The use of heavy nitrogen in quantitative proteomics experiments in plants

Application of Parallel Reaction Monitoring in 15N Labeled Samples for Quantification

Accurate relative quantification is critical in proteomic studies. The incorporation of stable isotope ¹⁵N to plant-expressed proteins in vivo is a powerful tool for accurate quantification with a major advantage of reducing preparative and analytical variabilities. However, ¹⁵N labeling quantification has several challenges. Less identifications are often observed in the heavy-labeled samples because of incomplete labeling, resulting in missing values in reciprocal labeling experiments. Inaccurate quantification can happen when there is contamination from co-eluting peptides or chemical noise in the MS¹ survey scan. These drawbacks in quantification can be more pronounced in less abundant but biologically interesting proteins, which often have very few identified peptides. Here, we demonstrate the application of parallel reaction monitoring (PRM) to ¹⁵N labeled samples on a high resolution, high mass accuracy Orbitrap mass spectrometer to achieve reliable quantification even of low abundance proteins in samples.

15N Metabolic Labeling Quantification Workflow in Arabidopsis Using Protein Prospector

Metabolic labeling using stable isotopes is widely used for the relative quantification of proteins in proteomic studies. In plants, metabolic labeling using 15N has great potential, but the associated complexity of data analysis has limited its usage. Here, we present the 15N stable-isotope labeled protein quantification workflow utilizing open-access web-based software Protein Prospector. Further, we discuss several important features of 15N labeling required to make reliable and precise protein quantification. These features include ratio adjustment based on labeling efficiency, median and interquartile range for protein ratios, isotope cluster pattern matching to flag incorrect monoisotopic peak assignment, and caching of quantification results for fast retrieval.

SILIA-Based 4C Quantitative PTM Proteomics

To absolutely and relatively quantitate the alteration of a posttranslationally modified (PTM) proteome in response to a specific internal or external signal, a ¹⁵N-stable isotope labeling in Arabidopsis (SILIA) protocol has been integrated into the 4C quantitative PTM proteomics, named as SILIA-based 4C quantitative PTM proteomics (S4Quap). The isotope metabolic labeling produces both forward (F) and reciprocal (R) mixings of either ¹⁴N/¹⁵N-coded tissues or the ¹⁴N/¹⁵N-coded total cellular proteins. Plant protein is isolated using a urea-based extraction buffer (UEB). The presence of 8 M urea, 2% polyvinylpolypyrrolidone (PVPP), and 5 mM ascorbic acid allows to instantly denature protein, remove the phenolic compounds, and curb the oxidation by free radicals once plant cells are broken. The total cellular proteins are routinely processed into peptides by trypsin. The PTM peptide yield of affinity enrichment and preparation is 0.1-0.2% in general. Ion exchange chromatographic fractionation prepares the PTM peptides for LC-MS/MS analysis. The collected mass spectrograms are subjected to a target-decoy sequence analysis using various search engines. The computational programs are subsequently applied to analyze the ratios of the extracted ion chromatogram (XIC) of the ¹⁴N/¹⁵N isotope-coded PTM peptide ions and to perform the statistical evaluation of the quantitation results. The Student t-test values of ratios of quantifiable ¹⁴N/¹⁵N-coded PTM peptides are normally corrected using a Benjamini-Hochberg (BH) multiple hypothesis test to select the significantly regulated PTM peptide groups (BH-FDR < 5%). Consequently, the highly selected prospect candidate(s) of PTM proteins are confirmed and validated using biochemical, molecular, cellular, and transgenic plant analysis.

