Networking at the Protein Society symposium.

PubMed

McKnight, C James; Cordes, Matthew H J

2005-10-01

From the complex behavior of multicomponent signaling networks to the structures of large protein complexes and aggregates, questions once viewed as daunting are now being tackled fearlessly by protein scientists. The 19th Annual Symposium of the Protein Society in Boston highlighted the maturation of systems biology as applied to proteins.

Reconstruction of the temporal signaling network in Salmonella-infected human cells.

PubMed

Budak, Gungor; Eren Ozsoy, Oyku; Aydin Son, Yesim; Can, Tolga; Tuncbag, Nurcan

2015-01-01

Salmonella enterica is a bacterial pathogen that usually infects its host through food sources. Translocation of the pathogen proteins into the host cells leads to changes in the signaling mechanism either by activating or inhibiting the host proteins. Given that the bacterial infection modifies the response network of the host, a more coherent view of the underlying biological processes and the signaling networks can be obtained by using a network modeling approach based on the reverse engineering principles. In this work, we have used a published temporal phosphoproteomic dataset of Salmonella-infected human cells and reconstructed the temporal signaling network of the human host by integrating the interactome and the phosphoproteomic dataset. We have combined two well-established network modeling frameworks, the Prize-collecting Steiner Forest (PCSF) approach and the Integer Linear Programming (ILP) based edge inference approach. The resulting network conserves the information on temporality, direction of interactions, while revealing hidden entities in the signaling, such as the SNARE binding, mTOR signaling, immune response, cytoskeleton organization, and apoptosis pathways. Targets of the Salmonella effectors in the host cells such as CDC42, RHOA, 14-3-3δ, Syntaxin family, Oxysterol-binding proteins were included in the reconstructed signaling network although they were not present in the initial phosphoproteomic data. We believe that integrated approaches, such as the one presented here, have a high potential for the identification of clinical targets in infectious diseases, especially in the Salmonella infections.

Prediction and functional analysis of the sweet orange protein-protein interaction network.

PubMed

Ding, Yu-Duan; Chang, Ji-Wei; Guo, Jing; Chen, Dijun; Li, Sen; Xu, Qiang; Deng, Xiu-Xin; Cheng, Yun-Jiang; Chen, Ling-Ling

2014-08-05

Sweet orange (Citrus sinensis) is one of the most important fruits world-wide. Because it is a woody plant with a long growth cycle, genetic studies of sweet orange are lagging behind those of other species. In this analysis, we employed ortholog identification and domain combination methods to predict the protein-protein interaction (PPI) network for sweet orange. The K-nearest neighbors (KNN) classification method was used to verify and filter the network. The final predicted PPI network, CitrusNet, contained 8,195 proteins with 124,491 interactions. The quality of CitrusNet was evaluated using gene ontology (GO) and Mapman annotations, which confirmed the reliability of the network. In addition, we calculated the expression difference of interacting genes (EDI) in CitrusNet using RNA-seq data from four sweet orange tissues, and also analyzed the EDI distribution and variation in different sub-networks. Gene expression in CitrusNet has significant modular features. Target of rapamycin (TOR) protein served as the central node of the hormone-signaling sub-network. All evidence supported the idea that TOR can integrate various hormone signals and affect plant growth. CitrusNet provides valuable resources for the study of biological functions in sweet orange.

The Hedgehog Signal Transduction Network

PubMed Central

Robbins, David J.; Fei, Dennis Liang; Riobo, Natalia A.

2013-01-01

Hedgehog (Hh) proteins regulate the development of a wide range of metazoan embryonic and adult structures, and disruption of Hh signaling pathways results in various human diseases. Here, we provide a comprehensive review of the signaling pathways regulated by Hh, consolidating data from a diverse array of organisms in a variety of scientific disciplines. Similar to the elucidation of many other signaling pathways, our knowledge of Hh signaling developed in a sequential manner centered on its earliest discoveries. Thus, our knowledge of Hh signaling has for the most part focused on elucidating the mechanism by which Hh regulates the Gli family of transcription factors, the so-called “canonical” Hh signaling pathway. However, in the past few years, numerous studies have shown that Hh proteins can also signal through Gli-independent mechanisms collectively referred to as “noncanonical” signaling pathways. Noncanonical Hh signaling is itself subdivided into two distinct signaling modules: (i) those not requiring Smoothened (Smo) and (ii) those downstream of Smo that do not require Gli transcription factors. Thus, Hh signaling is now proposed to occur through a variety of distinct context-dependent signaling modules that have the ability to crosstalk with one another to form an interacting, dynamic Hh signaling network. PMID:23074268

Calcium/calmodulin-mediated signal network in plants

NASA Technical Reports Server (NTRS)

Yang, Tianbao; Poovaiah, B. W.

2003-01-01

Various extracellular stimuli elicit specific calcium signatures that can be recognized by different calcium sensors. Calmodulin, the predominant calcium receptor, is one of the best-characterized calcium sensors in eukaryotes. In recent years, completion of the Arabidopsis genome project and advances in functional genomics have helped to identify and characterize numerous calmodulin-binding proteins in plants. There are some similarities in Ca(2+)/calmodulin-mediated signaling in plants and animals. However, plants possess multiple calmodulin genes and many calmodulin target proteins, including unique protein kinases and transcription factors. Some of these proteins are likely to act as "hubs" during calcium signal transduction. Hence, a better understanding of the function of these calmodulin target proteins should help in deciphering the Ca(2+)/calmodulin-mediated signal network and its role in plant growth, development and response to environmental stimuli.

Connecting G protein signaling to chemoattractant-mediated cell polarity and cytoskeletal reorganization.

PubMed

Liu, Youtao; Lacal, Jesus; Firtel, Richard A; Kortholt, Arjan

2018-07-04

The directional movement toward extracellular chemical gradients, a process called chemotaxis, is an important property of cells. Central to eukaryotic chemotaxis is the molecular mechanism by which chemoattractant-mediated activation of G-protein coupled receptors (GPCRs) induces symmetry breaking in the activated downstream signaling pathways. Studies with mainly Dictyostelium and mammalian neutrophils as experimental systems have shown that chemotaxis is mediated by a complex network of signaling pathways. Recently, several labs have used extensive and efficient proteomic approaches to further unravel this dynamic signaling network. Together these studies showed the critical role of the interplay between heterotrimeric G-protein subunits and monomeric G proteins in regulating cytoskeletal rearrangements during chemotaxis. Here we highlight how these proteomic studies have provided greater insight into the mechanisms by which the heterotrimeric G protein cycle is regulated, how heterotrimeric G proteins-induced symmetry breaking is mediated through small G protein signaling, and how symmetry breaking in G protein signaling subsequently induces cytoskeleton rearrangements and cell migration.

CBL-CIPK network for calcium signaling in higher plants

NASA Astrophysics Data System (ADS)

Luan, Sheng

Plants sense their environment by signaling mechanisms involving calcium. Calcium signals are encoded by a complex set of parameters and decoded by a large number of proteins including the more recently discovered CBL-CIPK network. The calcium-binding CBL proteins specifi-cally interact with a family of protein kinases CIPKs and regulate the activity and subcellular localization of these kinases, leading to the modification of kinase substrates. This represents a paradigm shift as compared to a calcium signaling mechanism from yeast and animals. One example of CBL-CIPK signaling pathways is the low-potassium response of Arabidopsis roots. When grown in low-K medium, plants develop stronger K-uptake capacity adapting to the low-K condition. Recent studies show that the increased K-uptake is caused by activation of a specific K-channel by the CBL-CIPK network. A working model for this regulatory pathway will be discussed in the context of calcium coding and decoding processes.

Yes-associated protein (YAP) in pancreatic cancer: at the epicenter of a targetable signaling network associated with patient survival.

PubMed

Rozengurt, Enrique; Sinnett-Smith, James; Eibl, Guido

2018-01-01

Pancreatic ductal adenocarcinoma (PDAC) is generally a fatal disease with no efficacious treatment modalities. Elucidation of signaling mechanisms that will lead to the identification of novel targets for therapy and chemoprevention is urgently needed. Here, we review the role of Yes-associated protein (YAP) and WW-domain-containing Transcriptional co-Activator with a PDZ-binding motif (TAZ) in the development of PDAC. These oncogenic proteins are at the center of a signaling network that involves multiple upstream signals and downstream YAP-regulated genes. We also discuss the clinical significance of the YAP signaling network in PDAC using a recently published interactive open-access database (www.proteinatlas.org/pathology) that allows genome-wide exploration of the impact of individual proteins on survival outcomes. Multiple YAP/TEAD-regulated genes, including AJUBA , ANLN , AREG , ARHGAP29 , AURKA , BUB1 , CCND1 , CDK6, CXCL5 , EDN2 , DKK1 , FOSL1,FOXM1 , HBEGF , IGFBP2 , JAG1 , NOTCH2 , RHAMM , RRM2 , SERP1 , and ZWILCH , are associated with unfavorable survival of PDAC patients. Similarly, components of AP-1 that synergize with YAP ( FOSL1 ), growth factors (TGFα, EPEG, and HBEGF), a specific integrin ( ITGA2 ), heptahelical receptors ( P2Y 2 R , GPR87 ) and an inhibitor of the Hippo pathway ( MUC1 ), all of which stimulate YAP activity, are associated with unfavorable survival of PDAC patients. By contrast, YAP inhibitory pathways (STRAD/LKB-1/AMPK, PKA/LATS, and TSC/mTORC1) indicate a favorable prognosis. These associations emphasize that the YAP signaling network correlates with poor survival of pancreatic cancer patients. We conclude that the YAP pathway is a major determinant of clinical aggressiveness in PDAC patients and a target for therapeutic and preventive strategies in this disease.

Recent coselection in human populations revealed by protein-protein interaction network.

PubMed

Qian, Wei; Zhou, Hang; Tang, Kun

2014-12-21

Genome-wide scans for signals of natural selection in human populations have identified a large number of candidate loci that underlie local adaptations. This is surprising given the relatively short evolutionary time since the divergence of the human population. One hypothesis that has not been formally examined is whether and how the recent human evolution may have been shaped by coselection in the context of complex molecular interactome. In this study, genome-wide signals of selection were scanned in East Asians, Europeans, and Africans using 1000 Genome data, and subsequently mapped onto the protein-protein interaction (PPI) network. We found that the candidate genes of recent positive selection localized significantly closer to each other on the PPI network than expected, revealing substantial clustering of selected genes. Furthermore, gene pairs of shorter PPI network distances showed higher similarities of their recent evolutionary paths than those further apart. Last, subnetworks enriched with recent coselection signals were identified, which are substantially overrepresented in biological pathways related to signal transduction, neurogenesis, and immune function. These results provide the first genome-wide evidence for association of recent selection signals with the PPI network, shedding light on the potential mechanisms of recent coselection in the human genome. © The Author(s) 2014. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution.

Pathway connectivity and signaling coordination in the yeast stress-activated signaling network

PubMed Central

Chasman, Deborah; Ho, Yi-Hsuan; Berry, David B; Nemec, Corey M; MacGilvray, Matthew E; Hose, James; Merrill, Anna E; Lee, M Violet; Will, Jessica L; Coon, Joshua J; Ansari, Aseem Z; Craven, Mark; Gasch, Audrey P

2014-01-01

Stressed cells coordinate a multi-faceted response spanning many levels of physiology. Yet knowledge of the complete stress-activated regulatory network as well as design principles for signal integration remains incomplete. We developed an experimental and computational approach to integrate available protein interaction data with gene fitness contributions, mutant transcriptome profiles, and phospho-proteome changes in cells responding to salt stress, to infer the salt-responsive signaling network in yeast. The inferred subnetwork presented many novel predictions by implicating new regulators, uncovering unrecognized crosstalk between known pathways, and pointing to previously unknown ‘hubs’ of signal integration. We exploited these predictions to show that Cdc14 phosphatase is a central hub in the network and that modification of RNA polymerase II coordinates induction of stress-defense genes with reduction of growth-related transcripts. We find that the orthologous human network is enriched for cancer-causing genes, underscoring the importance of the subnetwork's predictions in understanding stress biology. PMID:25411400

Prediction of Oncogenic Interactions and Cancer-Related Signaling Networks Based on Network Topology

PubMed Central

Acencio, Marcio Luis; Bovolenta, Luiz Augusto; Camilo, Esther; Lemke, Ney

2013-01-01

Cancer has been increasingly recognized as a systems biology disease since many investigators have demonstrated that this malignant phenotype emerges from abnormal protein-protein, regulatory and metabolic interactions induced by simultaneous structural and regulatory changes in multiple genes and pathways. Therefore, the identification of oncogenic interactions and cancer-related signaling networks is crucial for better understanding cancer. As experimental techniques for determining such interactions and signaling networks are labor-intensive and time-consuming, the development of a computational approach capable to accomplish this task would be of great value. For this purpose, we present here a novel computational approach based on network topology and machine learning capable to predict oncogenic interactions and extract relevant cancer-related signaling subnetworks from an integrated network of human genes interactions (INHGI). This approach, called graph2sig, is twofold: first, it assigns oncogenic scores to all interactions in the INHGI and then these oncogenic scores are used as edge weights to extract oncogenic signaling subnetworks from INHGI. Regarding the prediction of oncogenic interactions, we showed that graph2sig is able to recover 89% of known oncogenic interactions with a precision of 77%. Moreover, the interactions that received high oncogenic scores are enriched in genes for which mutations have been causally implicated in cancer. We also demonstrated that graph2sig is potentially useful in extracting oncogenic signaling subnetworks: more than 80% of constructed subnetworks contain more than 50% of original interactions in their corresponding oncogenic linear pathways present in the KEGG PATHWAY database. In addition, the potential oncogenic signaling subnetworks discovered by graph2sig are supported by experimental evidence. Taken together, these results suggest that graph2sig can be a useful tool for investigators involved in cancer research

Protein interaction networks from literature mining

NASA Astrophysics Data System (ADS)

Ihara, Sigeo

2005-03-01

The ability to accurately predict and understand physiological changes in the biological network system in response to disease or drug therapeutics is of crucial importance in life science. The extensive amount of gene expression data generated from even a single microarray experiment often proves difficult to fully interpret and comprehend the biological significance. An increasing knowledge of protein interactions stored in the PubMed database, as well as the advancement of natural language processing, however, makes it possible to construct protein interaction networks from the gene expression information that are essential for understanding the biological meaning. From the in house literature mining system we have developed, the protein interaction network for humans was constructed. By analysis based on the graph-theoretical characterization of the total interaction network in literature, we found that the network is scale-free and semantic long-ranged interactions (i.e. inhibit, induce) between proteins dominate in the total interaction network, reducing the degree exponent. Interaction networks generated based on scientific text in which the interaction event is ambiguously described result in disconnected networks. In contrast interaction networks based on text in which the interaction events are clearly stated result in strongly connected networks. The results of protein-protein interaction networks obtained in real applications from microarray experiments are discussed: For example, comparisons of the gene expression data indicative of either a good or a poor prognosis for acute lymphoblastic leukemia with MLL rearrangements, using our system, showed newly discovered signaling cross-talk.

Modeling Signaling Networks to Advance New Cancer Therapies.

PubMed

Saez-Rodriguez, Julio; MacNamara, Aidan; Cook, Simon

2015-01-01

Cell signaling pathways control cells' responses to their environment through an intricate network of proteins and small molecules partitioned by intracellular structures, such as the cytoskeleton and nucleus. Our understanding of these pathways has been revised recently with the advent of more advanced experimental techniques; no longer are signaling pathways viewed as linear cascades of information flowing from membrane-bound receptors to the nucleus. Instead, such pathways must be understood in the context of networks, and studying such networks requires an integration of computational and experimental approaches. This understanding is becoming more important in designing novel therapies for diseases such as cancer. Using the MAPK (mitogen-activated protein kinase) and PI3K (class I phosphoinositide-3' kinase) pathways as case studies of cellular signaling, we give an overview of these pathways and their functions. We then describe, using a number of case studies, how computational modeling has aided in understanding these pathways' deregulation in cancer, and how such understanding can be used to optimally tailor current therapies or help design new therapies against cancer.

Enzyme Sequestration as a Tuning Point in Controlling Response Dynamics of Signalling Networks

PubMed Central

Ollivier, Julien F.; Soyer, Orkun S.

2016-01-01

Signalling networks result from combinatorial interactions among many enzymes and scaffolding proteins. These complex systems generate response dynamics that are often essential for correct decision-making in cells. Uncovering biochemical design principles that underpin such response dynamics is a prerequisite to understand evolved signalling networks and to design synthetic ones. Here, we use in silico evolution to explore the possible biochemical design space for signalling networks displaying ultrasensitive and adaptive response dynamics. By running evolutionary simulations mimicking different biochemical scenarios, we find that enzyme sequestration emerges as a key mechanism for enabling such dynamics. Inspired by these findings, and to test the role of sequestration, we design a generic, minimalist model of a signalling cycle, featuring two enzymes and a single scaffolding protein. We show that this simple system is capable of displaying both ultrasensitive and adaptive response dynamics. Furthermore, we find that tuning the concentration or kinetics of the sequestering protein can shift system dynamics between these two response types. These empirical results suggest that enzyme sequestration through scaffolding proteins is exploited by evolution to generate diverse response dynamics in signalling networks and could provide an engineering point in synthetic biology applications. PMID:27163612

R4 RGS Proteins: Regulation of G Protein Signaling and Beyond

PubMed Central

Bansal, Geetanjali; Druey, Kirk M.; Xie, Zhihui

2007-01-01

The Regulators of G protein Signaling (RGS) proteins were initially characterized as inhibitors of signal transduction cascades initiated by G-protein-coupled receptors (GPCRs) because of their ability to increase the intrinsic GTPase activity of heterotrimeric G proteins. This GTPase accelerating (GAP) activity enhances G protein deactivation and promotes desensitization. However, in addition to this signature trait, emerging data have revealed an expanding network of proteins, lipids, and ions that interact with RGS proteins and confer additional regulatory functions. This review highlights recent advances in our understanding of the physiological functions of one subfamily of RGS proteins with a high degree of homology (B/R4) gleaned from recent studies of knockout mice or cells with reduced RGS expression. We also discuss some of the newly-appreciated interactions of RGS proteins with cellular factors that suggest RGS control of several components of G-protein-mediated pathways as well as a diverse array of non-GPCR-mediated biological responses. PMID:18006065

Evolution of synthetic signaling scaffolds by recombination of modular protein domains.

PubMed

Lai, Andicus; Sato, Paloma M; Peisajovich, Sergio G

2015-06-19

Signaling scaffolds are proteins that interact via modular domains with multiple partners, regulating signaling networks in space and time and providing an ideal platform from which to alter signaling functions. However, to better exploit scaffolds for signaling engineering, it is necessary to understand the full extent of their modularity. We used a directed evolution approach to identify, from a large library of randomly shuffled protein interaction domains, variants capable of rescuing the signaling defect of a yeast strain in which Ste5, the scaffold in the mating pathway, had been deleted. After a single round of selection, we identified multiple synthetic scaffold variants with diverse domain architectures, able to mediate mating pathway activation in a pheromone-dependent manner. The facility with which this signaling network accommodates changes in scaffold architecture suggests that the mating signaling complex does not possess a single, precisely defined geometry into which the scaffold has to fit. These relaxed geometric constraints may facilitate the evolution of signaling networks, as well as their engineering for applications in synthetic biology.

Insight into bacterial virulence mechanisms against host immune response via the Yersinia pestis-human protein-protein interaction network.

PubMed

Yang, Huiying; Ke, Yuehua; Wang, Jian; Tan, Yafang; Myeni, Sebenzile K; Li, Dong; Shi, Qinghai; Yan, Yanfeng; Chen, Hui; Guo, Zhaobiao; Yuan, Yanzhi; Yang, Xiaoming; Yang, Ruifu; Du, Zongmin

2011-11-01

A Yersinia pestis-human protein interaction network is reported here to improve our understanding of its pathogenesis. Up to 204 interactions between 66 Y. pestis bait proteins and 109 human proteins were identified by yeast two-hybrid assay and then combined with 23 previously published interactions to construct a protein-protein interaction network. Topological analysis of the interaction network revealed that human proteins targeted by Y. pestis were significantly enriched in the proteins that are central in the human protein-protein interaction network. Analysis of this network showed that signaling pathways important for host immune responses were preferentially targeted by Y. pestis, including the pathways involved in focal adhesion, regulation of cytoskeleton, leukocyte transendoepithelial migration, and Toll-like receptor (TLR) and mitogen-activated protein kinase (MAPK) signaling. Cellular pathways targeted by Y. pestis are highly relevant to its pathogenesis. Interactions with host proteins involved in focal adhesion and cytoskeketon regulation pathways could account for resistance of Y. pestis to phagocytosis. Interference with TLR and MAPK signaling pathways by Y. pestis reflects common characteristics of pathogen-host interaction that bacterial pathogens have evolved to evade host innate immune response by interacting with proteins in those signaling pathways. Interestingly, a large portion of human proteins interacting with Y. pestis (16/109) also interacted with viral proteins (Epstein-Barr virus [EBV] and hepatitis C virus [HCV]), suggesting that viral and bacterial pathogens attack common cellular functions to facilitate infections. In addition, we identified vasodilator-stimulated phosphoprotein (VASP) as a novel interaction partner of YpkA and showed that YpkA could inhibit in vitro actin assembly mediated by VASP.

Dynamic protein interaction networks and new structural paradigms in signaling

PubMed Central

Csizmok, Veronika; Follis, Ariele Viacava; Kriwacki, Richard W.; Forman-Kay, Julie D.

2017-01-01

Understanding signaling and other complex biological processes requires elucidating the critical roles of intrinsically disordered proteins and regions (IDPs/IDRs), which represent ~30% of the proteome and enable unique regulatory mechanisms. In this review we describe the structural heterogeneity of disordered proteins that underpins these mechanisms and the latest progress in obtaining structural descriptions of ensembles of disordered proteins that are needed for linking structure and dynamics to function. We describe the diverse interactions of IDPs that can have unusual characteristics such as “ultrasensitivity” and “regulated folding and unfolding”. We also summarize the mounting data showing that large-scale assembly and protein phase separation occurs within a variety of signaling complexes and cellular structures. In addition, we discuss efforts to therapeutically target disordered proteins with small molecules. Overall, we interpret the remodeling of disordered state ensembles due to binding and post-translational modifications within an expanded framework for allostery that provides significant insights into how disordered proteins transmit biological information. PMID:26922996

The statistical mechanics of complex signaling networks: nerve growth factor signaling

NASA Astrophysics Data System (ADS)

Brown, K. S.; Hill, C. C.; Calero, G. A.; Myers, C. R.; Lee, K. H.; Sethna, J. P.; Cerione, R. A.

2004-10-01

The inherent complexity of cellular signaling networks and their importance to a wide range of cellular functions necessitates the development of modeling methods that can be applied toward making predictions and highlighting the appropriate experiments to test our understanding of how these systems are designed and function. We use methods of statistical mechanics to extract useful predictions for complex cellular signaling networks. A key difficulty with signaling models is that, while significant effort is being made to experimentally measure the rate constants for individual steps in these networks, many of the parameters required to describe their behavior remain unknown or at best represent estimates. To establish the usefulness of our approach, we have applied our methods toward modeling the nerve growth factor (NGF)-induced differentiation of neuronal cells. In particular, we study the actions of NGF and mitogenic epidermal growth factor (EGF) in rat pheochromocytoma (PC12) cells. Through a network of intermediate signaling proteins, each of these growth factors stimulates extracellular regulated kinase (Erk) phosphorylation with distinct dynamical profiles. Using our modeling approach, we are able to predict the influence of specific signaling modules in determining the integrated cellular response to the two growth factors. Our methods also raise some interesting insights into the design and possible evolution of cellular systems, highlighting an inherent property of these systems that we call 'sloppiness.'

The Robustness of a Signaling Complex to Domain Rearrangements Facilitates Network Evolution

PubMed Central

Sato, Paloma M.; Yoganathan, Kogulan; Jung, Jae H.; Peisajovich, Sergio G.

2014-01-01

The rearrangement of protein domains is known to have key roles in the evolution of signaling networks and, consequently, is a major tool used to synthetically rewire networks. However, natural mutational events leading to the creation of proteins with novel domain combinations, such as in frame fusions followed by domain loss, retrotranspositions, or translocations, to name a few, often simultaneously replace pre-existing genes. Thus, while proteins with new domain combinations may establish novel network connections, it is not clear how the concomitant deletions are tolerated. We investigated the mechanisms that enable signaling networks to tolerate domain rearrangement-mediated gene replacements. Using as a model system the yeast mitogen activated protein kinase (MAPK)-mediated mating pathway, we analyzed 92 domain-rearrangement events affecting 11 genes. Our results indicate that, while domain rearrangement events that result in the loss of catalytic activities within the signaling complex are not tolerated, domain rearrangements can drastically alter protein interactions without impairing function. This suggests that signaling complexes can maintain function even when some components are recruited to alternative sites within the complex. Furthermore, we also found that the ability of the complex to tolerate changes in interaction partners does not depend on long disordered linkers that often connect domains. Taken together, our results suggest that some signaling complexes are dynamic ensembles with loose spatial constraints that could be easily re-shaped by evolution and, therefore, are ideal targets for cellular engineering. PMID:25490747

Network Modeling Reveals Prevalent Negative Regulatory Relationships between Signaling Sectors in Arabidopsis Immune Signaling

PubMed Central

Sato, Masanao; Tsuda, Kenichi; Wang, Lin; Coller, John; Watanabe, Yuichiro; Glazebrook, Jane; Katagiri, Fumiaki

2010-01-01

Biological signaling processes may be mediated by complex networks in which network components and network sectors interact with each other in complex ways. Studies of complex networks benefit from approaches in which the roles of individual components are considered in the context of the network. The plant immune signaling network, which controls inducible responses to pathogen attack, is such a complex network. We studied the Arabidopsis immune signaling network upon challenge with a strain of the bacterial pathogen Pseudomonas syringae expressing the effector protein AvrRpt2 (Pto DC3000 AvrRpt2). This bacterial strain feeds multiple inputs into the signaling network, allowing many parts of the network to be activated at once. mRNA profiles for 571 immune response genes of 22 Arabidopsis immunity mutants and wild type were collected 6 hours after inoculation with Pto DC3000 AvrRpt2. The mRNA profiles were analyzed as detailed descriptions of changes in the network state resulting from the genetic perturbations. Regulatory relationships among the genes corresponding to the mutations were inferred by recursively applying a non-linear dimensionality reduction procedure to the mRNA profile data. The resulting static network model accurately predicted 23 of 25 regulatory relationships reported in the literature, suggesting that predictions of novel regulatory relationships are also accurate. The network model revealed two striking features: (i) the components of the network are highly interconnected; and (ii) negative regulatory relationships are common between signaling sectors. Complex regulatory relationships, including a novel negative regulatory relationship between the early microbe-associated molecular pattern-triggered signaling sectors and the salicylic acid sector, were further validated. We propose that prevalent negative regulatory relationships among the signaling sectors make the plant immune signaling network a “sector-switching” network, which

Linking the proteins--elucidation of proteome-scale networks using mass spectrometry.

PubMed

Pflieger, Delphine; Gonnet, Florence; de la Fuente van Bentem, Sergio; Hirt, Heribert; de la Fuente, Alberto

2011-01-01

Proteomes are intricate. Typically, thousands of proteins interact through physical association and post-translational modifications (PTMs) to give rise to the emergent functions of cells. Understanding these functions requires one to study proteomes as "systems" rather than collections of individual protein molecules. The abstraction of the interacting proteome to "protein networks" has recently gained much attention, as networks are effective representations, that lose specific molecular details, but provide the ability to see the proteome as a whole. Mostly two aspects of the proteome have been represented by network models: proteome-wide physical protein-protein-binding interactions organized into Protein Interaction Networks (PINs), and proteome-wide PTM relations organized into Protein Signaling Networks (PSNs). Mass spectrometry (MS) techniques have been shown to be essential to reveal both of these aspects on a proteome-wide scale. Techniques such as affinity purification followed by MS have been used to elucidate protein-protein interactions, and MS-based quantitative phosphoproteomics is critical to understand the structure and dynamics of signaling through the proteome. We here review the current state-of-the-art MS-based analytical pipelines for the purpose to characterize proteome-scale networks. Copyright © 2010 Wiley Periodicals, Inc.

Construction and analysis of protein-protein interaction networks based on proteomics data of prostate cancer

PubMed Central

CHEN, CHEN; SHEN, HONG; ZHANG, LI-GUO; LIU, JIAN; CAO, XIAO-GE; YAO, AN-LIANG; KANG, SHAO-SAN; GAO, WEI-XING; HAN, HUI; CAO, FENG-HONG; LI, ZHI-GUO

2016-01-01

Currently, using human prostate cancer (PCa) tissue samples to conduct proteomics research has generated a large amount of data; however, only a very small amount has been thoroughly investigated. In this study, we manually carried out the mining of the full text of proteomics literature that involved comparisons between PCa and normal or benign tissue and identified 41 differentially expressed proteins verified or reported more than 2 times from different research studies. We regarded these proteins as seed proteins to construct a protein-protein interaction (PPI) network. The extended network included one giant network, which consisted of 1,264 nodes connected via 1,744 edges, and 3 small separate components. The backbone network was then constructed, which was derived from key nodes and the subnetwork consisting of the shortest path between seed proteins. Topological analyses of these networks were conducted to identify proteins essential for the genesis of PCa. Solute carrier family 2 (facilitated glucose transporter), member 4 (SLC2A4) had the highest closeness centrality located in the center of each network, and the highest betweenness centrality and largest degree in the backbone network. Tubulin, beta 2C (TUBB2C) had the largest degree in the giant network and subnetwork. In addition, using module analysis of the whole PPI network, we obtained a densely connected region. Functional annotation indicated that the Ras protein signal transduction biological process, mitogen-activated protein kinase (MAPK), neurotrophin and the gonadotropin-releasing hormone (GnRH) signaling pathway may play an important role in the genesis and development of PCa. Further investigation of the SLC2A4, TUBB2C proteins, and these biological processes and pathways may therefore provide a potential target for the diagnosis and treatment of PCa. PMID:27121963

Two Chimeric Regulators of G-protein Signaling (RGS) Proteins Differentially Modulate Soybean Heterotrimeric G-protein Cycle*

PubMed Central

Roy Choudhury, Swarup; Westfall, Corey S.; Laborde, John P.; Bisht, Naveen C.; Jez, Joseph M.; Pandey, Sona

2012-01-01

Heterotrimeric G-proteins and the regulator of G-protein signaling (RGS) proteins, which accelerate the inherent GTPase activity of Gα proteins, are common in animals and encoded by large gene families; however, in plants G-protein signaling is thought to be more limited in scope. For example, Arabidopsis thaliana contains one Gα, one Gβ, three Gγ, and one RGS protein. Recent examination of the Glycine max (soybean) genome reveals a larger set of G-protein-related genes and raises the possibility of more intricate G-protein networks than previously observed in plants. Stopped-flow analysis of GTP-binding and GDP/GTP exchange for the four soybean Gα proteins (GmGα1–4) reveals differences in their kinetic properties. The soybean genome encodes two chimeric RGS proteins with an N-terminal seven transmembrane domain and a C-terminal RGS box. Both GmRGS interact with each of the four GmGα and regulate their GTPase activity. The GTPase-accelerating activities of GmRGS1 and -2 differ for each GmGα, suggesting more than one possible rate of the G-protein cycle initiated by each of the Gα proteins. The differential effects of GmRGS1 and GmRGS2 on GmGα1–4 result from a single valine versus alanine difference. The emerging picture suggests complex regulation of the G-protein cycle in soybean and in other plants with expanded G-protein networks. PMID:22474294

Detection of allosteric signal transmission by information-theoretic analysis of protein dynamics

PubMed Central

Pandini, Alessandro; Fornili, Arianna; Fraternali, Franca; Kleinjung, Jens

2012-01-01

Allostery offers a highly specific way to modulate protein function. Therefore, understanding this mechanism is of increasing interest for protein science and drug discovery. However, allosteric signal transmission is difficult to detect experimentally and to model because it is often mediated by local structural changes propagating along multiple pathways. To address this, we developed a method to identify communication pathways by an information-theoretical analysis of molecular dynamics simulations. Signal propagation was described as information exchange through a network of correlated local motions, modeled as transitions between canonical states of protein fragments. The method was used to describe allostery in two-component regulatory systems. In particular, the transmission from the allosteric site to the signaling surface of the receiver domain NtrC was shown to be mediated by a layer of hub residues. The location of hubs preferentially connected to the allosteric site was found in close agreement with key residues experimentally identified as involved in the signal transmission. The comparison with the networks of the homologues CheY and FixJ highlighted similarities in their dynamics. In particular, we showed that a preorganized network of fragment connections between the allosteric and functional sites exists already in the inactive state of all three proteins.—Pandini, A., Fornili, A., Fraternali, F., Kleinjung, J. Detection of allosteric signal transmission by information-theoretic analysis of protein dynamics. PMID:22071506

Nonlinear signaling on biological networks: The role of stochasticity and spectral clustering

NASA Astrophysics Data System (ADS)

Hernandez-Hernandez, Gonzalo; Myers, Jesse; Alvarez-Lacalle, Enrique; Shiferaw, Yohannes

2017-03-01

Signal transduction within biological cells is governed by networks of interacting proteins. Communication between these proteins is mediated by signaling molecules which bind to receptors and induce stochastic transitions between different conformational states. Signaling is typically a cooperative process which requires the occurrence of multiple binding events so that reaction rates have a nonlinear dependence on the amount of signaling molecule. It is this nonlinearity that endows biological signaling networks with robust switchlike properties which are critical to their biological function. In this study we investigate how the properties of these signaling systems depend on the network architecture. Our main result is that these nonlinear networks exhibit bistability where the network activity can switch between states that correspond to a low and high activity level. We show that this bistable regime emerges at a critical coupling strength that is determined by the spectral structure of the network. In particular, the set of nodes that correspond to large components of the leading eigenvector of the adjacency matrix determines the onset of bistability. Above this transition the eigenvectors of the adjacency matrix determine a hierarchy of clusters, defined by its spectral properties, which are activated sequentially with increasing network activity. We argue further that the onset of bistability occurs either continuously or discontinuously depending upon whether the leading eigenvector is localized or delocalized. Finally, we show that at low network coupling stochastic transitions to the active branch are also driven by the set of nodes that contribute more strongly to the leading eigenvector. However, at high coupling, transitions are insensitive to network structure since the network can be activated by stochastic transitions of a few nodes. Thus this work identifies important features of biological signaling networks that may underlie their biological

Phosphotyrosine signaling proteins that drive oncogenesis tend to be highly interconnected.

PubMed

Koytiger, Grigoriy; Kaushansky, Alexis; Gordus, Andrew; Rush, John; Sorger, Peter K; MacBeath, Gavin

2013-05-01

Mutation and overexpression of receptor tyrosine kinases or the proteins they regulate serve as oncogenic drivers in diverse cancers. To better understand receptor tyrosine kinase signaling and its link to oncogenesis, we used protein microarrays to systematically and quantitatively measure interactions between virtually every SH2 or PTB domain encoded in the human genome and all known sites of tyrosine phosphorylation on 40 receptor tyrosine kinases and on most of the SH2 and PTB domain-containing adaptor proteins. We found that adaptor proteins, like RTKs, have many high affinity bindings sites for other adaptor proteins. In addition, proteins that drive cancer, including both receptors and adaptor proteins, tend to be much more highly interconnected via networks of SH2 and PTB domain-mediated interactions than nononcogenic proteins. Our results suggest that network topological properties such as connectivity can be used to prioritize new drug targets in this well-studied family of signaling proteins.

Position Matters: Network Centrality Considerably Impacts Rates of Protein Evolution in the Human Protein-Protein Interaction Network.

PubMed

Alvarez-Ponce, David; Feyertag, Felix; Chakraborty, Sandip

2017-06-01

The proteins of any organism evolve at disparate rates. A long list of factors affecting rates of protein evolution have been identified. However, the relative importance of each factor in determining rates of protein evolution remains unresolved. The prevailing view is that evolutionary rates are dominantly determined by gene expression, and that other factors such as network centrality have only a marginal effect, if any. However, this view is largely based on analyses in yeasts, and accurately measuring the importance of the determinants of rates of protein evolution is complicated by the fact that the different factors are often correlated with each other, and by the relatively poor quality of available functional genomics data sets. Here, we use correlation, partial correlation and principal component regression analyses to measure the contributions of several factors to the variability of the rates of evolution of human proteins. For this purpose, we analyzed the entire human protein-protein interaction data set and the human signal transduction network-a network data set of exceptionally high quality, obtained by manual curation, which is expected to be virtually free from false positives. In contrast with the prevailing view, we observe that network centrality (measured as the number of physical and nonphysical interactions, betweenness, and closeness) has a considerable impact on rates of protein evolution. Surprisingly, the impact of centrality on rates of protein evolution seems to be comparable, or even superior according to some analyses, to that of gene expression. Our observations seem to be independent of potentially confounding factors and from the limitations (biases and errors) of interactomic data sets. © The Author 2017. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution.

Proteins in phytohormone signaling pathways for abiotic stress in plants

USDA-ARS?s Scientific Manuscript database

Plant hormones and their signaling network systems have an essential role in activating and regulating plant responses to both biotic and abiotic stress factors. This chapter describes proteins that are involved in hormone biosynthesis, long distance and intra-cellular transport, the signaling sensi...

NSP-CAS Protein Complexes: Emerging Signaling Modules in Cancer.

PubMed

Wallez, Yann; Mace, Peter D; Pasquale, Elena B; Riedl, Stefan J

2012-05-01

The CAS (CRK-associated substrate) family of adaptor proteins comprises 4 members, which share a conserved modular domain structure that enables multiple protein-protein interactions, leading to the assembly of intracellular signaling platforms. Besides their physiological role in signal transduction downstream of a variety of cell surface receptors, CAS proteins are also critical for oncogenic transformation and cancer cell malignancy through associations with a variety of regulatory proteins and downstream effectors. Among the regulatory partners, the 3 recently identified adaptor proteins constituting the NSP (novel SH2-containing protein) family avidly bind to the conserved carboxy-terminal focal adhesion-targeting (FAT) domain of CAS proteins. NSP proteins use an anomalous nucleotide exchange factor domain that lacks catalytic activity to form NSP-CAS signaling modules. Additionally, the NSP SH2 domain can link NSP-CAS signaling assemblies to tyrosine-phosphorylated cell surface receptors. NSP proteins can potentiate CAS function by affecting key CAS attributes such as expression levels, phosphorylation state, and subcellular localization, leading to effects on cell adhesion, migration, and invasion as well as cell growth. The consequences of these activities are well exemplified by the role that members of both families play in promoting breast cancer cell invasiveness and resistance to antiestrogens. In this review, we discuss the intriguing interplay between the NSP and CAS families, with a particular focus on cancer signaling networks.

A Bayesian Active Learning Experimental Design for Inferring Signaling Networks.

PubMed

Ness, Robert O; Sachs, Karen; Mallick, Parag; Vitek, Olga

2018-06-21

Machine learning methods for learning network structure are applied to quantitative proteomics experiments and reverse-engineer intracellular signal transduction networks. They provide insight into the rewiring of signaling within the context of a disease or a phenotype. To learn the causal patterns of influence between proteins in the network, the methods require experiments that include targeted interventions that fix the activity of specific proteins. However, the interventions are costly and add experimental complexity. We describe an active learning strategy for selecting optimal interventions. Our approach takes as inputs pathway databases and historic data sets, expresses them in form of prior probability distributions on network structures, and selects interventions that maximize their expected contribution to structure learning. Evaluations on simulated and real data show that the strategy reduces the detection error of validated edges as compared with an unguided choice of interventions and avoids redundant interventions, thereby increasing the effectiveness of the experiment.

Biophysical constraints on the computational capacity of biochemical signaling networks

NASA Astrophysics Data System (ADS)

Wang, Ching-Hao; Mehta, Pankaj

Biophysics fundamentally constrains the computations that cells can carry out. Here, we derive fundamental bounds on the computational capacity of biochemical signaling networks that utilize post-translational modifications (e.g. phosphorylation). To do so, we combine ideas from the statistical physics of disordered systems and the observation by Tony Pawson and others that the biochemistry underlying protein-protein interaction networks is combinatorial and modular. Our results indicate that the computational capacity of signaling networks is severely limited by the energetics of binding and the need to achieve specificity. We relate our results to one of the theoretical pillars of statistical learning theory, Cover's theorem, which places bounds on the computational capacity of perceptrons. PM and CHW were supported by a Simons Investigator in the Mathematical Modeling of Living Systems Grant, and NIH Grant No. 1R35GM119461 (both to PM).

Construction of reliable protein-protein interaction networks with a new interaction generality measure.

PubMed

Saito, Rintaro; Suzuki, Harukazu; Hayashizaki, Yoshihide

2003-04-12

Recent screening techniques have made large amounts of protein-protein interaction data available, from which biologically important information such as the function of uncharacterized proteins, the existence of novel protein complexes, and novel signal-transduction pathways can be discovered. However, experimental data on protein interactions contain many false positives, making these discoveries difficult. Therefore computational methods of assessing the reliability of each candidate protein-protein interaction are urgently needed. We developed a new 'interaction generality' measure (IG2) to assess the reliability of protein-protein interactions using only the topological properties of their interaction-network structure. Using yeast protein-protein interaction data, we showed that reliable protein-protein interactions had significantly lower IG2 values than less-reliable interactions, suggesting that IG2 values can be used to evaluate and filter interaction data to enable the construction of reliable protein-protein interaction networks.

SignaLink 2 - a signaling pathway resource with multi-layered regulatory networks.

PubMed

Fazekas, Dávid; Koltai, Mihály; Türei, Dénes; Módos, Dezső; Pálfy, Máté; Dúl, Zoltán; Zsákai, Lilian; Szalay-Bekő, Máté; Lenti, Katalin; Farkas, Illés J; Vellai, Tibor; Csermely, Péter; Korcsmáros, Tamás

2013-01-18

Signaling networks in eukaryotes are made up of upstream and downstream subnetworks. The upstream subnetwork contains the intertwined network of signaling pathways, while the downstream regulatory part contains transcription factors and their binding sites on the DNA as well as microRNAs and their mRNA targets. Currently, most signaling and regulatory databases contain only a subsection of this network, making comprehensive analyses highly time-consuming and dependent on specific data handling expertise. The need for detailed mapping of signaling systems is also supported by the fact that several drug development failures were caused by undiscovered cross-talk or regulatory effects of drug targets. We previously created a uniformly curated signaling pathway resource, SignaLink, to facilitate the analysis of pathway cross-talks. Here, we present SignaLink 2, which significantly extends the coverage and applications of its predecessor. We developed a novel concept to integrate and utilize different subsections (i.e., layers) of the signaling network. The multi-layered (onion-like) database structure is made up of signaling pathways, their pathway regulators (e.g., scaffold and endocytotic proteins) and modifier enzymes (e.g., phosphatases, ubiquitin ligases), as well as transcriptional and post-transcriptional regulators of all of these components. The user-friendly website allows the interactive exploration of how each signaling protein is regulated. The customizable download page enables the analysis of any user-specified part of the signaling network. Compared to other signaling resources, distinctive features of SignaLink 2 are the following: 1) it involves experimental data not only from humans but from two invertebrate model organisms, C. elegans and D. melanogaster; 2) combines manual curation with large-scale datasets; 3) provides confidence scores for each interaction; 4) operates a customizable download page with multiple file formats (e.g., Bio

Genetic reductionist approach for dissecting individual roles of GGDEF proteins within the c-di-GMP signaling network in Salmonella

PubMed Central

Solano, Cristina; García, Begoña; Latasa, Cristina; Toledo-Arana, Alejandro; Zorraquino, Violeta; Valle, Jaione; Casals, Joan; Pedroso, Enrique; Lasa, Iñigo

2009-01-01

Bacteria have developed an exclusive signal transduction system involving multiple diguanylate cyclase and phosphodiesterase domain-containing proteins (GGDEF and EAL/HD-GYP, respectively) that modulate the levels of the same diffusible molecule, 3′-5′-cyclic diguanylic acid (c-di-GMP), to transmit signals and obtain specific cellular responses. Current knowledge about c-di-GMP signaling has been inferred mainly from the analysis of recombinant bacteria that either lack or overproduce individual members of the pathway, without addressing potential compensatory effects or interferences between them. Here, we dissected c-di-GMP signaling by constructing a Salmonella strain lacking all GGDEF-domain proteins and then producing derivatives, each restoring 1 protein. Our analysis showed that most GGDEF proteins are constitutively expressed and that their expression levels are not interdependent. Complete deletion of genes encoding GGDEF-domain proteins abrogated virulence, motility, long-term survival, and cellulose and fimbriae synthesis. Separate restoration revealed that 4 proteins from Salmonella and 1 from Yersinia pestis exclusively restored cellulose synthesis in a c-di-GMP–dependent manner, indicating that c-di-GMP produced by different GGDEF proteins can activate the same target. However, the restored strain containing the STM4551-encoding gene recovered all other phenotypes by means of gene expression modulation independently of c-di-GMP. Specifically, fimbriae synthesis and virulence were recovered through regulation of csgD and the plasmid-encoded spvAB mRNA levels, respectively. This study provides evidence that the regulation of the GGDEF-domain proteins network occurs at 2 levels: a level that strictly requires c-di-GMP to control enzymatic activities directly, restricted to cellulose synthesis in our experimental conditions, and another that involves gene regulation for which c-di-GMP synthesis can be dispensable. PMID:19416883

Emerging themes in heterotrimeric G-protein signaling in plants.

PubMed

Pandey, Sona; Vijayakumar, Anitha

2018-05-01

Heterotrimeric G-proteins are key signaling components involved during the regulation of a multitude of growth and developmental pathways in all eukaryotes. Although the core proteins (Gα, Gβ, Gγ subunits) and their basic biochemistries are conserved between plants and non-plant systems, seemingly different inherent properties of specific components, altered wirings of G-protein network architectures, and the presence of novel receptors and effector proteins make plant G-protein signaling mechanisms somewhat distinct from the well-established animal paradigm. G-protein research in plants is getting a lot of attention recently due to the emerging roles of these proteins in controlling many agronomically important traits. New findings on both canonical and novel G-protein components and their conserved and unique signaling mechanisms are expected to improve our understanding of this important module in affecting critical plant growth and development pathways and eventually their utilization to produce plants for the future needs. In this review, we briefly summarize what is currently known in plant G-protein research, describe new findings and how they are changing our perceptions of the field, and discuss important issues that still need to be addressed. Copyright © 2018 Elsevier B.V. All rights reserved.

An Interaction Library for the FcεRI Signaling Network

DOE PAGES

Chylek, Lily A.; Holowka, David A.; Baird, Barbara A.; ...

2014-04-15

Antigen receptors play a central role in adaptive immune responses. Although the molecular networks associated with these receptors have been extensively studied, we currently lack a systems-level understanding of how combinations of non-covalent interactions and post-translational modifications are regulated during signaling to impact cellular decision-making. To fill this knowledge gap, it will be necessary to formalize and piece together information about individual molecular mechanisms to form large-scale computational models of signaling networks. To this end, we have developed an interaction library for signaling by the high-affinity IgE receptor, FcεRI. The library consists of executable rules for protein–protein and protein–lipid interactions.more » This library extends earlier models for FcεRI signaling and introduces new interactions that have not previously been considered in a model. Thus, this interaction library is a toolkit with which existing models can be expanded and from which new models can be built. As an example, we present models of branching pathways from the adaptor protein Lat, which influence production of the phospholipid PIP 3 at the plasma membrane and the soluble second messenger IP 3. We find that inclusion of a positive feedback loop gives rise to a bistable switch, which may ensure robust responses to stimulation above a threshold level. In addition, the library is visualized to facilitate understanding of network circuitry and identification of network motifs.« less

An Interaction Library for the FcεRI Signaling Network

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chylek, Lily A.; Holowka, David A.; Baird, Barbara A.

Antigen receptors play a central role in adaptive immune responses. Although the molecular networks associated with these receptors have been extensively studied, we currently lack a systems-level understanding of how combinations of non-covalent interactions and post-translational modifications are regulated during signaling to impact cellular decision-making. To fill this knowledge gap, it will be necessary to formalize and piece together information about individual molecular mechanisms to form large-scale computational models of signaling networks. To this end, we have developed an interaction library for signaling by the high-affinity IgE receptor, FcεRI. The library consists of executable rules for protein–protein and protein–lipid interactions.more » This library extends earlier models for FcεRI signaling and introduces new interactions that have not previously been considered in a model. Thus, this interaction library is a toolkit with which existing models can be expanded and from which new models can be built. As an example, we present models of branching pathways from the adaptor protein Lat, which influence production of the phospholipid PIP 3 at the plasma membrane and the soluble second messenger IP 3. We find that inclusion of a positive feedback loop gives rise to a bistable switch, which may ensure robust responses to stimulation above a threshold level. In addition, the library is visualized to facilitate understanding of network circuitry and identification of network motifs.« less

Identification of Major Signaling Pathways in Prion Disease Progression Using Network Analysis

PubMed Central

Newaz, Khalique; Sriram, K.; Bera, Debajyoti

2015-01-01

Prion diseases are transmissible neurodegenerative diseases that arise due to conformational change of normal, cellular prion protein (PrPC) to protease-resistant isofrom (rPrPSc). Deposition of misfolded PrpSc proteins leads to an alteration of many signaling pathways that includes immunological and apoptotic pathways. As a result, this culminates in the dysfunction and death of neuronal cells. Earlier works on transcriptomic studies have revealed some affected pathways, but it is not clear which is (are) the prime network pathway(s) that change during the disease progression and how these pathways are involved in crosstalks with each other from the time of incubation to clinical death. We perform network analysis on large-scale transcriptomic data of differentially expressed genes obtained from whole brain in six different mouse strain-prion strain combination models to determine the pathways involved in prion diseases, and to understand the role of crosstalks in disease propagation. We employ a notion of differential network centrality measures on protein interaction networks to identify the potential biological pathways involved. We also propose a crosstalk ranking method based on dynamic protein interaction networks to identify the core network elements involved in crosstalk with different pathways. We identify 148 DEGs (differentially expressed genes) potentially related to the prion disease progression. Functional association of the identified genes implicates a strong involvement of immunological pathways. We extract a bow-tie structure that is potentially dysregulated in prion disease. We also propose an ODE model for the bow-tie network. Predictions related to diseased condition suggests the downregulation of the core signaling elements (PI3Ks and AKTs) of the bow-tie network. In this work, we show using transcriptomic data that the neuronal dysfunction in prion disease is strongly related to the immunological pathways. We conclude that these

Protein complexes and functional modules in molecular networks

NASA Astrophysics Data System (ADS)

Spirin, Victor; Mirny, Leonid A.

