Prostacyclin (PGI2) is a vasodilator and platelet inhibitor, properties consistent with cardioprotection. More than a decade ago, inhibition of cyclooxygenase-2 (COX-2) by the nonsteroidal anti-inflammatory drugs (NSAIDs) rofecoxib and celecoxib was found to reduce the amount of the major metabolite of PGI2 (PGI-M) in the urine of healthy volunteers. This suggested that NSAIDs might cause adverse cardiovascular events by reducing production of cardioprotective PGI2. This prediction was based on the assumption that the concentration of PGI-M in urine likely reflected vascular production of PGI2 and that other cardioprotective mediators, especially nitric oxide (NO), were not able to compensate for the loss of PGI2. Subsequently, eight placebo-controlled clinical trials showed that NSAIDs that block COX-2 increase adverse cardiovascular events. We connect tissue-specific effects of NSAID action and functional correlates in mice with clinical outcomes in humans by showing that deletion of COX-2 in the mouse vasculature reduces excretion of PGI-M in urine and predisposes the animals to both hypertension and thrombosis. Furthermore, vascular disruption of COX-2 depressed expression of endothelial NO synthase and the consequent release and function of NO. Thus, suppression of PGI2 formation resulting from deletion of vascular COX-2 is sufficient to explain the cardiovascular hazard from NSAIDs, which is likely to be augmented by secondary mechanisms such as suppression of NO production.
The model organism zebrafish (Danio rerio) is particularly amenable to studies deciphering regulatory genetic networks in vertebrate development, biology, and pharmacology. Unraveling the functional dynamics of such networks requires precise quantitation of protein expression during organismal growth, which is incrementally challenging with progressive complexity of the systems. In an approach toward such quantitative studies of dynamic network behavior, we applied mass spectrometric methodology and rigorous statistical analysis to create comprehensive, high quality profiles of proteins expressed at two stages of zebrafish development. Proteins of embryos 72 and 120 h postfertilization (hpf) were isolated and analyzed both by two-dimensional (2D) LC followed by ESI-MS/MS and by 2D PAGE followed by MALDI-TOF/TOF protein identification. We detected 1384 proteins from 327,906 peptide sequence identifications at 72 and 120 hpf with false identification rates of less than 1% using 2D LC-ESI-MS/MS. These included only ϳ30% of proteins that were identified by 2D PAGE-MALDI-TOF/TOF. Roughly 10% of all detected proteins were derived from hypothetical or predicted gene models or were entirely unannotated. Comparison of proteins expression by 2D DIGE revealed that proteins involved in energy production and transcription/translation were relatively more abundant at 72 hpf consistent with faster synthesis of cellular proteins during organismal growth at this time compared with 120 hpf. The data are accessible in a database that links protein identifications to existing resources including the Zebrafish Information Network database. This new resource should facilitate the selection of candidate proteins for targeted quantitation and refine systematic genetic network analysis in vertebrate development and biology. Molecular & Cellular Proteomics 7:981-994, 2008.
MS/MSProteomics methods aim to identify the proteins expressed in biological samples (1) just like transcriptomics methods detect and quantify the abundance of RNA molecules (2). Both techniques can point to particular tissue correlates, such as disease biomarkers, and find networks of co-regulated genes and proteins that reveal the mechanisms underlying biological processes (3). As thousands of simultaneous measurements are taken, however, balancing sensitivity of detection against rates of false positive error is a major challenge. With rapid advances in large scale proteomics technology, there is an urgent need for methods that maximize sensitivity and control of false protein identifications by integrating the results of multiple experiments.In "shotgun" proteomics studies, a protein sample is digested with a proteolytic enzyme, and the resulting peptide mixture is separated by LC prior to conducting collisioninduced dissociation and MS/MS (1). This generates thousands of peptide ion fragmentation spectra containing diagnostic amino acid sequence information. Peptide sequences are inferred by matching the resulting product ion spectra to theoretical or empirical spectra in peptide sequence databases (4). No match is exact, so the peptide identifications are not certain. Database search algorithms either calculate a probability that the spectrum results from a random fragmentation (e.g. MASCOT, Ref. 5) or use heuristic scores (such as SEQUEST, Ref. 6), which can then be converted into probabilities of accurate peptide identification (7,8). A variety of other statistical models have also been proposed to score peptide matches (9). These algorithms have to some extent complementary merits, and the sensitivity and specificity of peptide sequence identifications from MS/MS data can be improved by the use of more than one search algorithm (10 -12). A recent comparison of five database search programs concluded with the recommendation to use consensus scoring from at least two different algorithms and noted the need for appropriate methods of combining scores from different algorithms (11). Database search methods using different assumptions and procedures can result in markedly different sets of protein identifications (e.g. Ref. 13).Probable peptide sequences need to be assembled to proteins (14). A simple method for identifying proteins is to filter the peptide matches using empirically derived thresholds and then sort by parent protein. Variants of this approach do not attempt to estimate false identification rates, nor do they deal in a useful way with "degenerate" peptides whose sequence matches more than one protein in the database (15-17). More sophisticated methods use statistical models to calculate protein expression probabilities (12, 18 -20) or ranking scores (21) based on the number and quality of peptide sequence matches and other criteria, such as protein length, the size of the database, and the degeneracy of the peptide sequence identifications. These models can be empirically validated by search...
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