A rapid approach for phenotype-screening and database independent detection of cSNP/protein polymorphism using mass accuracy precursor alignment

Quantifying the impact of dynamic plant-environment interactions on metabolic regulation

A plant's genome encodes enzymes, transporters and many other proteins which constitute metabolism. Interactions of plants with their environment shape their growth, development and resilience towards adverse conditions. Although genome sequencing technologies and applications have experienced triumphantly rapid development during the last decades, enabling nowadays a fast and cheap sequencing of full genomes, prediction of metabolic phenotypes from genotype × environment interactions remains, at best, very incomplete. The main reasons are a lack of understanding of how different levels of molecular organisation depend on each other, and how they are constituted and expressed within a setup of growth conditions. Phenotypic plasticity, e.g., of the genetic model plant Arabidopsis thaliana, has provided important insights into plant-environment interactions and the resulting genotype x phenotype relationships. Here, we summarize previous and current findings about plant development in a changing environment and how this might be shaped and reflected in metabolism and its regulation. We identify current challenges in the study of plant development and metabolic regulation and provide an outlook of how methodological workflows might support the application of findings made in model systems to crops and their cultivation.

Physiological and Proteomic Signatures Reveal Mechanisms of Superior Drought Resilience in Pearl Millet Compared to Wheat

Presently, pearl millet and wheat are belonging to highly important cereal crops. Pearl millet, however, is an under-utilized crop, despite its superior resilience to drought and heat stress in contrast to wheat. To investigate this in more detail, we performed comparative physiological screening and large scale proteomics of drought stress responses in drought-tolerant and susceptible genotypes of pearl millet and wheat. These chosen genotypes are widely used in breeding and farming practices. The physiological responses demonstrated large differences in the regulation of root morphology and photosynthetic machinery, revealing a stay-green phenotype in pearl millet. Subsequent tissue-specific proteome analysis of leaves, roots and seeds led to the identification of 12,558 proteins in pearl millet and wheat under well-watered and stress conditions. To allow for this comparative proteome analysis and to provide a platform for future functional proteomics studies we performed a systematic phylogenetic analysis of all orthologues in pearl millet, wheat, foxtail millet, sorghum, barley, brachypodium, rice, maize, Arabidopsis, and soybean. In summary, we define (i) a stay-green proteome signature in the drought-tolerant pearl millet phenotype and (ii) differential senescence proteome signatures in contrasting wheat phenotypes not capable of coping with similar drought stress. These different responses have a significant effect on yield and grain filling processes reflected by the harvest index. Proteome signatures related to root morphology and seed yield demonstrated the unexpected intra- and interspecies-specific biochemical plasticity for stress adaptation for both pearl millet and wheat genotypes. These quantitative reference data provide tissue- and phenotype-specific marker proteins of stress defense mechanisms which are not predictable from the genome sequence itself and have potential value for marker-assisted breeding beyond genome assisted breeding.

Mass Spectrometry Untangles Plant Membrane Protein Signaling Networks

Plasma membranes (PMs) act as primary cellular checkpoints for sensing signals and controlling solute transport. Membrane proteins communicate with intracellular processes through protein interaction networks. Deciphering these signaling networks provides crucial information for elucidating in vivo cellular regulation. Large-scale proteomics enables system-wide characterization of the membrane proteome, identification of ligand-receptor pairs, and elucidation of signals originating at membranes. In this review we assess recent progress in the development of mass spectrometry (MS)-based proteomic pipelines for determining membrane signaling pathways. We focus in particular on current techniques for the analysis of membrane protein phosphorylation and interaction, and how these proteins may be connected to downstream changes in gene expression, metabolism, and physiology.