Plasticity of rosette size in response to nitrogen availability is controlled by an RCC1-family protein

Nitrogen (N) is fundamental to plant growth, development and yield. Genes underlying N utilization and assimilation are well-characterized, but mechanisms underpinning plasticity of different phenotypes in response to N remain elusive. Here, using Arabidopsis thaliana accessions, we dissected the genetic architecture of plasticity in early and late rosette diameter, flowering time and yield, in response to three levels of N in the soil. Furthermore, we found that the plasticity in levels of primary metabolites were related with the plasticities of the studied traits. Genome-wide association analysis identified three significant associations for phenotypic plasticity, one for early rosette diameter and two for flowering time. We confirmed that the gene At1g19880, hereafter named as PLASTICITY OF ROSETTE TO NITROGEN 1 (PROTON1), encoding for a regulator of chromatin condensation 1 (RCC1) family protein, conferred plasticity of rosette diameter in response to N. Treatment of PROTON1 T-DNA line with salt implied that the reduced plasticity of early rosette diameter was not a general growth response to stress. We further showed that plasticities of growth and flowering-related traits differed between environmental cues, indicating decoupled genetic programs regulating these traits. Our findings provide a prospective to identify genes that stabilize performance under fluctuating environments.

Combining Targeted and Untargeted Data Acquisition to Enhance Quantitative Plant Proteomics Experiments

Most quantitative proteomics experiments either target a limited number of selected proteins for quantification or quantify proteins on a broad scale in an untargeted manner. However, we recently demonstrated that experiments that have both targeted and untargeted components can be particularly advantageous. Using a combined targeted and untargeted liquid chromatography-tandem mass spectrometry data acquisition strategy termed TDA/DDA (shorthand for targeted data acquisition/data-dependent acquisition), which we applied to a model quantitative plant proteomics experiment performed on Arabidopsis, we demonstrated improved quantification of both targeted and untargeted proteins relative to purely untargeted experiments performed using conventional data-dependent acquisition (Hart-Smith et al. Front Plant Sci 8:1669, 2017). This suggests that many quantitative proteomics datasets earmarked for collection using data-dependent acquisition are likely to benefit from the use of TDA/DDA instead.This chapter describes how TDA/DDA liquid chromatography-tandem mass spectrometry methods can be created on commonly used mass spectrometric instrument platforms. It described how, using freely available software, tandem mass spectrometry inclusion lists designed to target proteins of hypothesized interest can be generated. Best practice implementation of these inclusion lists in TDA/DDA strategies is then described. Relative to conventional data-dependent acquisition, the liquid chromatography-tandem mass spectrometry methods created using these guidelines increase the chances of quantifying targeted proteins and can produce widespread improvements in the reproducibility of untargeted protein quantification, without compromising the total numbers of proteins quantified. They are compatible with different quantitative proteomics methodologies, including metabolic labeling, chemical labeling and label-free approaches, and can be used to create tailored assay libraries to aid the interpretation of quantitative proteomics data collected using data-independent acquisition.

A cost-effective approach to produce 15N-labelled amino acids employing Chlamydomonas reinhardtii CC503

BACKGROUND

The use of enriched stable isotopes is of outstanding importance in chemical metrology as it allows the application of isotope dilution mass spectrometry (IDMS). Primary methods based on IDMS ensure the quality of the analytical measurements and traceability of the results to the international system of units. However, the synthesis of isotopically labelled molecules from enriched stable isotopes is an expensive and a difficult task. Either chemical and biochemical methods to produce labelled molecules have been proposed, but so far, few cost-effective methods have been described.

RESULTS

The aim of this study was to use the microalgae Chlamydomonas reinhardtii to produce, at laboratory scale, 15N-labelled amino acids with a high isotopic enrichment. To do that, a culture media containing 15NH4Cl was used. No kinetic isotope effect (KIE) was observed. The labelled proteins biosynthesized by the microorganism were extracted from the biomass and the 15N-labelled amino acids were obtained after a protein hydrolysis with HCl. The use of the wall deficient strain CC503 cw92 mt+ is fit for purpose, as it only assimilates ammonia as nitrogen source, avoiding isotope contamination with nitrogen from the atmosphere or the reagents used in the culture medium, and enhancing the protein extraction efficiency compared to cell-walled wild type Chlamydomonas. The isotopic enrichment of the labelled amino acids was calculated from their isotopic composition measured by gas chromatography mass spectrometry (GC-MS). The average isotopic enrichment for the 16 amino acids characterized was 99.56 ± 0.05% and the concentration of the amino acids in the hydrolysate ranged from 18 to 90 µg/mL.

