ProtQuant: a tool for the label-free quantification of MudPIT proteomics data

<p>Abstract</p> <p>Background</p> <p>Effective and economical methods for quantitative analysis of high throughput mass spectrometry data are essential to meet the goals of directly identifying, characterizing, and quantifying proteins from a particular cell state. Multidimensional Protein Identification Technology (MudPIT) is a common approach used in protein identification. Two types of methods are used to detect differential protein expression in MudPIT experiments: those involving stable isotope labelling and the so-called label-free methods. Label-free methods are based on the relationship between protein abundance and sampling statistics such as peptide count, spectral count, probabilistic peptide identification scores, and sum of peptide Sequest XCorr scores (ΣXCorr). Although a number of label-free methods for protein quantification have been described in the literature, there are few publicly available tools that implement these methods. We describe ProtQuant, a Java-based tool for label-free protein quantification that uses the previously published ΣXCorr method for quantification and includes an improved method for handling missing data.</p> <p>Results</p> <p><it>ProtQuant </it>was designed for ease of use and portability for the bench scientist. It implements the ΣXCorr method for label free protein quantification from MudPIT datasets. <it>ProtQuant </it>has a graphical user interface, accepts multiple file formats, is not limited by the size of the input files, and can process any number of replicates and any number of treatments. In addition,<it>ProtQuant </it>implements a new method for dealing with missing values for peptide scores used for quantification. The new algorithm, called ΣXCorr*, uses "below threshold" peptide scores to provide meaningful non-zero values for missing data points. We demonstrate that ΣXCorr* produces an average reduction in false positive identifications of differential expression of 25% compared to ΣXCorr.</p> <p>Conclusion</p> <p><it>ProtQuant </it>is a tool for protein quantification built for multi-platform use with an intuitive user interface. <it>ProtQuant </it>efficiently and uniquely performs label-free quantification of protein datasets produced with Sequest and provides the user with facilities for data management and analysis. Importantly, <it>ProtQuant </it>is available as a self-installing executable for the Windows environment used by many bench scientists.</p

SIRT3 Blocks Aging-Associated Tissue Fibrosis in Mice by Deacetylating and Activating Glycogen Synthase Kinase 3β.

Tissue fibrosis is a major cause of organ dysfunction during chronic diseases and aging. A critical step in this process is transforming growth factor 1 (TGF-1)-mediated transformation of fibroblasts into myofibroblasts, cells capable of synthesizing extracellular matrix. Here, we show that SIRT3 controls transformation of fibroblasts into myofibroblasts via suppressing the profibrotic TGF-1 signaling. We found that Sirt3 knockout (KO) mice with age develop tissue fibrosis of multiple organs, including heart, liver, kidney, and lungs but not whole-body SIRT3-overexpressing mice. SIRT3 deficiency caused induction of TGF-1 expression and hyperacetylation of glycogen synthase kinase 3 (GSK3) at residue K15, which negatively regulated GSK3 activity to phosphorylate the substrates Smad3 and -catenin. Reduced phosphorylation led to stabilization and activation of these transcription factors regulating expression of the profibrotic genes. SIRT3 deacetylated and activated GSK3 and thereby blocked TGF-1 signaling and tissue fibrosis. These data reveal a new role of SIRT3 to negatively regulate aging-associated tissue fibrosis and discloses a novel phosphorylation-independent mechanism controlling the catalytic activity of GSK3. Fibrosis