2003-10-01

Proteins, nucleic acids, and small molecules form a dense network of molecular interactions in a cell. Molecules are nodes of this network, and the interactions between them are edges. The architecture of molecular networks can reveal important principles of cellular organization and function, similarly to the way that protein structure tells us about the function and organization of a protein. Computational analysis of molecular networks has been primarily concerned with node degree [Wagner, A. & Fell, D. A. (2001) Proc. R. Soc. London Ser. B 268, 1803-1810; Jeong, H., Tombor, B., Albert, R., Oltvai, Z. N. & Barabasi, A. L. (2000) Nature 407, 651-654] or degree correlation [Maslov, S. & Sneppen, K. (2002) Science 296, 910-913], and hence focused on single/two-body properties of these networks. Here, by analyzing the multibody structure of the network of protein-protein interactions, we discovered molecular modules that are densely connected within themselves but sparsely connected with the rest of the network. Comparison with experimental data and functional annotation of genes showed two types of modules: (i) protein complexes (splicing machinery, transcription factors, etc.) and (ii) dynamic functional units (signaling cascades, cell-cycle regulation, etc.). Discovered modules are highly statistically significant, as is evident from comparison with random graphs, and are robust to noise in the data. Our results provide strong support for the network modularity principle introduced by Hartwell et al. [Hartwell, L. H., Hopfield, J. J., Leibler, S. & Murray, A. W. (1999) Nature 402, C47-C52], suggesting that found modules constitute the "building blocks" of molecular networks.

APG: an Active Protein-Gene network model to quantify regulatory signals in complex biological systems.

PubMed

Wang, Jiguang; Sun, Yidan; Zheng, Si; Zhang, Xiang-Sun; Zhou, Huarong; Chen, Luonan

2013-01-01

Synergistic interactions among transcription factors (TFs) and their cofactors collectively determine gene expression in complex biological systems. In this work, we develop a novel graphical model, called Active Protein-Gene (APG) network model, to quantify regulatory signals of transcription in complex biomolecular networks through integrating both TF upstream-regulation and downstream-regulation high-throughput data. Firstly, we theoretically and computationally demonstrate the effectiveness of APG by comparing with the traditional strategy based only on TF downstream-regulation information. We then apply this model to study spontaneous type 2 diabetic Goto-Kakizaki (GK) and Wistar control rats. Our biological experiments validate the theoretical results. In particular, SP1 is found to be a hidden TF with changed regulatory activity, and the loss of SP1 activity contributes to the increased glucose production during diabetes development. APG model provides theoretical basis to quantitatively elucidate transcriptional regulation by modelling TF combinatorial interactions and exploiting multilevel high-throughput information.

APG: an Active Protein-Gene Network Model to Quantify Regulatory Signals in Complex Biological Systems

PubMed Central

Wang, Jiguang; Sun, Yidan; Zheng, Si; Zhang, Xiang-Sun; Zhou, Huarong; Chen, Luonan

2013-01-01

Synergistic interactions among transcription factors (TFs) and their cofactors collectively determine gene expression in complex biological systems. In this work, we develop a novel graphical model, called Active Protein-Gene (APG) network model, to quantify regulatory signals of transcription in complex biomolecular networks through integrating both TF upstream-regulation and downstream-regulation high-throughput data. Firstly, we theoretically and computationally demonstrate the effectiveness of APG by comparing with the traditional strategy based only on TF downstream-regulation information. We then apply this model to study spontaneous type 2 diabetic Goto-Kakizaki (GK) and Wistar control rats. Our biological experiments validate the theoretical results. In particular, SP1 is found to be a hidden TF with changed regulatory activity, and the loss of SP1 activity contributes to the increased glucose production during diabetes development. APG model provides theoretical basis to quantitatively elucidate transcriptional regulation by modelling TF combinatorial interactions and exploiting multilevel high-throughput information. PMID:23346354

Systematic Prediction of Scaffold Proteins Reveals New Design Principles in Scaffold-Mediated Signal Transduction

PubMed Central

Hu, Jianfei; Neiswinger, Johnathan; Zhang, Jin; Zhu, Heng; Qian, Jiang

2015-01-01

Scaffold proteins play a crucial role in facilitating signal transduction in eukaryotes by bringing together multiple signaling components. In this study, we performed a systematic analysis of scaffold proteins in signal transduction by integrating protein-protein interaction and kinase-substrate relationship networks. We predicted 212 scaffold proteins that are involved in 605 distinct signaling pathways. The computational prediction was validated using a protein microarray-based approach. The predicted scaffold proteins showed several interesting characteristics, as we expected from the functionality of scaffold proteins. We found that the scaffold proteins are likely to interact with each other, which is consistent with previous finding that scaffold proteins tend to form homodimers and heterodimers. Interestingly, a single scaffold protein can be involved in multiple signaling pathways by interacting with other scaffold protein partners. Furthermore, we propose two possible regulatory mechanisms by which the activity of scaffold proteins is coordinated with their associated pathways through phosphorylation process. PMID:26393507

Quantitative Analysis of Signaling Networks across Differentially Embedded Tumors Highlights Interpatient Heterogeneity in Human Glioblastoma

PubMed Central

2015-01-01

Glioblastoma multiforme (GBM) is the most aggressive malignant primary brain tumor, with a dismal mean survival even with the current standard of care. Although in vitro cell systems can provide mechanistic insight into the regulatory networks governing GBM cell proliferation and migration, clinical samples provide a more physiologically relevant view of oncogenic signaling networks. However, clinical samples are not widely available and may be embedded for histopathologic analysis. With the goal of accurately identifying activated signaling networks in GBM tumor samples, we investigated the impact of embedding in optimal cutting temperature (OCT) compound followed by flash freezing in LN2 vs immediate flash freezing (iFF) in LN2 on protein expression and phosphorylation-mediated signaling networks. Quantitative proteomic and phosphoproteomic analysis of 8 pairs of tumor specimens revealed minimal impact of the different sample processing strategies and highlighted the large interpatient heterogeneity present in these tumors. Correlation analyses of the differentially processed tumor sections identified activated signaling networks present in selected tumors and revealed the differential expression of transcription, translation, and degradation associated proteins. This study demonstrates the capability of quantitative mass spectrometry for identification of in vivo oncogenic signaling networks from human tumor specimens that were either OCT-embedded or immediately flash-frozen. PMID:24927040

The Hippo signal transduction network for exercise physiologists

PubMed Central

Hamilton, D. Lee; Tremblay, Annie M.

2016-01-01

The ubiquitous transcriptional coactivators Yap (gene symbol Yap1) and Taz (gene symbol Wwtr1) regulate gene expression mainly by coactivating the Tead transcription factors. Being at the center of the Hippo signaling network, Yap and Taz are regulated by the Hippo kinase cassette and additionally by a plethora of exercise-associated signals and signaling modules. These include mechanotransduction, the AKT-mTORC1 network, the SMAD transcription factors, hypoxia, glucose homeostasis, AMPK, adrenaline/epinephrine and angiotensin II through G protein-coupled receptors, and IL-6. Consequently, exercise should alter Hippo signaling in several organs to mediate at least some aspects of the organ-specific adaptations to exercise. Indeed, Tead1 overexpression in muscle fibers has been shown to promote a fast-to-slow fiber type switch, whereas Yap in muscle fibers and cardiomyocytes promotes skeletal muscle hypertrophy and cardiomyocyte adaptations, respectively. Finally, genome-wide association studies in humans have linked the Hippo pathway members LATS2, TEAD1, YAP1, VGLL2, VGLL3, and VGLL4 to body height, which is a key factor in sports. PMID:26940657

SignaLink 2 – a signaling pathway resource with multi-layered regulatory networks

PubMed Central

2013-01-01

Background Signaling networks in eukaryotes are made up of upstream and downstream subnetworks. The upstream subnetwork contains the intertwined network of signaling pathways, while the downstream regulatory part contains transcription factors and their binding sites on the DNA as well as microRNAs and their mRNA targets. Currently, most signaling and regulatory databases contain only a subsection of this network, making comprehensive analyses highly time-consuming and dependent on specific data handling expertise. The need for detailed mapping of signaling systems is also supported by the fact that several drug development failures were caused by undiscovered cross-talk or regulatory effects of drug targets. We previously created a uniformly curated signaling pathway resource, SignaLink, to facilitate the analysis of pathway cross-talks. Here, we present SignaLink 2, which significantly extends the coverage and applications of its predecessor. Description We developed a novel concept to integrate and utilize different subsections (i.e., layers) of the signaling network. The multi-layered (onion-like) database structure is made up of signaling pathways, their pathway regulators (e.g., scaffold and endocytotic proteins) and modifier enzymes (e.g., phosphatases, ubiquitin ligases), as well as transcriptional and post-transcriptional regulators of all of these components. The user-friendly website allows the interactive exploration of how each signaling protein is regulated. The customizable download page enables the analysis of any user-specified part of the signaling network. Compared to other signaling resources, distinctive features of SignaLink 2 are the following: 1) it involves experimental data not only from humans but from two invertebrate model organisms, C. elegans and D. melanogaster; 2) combines manual curation with large-scale datasets; 3) provides confidence scores for each interaction; 4) operates a customizable download page with multiple file formats

Signal Propagation in Proteins and Relation to Equilibrium Fluctuations

PubMed Central

Chennubhotla, Chakra; Bahar, Ivet

2007-01-01

Elastic network (EN) models have been widely used in recent years for describing protein dynamics, based on the premise that the motions naturally accessible to native structures are relevant to biological function. We posit that equilibrium motions also determine communication mechanisms inherent to the network architecture. To this end, we explore the stochastics of a discrete-time, discrete-state Markov process of information transfer across the network of residues. We measure the communication abilities of residue pairs in terms of hit and commute times, i.e., the number of steps it takes on an average to send and receive signals. Functionally active residues are found to possess enhanced communication propensities, evidenced by their short hit times. Furthermore, secondary structural elements emerge as efficient mediators of communication. The present findings provide us with insights on the topological basis of communication in proteins and design principles for efficient signal transduction. While hit/commute times are information-theoretic concepts, a central contribution of this work is to rigorously show that they have physical origins directly relevant to the equilibrium fluctuations of residues predicted by EN models. PMID:17892319

Characterization of essential proteins based on network topology in proteins interaction networks

NASA Astrophysics Data System (ADS)

Bakar, Sakhinah Abu; Taheri, Javid; Zomaya, Albert Y.

2014-06-01

The identification of essential proteins is theoretically and practically important as (1) it is essential to understand the minimal surviving requirements for cellular lives, and (2) it provides fundamental for development of drug. As conducting experimental studies to identify essential proteins are both time and resource consuming, here we present a computational approach in predicting them based on network topology properties from protein-protein interaction networks of Saccharomyces cerevisiae. The proposed method, namely EP3NN (Essential Proteins Prediction using Probabilistic Neural Network) employed a machine learning algorithm called Probabilistic Neural Network as a classifier to identify essential proteins of the organism of interest; it uses degree centrality, closeness centrality, local assortativity and local clustering coefficient of each protein in the network for such predictions. Results show that EP3NN managed to successfully predict essential proteins with an accuracy of 95% for our studied organism. Results also show that most of the essential proteins are close to other proteins, have assortativity behavior and form clusters/sub-graph in the network.

CD25 and CD69 induction by α4β1 outside-in signalling requires TCR early signalling complex proteins

PubMed Central

Cimo, Ann-Marie; Ahmed, Zamal; McIntyre, Bradley W.; Lewis, Dorothy E.; Ladbury, John E.

2013-01-01

Distinct signalling pathways producing diverse cellular outcomes can utilize similar subsets of proteins. For example, proteins from the TCR (T-cell receptor) ESC (early signalling complex) are also involved in interferon-α receptor signalling. Defining the mechanism for how these proteins function within a given pathway is important in understanding the integration and communication of signalling networks with one another. We investigated the contributions of the TCR ESC proteins Lck (lymphocyte-specific kinase), ZAP-70 (ζ-chain-associated protein of 70 kDa), Vav1, SLP-76 [SH2 (Src homology 2)-domain-containing leukocyte protein of 76 kDa] and LAT (linker for activation of T-cells) to integrin outside-in signalling in human T-cells. Lck, ZAP-70, SLP-76, Vav1 and LAT were activated by α4β1 outside-in signalling, but in a manner different from TCR signalling. TCR stimulation recruits ESC proteins to activate the mitogen-activated protein kinase ERK (extracellular-signal-regulated kinase). α4β1 outside-in-mediated ERK activation did not require TCR ESC proteins. However, α4β1 outside-in signalling induced CD25 and co-stimulated CD69 and this was dependent on TCR ESC proteins. TCR and α4β1 outside-in signalling are integrated through the common use of TCR ESC proteins; however, these proteins display functionally distinct roles in these pathways. These novel insights into the cross-talk between integrin outside-in and TCR signalling pathways are highly relevant to the development of therapeutic strategies to overcome disease associated with T-cell deregulation. PMID:23758320

Soft Cysteine Signaling Network: The Functional Significance of Cysteine in Protein Function and the Soft Acids/Bases Thiol Chemistry That Facilitates Cysteine Modification.

PubMed

Wible, Ryan S; Sutter, Thomas R

2017-03-20

The unique biophysical and electronic properties of cysteine make this molecule one of the most biologically critical amino acids in the proteome. The defining sulfur atom in cysteine is much larger than the oxygen and nitrogen atoms more commonly found in the other amino acids. As a result of its size, the valence electrons of sulfur are highly polarizable. Unique protein microenvironments favor the polarization of sulfur, thus increasing the overt reactivity of cysteine. Here, we provide a brief overview of the endogenous generation of reactive oxygen and electrophilic species and specific examples of enzymes and transcription factors in which the oxidation or covalent modification of cysteine in those proteins modulates their function. The perspective concludes with a discussion of cysteine chemistry and biophysics, the hard and soft acids and bases model, and the proposal of the Soft Cysteine Signaling Network: a hypothesis proposing the existence of a complex signaling network governed by layered chemical reactivity and cross-talk in which the chemical modification of reactive cysteine in biological networks triggers the reorganization of intracellular biochemistry to mitigate spikes in endogenous or exogenous oxidative or electrophilic stress.

Synaptic activity induces input-specific rearrangements in a targeted synaptic protein interaction network.

PubMed

Lautz, Jonathan D; Brown, Emily A; VanSchoiack, Alison A Williams; Smith, Stephen E P

2018-05-27

Cells utilize dynamic, network level rearrangements in highly interconnected protein interaction networks to transmit and integrate information from distinct signaling inputs. Despite the importance of protein interaction network dynamics, the organizational logic underlying information flow through these networks is not well understood. Previously, we developed the quantitative multiplex co-immunoprecipitation platform, which allows for the simultaneous and quantitative measurement of the amount of co-association between large numbers of proteins in shared complexes. Here, we adapt quantitative multiplex co-immunoprecipitation to define the activity dependent dynamics of an 18-member protein interaction network in order to better understand the underlying principles governing glutamatergic signal transduction. We first establish that immunoprecipitation detected by flow cytometry can detect activity dependent changes in two known protein-protein interactions (Homer1-mGluR5 and PSD-95-SynGAP). We next demonstrate that neuronal stimulation elicits a coordinated change in our targeted protein interaction network, characterized by the initial dissociation of Homer1 and SynGAP-containing complexes followed by increased associations among glutamate receptors and PSD-95. Finally, we show that stimulation of distinct glutamate receptor types results in different modular sets of protein interaction network rearrangements, and that cells activate both modules in order to integrate complex inputs. This analysis demonstrates that cells respond to distinct types of glutamatergic input by modulating different combinations of protein co-associations among a targeted network of proteins. Our data support a model of synaptic plasticity in which synaptic stimulation elicits dissociation of preexisting multiprotein complexes, opening binding slots in scaffold proteins and allowing for the recruitment of additional glutamatergic receptors. This article is protected by copyright. All

Ischemic time impacts biological integrity of phospho-proteins in PI3K/Akt, Erk/MAPK, and p38 MAPK signaling networks.

PubMed

Holzer, Timothy R; Fulford, Angie D; Arkins, Austin M; Grondin, Janet M; Mundy, Christopher W; Nasir, Aejaz; Schade, Andrew E

2011-06-01

Post-translational modifications of proteins, such as phosphorylation, are labile events dynamically regulated by opposing kinase and phosphatase activities. Preanalytical factors, such as ischemic time before fixation, affect these activities and can have a significant impact on the ability to elucidate signaling pathways in tissue. Immunohistochemical analysis of phosphorylated proteins involved in PI3K/Akt, Erk/MAPK, and p38 MAPK signaling networks was performed in human cell line xenografts from lung, brain, ovary, and prostate tumors. In order to replicate real-world practices, the tissues were subjected to ischemic times of 0 (baseline), 1, 4, and 24 hours before fixation in formalin. Two key concepts emerge from this analysis: (1) the stability of different phospho-epitopes within a given tumor type is variable (e.g. phospho-PRAS40 is more labile than phospho-S6 ribosomal protein) and (2) the stability of a given phospho-epitope (e.g. phospho-MAPKAPK2) varies significantly across different tumor types. These results highlight the importance of proper tissue acquisition and rapid fixation to preserve the biological integrity of signal transduction pathways that may guide therapeutic decision making.

Raf Kinase Inhibitory Protein (RKIP) as a Metastasis Suppressor: Regulation of Signaling Networks in Cancer

PubMed Central

Yesilkanal, Ali E.; Rosner, Marsha R.

2015-01-01

Cancer is one of the deadliest diseases worldwide, accounting for about 8 million deaths a year. For solid tumors, cancer patients die as a result of the metastatic spread of the tumor to the rest of the body. Therefore, there is a clinical need for understanding the molecular and cellular basis of metastasis, identifying patients whose tumors are more likely to metastasize, and developing effective therapies against metastatic progression. Over the years, Raf kinase inhibitory protein (RKIP) has emerged as a natural suppressor of the metastatic process, constituting a tool for studying metastasis and its clinical outcomes. Here, we review RKIP’s role as a metastasis suppressor and the signaling networks and genes regulated by RKIP in metastatic, triple-negative breast cancer. We also highlight the clinical implications and power of building gene signatures based on RKIP-regulated signaling modules in identifying cancer patients that are at higher risk for metastases. Finally, we highlight the potential of RKIP as a tool for developing new therapeutic strategies in cancer treatment. PMID:25597354

Construction and Deciphering of Human Phosphorylation-Mediated Signaling Transduction Networks.

PubMed

Zhang, Menghuan; Li, Hong; He, Ying; Sun, Han; Xia, Li; Wang, Lishun; Sun, Bo; Ma, Liangxiao; Zhang, Guoqing; Li, Jing; Li, Yixue; Xie, Lu

2015-07-02

Protein phosphorylation is the most abundant reversible covalent modification. Human protein kinases participate in almost all biological pathways, and approximately half of the kinases are associated with disease. PhoSigNet was designed to store and display human phosphorylation-mediated signal transduction networks, with additional information related to cancer. It contains 11 976 experimentally validated directed edges and 216 871 phosphorylation sites. Moreover, 3491 differentially expressed proteins in human cancer from dbDEPC, 18 907 human cancer variation sites from CanProVar, and 388 hyperphosphorylation sites from PhosphoSitePlus were collected as annotation information. Compared with other phosphorylation-related databases, PhoSigNet not only takes the kinase-substrate regulatory relationship pairs into account, but also extends regulatory relationships up- and downstream (e.g., from ligand to receptor, from G protein to kinase, and from transcription factor to targets). Furthermore, PhoSigNet allows the user to investigate the impact of phosphorylation modifications on cancer. By using one set of in-house time series phosphoproteomics data, the reconstruction of a conditional and dynamic phosphorylation-mediated signaling network was exemplified. We expect PhoSigNet to be a useful database and analysis platform benefiting both proteomics and cancer studies.

Topological, functional, and dynamic properties of the protein interaction networks rewired by benzo(a)pyrene

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ba, Qian; Key Laboratory of Food Safety Risk Assessment, Ministry of Health, Beijing; Li, Junyang

2015-03-01

Benzo(a)pyrene is a common environmental and foodborne pollutant that has been identified as a human carcinogen. Although the carcinogenicity of benzo(a)pyrene has been extensively reported, its precise molecular mechanisms and the influence on system-level protein networks are not well understood. To investigate the system-level influence of benzo(a)pyrene on protein interactions and regulatory networks, a benzo(a)pyrene-rewired protein interaction network was constructed based on 769 key proteins derived from more than 500 literature reports. The protein interaction network rewired by benzo(a)pyrene was a scale-free, highly-connected biological system. Ten modules were identified, and 25 signaling pathways were enriched, most of which belong tomore » the human diseases category, especially cancer and infectious disease. In addition, two lung-specific and two liver-specific pathways were identified. Three pathways were specific in short and medium-term networks (< 48 h), and five pathways were enriched only in the medium-term network (6 h–48 h). Finally, the expression of linker genes in the network was validated by Western blotting. These findings establish the overall, tissue- and time-specific benzo(a)pyrene-rewired protein interaction networks and provide insights into the biological effects and molecular mechanisms of action of benzo(a)pyrene. - Highlights: • Benzo(a)pyrene induced scale-free, highly-connected protein interaction networks. • 25 signaling pathways were enriched through modular analysis. • Tissue- and time-specific pathways were identified.« less

Position Matters: Network Centrality Considerably Impacts Rates of Protein Evolution in the Human Protein–Protein Interaction Network

PubMed Central

Feyertag, Felix; Chakraborty, Sandip

2017-01-01

Abstract The proteins of any organism evolve at disparate rates. A long list of factors affecting rates of protein evolution have been identified. However, the relative importance of each factor in determining rates of protein evolution remains unresolved. The prevailing view is that evolutionary rates are dominantly determined by gene expression, and that other factors such as network centrality have only a marginal effect, if any. However, this view is largely based on analyses in yeasts, and accurately measuring the importance of the determinants of rates of protein evolution is complicated by the fact that the different factors are often correlated with each other, and by the relatively poor quality of available functional genomics data sets. Here, we use correlation, partial correlation and principal component regression analyses to measure the contributions of several factors to the variability of the rates of evolution of human proteins. For this purpose, we analyzed the entire human protein–protein interaction data set and the human signal transduction network—a network data set of exceptionally high quality, obtained by manual curation, which is expected to be virtually free from false positives. In contrast with the prevailing view, we observe that network centrality (measured as the number of physical and nonphysical interactions, betweenness, and closeness) has a considerable impact on rates of protein evolution. Surprisingly, the impact of centrality on rates of protein evolution seems to be comparable, or even superior according to some analyses, to that of gene expression. Our observations seem to be independent of potentially confounding factors and from the limitations (biases and errors) of interactomic data sets. PMID:28854629

Role of the PDZ-scaffold protein NHERF1/EBP50 in cancer biology: from signaling regulation to clinical relevance.

PubMed

Vaquero, J; Nguyen Ho-Bouldoires, T H; Clapéron, A; Fouassier, L

2017-06-01

The transmission of cellular information requires fine and subtle regulation of proteins that need to interact in a coordinated and specific way to form efficient signaling networks. The spatial and temporal coordination relies on scaffold proteins. Thanks to protein interaction domains such as PDZ domains, scaffold proteins organize multiprotein complexes enabling the proper transmission of cellular information through intracellular networks. NHERF1/EBP50 is a PDZ-scaffold protein that was initially identified as an organizer and regulator of transporters and channels at the apical side of epithelia through actin-binding ezrin-moesin-radixin proteins. Since, NHERF1/EBP50 has emerged as a major regulator of cancer signaling network by assembling cancer-related proteins. The PDZ-scaffold EBP50 carries either anti-tumor or pro-tumor functions, two antinomic functions dictated by EBP50 expression or subcellular localization. The dual function of NHERF1/EBP50 encompasses the regulation of several major signaling pathways engaged in cancer, including the receptor tyrosine kinases PDGFR and EGFR, PI3K/PTEN/AKT and Wnt-β-catenin pathways.

Modular architecture of protein structures and allosteric communications: potential implications for signaling proteins and regulatory linkages

PubMed Central

del Sol, Antonio; Araúzo-Bravo, Marcos J; Amoros, Dolors; Nussinov, Ruth

2007-01-01

Background Allosteric communications are vital for cellular signaling. Here we explore a relationship between protein architectural organization and shortcuts in signaling pathways. Results We show that protein domains consist of modules interconnected by residues that mediate signaling through the shortest pathways. These mediating residues tend to be located at the inter-modular boundaries, which are more rigid and display a larger number of long-range interactions than intra-modular regions. The inter-modular boundaries contain most of the residues centrally conserved in the protein fold, which may be crucial for information transfer between amino acids. Our approach to modular decomposition relies on a representation of protein structures as residue-interacting networks, and removal of the most central residue contacts, which are assumed to be crucial for allosteric communications. The modular decomposition of 100 multi-domain protein structures indicates that modules constitute the building blocks of domains. The analysis of 13 allosteric proteins revealed that modules characterize experimentally identified functional regions. Based on the study of an additional functionally annotated dataset of 115 proteins, we propose that high-modularity modules include functional sites and are the basic functional units. We provide examples (the Gαs subunit and P450 cytochromes) to illustrate that the modular architecture of active sites is linked to their functional specialization. Conclusion Our method decomposes protein structures into modules, allowing the study of signal transmission between functional sites. A modular configuration might be advantageous: it allows signaling proteins to expand their regulatory linkages and may elicit a broader range of control mechanisms either via modular combinations or through modulation of inter-modular linkages. PMID:17531094

Elementary signaling modes predict the essentiality of signal transduction network components

PubMed Central

2011-01-01

Background Understanding how signals propagate through signaling pathways and networks is a central goal in systems biology. Quantitative dynamic models help to achieve this understanding, but are difficult to construct and validate because of the scarcity of known mechanistic details and kinetic parameters. Structural and qualitative analysis is emerging as a feasible and useful alternative for interpreting signal transduction. Results In this work, we present an integrative computational method for evaluating the essentiality of components in signaling networks. This approach expands an existing signaling network to a richer representation that incorporates the positive or negative nature of interactions and the synergistic behaviors among multiple components. Our method simulates both knockout and constitutive activation of components as node disruptions, and takes into account the possible cascading effects of a node's disruption. We introduce the concept of elementary signaling mode (ESM), as the minimal set of nodes that can perform signal transduction independently. Our method ranks the importance of signaling components by the effects of their perturbation on the ESMs of the network. Validation on several signaling networks describing the immune response of mammals to bacteria, guard cell abscisic acid signaling in plants, and T cell receptor signaling shows that this method can effectively uncover the essentiality of components mediating a signal transduction process and results in strong agreement with the results of Boolean (logical) dynamic models and experimental observations. Conclusions This integrative method is an efficient procedure for exploratory analysis of large signaling and regulatory networks where dynamic modeling or experimental tests are impractical. Its results serve as testable predictions, provide insights into signal transduction and regulatory mechanisms and can guide targeted computational or experimental follow-up studies. The

Functional modules by relating protein interaction networks and gene expression.

PubMed

Tornow, Sabine; Mewes, H W

2003-11-01

Genes and proteins are organized on the basis of their particular mutual relations or according to their interactions in cellular and genetic networks. These include metabolic or signaling pathways and protein interaction, regulatory or co-expression networks. Integrating the information from the different types of networks may lead to the notion of a functional network and functional modules. To find these modules, we propose a new technique which is based on collective, multi-body correlations in a genetic network. We calculated the correlation strength of a group of genes (e.g. in the co-expression network) which were identified as members of a module in a different network (e.g. in the protein interaction network) and estimated the probability that this correlation strength was found by chance. Groups of genes with a significant correlation strength in different networks have a high probability that they perform the same function. Here, we propose evaluating the multi-body correlations by applying the superparamagnetic approach. We compare our method to the presently applied mean Pearson correlations and show that our method is more sensitive in revealing functional relationships.

Functional modules by relating protein interaction networks and gene expression

PubMed Central

Tornow, Sabine; Mewes, H. W.

2003-01-01

Genes and proteins are organized on the basis of their particular mutual relations or according to their interactions in cellular and genetic networks. These include metabolic or signaling pathways and protein interaction, regulatory or co-expression networks. Integrating the information from the different types of networks may lead to the notion of a functional network and functional modules. To find these modules, we propose a new technique which is based on collective, multi-body correlations in a genetic network. We calculated the correlation strength of a group of genes (e.g. in the co-expression network) which were identified as members of a module in a different network (e.g. in the protein interaction network) and estimated the probability that this correlation strength was found by chance. Groups of genes with a significant correlation strength in different networks have a high probability that they perform the same function. Here, we propose evaluating the multi-body correlations by applying the superparamagnetic approach. We compare our method to the presently applied mean Pearson correlations and show that our method is more sensitive in revealing functional relationships. PMID:14576317

Integrated cellular network of transcription regulations and protein-protein interactions.

PubMed

Wang, Yu-Chao; Chen, Bor-Sen

2010-03-08

With the accumulation of increasing omics data, a key goal of systems biology is to construct networks at different cellular levels to investigate cellular machinery of the cell. However, there is currently no satisfactory method to construct an integrated cellular network that combines the gene regulatory network and the signaling regulatory pathway. In this study, we integrated different kinds of omics data and developed a systematic method to construct the integrated cellular network based on coupling dynamic models and statistical assessments. The proposed method was applied to S. cerevisiae stress responses, elucidating the stress response mechanism of the yeast. From the resulting integrated cellular network under hyperosmotic stress, the highly connected hubs which are functionally relevant to the stress response were identified. Beyond hyperosmotic stress, the integrated network under heat shock and oxidative stress were also constructed and the crosstalks of these networks were analyzed, specifying the significance of some transcription factors to serve as the decision-making devices at the center of the bow-tie structure and the crucial role for rapid adaptation scheme to respond to stress. In addition, the predictive power of the proposed method was also demonstrated. We successfully construct the integrated cellular network which is validated by literature evidences. The integration of transcription regulations and protein-protein interactions gives more insight into the actual biological network and is more predictive than those without integration. The method is shown to be powerful and flexible and can be used under different conditions and for different species. The coupling dynamic models of the whole integrated cellular network are very useful for theoretical analyses and for further experiments in the fields of network biology and synthetic biology.

Attractor Structures of Signaling Networks: Consequences of Different Conformational Barcode Dynamics and Their Relations to Network-Based Drug Design.

PubMed

Szalay, Kristóf Z; Nussinov, Ruth; Csermely, Peter

2014-06-01

Conformational barcodes tag functional sites of proteins and are decoded by interacting molecules transmitting the incoming signal. Conformational barcodes are modified by all co-occurring allosteric events induced by post-translational modifications, pathogen, drug binding, etc. We argue that fuzziness (plasticity) of conformational barcodes may be increased by disordered protein structures, by integrative plasticity of multi-phosphorylation events, by increased intracellular water content (decreased molecular crowding) and by increased action of molecular chaperones. This leads to increased plasticity of signaling and cellular networks. Increased plasticity is both substantiated by and inducing an increased noise level. Using the versatile network dynamics tool, Turbine (www.turbine.linkgroup.hu), here we show that the 10 % noise level expected in cellular systems shifts a cancer-related signaling network of human cells from its proliferative attractors to its largest, apoptotic attractor representing their health-preserving response in the carcinogen containing and tumor suppressor deficient environment modeled in our study. Thus, fuzzy conformational barcodes may not only make the cellular system more plastic, and therefore more adaptable, but may also stabilize the complex system allowing better access to its largest attractor. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

SoxB1-driven transcriptional network underlies neural-specific interpretation of morphogen signals.

PubMed

Oosterveen, Tony; Kurdija, Sanja; Ensterö, Mats; Uhde, Christopher W; Bergsland, Maria; Sandberg, Magnus; Sandberg, Rickard; Muhr, Jonas; Ericson, Johan

2013-04-30

The reiterative deployment of a small cadre of morphogen signals underlies patterning and growth of most tissues during embyogenesis, but how such inductive events result in tissue-specific responses remains poorly understood. By characterizing cis-regulatory modules (CRMs) associated with genes regulated by Sonic hedgehog (Shh), retinoids, or bone morphogenetic proteins in the CNS, we provide evidence that the neural-specific interpretation of morphogen signaling reflects a direct integration of these pathways with SoxB1 proteins at the CRM level. Moreover, expression of SoxB1 proteins in the limb bud confers on mesodermal cells the potential to activate neural-specific target genes upon Shh, retinoid, or bone morphogenetic protein signaling, and the collocation of binding sites for SoxB1 and morphogen-mediatory transcription factors in CRMs faithfully predicts neural-specific gene activity. Thus, an unexpectedly simple transcriptional paradigm appears to conceptually explain the neural-specific interpretation of pleiotropic signaling during vertebrate development. Importantly, genes induced in a SoxB1-dependent manner appear to constitute repressive gene regulatory networks that are directly interlinked at the CRM level to constrain the regional expression of patterning genes. Accordingly, not only does the topology of SoxB1-driven gene regulatory networks provide a tissue-specific mode of gene activation, but it also determines the spatial expression pattern of target genes within the developing neural tube.

Advances in dynamic modeling of colorectal cancer signaling-network regions, a path toward targeted therapies

PubMed Central

Kolch, Walter; Kholodenko, Boris N.; Ambrosi, Cristina De; Barla, Annalisa; Biganzoli, Elia M.; Nencioni, Alessio; Patrone, Franco; Ballestrero, Alberto; Zoppoli, Gabriele; Verri, Alessandro; Parodi, Silvio

2015-01-01

The interconnected network of pathways downstream of the TGFβ, WNT and EGF-families of receptor ligands play an important role in colorectal cancer pathogenesis. We studied and implemented dynamic simulations of multiple downstream pathways and described the section of the signaling network considered as a Molecular Interaction Map (MIM). Our simulations used Ordinary Differential Equations (ODEs), which involved 447 reactants and their interactions. Starting from an initial “physiologic condition”, the model can be adapted to simulate individual pathologic cancer conditions implementing alterations/mutations in relevant onco-proteins. We verified some salient model predictions using the mutated colorectal cancer lines HCT116 and HT29. We measured the amount of MYC and CCND1 mRNAs and AKT and ERK phosphorylated proteins, in response to individual or combination onco-protein inhibitor treatments. Experimental and simulation results were well correlated. Recent independently published results were also predicted by our model. Even in the presence of an approximate and incomplete signaling network information, a predictive dynamic modeling seems already possible. An important long term road seems to be open and can be pursued further, by incremental steps, toward even larger and better parameterized MIMs. Personalized treatment strategies with rational associations of signaling-proteins inhibitors, could become a realistic goal. PMID:25671297

An Ultrasensitive Bacterial Motor Revealed by Monitoring Signaling Proteins in Single Cells

NASA Astrophysics Data System (ADS)

Cluzel, Philippe; Surette, Michael; Leibler, Stanislas

2000-03-01

Understanding biology at the single-cell level requires simultaneous measurements of biochemical parameters and behavioral characteristics in individual cells. Here, the output of individual flagellar motors in Escherichia coli was measured as a function of the intracellular concentration of the chemotactic signaling protein. The concentration of this molecule, fused to green fluorescent protein, was monitored with fluorescence correlation spectroscopy. Motors from different bacteria exhibited an identical steep input-output relation, suggesting that they actively contribute to signal amplification in chemotaxis. This experimental approach can be extended to quantitative in vivo studies of other biochemical networks.

Adaptor proteins in protein kinase C-mediated signal transduction.

PubMed

Schechtman, D; Mochly-Rosen, D

2001-10-01

Spatial and temporal organization of signal transduction is essential in determining the speed and precision by which signaling events occur. Adaptor proteins are key to organizing signaling enzymes near their select substrates and away from others in order to optimize precision and speed of response. Here, we describe the role of adaptor proteins in determining the specific function of individual protein kinase C (PKC) isozymes. These isozyme-selective proteins were called collectively RACKs (receptors for activated C-kinase). The role of RACKs in PKC-mediated signaling was determined using isozyme-specific inhibitors and activators of the binding of each isozyme to its respective RACK. In addition to anchoring activated PKC isozymes, RACKs anchor other signaling enzymes. RACK1, the anchoring protein for activated betaIIPKC, binds for example, Src tyrosine kinase, integrin, and phosphodiesterase. RACK2, the epsilonPKC-specific RACK, is a coated-vesicle protein and thus is involved in vesicular release and cell-cell communication. Therefore, RACKs are not only adaptors for PKC, but also serve as adaptor proteins for several other signaling enzymes. Because at least some of the proteins that bind to RACKs, including PKC itself, regulate cell growth, modulating their interactions with RACKs may help elucidate signaling pathways leading to carcinogenesis and could result in the identification of novel therapeutic targets.

Amyloid precursor protein interaction network in human testis: sentinel proteins for male reproduction.

PubMed

Silva, Joana Vieira; Yoon, Sooyeon; Domingues, Sara; Guimarães, Sofia; Goltsev, Alexander V; da Cruz E Silva, Edgar Figueiredo; Mendes, José Fernando F; da Cruz E Silva, Odete Abreu Beirão; Fardilha, Margarida

2015-01-16

Amyloid precursor protein (APP) is widely recognized for playing a central role in Alzheimer's disease pathogenesis. Although APP is expressed in several tissues outside the human central nervous system, the functions of APP and its family members in other tissues are still poorly understood. APP is involved in several biological functions which might be potentially important for male fertility, such as cell adhesion, cell motility, signaling, and apoptosis. Furthermore, APP superfamily members are known to be associated with fertility. Knowledge on the protein networks of APP in human testis and spermatozoa will shed light on the function of APP in the male reproductive system. We performed a Yeast Two-Hybrid screen and a database search to study the interaction network of APP in human testis and sperm. To gain insights into the role of APP superfamily members in fertility, the study was extended to APP-like protein 2 (APLP2). We analyzed several topological properties of the APP interaction network and the biological and physiological properties of the proteins in the APP interaction network were also specified by gene ontologyand pathways analyses. We classified significant features related to the human male reproduction for the APP interacting proteins and identified modules of proteins with similar functional roles which may show cooperative behavior for male fertility. The present work provides the first report on the APP interactome in human testis. Our approach allowed the identification of novel interactions and recognition of key APP interacting proteins for male reproduction, particularly in sperm-oocyte interaction.

Neuron-Like Networks Between Ribosomal Proteins Within the Ribosome

NASA Astrophysics Data System (ADS)

Poirot, Olivier; Timsit, Youri

2016-05-01

From brain to the World Wide Web, information-processing networks share common scale invariant properties. Here, we reveal the existence of neural-like networks at a molecular scale within the ribosome. We show that with their extensions, ribosomal proteins form complex assortative interaction networks through which they communicate through tiny interfaces. The analysis of the crystal structures of 50S eubacterial particles reveals that most of these interfaces involve key phylogenetically conserved residues. The systematic observation of interactions between basic and aromatic amino acids at the interfaces and along the extension provides new structural insights that may contribute to decipher the molecular mechanisms of signal transmission within or between the ribosomal proteins. Similar to neurons interacting through “molecular synapses”, ribosomal proteins form a network that suggest an analogy with a simple molecular brain in which the “sensory-proteins” innervate the functional ribosomal sites, while the “inter-proteins” interconnect them into circuits suitable to process the information flow that circulates during protein synthesis. It is likely that these circuits have evolved to coordinate both the complex macromolecular motions and the binding of the multiple factors during translation. This opens new perspectives on nanoscale information transfer and processing.

Barcoding of GPCR trafficking and signaling through the various trafficking roadmaps by compartmentalized signaling networks.

PubMed

Bahouth, Suleiman W; Nooh, Mohammed M

2017-08-01

Proper signaling by G protein coupled receptors (GPCR) is dependent on the specific repertoire of transducing, enzymatic and regulatory kinases and phosphatases that shape its signaling output. Activation and signaling of the GPCR through its cognate G protein is impacted by G protein-coupled receptor kinase (GRK)-imprinted "barcodes" that recruit β-arrestins to regulate subsequent desensitization, biased signaling and endocytosis of the GPCR. The outcome of agonist-internalized GPCR in endosomes is also regulated by sequence motifs or "barcodes" within the GPCR that mediate its recycling to the plasma membrane or retention and eventual degradation as well as its subsequent signaling in endosomes. Given the vast number of diverse sequences in GPCR, several trafficking mechanisms for endosomal GPCR have been described. The majority of recycling GPCR, are sorted out of endosomes in a "sequence-dependent pathway" anchored around a type-1 PDZ-binding module found in their C-tails. For a subset of these GPCR, a second "barcode" imprinted onto specific GPCR serine/threonine residues by compartmentalized kinase networks was required for their efficient recycling through the "sequence-dependent pathway". Mutating the serine/threonine residues involved, produced dramatic effects on GPCR trafficking, indicating that they played a major role in setting the trafficking itinerary of these GPCR. While endosomal SNX27, retromer/WASH complexes and actin were required for efficient sorting and budding of all these GPCR, additional proteins were required for GPCR sorting via the second "barcode". Here we will review recent developments in GPCR trafficking in general and the human β 1 -adrenergic receptor in particular across the various trafficking roadmaps. In addition, we will discuss the role of GPCR trafficking in regulating endosomal GPCR signaling, which promote biochemical and physiological effects that are distinct from those generated by the GPCR signal transduction

The N and C Termini of ZO-1 Are Surrounded by Distinct Proteins and Functional Protein Networks*

PubMed Central

Van Itallie, Christina M.; Aponte, Angel; Tietgens, Amber Jean; Gucek, Marjan; Fredriksson, Karin; Anderson, James Melvin

2013-01-01

The proteins and functional protein networks of the tight junction remain incompletely defined. Among the currently known proteins are barrier-forming proteins like occludin and the claudin family; scaffolding proteins like ZO-1; and some cytoskeletal, signaling, and cell polarity proteins. To define a more complete list of proteins and infer their functional implications, we identified the proteins that are within molecular dimensions of ZO-1 by fusing biotin ligase to either its N or C terminus, expressing these fusion proteins in Madin-Darby canine kidney epithelial cells, and purifying and identifying the resulting biotinylated proteins by mass spectrometry. Of a predicted proteome of ∼9000, we identified more than 400 proteins tagged by biotin ligase fused to ZO-1, with both identical and distinct proteins near the N- and C-terminal ends. Those proximal to the N terminus were enriched in transmembrane tight junction proteins, and those proximal to the C terminus were enriched in cytoskeletal proteins. We also identified many unexpected but easily rationalized proteins and verified partial colocalization of three of these proteins with ZO-1 as examples. In addition, functional networks of interacting proteins were tagged, such as the basolateral but not apical polarity network. These results provide a rich inventory of proteins and potential novel insights into functions and protein networks that should catalyze further understanding of tight junction biology. Unexpectedly, the technique demonstrates high spatial resolution, which could be generally applied to defining other subcellular protein compartmentalization. PMID:23553632

Integrated Module and Gene-Specific Regulatory Inference Implicates Upstream Signaling Networks

PubMed Central

Roy, Sushmita; Lagree, Stephen; Hou, Zhonggang; Thomson, James A.; Stewart, Ron; Gasch, Audrey P.

2013-01-01

Regulatory networks that control gene expression are important in diverse biological contexts including stress response and development. Each gene's regulatory program is determined by module-level regulation (e.g. co-regulation via the same signaling system), as well as gene-specific determinants that can fine-tune expression. We present a novel approach, Modular regulatory network learning with per gene information (MERLIN), that infers regulatory programs for individual genes while probabilistically constraining these programs to reveal module-level organization of regulatory networks. Using edge-, regulator- and module-based comparisons of simulated networks of known ground truth, we find MERLIN reconstructs regulatory programs of individual genes as well or better than existing approaches of network reconstruction, while additionally identifying modular organization of the regulatory networks. We use MERLIN to dissect global transcriptional behavior in two biological contexts: yeast stress response and human embryonic stem cell differentiation. Regulatory modules inferred by MERLIN capture co-regulatory relationships between signaling proteins and downstream transcription factors thereby revealing the upstream signaling systems controlling transcriptional responses. The inferred networks are enriched for regulators with genetic or physical interactions, supporting the inference, and identify modules of functionally related genes bound by the same transcriptional regulators. Our method combines the strengths of per-gene and per-module methods to reveal new insights into transcriptional regulation in stress and development. PMID:24146602

Bimolecular fluorescence complementation: lighting up seven transmembrane domain receptor signalling networks

PubMed Central

Rose, Rachel H; Briddon, Stephen J; Holliday, Nicholas D

2010-01-01

There is increasing complexity in the organization of seven transmembrane domain (7TM) receptor signalling pathways, and in the ability of their ligands to modulate and direct this signalling. Underlying these events is a network of protein interactions between the 7TM receptors themselves and associated effectors, such as G proteins and β-arrestins. Bimolecular fluorescence complementation, or BiFC, is a technique capable of detecting these protein–protein events essential for 7TM receptor function. Fluorescent proteins, such as those from Aequorea victoria, are split into two non-fluorescent halves, which then tag the proteins under study. On association, these fragments refold and regenerate a mature fluorescent protein, producing a BiFC signal indicative of complex formation. Here, we review the experimental criteria for successful application of BiFC, considered in the context of 7TM receptor signalling events such as receptor dimerization, G protein and β-arrestin signalling. The advantages and limitations of BiFC imaging are compared with alternative resonance energy transfer techniques. We show that the essential simplicity of the fluorescent BiFC measurement allows high-content and advanced imaging applications, and that it can probe more complex multi-protein interactions alone or in combination with resonance energy transfer. These capabilities suggest that BiFC techniques will become ever more useful in the analysis of ligand and 7TM receptor pharmacology at the molecular level of protein–protein interactions. This article is part of a themed section on Imaging in Pharmacology. To view the editorial for this themed section visit http://dx.doi.org/10.1111/j.1476-5381.2010.00685.x PMID:20015298

Integrated cellular network of transcription regulations and protein-protein interactions

PubMed Central

2010-01-01

Background With the accumulation of increasing omics data, a key goal of systems biology is to construct networks at different cellular levels to investigate cellular machinery of the cell. However, there is currently no satisfactory method to construct an integrated cellular network that combines the gene regulatory network and the signaling regulatory pathway. Results In this study, we integrated different kinds of omics data and developed a systematic method to construct the integrated cellular network based on coupling dynamic models and statistical assessments. The proposed method was applied to S. cerevisiae stress responses, elucidating the stress response mechanism of the yeast. From the resulting integrated cellular network under hyperosmotic stress, the highly connected hubs which are functionally relevant to the stress response were identified. Beyond hyperosmotic stress, the integrated network under heat shock and oxidative stress were also constructed and the crosstalks of these networks were analyzed, specifying the significance of some transcription factors to serve as the decision-making devices at the center of the bow-tie structure and the crucial role for rapid adaptation scheme to respond to stress. In addition, the predictive power of the proposed method was also demonstrated. Conclusions We successfully construct the integrated cellular network which is validated by literature evidences. The integration of transcription regulations and protein-protein interactions gives more insight into the actual biological network and is more predictive than those without integration. The method is shown to be powerful and flexible and can be used under different conditions and for different species. The coupling dynamic models of the whole integrated cellular network are very useful for theoretical analyses and for further experiments in the fields of network biology and synthetic biology. PMID:20211003

Detection of protein complex from protein-protein interaction network using Markov clustering

NASA Astrophysics Data System (ADS)

Ochieng, P. J.; Kusuma, W. A.; Haryanto, T.

2017-05-01

Detection of complexes, or groups of functionally related proteins, is an important challenge while analysing biological networks. However, existing algorithms to identify protein complexes are insufficient when applied to dense networks of experimentally derived interaction data. Therefore, we introduced a graph clustering method based on Markov clustering algorithm to identify protein complex within highly interconnected protein-protein interaction networks. Protein-protein interaction network was first constructed to develop geometrical network, the network was then partitioned using Markov clustering to detect protein complexes. The interest of the proposed method was illustrated by its application to Human Proteins associated to type II diabetes mellitus. Flow simulation of MCL algorithm was initially performed and topological properties of the resultant network were analysed for detection of the protein complex. The results indicated the proposed method successfully detect an overall of 34 complexes with 11 complexes consisting of overlapping modules and 20 non-overlapping modules. The major complex consisted of 102 proteins and 521 interactions with cluster modularity and density of 0.745 and 0.101 respectively. The comparison analysis revealed MCL out perform AP, MCODE and SCPS algorithms with high clustering coefficient (0.751) network density and modularity index (0.630). This demonstrated MCL was the most reliable and efficient graph clustering algorithm for detection of protein complexes from PPI networks.