CONCLUSIONS

Previously reported biochemical methods to produce isotopically labelled proteins have been applied in the fields of proteomics and fluxomics. For these approaches, low amounts of products are required and the isotopic enrichment of the molecules has never been properly determined. So far, only 13C-labelled fatty acids have been isolated from labelled microalga biomass as valuable industrial products. In this study, we propose Chlamydomonas reinhardtii CC503 as a feasible microorganism and strain to produce labelled biomass from which a standard containing sixteen 15N-labelled amino acids could be obtained.

Kinetic 15N-isotope effects on algal growth

Stable isotope labeling is a standard technique for tracing material transfer in molecular, ecological and biogeochemical studies. The main assumption in this approach is that the enrichment with a heavy isotope has no effect on the organism metabolism and growth, which is not consistent with current theoretical and empirical knowledge on kinetic isotope effects. Here, we demonstrate profound changes in growth dynamics of the green alga Raphidocelis subcapitata grown in 15N-enriched media. With increasing 15N concentration (0.37 to 50 at%), the lag phase increased, whereas maximal growth rate and total yield decreased; moreover, there was a negative relationship between the growth and the lag phase across the treatments. The latter suggests that a trade-off between growth rate and the ability to adapt to the high 15N environment may exist. Remarkably, the lag-phase response at 3.5 at% 15N was the shortest and deviated from the overall trend, thus providing partial support to the recently proposed Isotopic Resonance hypothesis, which predicts that certain isotopic composition is particularly favorable for living organisms. These findings confirm the occurrence of KIE in isotopically enriched algae and underline the importance of considering these effects when using stable isotope labeling in field and experimental studies.

Improved Quantitative Plant Proteomics via the Combination of Targeted and Untargeted Data Acquisition

Quantitative proteomics strategies - which are playing important roles in the expanding field of plant molecular systems biology - are traditionally designated as either hypothesis driven or non-hypothesis driven. Many of these strategies aim to select individual peptide ions for tandem mass spectrometry (MS/MS), and to do this mixed hypothesis driven and non-hypothesis driven approaches are theoretically simple to implement. In-depth investigations into the efficacies of such approaches have, however, yet to be described. In this study, using combined samples of unlabeled and metabolically ¹⁵N-labeled Arabidopsis thaliana proteins, we investigate the mixed use of targeted data acquisition (TDA) and data dependent acquisition (DDA) - referred to as TDA/DDA - to facilitate both hypothesis driven and non-hypothesis driven quantitative data collection in individual LC-MS/MS experiments. To investigate TDA/DDA for hypothesis driven data collection, 7 miRNA target proteins of differing size and abundance were targeted using inclusion lists comprised of 1558 m/z values, using 3 different TDA/DDA experimental designs. In samples in which targeted peptide ions were of particularly low abundance (i.e., predominantly only marginally above mass analyser detection limits), TDA/DDA produced statistically significant increases in the number of targeted peptides identified (230 ± 8 versus 80 ± 3 for DDA; p = 1.1 × 10^-3) and quantified (35 ± 3 versus 21 ± 2 for DDA; p = 0.038) per experiment relative to the use of DDA only. These expected improvements in hypothesis driven data collection were observed alongside unexpected improvements in non-hypothesis driven data collection. Untargeted peptide ions with m/z values matching those in inclusion lists were repeatedly identified and quantified across technical replicate TDA/DDA experiments, resulting in significant increases in the percentages of proteins repeatedly quantified in TDA/DDA experiments only relative to DDA experiments only (33.0 ± 2.6% versus 8.0 ± 2.7%, respectively; p = 0.011). These results were observed together with uncompromised broad-scale MS/MS data collection in TDA/DDA experiments relative to DDA experiments. Using our observations we provide guidelines for TDA/DDA method design for quantitative plant proteomics studies, and suggest that TDA/DDA is a broadly underutilized proteomics data acquisition strategy.