PANOMICS meets germplasm

Genotyping-by-sequencing has enabled approaches for genomic selection to improve yield, stress resistance and nutritional value. More and more resource studies are emerging providing 1000 and more genotypes and millions of SNPs for one species covering a hitherto inaccessible intraspecific genetic variation. The larger the databases are growing, the better statistical approaches for genomic selection will be available. However, there are clear limitations on the statistical but also on the biological part. Intraspecific genetic variation is able to explain a high proportion of the phenotypes, but a large part of phenotypic plasticity also stems from environmentally driven transcriptional, post-transcriptional, translational, post-translational, epigenetic and metabolic regulation. Moreover, regulation of the same gene can have different phenotypic outputs in different environments. Consequently, to explain and understand environment-dependent phenotypic plasticity based on the available genotype variation we have to integrate the analysis of further molecular levels reflecting the complete information flow from the gene to metabolism to phenotype. Interestingly, metabolomics platforms are already more cost-effective than NGS platforms and are decisive for the prediction of nutritional value or stress resistance. Here, we propose three fundamental pillars for future breeding strategies in the framework of Green Systems Biology: (i) combining genome selection with environment-dependent PANOMICS analysis and deep learning to improve prediction accuracy for marker-dependent trait performance; (ii) PANOMICS resolution at subtissue, cellular and subcellular level provides information about fundamental functions of selected markers; (iii) combining PANOMICS with genome editing and speed breeding tools to accelerate and enhance large-scale functional validation of trait-specific precision breeding.

Physiological and Proteomic Signatures Reveal Mechanisms of Superior Drought Resilience in Pearl Millet Compared to Wheat

Presently, pearl millet and wheat are belonging to highly important cereal crops. Pearl millet, however, is an under-utilized crop, despite its superior resilience to drought and heat stress in contrast to wheat. To investigate this in more detail, we performed comparative physiological screening and large scale proteomics of drought stress responses in drought-tolerant and susceptible genotypes of pearl millet and wheat. These chosen genotypes are widely used in breeding and farming practices. The physiological responses demonstrated large differences in the regulation of root morphology and photosynthetic machinery, revealing a stay-green phenotype in pearl millet. Subsequent tissue-specific proteome analysis of leaves, roots and seeds led to the identification of 12,558 proteins in pearl millet and wheat under well-watered and stress conditions. To allow for this comparative proteome analysis and to provide a platform for future functional proteomics studies we performed a systematic phylogenetic analysis of all orthologues in pearl millet, wheat, foxtail millet, sorghum, barley, brachypodium, rice, maize, Arabidopsis, and soybean. In summary, we define (i) a stay-green proteome signature in the drought-tolerant pearl millet phenotype and (ii) differential senescence proteome signatures in contrasting wheat phenotypes not capable of coping with similar drought stress. These different responses have a significant effect on yield and grain filling processes reflected by the harvest index. Proteome signatures related to root morphology and seed yield demonstrated the unexpected intra- and interspecies-specific biochemical plasticity for stress adaptation for both pearl millet and wheat genotypes. These quantitative reference data provide tissue- and phenotype-specific marker proteins of stress defense mechanisms which are not predictable from the genome sequence itself and have potential value for marker-assisted breeding beyond genome assisted breeding.

Metabolomics in Plant Stress Physiology

Metabolomics is an essential technology for functional genomics and systems biology. It plays a key role in functional annotation of genes and understanding towards cellular and molecular, biotic and abiotic stress responses. Different analytical techniques are used to extend the coverage of a full metabolome. The commonly used techniques are NMR, CE-MS, LC-MS, and GC-MS. The choice of a suitable technique depends on the speed, sensitivity, and accuracy. This chapter provides insight into plant metabolomic techniques, databases used in the analysis, data mining and processing, compound identification, and limitations in metabolomics. It also describes the workflow of measuring metabolites in plants. Metabolomic studies in plant responses to stress are a key research topic in many laboratories worldwide. We summarize different approaches and provide a generic overview of stress responsive metabolite markers and processes compiled from a broad range of different studies. Graphical Abstract.

