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Mutations of comparative gene identification 58 (CGI-58) in humans cause Chanarin-Dorfman syndrome, a rare autosomal recessive disease in which excess triacylglycerol (TAG) accumulates in multiple tissues. CGI-58 recently has been ascribed two distinct biochemical activities, including coactivation of adipose triglyceride lipase and acylation of lysophosphatidic acid (LPA). It is noteworthy that both the substrate (LPA) and the product (phosphatidic acid) of the LPA acyltransferase reaction are well-known signaling lipids. Therefore, we hypothesized that CGI-58 is involved in generating lipid mediators that regulate TAG metabolism and insulin sensitivity. Here, we show that CGI-58 is required for the generation of signaling lipids in response to inflammatory stimuli and that lipid second messengers generated by CGI-58 play a critical role in maintaining the balance between inflammation and insulin action. Furthermore, we show that CGI-58 is necessary for maximal TH1 cytokine signaling in the liver. This novel role for CGI-58 in cytokine signaling may explain why diminished CGI-58 expression causes severe hepatic lipid accumulation yet paradoxically improves hepatic insulin action. Collectively, these findings establish that CGI-58 provides a novel source of signaling lipids. These findings contribute insight into the basic mechanisms linking TH1 cytokine signaling to nutrient metabolism.

Thrombin induces platelet activation through an early, reversible stage of platelet aggregation, which is followed by a later, irreversible stage of platelet aggregation. Without intervention, events leading to pathological platelet activation can result in vessel occlusion, acute coronary syndrome, and stroke. Therefore, a better understanding of events leading to platelet-mediated clot formation may provide insight into new therapeutic targets. Once activated, protease activated receptors (PARs) are essential in regulating events leading to platelet aggregation. We have determined a signaling cascade through PAR1, which involves phosphatidylinositol (PI) kinases, phosphatidylinositol bisphosphate (PIP(2)), and Rap1 activation (independent of P2Y12) in the formation of a stable platelet aggregate. The putative phosphatidylinositol-3 kinase (PI3K) inhibitor LY294002 was found to reduce basal and PAR-stimulated PIP(2) levels by mass spectrometry and to inhibit PAR1-mediated stable platelet aggregation. Rap1 activation in platelets (during time points corresponding to the late, irreversible phase of aggregation) was found to require the PI signaling pathway. Perturbation of PI3K signaling by isoform-selective inhibitors had differential effects on Rap1 activation through PAR1 and PAR4. Hence, it is possible to disrupt lipid signaling pathways involved in stable clot formation without inhibiting early clot formation, offering a new potential target for antiplatelet therapy.

Recent successes in defining the roles of lipids in cell signaling have stimulated greater interest in these versatile biomolecules. Until recently, analysis of these molecules at the species level has required labor-intensive techniques. The development of electrospray ionization mass spectrometry (ESI-MS) has made possible the detection and identification of thermally labile biological molecules, such as phospholipids. The "soft" ionization does not cause extensive fragmentation, is highly sensitive, accurate, and reproducible. Thus, this method is well suited for analyzing a broad range of phospholipids without elaborate chromatographic separation. Evaluating the vast amounts of data resulting from these measurements is a rate-limiting step in the assessment of phospholipid composition, requiring the development and application of computational algorithms for mass spectrometry data. Here we describe computational lipidomics, a novel analytical technique, coupling mass spectrometry with statistical algorithms to facilitate the comprehensive analysis of hundreds of lipid species from cellular extracts. As a result, lipid arrays are generated to indicate qualitative changes that occur in lipid composition between experimental or disease states, similar to proteomic and genomic analyses. This review presents a methodological strategy for using ESI-MS combined with a high-power computational analysis to profile time-dependent changes in cellular phospholipids after the addition of an agonist or to evaluate changes promoted by pathophysiological processes. As an illustration, we describe the methods and approaches used to generate lipid arrays for The Alliance for Cellular Signaling (AfCS). These arrays are contributing to a more complete understanding of the participants of cellular signaling pathways after activation of cell surface receptors.

Phospholipids are the structural building blocks of the membrane bilayer, which retains and regulates intra-cellular content. In addition to creating a protective barrier around the cell, lipids modulate membrane trafficking and are themselves precursors of important intracellular signaling molecules. Identification and quantification of these molecular species is essential for a more complete understanding of cell signaling pathways, and more reliable and sensitive methods are needed for determining membrane phospholipid content. Recent improvements in electrospray ionization mass spectrometry have made possible the direct identification of more than 400 phospholipid species from biological extracts of a single cell type. Changes in the cellular concentration of diverse lipids can be determined by analysis of the mass spectra by statistical algorithms. In the future, lipid arrays will be integrated with other high-throughput profiling technologies, and computational lipidomics will expand our understanding of the molecular basis of cellular processes and diseases.

The Alliance for Cellular Signaling is a large-scale collaboration designed to answer global questions about signalling networks. Pathways will be studied intensively in two cells--B lymphocytes (the cells of the immune system) and cardiac myocytes--to facilitate quantitative modelling. One goal is to catalyse complementary research in individual laboratories; to facilitate this, all alliance data are freely available for use by the entire research community.