The use of heavy nitrogen in quantitative proteomics experiments in plants.
ABSTRACT In the growing field of plant systems biology, there is an undisputed need for methods allowing accurate quantitation of proteins and metabolites. As autotrophic organisms, plants can easily metabolize different nitrogen isotopes, resulting in proteins and metabolites with distinct molecular mass that can be separated on a mass spectrometer. In comparative quantitative experiments, treated and untreated samples are differentially labeled by nitrogen isotopes and jointly processed, thereby minimizing sample-to-sample variation. In recent years, heavy nitrogen labeling has become a widely used strategy in quantitative proteomics and novel approaches have been developed for metabolite identification. Here, we present an overview of currently used experimental strategies in heavy nitrogen labeling in plants and provide background on the history and function of this quantitation technique.
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ABSTRACT: Plant breeders need new and more precise tools to accelerate breeding programs that address the increasing needs for food, feed, energy and raw materials, while facing a changing environment in which high salinity and drought have major impacts on crop losses worldwide. This review covers the achievements and bottlenecks in the identification and validation of proteins with relevance in abiotic stress tolerance, also mentioning the unexpected consequences of the stress in allergen expression. While addressing the key pathways regulating abiotic stress plant adaptation, comprehensive data is presented on the proteins confirmed as relevant to confer tolerance. Promising candidates still to be confirmed are also highlighted, as well as the specific protein families and protein modifications for which detection and characterization is still a challenge. This article is part of a Special Issue entitled: Translational Plant Proteomics SI: Translational Plant Proteomics.Journal of proteomics 07/2013; · 5.07 Impact Factor
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ABSTRACT: Protein phosphorylation is one of the most important post-translational modifications (PTMs) as it participates in regulating various cellular processes and biological functions. It is therefore crucial to identify phosphorylated proteins to construct a phosphor-relay network, and eventually to understand the underlying molecular regulatory mechanism in response to both internal and external stimuli. The changes in phosphorylation status at these novel phosphosites can be accurately measured using a (15)N-stable isotopic labeling in Arabidopsis (SILIA) quantitative proteomic approach in a high-throughput manner. One of the unique characteristics of the SILIA quantitative phosphoproteomic approach is the preservation of native PTM status on protein during the entire peptide preparation procedure. Evolved from SILIA is another quantitative PTM proteomic approach, AQUIP (absolute quantitation of isoforms of post-translationally modified proteins), which was developed by combining the advantages of targeted proteomics with SILIA. Bioinformatics-based phosphorylation site prediction coupled with an MS-based in vitro kinase assay is an additional way to extend the capability of phosphosite identification from the total cellular protein. The combined use of SILIA and AQUIP provides a novel strategy for molecular systems biological study and for investigation of in vivo biological functions of these phosphoprotein isoforms and combinatorial codes of PTMs.Frontiers in Plant Science 01/2012; 3:302.
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ABSTRACT: Quantitative comparative analyses of protein abundances using peptide ion intensities and their modifications have become a widely used technique in studying various biological questions. In the past years, several methods for quantitative proteomics were established using stable-isotope labeling and label-free approaches. We systematically evaluated the application of reference protein normalization (RPN) for proteomic experiments using a high mass accuracy LC-MS/MS platform. In RPN all sample peptide intensities were normalized to an average protein intensity of a spiked reference protein. The main advantage of this method is that it avoids fraction of total based relative analysis of proteomic data, which is often very much dependent on sample complexity. We could show that reference protein ion intensity sums are sufficiently reproducible to ensure a reliable normalization. We validated the RPN strategy by analyzing changes in protein abundances induced by nutrient starvation in . Beyond that, we provide a principle guideline for determining optimal combination of sample protein and reference protein load on individual LC-MS/MS systems.Frontiers in Plant Science 01/2013; 4:25.