Eco-Metabolomics and Metabolic Modeling: Making the Leap From Model Systems in the Lab to Native Populations in the Field

Experimental high-throughput analysis of molecular networks is a central approach to characterize the adaptation of plant metabolism to the environment. However, recent studies have demonstrated that it is hardly possible to predict in situ metabolic phenotypes from experiments under controlled conditions, such as growth chambers or greenhouses. This is particularly due to the high molecular variance of in situ samples induced by environmental fluctuations. An approach of functional metabolome interpretation of field samples would be desirable in order to be able to identify and trace back the impact of environmental changes on plant metabolism. To test the applicability of metabolomics studies for a characterization of plant populations in the field, we have identified and analyzed in situ samples of nearby grown natural populations of Arabidopsis thaliana in Austria. A. thaliana is the primary molecular biological model system in plant biology with one of the best functionally annotated genomes representing a reference system for all other plant genome projects. The genomes of these novel natural populations were sequenced and phylogenetically compared to a comprehensive genome database of A. thaliana ecotypes. Experimental results on primary and secondary metabolite profiling and genotypic variation were functionally integrated by a data mining strategy, which combines statistical output of metabolomics data with genome-derived biochemical pathway reconstruction and metabolic modeling. Correlations of biochemical model predictions and population-specific genetic variation indicated varying strategies of metabolic regulation on a population level which enabled the direct comparison, differentiation, and prediction of metabolic adaptation of the same species to different habitats. These differences were most pronounced at organic and amino acid metabolism as well as at the interface of primary and secondary metabolism and allowed for the direct classification of population-specific metabolic phenotypes within geographically contiguous sampling sites.

Field proteomics of Populus alba grown in a heavily modified environment - An example of a tannery waste landfill

Tannery waste is highly toxic and dangerous to living organisms because of the high heavy metal content, especially chromium [Cr(III)]. This study analysed the proteomic response of the Populus alba L. clone 'Villafranca' grown for 4years on a tannery waste landfill. In this extremely hostile environment, the plants struggled with continuous stress, which inhibited growth by 54%, with a 67% decrease in tree height and diameter at breast height compared to those of the forest reference plot, respectively. The leaves and roots of the tannery landfill-grown plants produced strong proteomic stress signals for protection against reactive oxygen species (ROS) and repair to ROS-damaged proteins and DNA as well as signals for protection of the photosynthetic apparatus. The content of HSP80 was also high. However, primary metabolic pathways were generally unaffected, and signals of increased protein protection, but not turnover, were found, indicating mechanisms of adaptation to long-term stress conditions present at the landfill. A proteomic tool, two-dimensional electrophoresis coupled with tandem mass spectrometry, was successfully applied in this environmental in situ study of distant plots (280km apart).

Proteomics survey of Solanaceae family: Current status and challenges ahead

Solanaceae is one of the major economically important families of higher plants and has played a central role in human nutrition since the dawn of human civilization. Therefore, researchers have always been interested in understanding the complex behavior of Solanaceae members to identify key transcripts, proteins or metabolites, which are potentially associated with major traits. Proteomics studies have contributed significantly to understanding the physiology of Solanaceae members. A compilation of all the published reports showed that both gel-based (75%) and gel-free (25%) proteomic technologies have been utilized to establish the proteomes of different tissues, organs, and organelles under normal and adverse environmental conditions. Among the Solanaceae members, most of the research has been focused on tomato (42%) followed by potato (28%) and tobacco (20%), owing to their economic importance. This review comprehensively covers the progress made so far in the field of Solanaceae proteomics including novel methods developed to isolate the proteins from different tissues. Moreover, key proteins presented in this review can serve as a resource to select potential targets for crop improvement. We envisage that information presented in this review would enable us to design the stress tolerant plants with enhanced yields.

Performance comparison of three trypsin columns used in liquid chromatography

Trypsin is the most widely used enzyme in proteomic research due to its high specificity. Although the in-solution digestion is predominantly used, it has several drawbacks, such as long digestion times, autolysis, and intolerance to high temperatures or organic solvents. To overcome these shortcomings trypsin was covalently immobilized on solid support and tested for its proteolytic activity. Trypsin was immobilized on bridge-ethyl hybrid silica sorbent with 300Å pores, packed in 2.1×30mm column and compared with Perfinity and Poroszyme trypsin columns. Catalytic efficiency of enzymatic reactors was tested using N_α-Benzoyl-l-arginine 4-nitroanilide hydrochloride as a substrate. The impact of buffer pH, mobile phase flow rate, and temperature on enzymatic activity was investigated. Digestion speed generally increased with the temperature from 20 to 37°C. Digestion speed also increased with pH from 7.0 to 9.0; the activity of prototype enzyme reactor was highest at pH 9.0, when it activity exceeded both commercial reactors. Preliminary data for fast protein digestion are presented.

