SILACtor: software to enable dynamic SILAC studies

TurnoveR: A Skyline External Tool for Analysis of Protein Turnover in Metabolic Labeling Studies

In spite of its central role in biology and disease, protein turnover is a largely understudied aspect of most proteomic studies due to the complexity of computational workflows that analyze in vivo turnover rates. To address this need, we developed a new computational tool, TurnoveR, to accurately calculate protein turnover rates from mass spectrometric analysis of metabolic labeling experiments in Skyline, a free and open-source proteomics software platform. TurnoveR is a straightforward graphical interface that enables seamless integration of protein turnover analysis into a traditional proteomics workflow in Skyline, allowing users to take advantage of the advanced and flexible data visualization and curation features built into the software. The computational pipeline of TurnoveR performs critical steps to determine protein turnover rates, including isotopologue demultiplexing, precursor-pool correction, statistical analysis, and generation of data reports and visualizations. This workflow is compatible with many mass spectrometric platforms and recapitulates turnover rates and differential changes in turnover rates between treatment groups calculated in previous studies. We expect that the addition of TurnoveR to the widely used Skyline proteomics software will facilitate wider utilization of protein turnover analysis in highly relevant biological models, including aging, neurodegeneration, and skeletal muscle atrophy.

Accumulation of "Old Proteins" and the Critical Need for MS-based Protein Turnover Measurements in Aging and Longevity

Aging and age-related diseases are accompanied by proteome remodeling and progressive declines in cellular machinery required to maintain protein homeostasis (proteostasis), such as autophagy, ubiquitin-mediated degradation, and protein synthesis. While many studies have focused on capturing changes in proteostasis, the identification of proteins that evade these cellular processes has recently emerged as an approach to studying the aging proteome. With advances in proteomic technology, it is possible to monitor protein half-lives and protein turnover at the level of individual proteins in vivo. For large-scale studies, these technologies typically include the use of stable isotope labeling coupled with MS and comprehensive assessment of protein turnover rates. Protein turnover studies have revealed groups of highly relevant long-lived proteins (LLPs), such as the nuclear pore complexes, extracellular matrix proteins, and protein aggregates. Here, the role of LLPs during aging and age-related diseases and the methods used to identify and quantify their changes are reviewed. The methods available to conduct studies of protein turnover, used in combination with traditional proteomic methods, will enable the field to perform studies in a systems biology context, as changes in proteostasis may not be revealed in studies that solely measure differential protein abundances.

Protein Turnover in Aging and Longevity

Progressive loss of proteostasis is a hallmark of aging that is marked by declines in various components of proteostasis machinery, including: autophagy, ubiquitin-mediated degradation, protein synthesis, and others. While declines in proteostasis have historically been observed as changes in these processes, or as bulk changes in the proteome, recent advances in proteomic methodologies have enabled the comprehensive measurement of turnover directly at the level of individual proteins in vivo. These methods, which utilize a combination of stable-isotope labeling, mass spectrometry, and specialized software analysis, have now been applied to various studies of aging and longevity. Here we review the role of proteostasis in aging and longevity, with a focus on the proteomic methods available to conduct protein turnover in aging models and the insights these studies have provided thus far.

Analysis of proteome dynamics in mice by isotopic labeling

Recent advances in mass spectrometry and in vivo isotopic labeling have enabled proteome-wide analyses of protein turnover in complex organisms. Here, we describe a protocol for analyzing protein turnover rates in mouse tissues by comprehensive (15)N labeling. The procedure involves the complete isotopic labeling of blue green algae (Spirulina platensis) with (15)N and utilizing it as a source of dietary nitrogen for mice. We outline a detailed protocol for in-house production of (15)N-labeled algae, labeling of mice, and analysis of isotope incorporation kinetics by mass spectrometry. The methodology can be adapted to analyze proteome dynamics in most murine tissues and may be particularly useful in the analysis of proteostatic disruptions in mouse models of disease.

SILAC peptide ratio calculator: a tool for SILAC quantitation of peptides and post-translational modifications

This paper describes an algorithm to assist in relative quantitation of peptide post-translational modifications using stable isotope labeling by amino acids in cell culture (SILAC). The described algorithm first determines the normalization factor and then calculates SILAC ratios for a list of target peptide masses using precursor ion abundances. Four yeast histone mutants were used to demonstrate the effectiveness of this approach for quantitation of peptide post-translational modifications changes. The details of the algorithm's approach for normalization and peptide ratio calculation are described. The examples demonstrate the robustness of the approach as well as its utility to rapidly determine changes in peptide post-translational modifications within a protein.

Find pairs: the module for protein quantification of the PeakQuant software suite

Accurate quantification of proteins is one of the major tasks in current proteomics research. To address this issue, a wide range of stable isotope labeling techniques have been developed, allowing one to quantitatively study thousands of proteins by means of mass spectrometry. In this article, the FindPairs module of the PeakQuant software suite is detailed. It facilitates the automatic determination of protein abundance ratios based on the automated analysis of stable isotope-coded mass spectrometric data. Furthermore, it implements statistical methods to determine outliers due to biological as well as technical variance of proteome data obtained in replicate experiments. This provides an important means to evaluate the significance in obtained protein expression data. For demonstrating the high applicability of FindPairs, we focused on the quantitative analysis of proteome data acquired in (14)N/(15)N labeling experiments. We further provide a comprehensive overview of the features of the FindPairs software, and compare these with existing quantification packages. The software presented here supports a wide range of proteomics applications, allowing one to quantitatively assess data derived from different stable isotope labeling approaches, such as (14)N/(15)N labeling, SILAC, and iTRAQ. The software is publicly available at http://www.medizinisches-proteom-center.de/software and free for academic use.

Protein turnover: measurement of proteome dynamics by whole animal metabolic labelling with stable isotope labelled amino acids

The measurement of protein turnover in tissues of intact animals is obtained by whole animal dynamic labelling studies, requiring dietary administration of precursor label. It is difficult to obtain full labelling of precursor amino acids in the diet and if partial labelling is used, calculation of the rate of turnover of each protein requires knowledge of the precursor relative isotope abundance (RIA). We describe an approach to dynamic labelling of proteins in the mouse with a commercial diet supplemented with a pure, deuterated essential amino acid. The pattern of isotopomer labelling can be used to recover the precursor RIA, and sampling of urinary secreted proteins can monitor the development of liver precursor RIA non-invasively. Time-series analysis of the labelling trajectories for individual proteins allows accurate determination of the first order rate constant for degradation. The acquisition of this parameter over multiple proteins permits turnover profiling of cellular proteins and comparisons of different tissues. The median rate of degradation of muscle protein is considerably lower than liver or kidney, with heart occupying an intermediate position.

Topograph, a software platform for precursor enrichment corrected global protein turnover measurements

Defects in protein turnover have been implicated in a broad range of diseases, but current proteomics methods of measuring protein turnover are limited by the software tools available. Conventional methods require indirect approaches to differentiate newly synthesized protein when synthesized from partially labeled precursor pools. To address this, we have developed Topograph, a software platform which calculates the fraction of peptides that are from newly synthesized proteins and their turnover rates. A unique feature of Topograph is the ability to calculate amino acid precursor pool enrichment levels which allows for accurate calculations when the precursor pool is not fully labeled, and the approach used by Topograph is applicable regardless of the stable isotope label used. We validate the Topograph algorithms using data acquired from a mouse labeling experiment and demonstrate the influence that precursor pool corrections can have on protein turnover measurements.