Perturbation Biology: Inferring Signaling Networks in Cellular Systems

PubMed Central

Miller, Martin L.; Gauthier, Nicholas P.; Jing, Xiaohong; Kaushik, Poorvi; He, Qin; Mills, Gordon; Solit, David B.; Pratilas, Christine A.; Weigt, Martin; Braunstein, Alfredo; Pagnani, Andrea; Zecchina, Riccardo; Sander, Chris

2013-01-01

We present a powerful experimental-computational technology for inferring network models that predict the response of cells to perturbations, and that may be useful in the design of combinatorial therapy against cancer. The experiments are systematic series of perturbations of cancer cell lines by targeted drugs, singly or in combination. The response to perturbation is quantified in terms of relative changes in the measured levels of proteins, phospho-proteins and cellular phenotypes such as viability. Computational network models are derived de novo, i.e., without prior knowledge of signaling pathways, and are based on simple non-linear differential equations. The prohibitively large solution space of all possible network models is explored efficiently using a probabilistic algorithm, Belief Propagation (BP), which is three orders of magnitude faster than standard Monte Carlo methods. Explicit executable models are derived for a set of perturbation experiments in SKMEL-133 melanoma cell lines, which are resistant to the therapeutically important inhibitor of RAF kinase. The resulting network models reproduce and extend known pathway biology. They empower potential discoveries of new molecular interactions and predict efficacious novel drug perturbations, such as the inhibition of PLK1, which is verified experimentally. This technology is suitable for application to larger systems in diverse areas of molecular biology. PMID:24367245

CellNOptR: a flexible toolkit to train protein signaling networks to data using multiple logic formalisms.

PubMed

Terfve, Camille; Cokelaer, Thomas; Henriques, David; MacNamara, Aidan; Goncalves, Emanuel; Morris, Melody K; van Iersel, Martijn; Lauffenburger, Douglas A; Saez-Rodriguez, Julio

2012-10-18

Cells process signals using complex and dynamic networks. Studying how this is performed in a context and cell type specific way is essential to understand signaling both in physiological and diseased situations. Context-specific medium/high throughput proteomic data measured upon perturbation is now relatively easy to obtain but formalisms that can take advantage of these features to build models of signaling are still comparatively scarce. Here we present CellNOptR, an open-source R software package for building predictive logic models of signaling networks by training networks derived from prior knowledge to signaling (typically phosphoproteomic) data. CellNOptR features different logic formalisms, from Boolean models to differential equations, in a common framework. These different logic model representations accommodate state and time values with increasing levels of detail. We provide in addition an interface via Cytoscape (CytoCopteR) to facilitate use and integration with Cytoscape network-based capabilities. Models generated with this pipeline have two key features. First, they are constrained by prior knowledge about the network but trained to data. They are therefore context and cell line specific, which results in enhanced predictive and mechanistic insights. Second, they can be built using different logic formalisms depending on the richness of the available data. Models built with CellNOptR are useful tools to understand how signals are processed by cells and how this is altered in disease. They can be used to predict the effect of perturbations (individual or in combinations), and potentially to engineer therapies that have differential effects/side effects depending on the cell type or context.

The SH2 Domain–Containing Proteins in 21 Species Establish the Provenance and Scope of Phosphotyrosine Signaling in Eukaryotes

PubMed Central

Liu, Bernard A.; Shah, Eshana; Jablonowski, Karl; Stergachis, Andrew; Engelmann, Brett; Nash, Piers D.

2014-01-01

The Src homology 2 (SH2) domains are participants in metazoan signal transduction, acting as primary mediators for regulated protein-protein interactions with tyrosine-phosphorylated substrates. Here, we describe the origin and evolution of SH2 domain proteins by means of sequence analysis from 21 eukaryotic organisms from the basal unicellular eukaryotes, where SH2 domains first appeared, through the multicellular animals and increasingly complex metazoans. On the basis of our results, SH2 domains and phosphotyrosine signaling emerged in the early Unikonta, and the numbers of SH2 domains expanded in the choanoflagellate and metazoan lineages with the development of tyrosine kinases, leading to rapid elaboration of phosphotyrosine signaling in early multicellular animals. Our results also indicated that SH2 domains coevolved and the number of the domains expanded alongside protein tyrosine kinases and tyrosine phosphatases, thereby coupling phosphotyrosine signaling to downstream signaling networks. Gene duplication combined with domain gain or loss produced novel SH2-containing proteins that function within phosphotyrosine signaling, which likely have contributed to diversity and complexity in metazoans. We found that intra- and intermolecular interactions within and between SH2 domain proteins increased in prevalence along with organismal complexity and may function to generate more highly connected and robust phosphotyrosine signaling networks. PMID:22155787

Protein complex prediction in large ontology attributed protein-protein interaction networks.

PubMed

Zhang, Yijia; Lin, Hongfei; Yang, Zhihao; Wang, Jian; Li, Yanpeng; Xu, Bo

2013-01-01

Protein complexes are important for unraveling the secrets of cellular organization and function. Many computational approaches have been developed to predict protein complexes in protein-protein interaction (PPI) networks. However, most existing approaches focus mainly on the topological structure of PPI networks, and largely ignore the gene ontology (GO) annotation information. In this paper, we constructed ontology attributed PPI networks with PPI data and GO resource. After constructing ontology attributed networks, we proposed a novel approach called CSO (clustering based on network structure and ontology attribute similarity). Structural information and GO attribute information are complementary in ontology attributed networks. CSO can effectively take advantage of the correlation between frequent GO annotation sets and the dense subgraph for protein complex prediction. Our proposed CSO approach was applied to four different yeast PPI data sets and predicted many well-known protein complexes. The experimental results showed that CSO was valuable in predicting protein complexes and achieved state-of-the-art performance.

Cellular Organization and Cytoskeletal Regulation of the Hippo Signaling Network

PubMed Central

Sun, Shuguo; Irvine, Kenneth D.

2016-01-01

The Hippo signaling network integrates diverse upstream signals to control cell fate decisions and regulate organ growth. Recent studies have provided new insights into the cellular organization of Hippo signaling, its relationship to cell-cell junctions, and how the cytoskeleton modulates Hippo signaling. Cell-cell junctions serve as platforms for Hippo signaling by localizing scaffolding proteins that interact with core components of the pathway. Interactions of Hippo pathway components with cell-cell junctions and the cytoskeleton also suggest potential mechanisms for the regulation of the pathway by cell contact and cell polarity. As our understanding of the complexity of Hippo signaling increases, a future challenge will be to understand how the diverse inputs into the pathway are integrated, and to define their respective contributions in vivo. PMID:27268910

Cellular Organization and Cytoskeletal Regulation of the Hippo Signaling Network.

PubMed

Sun, Shuguo; Irvine, Kenneth D

2016-09-01

The Hippo signaling network integrates diverse upstream signals to control cell fate decisions and regulate organ growth. Recent studies have provided new insights into the cellular organization of Hippo signaling, its relationship to cell-cell junctions, and how the cytoskeleton modulates Hippo signaling. Cell-cell junctions serve as platforms for Hippo signaling by localizing scaffolding proteins that interact with core components of the pathway. Interactions of Hippo pathway components with cell-cell junctions and the cytoskeleton also suggest potential mechanisms for the regulation of the pathway by cell contact and cell polarity. As our understanding of the complexity of Hippo signaling increases, a future challenge will be to understand how the diverse inputs into the pathway are integrated and to define their respective contributions in vivo. Copyright © 2016 Elsevier Ltd. All rights reserved.

Atypical Diabetic Foot Ulcer Keratinocyte Protein Signaling Correlates with Impaired Wound Healing.

PubMed

Hoke, Glenn D; Ramos, Corrine; Hoke, Nicholas N; Crossland, Mary C; Shawler, Lisa G; Boykin, Joseph V

2016-01-01

Diabetes mellitus is associated with chronic diabetic foot ulcers (DFUs) and wound infections often resulting in lower extremity amputations. The protein signaling architecture of the mechanisms responsible for impaired DFU healing has not been characterized. In this preliminary clinical study, the intracellular levels of proteins involved in signal transduction networks relevant to wound healing were non-biasedly measured using reverse-phase protein arrays (RPPA) in keratinocytes isolated from DFU wound biopsies. RPPA allows for the simultaneous documentation and assessment of the signaling pathways active in each DFU. Thus, RPPA provides for the accurate mapping of wound healing pathways associated with apoptosis, proliferation, senescence, survival, and angiogenesis. From the study data, we have identified potential diagnostic, or predictive, biomarkers for DFU wound healing derived from the ratios of quantified signaling protein expressions within interconnected pathways. These biomarkers may allow physicians to personalize therapeutic strategies for DFU management on an individual basis based upon the signaling architecture present in each wound. Additionally, we have identified altered, interconnected signaling pathways within DFU keratinocytes that may help guide the development of therapeutics to modulate these dysregulated pathways, many of which parallel the therapeutic targets which are the hallmarks of molecular therapies for treating cancer.

Atypical Diabetic Foot Ulcer Keratinocyte Protein Signaling Correlates with Impaired Wound Healing

PubMed Central

Hoke, Glenn D.; Ramos, Corrine; Hoke, Nicholas N.; Crossland, Mary C.; Shawler, Lisa G.

2016-01-01

Diabetes mellitus is associated with chronic diabetic foot ulcers (DFUs) and wound infections often resulting in lower extremity amputations. The protein signaling architecture of the mechanisms responsible for impaired DFU healing has not been characterized. In this preliminary clinical study, the intracellular levels of proteins involved in signal transduction networks relevant to wound healing were non-biasedly measured using reverse-phase protein arrays (RPPA) in keratinocytes isolated from DFU wound biopsies. RPPA allows for the simultaneous documentation and assessment of the signaling pathways active in each DFU. Thus, RPPA provides for the accurate mapping of wound healing pathways associated with apoptosis, proliferation, senescence, survival, and angiogenesis. From the study data, we have identified potential diagnostic, or predictive, biomarkers for DFU wound healing derived from the ratios of quantified signaling protein expressions within interconnected pathways. These biomarkers may allow physicians to personalize therapeutic strategies for DFU management on an individual basis based upon the signaling architecture present in each wound. Additionally, we have identified altered, interconnected signaling pathways within DFU keratinocytes that may help guide the development of therapeutics to modulate these dysregulated pathways, many of which parallel the therapeutic targets which are the hallmarks of molecular therapies for treating cancer. PMID:27840833

Cytoprophet: a Cytoscape plug-in for protein and domain interaction networks inference.

PubMed

Morcos, Faruck; Lamanna, Charles; Sikora, Marcin; Izaguirre, Jesús

2008-10-01

Cytoprophet is a software tool that allows prediction and visualization of protein and domain interaction networks. It is implemented as a plug-in of Cytoscape, an open source software framework for analysis and visualization of molecular networks. Cytoprophet implements three algorithms that predict new potential physical interactions using the domain composition of proteins and experimental assays. The algorithms for protein and domain interaction inference include maximum likelihood estimation (MLE) using expectation maximization (EM); the set cover approach maximum specificity set cover (MSSC) and the sum-product algorithm (SPA). After accepting an input set of proteins with Uniprot ID/Accession numbers and a selected prediction algorithm, Cytoprophet draws a network of potential interactions with probability scores and GO distances as edge attributes. A network of domain interactions between the domains of the initial protein list can also be generated. Cytoprophet was designed to take advantage of the visual capabilities of Cytoscape and be simple to use. An example of inference in a signaling network of myxobacterium Myxococcus xanthus is presented and available at Cytoprophet's website. http://cytoprophet.cse.nd.edu.

Design principles of nuclear receptor signaling: how complex networking improves signal transduction

PubMed Central

Kolodkin, Alexey N; Bruggeman, Frank J; Plant, Nick; Moné, Martijn J; Bakker, Barbara M; Campbell, Moray J; van Leeuwen, Johannes P T M; Carlberg, Carsten; Snoep, Jacky L; Westerhoff, Hans V

2010-01-01

The topology of nuclear receptor (NR) signaling is captured in a systems biological graphical notation. This enables us to identify a number of ‘design' aspects of the topology of these networks that might appear unnecessarily complex or even functionally paradoxical. In realistic kinetic models of increasing complexity, calculations show how these features correspond to potentially important design principles, e.g.: (i) cytosolic ‘nuclear' receptor may shuttle signal molecules to the nucleus, (ii) the active export of NRs may ensure that there is sufficient receptor protein to capture ligand at the cytoplasmic membrane, (iii) a three conveyor belts design dissipating GTP-free energy, greatly aids response, (iv) the active export of importins may prevent sequestration of NRs by importins in the nucleus and (v) the unspecific nature of the nuclear pore may ensure signal-flux robustness. In addition, the models developed are suitable for implementation in specific cases of NR-mediated signaling, to predict individual receptor functions and differential sensitivity toward physiological and pharmacological ligands. PMID:21179018

CellNOptR: a flexible toolkit to train protein signaling networks to data using multiple logic formalisms

PubMed Central

2012-01-01

Background Cells process signals using complex and dynamic networks. Studying how this is performed in a context and cell type specific way is essential to understand signaling both in physiological and diseased situations. Context-specific medium/high throughput proteomic data measured upon perturbation is now relatively easy to obtain but formalisms that can take advantage of these features to build models of signaling are still comparatively scarce. Results Here we present CellNOptR, an open-source R software package for building predictive logic models of signaling networks by training networks derived from prior knowledge to signaling (typically phosphoproteomic) data. CellNOptR features different logic formalisms, from Boolean models to differential equations, in a common framework. These different logic model representations accommodate state and time values with increasing levels of detail. We provide in addition an interface via Cytoscape (CytoCopteR) to facilitate use and integration with Cytoscape network-based capabilities. Conclusions Models generated with this pipeline have two key features. First, they are constrained by prior knowledge about the network but trained to data. They are therefore context and cell line specific, which results in enhanced predictive and mechanistic insights. Second, they can be built using different logic formalisms depending on the richness of the available data. Models built with CellNOptR are useful tools to understand how signals are processed by cells and how this is altered in disease. They can be used to predict the effect of perturbations (individual or in combinations), and potentially to engineer therapies that have differential effects/side effects depending on the cell type or context. PMID:23079107

Systematic Differences in Signal Emitting and Receiving Revealed by PageRank Analysis of a Human Protein Interactome

PubMed Central

Li, Xiu-Qing

2012-01-01

Most protein PageRank studies do not use signal flow direction information in protein interactions because this information was not readily available in large protein databases until recently. Therefore, four questions have yet to be answered: A) What is the general difference between signal emitting and receiving in a protein interactome? B) Which proteins are among the top ranked in directional ranking? C) Are high ranked proteins more evolutionarily conserved than low ranked ones? D) Do proteins with similar ranking tend to have similar subcellular locations? In this study, we address these questions using the forward, reverse, and non-directional PageRank approaches to rank an information-directional network of human proteins and study their evolutionary conservation. The forward ranking gives credit to information receivers, reverse ranking to information emitters, and non-directional ranking mainly to the number of interactions. The protein lists generated by the forward and non-directional rankings are highly correlated, but those by the reverse and non-directional rankings are not. The results suggest that the signal emitting/receiving system is characterized by key-emittings and relatively even receivings in the human protein interactome. Signaling pathway proteins are frequent in top ranked ones. Eight proteins are both informational top emitters and top receivers. Top ranked proteins, except a few species-related novel-function ones, are evolutionarily well conserved. Protein-subunit ranking position reflects subunit function. These results demonstrate the usefulness of different PageRank approaches in characterizing protein networks and provide insights to protein interaction in the cell. PMID:23028653

Using protein-protein interactions for refining gene networks estimated from microarray data by Bayesian networks.

PubMed

Nariai, N; Kim, S; Imoto, S; Miyano, S

2004-01-01

We propose a statistical method to estimate gene networks from DNA microarray data and protein-protein interactions. Because physical interactions between proteins or multiprotein complexes are likely to regulate biological processes, using only mRNA expression data is not sufficient for estimating a gene network accurately. Our method adds knowledge about protein-protein interactions to the estimation method of gene networks under a Bayesian statistical framework. In the estimated gene network, a protein complex is modeled as a virtual node based on principal component analysis. We show the effectiveness of the proposed method through the analysis of Saccharomyces cerevisiae cell cycle data. The proposed method improves the accuracy of the estimated gene networks, and successfully identifies some biological facts.

Protein-protein interaction networks (PPI) and complex diseases

PubMed Central

Safari-Alighiarloo, Nahid; Taghizadeh, Mohammad; Rezaei-Tavirani, Mostafa; Goliaei, Bahram

2014-01-01

The physical interaction of proteins which lead to compiling them into large densely connected networks is a noticeable subject to investigation. Protein interaction networks are useful because of making basic scientific abstraction and improving biological and biomedical applications. Based on principle roles of proteins in biological function, their interactions determine molecular and cellular mechanisms, which control healthy and diseased states in organisms. Therefore, such networks facilitate the understanding of pathogenic (and physiologic) mechanisms that trigger the onset and progression of diseases. Consequently, this knowledge can be translated into effective diagnostic and therapeutic strategies. Furthermore, the results of several studies have proved that the structure and dynamics of protein networks are disturbed in complex diseases such as cancer and autoimmune disorders. Based on such relationship, a novel paradigm is suggested in order to confirm that the protein interaction networks can be the target of therapy for treatment of complex multi-genic diseases rather than individual molecules with disrespect the network. PMID:25436094

Protein sensing by nanofluidic crystal and its signal enhancement

PubMed Central

Sang, Jianming; Du, Hongtan; Wang, Wei; Chu, Ming; Wang, Yuedan; Li, Haichao; Alice Zhang, Haixia; Wu, Wengang; Li, Zhihong

2013-01-01

Nanofluidics has a unique property that ionic conductance across a nanometer-sized confined space is strongly affected by the space surface charge density, which can be utilized to construct electrical read-out biosensor. Based on this principle, this work demonstrated a novel protein sensor along with a sandwich signal enhancement approach. Nanoparticles with designed aptamer onside are assembled in a suspended micropore to form a 3-dimensional network of nanometer-sized interstices, named as nanofluidic crystal hereafter, as the basic sensing unit. Proteins captured by aptamers will change the surface charge density of nanoparticles and thereby can be detected by monitoring the ionic conductance across this nanofluidic crystal. Another aptamer can further enlarge the variations of the surface charge density by forming a sandwich structure (capturing aptamer/protein/signal enhancement aptamer) and the read-out conductance as well. The preliminary experimental results indicated that human α-thrombin was successfully detected by the corresponding aptamer modified nanofluidic crystal with the limit of detection of 5 nM (0.18 μg/ml) and the read-out signal was enhanced up to 3 folds by using another thrombin aptamer. Being easy to graft probe, facile and low-cost to prepare the nano-device, and having an electrical read-out, the present nanofluidic crystal scheme is a promising and universal strategy for protein sensing. PMID:24404017

Signal propagation in cortical networks: a digital signal processing approach.

PubMed

Rodrigues, Francisco Aparecido; da Fontoura Costa, Luciano

2009-01-01

This work reports a digital signal processing approach to representing and modeling transmission and combination of signals in cortical networks. The signal dynamics is modeled in terms of diffusion, which allows the information processing undergone between any pair of nodes to be fully characterized in terms of a finite impulse response (FIR) filter. Diffusion without and with time decay are investigated. All filters underlying the cat and macaque cortical organization are found to be of low-pass nature, allowing the cortical signal processing to be summarized in terms of the respective cutoff frequencies (a high cutoff frequency meaning little alteration of signals through their intermixing). Several findings are reported and discussed, including the fact that the incorporation of temporal activity decay tends to provide more diversified cutoff frequencies. Different filtering intensity is observed for each community in those networks. In addition, the brain regions involved in object recognition tend to present the highest cutoff frequencies for both the cat and macaque networks.

A Global Protein Kinase and Phosphatase Interaction Network in Yeast

PubMed Central

Breitkreutz, Ashton; Choi, Hyungwon; Sharom, Jeffrey R.; Boucher, Lorrie; Neduva, Victor; Larsen, Brett; Lin, Zhen-Yuan; Breitkreutz, Bobby-Joe; Stark, Chris; Liu, Guomin; Ahn, Jessica; Dewar-Darch, Danielle; Reguly, Teresa; Tang, Xiaojing; Almeida, Ricardo; Qin, Zhaohui Steve; Pawson, Tony; Gingras, Anne-Claude; Nesvizhskii, Alexey I.; Tyers, Mike

2011-01-01

The interactions of protein kinases and phosphatases with their regulatory subunits and substrates underpin cellular regulation. We identified a kinase and phosphatase interaction (KPI) network of 1844 interactions in budding yeast by mass spectrometric analysis of protein complexes. The KPI network contained many dense local regions of interactions that suggested new functions. Notably, the cell cycle phosphatase Cdc14 associated with multiple kinases that revealed roles for Cdc14 in mitogen-activated protein kinase signaling, the DNA damage response, and metabolism, whereas interactions of the target of rapamycin complex 1 (TORC1) uncovered new effector kinases in nitrogen and carbon metabolism. An extensive backbone of kinase-kinase interactions cross-connects the proteome and may serve to coordinate diverse cellular responses. PMID:20489023

Minimal Network Topologies for Signal Processing during Collective Cell Chemotaxis.

PubMed

Yue, Haicen; Camley, Brian A; Rappel, Wouter-Jan

2018-06-19

Cell-cell communication plays an important role in collective cell migration. However, it remains unclear how cells in a group cooperatively process external signals to determine the group's direction of motion. Although the topology of signaling pathways is vitally important in single-cell chemotaxis, the signaling topology for collective chemotaxis has not been systematically studied. Here, we combine mathematical analysis and simulations to find minimal network topologies for multicellular signal processing in collective chemotaxis. We focus on border cell cluster chemotaxis in the Drosophila egg chamber, in which responses to several experimental perturbations of the signaling network are known. Our minimal signaling network includes only four elements: a chemoattractant, the protein Rac (indicating cell activation), cell protrusion, and a hypothesized global factor responsible for cell-cell interaction. Experimental data on cell protrusion statistics allows us to systematically narrow the number of possible topologies from more than 40,000,000 to only six minimal topologies with six interactions between the four elements. This analysis does not require a specific functional form of the interactions, and only qualitative features are needed; it is thus robust to many modeling choices. Simulations of a stochastic biochemical model of border cell chemotaxis show that the qualitative selection procedure accurately determines which topologies are consistent with the experiment. We fit our model for all six proposed topologies; each produces results that are consistent with all experimentally available data. Finally, we suggest experiments to further discriminate possible pathway topologies. Copyright © 2018 Biophysical Society. Published by Elsevier Inc. All rights reserved.

G-Protein/β-Arrestin-Linked Fluctuating Network of G-Protein-Coupled Receptors for Predicting Drug Efficacy and Bias Using Short-Term Molecular Dynamics Simulation

PubMed Central

Ichikawa, Osamu; Fujimoto, Kazushi; Yamada, Atsushi; Okazaki, Susumu; Yamazaki, Kazuto

2016-01-01

The efficacy and bias of signal transduction induced by a drug at a target protein are closely associated with the benefits and side effects of the drug. In particular, partial agonist activity and G-protein/β-arrestin-biased agonist activity for the G-protein-coupled receptor (GPCR) family, the family with the most target proteins of launched drugs, are key issues in drug discovery. However, designing GPCR drugs with appropriate efficacy and bias is challenging because the dynamic mechanism of signal transduction induced by ligand—receptor interactions is complicated. Here, we identified the G-protein/β-arrestin-linked fluctuating network, which initiates large-scale conformational changes, using sub-microsecond molecular dynamics (MD) simulations of the β2-adrenergic receptor (β2AR) with a diverse collection of ligands and correlation analysis of their G protein/β-arrestin efficacy. The G-protein-linked fluctuating network extends from the ligand-binding site to the G-protein-binding site through the connector region, and the β-arrestin-linked fluctuating network consists of the NPxxY motif and adjacent regions. We confirmed that the averaged values of fluctuation in the fluctuating network detected are good quantitative indexes for explaining G protein/β-arrestin efficacy. These results indicate that short-term MD simulation is a practical method to predict the efficacy and bias of any compound for GPCRs. PMID:27187591

Signaling through G protein coupled receptors.

PubMed

Tuteja, Narendra

2009-10-01

Heterotrimeric G proteins (Galpha, Gbeta/Ggamma subunits) constitute one of the most important components of cell signaling cascade. G Protein Coupled Receptors (GPCRs) perceive many extracellular signals and transduce them to heterotrimeric G proteins, which further transduce these signals intracellular to appropriate downstream effectors and thereby play an important role in various signaling pathways. GPCRs exist as a superfamily of integral membrane protein receptors that contain seven transmembrane alpha-helical regions, which bind to a wide range of ligands. Upon activation by a ligand, the GPCR undergoes a conformational change and then activate the G proteins by promoting the exchange of GDP/GTP associated with the Galpha subunit. This leads to the dissociation of Gbeta/Ggamma dimer from Galpha. Both these moieties then become free to act upon their downstream effectors and thereby initiate unique intracellular signaling responses. After the signal propagation, the GTP of Galpha-GTP is hydrolyzed to GDP and Galpha becomes inactive (Galpha-GDP), which leads to its re-association with the Gbeta/Ggamma dimer to form the inactive heterotrimeric complex. The GPCR can also transduce the signal through G protein independent pathway. GPCRs also regulate cell cycle progression. Till to date thousands of GPCRs are known from animal kingdom with little homology among them, but only single GPCR has been identified in plant system. The Arabidopsis GPCR was reported to be cell cycle regulated and also involved in ABA and in stress signaling. Here I have described a general mechanism of signal transduction through GPCR/G proteins, structure of GPCRs, family of GPCRs and plant GPCR and its role.

A Collaboration Network Model Of Cytokine-Protein Network

NASA Astrophysics Data System (ADS)

Zou, Sheng-Rong; Zhou, Ta; Peng, Yu-Jing; Guo, Zhong-Wei; Gu, Chang-Gui; He, Da-Ren

2008-03-01

Complex networks provide us a new view for investigation of immune systems. We collect data through STRING database and present a network description with cooperation network model. The cytokine-protein network model we consider is constituted by two kinds of nodes, one is immune cytokine types which can be regarded as collaboration acts, the other one is protein type which can be regarded as collaboration actors. From act degree distribution that can be well described by typical SPL (shifted power law) functions [1], we find that HRAS, TNFRSF13C, S100A8, S100A1, MAPK8, S100A7, LIF, CCL4, CXCL13 are highly collaborated with other proteins. It reveals that these mediators are important in cytokine-protein network to regulate immune activity. Dyad in the collaboration networks can be defined as two proteins and they appear in one cytokine collaboration relationship. The dyad act degree distribution can also be well described by typical SPL functions. [1] Assortativity and act degree distribution of some collaboration networks, Hui Chang, Bei-Bei Su, Yue-Ping Zhou, Daren He, Physica A, 383 (2007) 687-702

The Roles of NDR Protein Kinases in Hippo Signalling.

PubMed

Hergovich, Alexander

2016-05-18

The Hippo tumour suppressor pathway has emerged as a critical regulator of tissue growth through controlling cellular processes such as cell proliferation, death, differentiation and stemness. Traditionally, the core cassette of the Hippo pathway includes the MST1/2 protein kinases, the LATS1/2 protein kinases, and the MOB1 scaffold signal transducer, which together regulate the transcriptional co-activator functions of the proto-oncoproteins YAP and TAZ through LATS1/2-mediated phosphorylation of YAP/TAZ. Recent research has identified additional kinases, such as NDR1/2 (also known as STK38/STK38L) and MAP4Ks, which should be considered as novel members of the Hippo core cassette. While these efforts helped to expand our understanding of Hippo core signalling, they also began to provide insights into the complexity and redundancy of the Hippo signalling network. Here, we focus on summarising our current knowledge of the regulation and functions of mammalian NDR kinases, discussing parallels between the NDR pathways in Drosophila and mammals. Initially, we provide a general overview of the cellular functions of NDR kinases in cell cycle progression, centrosome biology, apoptosis, autophagy, DNA damage signalling, immunology and neurobiology. Finally, we put particular emphasis on discussing NDR1/2 as YAP kinases downstream of MST1/2 and MOB1 signalling in Hippo signalling.

Expression Patterns and Identified Protein-Protein Interactions Suggest That Cassava CBL-CIPK Signal Networks Function in Responses to Abiotic Stresses.

PubMed

Mo, Chunyan; Wan, Shumin; Xia, Youquan; Ren, Ning; Zhou, Yang; Jiang, Xingyu

2018-01-01

Cassava is an energy crop that is tolerant of multiple abiotic stresses. It has been reported that the interaction between Calcineurin B-like (CBL) protein and CBL-interacting protein kinase (CIPK) is implicated in plant development and responses to various stresses. However, little is known about their functions in cassava. Herein, 8 CBL ( MeCBL ) and 26 CIPK ( MeCIPK ) genes were isolated from cassava by genome searching and cloning of cDNA sequences of Arabidopsis CBL s and CIPK s. Reverse-transcriptase polymerase chain reaction (RT-PCR) analysis showed that the expression levels of MeCBL and MeCIPK genes were different in different tissues throughout the life cycle. The expression patterns of 7 CBL and 26 CIPK genes in response to NaCl, PEG, heat and cold stresses were analyzed by quantitative real-time PCR (qRT-PCR), and it was found that the expression of each was induced by multiple stimuli. Furthermore, we found that many pairs of CBLs and CIPKs could interact with each other via investigating the interactions between 8 CBL and 25 CIPK proteins using a yeast two-hybrid system. Yeast cells co-transformed with cassava MeCIPK24, MeCBL10 , and Na + /H + antiporter MeSOS1 genes exhibited higher salt tolerance compared to those with one or two genes. These results suggest that the cassava CBL-CIPK signal network might play key roles in response to abiotic stresses.

Expression Patterns and Identified Protein-Protein Interactions Suggest That Cassava CBL-CIPK Signal Networks Function in Responses to Abiotic Stresses

PubMed Central

Mo, Chunyan; Wan, Shumin; Xia, Youquan; Ren, Ning; Zhou, Yang; Jiang, Xingyu

2018-01-01

Cassava is an energy crop that is tolerant of multiple abiotic stresses. It has been reported that the interaction between Calcineurin B-like (CBL) protein and CBL-interacting protein kinase (CIPK) is implicated in plant development and responses to various stresses. However, little is known about their functions in cassava. Herein, 8 CBL (MeCBL) and 26 CIPK (MeCIPK) genes were isolated from cassava by genome searching and cloning of cDNA sequences of Arabidopsis CBLs and CIPKs. Reverse-transcriptase polymerase chain reaction (RT-PCR) analysis showed that the expression levels of MeCBL and MeCIPK genes were different in different tissues throughout the life cycle. The expression patterns of 7 CBL and 26 CIPK genes in response to NaCl, PEG, heat and cold stresses were analyzed by quantitative real-time PCR (qRT-PCR), and it was found that the expression of each was induced by multiple stimuli. Furthermore, we found that many pairs of CBLs and CIPKs could interact with each other via investigating the interactions between 8 CBL and 25 CIPK proteins using a yeast two-hybrid system. Yeast cells co-transformed with cassava MeCIPK24, MeCBL10, and Na+/H+ antiporter MeSOS1 genes exhibited higher salt tolerance compared to those with one or two genes. These results suggest that the cassava CBL-CIPK signal network might play key roles in response to abiotic stresses. PMID:29552024

Discovery of nitrate-CPK-NLP signalling in central nutrient-growth networks

PubMed Central

Liu, Kun-hsiang; Niu, Yajie; Konishi, Mineko; Wu, Yue; Du, Hao; Sun Chung, Hoo; Li, Lei; Boudsocq, Marie; McCormack, Matthew; Maekawa, Shugo; Ishida, Tetsuya; Zhang, Chao; Shokat, Kevan; Yanagisawa, Shuichi; Sheen, Jen

2018-01-01

Nutrient signalling integrates and coordinates gene expression, metabolism and growth. However, its primary molecular mechanisms remain incompletely understood in plants and animals. Here we report novel Ca2+ signalling triggered by nitrate with live imaging of an ultrasensitive biosensor in Arabidopsis leaves and roots. A nitrate-sensitized and targeted functional genomic screen identifies subgroup III Ca2+-sensor protein kinases (CPKs) as master regulators orchestrating primary nitrate responses. A chemical switch with the engineered CPK10(M141G) kinase enables conditional analyses of cpk10,30,32 to define comprehensive nitrate-associated regulatory and developmental programs, circumventing embryo lethality. Nitrate-CPK signalling phosphorylates conserved NIN-LIKE PROTEIN (NLP) transcription factors (TFs) to specify reprogramming of gene sets for downstream TFs, transporters, N-assimilation, C/N-metabolism, redox, signalling, hormones, and proliferation. Conditional cpk10,30,32 and nlp7 similarly impair nitrate-stimulated system-wide shoot growth and root establishment. The nutrient-coupled Ca2+ signalling network integrates transcriptome and cellular metabolism with shoot-root coordination and developmental plasticity in shaping organ biomass and architecture. PMID:28489820

Transmembrane Topology and Signal Peptide Prediction Using Dynamic Bayesian Networks

PubMed Central

Reynolds, Sheila M.; Käll, Lukas; Riffle, Michael E.; Bilmes, Jeff A.; Noble, William Stafford

2008-01-01

Hidden Markov models (HMMs) have been successfully applied to the tasks of transmembrane protein topology prediction and signal peptide prediction. In this paper we expand upon this work by making use of the more powerful class of dynamic Bayesian networks (DBNs). Our model, Philius, is inspired by a previously published HMM, Phobius, and combines a signal peptide submodel with a transmembrane submodel. We introduce a two-stage DBN decoder that combines the power of posterior decoding with the grammar constraints of Viterbi-style decoding. Philius also provides protein type, segment, and topology confidence metrics to aid in the interpretation of the predictions. We report a relative improvement of 13% over Phobius in full-topology prediction accuracy on transmembrane proteins, and a sensitivity and specificity of 0.96 in detecting signal peptides. We also show that our confidence metrics correlate well with the observed precision. In addition, we have made predictions on all 6.3 million proteins in the Yeast Resource Center (YRC) database. This large-scale study provides an overall picture of the relative numbers of proteins that include a signal-peptide and/or one or more transmembrane segments as well as a valuable resource for the scientific community. All DBNs are implemented using the Graphical Models Toolkit. Source code for the models described here is available at http://noble.gs.washington.edu/proj/philius. A Philius Web server is available at http://www.yeastrc.org/philius, and the predictions on the YRC database are available at http://www.yeastrc.org/pdr. PMID:18989393

PNMA family: Protein interaction network and cell signalling pathways implicated in cancer and apoptosis.

PubMed

Pang, Siew Wai; Lahiri, Chandrajit; Poh, Chit Laa; Tan, Kuan Onn

2018-05-01

Paraneoplastic Ma Family (PNMA) comprises a growing number of family members which share relatively conserved protein sequences encoded by the human genome and is localized to several human chromosomes, including the X-chromosome. Based on sequence analysis, PNMA family members share sequence homology to the Gag protein of LTR retrotransposon, and several family members with aberrant protein expressions have been reported to be closely associated with the human Paraneoplastic Disorder (PND). In addition, gene mutations of specific members of PNMA family are known to be associated with human mental retardation or 3-M syndrome consisting of restrictive post-natal growth or dwarfism, and development of skeletal abnormalities. Other than sequence homology, the physiological function of many members in this family remains unclear. However, several members of this family have been characterized, including cell signalling events mediated by these proteins that are associated with apoptosis, and cancer in different cell types. Furthermore, while certain PNMA family members show restricted gene expression in the human brain and testis, other PNMA family members exhibit broader gene expression or preferential and selective protein interaction profiles, suggesting functional divergence within the family. Functional analysis of some members of this family have identified protein domains that are required for subcellular localization, protein-protein interactions, and cell signalling events which are the focus of this review paper. Copyright © 2018 Elsevier Inc. All rights reserved.

Inositol polyphosphates intersect with signaling and metabolic networks via two distinct mechanisms.

PubMed

Wu, Mingxuan; Chong, Lucy S; Perlman, David H; Resnick, Adam C; Fiedler, Dorothea

2016-11-01

Inositol-based signaling molecules are central eukaryotic messengers and include the highly phosphorylated, diffusible inositol polyphosphates (InsPs) and inositol pyrophosphates (PP-InsPs). Despite the essential cellular regulatory functions of InsPs and PP-InsPs (including telomere maintenance, phosphate sensing, cell migration, and insulin secretion), the majority of their protein targets remain unknown. Here, the development of InsP and PP-InsP affinity reagents is described to comprehensively annotate the interactome of these messenger molecules. By using the reagents as bait, >150 putative protein targets were discovered from a eukaryotic cell lysate (Saccharomyces cerevisiae). Gene Ontology analysis of the binding partners revealed a significant overrepresentation of proteins involved in nucleotide metabolism, glucose metabolism, ribosome biogenesis, and phosphorylation-based signal transduction pathways. Notably, we isolated and characterized additional substrates of protein pyrophosphorylation, a unique posttranslational modification mediated by the PP-InsPs. Our findings not only demonstrate that the PP-InsPs provide a central line of communication between signaling and metabolic networks, but also highlight the unusual ability of these molecules to access two distinct modes of action.

Hypoxia induces a phase transition within a kinase signaling network in cancer cells

PubMed Central

Wei, Wei; Shi, Qihui; Remacle, Francoise; Qin, Lidong; Shackelford, David B.; Shin, Young Shik; Mischel, Paul S.; Levine, R. D.; Heath, James R.

2013-01-01

Hypoxia is a near-universal feature of cancer, promoting glycolysis, cellular proliferation, and angiogenesis. The molecular mechanisms of hypoxic signaling have been intensively studied, but the impact of changes in oxygen partial pressure (pO2) on the state of signaling networks is less clear. In a glioblastoma multiforme (GBM) cancer cell model, we examined the response of signaling networks to targeted pathway inhibition between 21% and 1% pO2. We used a microchip technology that facilitates quantification of a panel of functional proteins from statistical numbers of single cells. We find that near 1.5% pO2, the signaling network associated with mammalian target of rapamycin (mTOR) complex 1 (mTORC1)—a critical component of hypoxic signaling and a compelling cancer drug target—is deregulated in a manner such that it will be unresponsive to mTOR kinase inhibitors near 1.5% pO2, but will respond at higher or lower pO2 values. These predictions were validated through experiments on bulk GBM cell line cultures and on neurosphere cultures of a human-origin GBM xenograft tumor. We attempt to understand this behavior through the use of a quantitative version of Le Chatelier’s principle, as well as through a steady-state kinetic model of protein interactions, both of which indicate that hypoxia can influence mTORC1 signaling as a switch. The Le Chatelier approach also indicates that this switch may be thought of as a type of phase transition. Our analysis indicates that certain biologically complex cell behaviors may be understood using fundamental, thermodynamics-motivated principles. PMID:23530221

Hypoxia induces a phase transition within a kinase signaling network in cancer cells.

PubMed

Wei, Wei; Shi, Qihui; Remacle, Francoise; Qin, Lidong; Shackelford, David B; Shin, Young Shik; Mischel, Paul S; Levine, R D; Heath, James R

2013-04-09

Hypoxia is a near-universal feature of cancer, promoting glycolysis, cellular proliferation, and angiogenesis. The molecular mechanisms of hypoxic signaling have been intensively studied, but the impact of changes in oxygen partial pressure (pO2) on the state of signaling networks is less clear. In a glioblastoma multiforme (GBM) cancer cell model, we examined the response of signaling networks to targeted pathway inhibition between 21% and 1% pO2. We used a microchip technology that facilitates quantification of a panel of functional proteins from statistical numbers of single cells. We find that near 1.5% pO2, the signaling network associated with mammalian target of rapamycin (mTOR) complex 1 (mTORC1)--a critical component of hypoxic signaling and a compelling cancer drug target--is deregulated in a manner such that it will be unresponsive to mTOR kinase inhibitors near 1.5% pO2, but will respond at higher or lower pO2 values. These predictions were validated through experiments on bulk GBM cell line cultures and on neurosphere cultures of a human-origin GBM xenograft tumor. We attempt to understand this behavior through the use of a quantitative version of Le Chatelier's principle, as well as through a steady-state kinetic model of protein interactions, both of which indicate that hypoxia can influence mTORC1 signaling as a switch. The Le Chatelier approach also indicates that this switch may be thought of as a type of phase transition. Our analysis indicates that certain biologically complex cell behaviors may be understood using fundamental, thermodynamics-motivated principles.

Recent Coselection in Human Populations Revealed by Protein–Protein Interaction Network

PubMed Central

Qian, Wei; Zhou, Hang; Tang, Kun

2015-01-01

Genome-wide scans for signals of natural selection in human populations have identified a large number of candidate loci that underlie local adaptations. This is surprising given the relatively short evolutionary time since the divergence of the human population. One hypothesis that has not been formally examined is whether and how the recent human evolution may have been shaped by coselection in the context of complex molecular interactome. In this study, genome-wide signals of selection were scanned in East Asians, Europeans, and Africans using 1000 Genome data, and subsequently mapped onto the protein–protein interaction (PPI) network. We found that the candidate genes of recent positive selection localized significantly closer to each other on the PPI network than expected, revealing substantial clustering of selected genes. Furthermore, gene pairs of shorter PPI network distances showed higher similarities of their recent evolutionary paths than those further apart. Last, subnetworks enriched with recent coselection signals were identified, which are substantially overrepresented in biological pathways related to signal transduction, neurogenesis, and immune function. These results provide the first genome-wide evidence for association of recent selection signals with the PPI network, shedding light on the potential mechanisms of recent coselection in the human genome. PMID:25532814

Emerging Role of Protein-Protein Transnitrosylation in Cell Signaling Pathways

PubMed Central

2013-01-01

Abstract Significance: Protein S-nitrosylation, a covalent reaction of a nitric oxide (NO) group with a critical protein thiol (or more properly thiolate anion), mediates an important form of redox-related signaling as well as aberrant signaling in disease states. Recent Advances: A growing literature suggests that over 3000 proteins are S-nitrosylated in cell systems. Our laboratory and several others have demonstrated that protein S-nitrosylation can regulate protein function by directly inhibiting catalytically active cysteines, by reacting with allosteric sites, or via influencing protein-protein interaction. For example, S-nitrosylation of critical cysteine thiols in protein-disulfide isomerase and in parkin alters their activity, thus contributing to protein misfolding in Parkinson's disease. Critical Issues: However, the mechanism by which specific protein S-nitrosylation occurs in cell signaling pathways is less well investigated. Interestingly, the recent discovery of protein-protein transnitrosylation reactions (transfer of an NO group from one protein to another) has revealed a unique mechanism whereby NO can S-nitrosylate a particular set of protein thiols, and represents a major class of nitrosylating/denitrosylating enzymes in mammalian systems. In this review, we will discuss recent evidence for transnitrosylation reactions between (i) hemoglobin/anion exchanger 1, (ii) thioredoxin/caspase-3, (iii) X-linked inhibitor of apoptosis/caspase-3, (iv) GAPDH-HDAC2/SIRT1/DNA-PK, and (v) Cdk5/dynamin related protein 1 (Drp1). This review also discusses experimental techniques useful in characterizing protein-protein transnitrosylations. Future Directions: Elucidation of additional transnitrosylation cascades will further our understanding of the enzymes that catalyze nitrosation, thereby contributing to NO-mediated signaling pathways. Antioxid. Redox Signal. 18, 239–249. PMID:22657837

Unified Alignment of Protein-Protein Interaction Networks.

PubMed

Malod-Dognin, Noël; Ban, Kristina; Pržulj, Nataša

2017-04-19

Paralleling the increasing availability of protein-protein interaction (PPI) network data, several network alignment methods have been proposed. Network alignments have been used to uncover functionally conserved network parts and to transfer annotations. However, due to the computational intractability of the network alignment problem, aligners are heuristics providing divergent solutions and no consensus exists on a gold standard, or which scoring scheme should be used to evaluate them. We comprehensively evaluate the alignment scoring schemes and global network aligners on large scale PPI data and observe that three methods, HUBALIGN, L-GRAAL and NATALIE, regularly produce the most topologically and biologically coherent alignments. We study the collective behaviour of network aligners and observe that PPI networks are almost entirely aligned with a handful of aligners that we unify into a new tool, Ulign. Ulign enables complete alignment of two networks, which traditional global and local aligners fail to do. Also, multiple mappings of Ulign define biologically relevant soft clusterings of proteins in PPI networks, which may be used for refining the transfer of annotations across networks. Hence, PPI networks are already well investigated by current aligners, so to gain additional biological insights, a paradigm shift is needed. We propose such a shift come from aligning all available data types collectively rather than any particular data type in isolation from others.

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

PubMed

Stavrakas, Vassilis; Melas, Ioannis N; Sakellaropoulos, Theodore; Alexopoulos, Leonidas G

2015-01-01

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

SH2 Domains Serve as Lipid-Binding Modules for pTyr-Signaling Proteins.

PubMed

Park, Mi-Jeong; Sheng, Ren; Silkov, Antonina; Jung, Da-Jung; Wang, Zhi-Gang; Xin, Yao; Kim, Hyunjin; Thiagarajan-Rosenkranz, Pallavi; Song, Seohyeon; Yoon, Youngdae; Nam, Wonhee; Kim, Ilshin; Kim, Eui; Lee, Dong-Gyu; Chen, Yong; Singaram, Indira; Wang, Li; Jang, Myoung Ho; Hwang, Cheol-Sang; Honig, Barry; Ryu, Sungho; Lorieau, Justin; Kim, You-Me; Cho, Wonhwa

2016-04-07

The Src-homology 2 (SH2) domain is a protein interaction domain that directs myriad phosphotyrosine (pY)-signaling pathways. Genome-wide screening of human SH2 domains reveals that ∼90% of SH2 domains bind plasma membrane lipids and many have high phosphoinositide specificity. They bind lipids using surface cationic patches separate from pY-binding pockets, thus binding lipids and the pY motif independently. The patches form grooves for specific lipid headgroup recognition or flat surfaces for non-specific membrane binding and both types of interaction are important for cellular function and regulation of SH2 domain-containing proteins. Cellular studies with ZAP70 showed that multiple lipids bind its C-terminal SH2 domain in a spatiotemporally specific manner and thereby exert exquisite spatiotemporal control over its protein binding and signaling activities in T cells. Collectively, this study reveals how lipids control SH2 domain-mediated cellular protein-protein interaction networks and suggest a new strategy for therapeutic modulation of pY-signaling pathways. Copyright © 2016 Elsevier Inc. All rights reserved.

Mapping transcription factor interactome networks using HaloTag protein arrays.

PubMed

Yazaki, Junshi; Galli, Mary; Kim, Alice Y; Nito, Kazumasa; Aleman, Fernando; Chang, Katherine N; Carvunis, Anne-Ruxandra; Quan, Rosa; Nguyen, Hien; Song, Liang; Alvarez, José M; Huang, Shao-Shan Carol; Chen, Huaming; Ramachandran, Niroshan; Altmann, Stefan; Gutiérrez, Rodrigo A; Hill, David E; Schroeder, Julian I; Chory, Joanne; LaBaer, Joshua; Vidal, Marc; Braun, Pascal; Ecker, Joseph R

2016-07-19

Protein microarrays enable investigation of diverse biochemical properties for thousands of proteins in a single experiment, an unparalleled capacity. Using a high-density system called HaloTag nucleic acid programmable protein array (HaloTag-NAPPA), we created high-density protein arrays comprising 12,000 Arabidopsis ORFs. We used these arrays to query protein-protein interactions for a set of 38 transcription factors and transcriptional regulators (TFs) that function in diverse plant hormone regulatory pathways. The resulting transcription factor interactome network, TF-NAPPA, contains thousands of novel interactions. Validation in a benchmarked in vitro pull-down assay revealed that a random subset of TF-NAPPA validated at the same rate of 64% as a positive reference set of literature-curated interactions. Moreover, using a bimolecular fluorescence complementation (BiFC) assay, we confirmed in planta several interactions of biological interest and determined the interaction localizations for seven pairs. The application of HaloTag-NAPPA technology to plant hormone signaling pathways allowed the identification of many novel transcription factor-protein interactions and led to the development of a proteome-wide plant hormone TF interactome network.

Discrete dynamic modeling of cellular signaling networks.

PubMed

Albert, Réka; Wang, Rui-Sheng

2009-01-01

Understanding signal transduction in cellular systems is a central issue in systems biology. Numerous experiments from different laboratories generate an abundance of individual components and causal interactions mediating environmental and developmental signals. However, for many signal transduction systems there is insufficient information on the overall structure and the molecular mechanisms involved in the signaling network. Moreover, lack of kinetic and temporal information makes it difficult to construct quantitative models of signal transduction pathways. Discrete dynamic modeling, combined with network analysis, provides an effective way to integrate fragmentary knowledge of regulatory interactions into a predictive mathematical model which is able to describe the time evolution of the system without the requirement for kinetic parameters. This chapter introduces the fundamental concepts of discrete dynamic modeling, particularly focusing on Boolean dynamic models. We describe this method step-by-step in the context of cellular signaling networks. Several variants of Boolean dynamic models including threshold Boolean networks and piecewise linear systems are also covered, followed by two examples of successful application of discrete dynamic modeling in cell biology.

Network-Based Methods for Identifying Key Active Proteins in the Extracellular Electron Transfer Process in Shewanella oneidensis MR-1.

PubMed

Ding, Dewu; Sun, Xiao

2018-01-16

Shewanella oneidensis MR-1 can transfer electrons from the intracellular environment to the extracellular space of the cells to reduce the extracellular insoluble electron acceptors (Extracellular Electron Transfer, EET). Benefiting from this EET capability, Shewanella has been widely used in different areas, such as energy production, wastewater treatment, and bioremediation. Genome-wide proteomics data was used to determine the active proteins involved in activating the EET process. We identified 1012 proteins with decreased expression and 811 proteins with increased expression when the EET process changed from inactivation to activation. We then networked these proteins to construct the active protein networks, and identified the top 20 key active proteins by network centralization analysis, including metabolism- and energy-related proteins, signal and transcriptional regulatory proteins, translation-related proteins, and the EET-related proteins. We also constructed the integrated protein interaction and transcriptional regulatory networks for the active proteins, then found three exclusive active network motifs involved in activating the EET process-Bi-feedforward Loop, Regulatory Cascade with a Feedback, and Feedback with a Protein-Protein Interaction (PPI)-and identified the active proteins involved in these motifs. Both enrichment analysis and comparative analysis to the whole-genome data implicated the multiheme c -type cytochromes and multiple signal processing proteins involved in the process. Furthermore, the interactions of these motif-guided active proteins and the involved functional modules were discussed. Collectively, by using network-based methods, this work reported a proteome-wide search for the key active proteins that potentially activate the EET process.