Proteome Profiling of Wheat Shoots from Different Cultivars

Wheat is a cereal grain and one of the world's major food crops. Recent advances in wheat genome sequencing are by now facilitating its genomic and proteomic analyses. However, little is known about possible differences in total protein levels of hexaploid versus tetraploid wheat cultivars, and also knowledge of phosphorylated wheat proteins is still limited. Here, we performed a detailed analysis of the proteome of seedling leaves from two hexaploid wheat cultivars (Triticum aestivum L. Pavon 76 and USU-Apogee) and one tetraploid wheat (T. turgidum ssp. durum cv. Senatore Cappelli). Our shotgun proteomics data revealed that, whereas we observed some significant differences, overall a high similarity between hexaploid and tetraploid varieties with respect to protein abundance was observed. In addition, already at the seedling stage, a small set of proteins was differential between the small (USU-Apogee) and larger hexaploid wheat cultivars (Pavon 76), which could potentially act as growth predictors. Finally, the phosphosites identified in this study can be retrieved from the in-house developed plant PTM-Viewer (bioinformatics.psb.ugent.be/webtools/ptm_viewer/), making this the first searchable repository for phosphorylated wheat proteins. This paves the way for further in depth, quantitative (phospho)proteome-wide differential analyses upon a specific trigger or environmental change.

Comparative Phosphoproteomic Analysis under High-Nitrogen Fertilizer Reveals Central Phosphoproteins Promoting Wheat Grain Starch and Protein Synthesis

Nitrogen (N) is a macronutrient important for plant growth and development. It also strongly influences starch and protein synthesis, closely related to grain yield and quality. We performed the first comparative phosphoproteomic analysis of developing wheat grains in response to high-N fertilizer. Physiological and biochemical analyses showed that application of high-N fertilizer resulted in significant increases in leaf length and area, chlorophyll content, the activity of key enzymes in leaves such as nitrate reductase (NR), and in grains such as sucrose phosphate synthase (SPS), sucrose synthase (SuSy), and ADP glucose pyrophosphorylase (AGPase). This enhanced enzyme activity led to significant improvements in starch content, grain yield, and ultimately, bread making quality. Comparative phosphoproteomic analysis of developing grains under the application of high-N fertilizer performed 15 and 25 days post-anthesis identified 2470 phosphosites among 1372 phosphoproteins, of which 411 unique proteins displayed significant changes in phosphorylation level (>2-fold or <0.5-fold). These phosphoproteins are involved mainly in signaling transduction, starch synthesis, energy metabolism. Pro-Q diamond staining and Western blotting confirmed our phosphoproteomic results. We propose a putative pathway to elucidate the important roles of the central phosphoproteins regulating grain starch and protein synthesis. Our results provide new insights into the molecular mechanisms of protein phosphorylation modifications involved in grain development, yield and quality formation.

New flaxseed orbitides: Detection, sequencing, and (15)N incorporation

Three new orbitides (cyclolinopeptides 17, 18, and 19) were identified in flaxseed (Linum usitatissimum L.) extracts without any form of purification. Their structures were elucidated by a combination of (15) N-labeling experiments and extensive tandem mass spectrometry (MS/MS) with electrospray ionization (ESI). Putative linear peptide sequences of the new orbitides were used as the query in the Basic Local Alignment Search Tool (BLAST) searches of flax genome database. These searches returned linear sequences for the putative precursors of cyclolinopeptides 17 and 19 among others. Cyclolinopeptide 18 contains MetO (O) and is not directly encoded, but is a product of post-translation modification of the Met present in 17. The identification of precursor proteins in flax mRNA transcripts and DNA sequences confirmed the occurrence and amino acid sequences of these orbitides as [1-9-NαC]-MLKPFFFWI, [1-9-NαC]-OLKPFFFWI, and [1-9-NαC]-GIPPFWLTL for cyclolinopeptides 17, 18, and 19, respectively.

