Determination of strongly overlapping signaling activity from microarray data

Background  

As numerous diseases involve errors in signal transduction, modern therapeutics often target proteins involved in cellular signaling. Interpretation of the activity of signaling pathways during disease development or therapeutic intervention would assist in drug development, design of therapy, and target identification. Microarrays provide a global measure of cellular response, however linking these responses to signaling pathways requires an analytic approach tuned to the underlying biology. An ongoing issue in pattern recognition in microarrays has been how to determine the number of patterns (or clusters) to use for data interpretation, and this is a critical issue as measures of statistical significance in gene ontology or pathways rely on proper separation of genes into groups.     

SNAVI: Desktop application for analysis and visualization of large-scale signaling networks

Background  

Studies of cellular signaling indicate that signal transduction pathways combine to form large networks of interactions. Viewing protein-protein and ligand-protein interactions as graphs (networks), where biomolecules are represented as nodes and their interactions are represented as links, is a promising approach for integrating experimental results from different sources to achieve a systematic understanding of the molecular mechanisms driving cell phenotype. The emergence of large-scale signaling networks provides an opportunity for topological statistical analysis while visualization of such networks represents a challenge.     

cPath: open source software for collecting, storing, and querying biological pathways

Background  

Biological pathways, including metabolic pathways, protein interaction networks, signal transduction pathways, and gene regulatory networks, are currently represented in over 220 diverse databases. These data are crucial for the study of specific biological processes, including human diseases. Standard exchange formats for pathway information, such as BioPAX, CellML, SBML and PSI-MI, enable convenient collection of this data for biological research, but mechanisms for common storage and communication are required.     

Cytosolic phospholipase A2: a member of the signalling pathway of a new G protein α subunit in Sporothrix schenckii

Background  

Sporothrix schenckii is a pathogenic dimorphic fungus, the etiological agent of sporotrichosis, a lymphocutaneous disease that can remain localized or can disseminate, involving joints, lungs, and the central nervous system. Pathogenic fungi use signal transduction pathways to rapidly adapt to changing environmental conditions and S. schenckii is no exception. S. schenckii yeast cells, either proliferate (yeast cell cycle) or engage in a developmental program that includes proliferation accompanied by morphogenesis (yeast to mycelium transition) depending on the environmental conditions. The principal intracellular receptors of environmental signals are the heterotrimeric G proteins, suggesting their involvement in fungal dimorphism and pathogenicity. Identifying these G proteins in fungi and their involvement in protein-protein interactions will help determine their role in signal transduction pathways.     

Application of Petri net based analysis techniques to signal transduction pathways

Background  

Signal transduction pathways are usually modelled using classical quantitative methods, which are based on ordinary differential equations (ODEs). However, some difficulties are inherent in this approach. On the one hand, the kinetic parameters involved are often unknown and have to be estimated. With increasing size and complexity of signal transduction pathways, the estimation of missing kinetic data is not possible. On the other hand, ODEs based models do not support any explicit insights into possible (signal-) flows within the network. Moreover, a huge amount of qualitative data is available due to high-throughput techniques. In order to get information on the systems behaviour, qualitative analysis techniques have been developed. Applications of the known qualitative analysis methods concern mainly metabolic networks. Petri net theory provides a variety of established analysis techniques, which are also applicable to signal transduction models. In this context special properties have to be considered and new dedicated techniques have to be designed.     

Structure determination of human Lck unique and SH3 domains by nuclear magnetic resonance spectroscopy

Background  

Protein tyrosine kinases are involved in signal transduction pathways that regulate cell growth, differentiation, activation and transformation. Human lymphocyte specific kinase (Lck) is a 56 kDa protein involved in T-cell- and IL2-receptor signaling. Three-dimensional structures are known for SH3, SH2 and kinase domains of Lck as well as for other tyrosine kinases. No structure is known for the unique domain of any Src-type tyrosine kinase.     

A genetic network model of cellular responses to lithium treatment and cocaine abuse in bipolar disorder

Background  

Lithium is an effective treatment for Bipolar Disorder (BD) and significantly reduces suicide risk, though the molecular basis of lithium's effectiveness is not well understood. We seek to improve our understanding of this effectiveness by posing hypotheses based on new experimental data as well as published data, testing these hypotheses in silico, and posing new hypotheses for validation in future studies. We initially hypothesized a gene-by-environment interaction where lithium, acting as an environmental influence, impacts signal transduction pathways leading to differential expression of genes important in the etiology of BD mania.     

