In silico reconstruction of nutrient-sensing signal transduction pathways in Aspergillus nidulans

We report here probable nutrient-sensing signal transduction pathways in Aspergillus nidulans, a model filamentous fungus, based on sequence homology studies with known Saccharomyces cerevisiae and Schizosaccharomyces pombe proteins. Specifically, we identified A. nidulans homologs for yeast proteins involved in (1) filamentation-invasion, (2) cAMP-PKA, (3) pheromone response, (4) cell integrity and (5) TOR signaling pathways. We have also studied autophagy, one of the most important cellular responses regulated by TOR signaling. The Basic Local Alignment Search Tool program "blastp" was used to assess the homology of proteins. We note that by using a highly conservative approach, 70% of the S. cerevisiae signal transduction proteins (107 proteins out of 153 proteins studied) have significant homologs in A. nidulans. Using a slightly less conservative approach, we are able to identify homologs for as high as 91% of the S. cerevisiae signal transduction proteins (139 proteins out of 153 proteins studied). The filamentation-invasion, cell integrity and TOR signaling pathways showed greatest similarity with S. cerevisiae, while the cAMP-PKA and pheromone response pathways showed greater similarity with S. pombe. Based on these results, probable pathways in A. nidulans were constructed using well-established S. cerevisiae and S. pombe models.     

Discovery of intramolecular signal transduction network based on a new protein dynamics model of energy dissipation

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.     

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

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.     

大豆类钙调磷酸酶B亚基GmCBL1互作候选蛋白的筛选

Ca2+是非生物胁迫信号转导途径中的重要信号分子,植物类钙调磷酸酶B亚基蛋白(CBL,calcineurin B-like proteins)是一类重要的钙信号受体蛋白,主要通过与其他蛋白的特异结合传递信号,使植物形成对非生物胁迫的响应。本实验室已经获得大豆Gm CBL1基因,功能鉴定显示Gm CBL1增强了转基因拟南芥对非生物胁迫的耐性。为了进一步研究Gm CBL1的作用机理,本研究构建诱饵载体p GBKT7::Gm CBL1,利用酵母双杂交技术筛选大豆Gm CBL1的互作蛋白。通过对筛选获得的106个蛋白基因测序和Blast比对分析,并根据其可能的生理功能对这些候选蛋白归类,整理得到4类蛋白:能量代谢相关蛋白、修饰蛋白、防御蛋白、钙信号转导相关蛋白。筛选得到候选蛋白的功能预测初步表明,大豆Gm CBL1参与多条信号途径,为进一步研究探索大豆CBL介导的抗逆信号转导途径奠定了基础。     

Mapping signal transduction pathways by phage display

Rapid identification of proteins that interact with a novel gene product is an important element of functional genomics. Here we describe a phage display-based technique for interaction screening of complex cDNA libraries using proteins or synthetic peptides as baits. Starting with the epidermal growth factor receptor (EGFR) cytoplasmic tail, we identified known protein interactions that link EGFR to the Ras/MAP kinase signal transduction cascade and several novel interactions. This approach can be used as a rapid and efficient tool for elucidating protein networks and mapping intracellular signal transduction pathways.     

酵母HOG-MAPK途径

酿酒酵母Saccharomyces cerevisiae的高渗透性甘油促分裂原活化蛋白激酶(highos-molarity glycerol mitogen-activated protein kinase,HOG-MAPK)途径是高度保守的信号转导途径,很多方面和高等真核生物MAPK途径类似。该途径在高渗应激环境下控制信号转导和基因表达,是细胞生存所必需的。现对酵母HOG-MAPK途径的信号转导以及信号传递的专一性控制、HOG-MAPK途径各组分的亚细胞定位和基因表达调控机制进行综述。     

Delineating signal transduction pathways in smooth muscle through focused proteomics

This review will outline examples of the authors' focused proteomics approaches to studying signal transduction pathways in smooth muscle. By focusing the use of traditional proteomics techniques with hypothesis-driven selection methods, this approach efficiently addresses the identification of novel elements in a signal transduction pathway of interest. However, focused proteomics serves only as a starting point in the investigation of novel signaling proteins. While focused proteomics studies can suggest the involvement and general biochemical function of a protein in a signaling pathway, these findings must be further investigated and validated. Through the integrated use of focused proteomics with complementary approaches such as genetics, biochemistry and cell physiology, a complete and detailed mechanism of signal transduction can be determined.     

Integrated analysis of co-expressed MAP kinase substrates in Arabidopsis thaliana

MAP kinase (MAPK) signal transduction cascades are conserved eukaryotic pathways that modulate stress responses and developmental processes. In a recent report we have identified novel Arabidopsis MAPKK/MAPK/Substrate signaling pathways using microarrays containing 2,158 unique Arabidopsis proteins. Subsequently, several WRKY and TGA targets phosphorylated by MAPKs were verified in planta. We have also reported that specific MAPKK/MAPK modules expressed in Nicotiana benthamiana induced a cell death phenotype related to the immune response. We have generated a MAPK phosphorylation network based on our protein microarray experimental data. Here we further analyze our network by integrating phosphorylation and gene expression information to identify biologically relevant signaling modules. We have identified 108 phosphorylation events that occur among 96 annotated genes with highly similar pairwise expression profiles. Our analysis brings a new perspective on MAPK signaling by revealing new relationships between components of signaling pathways.Key words: MAPK, protein microarray, network, cell death, co-expression, signaling     

Signalogs: orthology-based identification of novel signaling pathway components in three metazoans

Background

Uncovering novel components of signal transduction pathways and their interactions within species is a central task in current biological research. Orthology alignment and functional genomics approaches allow the effective identification of signaling proteins by cross-species data integration. Recently, functional annotation of orthologs was transferred across organisms to predict novel roles for proteins. Despite the wide use of these methods, annotation of complete signaling pathways has not yet been transferred systematically between species.