Quantitation of Vacuolar Sugar Transporter Abundance Changes Using QconCAT Synthtetic Peptides

Measurements of protein abundance changes are important for biological conclusions on protein-related processes such as activity or complex formation. Proteomic analyses in general are almost routine tasks in many laboratories, but a precise and quantitative description of (absolute) protein abundance changes require careful experimental design and precise data quality. Today, a vast choice of metabolic labeling and label-free quantitation protocols are available, but the trade-off between quantitative precision and proteome coverage of quantified proteins including missing value problems remain. Here, we provide an example of a targeted proteomic approach using artificial standard proteins consisting of concatenated peptides of interest (QconCAT) to specifically quantify abiotic stress-induced abundance changes in low abundant vacuolar transporters. An advantage of this approach is the reliable quantitation of alimited set of low-abundant target proteins throughout different conditions. We show that vacuolar ATPase AVP1 and sugar transporters of the ERDL (early responsive to dehydration-like) family and TMT2 (tonoplast monosaccharide transporter 2) showed increased abundance upon salt stress.

Rapid Phosphoproteomic Effects of Abscisic Acid (ABA) on Wild-Type and ABA Receptor-Deficient A. thaliana Mutants

Abscisic acid (ABA)¹ is a plant hormone that controls many aspects of plant growth, including seed germination, stomatal aperture size, and cellular drought response. ABA interacts with a unique family of 14 receptor proteins. This interaction leads to the activation of a family of protein kinases, SnRK2s, which in turn phosphorylate substrates involved in many cellular processes. The family of receptors appears functionally redundant. To observe a measurable phenotype, four of the fourteen receptors have to be mutated to create a multilocus loss-of-function quadruple receptor (QR) mutant, which is much less sensitive to ABA than wild-type (WT) plants. Given these phenotypes, we asked whether or not a difference in ABA response between the WT and QR backgrounds would manifest on a phosphorylation level as well. We tested WT and QR mutant ABA response using isotope-assisted quantitative phosphoproteomics to determine what ABA-induced phosphorylation changes occur in WT plants within 5 min of ABA treatment and how that phosphorylation pattern is altered in the QR mutant. We found multiple ABA-induced phosphorylation changes that occur within 5 min of treatment, including three SnRK2 autophosphorylation events and phosphorylation on SnRK2 substrates. The majority of robust ABA-dependent phosphorylation changes observed were partially diminished in the QR mutant, whereas many smaller ABA-dependent phosphorylation changes observed in the WT were not responsive to ABA in the mutant. A single phosphorylation event was increased in response to ABA treatment in both the WT and QR mutant. A portion of the discovery data was validated using selected reaction monitoring-based targeted measurements on a triple quadrupole mass spectrometer. These data suggest that different subsets of phosphorylation events depend upon different subsets of the ABA receptor family to occur. Altogether, these data expand our understanding of the model by which the family of ABA receptors directs rapid phosphoproteomic changes.

Combining Metabolic ¹⁵N Labeling with Improved Tandem MOAC for Enhanced Probing of the Phosphoproteome