Predicting disease-related proteins based on clique backbone in protein-protein interaction network.

PubMed

Yang, Lei; Zhao, Xudong; Tang, Xianglong

2014-01-01

Network biology integrates different kinds of data, including physical or functional networks and disease gene sets, to interpret human disease. A clique (maximal complete subgraph) in a protein-protein interaction network is a topological module and possesses inherently biological significance. A disease-related clique possibly associates with complex diseases. Fully identifying disease components in a clique is conductive to uncovering disease mechanisms. This paper proposes an approach of predicting disease proteins based on cliques in a protein-protein interaction network. To tolerate false positive and negative interactions in protein networks, extending cliques and scoring predicted disease proteins with gene ontology terms are introduced to the clique-based method. Precisions of predicted disease proteins are verified by disease phenotypes and steadily keep to more than 95%. The predicted disease proteins associated with cliques can partly complement mapping between genotype and phenotype, and provide clues for understanding the pathogenesis of serious diseases.

Architecture of the human interactome defines protein communities and disease networks

PubMed Central

Huttlin, Edward L.; Bruckner, Raphael J.; Paulo, Joao A.; Cannon, Joe R.; Ting, Lily; Baltier, Kurt; Colby, Greg; Gebreab, Fana; Gygi, Melanie P.; Parzen, Hannah; Szpyt, John; Tam, Stanley; Zarraga, Gabriela; Pontano-Vaites, Laura; Swarup, Sharan; White, Anne E.; Schweppe, Devin K.; Rad, Ramin; Erickson, Brian K.; Obar, Robert A.; Guruharsha, K.G.; Li, Kejie; Artavanis-Tsakonas, Spyros; Gygi, Steven P.; Harper, J. Wade

2017-01-01

The physiology of a cell can be viewed as the product of thousands of proteins acting in concert to shape the cellular response. Coordination is achieved in part through networks of protein-protein interactions that assemble functionally related proteins into complexes, organelles, and signal transduction pathways. Understanding the architecture of the human proteome has the potential to inform cellular, structural, and evolutionary mechanisms and is critical to elucidation of how genome variation contributes to disease1–3. Here, we present BioPlex 2.0 (Biophysical Interactions of ORFEOME-derived complexes), which employs robust affinity purification-mass spectrometry (AP-MS) methodology4 to elucidate protein interaction networks and co-complexes nucleated by more than 25% of protein coding genes from the human genome, and constitutes the largest such network to date. With >56,000 candidate interactions, BioPlex 2.0 contains >29,000 previously unknown co-associations and provides functional insights into hundreds of poorly characterized proteins while enhancing network-based analyses of domain associations, subcellular localization, and co-complex formation. Unsupervised Markov clustering (MCL)5 of interacting proteins identified more than 1300 protein communities representing diverse cellular activities. Genes essential for cell fitness6,7 are enriched within 53 communities representing central cellular functions. Moreover, we identified 442 communities associated with more than 2000 disease annotations, placing numerous candidate disease genes into a cellular framework. BioPlex 2.0 exceeds previous experimentally derived interaction networks in depth and breadth, and will be a valuable resource for exploring the biology of incompletely characterized proteins and for elucidating larger-scale patterns of proteome organization. PMID:28514442

The Calcineurin Signaling Network Evolves Via Conserved Kinase–Phosphatase Modules That Transcend Substrate Identity

PubMed Central

Bodenmiller, Bernd; Wanka, Stefanie; Landry, Christian R.; Aebersold, Ruedi; Cyert, Martha S.

2014-01-01

Summary To define the first functional network for calcineurin, the conserved Ca2+/calmodulin-regulated phosphatase, we systematically identified its substrates in S. cerevisiae using phosphoproteomics and bioinformatics, followed by co-purification and dephosphorylation assays. This study establishes new calcineurin functions and reveals mechanisms that shape calcineurin network evolution. Analyses of closely related yeasts show that many proteins were recently recruited to the network by acquiring a calcineurin-recognition motif. Calcineurin substrates in yeast and mammals are distinct due to network rewiring but surprisingly are phosphorylated by similar kinases. We postulate that co-recognition of conserved substrate features, including phosphorylation and docking motifs, preserves calcineurin-kinase opposition during evolution. One example we document is a composite docking site that confers substrate recognition by both calcineurin and MAPK. We propose that conserved kinase-phosphatase pairs define the architecture of signaling networks and allow other connections between kinases and phosphatases to develop and establish common regulatory motifs in signaling networks. PMID:24930733

A Method for Predicting Protein Complexes from Dynamic Weighted Protein-Protein Interaction Networks.

PubMed

Liu, Lizhen; Sun, Xiaowu; Song, Wei; Du, Chao

2018-06-01

Predicting protein complexes from protein-protein interaction (PPI) network is of great significance to recognize the structure and function of cells. A protein may interact with different proteins under different time or conditions. Existing approaches only utilize static PPI network data that may lose much temporal biological information. First, this article proposed a novel method that combines gene expression data at different time points with traditional static PPI network to construct different dynamic subnetworks. Second, to further filter out the data noise, the semantic similarity based on gene ontology is regarded as the network weight together with the principal component analysis, which is introduced to deal with the weight computing by three traditional methods. Third, after building a dynamic PPI network, a predicting protein complexes algorithm based on "core-attachment" structural feature is applied to detect complexes from each dynamic subnetworks. Finally, it is revealed from the experimental results that our method proposed in this article performs well on detecting protein complexes from dynamic weighted PPI networks.

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

PubMed

Saez-Rodriguez, Julio; Gayer, Stefan; Ginkel, Martin; Gilles, Ernst Dieter

2008-08-15

The modularity of biochemical networks in general, and signaling networks in particular, has been extensively studied over the past few years. It has been proposed to be a useful property to analyze signaling networks: by decomposing the network into subsystems, more manageable units are obtained that are easier to analyze. While many powerful algorithms are available to identify modules in protein interaction networks, less attention has been paid to signaling networks de.ned as chemical systems. Such a decomposition would be very useful as most quantitative models are de.ned using the latter, more detailed formalism. Here, we introduce a novel method to decompose biochemical networks into modules so that the bidirectional (retroactive) couplings among the modules are minimized. Our approach adapts a method to detect community structures, and applies it to the so-called retroactivity matrix that characterizes the couplings of the network. Only the structure of the network, e.g. in SBML format, is required. Furthermore, the modularized models can be loaded into ProMoT, a modeling tool which supports modular modeling. This allows visualization of the models, exploiting their modularity and easy generation of models of one or several modules for further analysis. The method is applied to several relevant cases, including an entangled model of the EGF-induced MAPK cascade and a comprehensive model of EGF signaling, demonstrating its ability to uncover meaningful modules. Our approach can thus help to analyze large networks, especially when little a priori knowledge on the structure of the network is available. The decomposition algorithms implemented in MATLAB (Mathworks, Inc.) are freely available upon request. ProMoT is freely available at http://www.mpi-magdeburg.mpg.de/projects/promot. Supplementary data are available at Bioinformatics online.

Discovery of nitrate-CPK-NLP signalling in central nutrient-growth networks.

PubMed

Liu, Kun-Hsiang; Niu, Yajie; Konishi, Mineko; Wu, Yue; Du, Hao; Sun Chung, Hoo; Li, Lei; Boudsocq, Marie; McCormack, Matthew; Maekawa, Shugo; Ishida, Tetsuya; Zhang, Chao; Shokat, Kevan; Yanagisawa, Shuichi; Sheen, Jen

2017-05-18

Nutrient signalling integrates and coordinates gene expression, metabolism and growth. However, its primary molecular mechanisms remain incompletely understood in plants and animals. Here we report unique Ca 2+ signalling triggered by nitrate with live imaging of an ultrasensitive biosensor in Arabidopsis leaves and roots. A nitrate-sensitized and targeted functional genomic screen identifies subgroup III Ca 2+ -sensor protein kinases (CPKs) as master regulators that orchestrate primary nitrate responses. A chemical switch with the engineered mutant CPK10(M141G) circumvents embryo lethality and enables conditional analyses of cpk10 cpk30 cpk32 triple mutants to define comprehensive nitrate-associated regulatory and developmental programs. Nitrate-coupled CPK signalling phosphorylates conserved NIN-LIKE PROTEIN (NLP) transcription factors to specify the reprogramming of gene sets for downstream transcription factors, transporters, nitrogen assimilation, carbon/nitrogen metabolism, redox, signalling, hormones and proliferation. Conditional cpk10 cpk30 cpk32 and nlp7 mutants similarly impair nitrate-stimulated system-wide shoot growth and root establishment. The nutrient-coupled Ca 2+ signalling network integrates transcriptome and cellular metabolism with shoot-root coordination and developmental plasticity in shaping organ biomass and architecture.

Protein-Protein Interface and Disease: Perspective from Biomolecular Networks.

PubMed

Hu, Guang; Xiao, Fei; Li, Yuqian; Li, Yuan; Vongsangnak, Wanwipa

Protein-protein interactions are involved in many important biological processes and molecular mechanisms of disease association. Structural studies of interfacial residues in protein complexes provide information on protein-protein interactions. Characterizing protein-protein interfaces, including binding sites and allosteric changes, thus pose an imminent challenge. With special focus on protein complexes, approaches based on network theory are proposed to meet this challenge. In this review we pay attention to protein-protein interfaces from the perspective of biomolecular networks and their roles in disease. We first describe the different roles of protein complexes in disease through several structural aspects of interfaces. We then discuss some recent advances in predicting hot spots and communication pathway analysis in terms of amino acid networks. Finally, we highlight possible future aspects of this area with respect to both methodology development and applications for disease treatment.

A human functional protein interaction network and its application to cancer data analysis

PubMed Central

2010-01-01

Background One challenge facing biologists is to tease out useful information from massive data sets for further analysis. A pathway-based analysis may shed light by projecting candidate genes onto protein functional relationship networks. We are building such a pathway-based analysis system. Results We have constructed a protein functional interaction network by extending curated pathways with non-curated sources of information, including protein-protein interactions, gene coexpression, protein domain interaction, Gene Ontology (GO) annotations and text-mined protein interactions, which cover close to 50% of the human proteome. By applying this network to two glioblastoma multiforme (GBM) data sets and projecting cancer candidate genes onto the network, we found that the majority of GBM candidate genes form a cluster and are closer than expected by chance, and the majority of GBM samples have sequence-altered genes in two network modules, one mainly comprising genes whose products are localized in the cytoplasm and plasma membrane, and another comprising gene products in the nucleus. Both modules are highly enriched in known oncogenes, tumor suppressors and genes involved in signal transduction. Similar network patterns were also found in breast, colorectal and pancreatic cancers. Conclusions We have built a highly reliable functional interaction network upon expert-curated pathways and applied this network to the analysis of two genome-wide GBM and several other cancer data sets. The network patterns revealed from our results suggest common mechanisms in the cancer biology. Our system should provide a foundation for a network or pathway-based analysis platform for cancer and other diseases. PMID:20482850

Information processing in network architecture of genome controlled signal transduction circuit. A proposed theoretical explanation.

PubMed

Chakraborty, Chiranjib; Sarkar, Bimal Kumar; Patel, Pratiksha; Agoramoorthy, Govindasamy

2012-01-01

In this paper, Shannon information theory has been applied to elaborate cell signaling. It is proposed that in the cellular network architecture, four components viz. source (DNA), transmitter (mRNA), receiver (protein) and destination (another protein) are involved. The message transmits from source (DNA) to transmitter (mRNA) and then passes through a noisy channel reaching finally the receiver (protein). The protein synthesis process is here considered as the noisy channel. Ultimately, signal is transmitted from receiver to destination (another protein). The genome network architecture elements were compared with genetic alphabet L = {A, C, G, T} with a biophysical model based on the popular Shannon information theory. This study found the channel capacity as maximum for zero error (sigma = 0) and at this condition, transition matrix becomes a unit matrix with rank 4. The transition matrix will be erroneous and finally at sigma = 1 channel capacity will be localized maxima with a value of 0.415 due to the increased value at sigma. On the other hand, minima exists at sigma = 0.75, where all transition probabilities become 0.25 and uncertainty will be maximum resulting in channel capacity with the minima value of zero.

Cellular Signaling Pathways and Posttranslational Modifications Mediated by Nematode Effector Proteins.

PubMed

Hewezi, Tarek

2015-10-01

Plant-parasitic cyst and root-knot nematodes synthesize and secrete a suite of effector proteins into infected host cells and tissues. These effectors are the major virulence determinants mediating the transformation of normal root cells into specialized feeding structures. Compelling evidence indicates that these effectors directly hijack or manipulate refined host physiological processes to promote the successful parasitism of host plants. Here, we provide an update on recent progress in elucidating the molecular functions of nematode effectors. In particular, we emphasize how nematode effectors modify plant cell wall structure, mimic the activity of host proteins, alter auxin signaling, and subvert defense signaling and immune responses. In addition, we discuss the emerging evidence suggesting that nematode effectors target and recruit various components of host posttranslational machinery in order to perturb the host signaling networks required for immunity and to regulate their own activity and subcellular localization. © 2015 American Society of Plant Biologists. All Rights Reserved.

Neurodegeneration in Alzheimer disease: role of amyloid precursor protein and presenilin 1 intracellular signaling.

PubMed

Nizzari, Mario; Thellung, Stefano; Corsaro, Alessandro; Villa, Valentina; Pagano, Aldo; Porcile, Carola; Russo, Claudio; Florio, Tullio

2012-01-01

Alzheimer disease (AD) is a heterogeneous neurodegenerative disorder characterized by (1) progressive loss of synapses and neurons, (2) intracellular neurofibrillary tangles, composed of hyperphosphorylated Tau protein, and (3) amyloid plaques. Genetically, AD is linked to mutations in few proteins amyloid precursor protein (APP) and presenilin 1 and 2 (PS1 and PS2). The molecular mechanisms underlying neurodegeneration in AD as well as the physiological function of APP are not yet known. A recent theory has proposed that APP and PS1 modulate intracellular signals to induce cell-cycle abnormalities responsible for neuronal death and possibly amyloid deposition. This hypothesis is supported by the presence of a complex network of proteins, clearly involved in the regulation of signal transduction mechanisms that interact with both APP and PS1. In this review we discuss the significance of novel finding related to cell-signaling events modulated by APP and PS1 in the development of neurodegeneration.

Identification of Modules in Protein-Protein Interaction Networks

NASA Astrophysics Data System (ADS)

Erten, Sinan; Koyutürk, Mehmet

In biological systems, most processes are carried out through orchestration of multiple interacting molecules. These interactions are often abstracted using network models. A key feature of cellular networks is their modularity, which contributes significantly to the robustness, as well as adaptability of biological systems. Therefore, modularization of cellular networks is likely to be useful in obtaining insights into the working principles of cellular systems, as well as building tractable models of cellular organization and dynamics. A common, high-throughput source of data on molecular interactions is in the form of physical interactions between proteins, which are organized into protein-protein interaction (PPI) networks. This chapter provides an overview on identification and analysis of functional modules in PPI networks, which has been an active area of research in the last decade.

The protist, Monosiga brevicollis, has a tyrosine kinase signaling network more elaborate and diverse than found in any known metazoan.

PubMed

Manning, Gerard; Young, Susan L; Miller, W Todd; Zhai, Yufeng

2008-07-15

Tyrosine kinase signaling has long been considered a hallmark of intercellular communication, unique to multicellular animals. Our genomic analysis of the unicellular choanoflagellate Monosiga brevicollis discovers a remarkable count of 128 tyrosine kinases, 38 tyrosine phosphatases, and 123 phosphotyrosine (pTyr)-binding SH2 proteins, all higher counts than seen in any metazoan. This elaborate signaling network shows little orthology to metazoan counterparts yet displays many innovations reminiscent of metazoans. These include extracellular domains structurally related to those of metazoan receptor kinases, alternative methods for membrane anchoring and phosphotyrosine interaction in cytoplasmic kinases, and domain combinations that link kinases to small GTPase signaling and transcription. These proteins also display a wealth of combinations of known signaling domains. This uniquely divergent and elaborate signaling network illuminates the early evolution of pTyr signaling, explores innovative ways to traverse the cellular signaling circuitry, and shows extensive convergent evolution, highlighting pervasive constraints on pTyr signaling.

Signal-transducing protein phosphorylation cascades mediated by Ras/Rho proteins in the mammalian cell: the potential for multiplex signalling.

PubMed Central

Denhardt, D T

1996-01-01

The features of three distinct protein phosphorylation cascades in mammalian cells are becoming clear. These signalling pathways link receptor-mediated events at the cell surface or intracellular perturbations such as DNA damage to changes in cytoskeletal structure, vesicle transport and altered transcription factor activity. The best known pathway, the Ras-->Raf-->MEK-->ERK cascade [where ERK is extracellular-signal-regulated kinase and MEK is mitogen-activated protein (MAP) kinase/ERK kinase], is typically stimulated strongly by mitogens and growth factors. The other two pathways, stimulated primarily by assorted cytokines, hormones and various forms of stress, predominantly utilize p21 proteins of the Rho family (Rho, Rac and CDC42), although Ras can also participate. Diagnostic of each pathway is the MAP kinase component, which is phosphorylated by a unique dual-specificity kinase on both tyrosine and threonine in one of three motifs (Thr-Glu-Tyr, Thr-Phe-Tyr or Thr-Gly-Tyr), depending upon the pathway. In addition to activating one or more protein phosphorylation cascades, the initiating stimulus may also mobilize a variety of other signalling molecules (e.g. protein kinase C isoforms, phospholipid kinases, G-protein alpha and beta gamma subunits, phospholipases, intracellular Ca2+). These various signals impact to a greater or lesser extent on multiple downstream effectors. Important concepts are that signal transmission often entails the targeted relocation of specific proteins in the cell, and the reversible formation of protein complexes by means of regulated protein phosphorylation. The signalling circuits may be completed by the phosphorylation of upstream effectors by downstream kinases, resulting in a modulation of the signal. Signalling is terminated and the components returned to the ground state largely by dephosphorylation. There is an indeterminant amount of cross-talk among the pathways, and many of the proteins in the pathways belong to families

Protein profile and protein interaction network of Moniliophthora perniciosa basidiospores.

PubMed

Mares, Joise Hander; Gramacho, Karina Peres; Dos Santos, Everton Cruz; Santiago, André da Silva; Silva, Edson Mário de Andrade; Alvim, Fátima Cerqueira; Pirovani, Carlos Priminho

2016-06-24

Witches' broom, a disease caused by the basidiomycete Moniliophthora perniciosa, is considered to be the most important disease of the cocoa crop in Bahia, an area in the Brazilian Amazon, and also in the other countries where it is found. M. perniciosa germ tubes may penetrate into the host through intact or natural openings in the cuticle surface, in epidermis cell junctions, at the base of trichomes, or through the stomata. Despite its relevance to the fungal life cycle, basidiospore biology has not been extensively investigated. In this study, our goal was to optimize techniques for producing basidiospores for protein extraction, and to produce the first proteomics analysis map of ungerminated basidiospores. We then presented a protein interaction network by using Ustilago maydis as a model. The average pileus area ranged from 17.35 to 211.24 mm(2). The minimum and maximum productivity were 23,200 and 6,666,667 basidiospores per basidiome, respectively. The protein yield in micrograms per million basidiospores were approximately 0.161; 2.307, and 3.582 for germination times of 0, 2, and 4 h after germination, respectively. A total of 178 proteins were identified through mass spectrometry. These proteins were classified according to their molecular function and their involvement in biological processes such as cellular energy production, oxidative metabolism, stress, protein synthesis, and protein folding. Furthermore, to better understand the expression pattern, signaling, and interaction events of spore proteins, we presented an interaction network using orthologous proteins from Ustilago maydis as a model. Most of the orthologous proteins that were identified in this study were not clustered in the network, but several of them play a very important role in hypha development and branching. The quantities of basidiospores 7 × 10(9); 5.2 × 10(8), and 6.7 × 10(8) were sufficient to obtain enough protein mass for the three 2D-PAGE replicates, for

A protein interaction network analysis for yeast integral membrane protein.

PubMed

Shi, Ming-Guang; Huang, De-Shuang; Li, Xue-Ling

2008-01-01

Although the yeast Saccharomyces cerevisiae is the best exemplified single-celled eukaryote, the vast number of protein-protein interactions of integral membrane proteins of Saccharomyces cerevisiae have not been characterized by experiments. Here, based on the kernel method of Greedy Kernel Principal Component analysis plus Linear Discriminant Analysis, we identify 300 protein-protein interactions involving 189 membrane proteins and get the outcome of a highly connected protein-protein interactions network. Furthermore, we study the global topological features of integral membrane proteins network of Saccharomyces cerevisiae. These results give the comprehensive description of protein-protein interactions of integral membrane proteins and reveal global topological and robustness of the interactome network at a system level. This work represents an important step towards a comprehensive understanding of yeast protein interactions.

Extensive cross-talk and global regulators identified from an analysis of the integrated transcriptional and signaling network in Escherichia coli.

PubMed

Antiqueira, Lucas; Janga, Sarath Chandra; Costa, Luciano da Fontoura

2012-11-01

To understand the regulatory dynamics of transcription factors (TFs) and their interplay with other cellular components we have integrated transcriptional, protein-protein and the allosteric or equivalent interactions which mediate the physiological activity of TFs in Escherichia coli. To study this integrated network we computed a set of network measurements followed by principal component analysis (PCA), investigated the correlations between network structure and dynamics, and carried out a procedure for motif detection. In particular, we show that outliers identified in the integrated network based on their network properties correspond to previously characterized global transcriptional regulators. Furthermore, outliers are highly and widely expressed across conditions, thus supporting their global nature in controlling many genes in the cell. Motifs revealed that TFs not only interact physically with each other but also obtain feedback from signals delivered by signaling proteins supporting the extensive cross-talk between different types of networks. Our analysis can lead to the development of a general framework for detecting and understanding global regulatory factors in regulatory networks and reinforces the importance of integrating multiple types of interactions in underpinning the interrelationships between them.

ChiPPI: a novel method for mapping chimeric protein-protein interactions uncovers selection principles of protein fusion events in cancer.

PubMed

Frenkel-Morgenstern, Milana; Gorohovski, Alessandro; Tagore, Somnath; Sekar, Vaishnovi; Vazquez, Miguel; Valencia, Alfonso

2017-07-07

Fusion proteins, comprising peptides deriving from the translation of two parental genes, are produced in cancer by chromosomal aberrations. The expressed fusion protein incorporates domains of both parental proteins. Using a methodology that treats discrete protein domains as binding sites for specific domains of interacting proteins, we have cataloged the protein interaction networks for 11 528 cancer fusions (ChiTaRS-3.1). Here, we present our novel method, chimeric protein-protein interactions (ChiPPI) that uses the domain-domain co-occurrence scores in order to identify preserved interactors of chimeric proteins. Mapping the influence of fusion proteins on cell metabolism and pathways reveals that ChiPPI networks often lose tumor suppressor proteins and gain oncoproteins. Furthermore, fusions often induce novel connections between non-interactors skewing interaction networks and signaling pathways. We compared fusion protein PPI networks in leukemia/lymphoma, sarcoma and solid tumors finding distinct enrichment patterns for each disease type. While certain pathways are enriched in all three diseases (Wnt, Notch and TGF β), there are distinct patterns for leukemia (EGFR signaling, DNA replication and CCKR signaling), for sarcoma (p53 pathway and CCKR signaling) and solid tumors (FGFR and EGFR signaling). Thus, the ChiPPI method represents a comprehensive tool for studying the anomaly of skewed cellular networks produced by fusion proteins in cancer. © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.

Investigation of a protein complex network

NASA Astrophysics Data System (ADS)

Mashaghi, A. R.; Ramezanpour, A.; Karimipour, V.

2004-09-01

The budding yeast Saccharomyces cerevisiae is the first eukaryote whose genome has been completely sequenced. It is also the first eukaryotic cell whose proteome (the set of all proteins) and interactome (the network of all mutual interactions between proteins) has been analyzed. In this paper we study the structure of the yeast protein complex network in which weighted edges between complexes represent the number of shared proteins. It is found that the network of protein complexes is a small world network with scale free behavior for many of its distributions. However we find that there are no strong correlations between the weights and degrees of neighboring complexes. To reveal non-random features of the network we also compare it with a null model in which the complexes randomly select their proteins. Finally we propose a simple evolutionary model based on duplication and divergence of proteins.

A Discrete Ubiquitin-Mediated Network Regulates the Strength of NOD2 Signaling

PubMed Central

Tigno-Aranjuez, Justine T.; Bai, Xiaodong

2013-01-01

Dysregulation of NOD2 signaling is implicated in the pathology of various inflammatory diseases, including Crohn's disease, asthma, and sarcoidosis, making signaling proteins downstream of NOD2 potential therapeutic targets. Inhibitor-of-apoptosis (IAP) proteins, particularly cIAP1, are essential mediators of NOD2 signaling, and in this work, we describe a molecular mechanism for cIAP1's regulation in the NOD2 signaling pathway. While cIAP1 promotes RIP2's tyrosine phosphorylation and subsequent NOD2 signaling, this positive regulation is countered by another E3 ubiquitin ligase, ITCH, through direct ubiquitination of cIAP1. This ITCH-mediated ubiquitination leads to cIAP1's lysosomal degradation. Pharmacologic inhibition of cIAP1 expression in ITCH−/− macrophages attenuates heightened ITCH−/− macrophage muramyl dipeptide-induced responses. Transcriptome analysis, combined with pharmacologic inhibition of cIAP1, further defines specific pathways within the NOD2 signaling pathway that are targeted by cIAP1. This information provides genetic signatures that may be useful in repurposing cIAP1-targeted therapies to correct NOD2-hyperactive states and identifies a ubiquitin-regulated signaling network centered on ITCH and cIAP1 that controls the strength of NOD2 signaling. PMID:23109427

Coordination of the recruitment of the FANCD2 and PALB2 Fanconi anemia proteins by an ubiquitin signaling network.

PubMed

Bick, Gregory; Zhang, Fan; Meetei, A Ruhikanta; Andreassen, Paul R

2017-06-01

Fanconi anemia (FA) is a chromosome instability syndrome and the 20 identified FA proteins are organized into two main arms which are thought to function at distinct steps in the repair of DNA interstrand crosslinks (ICLs). These two arms include the upstream FA pathway, which culminates in the monoubiquitination of FANCD2 and FANCI, and downstream breast cancer (BRCA)-associated proteins that interact in protein complexes. How, and whether, these two groups of FA proteins are integrated is unclear. Here, we show that FANCD2 and PALB2, as indicators of the upstream and downstream arms, respectively, colocalize independently of each other in response to DNA damage induced by mitomycin C (MMC). We also show that ubiquitin chains are induced by MMC and colocalize with both FANCD2 and PALB2. Our finding that the RNF8 E3 ligase has a role in recruiting FANCD2 and PALB2 also provides support for the hypothesis that the two branches of the FA-BRCA pathway are coordinated by ubiquitin signaling. Interestingly, we find that the RNF8 partner, MDC1, as well as the ubiquitin-binding protein, RAP80, specifically recruit PALB2, while a different ubiquitin-binding protein, FAAP20, functions only in the recruitment of FANCD2. Thus, FANCD2 and PALB2 are not recruited in a single linear pathway, rather we define how their localization is coordinated and integrated by a network of ubiquitin-related proteins. We propose that such regulation may enable upstream and downstream FA proteins to act at distinct steps in the repair of ICLs.

Dynamic Bayesian Network Modeling of the Interplay between EGFR and Hedgehog Signaling.

PubMed

Fröhlich, Holger; Bahamondez, Gloria; Götschel, Frank; Korf, Ulrike

2015-01-01

Aberrant activation of sonic Hegdehog (SHH) signaling has been found to disrupt cellular differentiation in many human cancers and to increase proliferation. The SHH pathway is known to cross-talk with EGFR dependent signaling. Recent studies experimentally addressed this interplay in Daoy cells, which are presumable a model system for medulloblastoma, a highly malignant brain tumor that predominately occurs in children. Currently ongoing are several clinical trials for different solid cancers, which are designed to validate the clinical benefits of targeting the SHH in combination with other pathways. This has motivated us to investigate interactions between EGFR and SHH dependent signaling in greater depth. To our knowledge, there is no mathematical model describing the interplay between EGFR and SHH dependent signaling in medulloblastoma so far. Here we come up with a fully probabilistic approach using Dynamic Bayesian Networks (DBNs). To build our model, we made use of literature based knowledge describing SHH and EGFR signaling and integrated gene expression (Illumina) and cellular location dependent time series protein expression data (Reverse Phase Protein Arrays). We validated our model by sub-sampling training data and making Bayesian predictions on the left out test data. Our predictions focusing on key transcription factors and p70S6K, showed a high level of concordance with experimental data. Furthermore, the stability of our model was tested by a parametric bootstrap approach. Stable network features were in agreement with published data. Altogether we believe that our model improved our understanding of the interplay between two highly oncogenic signaling pathways in Daoy cells. This may open new perspectives for the future therapy of Hedghog/EGF-dependent solid tumors.

Signaling networks in joint development

PubMed Central

Salva, Joanna E.; Merrill, Amy E.

2016-01-01

Here we review studies identifying regulatory networks responsible for synovial, cartilaginous, and fibrous joint development. Synovial joints, characterized by the fluid-filled synovial space between the bones, are found in high-mobility regions and are the most common type of joint. Cartilaginous joints unite adjacent bones through either a hyaline cartilage or fibrocartilage intermediate. Fibrous joints, which include the cranial sutures, form a direct union between bones through fibrous connective tissue. We describe how the distinct morphologic and histogenic characteristics of these joint classes are established during embryonic development. Collectively, these studies reveal that despite the heterogeneity of joint strength and mobility, joint development throughout the skeleton utilizes common signaling networks via long-range morphogen gradients and direct cell-cell contact. This suggests that different joint types represent specialized variants of homologous developmental modules. Identifying the unifying aspects of the signaling networks between joint classes allows a more complete understanding of the signaling code for joint formation, which is critical to improving strategies for joint regeneration and repair. PMID:27859991

Natural Genetic Variation Influences Protein Abundances in C. elegans Developmental Signalling Pathways

PubMed Central

Singh, Kapil Dev; Roschitzki, Bernd; Snoek, L. Basten; Grossmann, Jonas; Zheng, Xue; Elvin, Mark; Kamkina, Polina; Schrimpf, Sabine P.; Poulin, Gino B.; Kammenga, Jan E.; Hengartner, Michael O.

2016-01-01

Complex traits, including common disease-related traits, are affected by many different genes that function in multiple pathways and networks. The apoptosis, MAPK, Notch, and Wnt signalling pathways play important roles in development and disease progression. At the moment we have a poor understanding of how allelic variation affects gene expression in these pathways at the level of translation. Here we report the effect of natural genetic variation on transcript and protein abundance involved in developmental signalling pathways in Caenorhabditis elegans. We used selected reaction monitoring to analyse proteins from the abovementioned four pathways in a set of recombinant inbred lines (RILs) generated from the wild-type strains N2 (Bristol) and CB4856 (Hawaii) to enable quantitative trait locus (QTL) mapping. About half of the cases from the 44 genes tested showed a statistically significant change in protein abundance between various strains, most of these were however very weak (below 1.3-fold change). We detected a distant QTL on the left arm of chromosome II that affected protein abundance of the phosphatidylserine receptor protein PSR-1, and two separate QTLs that influenced embryonic and ionizing radiation-induced apoptosis on chromosome IV. Our results demonstrate that natural variation in C. elegans is sufficient to cause significant changes in signalling pathways both at the gene expression (transcript and protein abundance) and phenotypic levels. PMID:26985669

Protein Translation and Signaling in Human Eosinophils

PubMed Central

Esnault, Stephane; Shen, Zhong-Jian; Malter, James S.

2017-01-01

We have recently reported that, unlike IL-5 and GM-CSF, IL-3 induces increased translation of a subset of mRNAs. In addition, we have demonstrated that Pin1 controls the activity of mRNA binding proteins, leading to enhanced mRNA stability, GM-CSF protein production and prolonged eosinophil (EOS) survival. In this review, discussion will include an overview of cap-dependent protein translation and its regulation by intracellular signaling pathways. We will address the more general process of mRNA post-transcriptional regulation, especially regarding mRNA binding proteins, which are critical effectors of protein translation. Furthermore, we will focus on (1) the roles of IL-3-driven sustained signaling on enhanced protein translation in EOS, (2) the mechanisms regulating mRNA binding proteins activity in EOS, and (3) the potential targeting of IL-3 signaling and the signaling leading to mRNA binding activity changes to identify therapeutic targets to treat EOS-associated diseases. PMID:28971096

Pharmacodynamic Assay Panel for Monitoring Phospho-Signaling Networks | Office of Cancer Clinical Proteomics Research

Cancer.gov

The DNA damage response (DDR) is a highly regulated signal transduction network that orchestrates the temporal and spatial organization of protein complexes required to repair (or tolerate) DNA damage (e.g., nucleotide excision repair, base excision repair, homologous recombination, non-homologous end joining, post-replication repair).

Protein-protein interaction network of gene expression in the hydrocortisone-treated keloid.

PubMed

Chen, Rui; Zhang, Zhiliang; Xue, Zhujia; Wang, Lin; Fu, Mingang; Lu, Yi; Bai, Ling; Zhang, Ping; Fan, Zhihong

2015-01-01

In order to explore the molecular mechanism of hydrocortisone in keloid tissue, the gene expression profiles of keloid samples treated with hydrocortisone were subjected to bioinformatics analysis. Firstly, the gene expression profiles (GSE7890) of five samples of keloid treated with hydrocortisone and five untreated keloid samples were downloaded from the Gene Expression Omnibus (GEO) database. Secondly, data were preprocessed using packages in R language and differentially expressed genes (DEGs) were screened using a significance analysis of microarrays (SAM) protocol. Thirdly, the DEGs were subjected to gene ontology (GO) function and KEGG pathway enrichment analysis. Finally, the interactions of DEGs in samples of keloid treated with hydrocortisone were explored in a human protein-protein interaction (PPI) network, and sub-modules of the DEGs interaction network were analyzed using Cytoscape software. Based on the analysis, 572 DEGs in the hydrocortisone-treated samples were screened; most of these were involved in the signal transduction and cell cycle. Furthermore, three critical genes in the module, including COL1A1, NID1, and PRELP, were screened in the PPI network analysis. These findings enhance understanding of the pathogenesis of the keloid and provide references for keloid therapy. © 2015 The International Society of Dermatology.

Aroclor 1254, a developmental neurotoxicant, alters energy metabolism- and intracellular signaling-associated protein networks in rat cerebellum and hippocampus

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kodavanti, Prasada Rao S., E-mail: kodavanti.prasada@epa.gov; Osorio, Cristina; Program on Molecular Biology and Biotechnology, University of North Carolina at Chapel Hill, North Carolina

2011-11-15

The vast literature on the mode of action of polychlorinated biphenyls (PCBs) indicates that PCBs are a unique model for understanding the mechanisms of toxicity of environmental mixtures of persistent chemicals. PCBs have been shown to adversely affect psychomotor function and learning and memory in humans. Although the molecular mechanisms for PCB effects are unclear, several studies indicate that the disruption of Ca{sup 2+}-mediated signal transduction plays significant roles in PCB-induced developmental neurotoxicity. Culminating events in signal transduction pathways include the regulation of gene and protein expression, which affects the growth and function of the nervous system. Our previous studiesmore » showed changes in gene expression related to signal transduction and neuronal growth. In this study, protein expression following developmental exposure to PCB is examined. Pregnant rats (Long Evans) were dosed with 0.0 or 6.0 mg/kg/day of Aroclor-1254 from gestation day 6 through postnatal day (PND) 21, and the cerebellum and hippocampus from PND14 animals were analyzed to determine Aroclor 1254-induced differential protein expression. Two proteins were found to be differentially expressed in the cerebellum following PCB exposure while 18 proteins were differentially expressed in the hippocampus. These proteins are related to energy metabolism in mitochondria (ATP synthase, sub unit {beta} (ATP5B), creatine kinase, and malate dehydrogenase), calcium signaling (voltage-dependent anion-selective channel protein 1 (VDAC1) and ryanodine receptor type II (RyR2)), and growth of the nervous system (dihydropyrimidinase-related protein 4 (DPYSL4), valosin-containing protein (VCP)). Results suggest that Aroclor 1254-like persistent chemicals may alter energy metabolism and intracellular signaling, which might result in developmental neurotoxicity. -- Highlights: Black-Right-Pointing-Pointer We performed brain proteomic analysis of rats exposed to the

Networks of blood proteins in the neuroimmunology of schizophrenia.

PubMed

Jeffries, Clark D; Perkins, Diana O; Fournier, Margot; Do, Kim Q; Cuenod, Michel; Khadimallah, Ines; Domenici, Enrico; Addington, Jean; Bearden, Carrie E; Cadenhead, Kristin S; Cannon, Tyrone D; Cornblatt, Barbara A; Mathalon, Daniel H; McGlashan, Thomas H; Seidman, Larry J; Tsuang, Ming; Walker, Elaine F; Woods, Scott W

2018-06-06

Levels of certain circulating cytokines and related immune system molecules are consistently altered in schizophrenia and related disorders. In addition to absolute analyte levels, we sought analytes in correlation networks that could be prognostic. We analyzed baseline blood plasma samples with a Luminex platform from 72 subjects meeting criteria for a psychosis clinical high-risk syndrome; 32 subjects converted to a diagnosis of psychotic disorder within two years while 40 other subjects did not. Another comparison group included 35 unaffected subjects. Assays of 141 analytes passed early quality control. We then used an unweighted co-expression network analysis to identify highly correlated modules in each group. Overall, there was a striking loss of network complexity going from unaffected subjects to nonconverters and thence to converters (applying standard, graph-theoretic metrics). Graph differences were largely driven by proteins regulating tissue remodeling (e.g. blood-brain barrier). In more detail, certain sets of antithetical proteins were highly correlated in unaffected subjects (e.g. SERPINE1 vs MMP9), as expected in homeostasis. However, for particular protein pairs this trend was reversed in converters (e.g. SERPINE1 vs TIMP1, being synthetical inhibitors of remodeling of extracellular matrix and vasculature). Thus, some correlation signals strongly predict impending conversion to a psychotic disorder and directly suggest pharmaceutical targets.

An artificial network model for estimating the network structure underlying partially observed neuronal signals.

PubMed

Komatsu, Misako; Namikawa, Jun; Chao, Zenas C; Nagasaka, Yasuo; Fujii, Naotaka; Nakamura, Kiyohiko; Tani, Jun

2014-01-01

Many previous studies have proposed methods for quantifying neuronal interactions. However, these methods evaluated the interactions between recorded signals in an isolated network. In this study, we present a novel approach for estimating interactions between observed neuronal signals by theorizing that those signals are observed from only a part of the network that also includes unobserved structures. We propose a variant of the recurrent network model that consists of both observable and unobservable units. The observable units represent recorded neuronal activity, and the unobservable units are introduced to represent activity from unobserved structures in the network. The network structures are characterized by connective weights, i.e., the interaction intensities between individual units, which are estimated from recorded signals. We applied this model to multi-channel brain signals recorded from monkeys, and obtained robust network structures with physiological relevance. Furthermore, the network exhibited common features that portrayed cortical dynamics as inversely correlated interactions between excitatory and inhibitory populations of neurons, which are consistent with the previous view of cortical local circuits. Our results suggest that the novel concept of incorporating an unobserved structure into network estimations has theoretical advantages and could provide insights into brain dynamics beyond what can be directly observed. Copyright © 2014 Elsevier Ireland Ltd and the Japan Neuroscience Society. All rights reserved.

The Brassinosteroid Signaling Pathway—New Key Players and Interconnections with Other Signaling Networks Crucial for Plant Development and Stress Tolerance

PubMed Central

Gruszka, Damian

2013-01-01

Brassinosteroids (BRs) are a class of steroid hormones regulating a wide range of physiological processes during the plant life cycle from seed development to the modulation of flowering and senescence. The last decades, and recent years in particular, have witnessed a significant advance in the elucidation of the molecular mechanisms of BR signaling from perception by the transmembrane receptor complex to the regulation of transcription factors influencing expression of the target genes. Application of the new approaches shed light on the molecular functions of the key players regulating the BR signaling cascade and allowed identification of new factors. Recent studies clearly indicated that some of the components of BR signaling pathway act as multifunctional proteins involved in other signaling networks regulating diverse physiological processes, such as photomorphogenesis, cell death control, stomatal development, flowering, plant immunity to pathogens and metabolic responses to stress conditions, including salinity. Regulation of some of these processes is mediated through a crosstalk between BR signalosome and the signaling cascades of other hormones, including auxin, abscisic acid, ethylene and salicylic acid. Unravelling the complicated mechanisms of BR signaling and its interconnections with other molecular networks may be of great importance for future practical applications in agriculture. PMID:23615468

Finding undetected protein associations in cell signaling by belief propagation.

PubMed

Bailly-Bechet, M; Borgs, C; Braunstein, A; Chayes, J; Dagkessamanskaia, A; François, J-M; Zecchina, R

2011-01-11

External information propagates in the cell mainly through signaling cascades and transcriptional activation, allowing it to react to a wide spectrum of environmental changes. High-throughput experiments identify numerous molecular components of such cascades that may, however, interact through unknown partners. Some of them may be detected using data coming from the integration of a protein-protein interaction network and mRNA expression profiles. This inference problem can be mapped onto the problem of finding appropriate optimal connected subgraphs of a network defined by these datasets. The optimization procedure turns out to be computationally intractable in general. Here we present a new distributed algorithm for this task, inspired from statistical physics, and apply this scheme to alpha factor and drug perturbations data in yeast. We identify the role of the COS8 protein, a member of a gene family of previously unknown function, and validate the results by genetic experiments. The algorithm we present is specially suited for very large datasets, can run in parallel, and can be adapted to other problems in systems biology. On renowned benchmarks it outperforms other algorithms in the field.

Radar signal transmission and switching over optical networks

NASA Astrophysics Data System (ADS)

Esmail, Maged A.; Ragheb, Amr; Seleem, Hussein; Fathallah, Habib; Alshebeili, Saleh

2018-03-01

In this paper, we experimentally demonstrate a radar signal distribution over optical networks. The use of fiber enables us to distribute radar signals to distant sites with a low power loss. Moreover, fiber networks can reduce the radar system cost, by sharing precise and expensive radar signal generation and processing equipment. In order to overcome the bandwidth challenges in electrical switches, a semiconductor optical amplifier (SOA) is used as an all-optical device for wavelength conversion to the desired port (or channel) of a wavelength division multiplexing (WDM) network. Moreover, the effect of chromatic dispersion in double sideband (DSB) signals is combated by generating optical single sideband (OSSB) signals. The optimal values of the SOA device parameters required to generate an OSSB with a high sideband suppression ratio (SSR) are determined. We considered various parameters such as injection current, pump power, and probe power. In addition, the effect of signal wavelength conversion and transmission over fiber are studied in terms of signal dynamic range.

Regulation of protease-activated receptor 1 signaling by the adaptor protein complex 2 and R4 subfamily of regulator of G protein signaling proteins.

PubMed

Chen, Buxin; Siderovski, David P; Neubig, Richard R; Lawson, Mark A; Trejo, Joann

2014-01-17

The G protein-coupled protease-activated receptor 1 (PAR1) is irreversibly proteolytically activated by thrombin. Hence, the precise regulation of PAR1 signaling is important for proper cellular responses. In addition to desensitization, internalization and lysosomal sorting of activated PAR1 are critical for the termination of signaling. Unlike most G protein-coupled receptors, PAR1 internalization is mediated by the clathrin adaptor protein complex 2 (AP-2) and epsin-1, rather than β-arrestins. However, the function of AP-2 and epsin-1 in the regulation of PAR1 signaling is not known. Here, we report that AP-2, and not epsin-1, regulates activated PAR1-stimulated phosphoinositide hydrolysis via two different mechanisms that involve, in part, a subset of R4 subfamily of "regulator of G protein signaling" (RGS) proteins. A significantly greater increase in activated PAR1 signaling was observed in cells depleted of AP-2 using siRNA or in cells expressing a PAR1 (420)AKKAA(424) mutant with defective AP-2 binding. This effect was attributed to AP-2 modulation of PAR1 surface expression and efficiency of G protein coupling. We further found that ectopic expression of R4 subfamily members RGS2, RGS3, RGS4, and RGS5 reduced activated PAR1 wild-type signaling, whereas signaling by the PAR1 AKKAA mutant was minimally affected. Intriguingly, siRNA-mediated depletion analysis revealed a function for RGS5 in the regulation of signaling by the PAR1 wild type but not the AKKAA mutant. Moreover, activation of the PAR1 wild type, and not the AKKAA mutant, induced Gαq association with RGS3 via an AP-2-dependent mechanism. Thus, AP-2 regulates activated PAR1 signaling by altering receptor surface expression and through recruitment of RGS proteins.

DiffSLC: A graph centrality method to detect essential proteins of a protein-protein interaction network.

PubMed

Mistry, Divya; Wise, Roger P; Dickerson, Julie A

2017-01-01

Identification of central genes and proteins in biomolecular networks provides credible candidates for pathway analysis, functional analysis, and essentiality prediction. The DiffSLC centrality measure predicts central and essential genes and proteins using a protein-protein interaction network. Network centrality measures prioritize nodes and edges based on their importance to the network topology. These measures helped identify critical genes and proteins in biomolecular networks. The proposed centrality measure, DiffSLC, combines the number of interactions of a protein and the gene coexpression values of genes from which those proteins were translated, as a weighting factor to bias the identification of essential proteins in a protein interaction network. Potentially essential proteins with low node degree are promoted through eigenvector centrality. Thus, the gene coexpression values are used in conjunction with the eigenvector of the network's adjacency matrix and edge clustering coefficient to improve essentiality prediction. The outcome of this prediction is shown using three variations: (1) inclusion or exclusion of gene co-expression data, (2) impact of different coexpression measures, and (3) impact of different gene expression data sets. For a total of seven networks, DiffSLC is compared to other centrality measures using Saccharomyces cerevisiae protein interaction networks and gene expression data. Comparisons are also performed for the top ranked proteins against the known essential genes from the Saccharomyces Gene Deletion Project, which show that DiffSLC detects more essential proteins and has a higher area under the ROC curve than other compared methods. This makes DiffSLC a stronger alternative to other centrality methods for detecting essential genes using a protein-protein interaction network that obeys centrality-lethality principle. DiffSLC is implemented using the igraph package in R, and networkx package in Python. The python package can be

Diversity in the origins of proteostasis networks- a driver for protein function in evolution

PubMed Central

Powers, Evan T.; Balch, William E.

2013-01-01

Although a protein’s primary sequence largely determines its function, proteins can adopt different folding states in response to changes in the environment, some of which may be deleterious to the organism. All organisms, including Bacteria, Archaea and Eukarya, have evolved a protein homeostasis network, or proteostasis network, that consists of chaperones and folding factors, degradation components, signalling pathways and specialized compartmentalized modules that manage protein folding in response to environmental stimuli and variation. Surveying the origins of proteostasis networks reveals that they have co-evolved with the proteome to regulate the physiological state of the cell, reflecting the unique stresses that different cells or organisms experience, and that they have a key role in driving evolution by closely managing the link between the phenotype and the genotype. PMID:23463216

Information flow in a network of dispersed signalers-receivers

NASA Astrophysics Data System (ADS)

Halupka, Konrad

2017-11-01

I consider a stochastic model of multi-agent communication in regular network. The model describes how dispersed animals exchange information. Each agent can initiate and transfer the signal to its nearest neighbors, who may pass it farther. For an external observer of busy networks, signaling activity may appear random, even though information flow actually thrives. Only when signal initiation and transfer are at low levels do spatiotemporal autocorrelations emerge as clumping signaling activity in space and pink noise time series. Under such conditions, the costs of signaling are moderate, but the signaler can reach a large audience. I propose that real-world networks of dispersed signalers-receivers may self-organize into this state and the flow of information maintains their integrity.

Protein function prediction using neighbor relativity in protein-protein interaction network.

PubMed

Moosavi, Sobhan; Rahgozar, Masoud; Rahimi, Amir

2013-04-01

There is a large gap between the number of discovered proteins and the number of functionally annotated ones. Due to the high cost of determining protein function by wet-lab research, function prediction has become a major task for computational biology and bioinformatics. Some researches utilize the proteins interaction information to predict function for un-annotated proteins. In this paper, we propose a novel approach called "Neighbor Relativity Coefficient" (NRC) based on interaction network topology which estimates the functional similarity between two proteins. NRC is calculated for each pair of proteins based on their graph-based features including distance, common neighbors and the number of paths between them. In order to ascribe function to an un-annotated protein, NRC estimates a weight for each neighbor to transfer its annotation to the unknown protein. Finally, the unknown protein will be annotated by the top score transferred functions. We also investigate the effect of using different coefficients for various types of functions. The proposed method has been evaluated on Saccharomyces cerevisiae and Homo sapiens interaction networks. The performance analysis demonstrates that NRC yields better results in comparison with previous protein function prediction approaches that utilize interaction network. Copyright © 2012 Elsevier Ltd. All rights reserved.