Principal Findings

Here we introduce the concept of ‘signalog’ to describe potential novel signaling function of a protein on the basis of the known signaling role(s) of its ortholog(s). To identify signalogs on genomic scale, we systematically transferred signaling pathway annotations among three animal species, the nematode Caenorhabditis elegans, the fruit fly Drosophila melanogaster, and humans. Using orthology data from InParanoid and signaling pathway information from the SignaLink database, we predict 88 worm, 92 fly, and 73 human novel signaling components. Furthermore, we developed an on-line tool and an interactive orthology network viewer to allow users to predict and visualize components of orthologous pathways. We verified the novelty of the predicted signalogs by literature search and comparison to known pathway annotations. In C. elegans, 6 out of the predicted novel Notch pathway members were validated experimentally. Our approach predicts signaling roles for 19 human orthodisease proteins and 5 known drug targets, and suggests 14 novel drug target candidates.

Conclusions

Orthology-based pathway membership prediction between species enables the identification of novel signaling pathway components that we referred to as signalogs. Signalogs can be used to build a comprehensive signaling network in a given species. Such networks may increase the biomedical utilization of C. elegans and D. melanogaster. In humans, signalogs may identify novel drug targets and new signaling mechanisms for approved drugs.     

How do oncoprotein mutations rewire protein–protein interaction networks?

The acquisition of mutations that activate oncogenes or inactivate tumor suppressors is a primary feature of most cancers. Mutations that directly alter protein sequence and structure drive the development of tumors through aberrant expression and modification of proteins, in many cases directly impacting components of signal transduction pathways and cellular architecture. Cancer-associated mutations may have direct or indirect effects on proteins and their interactions and while the effects of mutations on signaling pathways have been widely studied, how mutations alter underlying protein–protein interaction networks is much less well understood. Systematic mapping of oncoprotein protein interactions using proteomics techniques as well as computational network analyses is revealing how oncoprotein mutations perturb protein–protein interaction networks and drive the cancer phenotype.     

Global protein-protein interaction network in the human pathogen Mycobacterium tuberculosis H37Rv

Analysis of the protein-protein interaction network of a pathogen is a powerful approach for dissecting gene function, potential signal transduction, and virulence pathways. This study looks at the construction of a global protein-protein interaction (PPI) network for the human pathogen Mycobacterium tuberculosis H37Rv, based on a high-throughput bacterial two-hybrid method. Almost the entire ORFeome was cloned, and more than 8000 novel interactions were identified. The overall quality of the PPI network was validated through two independent methods, and a high success rate of more than 60% was obtained. The parameters of PPI networks were calculated. The average shortest path length was 4.31. The topological coefficient of the M. tuberculosis B2H network perfectly followed a power law distribution (correlation = 0.999; R-squared = 0.999) and represented the best fit in all currently available PPI networks. A cross-species PPI network comparison revealed 94 conserved subnetworks between M. tuberculosis and several prokaryotic organism PPI networks. The global network was linked to the protein secretion pathway. Two WhiB-like regulators were found to be highly connected proteins in the global network. This is the first systematic noncomputational PPI data for the human pathogen, and it provides a useful resource for studies of infection mechanisms, new signaling pathways, and novel antituberculosis drug development.     

Discovering pathways by orienting edges in protein interaction networks

Modern experimental technology enables the identification of the sensory proteins that interact with the cells' environment or various pathogens. Expression and knockdown studies can determine the downstream effects of these interactions. However, when attempting to reconstruct the signaling networks and pathways between these sources and targets, one faces a substantial challenge. Although pathways are directed, high-throughput protein interaction data are undirected. In order to utilize the available data, we need methods that can orient protein interaction edges and discover high-confidence pathways that explain the observed experimental outcomes. We formalize the orientation problem in weighted protein interaction graphs as an optimization problem and present three approximation algorithms based on either weighted Boolean satisfiability solvers or probabilistic assignments. We use these algorithms to identify pathways in yeast. Our approach recovers twice as many known signaling cascades as a recent unoriented signaling pathway prediction technique and over 13 times as many as an existing network orientation algorithm. The discovered paths match several known signaling pathways and suggest new mechanisms that are not currently present in signaling databases. For some pathways, including the pheromone signaling pathway and the high-osmolarity glycerol pathway, our method suggests interesting and novel components that extend current annotations.     

Regulation of the LPA2 receptor signaling through the carboxyl-terminal tail-mediated protein-protein interactions

While it is well known that lysophosphatidic acid (LPA) mediates diverse physiological and pathophysiological responses through the activation of G protein-coupled LPA receptors, the specificity and molecular mechanisms by which different LPA receptors mediate these biological responses remain largely unknown. Recent identification of several PDZ proteins and zinc finger proteins that interact with the carboxyl-terminal tail of the LPA(2) receptor provides a considerable progress towards the understanding of the mechanisms how the LPA(2) receptor specifically mediates LPA signaling pathways. These findings have led to the proposal that there are at least two distinct protein interaction motifs present in the carboxyl-terminus of the LPA(2) receptor. Together, these data provide a new concept that the efficiency and specificity of the LPA(2) receptor-mediated signal transduction can be achieved through the cross-regulation between the classical G protein-activated signaling cascades and the interacting partner-mediated signaling pathways.