Medicago truncatula proteomics for systems biology: novel rapid shotgun LC-MS approach for relative quantification based on full-scan selective peptide extraction (Selpex)

Subcellular Phenotyping: Using Proteomics to Quantitatively Link Subcellular Leaf Protein and Organelle Distribution Analyses of Pisum sativum Cultivars

Plant phenotyping to date typically comprises morphological and physiological profiling in a high-throughput manner. A powerful method that allows for subcellular characterization of organelle stoichiometric/functional characteristics is still missing. Organelle abundance and crosstalk in cell dynamics and signaling plays an important role for understanding crop growth and stress adaptations. However, microscopy cannot be considered a high-throughput technology. The aim of the present study was to develop an approach that enables the estimation of organelle functional stoichiometry and to determine differential subcellular dynamics within and across cultivars in a high-throughput manner. A combination of subcellular non-aqueous fractionation and liquid chromatography mass spectrometry was applied to assign membrane-marker proteins to cell compartmental abundances and functions of Pisum sativum leaves. Based on specific subcellular affiliation, proteotypic marker peptides of the chloroplast, mitochondria and vacuole membranes were selected and synthesized as heavy isotope labeled standards. The rapid and unbiased Mass Western approach for accurate stoichiometry and targeted absolute protein quantification allowed for a proportional organelle abundances measure linked to their functional properties. A 3D Confocal Laser Scanning Microscopy approach was developed to evaluate the Mass Western. Two P. sativum cultivars of varying morphology and physiology were compared. The Mass Western assay enabled a cultivar specific discrimination of the chloroplast to mitochondria to vacuole relations.

Label-free quantitative proteomic analysis of tolerance to drought in Pisum sativum

Abiotic stresses caused by adverse environmental conditions are responsible for heavy economic losses on pea crop, being drought one of the most important abiotic constraints. Development of pea cultivars well adapted to dry conditions has been one of the major tasks in breeding programs. The increasing food requirements drive the necessity to broaden the molecular basis of tolerance to drought to develop pea cultivars well adapted to dry conditions. We have used a shotgun proteomic approach (nLC-MSMS) to study the tolerance to drought in three pea genotypes that were selected based on differences in the level of water deficit tolerance. Multivariate statistical analysis of data unraveled 367 significant differences of 700 identified when genotypes and/or treatment were compared. More than half of the significantly changed proteins belong to primary metabolism and protein regulation categories. We propose different mechanisms to cope drought in the genotypes studied. Maintenance of the primary metabolism and protein protection seems a strategy for drought tolerance. On the other hand susceptibility might be related to maintenance of the homeostatic equilibrium, a very energy consuming process. Data are available via ProteomeXchange with identifier PXD004587.

Comparative shotgun proteomic analysis of wild and domesticated Opuntia spp. species shows a metabolic adaptation through domestication

The Opuntia genus is widely distributed in America, but the highest richness of wild species are found in Mexico, as well as the most domesticated Opuntia ficus-indica, which is the most domesticated species and an important crop in agricultural economies of arid and semiarid areas worldwide. During domestication process, the Opuntia morphological characteristics were favored, such as less and smaller spines in cladodes and less seeds in fruits, but changes at molecular level are almost unknown. To obtain more insights about the Opuntia molecular changes through domestication, a shotgun proteomic analysis and database-dependent searches by homology was carried out. >1000 protein species were identified and by using a label-free quantitation method, the Opuntia proteomes were compared in order to identify differentially accumulated proteins among wild and domesticated species. Most of the changes were observed in glucose, secondary, and 1C metabolism, which correlate with the observed protein, fiber and phenolic compounds accumulation in Opuntia cladodes. Regulatory proteins, ribosomal proteins, and proteins related with response to stress were also observed in differential accumulation. These results provide new valuable data that will help to the understanding of the molecular changes of Opuntia species through domestication.

BIOLOGICAL SIGNIFICANCE

Opuntia species are well adapted to dry and warm conditions in arid and semiarid regions worldwide, and they are highly productive plants showing considerable promises as an alternative food source. However, there is a gap regarding Opuntia molecular mechanisms that enable them to grow in extreme environmental conditions and how the domestication processes has changed them. In the present study, a shotgun analysis was carried out to characterize the proteomes of five Opuntia species selected by its domestication degree. Our results will help to a better understanding of proteomic features underlying the selection and specialization under evolution and domestication of Opuntia and will provide a platform for basic biology research and gene discovery.

Proteomics and Metabolomics: Two Emerging Areas for Legume Improvement

The crop legumes such as chickpea, common bean, cowpea, peanut, pigeonpea, soybean, etc. are important sources of nutrition and contribute to a significant amount of biological nitrogen fixation (>20 million tons of fixed nitrogen) in agriculture. However, the production of legumes is constrained due to abiotic and biotic stresses. It is therefore imperative to understand the molecular mechanisms of plant response to different stresses and identify key candidate genes regulating tolerance which can be deployed in breeding programs. The information obtained from transcriptomics has facilitated the identification of candidate genes for the given trait of interest and utilizing them in crop breeding programs to improve stress tolerance. However, the mechanisms of stress tolerance are complex due to the influence of multi-genes and post-transcriptional regulations. Furthermore, stress conditions greatly affect gene expression which in turn causes modifications in the composition of plant proteomes and metabolomes. Therefore, functional genomics involving various proteomics and metabolomics approaches have been obligatory for understanding plant stress tolerance. These approaches have also been found useful to unravel different pathways related to plant and seed development as well as symbiosis. Proteome and metabolome profiling using high-throughput based systems have been extensively applied in the model legume species, Medicago truncatula and Lotus japonicus, as well as in the model crop legume, soybean, to examine stress signaling pathways, cellular and developmental processes and nodule symbiosis. Moreover, the availability of protein reference maps as well as proteomics and metabolomics databases greatly support research and understanding of various biological processes in legumes. Protein-protein interaction techniques, particularly the yeast two-hybrid system have been advantageous for studying symbiosis and stress signaling in legumes. In this review, several studies on proteomics and metabolomics in model and crop legumes have been discussed. Additionally, applications of advanced proteomics and metabolomics approaches have also been included in this review for future applications in legume research. The integration of these "omics" approaches will greatly support the identification of accurate biomarkers in legume smart breeding programs.

Automated protein turnover calculations from 15N partial metabolic labeling LC/MS shotgun proteomics data

Protein turnover is a well-controlled process in which polypeptides are constantly being degraded and subsequently replaced with newly synthesized copies. Extraction of composite spectral envelopes from complex LC/MS shotgun proteomics data can be a challenging task, due to the inherent complexity of biological samples. With partial metabolic labeling experiments this complexity increases as a result of the emergence of additional isotopic peaks. Automated spectral extraction and subsequent protein turnover calculations enable the analysis of gigabytes of data within minutes, a prerequisite for systems biology high throughput studies. Here we present a fully automated method for protein turnover calculations from shotgun proteomics data. The approach enables the analysis of complex shotgun LC/MS 15N partial metabolic labeling experiments. Spectral envelopes of 1419 peptides can be extracted within an hour. The method quantifies turnover by calculating the Relative Isotope Abundance (RIA), which is defined as the ratio between the intensity sum of all heavy (15N) to the intensity sum of all light (14N) and heavy peaks. To facilitate this process, we have developed a computer program based on our method, which is freely available to download at http://promex.pph.univie.ac.at/protover.