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Towards Systematic Discovery of Signaling Networks in Budding Yeast Filamentous Growth Stress Response Using Interventional Phosphorylation Data  [PDF]
Yan Zhang,Hye Kyong Kweon,Christian Shively,Anuj Kumar,Philip C. Andrews
PLOS Computational Biology , 2013, DOI: 10.1371/journal.pcbi.1003077
Abstract: Reversible phosphorylation is one of the major mechanisms of signal transduction, and signaling networks are critical regulators of cell growth and development. However, few of these networks have been delineated completely. Towards this end, quantitative phosphoproteomics is emerging as a useful tool enabling large-scale determination of relative phosphorylation levels. However, phosphoproteomics differs from classical proteomics by a more extensive sampling limitation due to the limited number of detectable sites per protein. Here, we propose a comprehensive quantitative analysis pipeline customized for phosphoproteome data from interventional experiments for identifying key proteins in specific pathways, discovering the protein-protein interactions and inferring the signaling network. We also made an effort to partially compensate for the missing value problem, a chronic issue for proteomics studies. The dataset used for this study was generated using SILAC (Stable Isotope Labeling with Amino acids in Cell culture) technique with interventional experiments (kinase-dead mutations). The major components of the pipeline include phosphopeptide meta-analysis, correlation network analysis and causal relationship discovery. We have successfully applied our pipeline to interventional experiments identifying phosphorylation events underlying the transition to a filamentous growth form in Saccharomyces cerevisiae. We identified 5 high-confidence proteins from meta-analysis, and 19 hub proteins from correlation analysis (Pbi2p and Hsp42p were identified by both analyses). All these proteins are involved in stress responses. Nine of them have direct or indirect evidence of involvement in filamentous growth. In addition, we tested four of our predicted proteins, Nth1p, Pbi2p, Pdr12p and Rcn2p, by interventional phenotypic experiments and all of them present differential invasive growth, providing prospective validation of our approach. This comprehensive pipeline presents a systematic way for discovering signaling networks using interventional phosphoproteome data and can suggest candidate proteins for further investigation. We anticipate the methodology to be applicable as well to other interventional studies via different experimental platforms.
Systematic Prediction of Scaffold Proteins Reveals New Design Principles in Scaffold-Mediated Signal Transduction  [PDF]
Jianfei Hu?,Johnathan Neiswinger?,Jin Zhang?,Heng Zhu?,Jiang Qian
PLOS Computational Biology , 2015, DOI: 10.1371/journal.pcbi.1004508
Abstract: 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.
ATM acts in DNA damage signal transduction by catalyzing protein phosphorylation
Xiangbing Meng,Yan Dong,Zhixian Sun
Chinese Science Bulletin , 1999, DOI: 10.1007/BF02886160
Abstract: In order to investigate ATM in mediating DNA damage signal to p53 in the cellular response to IR, kinase activities of ATM and c-AbI immunoprecipitates and its activation by IR and damaged DNA have been analyzed. Results demonstrate that deficient ATM caused failure to activate phosphorylation of many proteins in response to radiation. ATM coimmunoprecipitates with c-AbI and can catalyze phosphorylation of many proteins including p53 in response to radiation. Kinase activity of ATM / c-AbI immunoprecipitate stimulated by damaged DNAin vitro phosphorylation demonstrates that ATM can detect damaged DNA and initiate DNA damage signals. ATM can be phosphorylatedin vitro and inhibited by wortmannin, a specific inhibitor of PI3K family. These results confirm that ATM acts in DNA damage detection and signal transduction.
Discovery of GPCR ligands for probing signal transduction pathways  [PDF]
Simone Brogi,Andrea Tafi,Laurent Désaubry,Canan G. Nebigil
Frontiers in Pharmacology , 2014, DOI: 10.3389/fphar.2014.00255
Abstract: G protein-coupled receptors (GPCRs) are seven integral transmembrane proteins that are the primary targets of almost 30% of approved drugs and continue to represent a major focus of pharmaceutical research. All of GPCR targeted medicines were discovered by classical medicinal chemistry approaches. After the first GPCR crystal structures were determined, the docking screens using these structures lead to discovery of more novel and potent ligands. There are over 360 pharmaceutically relevant GPCRs in the human genome and to date about only 30 of structures have been determined. For these reasons, computational techniques such as homology modeling and molecular dynamics simulations have proven their usefulness to explore the structure and function of GPCRs. Furthermore, structure-based drug design and in silico screening (High Throughput Docking) are still the most common computational procedures in GPCRs drug discovery. Moreover, ligand-based methods such as three-dimensional quantitative structure–selectivity relationships, are the ideal molecular modeling approaches to rationalize the activity of tested GPCR ligands and identify novel GPCR ligands. In this review, we discuss the most recent advances for the computational approaches to effectively guide selectivity and affinity of ligands. We also describe novel approaches in medicinal chemistry, such as the development of biased agonists, allosteric modulators, and bivalent ligands for class A GPCRs. Furthermore, we highlight some knockout mice models in discovering biased signaling selectivity.