In eukaryotic cells many diverse cellular functions are regulated by reversible protein phosphorylation. In recent years, phosphoproteomics has become a powerful tool for studying protein phosphorylation because it enables unbiased localization, and site-specific quantification of in vivo phosphorylation of hundreds of proteins in a single experiment. A common strategy for identifying phosphoproteins and their phosphorylation sites from complex biological samples is the enrichment of phosphopeptides from digested cellular lysates followed by mass spectrometry. However, despite high sensitivity of modern mass spectrometers the large dynamic range of protein abundance and the transient nature of protein phosphorylation remained major pitfalls in MS-based phosphoproteomics. This is particularly true for plants in which the presence of secondary metabolites and endogenous compounds, the overabundance of ribulose-1,5-bisphosphate carboxylase and other components of the photosynthetic apparatus, and the concurrent difficulties in protein extraction necessitate two-step phosphoprotein/phosphopeptide enrichment strategies (Nakagami et al., Plant Cell Physiol 53:118-124, 2012).Approaches for label-free peptide quantification are advantageous due to their low cost and experimental simplicity, but they lack precision. These drawbacks can be overcome by metabolic labeling of whole plants with heavy nitrogen ((15)N) which allows combining two samples very early in the phosphoprotein enrichment workflow. This avoids sample-to-sample variation introduced by the analytical procedures and it results in robust relative quantification values that need no further standardization. The integration of (15)N metabolic labeling into tandem metal-oxide affinity chromatography (MOAC) (Hoehenwarter et al., Mol Cell Proteomics 12:369-380, 2013) presents an improved and highly selective approach for the identification and accurate site-specific quantification of low-abundance phosphoproteins that is based on the successive enrichment of light and heavy nitrogen-labeled phosphoproteins and peptides. This improved strategy combines metabolic labeling of whole plants with the stable heavy nitrogen isotope ((15)N), protein extraction under denaturing conditions, phosphoprotein enrichment using Al(OH)3-based MOAC, and tryptic digest of enriched phosphoproteins followed by TiO2-based MOAC of phosphopeptides and quantitative phosphopeptide measurement by liquid chromatography (LC) and high-resolution accurate mass (HR/AM) mass spectrometry (MS). Thus, tandem MOAC effectively targets the phosphate moiety of phosphoproteins and phosphopeptides and allows probing of the phosphoproteome to unprecedented depth, while (15)N metabolic labeling enables accurate relative quantification of measured peptides and direct comparison between samples.

SILAC and alternatives in studying cellular proteomes of plants

Quantitative proteomics by metabolic labeling has a high impact on the growing field of plant systems biology. SILAC has been pioneered and optimized for plant cell culture systems allowing for SILAC-based quantitative experiments in specialized experimental setups. In comparison to other model organisms, the application of SILAC to whole plants is challenging. As autotrophic organisms, plants under their natural growth conditions can hardly be fully labeled with stable isotope-coded amino acids. The metabolic labeling with inorganic nitrogen is therefore the method of choice for most whole-plant physiological questions. Plants can easily metabolize different inorganic nitrogen isotopes. The incorporation of the labeled inorganic nitrogen then results in proteins and metabolites with distinct molecular mass, which can be detected on a mass spectrometer. In comparative quantitative experiments, similarly as in SILAC 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 were developed for metabolite identification. Here we present a typical hydroponics setup, the workflow for processing of samples, mass spectrometry and data analysis for large-scale metabolic labeling experiments of whole plants.

Metabolic labeling and membrane fractionation for comparative proteomic analysis of Arabidopsis thaliana suspension cell cultures

Plasma membrane microdomains are features based on the physical properties of the lipid and sterol environment and have particular roles in signaling processes. Extracting sterol-enriched membrane microdomains from plant cells for proteomic analysis is a difficult task mainly due to multiple preparation steps and sources for contaminations from other cellular compartments. The plasma membrane constitutes only about 5-20% of all the membranes in a plant cell, and therefore isolation of highly purified plasma membrane fraction is challenging. A frequently used method involves aqueous two-phase partitioning in polyethylene glycol and dextran, which yields plasma membrane vesicles with a purity of 95% (1). Sterol-rich membrane microdomains within the plasma membrane are insoluble upon treatment with cold nonionic detergents at alkaline pH. This detergent-resistant membrane fraction can be separated from the bulk plasma membrane by ultracentrifugation in a sucrose gradient (2). Subsequently, proteins can be extracted from the low density band of the sucrose gradient by methanol/chloroform precipitation. Extracted protein will then be trypsin digested, desalted and finally analyzed by LC-MS/MS. Our extraction protocol for sterol-rich microdomains is optimized for the preparation of clean detergent-resistant membrane fractions from Arabidopsis thaliana cell cultures. We use full metabolic labeling of Arabidopsis thaliana suspension cell cultures with K(15)NO3 as the only nitrogen source for quantitative comparative proteomic studies following biological treatment of interest (3). By mixing equal ratios of labeled and unlabeled cell cultures for joint protein extraction the influence of preparation steps on final quantitative result is kept at a minimum. Also loss of material during extraction will affect both control and treatment samples in the same way, and therefore the ratio of light and heave peptide will remain constant. In the proposed method either labeled or unlabeled cell culture undergoes a biological treatment, while the other serves as control (4).