Cereal Crop Proteomics: Systemic Analysis of Crop Drought Stress Responses Towards Marker-Assisted Selection Breeding

Sustainable crop production is the major challenge in the current global climate change scenario. Drought stress is one of the most critical abiotic factors which negatively impact crop productivity. In recent years, knowledge about molecular regulation has been generated to understand drought stress responses. For example, information obtained by transcriptome analysis has enhanced our knowledge and facilitated the identification of candidate genes which can be utilized for plant breeding. On the other hand, it becomes more and more evident that the translational and post-translational machinery plays a major role in stress adaptation, especially for immediate molecular processes during stress adaptation. Therefore, it is essential to measure protein levels and post-translational protein modifications to reveal information about stress inducible signal perception and transduction, translational activity and induced protein levels. This information cannot be revealed by genomic or transcriptomic analysis. Eventually, these processes will provide more direct insight into stress perception then genetic markers and might build a complementary basis for future marker-assisted selection of drought resistance. In this review, we survey the role of proteomic studies to illustrate their applications in crop stress adaptation analysis with respect to productivity. Cereal crops such as wheat, rice, maize, barley, sorghum and pearl millet are discussed in detail. We provide a comprehensive and comparative overview of all detected protein changes involved in drought stress in these crops and have summarized existing knowledge into a proposed scheme of drought response. Based on a recent proteome study of pearl millet under drought stress we compare our findings with wheat proteomes and another recent study which defined genetic marker in pearl millet.

System-Level and Granger Network Analysis of Integrated Proteomic and Metabolomic Dynamics Identifies Key Points of Grape Berry Development at the Interface of Primary and Secondary Metabolism

Grapevine is a fruit crop with worldwide economic importance. The grape berry undergoes complex biochemical changes from fruit set until ripening. This ripening process and production processes define the wine quality. Thus, a thorough understanding of berry ripening is crucial for the prediction of wine quality. For a systemic analysis of grape berry development we applied mass spectrometry based platforms to analyse the metabolome and proteome of Early Campbell at 12 stages covering major developmental phases. Primary metabolites involved in central carbon metabolism, such as sugars, organic acids and amino acids together with various bioactive secondary metabolites like flavonols, flavan-3-ols and anthocyanins were annotated and quantified. At the same time, the proteomic analysis revealed the protein dynamics of the developing grape berries. Multivariate statistical analysis of the integrated metabolomic and proteomic dataset revealed the growth trajectory and corresponding metabolites and proteins contributing most to the specific developmental process. K-means clustering analysis revealed 12 highly specific clusters of co-regulated metabolites and proteins. Granger causality network analysis allowed for the identification of time-shift correlations between metabolite-metabolite, protein- protein and protein-metabolite pairs which is especially interesting for the understanding of developmental processes. The integration of metabolite and protein dynamics with their corresponding biochemical pathways revealed an energy-linked metabolism before veraison with high abundances of amino acids and accumulation of organic acids, followed by protein and secondary metabolite synthesis. Anthocyanins were strongly accumulated after veraison whereas other flavonoids were in higher abundance at early developmental stages and decreased during the grape berry developmental processes. A comparison of the anthocyanin profile of Early Campbell to other cultivars revealed similarities to Concord grape and indicates the strong effect of genetic background on metabolic partitioning in primary and secondary metabolism.

System level analysis of cacao seed ripening reveals a sequential interplay of primary and secondary metabolism leading to polyphenol accumulation and preparation of stress resistance