Genetic Evidence for a Phosphorylation-Independent Signal Transduction Mechanism within the Bacillus subtilis Stressosome  [PDF]
Tatiana A. Gaidenko, Chester W. Price
PLOS ONE , 2014, DOI: 10.1371/journal.pone.0090741
Abstract: The stressosome is a 1.8 MDa cytoplasmic complex that controls diverse bacterial signaling pathways. Its role is best understood in Bacillus subtilis, where it activates the σB transcription factor in response to a variety of sharp environmental challenges, including acid, ethanol, heat or salt stress. However, details of the signaling mechanism within the stressosome remain uncertain. The core of the complex comprises one or more members of the RsbR co-antagonist family together with the RsbS antagonist protein, which binds the RsbT kinase in the absence of stress. As part of the response, RsbT first phosphorylates the RsbRA co-antagonist on T171 and then RsbS on S59; this latter event correlates with the stress-induced release of RsbT to activate downstream signaling. Here we examine the in vivo consequence of S59 phosphorylation in a model strain whose stressosome core is formed solely with the RsbRA co-antagonist and RsbS. A phosphorylation-deficient S59A substitution in RsbS blocked response to mild stress but had declining impact as stress increased: with strong ethanol challenge response with S59A was 60% as robust as with wild type RsbS. Genetic analysis narrowed this S59-independent activation to the stressosome and established that significant signaling still occurred in a strain bearing both the T171A and S59A substitutions. We infer that S59 phosphorylation increases signaling efficiency but is not essential, and that a second (or underlying) mechanism of signal transduction prevails in its absence. This interpretation nullifies models in which stressosome signaling is solely mediated by control of RsbT kinase activity toward S59.
SGK and 14-3-3 protein are involved in the serine/ threonine phosphorylation mechanism for TPO/MPL signal transduction
Libing Feng,Limin Yang,Weiguo Zhou,Li Huang,Min Wan,Shouyuan Zhao,Changben Li
Chinese Science Bulletin , 2001, DOI: 10.1007/BF02901163
Abstract: Thrombopioetin (TPO), the critical regulator of platelet production, acts by binding to its cell surface receptor, c-Mpl. Yeast two-hybrid screening was performed to isolate the proteins interacting with the cytoplasmic domain of c-Mpl. 48 positive clones were isolated from 5 × 106 independent transformants. The results of sequence analysis demonstrate that they represent 13 different protein encoding sequences. Among them there are a partial coding sequence of serine/threonine protein kinase SGK (serum and glucocorticoid-inducible kinase) and 14-3-3 theta protein partial coding sequence. GST-pull-down assay and co-immunoprecipitation in mammal cells have confirmed the interaction between these two proteins and c-Mpl. By constructing a series of deleted c-Mpl cytoplasmic domain, the interaction region in c-Mpl cytoplasmic tail was localized in amino acids 523–554. At the same time, the directed interaction between SGK and 14-3-3 proteins also has been verified by yeast two-hybrid assay. The present note is the first time to report that two proteins act with c-Mpl at the same time and put forward that SGK and 14-3-3 protein may be involved in the serine/threonine phosphorylation mechanism for signal transduction.
Discovery of Intramolecular Signal Transduction Network Based on a New Protein Dynamics Model of Energy Dissipation  [PDF]
Cheng-Wei Ma, Zhi-Long Xiu, An-Ping Zeng
PLOS ONE , 2012, DOI: 10.1371/journal.pone.0031529
Abstract: A novel approach to reveal intramolecular signal transduction network is proposed in this work. To this end, a new algorithm of network construction is developed, which is based on a new protein dynamics model of energy dissipation. A key feature of this approach is that direction information is specified after inferring protein residue-residue interaction network involved in the process of signal transduction. This enables fundamental analysis of the regulation hierarchy and identification of regulation hubs of the signaling network. A well-studied allosteric enzyme, E. coli aspartokinase III, is used as a model system to demonstrate the new method. Comparison with experimental results shows that the new approach is able to predict all the sites that have been experimentally proved to desensitize allosteric regulation of the enzyme. In addition, the signal transduction network shows a clear preference for specific structural regions, secondary structural types and residue conservation. Occurrence of super-hubs in the network indicates that allosteric regulation tends to gather residues with high connection ability to collectively facilitate the signaling process. Furthermore, a new parameter of propagation coefficient is defined to determine the propagation capability of residues within a signal transduction network. In conclusion, the new approach is useful for fundamental understanding of the process of intramolecular signal transduction and thus has significant impact on rational design of novel allosteric proteins.