A decade of plant proteomics and mass spectrometry: translation of technical advancements to food security and safety issues

Tremendous progress in plant proteomics driven by mass spectrometry (MS) techniques has been made since 2000 when few proteomics reports were published and plant proteomics was in its infancy. These achievements include the refinement of existing techniques and the search for new techniques to address food security, safety, and health issues. It is projected that in 2050, the world's population will reach 9-12 billion people demanding a food production increase of 34-70% (FAO, 2009) from today's food production. Provision of food in a sustainable and environmentally committed manner for such a demand without threatening natural resources, requires that agricultural production increases significantly and that postharvest handling and food manufacturing systems become more efficient requiring lower energy expenditure, a decrease in postharvest losses, less waste generation and food with longer shelf life. There is also a need to look for alternative protein sources to animal based (i.e., plant based) to be able to fulfill the increase in protein demands by 2050. Thus, plant biology has a critical role to play as a science capable of addressing such challenges. In this review, we discuss proteomics especially MS, as a platform, being utilized in plant biology research for the past 10 years having the potential to expedite the process of understanding plant biology for human benefits. The increasing application of proteomics technologies in food security, analysis, and safety is emphasized in this review. But, we are aware that no unique approach/technology is capable to address the global food issues. Proteomics-generated information/resources must be integrated and correlated with other omics-based approaches, information, and conventional programs to ensure sufficient food and resources for human development now and in the future.

Coping with abiotic stress: proteome changes for crop improvement

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.

Systematic evaluation of reference protein normalization in proteomic experiments

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 Arabidopsis thaliana. Beyond that, we provide a principle guideline for determining optimal combination of sample protein and reference protein load on individual LC-MS/MS systems.

Proteomics wants cRacker: automated standardized data analysis of LC-MS derived proteomic data

The large-scale analysis of thousands of proteins under various experimental conditions or in mutant lines has gained more and more importance in hypothesis-driven scientific research and systems biology in the past years. Quantitative analysis by large scale proteomics using modern mass spectrometry usually results in long lists of peptide ion intensities. The main interest for most researchers, however, is to draw conclusions on the protein level. Postprocessing and combining peptide intensities of a proteomic data set requires expert knowledge, and the often repetitive and standardized manual calculations can be time-consuming. The analysis of complex samples can result in very large data sets (lists with several 1000s to 100,000 entries of different peptides) that cannot easily be analyzed using standard spreadsheet programs. To improve speed and consistency of the data analysis of LC-MS derived proteomic data, we developed cRacker. cRacker is an R-based program for automated downstream proteomic data analysis including data normalization strategies for metabolic labeling and label free quantitation. In addition, cRacker includes basic statistical analysis, such as clustering of data, or ANOVA and t tests for comparison between treatments. Results are presented in editable graphic formats and in list files.

Precision, proteome coverage, and dynamic range of Arabidopsis proteome profiling using (15)N metabolic labeling and label-free approaches

This study reports the comprehensive comparison of (15)N metabolic labeling and label free proteomic strategies for quantitation, with particular focus on plant proteomics. Our investigation of proteome coverage, dynamic range and quantitative precision for a wide range of mixing ratios and protein loadings aim to aid the investigators in the decision making process during experimental design. One of the main characteristics of the label free strategy is the applicability to all starting material, which is a limitation to the metabolic labeling. However, particularly at mixing ratios up to 10-fold the (15)N metabolic labeling proved to be more precise. Contrary to usual practice based on the results from this study, we suggest that nonequal mixing ratios in metabolic labeling could further increase the proteome coverage for quantitation. On the other hand, the label free strategy, in combination with low protein loading allows the extension of the dynamic range for quantitation and it is more precise at very high ratios, which could be important for certain types of experiments.