Calcium-Oxidant Signaling Network Regulates AMP-activated Protein Kinase (AMPK) Activation upon Matrix Deprivation*

PubMed Central

Sundararaman, Ananthalakshmy; Amirtham, Usha; Rangarajan, Annapoorni

2016-01-01

The AMP-activated protein kinase (AMPK) has recently been implicated in anoikis resistance. However, the molecular mechanisms that activate AMPK upon matrix detachment remain unexplored. In this study, we show that AMPK activation is a rapid and sustained phenomenon upon matrix deprivation, whereas re-attachment to the matrix leads to its dephosphorylation and inactivation. Because matrix detachment leads to loss of integrin signaling, we investigated whether integrin signaling negatively regulates AMPK activation. However, modulation of focal adhesion kinase or Src, the major downstream components of integrin signaling, failed to cause a corresponding change in AMPK signaling. Further investigations revealed that the upstream AMPK kinases liver kinase B1 (LKB1) and Ca2+/calmodulin-dependent protein kinase kinase β (CaMKKβ) contribute to AMPK activation upon detachment. In LKB1-deficient cells, we found AMPK activation to be predominantly dependent on CaMKKβ. We observed no change in ATP levels under detached conditions at early time points suggesting that rapid AMPK activation upon detachment was not triggered by energy stress. We demonstrate that matrix deprivation leads to a spike in intracellular calcium as well as oxidant signaling, and both these intracellular messengers contribute to rapid AMPK activation upon detachment. We further show that endoplasmic reticulum calcium release-induced store-operated calcium entry contributes to intracellular calcium increase, leading to reactive oxygen species production, and AMPK activation. We additionally show that the LKB1/CaMKK-AMPK axis and intracellular calcium levels play a critical role in anchorage-independent cancer sphere formation. Thus, the Ca2+/reactive oxygen species-triggered LKB1/CaMKK-AMPK signaling cascade may provide a quick, adaptable switch to promote survival of metastasizing cancer cells. PMID:27226623

Impulse-induced optimum signal amplification in scale-free networks.

PubMed

Martínez, Pedro J; Chacón, Ricardo

2016-04-01

Optimizing information transmission across a network is an essential task for controlling and manipulating generic information-processing systems. Here, we show how topological amplification effects in scale-free networks of signaling devices are optimally enhanced when the impulse transmitted by periodic external signals (time integral over two consecutive zeros) is maximum. This is demonstrated theoretically by means of a star-like network of overdamped bistable systems subjected to generic zero-mean periodic signals and confirmed numerically by simulations of scale-free networks of such systems. Our results show that the enhancer effect of increasing values of the signal's impulse is due to a correlative increase of the energy transmitted by the periodic signals, while it is found to be resonant-like with respect to the topology-induced amplification mechanism.

Defining the Protein–Protein Interaction Network of the Human Hippo Pathway*

PubMed Central

Wang, Wenqi; Li, Xu; Huang, Jun; Feng, Lin; Dolinta, Keithlee G.; Chen, Junjie

2014-01-01

The Hippo pathway, which is conserved from Drosophila to mammals, has been recognized as a tumor suppressor signaling pathway governing cell proliferation and apoptosis, two key events involved in organ size control and tumorigenesis. Although several upstream regulators, the conserved kinase cascade and key downstream effectors including nuclear transcriptional factors have been defined, the global organization of this signaling pathway is not been fully understood. Thus, we conducted a proteomic analysis of human Hippo pathway, which revealed the involvement of an extensive protein–protein interaction network in this pathway. The mass spectrometry data were deposited to ProteomeXchange with identifier PXD000415. Our data suggest that 550 interactions within 343 unique protein components constitute the central protein–protein interaction landscape of human Hippo pathway. Our study provides a glimpse into the global organization of Hippo pathway, reveals previously unknown interactions within this pathway, and uncovers new potential components involved in the regulation of this pathway. Understanding these interactions will help us further dissect the Hippo signaling-pathway and extend our knowledge of organ size control. PMID:24126142

Topology and weights in a protein domain interaction network--a novel way to predict protein interactions.

PubMed

Wuchty, Stefan

2006-05-23

While the analysis of unweighted biological webs as diverse as genetic, protein and metabolic networks allowed spectacular insights in the inner workings of a cell, biological networks are not only determined by their static grid of links. In fact, we expect that the heterogeneity in the utilization of connections has a major impact on the organization of cellular activities as well. We consider a web of interactions between protein domains of the Protein Family database (PFAM), which are weighted by a probability score. We apply metrics that combine the static layout and the weights of the underlying interactions. We observe that unweighted measures as well as their weighted counterparts largely share the same trends in the underlying domain interaction network. However, we only find weak signals that weights and the static grid of interactions are connected entities. Therefore assuming that a protein interaction is governed by a single domain interaction, we observe strong and significant correlations of the highest scoring domain interaction and the confidence of protein interactions in the underlying interactions of yeast and fly. Modeling an interaction between proteins if we find a high scoring protein domain interaction we obtain 1, 428 protein interactions among 361 proteins in the human malaria parasite Plasmodium falciparum. Assessing their quality by a logistic regression method we observe that increasing confidence of predicted interactions is accompanied by high scoring domain interactions and elevated levels of functional similarity and evolutionary conservation. Our results indicate that probability scores are randomly distributed, allowing to treat static grid and weights of domain interactions as separate entities. In particular, these finding confirms earlier observations that a protein interaction is a matter of a single interaction event on domain level. As an immediate application, we show a simple way to predict potential protein interactions

An Integrated Phosphoproteomics Work Flow Reveals Extensive Network Regulation in Early Lysophosphatidic Acid Signaling*

PubMed Central

Schreiber, Thiemo B.; Mäusbacher, Nina; Kéri, György; Cox, Jürgen; Daub, Henrik

2010-01-01

Lysophosphatidic acid (LPA) induces a variety of cellular signaling pathways through the activation of its cognate G protein-coupled receptors. To investigate early LPA responses and assess the contribution of epidermal growth factor (EGF) receptor transactivation in LPA signaling, we performed phosphoproteomics analyses of both total cell lysate and protein kinase-enriched fractions as complementary strategies to monitor phosphorylation changes in A498 kidney carcinoma cells. Our integrated work flow enabled the identification and quantification of more than 5,300 phosphorylation sites of which 224 were consistently regulated by LPA. In addition to induced phosphorylation events, we also obtained evidence for early dephosphorylation reactions due to rapid phosphatase regulation upon LPA treatment. Phosphorylation changes induced by direct heparin-binding EGF-like growth factor-mediated EGF receptor activation were typically weaker and only detected on a subset of LPA-regulated sites, indicating signal integration among EGF receptor transactivation and other LPA-triggered pathways. Our results reveal rapid phosphoregulation of many proteins not yet implicated in G protein-coupled receptor signaling and point to various additional mechanisms by which LPA might regulate cell survival and migration as well as gene transcription on the molecular level. Moreover, our phosphoproteomics analysis of both total lysate and kinase-enriched fractions provided highly complementary parts of the LPA-regulated signaling network and thus represents a useful and generic strategy toward comprehensive signaling studies on a system-wide level. PMID:20071362

SH2/SH3 signaling proteins.

PubMed

Schlessinger, J

1994-02-01

SH2 and SH3 domains are small protein modules that mediate protein-protein interactions in signal transduction pathways that are activated by protein tyrosine kinases. SH2 domains bind to short phosphotyrosine-containing sequences in growth factor receptors and other phosphoproteins. SH3 domains bind to target proteins through sequences containing proline and hydrophobic amino acids. SH2 and SH3 domain containing proteins, such as Grb2 and phospholipase C gamma, utilize these modules in order to link receptor and cytoplasmic protein tyrosine kinases to the Ras signaling pathway and to phosphatidylinositol hydrolysis, respectively. The three-dimensional structures of several SH2 and SH3 domains have been determined by NMR and X-ray crystallography, and the molecular basis of their specificity is beginning to be unveiled.

Improved Protein Arrays for Quantitative Systems Analysis of the Dynamics of Signaling Pathway Interactions

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yang, Chin-Rang

Astronauts and workers in nuclear plants who repeatedly exposed to low doses of ionizing radiation (IR, <10 cGy) are likely to incur specific changes in signal transduction and gene expression in various tissues of their body. Remarkable advances in high throughput genomics and proteomics technologies enable researchers to broaden their focus from examining single gene/protein kinetics to better understanding global gene/protein expression profiling and biological pathway analyses, namely Systems Biology. An ultimate goal of systems biology is to develop dynamic mathematical models of interacting biological systems capable of simulating living systems in a computer. This Glue Grant is to complementmore » Dr. Boothman’s existing DOE grant (No. DE-FG02-06ER64186) entitled “The IGF1/IGF-1R-MAPK-Secretory Clusterin (sCLU) Pathway: Mediator of a Low Dose IR-Inducible Bystander Effect” to develop sensitive and quantitative proteomic technology that suitable for low dose radiobiology researches. An improved version of quantitative protein array platform utilizing linear Quantum dot signaling for systematically measuring protein levels and phosphorylation states for systems biology modeling is presented. The signals are amplified by a confocal laser Quantum dot scanner resulting in ~1000-fold more sensitivity than traditional Western blots and show the good linearity that is impossible for the signals of HRP-amplification. Therefore this improved protein array technology is suitable to detect weak responses of low dose radiation. Software is developed to facilitate the quantitative readout of signaling network activities. Kinetics of EGFRvIII mutant signaling was analyzed to quantify cross-talks between EGFR and other signaling pathways.« less

Regulation of PXR and CAR by protein-protein interaction and signaling crosstalk

PubMed Central

Oladimeji, Peter; Cui, Hongmei; Zhang, Chen; Chen, Taosheng

2016-01-01

Introduction Protein-protein interaction and signaling crosstalk contribute to the regulation of pregnane X receptor (PXR) and constitutive androstane receptor (CAR) and broaden their cellular function. Area covered This review covers key historic discoveries and recent advances in our understanding of the broad function of PXR and CAR and their regulation by protein-protein interaction and signaling crosstalk. Expert opinion PXR and CAR were first discovered as xenobiotic receptors. However, it is clear that PXR and CAR perform a much broader range of cellular functions through protein-protein interaction and signaling crosstalk, which typically mutually affect the function of all the partners involved. Future research on PXR and CAR should, therefore, look beyond their xenobiotic function. PMID:27295009

Regulation, Signaling, and Physiological Functions of G-Proteins.

PubMed

Syrovatkina, Viktoriya; Alegre, Kamela O; Dey, Raja; Huang, Xin-Yun

2016-09-25

Heterotrimeric guanine-nucleotide-binding regulatory proteins (G-proteins) mainly relay the information from G-protein-coupled receptors (GPCRs) on the plasma membrane to the inside of cells to regulate various biochemical functions. Depending on the targeted cell types, tissues, and organs, these signals modulate diverse physiological functions. The basic schemes of heterotrimeric G-proteins have been outlined. In this review, we briefly summarize what is known about the regulation, signaling, and physiological functions of G-proteins. We then focus on a few less explored areas such as the regulation of G-proteins by non-GPCRs and the physiological functions of G-proteins that cannot be easily explained by the known G-protein signaling pathways. There are new signaling pathways and physiological functions for G-proteins to be discovered and further interrogated. With the advancements in structural and computational biological techniques, we are closer to having a better understanding of how G-proteins are regulated and of the specificity of G-protein interactions with their regulators. Copyright © 2016 Elsevier Ltd. All rights reserved.

Exploring the interactions of the RAS family in the human protein network and their potential implications in RAS-directed therapies

PubMed Central

Bueno, Anibal; Morilla, Ian; Diez, Diego; Moya-Garcia, Aurelio A.; Lozano, José; Ranea, Juan A.G.

2016-01-01

RAS proteins are the founding members of the RAS superfamily of GTPases. They are involved in key signaling pathways regulating essential cellular functions such as cell growth and differentiation. As a result, their deregulation by inactivating mutations often results in aberrant cell proliferation and cancer. With the exception of the relatively well-known KRAS, HRAS and NRAS proteins, little is known about how the interactions of the other RAS human paralogs affect cancer evolution and response to treatment. In this study we performed a comprehensive analysis of the relationship between the phylogeny of RAS proteins and their location in the protein interaction network. This analysis was integrated with the structural analysis of conserved positions in available 3D structures of RAS complexes. Our results show that many RAS proteins with divergent sequences are found close together in the human interactome. We found specific conserved amino acid positions in this group that map to the binding sites of RAS with many of their signaling effectors, suggesting that these pairs could share interacting partners. These results underscore the potential relevance of cross-talking in the RAS signaling network, which should be taken into account when considering the inhibitory activity of drugs targeting specific RAS oncoproteins. This study broadens our understanding of the human RAS signaling network and stresses the importance of considering its potential cross-talk in future therapies. PMID:27713118

Signaling in large-scale neural networks.

PubMed

Berg, Rune W; Hounsgaard, Jørn

2009-02-01

We examine the recent finding that neurons in spinal motor circuits enter a high conductance state during functional network activity. The underlying concomitant increase in random inhibitory and excitatory synaptic activity leads to stochastic signal processing. The possible advantages of this metabolically costly organization are analyzed by comparing with synaptically less intense networks driven by the intrinsic response properties of the network neurons.

Parameter space exploration within dynamic simulations of signaling networks.

PubMed

De Ambrosi, Cristina; Barla, Annalisa; Tortolina, Lorenzo; Castagnino, Nicoletta; Pesenti, Raffaele; Verri, Alessandro; Ballestrero, Alberto; Patrone, Franco; Parodi, Silvio

2013-02-01

We started offering an introduction to very basic aspects of molecular biology, for the reader coming from computer sciences, information technology, mathematics. Similarly we offered a minimum of information about pathways and networks in graph theory, for a reader coming from the bio-medical sector. At the crossover about the two different types of expertise, we offered some definition about Systems Biology. The core of the article deals with a Molecular Interaction Map (MIM), a network of biochemical interactions involved in a small signaling-network sub-region relevant in breast cancer. We explored robustness/sensitivity to random perturbations. It turns out that our MIM is a non-isomorphic directed graph. For non physiological directions of propagation of the signal the network is quite resistant to perturbations. The opposite happens for biologically significant directions of signal propagation. In these cases we can have no signal attenuation, and even signal amplification. Signal propagation along a given pathway is highly unidirectional, with the exception of signal-feedbacks, that again have a specific biological role and significance. In conclusion, even a relatively small network like our present MIM reveals the preponderance of specific biological functions over unspecific isomorphic behaviors. This is perhaps the consequence of hundreds of millions of years of biological evolution.

Synchronization transmission of laser pattern signal within uncertain switched network

NASA Astrophysics Data System (ADS)

Lü, Ling; Li, Chengren; Li, Gang; Sun, Ao; Yan, Zhe; Rong, Tingting; Gao, Yan

2017-06-01

We propose a new technology for synchronization transmission of laser pattern signal within uncertain network with controllable topology. In synchronization process, the connection of dynamic network can vary at all time according to different demands. Especially, we construct the Lyapunov function of network through designing a special semi-positive definite function, and the synchronization transmission of laser pattern signal within uncertain network with controllable topology can be realized perfectly, which effectively avoids the complicated calculation for solving the second largest eignvalue of the coupling matrix of the dynamic network in order to obtain the network synchronization condition. At the same time, the uncertain parameters in dynamic equations belonging to network nodes can also be identified accurately via designing the identification laws of uncertain parameters. In addition, there are not any limitations for the synchronization target of network in the new technology, in other words, the target can either be a state variable signal of an arbitrary node within the network or an exterior signal.

The signaling helix: a common functional theme in diverse signaling proteins

PubMed Central

Anantharaman, Vivek; Balaji, S; Aravind, L

2006-01-01

Background The mechanism by which the signals are transmitted between receptor and effector domains in multi-domain signaling proteins is poorly understood. Results Using sensitive sequence analysis methods we identify a conserved helical segment of around 40 residues in a wide range of signaling proteins, including numerous sensor histidine kinases such as Sln1p, and receptor guanylyl cyclases such as the atrial natriuretic peptide receptor and nitric oxide receptors. We term this helical segment the signaling (S)-helix and present evidence that it forms a novel parallel coiled-coil element, distinct from previously known helical segments in signaling proteins, such as the Dimerization-Histidine phosphotransfer module of histidine kinases, the intra-cellular domains of the chemotaxis receptors, inter-GAF domain helical linkers and the α-helical HAMP module. Analysis of domain architectures allowed us to reconstruct the domain-neighborhood graph for the S-helix, which showed that the S-helix almost always occurs between two signaling domains. Several striking patterns in the domain neighborhood of the S-helix also became evident from the graph. It most often separates diverse N-terminal sensory domains from various C-terminal catalytic signaling domains such as histidine kinases, cNMP cyclase, PP2C phosphatases, NtrC-like AAA+ ATPases and diguanylate cyclases. It might also occur between two sensory domains such as PAS domains and occasionally between a DNA-binding HTH domain and a sensory domain. The sequence conservation pattern of the S-helix revealed the presence of a unique constellation of polar residues in the dimer-interface positions within the central heptad of the coiled-coil formed by the S-helix. Conclusion Combining these observations with previously reported mutagenesis studies on different S-helix-containing proteins we suggest that it functions as a switch that prevents constitutive activation of linked downstream signaling domains. However, upon

Interplay Between Protein Homeostasis Networks in Protein Aggregation and Proteotoxicity

PubMed Central

Douglas, Peter M.; Cyr, Douglas M.

2010-01-01

The misfolding and aggregation of disease proteins is characteristic of numerous neurodegenerative diseases. Particular neuronal populations are more vulnerable to proteotoxicity while others are more apt to tolerate the misfolding and aggregation of disease proteins. Thus, the cellular environment must play a significant role in determining whether disease proteins are converted into toxic or benign forms. The endomembrane network of eukaryotes divides the cell into different subcellular compartments that possess distinct sets of molecular chaperones and protein interaction networks. Chaperones act as agonists and antagonists of disease protein aggregation to prevent the accumulation of toxic intermediates in the aggregation pathway. Interacting partners can also modulate the conformation and localization of disease proteins and thereby influence proteotoxicity. Thus, interplay between these protein homeostasis network components can modulate the self-association of disease proteins and determine whether they elicit a toxic or benign outcome. PMID:19768782

Plant Bio-Wars: Maize Protein Networks Reveal Tissue-Specific Defense Strategies in Response to a Root Herbivore.

PubMed

Castano-Duque, Lina; Helms, Anjel; Ali, Jared Gregory; Luthe, Dawn S

2018-06-21

In this study we examined global changes in protein expression in both roots and leaves of maize plants attacked by the root herbivore, Western corn rootworm (WCR, Diabrotica virgifera virgifera). The changes in protein expression Are indicative of metabolic changes during WCR feeding that enable the plant to defend itself. This is one of the first studies to look above- and below-ground at global protein expression patterns of maize plants grown in soil and infested with a root herbivore. We used advanced proteomic and network analyses to identify metabolic pathways that contribute to global defenses deployed by the insect resistant maize genotype, Mp708, infested with WCR. Using proteomic analysis, 4878 proteins in roots and leaves were detected and of these 863 showed significant changes of abundance during WCR infestation. Protein abundance patterns were analyzed using hierarchical clustering, protein correlation and protein-protein interaction networks. All three data analysis pipelines showed that proteins such as jasmonic acid biosynthetic enzymes, serine proteases, protease inhibitors, proteins involved in biosynthesis and signaling of ethylene, and enzymes producing reactive oxygen species and isopentenyl pyrophosphate, a precursor for volatile production, were upregulated in roots during WCR infestation. In leaves, highly abundant proteins were involved in signal perception suggesting activation of systemic signaling. We conclude that these protein networks contribute to the overall herbivore defense mechanisms in Mp708. Because the plants were grown in potting mix and not sterilized sand, we found that both microbial and insect defense-related proteins were present in the roots. The presence of the high constitutive levels of reduced ascorbate in roots and benzothiazole in the root volatile profiles suggest a tight tri-trophic interaction among the plant, soil microbiomes and WCR-infested roots suggesting that defenses against insects coexist with

AP2/EREBP transcription factors are part of gene regulatory networks and integrate metabolic, hormonal and environmental signals in stress acclimation and retrograde signalling.

PubMed

Dietz, Karl-Josef; Vogel, Marc Oliver; Viehhauser, Andrea

2010-09-01

To optimize acclimation responses to environmental growth conditions, plants integrate and weigh a diversity of input signals. Signal integration within the signalling networks occurs at different sites including the level of transcription factor activation. Accumulating evidence assigns a major and diversified role in environmental signal integration to the family of APETALA 2/ethylene response element binding protein (AP2/EREBP) transcription factors. Presently, the Plant Transcription Factor Database 3.0 assigns 147 gene loci to this family in Arabidopsis thaliana, 200 in Populus trichocarpa and 163 in Oryza sativa subsp. japonica as compared to 13 to 14 in unicellular algae ( http://plntfdb.bio.uni-potsdam.de/v3.0/ ). AP2/EREBP transcription factors have been implicated in hormone, sugar and redox signalling in context of abiotic stresses such as cold and drought. This review exemplarily addresses present-day knowledge of selected AP2/EREBP with focus on a function in stress signal integration and retrograde signalling and defines AP2/EREBP-linked gene networks from transcriptional profiling-based graphical Gaussian models. The latter approach suggests highly interlinked functions of AP2/EREBPs in retrograde and stress signalling.

Analysis and logical modeling of biological signaling transduction networks

NASA Astrophysics Data System (ADS)

Sun, Zhongyao

The study of network theory and its application span across a multitude of seemingly disparate fields of science and technology: computer science, biology, social science, linguistics, etc. It is the intrinsic similarities embedded in the entities and the way they interact with one another in these systems that link them together. In this dissertation, I present from both the aspect of theoretical analysis and the aspect of application three projects, which primarily focus on signal transduction networks in biology. In these projects, I assembled a network model through extensively perusing literature, performed model-based simulations and validation, analyzed network topology, and proposed a novel network measure. The application of network modeling to the system of stomatal opening in plants revealed a fundamental question about the process that has been left unanswered in decades. The novel measure of the redundancy of signal transduction networks with Boolean dynamics by calculating its maximum node-independent elementary signaling mode set accurately predicts the effect of single node knockout in such signaling processes. The three projects as an organic whole advance the understanding of a real system as well as the behavior of such network models, giving me an opportunity to take a glimpse at the dazzling facets of the immense world of network science.

Application of Neutral Networks to Seismic Signal Discrimination

DTIC Science & Technology

1993-05-15

AD-A276 626 PL-TR-93-2154 Application of Neural Networks to Seismic Signal Discrimination James A. Cercone V. Shane Foster W. Mike Clark Larry... Networks to Seismic Signal Discrimination PE 61101E PR 1DMO TA DA WU AA .AUTHOR(S) Stephen Goodman John Martin C James A. Cercone Don J. Smith G...of Technology Applications of Neural Networks to Seismic Classification project. The first year of research focused on identification and collection

Network Compression as a Quality Measure for Protein Interaction Networks

PubMed Central

Royer, Loic; Reimann, Matthias; Stewart, A. Francis; Schroeder, Michael

2012-01-01

With the advent of large-scale protein interaction studies, there is much debate about data quality. Can different noise levels in the measurements be assessed by analyzing network structure? Because proteomic regulation is inherently co-operative, modular and redundant, it is inherently compressible when represented as a network. Here we propose that network compression can be used to compare false positive and false negative noise levels in protein interaction networks. We validate this hypothesis by first confirming the detrimental effect of false positives and false negatives. Second, we show that gold standard networks are more compressible. Third, we show that compressibility correlates with co-expression, co-localization, and shared function. Fourth, we also observe correlation with better protein tagging methods, physiological expression in contrast to over-expression of tagged proteins, and smart pooling approaches for yeast two-hybrid screens. Overall, this new measure is a proxy for both sensitivity and specificity and gives complementary information to standard measures such as average degree and clustering coefficients. PMID:22719828

A spatial focusing model for G protein signals. Regulator of G protein signaling (RGS) protien-mediated kinetic scaffolding.

PubMed

Zhong, Huailing; Wade, Susan M; Woolf, Peter J; Linderman, Jennifer J; Traynor, John R; Neubig, Richard R

2003-02-28

Regulators of G protein signaling (RGS) are GTPase-accelerating proteins (GAPs), which can inhibit heterotrimeric G protein pathways. In this study, we provide experimental and theoretical evidence that high concentrations of receptors (as at a synapse) can lead to saturation of GDP-GTP exchange making GTP hydrolysis rate-limiting. This results in local depletion of inactive heterotrimeric G-GDP, which is reversed by RGS GAP activity. Thus, RGS enhances receptor-mediated G protein activation even as it deactivates the G protein. Evidence supporting this model includes a GTP-dependent enhancement of guanosine 5'-3-O-(thio)triphosphate (GTPgammaS) binding to G(i) by RGS. The RGS domain of RGS4 is sufficient for this, not requiring the NH(2)- or COOH-terminal extensions. Furthermore, a kinetic model including only the GAP activity of RGS replicates the GTP-dependent enhancement of GTPgammaS binding observed experimentally. Finally in a Monte Carlo model, this mechanism results in a dramatic "spatial focusing" of active G protein. Near the receptor, G protein activity is maintained even with RGS due to the ability of RGS to reduce depletion of local Galpha-GDP levels permitting rapid recoupling to receptor and maintained G protein activation near the receptor. In contrast, distant signals are suppressed by the RGS, since Galpha-GDP is not depleted there. Thus, a novel RGS-mediated "kinetic scaffolding" mechanism is proposed which narrows the spatial range of active G protein around a cluster of receptors limiting the spill-over of G protein signals to more distant effector molecules, thus enhancing the specificity of G(i) protein signals.

Comment on "A dynamic network model of mTOR signaling reveals TSC-independent mTORC2 regulation": building a model of the mTOR signaling network with a potentially faulty tool.

PubMed

Manning, Brendan D

2012-07-10

In their study published in Science Signaling (Research Article, 27 March 2012, DOI: 10.1126/scisignal.2002469), Dalle Pezze et al. tackle the dynamic and complex wiring of the signaling network involving the protein kinase mTOR, which exists within two distinct protein complexes (mTORC1 and mTORC2) that differ in their regulation and function. The authors use a combination of immunoblotting for specific phosphorylation events and computational modeling. The primary experimental tool employed is to monitor the autophosphorylation of mTOR on Ser(2481) in cell lysates as a surrogate for mTOR activity, which the authors conclude is a specific readout for mTORC2. However, Ser(2481) phosphorylation occurs on both mTORC1 and mTORC2 and will dynamically change as the network through which these two complexes are connected is manipulated. Therefore, models of mTOR network regulation built using this tool are inherently imperfect and open to alternative explanations. Specific issues with the main conclusion made in this study, involving the TSC1-TSC2 (tuberous sclerosis complex 1 and 2) complex and its potential regulation of mTORC2, are discussed here. A broader goal of this Letter is to clarify to other investigators the caveats of using mTOR Ser(2481) phosphorylation in cell lysates as a specific readout for either of the two mTOR complexes.

Extracellular chloride signals collagen IV network assembly during basement membrane formation

PubMed Central

Cummings, Christopher F.; Pedchenko, Vadim; Brown, Kyle L.; Colon, Selene; Rafi, Mohamed; Jones-Paris, Celestial; Pokydeshava, Elena; Liu, Min; Pastor-Pareja, Jose C.; Stothers, Cody; Ero-Tolliver, Isi A.; McCall, A. Scott; Vanacore, Roberto; Bhave, Gautam; Santoro, Samuel; Blackwell, Timothy S.; Zent, Roy; Pozzi, Ambra

2016-01-01

Basement membranes are defining features of the cellular microenvironment; however, little is known regarding their assembly outside cells. We report that extracellular Cl− ions signal the assembly of collagen IV networks outside cells by triggering a conformational switch within collagen IV noncollagenous 1 (NC1) domains. Depletion of Cl− in cell culture perturbed collagen IV networks, disrupted matrix architecture, and repositioned basement membrane proteins. Phylogenetic evidence indicates this conformational switch is a fundamental mechanism of collagen IV network assembly throughout Metazoa. Using recombinant triple helical protomers, we prove that NC1 domains direct both protomer and network assembly and show in Drosophila that NC1 architecture is critical for incorporation into basement membranes. These discoveries provide an atomic-level understanding of the dynamic interactions between extracellular Cl− and collagen IV assembly outside cells, a critical step in the assembly and organization of basement membranes that enable tissue architecture and function. Moreover, this provides a mechanistic framework for understanding the molecular pathobiology of NC1 domains. PMID:27216258

Do cancer proteins really interact strongly in the human protein-protein interaction network?

PubMed Central

Xia, Junfeng; Sun, Jingchun; Jia, Peilin; Zhao, Zhongming

2011-01-01

Protein-protein interaction (PPI) network analysis has been widely applied in the investigation of the mechanisms of diseases, especially cancer. Recent studies revealed that cancer proteins tend to interact more strongly than other categories of proteins, even essential proteins, in the human interactome. However, it remains unclear whether this observation was introduced by the bias towards more cancer studies in humans. Here, we examined this important issue by uniquely comparing network characteristics of cancer proteins with three other sets of proteins in four organisms, three of which (fly, worm, and yeast) whose interactomes are essentially not biased towards cancer or other diseases. We confirmed that cancer proteins had stronger connectivity, shorter distance, and larger betweenness centrality than non-cancer disease proteins, essential proteins, and control proteins. Our statistical evaluation indicated that such observations were overall unlikely attributed to random events. Considering the large size and high quality of the PPI data in the four organisms, the conclusion that cancer proteins interact strongly in the PPI networks is reliable and robust. This conclusion suggests that perturbation of cancer proteins might cause major changes of cellular systems and result in abnormal cell function leading to cancer. PMID:21666777

The heat-shock protein/chaperone network and multiple stress resistance.

PubMed

Jacob, Pierre; Hirt, Heribert; Bendahmane, Abdelhafid

2017-04-01

Crop yield has been greatly enhanced during the last century. However, most elite cultivars are adapted to temperate climates and are not well suited to more stressful conditions. In the context of climate change, stress resistance is a major concern. To overcome these difficulties, scientists may help breeders by providing genetic markers associated with stress resistance. However, multistress resistance cannot be obtained from the simple addition of single stress resistance traits. In the field, stresses are unpredictable and several may occur at once. Consequently, the use of single stress resistance traits is often inadequate. Although it has been historically linked with the heat stress response, the heat-shock protein (HSP)/chaperone network is a major component of multiple stress responses. Among the HSP/chaperone 'client proteins', many are primary metabolism enzymes and signal transduction components with essential roles for the proper functioning of a cell. HSPs/chaperones are controlled by the action of diverse heat-shock factors, which are recruited under stress conditions. In this review, we give an overview of the regulation of the HSP/chaperone network with a focus on Arabidopsis thaliana. We illustrate the role of HSPs/chaperones in regulating diverse signalling pathways and discuss several basic principles that should be considered for engineering multiple stress resistance in crops through the HSP/chaperone network. © 2016 The Authors. Plant Biotechnology Journal published by Society for Experimental Biology and The Association of Applied Biologists and John Wiley & Sons Ltd.

Identifying Functional Mechanisms of Gene and Protein Regulatory Networks in Response to a Broader Range of Environmental Stresses

PubMed Central

Li, Cheng-Wei; Chen, Bor-Sen

2010-01-01

Cellular responses to sudden environmental stresses or physiological changes provide living organisms with the opportunity for final survival and further development. Therefore, it is an important topic to understand protective mechanisms against environmental stresses from the viewpoint of gene and protein networks. We propose two coupled nonlinear stochastic dynamic models to reconstruct stress-activated gene and protein regulatory networks via microarray data in response to environmental stresses. According to the reconstructed gene/protein networks, some possible mutual interactions, feedforward and feedback loops are found for accelerating response and filtering noises in these signaling pathways. A bow-tie core network is also identified to coordinate mutual interactions and feedforward loops, feedback inhibitions, feedback activations, and cross talks to cope efficiently with a broader range of environmental stresses with limited proteins and pathways. PMID:20454442

Prokaryotic ancestry of eukaryotic protein networks mediating innate immunity and apoptosis.

PubMed

Dunin-Horkawicz, Stanislaw; Kopec, Klaus O; Lupas, Andrei N

2014-04-03

Protein domains characteristic of eukaryotic innate immunity and apoptosis have many prokaryotic counterparts of unknown function. By reconstructing interactomes computationally, we found that bacterial proteins containing these domains are part of a network that also includes other domains not hitherto associated with immunity. This network is connected to the network of prokaryotic signal transduction proteins, such as histidine kinases and chemoreceptors. The network varies considerably in domain composition and degree of paralogy, even between strains of the same species, and its repetitive domains are often amplified recently, with individual repeats sharing up to 100% sequence identity. Both phenomena are evidence of considerable evolutionary pressure and thus compatible with a role in the "arms race" between host and pathogen. In order to investigate the relationship of this network to its eukaryotic counterparts, we performed a cluster analysis of organisms based on a census of its constituent domains across all fully sequenced genomes. We obtained a large central cluster of mainly unicellular organisms, from which multicellular organisms radiate out in two main directions. One is taken by multicellular bacteria, primarily cyanobacteria and actinomycetes, and plants form an extension of this direction, connected via the basal, unicellular cyanobacteria. The second main direction is taken by animals and fungi, which form separate branches with a common root in the α-proteobacteria of the central cluster. This analysis supports the notion that the innate immunity networks of eukaryotes originated from their endosymbionts and that increases in the complexity of these networks accompanied the emergence of multicellularity. Copyright © 2014 The Authors. Published by Elsevier Ltd.. All rights reserved.

Mining protein-protein interaction networks: denoising effects

NASA Astrophysics Data System (ADS)

Marras, Elisabetta; Capobianco, Enrico

2009-01-01

A typical instrument to pursue analysis in complex network studies is the analysis of the statistical distributions. They are usually computed for measures which characterize network topology, and are aimed at capturing both structural and dynamics aspects. Protein-protein interaction networks (PPIN) have also been studied through several measures. It is in general observed that a power law is expected to characterize scale-free networks. However, mixing the original noise cover with outlying information and other system-dependent fluctuations makes the empirical detection of the power law a difficult task. As a result the uncertainty level increases when looking at the observed sample; in particular, one may wonder whether the computed features may be sufficient to explain the interactome. We then address noise problems by implementing both decomposition and denoising techniques that reduce the impact of factors known to affect the accuracy of power law detection.

Fluorescence-based assay probing regulator of G protein signaling partner proteins.

PubMed

Huang, Po-Shiun; Yeh, Hsin-Sung; Yi, Hsiu-Ping; Lin, Chain-Jia; Yang, Chii-Shen

2012-04-01

The regulator of G protein signaling (RGS) proteins are one of the essential modulators for the G protein system. Besides regulating G protein signaling by accelerating the GTPase activity of Gα subunits, RGS proteins are implicated in exerting other functions; they are also known to be involved in several diseases. Moreover, the existence of a single RGS protein in plants and its seven-transmembrane domain found in 2003 triggered efforts to unveil detailed structural and functional information of RGS proteins. We present a method for real-time examination of the protein-protein interactions between RGS and Gα subunits. AtRGS1 from plants and RGS4 from mammals were site-directedly labeled with the fluorescent probe Lucifer yellow on engineered cysteine residues and used to interact with different Gα subunits. The physical interactions can be revealed by monitoring the real-time fluorescence changes (8.6% fluorescence increase in mammals and 27.6% in plants); their correlations to functional exertion were shown with a GTPase accelerating activity assay and further confirmed by measurement of K(d). We validate the effectiveness of this method and suggest its application to the exploration of more RGS signaling partner proteins in physiological and pathological studies. Copyright © 2012 Elsevier Inc. All rights reserved.

Detection test of wireless network signal strength and GPS positioning signal in underground pipeline

NASA Astrophysics Data System (ADS)

Li, Li; Zhang, Yunwei; Chen, Ling

2018-03-01

In order to solve the problem of selecting positioning technology for inspection robot in underground pipeline environment, the wireless network signal strength and GPS positioning signal testing are carried out in the actual underground pipeline environment. Firstly, the strength variation of the 3G wireless network signal and Wi-Fi wireless signal provided by China Telecom and China Unicom ground base stations are tested, and the attenuation law of these wireless signals along the pipeline is analyzed quantitatively and described. Then, the receiving data of the GPS satellite signal in the pipeline are tested, and the attenuation of GPS satellite signal under underground pipeline is analyzed. The testing results may be reference for other related research which need to consider positioning in pipeline.

LocSigDB: a database of protein localization signals

PubMed Central

Negi, Simarjeet; Pandey, Sanjit; Srinivasan, Satish M.; Mohammed, Akram; Guda, Chittibabu

2015-01-01

LocSigDB (http://genome.unmc.edu/LocSigDB/) is a manually curated database of experimental protein localization signals for eight distinct subcellular locations; primarily in a eukaryotic cell with brief coverage of bacterial proteins. Proteins must be localized at their appropriate subcellular compartment to perform their desired function. Mislocalization of proteins to unintended locations is a causative factor for many human diseases; therefore, collection of known sorting signals will help support many important areas of biomedical research. By performing an extensive literature study, we compiled a collection of 533 experimentally determined localization signals, along with the proteins that harbor such signals. Each signal in the LocSigDB is annotated with its localization, source, PubMed references and is linked to the proteins in UniProt database along with the organism information that contain the same amino acid pattern as the given signal. From LocSigDB webserver, users can download the whole database or browse/search for data using an intuitive query interface. To date, LocSigDB is the most comprehensive compendium of protein localization signals for eight distinct subcellular locations. Database URL: http://genome.unmc.edu/LocSigDB/ PMID:25725059

Sweet Taste Receptor Signaling Network: Possible Implication for Cognitive Functioning

PubMed Central

Welcome, Menizibeya O.; Mastorakis, Nikos E.; Pereverzev, Vladimir A.

2015-01-01

Sweet taste receptors are transmembrane protein network specialized in the transmission of information from special “sweet” molecules into the intracellular domain. These receptors can sense the taste of a range of molecules and transmit the information downstream to several acceptors, modulate cell specific functions and metabolism, and mediate cell-to-cell coupling through paracrine mechanism. Recent reports indicate that sweet taste receptors are widely distributed in the body and serves specific function relative to their localization. Due to their pleiotropic signaling properties and multisubstrate ligand affinity, sweet taste receptors are able to cooperatively bind multiple substances and mediate signaling by other receptors. Based on increasing evidence about the role of these receptors in the initiation and control of absorption and metabolism, and the pivotal role of metabolic (glucose) regulation in the central nervous system functioning, we propose a possible implication of sweet taste receptor signaling in modulating cognitive functioning. PMID:25653876

Controllability of protein-protein interaction phosphorylation-based networks: Participation of the hub 14-3-3 protein family

PubMed Central

Uhart, Marina; Flores, Gabriel; Bustos, Diego M.

2016-01-01

Posttranslational regulation of protein function is an ubiquitous mechanism in eukaryotic cells. Here, we analyzed biological properties of nodes and edges of a human protein-protein interaction phosphorylation-based network, especially of those nodes critical for the network controllability. We found that the minimal number of critical nodes needed to control the whole network is 29%, which is considerably lower compared to other real networks. These critical nodes are more regulated by posttranslational modifications and contain more binding domains to these modifications than other kinds of nodes in the network, suggesting an intra-group fast regulation. Also, when we analyzed the edges characteristics that connect critical and non-critical nodes, we found that the former are enriched in domain-to-eukaryotic linear motif interactions, whereas the later are enriched in domain-domain interactions. Our findings suggest a possible structure for protein-protein interaction networks with a densely interconnected and self-regulated central core, composed of critical nodes with a high participation in the controllability of the full network, and less regulated peripheral nodes. Our study offers a deeper understanding of complex network control and bridges the controllability theorems for complex networks and biological protein-protein interaction phosphorylation-based networked systems. PMID:27195976

Modeling and simulating networks of interdependent protein interactions.

PubMed

Stöcker, Bianca K; Köster, Johannes; Zamir, Eli; Rahmann, Sven

2018-05-21

Protein interactions are fundamental building blocks of biochemical reaction systems underlying cellular functions. The complexity and functionality of these systems emerge not only from the protein interactions themselves but also from the dependencies between these interactions, as generated by allosteric effects or mutual exclusion due to steric hindrance. Therefore, formal models for integrating and utilizing information about interaction dependencies are of high interest. Here, we describe an approach for endowing protein networks with interaction dependencies using propositional logic, thereby obtaining constrained protein interaction networks ("constrained networks"). The construction of these networks is based on public interaction databases as well as text-mined information about interaction dependencies. We present an efficient data structure and algorithm to simulate protein complex formation in constrained networks. The efficiency of the model allows fast simulation and facilitates the analysis of many proteins in large networks. In addition, this approach enables the simulation of perturbation effects, such as knockout of single or multiple proteins and changes of protein concentrations. We illustrate how our model can be used to analyze a constrained human adhesome protein network, which is responsible for the formation of diverse and dynamic cell-matrix adhesion sites. By comparing protein complex formation under known interaction dependencies versus without dependencies, we investigate how these dependencies shape the resulting repertoire of protein complexes. Furthermore, our model enables investigating how the interplay of network topology with interaction dependencies influences the propagation of perturbation effects across a large biochemical system. Our simulation software CPINSim (for Constrained Protein Interaction Network Simulator) is available under the MIT license at http://github.com/BiancaStoecker/cpinsim and as a Bioconda package

Do cancer proteins really interact strongly in the human protein-protein interaction network?

PubMed

Xia, Junfeng; Sun, Jingchun; Jia, Peilin; Zhao, Zhongming

2011-06-01

Protein-protein interaction (PPI) network analysis has been widely applied in the investigation of the mechanisms of diseases, especially cancer. Recent studies revealed that cancer proteins tend to interact more strongly than other categories of proteins, even essential proteins, in the human interactome. However, it remains unclear whether this observation was introduced by the bias towards more cancer studies in humans. Here, we examined this important issue by uniquely comparing network characteristics of cancer proteins with three other sets of proteins in four organisms, three of which (fly, worm, and yeast) whose interactomes are essentially not biased towards cancer or other diseases. We confirmed that cancer proteins had stronger connectivity, shorter distance, and larger betweenness centrality than non-cancer disease proteins, essential proteins, and control proteins. Our statistical evaluation indicated that such observations were overall unlikely attributed to random events. Considering the large size and high quality of the PPI data in the four organisms, the conclusion that cancer proteins interact strongly in the PPI networks is reliable and robust. This conclusion suggests that perturbation of cancer proteins might cause major changes of cellular systems and result in abnormal cell function leading to cancer. © 2011 Elsevier Ltd. All rights reserved.

Effective comparative analysis of protein-protein interaction networks by measuring the steady-state network flow using a Markov model.

PubMed

Jeong, Hyundoo; Qian, Xiaoning; Yoon, Byung-Jun

2016-10-06

Comparative analysis of protein-protein interaction (PPI) networks provides an effective means of detecting conserved functional network modules across different species. Such modules typically consist of orthologous proteins with conserved interactions, which can be exploited to computationally predict the modules through network comparison. In this work, we propose a novel probabilistic framework for comparing PPI networks and effectively predicting the correspondence between proteins, represented as network nodes, that belong to conserved functional modules across the given PPI networks. The basic idea is to estimate the steady-state network flow between nodes that belong to different PPI networks based on a Markov random walk model. The random walker is designed to make random moves to adjacent nodes within a PPI network as well as cross-network moves between potential orthologous nodes with high sequence similarity. Based on this Markov random walk model, we estimate the steady-state network flow - or the long-term relative frequency of the transitions that the random walker makes - between nodes in different PPI networks, which can be used as a probabilistic score measuring their potential correspondence. Subsequently, the estimated scores can be used for detecting orthologous proteins in conserved functional modules through network alignment. Through evaluations based on multiple real PPI networks, we demonstrate that the proposed scheme leads to improved alignment results that are biologically more meaningful at reduced computational cost, outperforming the current state-of-the-art algorithms. The source code and datasets can be downloaded from http://www.ece.tamu.edu/~bjyoon/CUFID .

Network Analysis of Epidermal Growth Factor Signaling Using Integrated Genomic, Proteomic and Phosphorylation Data

PubMed Central

Waters, Katrina M.; Liu, Tao; Quesenberry, Ryan D.; Willse, Alan R.; Bandyopadhyay, Somnath; Kathmann, Loel E.; Weber, Thomas J.; Smith, Richard D.; Wiley, H. Steven; Thrall, Brian D.

2012-01-01

To understand how integration of multiple data types can help decipher cellular responses at the systems level, we analyzed the mitogenic response of human mammary epithelial cells to epidermal growth factor (EGF) using whole genome microarrays, mass spectrometry-based proteomics and large-scale western blots with over 1000 antibodies. A time course analysis revealed significant differences in the expression of 3172 genes and 596 proteins, including protein phosphorylation changes measured by western blot. Integration of these disparate data types showed that each contributed qualitatively different components to the observed cell response to EGF and that varying degrees of concordance in gene expression and protein abundance measurements could be linked to specific biological processes. Networks inferred from individual data types were relatively limited, whereas networks derived from the integrated data recapitulated the known major cellular responses to EGF and exhibited more highly connected signaling nodes than networks derived from any individual dataset. While cell cycle regulatory pathways were altered as anticipated, we found the most robust response to mitogenic concentrations of EGF was induction of matrix metalloprotease cascades, highlighting the importance of the EGFR system as a regulator of the extracellular environment. These results demonstrate the value of integrating multiple levels of biological information to more accurately reconstruct networks of cellular response. PMID:22479638

3DProIN: Protein-Protein Interaction Networks and Structure Visualization.

PubMed

Li, Hui; Liu, Chunmei

2014-06-14

3DProIN is a computational tool to visualize protein-protein interaction networks in both two dimensional (2D) and three dimensional (3D) view. It models protein-protein interactions in a graph and explores the biologically relevant features of the tertiary structures of each protein in the network. Properties such as color, shape and name of each node (protein) of the network can be edited in either 2D or 3D views. 3DProIN is implemented using 3D Java and C programming languages. The internet crawl technique is also used to parse dynamically grasped protein interactions from protein data bank (PDB). It is a java applet component that is embedded in the web page and it can be used on different platforms including Linux, Mac and Window using web browsers such as Firefox, Internet Explorer, Chrome and Safari. It also was converted into a mac app and submitted to the App store as a free app. Mac users can also download the app from our website. 3DProIN is available for academic research at http://bicompute.appspot.com.

Construction of ontology augmented networks for protein complex prediction.

PubMed

Zhang, Yijia; Lin, Hongfei; Yang, Zhihao; Wang, Jian

2013-01-01

Protein complexes are of great importance in understanding the principles of cellular organization and function. The increase in available protein-protein interaction data, gene ontology and other resources make it possible to develop computational methods for protein complex prediction. Most existing methods focus mainly on the topological structure of protein-protein interaction networks, and largely ignore the gene ontology annotation information. In this article, we constructed ontology augmented networks with protein-protein interaction data and gene ontology, which effectively unified the topological structure of protein-protein interaction networks and the similarity of gene ontology annotations into unified distance measures. After constructing ontology augmented networks, a novel method (clustering based on ontology augmented networks) was proposed to predict protein complexes, which was capable of taking into account the topological structure of the protein-protein interaction network, as well as the similarity of gene ontology annotations. Our method was applied to two different yeast protein-protein interaction datasets and predicted many well-known complexes. The experimental results showed that (i) ontology augmented networks and the unified distance measure can effectively combine the structure closeness and gene ontology annotation similarity; (ii) our method is valuable in predicting protein complexes and has higher F1 and accuracy compared to other competing methods.

In Silico Enhancing M. tuberculosis Protein Interaction Networks in STRING To Predict Drug-Resistance Pathways and Pharmacological Risks.

PubMed

Mei, Suyu

2018-05-04

Bacterial protein-protein interaction (PPI) networks are significant to reveal the machinery of signal transduction and drug resistance within bacterial cells. The database STRING has collected a large number of bacterial pathogen PPI networks, but most of the data are of low quality without being experimentally or computationally validated, thus restricting its further biomedical applications. We exploit the experimental data via four solutions to enhance the quality of M. tuberculosis H37Rv (MTB) PPI networks in STRING. Computational results show that the experimental data derived jointly by two-hybrid and copurification approaches are the most reliable to train an L 2 -regularized logistic regression model for MTB PPI network validation. On the basis of the validated MTB PPI networks, we further study the three problems via breadth-first graph search algorithm: (1) discovery of MTB drug-resistance pathways through searching for the paths between known drug-target genes and drug-resistance genes, (2) choosing potential cotarget genes via searching for the critical genes located on multiple pathways, and (3) choosing essential drug-target genes via analysis of network degree distribution. In addition, we further combine the validated MTB PPI networks with human PPI networks to analyze the potential pharmacological risks of known and candidate drug-target genes from the point of view of system pharmacology. The evidence from protein structure alignment demonstrates that the drugs that act on MTB target genes could also adversely act on human signaling pathways.

Discovering disease-associated genes in weighted protein-protein interaction networks

NASA Astrophysics Data System (ADS)

Cui, Ying; Cai, Meng; Stanley, H. Eugene

2018-04-01

Although there have been many network-based attempts to discover disease-associated genes, most of them have not taken edge weight - which quantifies their relative strength - into consideration. We use connection weights in a protein-protein interaction (PPI) network to locate disease-related genes. We analyze the topological properties of both weighted and unweighted PPI networks and design an improved random forest classifier to distinguish disease genes from non-disease genes. We use a cross-validation test to confirm that weighted networks are better able to discover disease-associated genes than unweighted networks, which indicates that including link weight in the analysis of network properties provides a better model of complex genotype-phenotype associations.