Cancer, oncogenes and signal transduction
Edward J McManus, Dario R Alessi
Genome Biology , 2004, DOI: 10.1186/gb-2004-5-7-332
Abstract: The four-day meeting at the European Molecular Biology Laboratory (EMBL) brought together many of the specialists, mainly from Europe and the USA, working on cancer and signal transduction. It was the 2Oth meeting in this series, and celebrated 50 years since the discovery of protein kinases by Burnett and Kennedy (J Biol Chem 1954, 211:969-980) and 25 years since the discovery of tyrosine phosphorylation by Hunter and colleagues (Eckhart et al., Cell 1979, 18:925-933). Much of the meeting focused on advances obtained using murine models of oncogenesis, and this aspect was nicely complemented by more mechanistic talks.One of the most important recent advances in the field of kinase research was the characterization by Tony Hunter (Salk Institute for Biological Sciences, La Jolla, USA) and his colleagues of the evolutionary relationships between the 518 mammalian protein kinases encoded in the human genome. The kinases represent the largest family of human enzymes, collectively termed the kinome, and knowledge about their evolution has greatly facilitated research into these important enzymes. The value of the kinome data was exemplified by the many talks throughout the meeting describing work in which information from Hunter's study was used. In his talk, Hunter described the families of human kinases and the domains that they contain. Interestingly, 40% of all kinases have multiple splice variants and 10% of the total encode catalytically deficient enzymes that have been termed pseudokinases. The roles of these inactive enzymes are poorly defined, but recent examples indicate that a number of pseudokinases, such as ErbB3 and STRAD, play roles in activating conventional protein kinases, namely the epidermal growth factor (EGF) receptor and the serine-threonine kinase LKB1, respectively.Julian Downward (Cancer Research UK, London, UK), made use of Hunter's kinome database in an RNA interference (RNAi) screen of all human kinases, to search for enzymes that regulate s
Automated modelling of signal transduction networks
Martin Steffen, Allegra Petti, John Aach, Patrik D'haeseleer, George Church
BMC Bioinformatics , 2002, DOI: 10.1186/1471-2105-3-34
Abstract: We have developed a computational approach for generating static models of signal transduction networks which utilizes protein-interaction maps generated from large-scale two-hybrid screens and expression profiles from DNA microarrays. Networks are determined entirely by integrating protein-protein interaction data with microarray expression data, without prior knowledge of any pathway intermediates. In effect, this is equivalent to extracting subnetworks of the protein interaction dataset whose members have the most correlated expression profiles.We show that our technique accurately reconstructs MAP Kinase signaling networks in Saccharomyces cerevisiae. This approach should enhance our ability to model signaling networks and to discover new components of known networks. More generally, it provides a method for synthesizing molecular data, either individual transcript abundance measurements or pairwise protein interactions, into higher level structures, such as pathways and networks.Signal transduction is the primary means by which cells coordinate their metabolic, morphologic, and genetic responses to environmental cues such as growth factors, hormones, nutrients, osmolarity, and other chemical and tactile stimuli. Traditionally, the discovery of molecular components of signaling networks in yeast and mammals has relied upon the use of gene knockouts and epistasis analysis. Although these methods have been highly effective in generating detailed descriptions of specific linear signaling pathways, our knowledge of complex signaling networks and their interactions remains incomplete. New computational methods that capture molecular details from high-throughput genomic data in an automated fashion are desirable and can help direct the established techniques of molecular biology and genetics.DNA microarray technology has evolved to the point where one can simultaneously measure the transcript abundance of thousands of genes under hundreds of conditions, producing hund
An investigation of spatial signal transduction in cellular networks
Aiman Alam-Nazki, J Krishnan
BMC Systems Biology , 2012, DOI: 10.1186/1752-0509-6-83
Abstract: In this work we examine spatial signal transduction in a series of standard motifs/networks. These networks include coherent and incoherent feedforward, positive and negative feedback, cyclic motifs, monostable switches, bistable switches and negative feedback oscillators. In all these cases, the driving signal has spatial variation. For each network we consider two cases, one where all elements are essentially non-diffusible, and the other where one of the network elements may be highly diffusible. A careful analysis of steady state signal transduction provides many insights into the behaviour of all these modules. While in the non-diffusible case for the most part, spatial signalling reflects the temporal signalling behaviour, in the diffusible cases, we see significant differences between spatial and temporal signalling characteristics. Our results demonstrate that the presence of diffusible elements in the networks provides important constraints and capabilities for signalling.Our results provide a systematic basis for understanding spatial signalling in networks and the role of diffusible elements therein. This provides many insights into the signal transduction capabilities and constraints in such networks and suggests ways in which cellular signalling and information processing is organized to conform to or bypass those constraints. It also provides a framework for starting to understand the organization and regulation of spatial signal transduction in individual processes.Cells consist of highly complex genetic and protein networks which allow them to respond to a variety of cues and make appropriate decisions. Thus much of the decision making in response to external cues, as well as the regulation and control of intracellular processes is achieved through complex, non-linear, and often sophisticated and subtle chemical signal processing [1]. A considerable body of work has focussed on the understanding of signal transduction and gene regulatory networks und
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