Abstract
In a recent study, in vivo metabolic labeling using (15)N traced the rate of label incorporation among more than 1700 proteins simultaneously and enabled the determination of individual protein turnover rate constants over a dynamic range of three orders of magnitude (Price, J. C., Guan, S., Burlingame, A., Prusiner, S. B., and Ghaemmaghami, S. (2010) Analysis of proteome dynamics in the mouse brain. Proc. Natl. Acad. Sci. U. S. A. 107, 14508-14513). These studies of protein dynamics provide a deeper understanding of healthy development and well-being of complex organisms, as well as the possible causes and progression of disease. In addition to a fully labeled food source and appropriate mass spectrometry platform, an essential and enabling component of such large scale investigations is a robust data processing and analysis pipeline, which is capable of the reduction of large sets of liquid chromatography tandem MS raw data files into the desired protein turnover rate constants. The data processing pipeline described in this contribution is comprised of a suite of software modules required for the workflow that fulfills such requirements. This software platform includes established software tools such as a mass spectrometry database search engine together with several additional, novel data processing modules specifically developed for (15)N metabolic labeling. These fulfill the following functions: (1) cross-extraction of (15)N-containing ion intensities from raw data files at varying biosynthetic incorporation times, (2) computation of peptide (15)N isotopic incorporation distributions, and (3) aggregation of relative isotope abundance curves for multiple peptides into single protein curves. In addition, processing parameter optimization and noise reduction procedures were found to be necessary in the processing modules in order to reduce propagation of errors in the long chain of the processing steps of the entire workflow.
Highlights
From the ‡Department of Pharmaceutical Chemistry and Mass Spectrometry Facility, University of California, San Francisco, CA 94158-2517 and §Institute for Neurodegenerative Diseases and Department of Neurology, University of California, San Francisco, CA 94143-0518
Both radioactive [3] and stable isotope labeling techniques [4] have been employed in metabolic research. 15N isotope labeling provided some of the first evidence that dietary nutrients are used to maintain cell homeostasis as well as to provide energy [5]. 15N metabolic labeling has been applied to studies of a variety of model organisms [6] for several decades, but only recently has been employed for in vivo studies of mammals, such as Rattus norvegicus [7, 8]
In one method reported by Yates and coworkers, termed stable isotope labeling of mammals (SILAM)1, 15N incorporation was achieved by feeding rats a diet of 15N-enriched algae
Summary
Experimental procedures are described in detail elsewhere [1]. Briefly, 9-week-old FVB mice were fed with natural algae for 7 days. Gel lanes corresponding to the nine 15N incorporation time points were cut from the gel and each lane was further cut into nine slices, approximately corresponding to the molecular weight bands of Precision Plus Protein standard from Bio-Rad, Hercules, CA (10 –15, 15–20, 20 –25, 25–37, 37–50, 50 –75, 75–100, 100 –150, 150 –250 kDa). A utility program (a separate code from the main body of the program described below) was written in MATLAB to calculate the darkness of the gel slices, subjected to blank gel background subtraction. Injection of a similar amount of peptides reduces retention time variation between different samples (gel slices). The natural and 15N-labeled algae proteins were subjected to the SDS-PAGE separation. A novel software strategy was developed to extract protein turnover rate constants from raw LC MS/MS data files. The software described here and data files can be provided upon request
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