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Automated online sequential isotope labeling for protein quantitation applied to proteasome tissue-specific diversity.

ABSTRACT: Quantitation of protein abundance is a vital component in the proteomic analysis of biological systems, which can be achieved by differential stable isotopic labeling. To analyze tissue-derived samples, the isotopic labeling can be performed using chemical labeling of the peptides post-digestion. Standard chemical labeling procedures often require many manual sample handling steps, reducing the accuracy of measurements. Here, we describe a fully automated, online (in nanoLC columns), labeling procedure, which allows protein quantitation using differential isotopic dimethyl labeling of peptide N termini and lysine residues. We show that the method allows reliable quantitation over a wide dynamic range and can be used to quantify differential protein abundances in lysates and, more targeted, differences in composition between purified protein complexes. We apply the method to determine the differences in composition between bovine liver and spleen 20 S core proteasome complexes. We find that although all catalytically active immunoproteasome subunits were up-regulated in spleen (compared with liver), only one of the normal catalytic subunits was down-regulated, suggesting that the tissue-specific immunoproteasome assembly is more diverse than previously assumed.

SUBMITTER: Raijmakers R

PROVIDER: S-EPMC2556017 | biostudies-other | 2008 Sep

REPOSITORIES: biostudies-other

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Automated online sequential isotope labeling for protein quantitation applied to proteasome tissue-specific diversity.

Raijmakers Reinout R Berkers Celia R CR de Jong Annemieke A Ovaa Huib H Heck Albert J R AJ Mohammed Shabaz S

Molecular & cellular proteomics : MCP 20080604 9

Quantitation of protein abundance is a vital component in the proteomic analysis of biological systems, which can be achieved by differential stable isotopic labeling. To analyze tissue-derived samples, the isotopic labeling can be performed using chemical labeling of the peptides post-digestion. Standard chemical labeling procedures often require many manual sample handling steps, reducing the accuracy of measurements. Here, we describe a fully automated, online (in nanoLC columns), labeling pr ...[more]

PMID: 18534969

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Project description:Dexamethasone (Dex) is a synthetic glucocorticoid (GC) drug commonly used clinically for the treatment of several inflammatory and immune-mediated diseases. Despite its broad range of indications, the long-term use of Dex is known to be associated with specific abnormalities in several tissues and organs. In this study, the metabolomic effects on five different organs induced by the chronic administration of Dex in the Sprague-Dawley rat model were investigated using the chemical isotope labeling liquid chromatography-mass spectrometry (CIL LC-MS) platform, which targets the amine/phenol submetabolomes. Compared to controls, a prolonged intake of Dex resulted in significant perturbations in the levels of 492, 442, 300, 186, and 105 metabolites in the brain, skeletal muscle, liver, kidney, and heart tissues, respectively. The positively identified metabolites were mapped to diverse molecular pathways in different organs. In the brain, perturbations in protein biosynthesis, amino acid metabolism, and monoamine neurotransmitter synthesis were identified, while in the heart, pyrimidine metabolism and branched amino acid biosynthesis were the most significantly impaired pathways. In the kidney, several amino acid pathways were dysregulated, which reflected impairments in several biological functions, including gluconeogenesis and ureagenesis. Beta-alanine metabolism and uridine homeostasis were profoundly affected in liver tissues, whereas alterations of glutathione, arginine, glutamine, and nitrogen metabolism pointed to the modulation of muscle metabolism and disturbances in energy production and muscle mass in skeletal muscle. The differential expression of multiple dipeptides was most significant in the liver (down-regulated), brain (up-regulation), and kidney tissues, but not in the heart or skeletal muscle tissues. The identification of clinically relevant pathways provides holistic insights into the tissue molecular responses induced by Dex and understanding of the underlying mechanisms associated with their side effects. Our data suggest a potential role for glutathione supplementation and dipeptide modulators as novel therapeutic interventions to mitigate the side effects induced by Dex therapy.

Project description:BackgroundMetabolic Flux Analysis (MFA) based on isotope labeling experiments (ILEs) is a widely established tool for determining fluxes in metabolic pathways. Isotope labeling networks (ILNs) contain all essential information required to describe the flow of labeled material in an ILE. Whereas recent experimental progress paves the way for high-throughput MFA, large network investigations and exact statistical methods, these developments are still limited by the poor performance of computational routines used for the evaluation and design of ILEs. In this context, the global analysis of ILN topology turns out to be a clue for realizing large speedup factors in all required computational procedures.ResultsWith a strong focus on the speedup of algorithms the topology of ILNs is investigated using graph theoretic concepts and algorithms. A rigorous determination of all cyclic and isomorphic subnetworks, accompanied by the global analysis of ILN connectivity is performed. Particularly, it is proven that ILNs always brake up into a large number of small strongly connected components (SCCs) and, moreover, there are natural isomorphisms between many of these SCCs. All presented techniques are universal, i.e. they do not require special assumptions on the network structure, bidirectionality of fluxes, measurement configuration, or label input. The general results are exemplified with a practically relevant metabolic network which describes the central metabolism of E. coli comprising 10390 isotopomer pools.ConclusionExploiting the topological features of ILNs leads to a significant speedup of all universal algorithms for ILE evaluation. It is proven in theory and exemplified with the E. coli example that a speedup factor of about 1000 compared to standard algorithms is achieved. This widely opens the door for new high performance algorithms suitable for high throughput applications and large ILNs. Moreover, for the first time the global topological analysis of ILNs allows to comprehensively describe and understand the general patterns of label flow in complex networks. This is an invaluable tool for the structural design of new experiments and the interpretation of measured data.

Dataset Information

Automated online sequential isotope labeling for protein quantitation applied to proteasome tissue-specific diversity.

Publications

Automated online sequential isotope labeling for protein quantitation applied to proteasome tissue-specific diversity.

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