Theobroma cacao and its popular product, chocolate, are attracting attention due to potential health benefits including antioxidative effects by polyphenols, anti-depressant effects by high serotonin levels, inhibition of platelet aggregation and prevention of obesity-dependent insulin resistance. The development of cacao seeds during fruit ripening is the most crucial process for the accumulation of these compounds. In this study, we analyzed the primary and the secondary metabolome as well as the proteome during Theobroma cacao cv. Forastero seed development by applying an integrative extraction protocol. The combination of multivariate statistics and mathematical modelling revealed a complex consecutive coordination of primary and secondary metabolism and corresponding pathways. Tricarboxylic acid (TCA) cycle and aromatic amino acid metabolism dominated during the early developmental stages (stages 1 and 2; cell division and expansion phase). This was accompanied with a significant shift of proteins from phenylpropanoid metabolism to flavonoid biosynthesis. At stage 3 (reserve accumulation phase), metabolism of sucrose switched from hydrolysis into raffinose synthesis. Lipids as well as proteins involved in lipid metabolism increased whereas amino acids and N-phenylpropenoyl amino acids decreased. Purine alkaloids, polyphenols, and raffinose as well as proteins involved in abiotic and biotic stress accumulated at stage 4 (maturation phase) endowing cacao seeds the characteristic astringent taste and resistance to stress. In summary, metabolic key points of cacao seed development comprise the sequential coordination of primary metabolites, phenylpropanoid, N-phenylpropenoyl amino acid, serotonin, lipid and polyphenol metabolism thereby covering the major compound classes involved in cacao aroma and health benefits.

Pollen proteomics: from stress physiology to developmental priming

Pollen development and stress. In angiosperms, pollen or pollen grain (male gametophyte) is a highly reduced two- or three-cell structure which plays a decisive role in plant reproduction. Male gametophyte development takes place in anther locules where diploid sporophytic cells undergo meiotic division followed by two consecutive mitotic processes. A desiccated and metabolically quiescent form of mature pollen is released from the anther which lands on the stigma. Pollen tube growth takes place followed by double fertilization. Apart from its importance in sexual reproduction, pollen is also an interesting model system which integrates fundamental cellular processes like cell division, differentiation, fate determination, polar establishment, cell to cell recognition and communication. Recently, pollen functionality has been studied by multidisciplinary approaches which also include OMICS analyses like transcriptomics, proteomics and metabolomics. Here, we review recent advances in proteomics of pollen development and propose the process of developmental priming playing a key role to guard highly sensitive developmental processes.

Methods and applications of absolute protein quantification in microbial systems

In the last years the scientific community faced an increased need to provide high-quality data on the concentration of single proteins within a cell. Especially against the background of the fast evolving field of systems biology this does not only apply for a few proteins but preferably for the whole proteome of the organism. Therefore there has been a rapid development from pure identification of proteins via characterization of changes between different conditions by relative protein quantification towards determination of absolute protein amounts for hundreds of protein species in a cell. This review aims for discussion of different small-scale and large-scale approaches for absolute protein quantification in bacterial cells to picture biological processes and explore life in deeper detail. The presented advantages and limitations of various methods may provide interested researchers help to appraise available methods, select the most appropriate technique and avoid common pitfalls during determination of protein concentration in a complex sample.

Metabolomic and Proteomic Profiles Reveal the Dynamics of Primary Metabolism during Seed Development of Lotus (Nelumbo nucifera)

Sacred lotus (Nelumbo nucifera) belongs to the Nelumbonaceae family. Its seeds are widely consumed in Asian countries as snacks or even medicine. Besides the market value, lotus seed also plays a crucial role in the lotus life cycle. Consequently, it is essential to gain a comprehensive understanding of the development of lotus seed. During its development, lotus seed undergoes cell division, expansion, reserve accumulation, desiccation, and maturation phases. We observed morphological and biochemical changes from 10 to 25 days after pollination (DAP) which corresponded to the reserve synthesis and accumulation phase. The volume of the seed expanded until 20 DAP with the color of the seed coat changing from yellow-green to dark green and gradually fading again. Starch and protein rapidly accumulated from 15 to 20 DAP. To further reveal metabolic adaptation, primary metabolites and proteins profiles were obtained using mass spectrometry based platforms. Metabolites and enzymes involved in sugar metabolism, glycolysis, TCA cycle and amino acid metabolism showed sequential dynamics enabling the clear separation of the different metabolic states during lotus seed development. The integration of the data revealed a highly significant metabolic switch at 15 DAP going through a transition of metabolically highly active tissue to the preparation of storage tissue. The results provide a reference data set for the evaluation of primary metabolism during lotus seed development.

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.

