Single-click, self-updating web installation is available at http://proteome.gs.washington.edu/software/skyline. This web site also provides access to instructional videos, a support board, an issues list and a link to the source code project.

Background-Age is a major risk for cardiovascular diseases. Although mitochondrial reactive oxygen species have been proposed as one of the causes of aging, their role in cardiac aging remains unclear. We have previously shown that overexpression of catalase targeted to mitochondria (mCAT) prolongs murine median lifespan by 17% to 21%. Methods and Results-We used echocardiography to study cardiac function in aging cohorts of wild-type and mCAT mice.Changes found in wild-type mice recapitulate human aging: age-dependent increases in left ventricular mass index and left atrial dimension, worsening of the myocardial performance index, and a decline in diastolic function. Cardiac aging in mice is accompanied by accumulation of mitochondrial protein oxidation, increased mitochondrial DNA mutations and deletions and mitochondrial biogenesis, increased ventricular fibrosis, enlarged myocardial fiber size, decreased cardiac SERCA2 protein, and activation of the calcineurin-nuclear factor of activated T-cell pathway. All of these age-related changes were significantly attenuated in mCAT mice. Analysis of survival of 130 mice demonstrated that echocardiographic cardiac aging risk scores were significant predictors of mortality. The estimated attributable risk to mortality for these 2 parameters was 55%. Conclusions-This study shows that cardiac aging in the mouse closely recapitulates human aging and demonstrates the critical role of mitochondrial reactive oxygen species in cardiac aging and the impact of cardiac aging on survival. These findings also support the potential application of mitochondrial antioxidants in reactive oxygen species-related cardiovascular diseases.

Proteomics experiments based on Selected Reaction Monitoring (SRM, also referred to as Multiple Reaction Monitoring or MRM) are being used to target large numbers of protein candidates in complex mixtures. At present, instrument parameters are often optimized for each peptide, a time and resource intensive process. Large SRM experiments are greatly facilitated by having the ability to predict MS instrument parameters that work well with the broad diversity of peptides they target. For this reason, we investigated the impact of using simple linear equations to predict the collision energy (CE) on peptide signal intensity and compared it with the empirical optimization of the CE for each peptide and transition individually. Using optimized linear equations, the difference between predicted and empirically derived CE values was found to be an average gain of only 7.8% of total peak area. We also found that existing commonly used linear equations fall short of their potential, and should be recalculated for each charge state and when introducing new instrument platforms. We provide a fully automated pipeline for calculating these equations and individually optimizing CE of each transition on SRM instruments from Agilent, Applied Biosystems, Thermo-Scientific and Waters in the open source Skyline software tool (http://proteome.gs.washington.edu/software/skyline).