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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.

Recent successes in the pharmacotherapeutic treatment of breast cancer are associated with the use of selective estrogen receptor modulators. Two commonly prescribed pharmaceuticals in this class, tamoxifen and raloxifene, have been shown to have effects through estrogen receptor (ER)-independent mechanisms. Hyperactivation of phospholipase D (PLD) in certain tumor-derived cell lines have been reported, and recent findings suggest a role for PLD in transformation and metastasis. In the present study, we compare the effects of tamoxifen and raloxifene on PLD in the ER-positive mammary epithelial cell line MCF-12A, and the ER-negative, highly tumorigenic mammary carcinoma cell line MDA-MB-231. Our data demonstrate that tamoxifen and raloxifene have differential effects on PLD catalytic activity. Tamoxifen stimulates PLD in both ER-positive and -negative cells in vivo, whereas raloxifene inhibits PLD activity in these same cell types. In addition, we show that the active metabolite 4-OH-tamoxifen can be used to pharmacologically discriminate the two isoforms of PLD, through a stimulatory effect on PLD1 and an inhibitory effect on PLD2. Using recombinant PLD1, we show stimulation by tamoxifen requires a factor present in Sf21 insect cells that is not required for inhibition of PLD1 by raloxifene. Furthermore, tamoxifen stimulation and raloxifene inhibition of PLD activities are independent of the amino-terminal portion of PLD1 (amino acids 1-324). Knowledge of the mechanisms of action of these drugs on PLD may provide insights into the pharmacological action of these drugs and the role of PLD in some cancers.