De Novo Assembly of the Donkey White Blood Cell Transcriptome and a Comparative Analysis of Phenotype-Associated Genes between Donkeys and Horses

Prior to the mechanization of agriculture and labor-intensive tasks, humans used donkeys (Equus africanus asinus) for farm work and packing. However, as mechanization increased, donkeys have been increasingly raised for meat, milk, and fur in China. To maintain the development of the donkey industry, breeding programs should focus on traits related to these new uses. Compared to conventional marker-assisted breeding plans, genome- and transcriptome-based selection methods are more efficient and effective. To analyze the coding genes of the donkey genome, we assembled the transcriptome of donkey white blood cells de novo. Using transcriptomic deep-sequencing data, we identified 264,714 distinct donkey unigenes and predicted 38,949 protein fragments. We annotated the donkey unigenes by BLAST searches against the non-redundant (NR) protein database. We also compared the donkey protein sequences with those of the horse (E. caballus) and wild horse (E. przewalskii), and linked the donkey protein fragments with mammalian phenotypes. As the outer ear size of donkeys and horses are obviously different, we compared the outer ear size-associated proteins in donkeys and horses. We identified three ear size-associated proteins, HIC1, PRKRA, and KMT2A, with sequence differences among the donkey, horse, and wild horse loci. Since the donkey genome sequence has not been released, the de novo assembled donkey transcriptome is helpful for preliminary investigations of donkey cultivars and for genetic improvement.

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

Medicago truncatula has become the focus of systems biology research for improved legume crop breeding. In plant systems biology, several comparative studies have been carried out using liquid chromatography shotgun mass spectrometry (LC-MS/MS) and database-dependent protein identification analyses in combination with the spectral count for relative quantification. In order to receive optimal protein identification rates and spectral count quantification, data-dependent tandem mass spectrometry with LC separation of more than 1 h is required. Thus LC-MS/MS analyses time is the bottleneck for high-throughput research of experiments with high sample number.We describe a novel method, called full-scan (FS) selective peptide extraction, that allows for comparative quantification of target peptides combined with a significant reduction in LC-MS analysis time. In future, it will be a useful tool to detect (15)N-labeled selected peptide patterns for the targeted analysis of protein turnover and synthesis. We provide a first reference library of selected target peptides generated for M. truncatula leaf tissue. These peptides are also suitable candidates for selective reaction monitoring approaches.

Field-omics-understanding large-scale molecular data from field crops

The recent advances in gene expression analysis as well as protein and metabolite quantification enable genome-scale capturing of complex biological processes at the molecular level in crop field trials. This opens up new possibilities for understanding the molecular and environmental complexity of field-based systems and thus shedding light on the black box between genotype and environment, which in agriculture always is influenced by a multi-stress environment and includes management interventions. Nevertheless, combining different types of data obtained from the field and making biological sense out of large datasets remain challenging. Here we highlight the need to create a cross-disciplinary platform for innovative experimental design, sampling and subsequent analysis of large-scale molecular data obtained in field trials. For these reasons we put forward the term field-omics: "Field-omics strives to couple information from genomes, transcriptomes, proteomes, metabolomes and metagenomes to the long-established practice in crop science of conducting field trials as well as to adapt current strategies for recording and analysing field data to facilitate integration with '-omics' data."

Tandem metal-oxide affinity chromatography for enhanced depth of phosphoproteome analysis

In eukaryotic cells many diverse cellular functions are regulated by reversible protein phosphorylation. In recent years, phosphoproteomics has become a powerful tool to study protein phosphorylation because it allows unbiased localization, and site-specific quantification, of in vivo phosphorylation of hundreds of proteins in a single experiment. A common strategy to identify phosphoproteins and their phosphorylation sites from complex biological samples is the enrichment of phosphopeptides from digested cellular lysates followed by mass spectrometry. However, despite the 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. Tandem metal-oxide affinity chromatography (MOAC) represents a robust and highly selective approach for the identification and site-specific quantification of low abundant phosphoproteins that is based on the successive enrichment of phosphoproteins and -peptides. This strategy combines protein extraction under denaturing conditions, phosphoprotein enrichment using Al(OH)3-based MOAC, tryptic digestion of enriched phosphoproteins followed by TiO2-based MOAC of phosphopeptides. Thus, tandem MOAC effectively targets the phosphate moiety of phosphoproteins and phosphopeptides and, thus, allows probing of the phosphoproteome to unprecedented depth.