Structural principles within the human-virus protein-protein interaction network

PubMed Central

Franzosa, Eric A.; Xia, Yu

2011-01-01

General properties of the antagonistic biomolecular interactions between viruses and their hosts (exogenous interactions) remain poorly understood, and may differ significantly from known principles governing the cooperative interactions within the host (endogenous interactions). Systems biology approaches have been applied to study the combined interaction networks of virus and human proteins, but such efforts have so far revealed only low-resolution patterns of host-virus interaction. Here, we layer curated and predicted 3D structural models of human-virus and human-human protein complexes on top of traditional interaction networks to reconstruct the human-virus structural interaction network. This approach reveals atomic resolution, mechanistic patterns of host-virus interaction, and facilitates systematic comparison with the host’s endogenous interactions. We find that exogenous interfaces tend to overlap with and mimic endogenous interfaces, thereby competing with endogenous binding partners. The endogenous interfaces mimicked by viral proteins tend to participate in multiple endogenous interactions which are transient and regulatory in nature. While interface overlap in the endogenous network results largely from gene duplication followed by divergent evolution, viral proteins frequently achieve interface mimicry without any sequence or structural similarity to an endogenous binding partner. Finally, while endogenous interfaces tend to evolve more slowly than the rest of the protein surface, exogenous interfaces—including many sites of endogenous-exogenous overlap—tend to evolve faster, consistent with an evolutionary “arms race” between host and pathogen. These significant biophysical, functional, and evolutionary differences between host-pathogen and within-host protein-protein interactions highlight the distinct consequences of antagonism versus cooperation in biological networks. PMID:21680884

Thematic minireview series: cell biology of G protein signaling.

PubMed

Dohlman, Henrik G

2015-03-13

This thematic series is on the topic of cell signaling from a cell biology perspective, with a particular focus on G proteins. G protein-coupled receptors (GPCRs, also known as seven-transmembrane receptors) are typically found at the cell surface. Upon agonist binding, these receptors will activate a GTP-binding G protein at the cytoplasmic face of the plasma membrane. Additionally, there is growing evidence that G proteins can also be activated by non-receptor binding partners, and they can signal from non-plasma membrane compartments. The production of second messengers at multiple, spatially distinct locations represents a type of signal encoding that has been largely neglected. The first minireview in the series describes biosensors that are being used to monitor G protein signaling events in live cells. The second describes the implementation of antibody-based biosensors to dissect endosome signaling by G proteins and their receptors. The third describes the function of a non-receptor, cytoplasmic activator of G protein signaling, called GIV (Girdin). Collectively, the advances described in these articles provide a deeper understanding and emerging opportunities for new pharmacology. © 2015 by The American Society for Biochemistry and Molecular Biology, Inc.

The HER2 Signaling Network in Breast Cancer--Like a Spider in its Web.

PubMed

Dittrich, A; Gautrey, H; Browell, D; Tyson-Capper, A

2014-12-01

The human epidermal growth factor receptor 2 (HER2) is a major player in the survival and proliferation of tumour cells and is overexpressed in up to 30 % of breast cancer cases. A considerable amount of work has been undertaken to unravel the activity and function of HER2 to try and develop effective therapies that impede its action in HER2 positive breast tumours. Research has focused on exploring the HER2 activated phosphoinositide-3-kinase (PI3K)/AKT and rat sarcoma/mitogen-activated protein kinase (RAS/MAPK) pathways for therapies. Despite the advances, cases of drug resistance and recurrence of disease still remain a challenge to overcome. An important aspect for drug resistance is the complexity of the HER2 signaling network. This includes the crosstalk between HER2 and hormone receptors; its function as a transcription factor; the regulation of HER2 by protein-tyrosine phosphatases and a complex network of positive and negative feedback-loops. This review summarises the current knowledge of many different HER2 interactions to illustrate the complexity of the HER2 network from the transcription of HER2 to the effect of its downstream targets. Exploring the novel avenues of the HER2 signaling could yield a better understanding of treatment resistance and give rise to developing new and more effective therapies.

Modeling evolution of crosstalk in noisy signal transduction networks

NASA Astrophysics Data System (ADS)

Tareen, Ammar; Wingreen, Ned S.; Mukhopadhyay, Ranjan

2018-02-01

Signal transduction networks can form highly interconnected systems within cells due to crosstalk between constituent pathways. To better understand the evolutionary design principles underlying such networks, we study the evolution of crosstalk for two parallel signaling pathways that arise via gene duplication. We use a sequence-based evolutionary algorithm and evolve the network based on two physically motivated fitness functions related to information transmission. We find that one fitness function leads to a high degree of crosstalk while the other leads to pathway specificity. Our results offer insights on the relationship between network architecture and information transmission for noisy biomolecular networks.

Physical signals for protein-DNA recognition

NASA Astrophysics Data System (ADS)

Cao, Xiao-Qin; Zeng, Jia; Yan, Hong

2009-09-01

This paper discovers consensus physical signals around eukaryotic splice sites, transcription start sites, and replication origin start and end sites on a genome-wide scale based on their DNA flexibility profiles calculated by three different flexibility models. These salient physical signals are localized highly rigid and flexible DNAs, which may play important roles in protein-DNA recognition by the sliding search mechanism. The found physical signals lead us to a detailed hypothetical view of the search process in which a DNA-binding protein first finds a genomic region close to the target site from an arbitrary starting location by three-dimensional (3D) hopping and intersegment transfer mechanisms for long distances, and subsequently uses the one-dimensional (1D) sliding mechanism facilitated by the localized highly rigid DNAs to accurately locate the target flexible binding site within 30 bp (base pair) short distances. Guided by these physical signals, DNA-binding proteins rapidly search the entire genome to recognize a specific target site from the 3D to 1D pathway. Our findings also show that current promoter prediction programs (PPPs) based on DNA physical properties may suffer from lots of false positives because other functional sites such as splice sites and replication origins have similar physical signals as promoters do.

Microscopy and bioinformatic analyses of lipid metabolism implicate a sporophytic signaling network supporting pollen development in Arabidopsis.

PubMed

Wang, Yixing; Wu, Hong; Yang, Ming

2008-07-01

The Arabidopsis sporophytic tapetum undergoes a programmed degeneration process to secrete lipid and other materials to support pollen development. However, the molecular mechanism regulating the degeneration process is unknown. To gain insight into this molecular mechanism, we first determined that the most critical period for tapetal secretion to support pollen development is from the vacuolate microspore stage to the early binucleate pollen stage. We then analyzed the expression of enzymes responsible for lipid biosynthesis and degradation with available in-silico data. The genes for these enzymes that are expressed in the stamen but not in the concurrent uninucleate microspore and binucleate pollen are of particular interest, as they presumably hold the clues to unique molecular processes in the sporophytic tissues compared to the gametophytic tissue. No gene for lipid biosynthesis but a single gene encoding a patatin-like protein likely for lipid mobilization was identified based on the selection criterion. A search for genes co-expressed with this gene identified additional genes encoding typical signal transduction components such as a leucine-rich repeat receptor kinase, an extra-large G-protein, other protein kinases, and transcription factors. In addition, proteases, cell wall degradation enzymes, and other proteins were also identified. These proteins thus may be components of a signaling network leading to degradation of a broad range of cellular components. Since a broad range of degradation activities is expected to occur only in the tapetal degeneration process at this stage in the stamen, it is further hypothesized that the signaling network acts in the tapetal degeneration process.

Fiber fault location utilizing traffic signal in optical network.

PubMed

Zhao, Tong; Wang, Anbang; Wang, Yuncai; Zhang, Mingjiang; Chang, Xiaoming; Xiong, Lijuan; Hao, Yi

2013-10-07

We propose and experimentally demonstrate a method for fault location in optical communication network. This method utilizes the traffic signal transmitted across the network as probe signal, and then locates the fault by correlation technique. Compared with conventional techniques, our method has a simple structure and low operation expenditure, because no additional device is used, such as light source, modulator and signal generator. The correlation detection in this method overcomes the tradeoff between spatial resolution and measurement range in pulse ranging technique. Moreover, signal extraction process can improve the location result considerably. Experimental results show that we achieve a spatial resolution of 8 cm and detection range of over 23 km with -8-dBm mean launched power in optical network based on synchronous digital hierarchy protocols.

Inhibition of Protein-Protein Interactions and Signaling by Small Molecules

NASA Astrophysics Data System (ADS)

Freire, Ernesto

2010-03-01

Protein-protein interactions are at the core of cell signaling pathways as well as many bacterial and viral infection processes. As such, they define critical targets for drug development against diseases such as cancer, arthritis, obesity, AIDS and many others. Until now, the clinical inhibition of protein-protein interactions and signaling has been accomplished with the use of antibodies or soluble versions of receptor molecules. Small molecule replacements of these therapeutic agents have been extremely difficult to develop; either the necessary potency has been hard to achieve or the expected biological effect has not been obtained. In this presentation, we show that a rigorous thermodynamic approach that combines differential scanning calorimetry (DSC) and isothermal titration calorimetry (ITC) provides a unique platform for the identification and optimization of small molecular weight inhibitors of protein-protein interactions. Recent advances in the development of cell entry inhibitors of HIV-1 using this approach will be discussed.

G protein signaling in plants: minus times minus equals plus.

PubMed

Stateczny, Dave; Oppenheimer, Jara; Bommert, Peter

2016-12-01

Heterotrimeric G proteins are key regulators in the transduction of extracellular signals both in animals and plants. In plants, heterotrimeric G protein signaling plays essential roles in development and in response to biotic and abiotic stress. However, over the last decade it has become clear that plants have unique mechanisms of G protein signaling. Although plants share most of the core components of heterotrimeric G proteins, some of them exhibit unusual properties compared to their animal counterparts. In addition, plants do not share functional GPCRs. Therefore the well-established paradigm of the animal G protein signaling cycle is not applicable in plants. In this review, we summarize recent insights into these unique mechanisms of G protein signaling in plants with special focus on the evident potential of G protein signaling as a target to modify developmental and physiological parameters important for yield increase. Copyright © 2016 Elsevier Ltd. All rights reserved.

Dopamine D1 signaling organizes network dynamics underlying working memory.

PubMed

Roffman, Joshua L; Tanner, Alexandra S; Eryilmaz, Hamdi; Rodriguez-Thompson, Anais; Silverstein, Noah J; Ho, New Fei; Nitenson, Adam Z; Chonde, Daniel B; Greve, Douglas N; Abi-Dargham, Anissa; Buckner, Randy L; Manoach, Dara S; Rosen, Bruce R; Hooker, Jacob M; Catana, Ciprian

2016-06-01

Local prefrontal dopamine signaling supports working memory by tuning pyramidal neurons to task-relevant stimuli. Enabled by simultaneous positron emission tomography-magnetic resonance imaging (PET-MRI), we determined whether neuromodulatory effects of dopamine scale to the level of cortical networks and coordinate their interplay during working memory. Among network territories, mean cortical D1 receptor densities differed substantially but were strongly interrelated, suggesting cross-network regulation. Indeed, mean cortical D1 density predicted working memory-emergent decoupling of the frontoparietal and default networks, which respectively manage task-related and internal stimuli. In contrast, striatal D1 predicted opposing effects within these two networks but no between-network effects. These findings specifically link cortical dopamine signaling to network crosstalk that redirects cognitive resources to working memory, echoing neuromodulatory effects of D1 signaling on the level of cortical microcircuits.

Dopamine D1 signaling organizes network dynamics underlying working memory

PubMed Central

Roffman, Joshua L.; Tanner, Alexandra S.; Eryilmaz, Hamdi; Rodriguez-Thompson, Anais; Silverstein, Noah J.; Ho, New Fei; Nitenson, Adam Z.; Chonde, Daniel B.; Greve, Douglas N.; Abi-Dargham, Anissa; Buckner, Randy L.; Manoach, Dara S.; Rosen, Bruce R.; Hooker, Jacob M.; Catana, Ciprian

2016-01-01

Local prefrontal dopamine signaling supports working memory by tuning pyramidal neurons to task-relevant stimuli. Enabled by simultaneous positron emission tomography–magnetic resonance imaging (PET-MRI), we determined whether neuromodulatory effects of dopamine scale to the level of cortical networks and coordinate their interplay during working memory. Among network territories, mean cortical D1 receptor densities differed substantially but were strongly interrelated, suggesting cross-network regulation. Indeed, mean cortical D1 density predicted working memory–emergent decoupling of the frontoparietal and default networks, which respectively manage task-related and internal stimuli. In contrast, striatal D1 predicted opposing effects within these two networks but no between-network effects. These findings specifically link cortical dopamine signaling to network crosstalk that redirects cognitive resources to working memory, echoing neuromodulatory effects of D1 signaling on the level of cortical microcircuits. PMID:27386561

JNK Signaling: Regulation and Functions Based on Complex Protein-Protein Partnerships

PubMed Central

Zeke, András; Misheva, Mariya

2016-01-01

SUMMARY The c-Jun N-terminal kinases (JNKs), as members of the mitogen-activated protein kinase (MAPK) family, mediate eukaryotic cell responses to a wide range of abiotic and biotic stress insults. JNKs also regulate important physiological processes, including neuronal functions, immunological actions, and embryonic development, via their impact on gene expression, cytoskeletal protein dynamics, and cell death/survival pathways. Although the JNK pathway has been under study for >20 years, its complexity is still perplexing, with multiple protein partners of JNKs underlying the diversity of actions. Here we review the current knowledge of JNK structure and isoforms as well as the partnerships of JNKs with a range of intracellular proteins. Many of these proteins are direct substrates of the JNKs. We analyzed almost 100 of these target proteins in detail within a framework of their classification based on their regulation by JNKs. Examples of these JNK substrates include a diverse assortment of nuclear transcription factors (Jun, ATF2, Myc, Elk1), cytoplasmic proteins involved in cytoskeleton regulation (DCX, Tau, WDR62) or vesicular transport (JIP1, JIP3), cell membrane receptors (BMPR2), and mitochondrial proteins (Mcl1, Bim). In addition, because upstream signaling components impact JNK activity, we critically assessed the involvement of signaling scaffolds and the roles of feedback mechanisms in the JNK pathway. Despite a clarification of many regulatory events in JNK-dependent signaling during the past decade, many other structural and mechanistic insights are just beginning to be revealed. These advances open new opportunities to understand the role of JNK signaling in diverse physiological and pathophysiological states. PMID:27466283

JNK Signaling: Regulation and Functions Based on Complex Protein-Protein Partnerships.

PubMed

Zeke, András; Misheva, Mariya; Reményi, Attila; Bogoyevitch, Marie A

2016-09-01

The c-Jun N-terminal kinases (JNKs), as members of the mitogen-activated protein kinase (MAPK) family, mediate eukaryotic cell responses to a wide range of abiotic and biotic stress insults. JNKs also regulate important physiological processes, including neuronal functions, immunological actions, and embryonic development, via their impact on gene expression, cytoskeletal protein dynamics, and cell death/survival pathways. Although the JNK pathway has been under study for >20 years, its complexity is still perplexing, with multiple protein partners of JNKs underlying the diversity of actions. Here we review the current knowledge of JNK structure and isoforms as well as the partnerships of JNKs with a range of intracellular proteins. Many of these proteins are direct substrates of the JNKs. We analyzed almost 100 of these target proteins in detail within a framework of their classification based on their regulation by JNKs. Examples of these JNK substrates include a diverse assortment of nuclear transcription factors (Jun, ATF2, Myc, Elk1), cytoplasmic proteins involved in cytoskeleton regulation (DCX, Tau, WDR62) or vesicular transport (JIP1, JIP3), cell membrane receptors (BMPR2), and mitochondrial proteins (Mcl1, Bim). In addition, because upstream signaling components impact JNK activity, we critically assessed the involvement of signaling scaffolds and the roles of feedback mechanisms in the JNK pathway. Despite a clarification of many regulatory events in JNK-dependent signaling during the past decade, many other structural and mechanistic insights are just beginning to be revealed. These advances open new opportunities to understand the role of JNK signaling in diverse physiological and pathophysiological states. Copyright © 2016, American Society for Microbiology. All Rights Reserved.

Analysis of network motifs in cellular regulation: Structural similarities, input-output relations and signal integration.

PubMed

Straube, Ronny

2017-12-01

Much of the complexity of regulatory networks derives from the necessity to integrate multiple signals and to avoid malfunction due to cross-talk or harmful perturbations. Hence, one may expect that the input-output behavior of larger networks is not necessarily more complex than that of smaller network motifs which suggests that both can, under certain conditions, be described by similar equations. In this review, we illustrate this approach by discussing the similarities that exist in the steady state descriptions of a simple bimolecular reaction, covalent modification cycles and bacterial two-component systems. Interestingly, in all three systems fundamental input-output characteristics such as thresholds, ultrasensitivity or concentration robustness are described by structurally similar equations. Depending on the system the meaning of the parameters can differ ranging from protein concentrations and affinity constants to complex parameter combinations which allows for a quantitative understanding of signal integration in these systems. We argue that this approach may also be extended to larger regulatory networks. Copyright © 2017 Elsevier B.V. All rights reserved.

Context-based retrieval of functional modules in protein-protein interaction networks.

PubMed

Dobay, Maria Pamela; Stertz, Silke; Delorenzi, Mauro

2017-03-27

Various techniques have been developed for identifying the most probable interactants of a protein under a given biological context. In this article, we dissect the effects of the choice of the protein-protein interaction network (PPI) and the manipulation of PPI settings on the network neighborhood of the influenza A virus (IAV) network, as well as hits in genome-wide small interfering RNA screen results for IAV host factors. We investigate the potential of context filtering, which uses text mining evidence linked to PPI edges, as a complement to the edge confidence scores typically provided in PPIs for filtering, for obtaining more biologically relevant network neighborhoods. Here, we estimate the maximum performance of context filtering to isolate a Kyoto Encyclopedia of Genes and Genomes (KEGG) network Ki from a union of KEGG networks and its network neighborhood. The work gives insights on the use of human PPIs in network neighborhood approaches for functional inference. © The Author 2017. Published by Oxford University Press. All rights reserved. For permissions, please email: journals.permissions@oup.com.

Alignment-free protein interaction network comparison

PubMed Central

Ali, Waqar; Rito, Tiago; Reinert, Gesine; Sun, Fengzhu; Deane, Charlotte M.

2014-01-01

Motivation: Biological network comparison software largely relies on the concept of alignment where close matches between the nodes of two or more networks are sought. These node matches are based on sequence similarity and/or interaction patterns. However, because of the incomplete and error-prone datasets currently available, such methods have had limited success. Moreover, the results of network alignment are in general not amenable for distance-based evolutionary analysis of sets of networks. In this article, we describe Netdis, a topology-based distance measure between networks, which offers the possibility of network phylogeny reconstruction. Results: We first demonstrate that Netdis is able to correctly separate different random graph model types independent of network size and density. The biological applicability of the method is then shown by its ability to build the correct phylogenetic tree of species based solely on the topology of current protein interaction networks. Our results provide new evidence that the topology of protein interaction networks contains information about evolutionary processes, despite the lack of conservation of individual interactions. As Netdis is applicable to all networks because of its speed and simplicity, we apply it to a large collection of biological and non-biological networks where it clusters diverse networks by type. Availability and implementation: The source code of the program is freely available at http://www.stats.ox.ac.uk/research/proteins/resources. Contact: w.ali@stats.ox.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online. PMID:25161230

In silico modeling of the yeast protein and protein family interaction network

NASA Astrophysics Data System (ADS)

Goh, K.-I.; Kahng, B.; Kim, D.

2004-03-01

Understanding of how protein interaction networks of living organisms have evolved or are organized can be the first stepping stone in unveiling how life works on a fundamental ground. Here we introduce an in silico ``coevolutionary'' model for the protein interaction network and the protein family network. The essential ingredient of the model includes the protein family identity and its robustness under evolution, as well as the three previously proposed: gene duplication, divergence, and mutation. This model produces a prototypical feature of complex networks in a wide range of parameter space, following the generalized Pareto distribution in connectivity. Moreover, we investigate other structural properties of our model in detail with some specific values of parameters relevant to the yeast Saccharomyces cerevisiae, showing excellent agreement with the empirical data. Our model indicates that the physical constraints encoded via the domain structure of proteins play a crucial role in protein interactions.

Building protein-protein interaction networks for Leishmania species through protein structural information.

PubMed

Dos Santos Vasconcelos, Crhisllane Rafaele; de Lima Campos, Túlio; Rezende, Antonio Mauro

2018-03-06

Systematic analysis of a parasite interactome is a key approach to understand different biological processes. It makes possible to elucidate disease mechanisms, to predict protein functions and to select promising targets for drug development. Currently, several approaches for protein interaction prediction for non-model species incorporate only small fractions of the entire proteomes and their interactions. Based on this perspective, this study presents an integration of computational methodologies, protein network predictions and comparative analysis of the protozoan species Leishmania braziliensis and Leishmania infantum. These parasites cause Leishmaniasis, a worldwide distributed and neglected disease, with limited treatment options using currently available drugs. The predicted interactions were obtained from a meta-approach, applying rigid body docking tests and template-based docking on protein structures predicted by different comparative modeling techniques. In addition, we trained a machine-learning algorithm (Gradient Boosting) using docking information performed on a curated set of positive and negative protein interaction data. Our final model obtained an AUC = 0.88, with recall = 0.69, specificity = 0.88 and precision = 0.83. Using this approach, it was possible to confidently predict 681 protein structures and 6198 protein interactions for L. braziliensis, and 708 protein structures and 7391 protein interactions for L. infantum. The predicted networks were integrated to protein interaction data already available, analyzed using several topological features and used to classify proteins as essential for network stability. The present study allowed to demonstrate the importance of integrating different methodologies of interaction prediction to increase the coverage of the protein interaction of the studied protocols, besides it made available protein structures and interactions not previously reported.

Reverse Nearest Neighbor Search on a Protein-Protein Interaction Network to Infer Protein-Disease Associations.

PubMed

Suratanee, Apichat; Plaimas, Kitiporn

2017-01-01

The associations between proteins and diseases are crucial information for investigating pathological mechanisms. However, the number of known and reliable protein-disease associations is quite small. In this study, an analysis framework to infer associations between proteins and diseases was developed based on a large data set of a human protein-protein interaction network integrating an effective network search, namely, the reverse k -nearest neighbor (R k NN) search. The R k NN search was used to identify an impact of a protein on other proteins. Then, associations between proteins and diseases were inferred statistically. The method using the R k NN search yielded a much higher precision than a random selection, standard nearest neighbor search, or when applying the method to a random protein-protein interaction network. All protein-disease pair candidates were verified by a literature search. Supporting evidence for 596 pairs was identified. In addition, cluster analysis of these candidates revealed 10 promising groups of diseases to be further investigated experimentally. This method can be used to identify novel associations to better understand complex relationships between proteins and diseases.

High-throughput profiling of signaling networks identifies mechanism-based combination therapy to eliminate microenvironmental resistance in acute myeloid leukemia.

PubMed

Zeng, Zhihong; Liu, Wenbin; Tsao, Twee; Qiu, YiHua; Zhao, Yang; Samudio, Ismael; Sarbassov, Dos D; Kornblau, Steven M; Baggerly, Keith A; Kantarjian, Hagop M; Konopleva, Marina; Andreeff, Michael

2017-09-01

The bone marrow microenvironment is known to provide a survival advantage to residual acute myeloid leukemia cells, possibly contributing to disease recurrence. The mechanisms by which stroma in the microenvironment regulates leukemia survival remain largely unknown. Using reverse-phase protein array technology, we profiled 53 key protein molecules in 11 signaling pathways in 20 primary acute myeloid leukemia samples and two cell lines, aiming to understand stroma-mediated signaling modulation in response to the targeted agents temsirolimus (MTOR), ABT737 (BCL2/BCL-XL), and Nutlin-3a (MDM2), and to identify the effective combination therapy targeting acute myeloid leukemia in the context of the leukemia microenvironment. Stroma reprogrammed signaling networks and modified the sensitivity of acute myeloid leukemia samples to all three targeted inhibitors. Stroma activated AKT at Ser473 in the majority of samples treated with single-agent ABT737 or Nutlin-3a. This survival mechanism was partially abrogated by concomitant treatment with temsirolimus plus ABT737 or Nutlin-3a. Mapping the signaling networks revealed that combinations of two inhibitors increased the number of affected proteins in the targeted pathways and in multiple parallel signaling, translating into facilitated cell death. These results demonstrated that a mechanism-based selection of combined inhibitors can be used to guide clinical drug selection and tailor treatment regimens to eliminate microenvironment-mediated resistance in acute myeloid leukemia. Copyright© 2017 Ferrata Storti Foundation.

The T cell STAT signaling network is reprogrammed within hours of bacteremia via secondary signals1

PubMed Central

Hotson, Andrew N.; Hardy, Jonathan W.; Hale, Matthew B.; Contag, Christopher H.; Nolan, Garry P.

2014-01-01

The delicate balance between protective immunity and inflammatory disease is challenged during sepsis, a pathologic state characterized by aspects of both a hyper-active immune response and immunosuppression. The events driven by systemic infection by bacterial pathogens on the T cell signaling network that likely control these responses have not been illustrated in great detail. We characterized how intracellular signaling within the immune compartment is reprogrammed at the single cell level when the host is challenged with a high levels of pathogen. To accomplish this, we applied flow cytometry to measure the phosphorylation potential of key signal transduction proteins during acute bacterial challenge. We modeled the onset of sepsis by intravenous administration of avirulent strains of Listeria and E. coli to mice. Within six hours of bacterial challenge, T cells were globally restricted in their ability to respond to specific cytokine stimulations as determined by assessing the extent of STAT protein phosphorylation. Mechanisms by which this negative feedback response occurred included SOCS1 and SOCS3 gene up regulation and IL-6 induced endocystosis of the IL-6 receptor. In addition, macrophages were partially tolerized in their ability to respond to TLR agonists. Thus, in contrast to the view that there is a wholesale immune activation during sepsis, one immediate host response to blood borne bacteria was induction of a refractory period during which leukocyte activation by specific stimulations was attenuated. PMID:19494279

Role of Regulators of G Protein Signaling Proteins in Bone Physiology and Pathophysiology.

PubMed

Jules, Joel; Yang, Shuying; Chen, Wei; Li, Yi-Ping

2015-01-01

Regulators of G protein signaling (RGS) proteins enhance the intrinsic GTPase activity of α subunits of the heterotrimeric G protein complex of G protein-coupled receptors (GPCRs) and thereby inactivate signal transduction initiated by GPCRs. The RGS family consists of nearly 37 members with a conserved RGS homology domain which is critical for their GTPase accelerating activity. RGS proteins are expressed in most tissues, including heart, lung, brain, kidney, and bone and play essential roles in many physiological and pathological processes. In skeletal development and bone homeostasis as well as in many bone disorders, RGS proteins control the functions of various GPCRs, including the parathyroid hormone receptor type 1 and calcium-sensing receptor and also regulate various critical signaling pathways, such as Wnt and calcium oscillations. This chapter will discuss the current findings on the roles of RGS proteins in regulating signaling of key GPCRs in skeletal development and bone homeostasis. We also will examine the current updates of RGS proteins' regulation of calcium oscillations in bone physiology and highlight the roles of RGS proteins in selected bone pathological disorders. Despite the recent advances in bone and mineral research, RGS proteins remain understudied in the skeletal system. Further understanding of the roles of RGS proteins in bone should not only provide great insights into the molecular basis of various bone diseases but also generate great therapeutic drug targets for many bone diseases. © 2015 Elsevier Inc. All rights reserved.

Defective chemokine signal integration in leukocytes lacking activator of G protein signaling 3 (AGS3).

PubMed

Branham-O'Connor, Melissa; Robichaux, William G; Zhang, Xian-Kui; Cho, Hyeseon; Kehrl, John H; Lanier, Stephen M; Blumer, Joe B

2014-04-11

Activator of G-protein signaling 3 (AGS3, gene name G-protein signaling modulator-1, Gpsm1), an accessory protein for G-protein signaling, has functional roles in the kidney and CNS. Here we show that AGS3 is expressed in spleen, thymus, and bone marrow-derived dendritic cells, and is up-regulated upon leukocyte activation. We explored the role of AGS3 in immune cell function by characterizing chemokine receptor signaling in leukocytes from mice lacking AGS3. No obvious differences in lymphocyte subsets were observed. Interestingly, however, AGS3-null B and T lymphocytes and bone marrow-derived dendritic cells exhibited significant chemotactic defects as well as reductions in chemokine-stimulated calcium mobilization and altered ERK and Akt activation. These studies indicate a role for AGS3 in the regulation of G-protein signaling in the immune system, providing unexpected venues for the potential development of therapeutic agents that modulate immune function by targeting these regulatory mechanisms.

DiffSLc: A graph centrality method to detect essential proteins of a protein-protein interaction network

USDA-ARS?s Scientific Manuscript database

Network centrality measures prioritize nodes and edges based on their importance to the network topology. These measures have been helpful in identifying critical genes and proteins in biomolecular networks. The proposed centrality measure DiffSLc uses the number of interactions of a protein and gen...

Evidence for dynamically organized modularity in the yeast protein-protein interaction network

NASA Astrophysics Data System (ADS)

Han, Jing-Dong J.; Bertin, Nicolas; Hao, Tong; Goldberg, Debra S.; Berriz, Gabriel F.; Zhang, Lan V.; Dupuy, Denis; Walhout, Albertha J. M.; Cusick, Michael E.; Roth, Frederick P.; Vidal, Marc

2004-07-01

In apparently scale-free protein-protein interaction networks, or `interactome' networks, most proteins interact with few partners, whereas a small but significant proportion of proteins, the `hubs', interact with many partners. Both biological and non-biological scale-free networks are particularly resistant to random node removal but are extremely sensitive to the targeted removal of hubs. A link between the potential scale-free topology of interactome networks and genetic robustness seems to exist, because knockouts of yeast genes encoding hubs are approximately threefold more likely to confer lethality than those of non-hubs. Here we investigate how hubs might contribute to robustness and other cellular properties for protein-protein interactions dynamically regulated both in time and in space. We uncovered two types of hub: `party' hubs, which interact with most of their partners simultaneously, and `date' hubs, which bind their different partners at different times or locations. Both in silico studies of network connectivity and genetic interactions described in vivo support a model of organized modularity in which date hubs organize the proteome, connecting biological processes-or modules -to each other, whereas party hubs function inside modules.

Protein enriched pasta: structure and digestibility of its protein network.

PubMed

Laleg, Karima; Barron, Cécile; Santé-Lhoutellier, Véronique; Walrand, Stéphane; Micard, Valérie

2016-02-01

Wheat (W) pasta was enriched in 6% gluten (G), 35% faba (F) or 5% egg (E) to increase its protein content (13% to 17%). The impact of the enrichment on the multiscale structure of the pasta and on in vitro protein digestibility was studied. Increasing the protein content (W- vs. G-pasta) strengthened pasta structure at molecular and macroscopic scales but reduced its protein digestibility by 3% by forming a higher covalently linked protein network. Greater changes in the macroscopic and molecular structure of the pasta were obtained by varying the nature of protein used for enrichment. Proteins in G- and E-pasta were highly covalently linked (28-32%) resulting in a strong pasta structure. Conversely, F-protein (98% SDS-soluble) altered the pasta structure by diluting gluten and formed a weak protein network (18% covalent link). As a result, protein digestibility in F-pasta was significantly higher (46%) than in E- (44%) and G-pasta (39%). The effect of low (55 °C, LT) vs. very high temperature (90 °C, VHT) drying on the protein network structure and digestibility was shown to cause greater molecular changes than pasta formulation. Whatever the pasta, a general strengthening of its structure, a 33% to 47% increase in covalently linked proteins and a higher β-sheet structure were observed. However, these structural differences were evened out after the pasta was cooked, resulting in identical protein digestibility in LT and VHT pasta. Even after VHT drying, F-pasta had the best amino acid profile with the highest protein digestibility, proof of its nutritional interest.

Protein-protein interaction network-based detection of functionally similar proteins within species.

PubMed

Song, Baoxing; Wang, Fen; Guo, Yang; Sang, Qing; Liu, Min; Li, Dengyun; Fang, Wei; Zhang, Deli

2012-07-01

Although functionally similar proteins across species have been widely studied, functionally similar proteins within species showing low sequence similarity have not been examined in detail. Identification of these proteins is of significant importance for understanding biological functions, evolution of protein families, progression of co-evolution, and convergent evolution and others which cannot be obtained by detection of functionally similar proteins across species. Here, we explored a method of detecting functionally similar proteins within species based on graph theory. After denoting protein-protein interaction networks using graphs, we split the graphs into subgraphs using the 1-hop method. Proteins with functional similarities in a species were detected using a method of modified shortest path to compare these subgraphs and to find the eligible optimal results. Using seven protein-protein interaction networks and this method, some functionally similar proteins with low sequence similarity that cannot detected by sequence alignment were identified. By analyzing the results, we found that, sometimes, it is difficult to separate homologous from convergent evolution. Evaluation of the performance of our method by gene ontology term overlap showed that the precision of our method was excellent. Copyright © 2012 Wiley Periodicals, Inc.

The beta-arrestin pathway-selective type 1A angiotensin receptor (AT1A) agonist [Sar1,Ile4,Ile8]angiotensin II regulates a robust G protein-independent signaling network.

PubMed

Kendall, Ryan T; Strungs, Erik G; Rachidi, Saleh M; Lee, Mi-Hye; El-Shewy, Hesham M; Luttrell, Deirdre K; Janech, Michael G; Luttrell, Louis M

2011-06-03

The angiotensin II peptide analog [Sar(1),Ile(4),Ile(8)]AngII (SII) is a biased AT(1A) receptor agonist that stimulates receptor phosphorylation, β-arrestin recruitment, receptor internalization, and β-arrestin-dependent ERK1/2 activation without activating heterotrimeric G-proteins. To determine the scope of G-protein-independent AT(1A) receptor signaling, we performed a gel-based phosphoproteomic analysis of AngII and SII-induced signaling in HEK cells stably expressing AT(1A) receptors. A total of 34 differentially phosphorylated proteins were detected, of which 16 were unique to SII and eight to AngII stimulation. MALDI-TOF/TOF mass fingerprinting was employed to identify 24 SII-sensitive phosphoprotein spots, of which three (two peptide inhibitors of protein phosphatase 2A (I1PP2A and I2PP2A) and prostaglandin E synthase 3 (PGES3)) were selected for validation and further study. We found that phosphorylation of I2PP2A was associated with rapid and transient inhibition of a β-arrestin 2-associated pool of protein phosphatase 2A, leading to activation of Akt and increased phosphorylation of glycogen synthase kinase 3β in an arrestin signalsome complex. SII-stimulated PGES3 phosphorylation coincided with an increase in β-arrestin 1-associated PGES3 and an arrestin-dependent increase in cyclooxygenase 1-dependent prostaglandin E(2) synthesis. These findings suggest that AT(1A) receptors regulate a robust G protein-independent signaling network that affects protein phosphorylation and autocrine/paracrine prostaglandin production and that these pathways can be selectively modulated by biased ligands that antagonize G protein activation.

Role of Regulators of G Protein Signaling Proteins in Bone Physiology and Pathophysiology

PubMed Central

Jules, Joel; Yang, Shuying; Chen, Wei; Li, Yi-Ping

2016-01-01

Regulators of G protein signaling (RGS) proteins enhance the intrinsic GTPase activity of α subunits of the heterotrimeric G protein complex of G protein-coupled receptors (GPCRs) and thereby inactivate signal transduction initiated by GPCRs. The RGS family consists of nearly 37 members with a conserved RGS homology domain which is critical for their GTPase accelerating activity. RGS proteins are expressed in most tissues, including heart, lung, brain, kidney, and bone and play essential roles in many physiological and pathological processes. In skeletal development and bone homeostasis as well as in many bone disorders, RGS proteins control the functions of various GPCRs, including the parathyroid hormone receptor type 1 and calcium-sensing receptor and also regulate various critical signaling pathways, such as Wnt and calcium oscillations. This chapter will discuss the current findings on the roles of RGS proteins in regulating signaling of key GPCRs in skeletal development and bone homeostasis. We also will examine the current updates of RGS proteins’ regulation of calcium oscillations in bone physiology and highlight the roles of RGS proteins in selected bone pathological disorders. Despite the recent advances in bone and mineral research, RGS proteins remain understudied in the skeletal system. Further understanding of the roles of RGS proteins in bone should not only provide great insights into the molecular basis of various bone diseases but also generate great therapeutic drug targets for many bone diseases. PMID:26123302

Structure-based Reassessment of the Caveolin Signaling Model: Do Caveolae Regulate Signaling Through Caveolin-Protein Interactions?

PubMed Central

Collins, Brett M.; Davis, Melissa J.; Hancock, John F.; Parton, Robert G.

2012-01-01

Summary Caveolin proteins drive formation of caveolae, specialized cell-surface microdomains that influence cell signaling. Signaling proteins are proposed to use conserved caveolin-binding motifs (CBMs) to associate with caveolae via the caveolin scaffolding domain (CSD). However, structural and bioinformatic analyses argue against such direct physical interactions: In the majority of signaling proteins, the CBM is buried and inaccessible. Putative CBMs do not form a common structure for caveolin recognition, are not enriched amongst caveolin-binding proteins, and are even more common in yeast, which lack caveolae. We propose that CBM/CSD-dependent interactions are unlikely to mediate caveolar signaling, and the basis for signaling effects should therefore be reassessed. PMID:22814599

Protein complex prediction for large protein protein interaction networks with the Core&Peel method.

PubMed

Pellegrini, Marco; Baglioni, Miriam; Geraci, Filippo

2016-11-08

Biological networks play an increasingly important role in the exploration of functional modularity and cellular organization at a systemic level. Quite often the first tools used to analyze these networks are clustering algorithms. We concentrate here on the specific task of predicting protein complexes (PC) in large protein-protein interaction networks (PPIN). Currently, many state-of-the-art algorithms work well for networks of small or moderate size. However, their performance on much larger networks, which are becoming increasingly common in modern proteome-wise studies, needs to be re-assessed. We present a new fast algorithm for clustering large sparse networks: Core&Peel, which runs essentially in time and storage O(a(G)m+n) for a network G of n nodes and m arcs, where a(G) is the arboricity of G (which is roughly proportional to the maximum average degree of any induced subgraph in G). We evaluated Core&Peel on five PPI networks of large size and one of medium size from both yeast and homo sapiens, comparing its performance against those of ten state-of-the-art methods. We demonstrate that Core&Peel consistently outperforms the ten competitors in its ability to identify known protein complexes and in the functional coherence of its predictions. Our method is remarkably robust, being quite insensible to the injection of random interactions. Core&Peel is also empirically efficient attaining the second best running time over large networks among the tested algorithms. Our algorithm Core&Peel pushes forward the state-of the-art in PPIN clustering providing an algorithmic solution with polynomial running time that attains experimentally demonstrable good output quality and speed on challenging large real networks.

Protein phosphorylation and its role in archaeal signal transduction

PubMed Central

Esser, Dominik; Hoffmann, Lena; Pham, Trong Khoa; Bräsen, Christopher; Qiu, Wen; Wright, Phillip C.; Albers, Sonja-Verena; Siebers, Bettina

2016-01-01

Reversible protein phosphorylation is the main mechanism of signal transduction that enables cells to rapidly respond to environmental changes by controlling the functional properties of proteins in response to external stimuli. However, whereas signal transduction is well studied in Eukaryotes and Bacteria, the knowledge in Archaea is still rather scarce. Archaea are special with regard to protein phosphorylation, due to the fact that the two best studied phyla, the Euryarchaeota and Crenarchaeaota, seem to exhibit fundamental differences in regulatory systems. Euryarchaeota (e.g. halophiles, methanogens, thermophiles), like Bacteria and Eukaryotes, rely on bacterial-type two-component signal transduction systems (phosphorylation on His and Asp), as well as on the protein phosphorylation on Ser, Thr and Tyr by Hanks-type protein kinases. Instead, Crenarchaeota (e.g. acidophiles and (hyper)thermophiles) only depend on Hanks-type protein phosphorylation. In this review, the current knowledge of reversible protein phosphorylation in Archaea is presented. It combines results from identified phosphoproteins, biochemical characterization of protein kinases and protein phosphatases as well as target enzymes and first insights into archaeal signal transduction by biochemical, genetic and polyomic studies. PMID:27476079

PTP-ε HAS A CRITICAL ROLE IN SIGNALING TRANSDUCTION PATHWAYS AND PHOSPHOPROTEIN NETWORK TOPOLOGY IN RED CELLS

PubMed Central

De Franceschi, Lucia; Biondani, Andrea; Carta, Franco; Turrini, Franco; Laudanna, Carlo; Deana, Renzo; Brunati, Anna Maria; Turretta, Loris; Iolascon, Achille; Perrotta, Silverio; Elson, Ari; Bulato, Cristina; Brugnara, Carlo

2010-01-01

Protein tyrosine phosphatases (PTPs) are crucial components of cellular signal transduction pathways. We report here that red blood cells (RBCs) from mice lacking PTPε (Ptpre−/−) exhibit abnormal morphology and increased Ca2+-activated-K+ channel activity, which was partially blocked by the Src-Family-Kinases (SFKs) inhibitor PP1. In Ptpre−/− mouse RBCs, the activity of Fyn and Yes, two SFKs, were increased, suggesting a functional relationship between SFKs, PTPε and Ca2+-activated-K+-channel. The absence of PTPε markedly affected the RBC membrane tyrosine (Tyr-) phosphoproteome, indicating a perturbation of RBCs signal transduction pathways. Using signaling network computational analysis of the Tyr-phosphoproteomic data, we identified 7 topological clusters. We studied cluster 1, containing Syk-Tyr-kinase: Syk-kinase activity was higher in wild-type than in Ptpre−/− RBCs, validating the network computational analysis and indicating a novel signaling pathway, which involves Fyn and Syk in regulation of red cell morphology. PMID:18924107

Quality control methodology for high-throughput protein-protein interaction screening.

PubMed

Vazquez, Alexei; Rual, Jean-François; Venkatesan, Kavitha

2011-01-01

Protein-protein interactions are key to many aspects of the cell, including its cytoskeletal structure, the signaling processes in which it is involved, or its metabolism. Failure to form protein complexes or signaling cascades may sometimes translate into pathologic conditions such as cancer or neurodegenerative diseases. The set of all protein interactions between the proteins encoded by an organism constitutes its protein interaction network, representing a scaffold for biological function. Knowing the protein interaction network of an organism, combined with other sources of biological information, can unravel fundamental biological circuits and may help better understand the molecular basics of human diseases. The protein interaction network of an organism can be mapped by combining data obtained from both low-throughput screens, i.e., "one gene at a time" experiments and high-throughput screens, i.e., screens designed to interrogate large sets of proteins at once. In either case, quality controls are required to deal with the inherent imperfect nature of experimental assays. In this chapter, we discuss experimental and statistical methodologies to quantify error rates in high-throughput protein-protein interactions screens.

Protein Thiol Redox Signaling in Monocytes and Macrophages.

PubMed

Short, John D; Downs, Kevin; Tavakoli, Sina; Asmis, Reto

2016-11-20

Monocyte and macrophage dysfunction plays a critical role in a wide range of inflammatory disease processes, including obesity, impaired wound healing diabetic complications, and atherosclerosis. Emerging evidence suggests that the earliest events in monocyte or macrophage dysregulation include elevated reactive oxygen species production, thiol modifications, and disruption of redox-sensitive signaling pathways. This review focuses on the current state of research in thiol redox signaling in monocytes and macrophages, including (i) the molecular mechanisms by which reversible protein-S-glutathionylation occurs, (ii) the identification of bona fide S-glutathionylated proteins that occur under physiological conditions, and (iii) how disruptions of thiol redox signaling affect monocyte and macrophage functions and contribute to atherosclerosis. Recent Advances: Recent advances in redox biochemistry and biology as well as redox proteomic techniques have led to the identification of many new thiol redox-regulated proteins and pathways. In addition, major advances have been made in expanding the list of S-glutathionylated proteins and assessing the role that protein-S-glutathionylation and S-glutathionylation-regulating enzymes play in monocyte and macrophage functions, including monocyte transmigration, macrophage polarization, foam cell formation, and macrophage cell death. Protein-S-glutathionylation/deglutathionylation in monocytes and macrophages has emerged as a new and important signaling paradigm, which provides a molecular basis for the well-established relationship between metabolic disorders, oxidative stress, and cardiovascular diseases. The identification of specific S-glutathionylated proteins as well as the mechanisms that control this post-translational protein modification in monocytes and macrophages will facilitate the development of new preventive and therapeutic strategies to combat atherosclerosis and other metabolic diseases. Antioxid. Redox Signal

Assuring SS7 dependability: A robustness characterization of signaling network elements

NASA Astrophysics Data System (ADS)

Karmarkar, Vikram V.

1994-04-01

Current and evolving telecommunication services will rely on signaling network performance and reliability properties to build competitive call and connection control mechanisms under increasing demands on flexibility without compromising on quality. The dimensions of signaling dependability most often evaluated are the Rate of Call Loss and End-to-End Route Unavailability. A third dimension of dependability that captures the concern about large or catastrophic failures can be termed Network Robustness. This paper is concerned with the dependability aspects of the evolving Signaling System No. 7 (SS7) networks and attempts to strike a balance between the probabilistic and deterministic measures that must be evaluated to accomplish a risk-trend assessment to drive architecture decisions. Starting with high-level network dependability objectives and field experience with SS7 in the U.S., potential areas of growing stringency in network element (NE) dependability are identified to improve against current measures of SS7 network quality, as per-call signaling interactions increase. A sensitivity analysis is presented to highlight the impact due to imperfect coverage of duplex network component or element failures (i.e., correlated failures), to assist in the setting of requirements on NE robustness. A benefit analysis, covering several dimensions of dependability, is used to generate the domain of solutions available to the network architect in terms of network and network element fault tolerance that may be specified to meet the desired signaling quality goals.

Molecular analysis of HER2 signaling in human breast cancer by functional protein pathway activation mapping

PubMed Central

Wulfkuhle, Julia D.; Berg, Daniela; Wolff, Claudia; Langer, Rupert; Tran, Kai; Illi, Julie; Espina, Virginia; Pierobon, Mariaelena; Deng, Jianghong; DeMichele, Angela; Walch, Axel; Bronger, Holger; Becker, Ingrid; Waldhör, Christine; Höfler, Heinz; Esserman, Laura; Liotta, Lance A.; Becker, Karl-Friedrich; Petricoin, Emanuel F.

2017-01-01

Purpose Targeting of the HER2 protein in human breast cancer represents a major advance in oncology, but relies on measurements of total HER2 protein and not HER2 signaling network activation. We utilized reverse phase protein microarrays (RPMAs) to measure total and phosphorylated HER2 in the context of HER family signaling to understand correlations between phosphorylated and total levels of HER2 and downstream signaling activity. Experimental Design Three independent study sets, comprising a total of 415 individual patient samples from flash frozen core biopsy samples and FFPE surgical and core samples, were analyzed via RPMA. The phosphorylation and total levels of the HER receptor family proteins and downstream signaling molecules were measured in laser capture microdissected (LCM) enriched tumor epithelium from 127 frozen pre-treatment core biopsy samples and whole tissue lysates from 288 FFPE samples and these results were compared to FISH and IHC. Results RPMA measurements of total HER2 were highly concordant (> 90% all sets) with FISH and/or IHC data, as was phosphorylation of HER2 in the FISH/IHC+ population. Phosphorylation analysis of HER family signaling identified HER2 activation in some FISH/IHC- tumors and, identical to that seen with FISH/IHC+ tumors, the HER2 activation was concordant with EGFR and HER3 phosphorylation and downstream signaling endpoint activation. Conclusions Molecular profiling of HER2 signaling of a large cohort of human breast cancer specimens using a quantitative and sensitive functional pathway activation mapping technique reveals IHC-/FISH-/pHER2+ tumors with HER2 pathway activation independent of total HER2 levels and functional signaling through HER3 and EGFR. PMID:23045247

Fascin- and α-Actinin-Bundled Networks Contain Intrinsic Structural Features that Drive Protein Sorting.

PubMed

Winkelman, Jonathan D; Suarez, Cristian; Hocky, Glen M; Harker, Alyssa J; Morganthaler, Alisha N; Christensen, Jenna R; Voth, Gregory A; Bartles, James R; Kovar, David R

2016-10-24

Cells assemble and maintain functionally distinct actin cytoskeleton networks with various actin filament organizations and dynamics through the coordinated action of different sets of actin-binding proteins. The biochemical and functional properties of diverse actin-binding proteins, both alone and in combination, have been increasingly well studied. Conversely, how different sets of actin-binding proteins properly sort to distinct actin filament networks in the first place is not nearly as well understood. Actin-binding protein sorting is critical for the self-organization of diverse dynamic actin cytoskeleton networks within a common cytoplasm. Using in vitro reconstitution techniques including biomimetic assays and single-molecule multi-color total internal reflection fluorescence microscopy, we discovered that sorting of the prominent actin-bundling proteins fascin and α-actinin to distinct networks is an intrinsic behavior, free of complicated cellular signaling cascades. When mixed, fascin and α-actinin mutually exclude each other by promoting their own recruitment and inhibiting recruitment of the other, resulting in the formation of distinct fascin- or α-actinin-bundled domains. Subdiffraction-resolution light microscopy and negative-staining electron microscopy revealed that fascin domains are densely packed, whereas α-actinin domains consist of widely spaced parallel actin filaments. Importantly, other actin-binding proteins such as fimbrin and espin show high specificity between these two bundle types within the same reaction. Here we directly observe that fascin and α-actinin intrinsically segregate to discrete bundled domains that are specifically recognized by other actin-binding proteins. Copyright © 2016 Elsevier Ltd. All rights reserved.

Energy transport pathway in proteins: Insights from non-equilibrium molecular dynamics with elastic network model.

PubMed

Wang, Wei Bu; Liang, Yu; Zhang, Jing; Wu, Yi Dong; Du, Jian Jun; Li, Qi Ming; Zhu, Jian Zhuo; Su, Ji Guo

2018-06-22

Intra-molecular energy transport between distant functional sites plays important roles in allosterically regulating the biochemical activity of proteins. How to identify the specific intra-molecular signaling pathway from protein tertiary structure remains a challenging problem. In the present work, a non-equilibrium dynamics method based on the elastic network model (ENM) was proposed to simulate the energy propagation process and identify the specific signaling pathways within proteins. In this method, a given residue was perturbed and the propagation of energy was simulated by non-equilibrium dynamics in the normal modes space of ENM. After that, the simulation results were transformed from the normal modes space to the Cartesian coordinate space to identify the intra-protein energy transduction pathways. The proposed method was applied to myosin and the third PDZ domain (PDZ3) of PSD-95 as case studies. For myosin, two signaling pathways were identified, which mediate the energy transductions form the nucleotide binding site to the 50 kDa cleft and the converter subdomain, respectively. For PDZ3, one specific signaling pathway was identified, through which the intra-protein energy was transduced from ligand binding site to the distant opposite side of the protein. It is also found that comparing with the commonly used cross-correlation analysis method, the proposed method can identify the anisotropic energy transduction pathways more effectively.

