Endothelin-1- and isoproterenol-induced differential protein expression and signaling pathway in HL-1 cardiomyocytes.

It is well known that the two chemical compounds endothelin-1 (ET-1) and isoproterenol (ISO) can individually induce cardiac hypertrophy through G protein-coupled receptors in cardiomyocytes. However, the cardiac hypertrophy signaling pathway activated by ET-1 and ISO is not well defined. Therefore, we investigated the protein expression profile and signaling transduction in HL-l cardiomyocyte cells treated with ET-1 and ISO. Following separation of the cell lysates by using 2-DE and silver staining, we identified 16 protein spots that were differentially expressed as compared to the controls. Of these 16 spots, three changed only after treatment with ET-1, whereas four changed only after treatment with ISO, suggesting that these two stimuli could induce different signaling pathways. In order to reveal the differences between ET-1- and ISO-induced signaling, we studied the different events that occur at each step of the signaling pathways, when selected biocomponents were blocked by inhibitors. Our results indicated that ET-1 and ISO used different pathways for phosphorylation of glycogen synthase kinase-3ß (GSK3ß). ET-1 mainly used the mitogen-activated protein kinase and phosphatidylinositol-3-kinase/AKT pathways to activate GSK3ß, whereas under ISO stimulation, only the phosphatidylinositol-3-kinase/AKT pathway was required to trigger the GSK3ß pathway. Furthermore, the strength of the GSK3ß signal in ISO-induced cardiac hypertrophy was stronger than that in ET-1-induced cardiac hypertrophy. We found that these two agonists brought about different changes in the protein expression of HL-1 cardiomyocytes through distinct signaling pathways even though the destination of the two signaling pathways was the same.

Growing trend of CE at the omics level: the frontier of systems biology.

In a novel attempt to comprehend the complexity of life, systems biology has recently emerged as a state-of-the-art approach for biological research in contrast to the reductionist approaches that have been used in molecular cell biology since the 1950s. Because a massive amount of information is required in many systems biology studies of life processes, we have increasingly come to depend on techniques that provide high-throughput omics data. CE and CE coupled to MS have served as powerful analytical tools for providing qualitative and quantitative omics data. Recent systems biology studies have focused strongly on the diagnosis and treatment of diseases. The increasing number of clinical research papers on drug discovery and disease therapies reflects this growing interest among scientists. Since such clinical research reflects one of the ultimate purposes of bioscience, these trends will be sustained for a long time. Thus, this review mainly focuses on the application of CE and CE-MS in diagnosis as well as on the latest CE methods developed. Furthermore, we outline the new challenges that arose in 2008 and later in elucidating the system-level functions of the bioconstituents of living organisms.

Sensitivity enhancement of CE and CE-MS for the analysis of peptides by a dynamic pH junction.

An analytical method of CE-MS and CE with an online preconcentration technique induced by a dynamic pH junction, addition of organic solvent and large volume injection was developed for sensitive determination of peptides in biological samples. Leucine enkephalin, methionine enkephalin, dynorphin A, ß-endorphin and angiotensin II were used as model peptides. The optimal online preconcentration conditions were obtained at a sample matrix consisting of 100 mM borate buffer (pH 10.0) with 50% v/v acetonitrile and a BGE containing 1 M formic acid at pH 2.0, along with a 25-cm injection length. Under the optimized conditions, a 4.0×10(3)-1.1×10(4)-fold increase in peak intensity was achieved without degrading the peak shape. This online preconcentration method was applied to analyze the intracellular angiotensin II within the peptides extracted from HL1 cells and approximately increase of 1×10(4)-fold sensitivity was achieved compared to normal condition. Thus, the developed method could be applied to the analysis of various peptides for peptidomics study in biological samples.

Kinetic analysis of the MAPK and PI3K/Akt signaling pathways.

Computational modeling of signal transduction is currently attracting much attention as it can promote the understanding of complex signal transduction mechanisms. Although several mathematical models have been used to examine signaling pathways, little attention has been given to crosstalk mechanisms. In this study, an attempt was made to develop a computational model for the pathways involving growth-factor-mediated mitogen-activated protein kinase (MAPK) and phosphatidylinositol 3'-kinase/protein kinase B (PI3K/Akt). In addition, the dynamics of the protein activities were analyzed based on a set of kinetic data. The simulation approach integrates the information on several levels and predicts systems behavior. The in-silico analysis conducted revealed that the Raf and Akt pathways act independently.

CE at the omics level: towards systems biology--an update.

This review provides an updated overview of recent developments and applications of CE based on previously published reports in the field of omic research. The increased number of published articles on omics shows that the field is growing and attracting the attention of many life science researchers. Due to developments in the omics sciences, many researchers have been studying systems biology, in which biological events in organisms are systematically interpreted through the combination of complex measurements from various methods resulting in high-throughput data. Given the challenges of such complex forms of analysis, CE is a strong candidate for generating omics data useful for acquiring the qualitative and quantitative knowledge necessary for systems-level investigation. By emphasizing CE for systems biology, this review will discuss and focus on the applicability of CE to systems-based analytical data at the genomic, transcriptomic, proteomic, and metabolomic levels from 2005 to the present.

Classification of scale-free networks.

While the emergence of a power-law degree distribution in complex networks is intriguing, the degree exponent is not universal. Here we show that the between ness centrality displays a power-law distribution with an exponent eta, which is robust, and use it to classify the scale-free networks. We have observed two universality classes with eta approximately equal 2.2(1) and 2.0, respectively. Real-world networks for the former are the protein-interaction networks, the metabolic networks for eukaryotes and bacteria, and the coauthorship network, and those for the latter one are the Internet, the World Wide Web, and the metabolic networks for Archaea. Distinct features of the mass-distance relation, generic topology of geodesics, and resilience under attack of the two classes are identified. Various model networks also belong to either of the two classes, while their degree exponents are tunable.