Robust MS quantification method for phospho-peptides using 18O/16O labeling

Background  

Quantitative measurements of specific protein phosphorylation sites, as presented here, can be used to investigate signal transduction pathways, which is an important aspect of cell dynamics. The presented method quantitatively compares peptide abundances from experiments using ¹⁸O/¹⁶O labeling starting from elaborated MS spectra. It was originally developed to study signaling cascades activated by amyloid-β treatment of neurons used as a cellular model system with relevance to Alzheimer's disease, but is generally applicable.     

Reduced modeling of signal transduction – a modular approach

Background  

Combinatorial complexity is a challenging problem in detailed and mechanistic mathematical modeling of signal transduction. This subject has been discussed intensively and a lot of progress has been made within the last few years. A software tool (BioNetGen) was developed which allows an automatic rule-based set-up of mechanistic model equations. In many cases these models can be reduced by an exact domain-oriented lumping technique. However, the resulting models can still consist of a very large number of differential equations.     

Functional network of glycan-related molecules: Glyco-Net in Glycoconjugate Data Bank

Background  

Glycans are involved in a wide range of biological process, and they play an essential role in functions such as cell differentiation, cell adhesion, pathogen-host recognition, toxin-receptor interactions, signal transduction, cancer metastasis, and immune responses. Elucidating pathways related to post-translational modifications (PTMs) such as glycosylation are of growing importance in post-genome science and technology. Graphical networks describing the relationships among glycan-related molecules, including genes, proteins, lipids and various biological events are considered extremely valuable and convenient tools for the systematic investigation of PTMs. However, there is no database which dynamically draws functional networks related to glycans.     

PKG II Inhibits EGF/EGFR-Induced Migration of Gastric Cancer Cells

Background

Our previous research results showed that Type II cGMP dependent protein kinase (PKG II) could block the activation of epidermal growth factor receptor (EGFR) and consequently inhibit the proliferation and the related MAPK/ERK-mediated signal transduction of gastric cancer cell line BGC-823, suggesting that PKG II might inhibit other EGFR-triggered signal transduction pathways and related biological activities of gastric cancer cells. This paper was designed to investigate the potential inhibition of PKG II on EGF/EGFR-induced migration activity and the related signal transduction pathways.

Methodology/Principal Findings

In gastric cancer cell line AGS, expression and activity of PKG II were increased by infecting the cells with adenoviral construct encoding PKG II cDNA (Ad-PKG II) and treating the cells with cGMP analogue 8-pCPT-cGMP. Phosphorylation of proteins was detected by Western Blotting and active small G protein Ras and Rac1 was measured by “Pull-down” method. Cell migration activity was detected with trans-well equipment. Binding between PKG II and EGFR was detected with Co-IP. The results showed EGF stimulated migration of AGS cell and the effect was related to PLCγ1 and ERK-mediated signal transduction pathways. PKG II inhibited EGF-induced migration activity and blocked EGF-initiated signal transduction of PLCγ1 and MAPK/ERK-mediated pathways through preventing EGF-induced Tyr 992 and Tyr 1068 phosphorylation of EGFR. PKG II bound with EGFR and caused threonine phosphorylation of it.

Conclusion/Significance

Our results systemically confirms the inhibition of PKG II on EGF-induced migration and related signal transduction of PLCγ1 and MAPK/ERK-mediated pathways, indicating that PKG II has a fargoing inhibition on EGF/EGFR related signal transduction and biological activities of gastric cancer cells through phosphorylating EGFR and blocking the activation of it.     

Chromosomal loci important for cotyledon opening under UV-B in Arabidopsis thaliana

Background  

Understanding of the genetic architecture of plant UV-B responses allows extensive targeted testing of candidate genes or regions, along with combinations of those genes, for placement in metabolic or signal transduction pathways.     

Elementary signaling modes predict the essentiality of signal transduction network components

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.     

PKC translocation and ERK1/2 activation in compensated right ventricular hypertrophy secondary to chronic emphysema.

Background  

Right ventricular hypertrophy (RVH) is an important complication of chronic lung disease. However, the signal transduction pathways involved as well as the physiological changes to the right ventricle have not been investigated. Emphysema was produced in male, Syrian Golden hamsters by intra-tracheal instillation of 250 IU/kg elastase (Emp, n = 17). Saline treated animals served as controls (Con, n = 15).