Functional Module Search in Protein Networks based on Semantic Similarity Improves the Analysis of Proteomics Data*

PubMed Central

Boyanova, Desislava; Nilla, Santosh; Klau, Gunnar W.; Dandekar, Thomas; Müller, Tobias; Dittrich, Marcus

2014-01-01

The continuously evolving field of proteomics produces increasing amounts of data while improving the quality of protein identifications. Albeit quantitative measurements are becoming more popular, many proteomic studies are still based on non-quantitative methods for protein identification. These studies result in potentially large sets of identified proteins, where the biological interpretation of proteins can be challenging. Systems biology develops innovative network-based methods, which allow an integrated analysis of these data. Here we present a novel approach, which combines prior knowledge of protein-protein interactions (PPI) with proteomics data using functional similarity measurements of interacting proteins. This integrated network analysis exactly identifies network modules with a maximal consistent functional similarity reflecting biological processes of the investigated cells. We validated our approach on small (H9N2 virus-infected gastric cells) and large (blood constituents) proteomic data sets. Using this novel algorithm, we identified characteristic functional modules in virus-infected cells, comprising key signaling proteins (e.g. the stress-related kinase RAF1) and demonstrate that this method allows a module-based functional characterization of cell types. Analysis of a large proteome data set of blood constituents resulted in clear separation of blood cells according to their developmental origin. A detailed investigation of the T-cell proteome further illustrates how the algorithm partitions large networks into functional subnetworks each representing specific cellular functions. These results demonstrate that the integrated network approach not only allows a detailed analysis of proteome networks but also yields a functional decomposition of complex proteomic data sets and thereby provides deeper insights into the underlying cellular processes of the investigated system. PMID:24807868

Weak signal transmission in complex networks and its application in detecting connectivity.

PubMed

Liang, Xiaoming; Liu, Zonghua; Li, Baowen

2009-10-01

We present a network model of coupled oscillators to study how a weak signal is transmitted in complex networks. Through both theoretical analysis and numerical simulations, we find that the response of other nodes to the weak signal decays exponentially with their topological distance to the signal source and the coupling strength between two neighboring nodes can be figured out by the responses. This finding can be conveniently used to detect the topology of unknown network, such as the degree distribution, clustering coefficient and community structure, etc., by repeatedly choosing different nodes as the signal source. Through four typical networks, i.e., the regular one dimensional, small world, random, and scale-free networks, we show that the features of network can be approximately given by investigating many fewer nodes than the network size, thus our approach to detect the topology of unknown network may be efficient in practical situations with large network size.

Endoplasmic reticulum mediated signaling in cellular microdomains

PubMed Central

Biwer, Lauren; Isakson, Brant E

2016-01-01

The endoplasmic reticulum (ER) is a prime mediator of cellular signaling due to its functions as an internal cellular store for calcium, as well as a site for synthesis of proteins and lipids. Its peripheral network of sheets and tubules facilitate calcium and lipid signaling, especially in areas of the cell that are more distant to the main cytoplasmic network. Specific membrane proteins shape the peripheral ER architecture and influence the network stability in order to project into restricted spaces. The signaling microdomains are anatomically separate from the cytoplasm as a whole and exhibit localized protein, ion channel and cytoskeletal element expression. Signaling can also occur between the ER and other organelles, such as the Golgi or mitochondria. Lipids made in the ER membrane can be sent to the Golgi via specialized transfer proteins and specific phospholipid synthases are enriched at ER-mitochondria junctions to more efficiently expedite phospholipid transfer. As a hub for protein and lipid synthesis, a store for intracellular calcium [Ca2+]i, and a mediator of cellular stress, the ER is an important cellular organelle. Its ability to organize into tubules and project into restricted spaces allows for discrete and temporal signaling, which is important for cellular physiology and organism homeostasis. PMID:26973141

Minireview: Role of Intracellular Scaffolding Proteins in the Regulation of Endocrine G Protein-Coupled Receptor Signaling

PubMed Central

Walther, Cornelia

2015-01-01

The majority of hormones stimulates and mediates their signal transduction via G protein-coupled receptors (GPCRs). The signal is transmitted into the cell due to the association of the GPCRs with heterotrimeric G proteins, which in turn activates an extensive array of signaling pathways to regulate cell physiology. However, GPCRs also function as scaffolds for the recruitment of a variety of cytoplasmic protein-interacting proteins that bind to both the intracellular face and protein interaction motifs encoded by GPCRs. The structural scaffolding of these proteins allows GPCRs to recruit large functional complexes that serve to modulate both G protein-dependent and -independent cellular signaling pathways and modulate GPCR intracellular trafficking. This review focuses on GPCR interacting PSD95-disc large-zona occludens domain containing scaffolds in the regulation of endocrine receptor signaling as well as their potential role as therapeutic targets for the treatment of endocrinopathies. PMID:25942107

Frodo proteins: modulators of Wnt signaling in vertebrate development.

PubMed

Brott, Barbara K; Sokol, Sergei Y

2005-09-01

The Frodo/dapper (Frd) proteins are recently discovered signaling adaptors, which functionally and physically interact with Wnt and Nodal signaling pathways during vertebrate development. The Frd1 and Frd2 genes are expressed in dynamic patterns in early embryos, frequently in cells undergoing epithelial-mesenchymal transition. The Frd proteins function in multiple developmental processes, including mesoderm and neural tissue specification, early morphogenetic cell movements, and organogenesis. Loss-of-function studies using morpholino antisense oligonucleotides demonstrate that the Frd proteins regulate Wnt signal transduction in a context-dependent manner and may be involved in Nodal signaling. The identification of Frd-associated factors and cellular targets of the Frd proteins should shed light on the molecular mechanisms underlying Frd functions in embryonic development and in cancer.

17-Beta-estradiol inhibits transforming growth factor-beta signaling and function in breast cancer cells via activation of extracellular signal-regulated kinase through the G protein-coupled receptor 30.

PubMed

Kleuser, Burkhard; Malek, Daniela; Gust, Ronald; Pertz, Heinz H; Potteck, Henrik

2008-12-01

Breast cancer development and breast cancer progression involves the deregulation of growth factors leading to uncontrolled cellular proliferation, invasion and metastasis. Transforming growth factor (TGF)-beta plays a crucial role in breast cancer because it has the potential to act as either a tumor suppressor or a pro-oncogenic chemokine. A cross-communication between the TGF-beta signaling network and estrogens has been postulated, which is important for breast tumorigenesis. Here, we provide evidence that inhibition of TGF-beta signaling is associated with a rapid estrogen-dependent nongenomic action. Moreover, we were able to demonstrate that estrogens disrupt the TGF-beta signaling network as well as TGF-beta functions in breast cancer cells via the G protein-coupled receptor 30 (GPR30). Silencing of GPR30 in MCF-7 cells completely reduced the ability of 17-beta-estradiol (E2) to inhibit the TGF-beta pathway. Likewise, in GPR30-deficient MDA-MB-231 breast cancer cells, E2 achieved the ability to suppress TGF-beta signaling only after transfection with GPR30-encoding plasmids. It is most interesting that the antiestrogen fulvestrant (ICI 182,780), which possesses agonistic activity at the GPR30, also diminished TGF-beta signaling. Further experiments attempted to characterize the molecular mechanism by which activated GPR30 inhibits the TGF-beta pathway. Our results indicate that GPR30 induces the stimulation of the mitogen-activated protein kinases (MAPKs), which interferes with the activation of Smad proteins. Inhibition of MAPK activity prevented the ability of E2 from suppressing TGF-beta signaling. These findings are of great clinical relevance, because down-regulation of TGF-beta signaling is associated with the development of breast cancer resistance in response to antiestrogens.

Detection of dysregulated protein-association networks by high-throughput proteomics predicts cancer vulnerabilities.

PubMed

Lapek, John D; Greninger, Patricia; Morris, Robert; Amzallag, Arnaud; Pruteanu-Malinici, Iulian; Benes, Cyril H; Haas, Wilhelm

2017-10-01

The formation of protein complexes and the co-regulation of the cellular concentrations of proteins are essential mechanisms for cellular signaling and for maintaining homeostasis. Here we use isobaric-labeling multiplexed proteomics to analyze protein co-regulation and show that this allows the identification of protein-protein associations with high accuracy. We apply this 'interactome mapping by high-throughput quantitative proteome analysis' (IMAHP) method to a panel of 41 breast cancer cell lines and show that deviations of the observed protein co-regulations in specific cell lines from the consensus network affects cellular fitness. Furthermore, these aberrant interactions serve as biomarkers that predict the drug sensitivity of cell lines in screens across 195 drugs. We expect that IMAHP can be broadly used to gain insight into how changing landscapes of protein-protein associations affect the phenotype of biological systems.

Ultrasensitive response motifs: basic amplifiers in molecular signalling networks

PubMed Central

Zhang, Qiang; Bhattacharya, Sudin; Andersen, Melvin E.

2013-01-01

Multi-component signal transduction pathways and gene regulatory circuits underpin integrated cellular responses to perturbations. A recurring set of network motifs serve as the basic building blocks of these molecular signalling networks. This review focuses on ultrasensitive response motifs (URMs) that amplify small percentage changes in the input signal into larger percentage changes in the output response. URMs generally possess a sigmoid input–output relationship that is steeper than the Michaelis–Menten type of response and is often approximated by the Hill function. Six types of URMs can be commonly found in intracellular molecular networks and each has a distinct kinetic mechanism for signal amplification. These URMs are: (i) positive cooperative binding, (ii) homo-multimerization, (iii) multistep signalling, (iv) molecular titration, (v) zero-order covalent modification cycle and (vi) positive feedback. Multiple URMs can be combined to generate highly switch-like responses. Serving as basic signal amplifiers, these URMs are essential for molecular circuits to produce complex nonlinear dynamics, including multistability, robust adaptation and oscillation. These dynamic properties are in turn responsible for higher-level cellular behaviours, such as cell fate determination, homeostasis and biological rhythm. PMID:23615029

Modeling of cell signaling pathways in macrophages by semantic networks

PubMed Central

Hsing, Michael; Bellenson, Joel L; Shankey, Conor; Cherkasov, Artem

2004-01-01

Background Substantial amounts of data on cell signaling, metabolic, gene regulatory and other biological pathways have been accumulated in literature and electronic databases. Conventionally, this information is stored in the form of pathway diagrams and can be characterized as highly "compartmental" (i.e. individual pathways are not connected into more general networks). Current approaches for representing pathways are limited in their capacity to model molecular interactions in their spatial and temporal context. Moreover, the critical knowledge of cause-effect relationships among signaling events is not reflected by most conventional approaches for manipulating pathways. Results We have applied a semantic network (SN) approach to develop and implement a model for cell signaling pathways. The semantic model has mapped biological concepts to a set of semantic agents and relationships, and characterized cell signaling events and their participants in the hierarchical and spatial context. In particular, the available information on the behaviors and interactions of the PI3K enzyme family has been integrated into the SN environment and a cell signaling network in human macrophages has been constructed. A SN-application has been developed to manipulate the locations and the states of molecules and to observe their actions under different biological scenarios. The approach allowed qualitative simulation of cell signaling events involving PI3Ks and identified pathways of molecular interactions that led to known cellular responses as well as other potential responses during bacterial invasions in macrophages. Conclusions We concluded from our results that the semantic network is an effective method to model cell signaling pathways. The semantic model allows proper representation and integration of information on biological structures and their interactions at different levels. The reconstruction of the cell signaling network in the macrophage allowed detailed

Subsurface event detection and classification using Wireless Signal Networks.

PubMed

Yoon, Suk-Un; Ghazanfari, Ehsan; Cheng, Liang; Pamukcu, Sibel; Suleiman, Muhannad T

2012-11-05

Subsurface environment sensing and monitoring applications such as detection of water intrusion or a landslide, which could significantly change the physical properties of the host soil, can be accomplished using a novel concept, Wireless Signal Networks (WSiNs). The wireless signal networks take advantage of the variations of radio signal strength on the distributed underground sensor nodes of WSiNs to monitor and characterize the sensed area. To characterize subsurface environments for event detection and classification, this paper provides a detailed list and experimental data of soil properties on how radio propagation is affected by soil properties in subsurface communication environments. Experiments demonstrated that calibrated wireless signal strength variations can be used as indicators to sense changes in the subsurface environment. The concept of WSiNs for the subsurface event detection is evaluated with applications such as detection of water intrusion, relative density change, and relative motion using actual underground sensor nodes. To classify geo-events using the measured signal strength as a main indicator of geo-events, we propose a window-based minimum distance classifier based on Bayesian decision theory. The window-based classifier for wireless signal networks has two steps: event detection and event classification. With the event detection, the window-based classifier classifies geo-events on the event occurring regions that are called a classification window. The proposed window-based classification method is evaluated with a water leakage experiment in which the data has been measured in laboratory experiments. In these experiments, the proposed detection and classification method based on wireless signal network can detect and classify subsurface events.

UDoNC: An Algorithm for Identifying Essential Proteins Based on Protein Domains and Protein-Protein Interaction Networks.

PubMed

Peng, Wei; Wang, Jianxin; Cheng, Yingjiao; Lu, Yu; Wu, Fangxiang; Pan, Yi

2015-01-01

Prediction of essential proteins which are crucial to an organism's survival is important for disease analysis and drug design, as well as the understanding of cellular life. The majority of prediction methods infer the possibility of proteins to be essential by using the network topology. However, these methods are limited to the completeness of available protein-protein interaction (PPI) data and depend on the network accuracy. To overcome these limitations, some computational methods have been proposed. However, seldom of them solve this problem by taking consideration of protein domains. In this work, we first analyze the correlation between the essentiality of proteins and their domain features based on data of 13 species. We find that the proteins containing more protein domain types which rarely occur in other proteins tend to be essential. Accordingly, we propose a new prediction method, named UDoNC, by combining the domain features of proteins with their topological properties in PPI network. In UDoNC, the essentiality of proteins is decided by the number and the frequency of their protein domain types, as well as the essentiality of their adjacent edges measured by edge clustering coefficient. The experimental results on S. cerevisiae data show that UDoNC outperforms other existing methods in terms of area under the curve (AUC). Additionally, UDoNC can also perform well in predicting essential proteins on data of E. coli.

DLG5 connects cell polarity and Hippo signaling protein networks by linking PAR-1 with MST1/2

PubMed Central

Kwan, Julian; Sczaniecka, Anna; Heidary Arash, Emad; Nguyen, Liem; Chen, Chia-Chun; Ratkovic, Srdjana; Klezovitch, Olga; Attisano, Liliana; McNeill, Helen; Emili, Andrew; Vasioukhin, Valeri

2016-01-01

Disruption of apical–basal polarity is implicated in developmental disorders and cancer; however, the mechanisms connecting cell polarity proteins with intracellular signaling pathways are largely unknown. We determined previously that membrane-associated guanylate kinase (MAGUK) protein discs large homolog 5 (DLG5) functions in cell polarity and regulates cellular proliferation and differentiation via undefined mechanisms. We report here that DLG5 functions as an evolutionarily conserved scaffold and negative regulator of Hippo signaling, which controls organ size through the modulation of cell proliferation and differentiation. Affinity purification/mass spectrometry revealed a critical role of DLG5 in the formation of protein assemblies containing core Hippo kinases mammalian ste20 homologs 1/2 (MST1/2) and Par-1 polarity proteins microtubule affinity-regulating kinases 1/2/3 (MARK1/2/3). Consistent with this finding, Hippo signaling is markedly hyperactive in mammalian Dlg5−/− tissues and cells in vivo and ex vivo and in Drosophila upon dlg5 knockdown. Conditional deletion of Mst1/2 fully rescued the phenotypes of brain-specific Dlg5 knockout mice. Dlg5 also interacts genetically with Hippo effectors Yap1/Taz. Mechanistically, we show that DLG5 inhibits the association between MST1/2 and large tumor suppressor homologs 1/2 (LATS1/2), uses its scaffolding function to link MST1/2 with MARK3, and inhibits MST1/2 kinase activity. These data reveal a direct connection between cell polarity proteins and Hippo, which is essential for proper development of multicellular organisms. PMID:28087714

Clustering and Network Analysis of Reverse Phase Protein Array Data.

PubMed

Byron, Adam

2017-01-01

Molecular profiling of proteins and phosphoproteins using a reverse phase protein array (RPPA) platform, with a panel of target-specific antibodies, enables the parallel, quantitative proteomic analysis of many biological samples in a microarray format. Hence, RPPA analysis can generate a high volume of multidimensional data that must be effectively interrogated and interpreted. A range of computational techniques for data mining can be applied to detect and explore data structure and to form functional predictions from large datasets. Here, two approaches for the computational analysis of RPPA data are detailed: the identification of similar patterns of protein expression by hierarchical cluster analysis and the modeling of protein interactions and signaling relationships by network analysis. The protocols use freely available, cross-platform software, are easy to implement, and do not require any programming expertise. Serving as data-driven starting points for further in-depth analysis, validation, and biological experimentation, these and related bioinformatic approaches can accelerate the functional interpretation of RPPA data.

Endocytosis of the seven-transmembrane RGS1 protein activates G-protein-coupled signalling in Arabidopsis.

PubMed

Urano, Daisuke; Phan, Nguyen; Jones, Janice C; Yang, Jing; Huang, Jirong; Grigston, Jeffrey; Taylor, J Philip; Jones, Alan M

2012-10-01

Signal transduction typically begins by ligand-dependent activation of a concomitant partner that is otherwise in its resting state. However, in cases where signal activation is constitutive by default, the mechanism of regulation is unknown. The Arabidopsis thaliana heterotrimeric Gα protein self-activates without accessory proteins, and is kept in its resting state by the negative regulator, AtRGS1 (regulator of G-protein signalling 1), which is the prototype of a seven-transmembrane receptor fused with an RGS domain. Endocytosis of AtRGS1 by ligand-dependent endocytosis physically uncouples the GTPase-accelerating activity of AtRGS1 from the Gα protein, permitting sustained activation. Phosphorylation of AtRGS1 by AtWNK8 kinase causes AtRGS1 endocytosis, required for both G-protein-mediated sugar signalling and cell proliferation. In animals, receptor endocytosis results in signal desensitization, whereas in plants, endocytosis results in signal activation. These findings reveal how different organisms rearrange a regulatory system to result in opposite outcomes using similar phosphorylation-dependent endocytosis mechanisms.

Signalling Network Construction for Modelling Plant Defence Response

PubMed Central

Miljkovic, Dragana; Stare, Tjaša; Mozetič, Igor; Podpečan, Vid; Petek, Marko; Witek, Kamil; Dermastia, Marina; Lavrač, Nada; Gruden, Kristina

2012-01-01

Plant defence signalling response against various pathogens, including viruses, is a complex phenomenon. In resistant interaction a plant cell perceives the pathogen signal, transduces it within the cell and performs a reprogramming of the cell metabolism leading to the pathogen replication arrest. This work focuses on signalling pathways crucial for the plant defence response, i.e., the salicylic acid, jasmonic acid and ethylene signal transduction pathways, in the Arabidopsis thaliana model plant. The initial signalling network topology was constructed manually by defining the representation formalism, encoding the information from public databases and literature, and composing a pathway diagram. The manually constructed network structure consists of 175 components and 387 reactions. In order to complement the network topology with possibly missing relations, a new approach to automated information extraction from biological literature was developed. This approach, named Bio3graph, allows for automated extraction of biological relations from the literature, resulting in a set of (component1, reaction, component2) triplets and composing a graph structure which can be visualised, compared to the manually constructed topology and examined by the experts. Using a plant defence response vocabulary of components and reaction types, Bio3graph was applied to a set of 9,586 relevant full text articles, resulting in 137 newly detected reactions between the components. Finally, the manually constructed topology and the new reactions were merged to form a network structure consisting of 175 components and 524 reactions. The resulting pathway diagram of plant defence signalling represents a valuable source for further computational modelling and interpretation of omics data. The developed Bio3graph approach, implemented as an executable language processing and graph visualisation workflow, is publically available at http://ropot.ijs.si/bio3graph/and can be utilised for

Inflammatory Mediators Alter the Astrocyte Transcriptome and Calcium Signaling Elicited by Multiple G-Protein-Coupled Receptors

PubMed Central

Hamby, Mary E.; Coppola, Giovanni; Ao, Yan; Geschwind, Daniel H.; Khakh, Baljit S.; Sofroniew, Michael V.

2012-01-01

Inflammation features in CNS disorders such as stroke, trauma, neurodegeneration, infection, and autoimmunity in which astrocytes play critical roles. To elucidate how inflammatory mediators alter astrocyte functions, we examined effects of transforming growth factor-β1 (TGF-β1), lipopolysaccharide (LPS), and interferon-gamma (IFNγ), alone and in combination, on purified, mouse primary cortical astrocyte cultures. We used microarrays to conduct whole-genome expression profiling, and measured calcium signaling, which is implicated in mediating dynamic astrocyte functions. Combinatorial exposure to TGF-β1, LPS, and IFNγ significantly modulated astrocyte expression of >6800 gene probes, including >380 synergistic changes not predicted by summing individual treatment effects. Bioinformatic analyses revealed significantly and markedly upregulated molecular networks and pathways associated in particular with immune signaling and regulation of cell injury, death, growth, and proliferation. Highly regulated genes included chemokines, growth factors, enzymes, channels, transporters, and intercellular and intracellular signal transducers. Notably, numerous genes for G-protein-coupled receptors (GPCRs) and G-protein effectors involved in calcium signaling were significantly regulated, mostly down (for example, Cxcr4, Adra2a, Ednra, P2y1, Gnao1, Gng7), but some up (for example, P2y14, P2y6, Ccrl2, Gnb4). We tested selected cases and found that changes in GPCR gene expression were accompanied by significant, parallel changes in astrocyte calcium signaling evoked by corresponding GPCR-specific ligands. These findings identify pronounced changes in the astrocyte transcriptome induced by TGF-β1, LPS, and IFNγ, and show that these inflammatory stimuli upregulate astrocyte molecular networks associated with immune- and injury-related functions and significantly alter astrocyte calcium signaling stimulated by multiple GPCRs. PMID:23077035

A comprehensive map of the mTOR signaling network

PubMed Central

Caron, Etienne; Ghosh, Samik; Matsuoka, Yukiko; Ashton-Beaucage, Dariel; Therrien, Marc; Lemieux, Sébastien; Perreault, Claude; Roux, Philippe P; Kitano, Hiroaki

2010-01-01

The mammalian target of rapamycin (mTOR) is a central regulator of cell growth and proliferation. mTOR signaling is frequently dysregulated in oncogenic cells, and thus an attractive target for anticancer therapy. Using CellDesigner, a modeling support software for graphical notation, we present herein a comprehensive map of the mTOR signaling network, which includes 964 species connected by 777 reactions. The map complies with both the systems biology markup language (SBML) and graphical notation (SBGN) for computational analysis and graphical representation, respectively. As captured in the mTOR map, we review and discuss our current understanding of the mTOR signaling network and highlight the impact of mTOR feedback and crosstalk regulations on drug-based cancer therapy. This map is available on the Payao platform, a Web 2.0 based community-wide interactive process for creating more accurate and information-rich databases. Thus, this comprehensive map of the mTOR network will serve as a tool to facilitate systems-level study of up-to-date mTOR network components and signaling events toward the discovery of novel regulatory processes and therapeutic strategies for cancer. PMID:21179025

The signaling petri net-based simulator: a non-parametric strategy for characterizing the dynamics of cell-specific signaling networks.

PubMed

Ruths, Derek; Muller, Melissa; Tseng, Jen-Te; Nakhleh, Luay; Ram, Prahlad T

2008-02-29

Reconstructing cellular signaling networks and understanding how they work are major endeavors in cell biology. The scale and complexity of these networks, however, render their analysis using experimental biology approaches alone very challenging. As a result, computational methods have been developed and combined with experimental biology approaches, producing powerful tools for the analysis of these networks. These computational methods mostly fall on either end of a spectrum of model parameterization. On one end is a class of structural network analysis methods; these typically use the network connectivity alone to generate hypotheses about global properties. On the other end is a class of dynamic network analysis methods; these use, in addition to the connectivity, kinetic parameters of the biochemical reactions to predict the network's dynamic behavior. These predictions provide detailed insights into the properties that determine aspects of the network's structure and behavior. However, the difficulty of obtaining numerical values of kinetic parameters is widely recognized to limit the applicability of this latter class of methods. Several researchers have observed that the connectivity of a network alone can provide significant insights into its dynamics. Motivated by this fundamental observation, we present the signaling Petri net, a non-parametric model of cellular signaling networks, and the signaling Petri net-based simulator, a Petri net execution strategy for characterizing the dynamics of signal flow through a signaling network using token distribution and sampling. The result is a very fast method, which can analyze large-scale networks, and provide insights into the trends of molecules' activity-levels in response to an external stimulus, based solely on the network's connectivity. We have implemented the signaling Petri net-based simulator in the PathwayOracle toolkit, which is publicly available at http://bioinfo.cs.rice.edu/pathwayoracle. Using

The Signaling Petri Net-Based Simulator: A Non-Parametric Strategy for Characterizing the Dynamics of Cell-Specific Signaling Networks

PubMed Central

Ruths, Derek; Muller, Melissa; Tseng, Jen-Te; Nakhleh, Luay; Ram, Prahlad T.

2008-01-01

Reconstructing cellular signaling networks and understanding how they work are major endeavors in cell biology. The scale and complexity of these networks, however, render their analysis using experimental biology approaches alone very challenging. As a result, computational methods have been developed and combined with experimental biology approaches, producing powerful tools for the analysis of these networks. These computational methods mostly fall on either end of a spectrum of model parameterization. On one end is a class of structural network analysis methods; these typically use the network connectivity alone to generate hypotheses about global properties. On the other end is a class of dynamic network analysis methods; these use, in addition to the connectivity, kinetic parameters of the biochemical reactions to predict the network's dynamic behavior. These predictions provide detailed insights into the properties that determine aspects of the network's structure and behavior. However, the difficulty of obtaining numerical values of kinetic parameters is widely recognized to limit the applicability of this latter class of methods. Several researchers have observed that the connectivity of a network alone can provide significant insights into its dynamics. Motivated by this fundamental observation, we present the signaling Petri net, a non-parametric model of cellular signaling networks, and the signaling Petri net-based simulator, a Petri net execution strategy for characterizing the dynamics of signal flow through a signaling network using token distribution and sampling. The result is a very fast method, which can analyze large-scale networks, and provide insights into the trends of molecules' activity-levels in response to an external stimulus, based solely on the network's connectivity. We have implemented the signaling Petri net-based simulator in the PathwayOracle toolkit, which is publicly available at http://bioinfo.cs.rice.edu/pathwayoracle. Using

Filtering Gene Ontology semantic similarity for identifying protein complexes in large protein interaction networks.

PubMed

Wang, Jian; Xie, Dong; Lin, Hongfei; Yang, Zhihao; Zhang, Yijia

2012-06-21

Many biological processes recognize in particular the importance of protein complexes, and various computational approaches have been developed to identify complexes from protein-protein interaction (PPI) networks. However, high false-positive rate of PPIs leads to challenging identification. A protein semantic similarity measure is proposed in this study, based on the ontology structure of Gene Ontology (GO) terms and GO annotations to estimate the reliability of interactions in PPI networks. Interaction pairs with low GO semantic similarity are removed from the network as unreliable interactions. Then, a cluster-expanding algorithm is used to detect complexes with core-attachment structure on filtered network. Our method is applied to three different yeast PPI networks. The effectiveness of our method is examined on two benchmark complex datasets. Experimental results show that our method performed better than other state-of-the-art approaches in most evaluation metrics. The method detects protein complexes from large scale PPI networks by filtering GO semantic similarity. Removing interactions with low GO similarity significantly improves the performance of complex identification. The expanding strategy is also effective to identify attachment proteins of complexes.

Rap G protein signal in normal and disordered lymphohematopoiesis

DOE Office of Scientific and Technical Information (OSTI.GOV)

Minato, Nagahiro, E-mail: minato@imm.med.kyoto-u.ac.jp

2013-09-10

Rap proteins (Rap1, Rap2a, b, c) are small molecular weight GTPases of the Ras family. Rap G proteins mediate diverse cellular events such as cell adhesion, proliferation, and gene activation through various signaling pathways. Activation of Rap signal is regulated tightly by several specific regulatory proteins including guanine nucleotide exchange factors and GTPase-activating proteins. Beyond cell biological studies, increasing attempts have been made in the past decade to define the roles of Rap signal in specific functions of normal tissue systems as well as in cancer. In the immune and hematopoietic systems, Rap signal plays crucial roles in the developmentmore » and function of essentially all lineages of lymphocytes and hematopoietic cells, and importantly, deregulated Rap signal may lead to unique pathological conditions depending on the affected cell types, including various types of leukemia and autoimmunity. The phenotypical studies have unveiled novel, even unexpected functional aspects of Rap signal in cells from a variety of tissues, providing potentially important clues for controlling human diseases, including malignancy.« less

Predicting protein complex geometries with a neural network.

PubMed

Chae, Myong-Ho; Krull, Florian; Lorenzen, Stephan; Knapp, Ernst-Walter

2010-03-01

A major challenge of the protein docking problem is to define scoring functions that can distinguish near-native protein complex geometries from a large number of non-native geometries (decoys) generated with noncomplexed protein structures (unbound docking). In this study, we have constructed a neural network that employs the information from atom-pair distance distributions of a large number of decoys to predict protein complex geometries. We found that docking prediction can be significantly improved using two different types of polar hydrogen atoms. To train the neural network, 2000 near-native decoys of even distance distribution were used for each of the 185 considered protein complexes. The neural network normalizes the information from different protein complexes using an additional protein complex identity input neuron for each complex. The parameters of the neural network were determined such that they mimic a scoring funnel in the neighborhood of the native complex structure. The neural network approach avoids the reference state problem, which occurs in deriving knowledge-based energy functions for scoring. We show that a distance-dependent atom pair potential performs much better than a simple atom-pair contact potential. We have compared the performance of our scoring function with other empirical and knowledge-based scoring functions such as ZDOCK 3.0, ZRANK, ITScore-PP, EMPIRE, and RosettaDock. In spite of the simplicity of the method and its functional form, our neural network-based scoring function achieves a reasonable performance in rigid-body unbound docking of proteins. Proteins 2010. (c) 2009 Wiley-Liss, Inc.

Conformational transition in signal transduction: metastable states and transition pathways in the activation of a signaling protein.

PubMed

Banerjee, Rahul; Yan, Honggao; Cukier, Robert I

2015-06-04

Signal transduction is of vital importance to the growth and adaptation of living organisms. The key to understand mechanisms of biological signal transduction is elucidation of the conformational dynamics of its signaling proteins, as the activation of a signaling protein is fundamentally a process of conformational transition from an inactive to an active state. A predominant form of signal transduction for bacterial sensing of environmental changes in the wild or inside their hosts is a variety of two-component systems, in which the conformational transition of a response regulator (RR) from an inactive to an active state initiates responses to the environmental changes. Here, RR activation has been investigated using RR468 as a model system by extensive unbiased all-atom molecular dynamics (MD) simulations in explicit solvent, starting from snapshots along a targeted MD trajectory that covers the conformational transition. Markov state modeling, transition path theory, and geometric analyses of the wealth of the MD data have provided a comprehensive description of the RR activation. It involves a network of metastable states, with one metastable state essentially the same as the inactive state and another very similar to the active state that are connected via a small set of intermediates. Five major pathways account for >75% of the fluxes of the conformational transition from the inactive to the active-like state. The thermodynamic stability of the states and the activation barriers between states are found, to identify rate-limiting steps. The conformal transition is initiated predominantly by movements of the β3α3 loop, followed by movements of the β4α4-loop and neighboring α4 helix region, and capped by additional movements of the β3α3 loop. A number of transient hydrophobic and hydrogen bond interactions are revealed, and they may be important for the conformational transition.

A conserved signaling network monitors delivery of sphingolipids to the plasma membrane in budding yeast

PubMed Central

Clarke, Jesse; Dephoure, Noah; Horecka, Ira; Gygi, Steven; Kellogg, Douglas

2017-01-01

In budding yeast, cell cycle progression and ribosome biogenesis are dependent on plasma membrane growth, which ensures that events of cell growth are coordinated with each other and with the cell cycle. However, the signals that link the cell cycle and ribosome biogenesis to membrane growth are poorly understood. Here we used proteome-wide mass spectrometry to systematically discover signals associated with membrane growth. The results suggest that membrane trafficking events required for membrane growth generate sphingolipid-dependent signals. A conserved signaling network appears to play an essential role in signaling by responding to delivery of sphingolipids to the plasma membrane. In addition, sphingolipid-dependent signals control phosphorylation of protein kinase C (Pkc1), which plays an essential role in the pathways that link the cell cycle and ribosome biogenesis to membrane growth. Together these discoveries provide new clues as to how growth-­dependent signals control cell growth and the cell cycle. PMID:28794263

Primary Respiratory Chain Disease Causes Tissue-Specific Dysregulation of the Global Transcriptome and Nutrient-Sensing Signaling Network

PubMed Central

Zhang, Zhe; Tsukikawa, Mai; Peng, Min; Polyak, Erzsebet; Nakamaru-Ogiso, Eiko; Ostrovsky, Julian; McCormack, Shana; Place, Emily; Clarke, Colleen; Reiner, Gail; McCormick, Elizabeth; Rappaport, Eric; Haas, Richard; Baur, Joseph A.; Falk, Marni J.

2013-01-01

Primary mitochondrial respiratory chain (RC) diseases are heterogeneous in etiology and manifestations but collectively impair cellular energy metabolism. Mechanism(s) by which RC dysfunction causes global cellular sequelae are poorly understood. To identify a common cellular response to RC disease, integrated gene, pathway, and systems biology analyses were performed in human primary RC disease skeletal muscle and fibroblast transcriptomes. Significant changes were evident in muscle across diverse RC complex and genetic etiologies that were consistent with prior reports in other primary RC disease models and involved dysregulation of genes involved in RNA processing, protein translation, transport, and degradation, and muscle structure. Global transcriptional and post-transcriptional dysregulation was also found to occur in a highly tissue-specific fashion. In particular, RC disease muscle had decreased transcription of cytosolic ribosomal proteins suggestive of reduced anabolic processes, increased transcription of mitochondrial ribosomal proteins, shorter 5′-UTRs that likely improve translational efficiency, and stabilization of 3′-UTRs containing AU-rich elements. RC disease fibroblasts showed a strikingly similar pattern of global transcriptome dysregulation in a reverse direction. In parallel with these transcriptional effects, RC disease dysregulated the integrated nutrient-sensing signaling network involving FOXO, PPAR, sirtuins, AMPK, and mTORC1, which collectively sense nutrient availability and regulate cellular growth. Altered activities of central nodes in the nutrient-sensing signaling network were validated by phosphokinase immunoblot analysis in RC inhibited cells. Remarkably, treating RC mutant fibroblasts with nicotinic acid to enhance sirtuin and PPAR activity also normalized mTORC1 and AMPK signaling, restored NADH/NAD+ redox balance, and improved cellular respiratory capacity. These data specifically highlight a common pathogenesis

Protein S-glutathionlyation links energy metabolism to redox signaling in mitochondria

PubMed Central

Mailloux, Ryan J.; Treberg, Jason R.

2015-01-01

At its core mitochondrial function relies on redox reactions. Electrons stripped from nutrients are used to form NADH and NADPH, electron carriers that are similar in structure but support different functions. NADH supports ATP production but also generates reactive oxygen species (ROS), superoxide (O2·-) and hydrogen peroxide (H2O2). NADH-driven ROS production is counterbalanced by NADPH which maintains antioxidants in an active state. Mitochondria rely on a redox buffering network composed of reduced glutathione (GSH) and peroxiredoxins (Prx) to quench ROS generated by nutrient metabolism. As H2O2 is quenched, NADPH is expended to reactivate antioxidant networks and reset the redox environment. Thus, the mitochondrial redox environment is in a constant state of flux reflecting changes in nutrient and ROS metabolism. Changes in redox environment can modulate protein function through oxidation of protein cysteine thiols. Typically cysteine oxidation is considered to be mediated by H2O2 which oxidizes protein thiols (SH) forming sulfenic acid (SOH). However, problems begin to emerge when one critically evaluates the regulatory function of SOH. Indeed SOH formation is slow, non-specific, and once formed SOH reacts rapidly with a variety of molecules. By contrast, protein S-glutathionylation (PGlu) reactions involve the conjugation and removal of glutathione moieties from modifiable cysteine residues. PGlu reactions are driven by fluctuations in the availability of GSH and oxidized glutathione (GSSG) and thus should be exquisitely sensitive to changes ROS flux due to shifts in the glutathione pool in response to varying H2O2 availability. Here, we propose that energy metabolism-linked redox signals originating from mitochondria are mediated indirectly by H2O2 through the GSH redox buffering network in and outside mitochondria. This proposal is based on several observations that have shown that unlike other redox modifications PGlu reactions fulfill the requisite

Protein S-glutathionlyation links energy metabolism to redox signaling in mitochondria.

PubMed

Mailloux, Ryan J; Treberg, Jason R

2016-08-01

At its core mitochondrial function relies on redox reactions. Electrons stripped from nutrients are used to form NADH and NADPH, electron carriers that are similar in structure but support different functions. NADH supports ATP production but also generates reactive oxygen species (ROS), superoxide (O2(·-)) and hydrogen peroxide (H2O2). NADH-driven ROS production is counterbalanced by NADPH which maintains antioxidants in an active state. Mitochondria rely on a redox buffering network composed of reduced glutathione (GSH) and peroxiredoxins (Prx) to quench ROS generated by nutrient metabolism. As H2O2 is quenched, NADPH is expended to reactivate antioxidant networks and reset the redox environment. Thus, the mitochondrial redox environment is in a constant state of flux reflecting changes in nutrient and ROS metabolism. Changes in redox environment can modulate protein function through oxidation of protein cysteine thiols. Typically cysteine oxidation is considered to be mediated by H2O2 which oxidizes protein thiols (SH) forming sulfenic acid (SOH). However, problems begin to emerge when one critically evaluates the regulatory function of SOH. Indeed SOH formation is slow, non-specific, and once formed SOH reacts rapidly with a variety of molecules. By contrast, protein S-glutathionylation (PGlu) reactions involve the conjugation and removal of glutathione moieties from modifiable cysteine residues. PGlu reactions are driven by fluctuations in the availability of GSH and oxidized glutathione (GSSG) and thus should be exquisitely sensitive to changes ROS flux due to shifts in the glutathione pool in response to varying H2O2 availability. Here, we propose that energy metabolism-linked redox signals originating from mitochondria are mediated indirectly by H2O2 through the GSH redox buffering network in and outside mitochondria. This proposal is based on several observations that have shown that unlike other redox modifications PGlu reactions fulfill the requisite

A parapoxviral virion protein targets the retinoblastoma protein to inhibit NF-κB signaling

PubMed Central

Nagendraprabhu, Ponnuraj; Khatiwada, Sushil; Chaulagain, Sabal

2017-01-01

Poxviruses have evolved multiple strategies to subvert signaling by Nuclear Factor κB (NF-κB), a crucial regulator of host innate immune responses. Here, we describe an orf virus (ORFV) virion-associated protein, ORFV119, which inhibits NF-κB signaling very early in infection (≤ 30 min post infection). ORFV119 NF-κB inhibitory activity was found unimpaired upon translation inhibition, suggesting that virion ORFV119 alone is responsible for early interference in signaling. A C-terminal LxCxE motif in ORFV119 enabled the protein to interact with the retinoblastoma protein (pRb) a multifunctional protein best known for its tumor suppressor activity. Notably, experiments using a recombinant virus containing an ORFV119 mutation which abrogates its interaction with pRb together with experiments performed in cells lacking or with reduced pRb levels indicate that ORFV119 mediated inhibition of NF-κB signaling is largely pRb dependent. ORFV119 was shown to inhibit IKK complex activation early in infection. Consistent with IKK inhibition, ORFV119 also interacted with TNF receptor associated factor 2 (TRAF2), an adaptor protein recruited to signaling complexes upstream of IKK in infected cells. ORFV119-TRAF2 interaction was enhanced in the presence of pRb, suggesting that ORFV119-pRb complex is required for efficient interaction with TRAF2. Additionally, transient expression of ORFV119 in uninfected cells was sufficient to inhibit TNFα-induced IKK activation and NF-κB signaling, indicating that no other viral proteins are required for the effect. Infection of sheep with ORFV lacking the ORFV119 gene led to attenuated disease phenotype, indicating that ORFV119 contributes to virulence in the natural host. ORFV119 represents the first poxviral protein to interfere with NF-κB signaling through interaction with pRb. PMID:29244863

Oncogenic Signaling by Leukemia-Associated Mutant Cbl Proteins

PubMed Central

Nadeau, Scott; An, Wei; Palermo, Nick; Feng, Dan; Ahmad, Gulzar; Dong, Lin; Borgstahl, Gloria E. O.; Natarajan, Amarnath; Naramura, Mayumi; Band, Vimla; Band, Hamid

2013-01-01

Members of the Cbl protein family (Cbl, Cbl-b, and Cbl-c) are E3 ubiquitin ligases that have emerged as critical negative regulators of protein tyrosine kinase (PTK) signaling. This function reflects their ability to directly interact with activated PTKs and to target them as well as their associated signaling components for ubiquitination. Given the critical roles of PTK signaling in driving oncogenesis, recent studies in animal models and genetic analyses in human cancer have firmly established that Cbl proteins function as tumor suppressors. Missense mutations or small in-frame deletions within the regions of Cbl protein that are essential for its E3 activity have been identified in nearly 5% of leukemia patients with myelodysplastic/myeloproliferative disorders. Based on evidence from cell culture studies, in vivo models and clinical data, we discuss the potential signaling mechanisms of mutant Cbl-driven oncogenesis. Mechanistic insights into oncogenic Cbl mutants and associated animal models are likely to enhance our understanding of normal hematopoietic stem cell homeostasis and provide avenues for targeted therapy of mutant Cbl-driven cancers. PMID:23997989

PodNet, a protein-protein interaction network of the podocyte.

PubMed

Warsow, Gregor; Endlich, Nicole; Schordan, Eric; Schordan, Sandra; Chilukoti, Ravi K; Homuth, Georg; Moeller, Marcus J; Fuellen, Georg; Endlich, Karlhans

2013-07-01

Interactions between proteins crucially determine cellular structure and function. Differential analysis of the interactome may help elucidate molecular mechanisms during disease development; however, this analysis necessitates mapping of expression data on protein-protein interaction networks. These networks do not exist for the podocyte; therefore, we built PodNet, a literature-based mouse podocyte network in Cytoscape format. Using database protein-protein interactions, we expanded PodNet to XPodNet with enhanced connectivity. In order to test the performance of XPodNet in differential interactome analysis, we examined podocyte developmental differentiation and the effect of cell culture. Transcriptomes of podocytes in 10 different states were mapped on XPodNet and analyzed with the Cytoscape plugin ExprEssence, based on the law of mass action. Interactions between slit diaphragm proteins are most significantly upregulated during podocyte development and most significantly downregulated in culture. On the other hand, our analysis revealed that interactions lost during podocyte differentiation are not regained in culture, suggesting a loss rather than a reversal of differentiation for podocytes in culture. Thus, we have developed PodNet as a valuable tool for differential interactome analysis in podocytes, and we have identified established and unexplored regulated interactions in developing and cultured podocytes.

Subsurface Event Detection and Classification Using Wireless Signal Networks

PubMed Central

Yoon, Suk-Un; Ghazanfari, Ehsan; Cheng, Liang; Pamukcu, Sibel; Suleiman, Muhannad T.

2012-01-01

Subsurface environment sensing and monitoring applications such as detection of water intrusion or a landslide, which could significantly change the physical properties of the host soil, can be accomplished using a novel concept, Wireless Signal Networks (WSiNs). The wireless signal networks take advantage of the variations of radio signal strength on the distributed underground sensor nodes of WSiNs to monitor and characterize the sensed area. To characterize subsurface environments for event detection and classification, this paper provides a detailed list and experimental data of soil properties on how radio propagation is affected by soil properties in subsurface communication environments. Experiments demonstrated that calibrated wireless signal strength variations can be used as indicators to sense changes in the subsurface environment. The concept of WSiNs for the subsurface event detection is evaluated with applications such as detection of water intrusion, relative density change, and relative motion using actual underground sensor nodes. To classify geo-events using the measured signal strength as a main indicator of geo-events, we propose a window-based minimum distance classifier based on Bayesian decision theory. The window-based classifier for wireless signal networks has two steps: event detection and event classification. With the event detection, the window-based classifier classifies geo-events on the event occurring regions that are called a classification window. The proposed window-based classification method is evaluated with a water leakage experiment in which the data has been measured in laboratory experiments. In these experiments, the proposed detection and classification method based on wireless signal network can detect and classify subsurface events. PMID:23202191

Dense fibrillar collagen is a potent inducer of invadopodia via a specific signaling network

PubMed Central

Swatkoski, Stephen; Matsumoto, Kazue; Campbell, Catherine B.; Petrie, Ryan J.; Dimitriadis, Emilios K.; Li, Xin; Mueller, Susette C.; Bugge, Thomas H.; Gucek, Marjan

2015-01-01

Cell interactions with the extracellular matrix (ECM) can regulate multiple cellular activities and the matrix itself in dynamic, bidirectional processes. One such process is local proteolytic modification of the ECM. Invadopodia of tumor cells are actin-rich proteolytic protrusions that locally degrade matrix molecules and mediate invasion. We report that a novel high-density fibrillar collagen (HDFC) matrix is a potent inducer of invadopodia, both in carcinoma cell lines and in primary human fibroblasts. In carcinoma cells, HDFC matrix induced formation of invadopodia via a specific integrin signaling pathway that did not require growth factors or even altered gene and protein expression. In contrast, phosphoproteomics identified major changes in a complex phosphosignaling network with kindlin2 serine phosphorylation as a key regulatory element. This kindlin2-dependent signal transduction network was required for efficient induction of invadopodia on dense fibrillar collagen and for local degradation of collagen. This novel phosphosignaling mechanism regulates cell surface invadopodia via kindlin2 for local proteolytic remodeling of the ECM. PMID:25646088

Multi-agent-based bio-network for systems biology: protein-protein interaction network as an example.

PubMed

Ren, Li-Hong; Ding, Yong-Sheng; Shen, Yi-Zhen; Zhang, Xiang-Feng

2008-10-01

Recently, a collective effort from multiple research areas has been made to understand biological systems at the system level. This research requires the ability to simulate particular biological systems as cells, organs, organisms, and communities. In this paper, a novel bio-network simulation platform is proposed for system biology studies by combining agent approaches. We consider a biological system as a set of active computational components interacting with each other and with an external environment. Then, we propose a bio-network platform for simulating the behaviors of biological systems and modelling them in terms of bio-entities and society-entities. As a demonstration, we discuss how a protein-protein interaction (PPI) network can be seen as a society of autonomous interactive components. From interactions among small PPI networks, a large PPI network can emerge that has a remarkable ability to accomplish a complex function or task. We also simulate the evolution of the PPI networks by using the bio-operators of the bio-entities. Based on the proposed approach, various simulators with different functions can be embedded in the simulation platform, and further research can be done from design to development, including complexity validation of the biological system.

Ras plasma membrane signalling platforms

PubMed Central

2005-01-01

The plasma membrane is a complex, dynamic structure that provides platforms for the assembly of many signal transduction pathways. These platforms have the capacity to impose an additional level of regulation on cell signalling networks. In this review, we will consider specifically how Ras proteins interact with the plasma membrane. The focus will be on recent studies that provide novel spatial and dynamic insights into the micro-environments that different Ras proteins utilize for signal transduction. We will correlate these recent studies suggesting Ras proteins might operate within a heterogeneous plasma membrane with earlier biochemical work on Ras signal transduction. PMID:15954863

PDZ Protein Regulation of G Protein-Coupled Receptor Trafficking and Signaling Pathways.

PubMed

Dunn, Henry A; Ferguson, Stephen S G

2015-10-01

G protein-coupled receptors (GPCRs) contribute to the regulation of every aspect of human physiology and are therapeutic targets for the treatment of numerous diseases. As a consequence, understanding the myriad of mechanisms controlling GPCR signaling and trafficking is essential for the development of new pharmacological strategies for the treatment of human pathologies. Of the many GPCR-interacting proteins, postsynaptic density protein of 95 kilodaltons, disc large, zona occludens-1 (PDZ) domain-containing proteins appear most abundant and have similarly been implicated in disease mechanisms. PDZ proteins play an important role in regulating receptor and channel protein localization within synapses and tight junctions and function to scaffold intracellular signaling protein complexes. In the current study, we review the known functional interactions between PDZ domain-containing proteins and GPCRs and provide insight into the potential mechanisms of action. These PDZ domain-containing proteins include the membrane-associated guanylate-like kinases [postsynaptic density protein of 95 kilodaltons; synapse-associated protein of 97 kilodaltons; postsynaptic density protein of 93 kilodaltons; synapse-associated protein of 102 kilodaltons; discs, large homolog 5; caspase activation and recruitment domain and membrane-associated guanylate-like kinase domain-containing protein 3; membrane protein, palmitoylated 3; calcium/calmodulin-dependent serine protein kinase; membrane-associated guanylate kinase protein (MAGI)-1, MAGI-2, and MAGI-3], Na(+)/H(+) exchanger regulatory factor proteins (NHERFs) (NHERF1, NHERF2, PDZ domain-containing kidney protein 1, and PDZ domain-containing kidney protein 2), Golgi-associated PDZ proteins (Gα-binding protein interacting protein, C-terminus and CFTR-associated ligand), PDZ domain-containing guanine nucleotide exchange factors (GEFs) 1 and 2, regulator of G protein signaling (RGS)-homology-RhoGEFs (PDZ domain-containing RhoGEF and

Hierarchical Feedback Modules and Reaction Hubs in Cell Signaling Networks

PubMed Central

Xu, Jianfeng; Lan, Yueheng

2015-01-01

Despite much effort, identification of modular structures and study of their organizing and functional roles remain a formidable challenge in molecular systems biology, which, however, is essential in reaching a systematic understanding of large-scale cell regulation networks and hence gaining capacity of exerting effective interference to cell activity. Combining graph theoretic methods with available dynamics information, we successfully retrieved multiple feedback modules of three important signaling networks. These feedbacks are structurally arranged in a hierarchical way and dynamically produce layered temporal profiles of output signals. We found that global and local feedbacks act in very different ways and on distinct features of the information flow conveyed by signal transduction but work highly coordinately to implement specific biological functions. The redundancy embodied with multiple signal-relaying channels and feedback controls bestow great robustness and the reaction hubs seated at junctions of different paths announce their paramount importance through exquisite parameter management. The current investigation reveals intriguing general features of the organization of cell signaling networks and their relevance to biological function, which may find interesting applications in analysis, design and control of bio-networks. PMID:25951347

Drug Target Protein-Protein Interaction Networks: A Systematic Perspective

PubMed Central

2017-01-01

The identification and validation of drug targets are crucial in biomedical research and many studies have been conducted on analyzing drug target features for getting a better understanding on principles of their mechanisms. But most of them are based on either strong biological hypotheses or the chemical and physical properties of those targets separately. In this paper, we investigated three main ways to understand the functional biomolecules based on the topological features of drug targets. There are no significant differences between targets and common proteins in the protein-protein interactions network, indicating the drug targets are neither hub proteins which are dominant nor the bridge proteins. According to some special topological structures of the drug targets, there are significant differences between known targets and other proteins. Furthermore, the drug targets mainly belong to three typical communities based on their modularity. These topological features are helpful to understand how the drug targets work in the PPI network. Particularly, it is an alternative way to predict potential targets or extract nontargets to test a new drug target efficiently and economically. By this way, a drug target's homologue set containing 102 potential target proteins is predicted in the paper. PMID:28691014

IP-FCM measures physiologic protein-protein interactions modulated by signal transduction and small-molecule drug inhibition.

PubMed

Smith, Stephen E P; Bida, Anya T; Davis, Tessa R; Sicotte, Hugues; Patterson, Steven E; Gil, Diana; Schrum, Adam G

2012-01-01

Protein-protein interactions (PPI) mediate the formation of intermolecular networks that control biological signaling. For this reason, PPIs are of outstanding interest in pharmacology, as they display high specificity and may represent a vast pool of potentially druggable targets. However, the study of physiologic PPIs can be limited by conventional assays that often have large sample requirements and relatively low sensitivity. Here, we build on a novel method, immunoprecipitation detected by flow cytometry (IP-FCM), to assess PPI modulation during either signal transduction or pharmacologic inhibition by two different classes of small-molecule compounds. First, we showed that IP-FCM can detect statistically significant differences in samples possessing a defined PPI change as low as 10%. This sensitivity allowed IP-FCM to detect a PPI that increases transiently during T cell signaling, the antigen-inducible interaction between ZAP70 and the T cell antigen receptor (TCR)/CD3 complex. In contrast, IP-FCM detected no ZAP70 recruitment when T cells were stimulated with antigen in the presence of the src-family kinase inhibitor, PP2. Further, we tested whether IP-FCM possessed sufficient sensitivity to detect the effect of a second, rare class of compounds called SMIPPI (small-molecule inhibitor of PPI). We found that the first-generation non-optimized SMIPPI, Ro-26-4550, inhibited the IL-2:CD25 interaction detected by IP-FCM. This inhibition was detectable using either a recombinant CD25-Fc chimera or physiologic full-length CD25 captured from T cell lysates. Thus, we demonstrate that IP-FCM is a sensitive tool for measuring physiologic PPIs that are modulated by signal transduction and pharmacologic inhibition.

Integrating In Silico Resources to Map a Signaling Network

PubMed Central

Liu, Hanqing; Beck, Tim N.; Golemis, Erica A.; Serebriiskii, Ilya G.

2013-01-01

The abundance of publicly available life science databases offer a wealth of information that can support interpretation of experimentally derived data and greatly enhance hypothesis generation. Protein interaction and functional networks are not simply new renditions of existing data: they provide the opportunity to gain insights into the specific physical and functional role a protein plays as part of the biological system. In this chapter, we describe different in silico tools that can quickly and conveniently retrieve data from existing data repositories and discuss how the available tools are best utilized for different purposes. While emphasizing protein-protein interaction databases (e.g., BioGrid and IntAct), we also introduce metasearch platforms such as STRING and GeneMANIA, pathway databases (e.g., BioCarta and Pathway Commons), text mining approaches (e.g., PubMed and Chilibot), and resources for drug-protein interactions, genetic information for model organisms and gene expression information based on microarray data mining. Furthermore, we provide a simple step-by-step protocol to building customized protein-protein interaction networks in Cytoscape, a powerful network assembly and visualization program, integrating data retrieved from these various databases. As we illustrate, generation of composite interaction networks enables investigators to extract significantly more information about a given biological system than utilization of a single database or sole reliance on primary literature. PMID:24233784

Atopic dermatitis-associated protein interaction network lead to new insights in chronic sulfur mustard skin lesion mechanisms.

PubMed

Amiri, Mojtaba; Jafari, Mohieddin; Azimzadeh Jamalkandi, Sadegh; Davoodi, Seyed-Masoud

2013-10-01

Chronic sulfur mustard skin lesions (CSMSLs) are the most common complications of sulfur mustard exposure; however, its mechanism is not completely understood.According to clinical signs, there are similarities between CSMSL and atopic dermatitis (AD). In this study, proteomic results of AD were reviewed and the AD-associated protein-protein interaction network (PIN) was analyzed. According to centrality measurements, 16 proteins were designated as pivotal elements in AD mechanisms. Interestingly, most of these proteins had been reported in some sulfur mustard-related studies in late and acute phases separately. Based on the gene enrichment analysis, aging, cell response to stress, cancer, Toll- and NOD-like receptor and apoptosis signaling pathways have the greatest impact on the disease. By the analysis of directed protein interaction networks, it is concluded that TNF, IL-6, AKT1, NOS3 and CDKN1A are the most important proteins. It is possible that these proteins play role in the shared complications of AD and CSMSL including xerosis and itching.

G protein signaling in the parasite Entamoeba histolytica

PubMed Central

Bosch, Dustin E; Siderovski, David P

2013-01-01

The parasite Entamoeba histolytica causes amebic colitis and systemic amebiasis. Among the known amebic factors contributing to pathogenesis are signaling pathways involving heterotrimeric and Ras superfamily G proteins. Here, we review the current knowledge of the roles of heterotrimeric G protein subunits, Ras, Rho and Rab GTPase families in E. histolytica pathogenesis, as well as of their downstream signaling effectors and nucleotide cycle regulators. Heterotrimeric G protein signaling likely modulates amebic motility and attachment to and killing of host cells, in part through activation of an RGS-RhoGEF (regulator of G protein signaling–Rho guanine nucleotide exchange factor) effector. Rho family GTPases, as well as RhoGEFs and Rho effectors (formins and p21-activated kinases) regulate the dynamic actin cytoskeleton of E. histolytica and associated pathogenesis-related cellular processes, such as migration, invasion, phagocytosis and evasion of the host immune response by surface receptor capping. A remarkably large family of 91 Rab GTPases has multiple roles in a complex amebic vesicular trafficking system required for phagocytosis and pinocytosis and secretion of known virulence factors, such as amebapores and cysteine proteases. Although much remains to be discovered, recent studies of G protein signaling in E. histolytica have enhanced our understanding of parasitic pathogenesis and have also highlighted possible targets for pharmacological manipulation. PMID:23519208

Computational study of noise in a large signal transduction network.

PubMed

Intosalmi, Jukka; Manninen, Tiina; Ruohonen, Keijo; Linne, Marja-Leena

2011-06-21

Biochemical systems are inherently noisy due to the discrete reaction events that occur in a random manner. Although noise is often perceived as a disturbing factor, the system might actually benefit from it. In order to understand the role of noise better, its quality must be studied in a quantitative manner. Computational analysis and modeling play an essential role in this demanding endeavor. We implemented a large nonlinear signal transduction network combining protein kinase C, mitogen-activated protein kinase, phospholipase A2, and β isoform of phospholipase C networks. We simulated the network in 300 different cellular volumes using the exact Gillespie stochastic simulation algorithm and analyzed the results in both the time and frequency domain. In order to perform simulations in a reasonable time, we used modern parallel computing techniques. The analysis revealed that time and frequency domain characteristics depend on the system volume. The simulation results also indicated that there are several kinds of noise processes in the network, all of them representing different kinds of low-frequency fluctuations. In the simulations, the power of noise decreased on all frequencies when the system volume was increased. We concluded that basic frequency domain techniques can be applied to the analysis of simulation results produced by the Gillespie stochastic simulation algorithm. This approach is suited not only to the study of fluctuations but also to the study of pure noise processes. Noise seems to have an important role in biochemical systems and its properties can be numerically studied by simulating the reacting system in different cellular volumes. Parallel computing techniques make it possible to run massive simulations in hundreds of volumes and, as a result, accurate statistics can be obtained from computational studies. © 2011 Intosalmi et al; licensee BioMed Central Ltd.

Neural Networks For Demodulation Of Phase-Modulated Signals

NASA Technical Reports Server (NTRS)

Altes, Richard A.

1995-01-01

Hopfield neural networks proposed for demodulating quadrature phase-shift-keyed (QPSK) signals carrying digital information. Networks solve nonlinear integral equations prior demodulation circuits cannot solve. Consists of set of N operational amplifiers connected in parallel, with weighted feedback from output terminal of each amplifier to input terminals of other amplifiers. Used to solve signal processing problems. Implemented as analog very-large-scale integrated circuit that achieves rapid convergence. Alternatively, implemented as digital simulation of such circuit. Also used to improve phase estimation performance over that of phase-locked loop.

Load-induced modulation of signal transduction networks.

PubMed

Jiang, Peng; Ventura, Alejandra C; Sontag, Eduardo D; Merajver, Sofia D; Ninfa, Alexander J; Del Vecchio, Domitilla

2011-10-11

Biological signal transduction networks are commonly viewed as circuits that pass along information--in the process amplifying signals, enhancing sensitivity, or performing other signal-processing tasks--to transcriptional and other components. Here, we report on a "reverse-causality" phenomenon, which we call load-induced modulation. Through a combination of analytical and experimental tools, we discovered that signaling was modulated, in a surprising way, by downstream targets that receive the signal and, in doing so, apply what in physics is called a load. Specifically, we found that non-intuitive changes in response dynamics occurred for a covalent modification cycle when load was present. Loading altered the response time of a system, depending on whether the activity of one of the enzymes was maximal and the other was operating at its minimal rate or whether both enzymes were operating at submaximal rates. These two conditions, which we call "limit regime" and "intermediate regime," were associated with increased or decreased response times, respectively. The bandwidth, the range of frequency in which the system can process information, decreased in the presence of load, suggesting that downstream targets participate in establishing a balance between noise-filtering capabilities and a circuit's ability to process high-frequency stimulation. Nodes in a signaling network are not independent relay devices, but rather are modulated by their downstream targets.

Evolution of Hormone Signaling Networks in Plant Defense.

PubMed

Berens, Matthias L; Berry, Hannah M; Mine, Akira; Argueso, Cristiana T; Tsuda, Kenichi

2017-08-04

Studies with model plants such as Arabidopsis thaliana have revealed that phytohormones are central regulators of plant defense. The intricate network of phytohormone signaling pathways enables plants to activate appropriate and effective defense responses against pathogens as well as to balance defense and growth. The timing of the evolution of most phytohormone signaling pathways seems to coincide with the colonization of land, a likely requirement for plant adaptations to the more variable terrestrial environments, which included the presence of pathogens. In this review, we explore the evolution of defense hormone signaling networks by combining the model plant-based knowledge about molecular components mediating phytohormone signaling and cross talk with available genome information of other plant species. We highlight conserved hubs in hormone cross talk and discuss evolutionary advantages of defense hormone cross talk. Finally, we examine possibilities of engineering hormone cross talk for improvement of plant fitness and crop production.

Trans-species learning of cellular signaling systems with bimodal deep belief networks

PubMed Central

Chen, Lujia; Cai, Chunhui; Chen, Vicky; Lu, Xinghua

2015-01-01

Motivation: Model organisms play critical roles in biomedical research of human diseases and drug development. An imperative task is to translate information/knowledge acquired from model organisms to humans. In this study, we address a trans-species learning problem: predicting human cell responses to diverse stimuli, based on the responses of rat cells treated with the same stimuli. Results: We hypothesized that rat and human cells share a common signal-encoding mechanism but employ different proteins to transmit signals, and we developed a bimodal deep belief network and a semi-restricted bimodal deep belief network to represent the common encoding mechanism and perform trans-species learning. These ‘deep learning’ models include hierarchically organized latent variables capable of capturing the statistical structures in the observed proteomic data in a distributed fashion. The results show that the models significantly outperform two current state-of-the-art classification algorithms. Our study demonstrated the potential of using deep hierarchical models to simulate cellular signaling systems. Availability and implementation: The software is available at the following URL: http://pubreview.dbmi.pitt.edu/TransSpeciesDeepLearning/. The data are available through SBV IMPROVER website, https://www.sbvimprover.com/challenge-2/overview, upon publication of the report by the organizers. Contact: xinghua@pitt.edu Supplementary information: Supplementary data are available at Bioinformatics online. PMID:25995230

Trans-species learning of cellular signaling systems with bimodal deep belief networks.

PubMed

Chen, Lujia; Cai, Chunhui; Chen, Vicky; Lu, Xinghua

2015-09-15

Model organisms play critical roles in biomedical research of human diseases and drug development. An imperative task is to translate information/knowledge acquired from model organisms to humans. In this study, we address a trans-species learning problem: predicting human cell responses to diverse stimuli, based on the responses of rat cells treated with the same stimuli. We hypothesized that rat and human cells share a common signal-encoding mechanism but employ different proteins to transmit signals, and we developed a bimodal deep belief network and a semi-restricted bimodal deep belief network to represent the common encoding mechanism and perform trans-species learning. These 'deep learning' models include hierarchically organized latent variables capable of capturing the statistical structures in the observed proteomic data in a distributed fashion. The results show that the models significantly outperform two current state-of-the-art classification algorithms. Our study demonstrated the potential of using deep hierarchical models to simulate cellular signaling systems. The software is available at the following URL: http://pubreview.dbmi.pitt.edu/TransSpeciesDeepLearning/. The data are available through SBV IMPROVER website, https://www.sbvimprover.com/challenge-2/overview, upon publication of the report by the organizers. xinghua@pitt.edu Supplementary data are available at Bioinformatics online. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Computational Framework for Analysis of Prey–Prey Associations in Interaction Proteomics Identifies Novel Human Protein–Protein Interactions and Networks

PubMed Central

Saha, Sudipto; Dazard, Jean-Eudes; Xu, Hua; Ewing, Rob M.

2013-01-01

Large-scale protein–protein interaction data sets have been generated for several species including yeast and human and have enabled the identification, quantification, and prediction of cellular molecular networks. Affinity purification-mass spectrometry (AP-MS) is the preeminent methodology for large-scale analysis of protein complexes, performed by immunopurifying a specific “bait” protein and its associated “prey” proteins. The analysis and interpretation of AP-MS data sets is, however, not straightforward. In addition, although yeast AP-MS data sets are relatively comprehensive, current human AP-MS data sets only sparsely cover the human interactome. Here we develop a framework for analysis of AP-MS data sets that addresses the issues of noise, missing data, and sparsity of coverage in the context of a current, real world human AP-MS data set. Our goal is to extend and increase the density of the known human interactome by integrating bait–prey and cocomplexed preys (prey–prey associations) into networks. Our framework incorporates a score for each identified protein, as well as elements of signal processing to improve the confidence of identified protein–protein interactions. We identify many protein networks enriched in known biological processes and functions. In addition, we show that integrated bait–prey and prey–prey interactions can be used to refine network topology and extend known protein networks. PMID:22845868

The Signaling Networks of the Herpesvirus Entry Mediator (TNFRSF14) in Immune Regulation

PubMed Central

Steinberg, Marcos; Cheung, Timothy C.; Ware, Carl F.

2012-01-01

Summary The tumor necrosis factor (TNF) receptor superfamily member herpesvirus entry mediator (HVEM) (TNFRSF14) regulates T-cell immune responses by activating both inflammatory and inhibitory signaling pathways. HVEM acts as both a receptor for the canonical TNF-related ligands, LIGHT [lymphotoxin-like, exhibits inducible expression, and competes with herpes simplex virus glycoprotein D for HVEM, a receptor expressed on T lymphocytes] and lymphotoxin-α, and as a ligand for the immunoglobulin superfamily proteins BTLA (B and T lymphocyte attenuator) and CD160, a feature distinguishing HVEM from other immune regulatory molecules. The ability of HVEM to interact with multiple ligands in distinct configurations creates a functionally diverse set of intrinsic and bidirectional signaling pathways that control both inflammatory and inhibitory responses. The HVEM system is integrated into the larger LTβR and TNFR network through extensive shared ligand and receptor usage. Experimental mouse models and human diseases indicate that dysregulation of HVEM network may contribute to autoimmune pathogenesis, making it an attractive target for drug intervention. PMID:22017438

Topological properties of complex networks in protein structures

NASA Astrophysics Data System (ADS)

Kim, Kyungsik; Jung, Jae-Won; Min, Seungsik

2014-03-01

We study topological properties of networks in structural classification of proteins. We model the native-state protein structure as a network made of its constituent amino-acids and their interactions. We treat four structural classes of proteins composed predominantly of α helices and β sheets and consider several proteins from each of these classes whose sizes range from amino acids of the Protein Data Bank. Particularly, we simulate and analyze the network metrics such as the mean degree, the probability distribution of degree, the clustering coefficient, the characteristic path length, the local efficiency, and the cost. This work was supported by the KMAR and DP under Grant WISE project (153-3100-3133-302-350).

Engineering Synthetic Proteins to Generate Ca2+ Signals in Mammalian Cells.

PubMed

Qudrat, Anam; Truong, Kevin

2017-03-17

The versatility of Ca 2+ signals allows it to regulate diverse cellular processes such as migration, apoptosis, motility and exocytosis. In some receptors (e.g., VEGFR2), Ca 2+ signals are generated upon binding their ligand(s) (e.g., VEGF-A). Here, we employed a design strategy to engineer proteins that generate a Ca 2+ signal upon binding various extracellular stimuli by creating fusions of protein domains that oligomerize to the transmembrane domain and the cytoplasmic tail of the VEGFR2. To test the strategy, we created chimeric proteins that generate Ca 2+ signals upon stimulation with various extracellular stimuli (e.g., rapamycin, EDTA or extracellular free Ca 2+ ). By coupling these chimeric proteins that generate Ca 2+ signals with proteins that respond to Ca 2+ signals, we rewired, for example, dynamic cellular blebbing to increases in extracellular free Ca 2+ . Thus, using this design strategy, it is possible to engineer proteins to generate a Ca 2+ signal to rewire a wide range of extracellular stimuli to a wide range of Ca 2+ -activated processes.

Noncanonical Gβ Gib2 is a scaffolding protein promoting cAMP signaling through functions of Ras1 and Cac1 proteins in Cryptococcus neoformans.

PubMed

Wang, Yanli; Shen, Gui; Gong, Jinjun; Shen, Danyu; Whittington, Amy; Qing, Jiang; Treloar, Joshua; Boisvert, Scott; Zhang, Zhengguang; Yang, Cai; Wang, Ping

2014-05-02

Gβ-like/RACK1 functions as a key mediator of various pathways and contributes to numerous cellular functions in eukaryotic organisms. In the pathogenic fungus Cryptococcus neoformans, noncanonical Gβ Gib2 promotes cAMP signaling in cells lacking normal Gpa1 function while displaying versatility in interactions with Gα Gpa1, protein kinase Pkc1, and endocytic intersectin Cin1. To elucidate the Gib2 functional mechanism(s), we demonstrate that Gib2 is required for normal growth and virulence. We show that Gib2 directly binds to Gpa1 and Gγ Gpg1/Gpg2 and that it interacts with phosphodiesterase Pde2 and monomeric GTPase Ras1. Pde2 remains functionally dispensable, but Ras1 is found to associate with adenylyl cyclase Cac1 through the conserved Ras association domain. In addition, the ras1 mutant exhibits normal capsule formation, whereas the ras1 gpa1 mutant displays enhanced capsule formation, and the ras1 gpa1 cac1 mutant is acapsular. Collectively, these findings suggest that Gib2 promotes cAMP levels by relieving an inhibitory function of Ras1 on Cac1 in the absence of Gpa1. In addition, using GST affinity purification combined with mass spectrometry, we identified 47 additional proteins that interact with Gib2. These proteins have putative functions ranging from signal transduction, energy generation, metabolism, and stress response to ribosomal function. After establishing and validating a protein-protein interactive network, we believe Gib2 to be a key adaptor/scaffolding protein that drives the formation of various protein complexes required for growth and virulence. Our study reveals Gib2 as an essential component in deciphering the complexity of regulatory networks that control growth and virulence in C. neoformans.

Signaling mechanisms underlying the robustness and tunability of the plant immune network

PubMed Central

Kim, Yungil; Tsuda, Kenichi; Igarashi, Daisuke; Hillmer, Rachel A.; Sakakibara, Hitoshi; Myers, Chad L.; Katagiri, Fumiaki

2014-01-01

Summary How does robust and tunable behavior emerge in a complex biological network? We sought to understand this for the signaling network controlling pattern-triggered immunity (PTI) in Arabidopsis. A dynamic network model containing four major signaling sectors, the jasmonate, ethylene, PAD4, and salicylate sectors, which together explain up to 80% of the PTI level, was built using data for dynamic sector activities and PTI levels under exhaustive combinatorial sector perturbations. Our regularized multiple regression model had a high level of predictive power and captured known and unexpected signal flows in the network. The sole inhibitory sector in the model, the ethylene sector, was central to the network robustness via its inhibition of the jasmonate sector. The model's multiple input sites linked specific signal input patterns varying in strength and timing to different network response patterns, indicating a mechanism enabling tunability. PMID:24439900

Protein targeting and integration signal for the chloroplastic outer envelope membrane.

PubMed Central

Li, H M; Chen, L J

1996-01-01

Most proteins in chloroplasts are encoded by the nuclear genome and synthesized in the cytosol. With the exception of most quter envelope membrane proteins, nuclear-encoded chloroplastic proteins are synthesized with N-terminal extensions that contain the chloroplast targeting information of these proteins. Most outer membrane proteins, however, are synthesized without extensions in the cytosol. Therefore, it is not clear where the chloroplastic outer membrane targeting information resides within these polypeptides. We have analyzed a chloroplastic outer membrane protein, OEP14 (outer envelope membrane protein of 14 kD, previously named OM14), and localized its outer membrane targeting and integration signal to the first 30 amino acids of the protein. This signal consists of a positively charged N-terminal portion followed by a hydrophobic core, bearing resemblance to the signal peptides of proteins targeted to the endoplasmic reticulum. However, a chimeric protein containing this signal fused to a passenger protein did not integrate into the endoplasmic reticulum membrane. Furthermore, membrane topology analysis indicated that the signal inserts into the chloroplastic outer membrane in an orientation opposite to that predicted by the "positive inside" rule. PMID:8953775

Protein intrinsic disorder in plants.

PubMed

Pazos, Florencio; Pietrosemoli, Natalia; García-Martín, Juan A; Solano, Roberto

2013-09-12

To some extent contradicting the classical paradigm of the relationship between protein 3D structure and function, now it is clear that large portions of the proteomes, especially in higher organisms, lack a fixed structure and still perform very important functions. Proteins completely or partially unstructured in their native (functional) form are involved in key cellular processes underlain by complex networks of protein interactions. The intrinsic conformational flexibility of these disordered proteins allows them to bind multiple partners in transient interactions of high specificity and low affinity. In concordance, in plants this type of proteins has been found in processes requiring these complex and versatile interaction networks. These include transcription factor networks, where disordered proteins act as integrators of different signals or link different transcription factor subnetworks due to their ability to interact (in many cases simultaneously) with different partners. Similarly, they also serve as signal integrators in signaling cascades, such as those related to response to external stimuli. Disordered proteins have also been found in plants in many stress-response processes, acting as protein chaperones or protecting other cellular components and structures. In plants, it is especially important to have complex and versatile networks able to quickly and efficiently respond to changing environmental conditions since these organisms cannot escape and have no other choice than adapting to them. Consequently, protein disorder can play an especially important role in plants, providing them with a fast mechanism to obtain complex, interconnected and versatile molecular networks.

Protein intrinsic disorder in plants

PubMed Central

Pazos, Florencio; Pietrosemoli, Natalia; García-Martín, Juan A.; Solano, Roberto

2013-01-01

To some extent contradicting the classical paradigm of the relationship between protein 3D structure and function, now it is clear that large portions of the proteomes, especially in higher organisms, lack a fixed structure and still perform very important functions. Proteins completely or partially unstructured in their native (functional) form are involved in key cellular processes underlain by complex networks of protein interactions. The intrinsic conformational flexibility of these disordered proteins allows them to bind multiple partners in transient interactions of high specificity and low affinity. In concordance, in plants this type of proteins has been found in processes requiring these complex and versatile interaction networks. These include transcription factor networks, where disordered proteins act as integrators of different signals or link different transcription factor subnetworks due to their ability to interact (in many cases simultaneously) with different partners. Similarly, they also serve as signal integrators in signaling cascades, such as those related to response to external stimuli. Disordered proteins have also been found in plants in many stress-response processes, acting as protein chaperones or protecting other cellular components and structures. In plants, it is especially important to have complex and versatile networks able to quickly and efficiently respond to changing environmental conditions since these organisms cannot escape and have no other choice than adapting to them. Consequently, protein disorder can play an especially important role in plants, providing them with a fast mechanism to obtain complex, interconnected and versatile molecular networks. PMID:24062761

An ensemble framework for clustering protein-protein interaction networks.

PubMed

Asur, Sitaram; Ucar, Duygu; Parthasarathy, Srinivasan

2007-07-01

Protein-Protein Interaction (PPI) networks are believed to be important sources of information related to biological processes and complex metabolic functions of the cell. The presence of biologically relevant functional modules in these networks has been theorized by many researchers. However, the application of traditional clustering algorithms for extracting these modules has not been successful, largely due to the presence of noisy false positive interactions as well as specific topological challenges in the network. In this article, we propose an ensemble clustering framework to address this problem. For base clustering, we introduce two topology-based distance metrics to counteract the effects of noise. We develop a PCA-based consensus clustering technique, designed to reduce the dimensionality of the consensus problem and yield informative clusters. We also develop a soft consensus clustering variant to assign multifaceted proteins to multiple functional groups. We conduct an empirical evaluation of different consensus techniques using topology-based, information theoretic and domain-specific validation metrics and show that our approaches can provide significant benefits over other state-of-the-art approaches. Our analysis of the consensus clusters obtained demonstrates that ensemble clustering can (a) produce improved biologically significant functional groupings; and (b) facilitate soft clustering by discovering multiple functional associations for proteins. Supplementary data are available at Bioinformatics online.

Regulation of Phagocyte Migration by Signal Regulatory Protein-Alpha Signaling

PubMed Central

Alvarez-Zarate, Julian; Matlung, Hanke L.; Matozaki, Takashi; Kuijpers, Taco W.; Maridonneau-Parini, Isabelle; van den Berg, Timo K.

2015-01-01

Signaling through the inhibitory receptor signal regulatory protein-alpha (SIRPα) controls effector functions in phagocytes. However, there are also indications that interactions between SIRPα and its ligand CD47 are involved in phagocyte transendothelial migration. We have investigated the involvement of SIRPα signaling in phagocyte migration in vitro and in vivo using mice that lack the SIRPα cytoplasmic tail. During thioglycolate-induced peritonitis in SIRPα mutant mice, both neutrophil and macrophage influx were found to occur, but to be significantly delayed. SIRPα signaling appeared to be essential for an optimal transendothelial migration and chemotaxis, and for the amoeboid type of phagocyte migration in 3-dimensional environments. These findings demonstrate, for the first time, that SIRPα signaling can directly control phagocyte migration, and this may contribute to the impaired inflammatory phenotype that has been observed in the absence of SIRPα signaling. PMID:26057870

Regulation of Phagocyte Migration by Signal Regulatory Protein-Alpha Signaling.

PubMed

Alvarez-Zarate, Julian; Matlung, Hanke L; Matozaki, Takashi; Kuijpers, Taco W; Maridonneau-Parini, Isabelle; van den Berg, Timo K

2015-01-01

Signaling through the inhibitory receptor signal regulatory protein-alpha (SIRPα) controls effector functions in phagocytes. However, there are also indications that interactions between SIRPα and its ligand CD47 are involved in phagocyte transendothelial migration. We have investigated the involvement of SIRPα signaling in phagocyte migration in vitro and in vivo using mice that lack the SIRPα cytoplasmic tail. During thioglycolate-induced peritonitis in SIRPα mutant mice, both neutrophil and macrophage influx were found to occur, but to be significantly delayed. SIRPα signaling appeared to be essential for an optimal transendothelial migration and chemotaxis, and for the amoeboid type of phagocyte migration in 3-dimensional environments. These findings demonstrate, for the first time, that SIRPα signaling can directly control phagocyte migration, and this may contribute to the impaired inflammatory phenotype that has been observed in the absence of SIRPα signaling.

Gene essentiality and the topology of protein interaction networks

PubMed Central

Coulomb, Stéphane; Bauer, Michel; Bernard, Denis; Marsolier-Kergoat, Marie-Claude

2005-01-01

The mechanistic bases for gene essentiality and for cell mutational resistance have long been disputed. The recent availability of large protein interaction databases has fuelled the analysis of protein interaction networks and several authors have proposed that gene dispensability could be strongly related to some topological parameters of these networks. However, many results were based on protein interaction data whose biases were not taken into account. In this article, we show that the essentiality of a gene in yeast is poorly related to the number of interactants (or degree) of the corresponding protein and that the physiological consequences of gene deletions are unrelated to several other properties of proteins in the interaction networks, such as the average degrees of their nearest neighbours, their clustering coefficients or their relative distances. We also found that yeast protein interaction networks lack degree correlation, i.e. a propensity for their vertices to associate according to their degrees. Gene essentiality and more generally cell resistance against mutations thus seem largely unrelated to many parameters of protein network topology. PMID:16087428

Leucine facilitates insulin signaling through a Gαi protein-dependent signaling pathway in hepatocytes.

PubMed

Yang, Xuefeng; Mei, Shuang; Wang, Xiaolei; Li, Xiang; Liu, Rui; Ma, Yan; Hao, Liping; Yao, Ping; Liu, Liegang; Sun, Xiufa; Gu, Haihua; Liu, Zhenqi; Cao, Wenhong

2013-03-29

In this study, we addressed the direct effect of leucine on insulin signaling. In investigating the associated mechanisms, we found that leucine itself does not activate the classical Akt- or ERK1/2 MAP kinase-dependent signaling pathways but can facilitate the insulin-induced phosphorylations of Akt(473) and ERK1/2 in a time- and dose-dependent manner in cultured hepatocytes. The leucine-facilitated insulin-induced phosphorylation of Akt at residue 473 was not affected by knocking down the key component of mTORC1 or -2 complexes but was blocked by inhibition of c-Src (PP2), PI3K (LY294002), Gαi protein (pertussis toxin or siRNA against Gαi1 gene, or β-arrestin 2 (siRNA)). Similarly, the leucine-facilitated insulin activation of ERK1/2 was also blunted by pertussis toxin. We further show that leucine facilitated the insulin-mediated suppression of glucose production and expression of key gluconeogenic genes in a Gαi1 protein-dependent manner in cultured primary hepatocytes. Together, these results show that leucine can directly facilitate insulin signaling through a Gαi protein-dependent intracellular signaling pathway. This is the first evidence showing that macronutrients like amino acid leucine can facilitate insulin signaling through G proteins directly.

Leucine Facilitates Insulin Signaling through a Gαi Protein-dependent Signaling Pathway in Hepatocytes*

PubMed Central

Yang, Xuefeng; Mei, Shuang; Wang, Xiaolei; Li, Xiang; Liu, Rui; Ma, Yan; Hao, Liping; Yao, Ping; Liu, Liegang; Sun, Xiufa; Gu, Haihua; Liu, Zhenqi; Cao, Wenhong

2013-01-01

In this study, we addressed the direct effect of leucine on insulin signaling. In investigating the associated mechanisms, we found that leucine itself does not activate the classical Akt- or ERK1/2 MAP kinase-dependent signaling pathways but can facilitate the insulin-induced phosphorylations of Akt473 and ERK1/2 in a time- and dose-dependent manner in cultured hepatocytes. The leucine-facilitated insulin-induced phosphorylation of Akt at residue 473 was not affected by knocking down the key component of mTORC1 or -2 complexes but was blocked by inhibition of c-Src (PP2), PI3K (LY294002), Gαi protein (pertussis toxin or siRNA against Gαi1 gene, or β-arrestin 2 (siRNA)). Similarly, the leucine-facilitated insulin activation of ERK1/2 was also blunted by pertussis toxin. We further show that leucine facilitated the insulin-mediated suppression of glucose production and expression of key gluconeogenic genes in a Gαi1 protein-dependent manner in cultured primary hepatocytes. Together, these results show that leucine can directly facilitate insulin signaling through a Gαi protein-dependent intracellular signaling pathway. This is the first evidence showing that macronutrients like amino acid leucine can facilitate insulin signaling through G proteins directly. PMID:23404499

Signal Processing in Periodically Forced Gradient Frequency Neural Networks

PubMed Central

Kim, Ji Chul; Large, Edward W.

2015-01-01

Oscillatory instability at the Hopf bifurcation is a dynamical phenomenon that has been suggested to characterize active non-linear processes observed in the auditory system. Networks of oscillators poised near Hopf bifurcation points and tuned to tonotopically distributed frequencies have been used as models of auditory processing at various levels, but systematic investigation of the dynamical properties of such oscillatory networks is still lacking. Here we provide a dynamical systems analysis of a canonical model for gradient frequency neural networks driven by a periodic signal. We use linear stability analysis to identify various driven behaviors of canonical oscillators for all possible ranges of model and forcing parameters. The analysis shows that canonical oscillators exhibit qualitatively different sets of driven states and transitions for different regimes of model parameters. We classify the parameter regimes into four main categories based on their distinct signal processing capabilities. This analysis will lead to deeper understanding of the diverse behaviors of neural systems under periodic forcing and can inform the design of oscillatory network models of auditory signal processing. PMID:26733858

Arrestin-related proteins mediate pH signaling in fungi.

PubMed

Herranz, Silvia; Rodríguez, José M; Bussink, Henk-Jan; Sánchez-Ferrero, Juan C; Arst, Herbert N; Peñalva, Miguel A; Vincent, Olivier

2005-08-23

Metazoan arrestins bind to seven-transmembrane (7TM) receptors to regulate function. Aspergillus nidulans PalF, a protein involved in the fungal ambient pH signaling pathway, contains arrestin N-terminal and C-terminal domains and binds strongly to two different regions within the C-terminal cytoplasmic tail of the 7TM, putative pH sensor PalH. Upon exposure to alkaline ambient pH, PalF is phosphorylated and, like mammalian beta-arrestins, ubiquitinated in a signal-dependent and 7TM protein-dependent manner. Substitution in PalF of a highly conserved arrestin N-terminal domain Ser residue prevents PalF-PalH interaction and pH signaling in vivo. Thus, PalF is the first experimentally documented fungal arrestin-related protein, dispelling the notion that arrestins are restricted to animal proteomes. Epistasis analyses demonstrate that PalF posttranslational modification is partially dependent on the 4TM protein PalI but independent of the remaining pH signal transduction pathway proteins PalA, PalB, and PalC, yielding experimental evidence bearing on the order of participation of the six components of the pH signal transduction pathway. Our data strongly implicate PalH as an ambient pH sensor, possibly with the cooperation of PalI.

Solvated dissipative electro-elastic network model of hydrated proteins

NASA Astrophysics Data System (ADS)

Martin, Daniel R.; Matyushov, Dmitry V.

2012-10-01

Elastic network models coarse grain proteins into a network of residue beads connected by springs. We add dissipative dynamics to this mechanical system by applying overdamped Langevin equations of motion to normal-mode vibrations of the network. In addition, the network is made heterogeneous and softened at the protein surface by accounting for hydration of the ionized residues. Solvation changes the network Hessian in two ways. Diagonal solvation terms soften the spring constants and off-diagonal dipole-dipole terms correlate displacements of the ionized residues. The model is used to formulate the response functions of the electrostatic potential and electric field appearing in theories of redox reactions and spectroscopy. We also formulate the dielectric response of the protein and find that solvation of the surface ionized residues leads to a slow relaxation peak in the dielectric loss spectrum, about two orders of magnitude slower than the main peak of protein relaxation. Finally, the solvated network is used to formulate the allosteric response of the protein to ion binding. The global thermodynamics of ion binding is not strongly affected by the network solvation, but it dramatically enhances conformational changes in response to placing a charge at the active site of the protein.

Protein kinase N2 regulates AMP kinase signaling and insulin responsiveness of glucose metabolism in skeletal muscle.

PubMed

Ruby, Maxwell A; Riedl, Isabelle; Massart, Julie; Åhlin, Marcus; Zierath, Juleen R

2017-10-01

Insulin resistance is central to the development of type 2 diabetes and related metabolic disorders. Because skeletal muscle is responsible for the majority of whole body insulin-stimulated glucose uptake, regulation of glucose metabolism in this tissue is of particular importance. Although Rho GTPases and many of their affecters influence skeletal muscle metabolism, there is a paucity of information on the protein kinase N (PKN) family of serine/threonine protein kinases. We investigated the impact of PKN2 on insulin signaling and glucose metabolism in primary human skeletal muscle cells in vitro and mouse tibialis anterior muscle in vivo. PKN2 knockdown in vitro decreased insulin-stimulated glucose uptake, incorporation into glycogen, and oxidation. PKN2 siRNA increased 5'-adenosine monophosphate-activated protein kinase (AMPK) signaling while stimulating fatty acid oxidation and incorporation into triglycerides and decreasing protein synthesis. At the transcriptional level, PKN2 knockdown increased expression of PGC-1α and SREBP-1c and their target genes. In mature skeletal muscle, in vivo PKN2 knockdown decreased glucose uptake and increased AMPK phosphorylation. Thus, PKN2 alters key signaling pathways and transcriptional networks to regulate glucose and lipid metabolism. Identification of PKN2 as a novel regulator of insulin and AMPK signaling may provide an avenue for manipulation of skeletal muscle metabolism. Copyright © 2017 the American Physiological Society.

CCN proteins: A centralized communication network.

PubMed

Perbal, Bernard

2013-08-01

The CCN family of proteins includes six members presently known as CCN1, CCN2, CCN3, CCN4, CCN5 and CCN6. These proteins were originally designated CYR61, CTGF, NOV, and WISP-1, WISP-2, WISP-3. Although these proteins share a significant amount of structural features and a partial identity with other large families of regulatory proteins, they exhibit different biological functions. A critical examination of the progress made over the past two decades, since the first CCN proteins were discovered brings me to the conclusion that most of our present knowledge regarding the functions of these proteins was predicted very early after their discovery. In an effort to point out some of the gaps that prevent us to reach a comprehensive view of the functional interactions between CCN proteins, it is necessary to reconsider carefully data that was already published and put aside, either because the scientific community was not ready to accept them, or because they were not fitting with the « consensus » when they were published. This review article points to avenues that were not attracting the attention that they deserved. However, it is quite obvious that the six members of this unique family of tetra-modular proteins must act in concert, either simultaneously or sequentially, on the same sites or at different times in the life of living organisms. A better understanding of the spatio-temporal regulation of CCN proteins expression requires considering the family as such, not as a set of single proteins related only by their name. As proposed in this review, there is enough convincing pieces of evidence, at the present time, in favor of these proteins playing a role in the coordination of multiple signaling pathways, and constituting a Centralized Communication Network. Deciphering the hierarchy of regulatory circuits involved in this complex system is an important challenge for the near future. In this article, I would like to briefly review the concept of a CCN family

Gγ recruitment system incorporating a novel signal amplification circuit to screen transient protein-protein interactions.

PubMed

Fukuda, Nobuo; Ishii, Jun; Kondo, Akihiko

2011-09-01

Weak and transient protein-protein interactions are associated with biological processes, but many are still undefined because of the difficulties in their identification. Here, we describe a redesigned method to screen transient protein-protein interactions by using a novel signal amplification circuit, which is incorporated into yeast to artificially magnify the signal responding to the interactions. This refined method is based on the previously established Gγ recruitment system, which utilizes yeast G-protein signaling and mating growth selection to screen interacting protein pairs. In the current study, to test the capability of our method, we chose mutants of the Z-domain derived from Staphylococcus aureus protein A as candidate proteins, and the Fc region of human IgG as the counterpart. By introduction of an artificial signal amplifier into the previous Gγ recruitment system, the signal transduction responding to transient interactions between Z-domain mutants and the Fc region with significantly low affinity (8.0 × 10(3) M(-1)) was successfully amplified in recombinant haploid yeast cells. As a result of zygosis with the opposite mating type of wild-type haploid cells, diploid colonies were vigorously and selectively generated on the screening plates, whereas our previous system rarely produced positive colonies. This new approach will be useful for exploring the numerous transient interactions that remain undefined because of the lack of powerful screening tools for their identification. © 2011 The Authors Journal compilation © 2011 FEBS.

Kinome signaling through regulated protein-protein interactions in normal and cancer cells.

PubMed

Pawson, Tony; Kofler, Michael

2009-04-01

The flow of molecular information through normal and oncogenic signaling pathways frequently depends on protein phosphorylation, mediated by specific kinases, and the selective binding of the resulting phosphorylation sites to interaction domains present on downstream targets. This physical and functional interplay of catalytic and interaction domains can be clearly seen in cytoplasmic tyrosine kinases such as Src, Abl, Fes, and ZAP-70. Although the kinase and SH2 domains of these proteins possess similar intrinsic properties of phosphorylating tyrosine residues or binding phosphotyrosine sites, they also undergo intramolecular interactions when linked together, in a fashion that varies from protein to protein. These cooperative interactions can have diverse effects on substrate recognition and kinase activity, and provide a variety of mechanisms to link the stimulation of catalytic activity to substrate recognition. Taken together, these data have suggested how protein kinases, and the signaling pathways in which they are embedded, can evolve complex properties through the stepwise linkage of domains within single polypeptides or multi-protein assemblies.

Protein-protein interaction networks identify targets which rescue the MPP+ cellular model of Parkinson’s disease

NASA Astrophysics Data System (ADS)

Keane, Harriet; Ryan, Brent J.; Jackson, Brendan; Whitmore, Alan; Wade-Martins, Richard

2015-11-01

Neurodegenerative diseases are complex multifactorial disorders characterised by the interplay of many dysregulated physiological processes. As an exemplar, Parkinson’s disease (PD) involves multiple perturbed cellular functions, including mitochondrial dysfunction and autophagic dysregulation in preferentially-sensitive dopamine neurons, a selective pathophysiology recapitulated in vitro using the neurotoxin MPP+. Here we explore a network science approach for the selection of therapeutic protein targets in the cellular MPP+ model. We hypothesised that analysis of protein-protein interaction networks modelling MPP+ toxicity could identify proteins critical for mediating MPP+ toxicity. Analysis of protein-protein interaction networks constructed to model the interplay of mitochondrial dysfunction and autophagic dysregulation (key aspects of MPP+ toxicity) enabled us to identify four proteins predicted to be key for MPP+ toxicity (P62, GABARAP, GBRL1 and GBRL2). Combined, but not individual, knockdown of these proteins increased cellular susceptibility to MPP+ toxicity. Conversely, combined, but not individual, over-expression of the network targets provided rescue of MPP+ toxicity associated with the formation of autophagosome-like structures. We also found that modulation of two distinct proteins in the protein-protein interaction network was necessary and sufficient to mitigate neurotoxicity. Together, these findings validate our network science approach to multi-target identification in complex neurological diseases.

Gene regulatory and signaling networks exhibit distinct topological distributions of motifs

NASA Astrophysics Data System (ADS)

Ferreira, Gustavo Rodrigues; Nakaya, Helder Imoto; Costa, Luciano da Fontoura

2018-04-01

The biological processes of cellular decision making and differentiation involve a plethora of signaling pathways and gene regulatory circuits. These networks in turn exhibit a multitude of motifs playing crucial parts in regulating network activity. Here we compare the topological placement of motifs in gene regulatory and signaling networks and observe that it suggests different evolutionary strategies in motif distribution for distinct cellular subnetworks.

Topology-function conservation in protein-protein interaction networks.

PubMed

Davis, Darren; Yaveroğlu, Ömer Nebil; Malod-Dognin, Noël; Stojmirovic, Aleksandar; Pržulj, Nataša

2015-05-15

Proteins underlay the functioning of a cell and the wiring of proteins in protein-protein interaction network (PIN) relates to their biological functions. Proteins with similar wiring in the PIN (topology around them) have been shown to have similar functions. This property has been successfully exploited for predicting protein functions. Topological similarity is also used to guide network alignment algorithms that find similarly wired proteins between PINs of different species; these similarities are used to transfer annotation across PINs, e.g. from model organisms to human. To refine these functional predictions and annotation transfers, we need to gain insight into the variability of the topology-function relationships. For example, a function may be significantly associated with specific topologies, while another function may be weakly associated with several different topologies. Also, the topology-function relationships may differ between different species. To improve our understanding of topology-function relationships and of their conservation among species, we develop a statistical framework that is built upon canonical correlation analysis. Using the graphlet degrees to represent the wiring around proteins in PINs and gene ontology (GO) annotations to describe their functions, our framework: (i) characterizes statistically significant topology-function relationships in a given species, and (ii) uncovers the functions that have conserved topology in PINs of different species, which we term topologically orthologous functions. We apply our framework to PINs of yeast and human, identifying seven biological process and two cellular component GO terms to be topologically orthologous for the two organisms. © The Author 2015. Published by Oxford University Press.

P-glycoprotein (ABCB1) inhibited network of mitochondrion transport along microtubule and BMP signal-induced cell shape in chimpanzee left cerebrum by systems-theoretical analysis.

PubMed

Lin, Hong; Wang, Lin; Jiang, Minghu; Huang, Juxiang; Qi, Lianxiu

2012-10-01

We constructed the significant low-expression P-glycoprotein (ABCB1) inhibited transport and signal network in chimpanzee compared with high-expression (fold change ≥2) the human left cerebrum in GEO data set, by using integration of gene regulatory activated and inhibited network inference method with gene ontology (GO) analysis. Our result showed that ABCB1 transport and signal upstream network RAB2A inhibited ABCB1, and downstream ABCB1-inhibited SMAD1_2, NCK2, SLC25A46, GDF10, RASGRP1, EGFR, LRPPRC, RASSF2, RASA4, CA2, CBLB, UBR5, SLC25A16, ITGB3BP, DDIT4, PDPN, RAB2A in chimpanzee left cerebrum. We obtained that the different biological processes of ABCB1 inhibited transport and signal network repressed carbon dioxide transport, ER to Golgi vesicle-mediated transport, folic acid transport, mitochondrion transport along microtubule, water transport, BMP signaling pathway, Ras protein signal transduction, transforming growth factor beta receptor signaling pathway in chimpanzee compared with the inhibited network of the human left cerebrum, as a result of inducing inhibition of mitochondrion transport along microtubule and BMP signal-induced cell shape in chimpanzee left cerebrum. Our hypothesis was verified by the same and different biological processes of ABCB1 inhibited transport and signal network of chimpanzee compared with the corresponding activated network of chimpanzee and the human left cerebrum, respectively. Copyright © 2012 John Wiley & Sons, Ltd.

Whole genome siRNA cell-based screen links mitochondria to Akt signaling network through uncoupling of electron transport chain

PubMed Central

Senapedis, William T.; Kennedy, Caleb J.; Boyle, Patrick M.; Silver, Pamela A.

2011-01-01

Forkhead transcription factors (FOXOs) alter a diverse array of cellular processes including the cell cycle, oxidative stress resistance, and aging. Insulin/Akt activation directs phosphorylation and cytoplasmic sequestration of FOXO away from its target genes and serves as an endpoint of a complex signaling network. Using a human genome small interfering RNA (siRNA) library in a cell-based assay, we identified an extensive network of proteins involved in nuclear export, focal adhesion, and mitochondrial respiration not previously implicated in FOXO localization. Furthermore, a detailed examination of mitochondrial factors revealed that loss of uncoupling protein 5 (UCP5) modifies the energy balance and increases free radicals through up-regulation of uncoupling protein 3 (UCP3). The increased superoxide content induces c-Jun N-terminal kinase 1 (JNK1) kinase activity, which in turn affects FOXO localization through a compensatory dephosphorylation of Akt. The resulting nuclear FOXO increases expression of target genes, including mitochondrial superoxide dismutase. By connecting free radical defense and mitochondrial uncoupling to Akt/FOXO signaling, these results have implications in obesity and type 2 diabetes development and the potential for therapeutic intervention. PMID:21460183

Whole genome siRNA cell-based screen links mitochondria to Akt signaling network through uncoupling of electron transport chain.

PubMed