An improved detergent-compatible gel-fractionation LC-LTQ-Orbitrap-MS workflow for plant and microbial proteomics

In proteomics, liquid chromatography coupled to mass spectrometry (LC-MS/MS) is an invaluable technique to accurately identify and quantify large sets of proteins. In this chapter we show a time-effective, and detergent compatible, Ge-LC-LTQ-Orbitrap/MS proteomics workflow. The compatibility of this protocol with high concentrations of detergents significantly increases the extraction yield and the abundance of membrane proteins while gel fractionation increases the number of protein identifications. In our hands this workflow allows the identification of more than 1,500 proteins per sample, harvesting less than 20 mg of fresh weight, in many different organisms such as Chlamydomonas, Cyanothece, Arabidopsis, or Nicotiana, various microbes and enriched microbial samples.

From proteomics to systems biology: MAPA, MASS WESTERN, PROMEX, and COVAIN as a user-oriented platform

Genome sequencing and systems biology are revolutionizing life sciences. Proteomics emerged as a fundamental technique of this novel research area as it is the basis for gene function analysis and modeling of dynamic protein networks. Here a complete proteomics platform suited for functional genomics and systems biology is presented. The strategy includes MAPA (mass accuracy precursor alignment; http://www.univie.ac.at/mosys/software.html ) as a rapid exploratory analysis step; MASS WESTERN for targeted proteomics; COVAIN ( http://www.univie.ac.at/mosys/software.html ) for multivariate statistical analysis, data integration, and data mining; and PROMEX ( http://www.univie.ac.at/mosys/databases.html ) as a database module for proteogenomics and proteotypic peptides for targeted analysis. Moreover, the presented platform can also be utilized to integrate metabolomics and transcriptomics data for the analysis of metabolite-protein-transcript correlations and time course analysis using COVAIN. Examples for the integration of MAPA and MASS WESTERN data, proteogenomic and metabolic modeling approaches for functional genomics, phosphoproteomics by integration of MOAC (metal-oxide affinity chromatography) with MAPA, and the integration of metabolomics, transcriptomics, proteomics, and physiological data using this platform are presented. All software and step-by-step tutorials for data processing and data mining can be downloaded from http://www.univie.ac.at/mosys/software.html.

Metabolo-proteomics to discover plant biotic stress resistance genes

Plants continuously encounter various environmental stresses and use qualitative and quantitative measures to resist pathogen attack. Qualitative stress responses, based on monogenic inheritance, have been elucidated and successfully used in plant improvement. By contrast, quantitative stress responses remain largely unexplored in plant breeding, due to complex polygenic inheritance, although hundreds of quantitative trait loci for resistance have been identified. Recent advances in metabolomic and proteomic technologies now offer opportunities to overcome the hurdle of polygenic inheritance and identify candidate genes for use in plant breeding, thus improving the global food security. In this review, we describe a conceptual background to the plant-pathogen relationship and propose ten heuristic steps streamlining the application of metabolo-proteomics to improve plant resistance to biotic stress.

Novel candidate genes influencing natural variation in potato tuber cold sweetening identified by comparative proteomics and association mapping

BACKGROUND

Higher plants evolved various strategies to adapt to chilling conditions. Among other transcriptional and metabolic responses to cold temperatures plants accumulate a range of solutes including sugars. The accumulation of the reducing sugars glucose and fructose in mature potato tubers during exposure to cold temperatures is referred to as cold induced sweetening (CIS). The molecular basis of CIS in potato tubers is of interest not only in basic research on plant adaptation to environmental stress but also in applied research, since high amounts of reducing sugars affect negatively the quality of processed food products such as potato chips. CIS-tolerance varies considerably among potato cultivars. Our objective was to identify by an unbiased approach genes and cellular processes influencing natural variation of tuber sugar content before and during cold storage in potato cultivars used in breeding programs. We compared by two-dimensional polyacrylamide gel electrophoresis the tuber proteomes of cultivars highly diverse for CIS. DNA polymorphisms in genomic sequences encoding differentially expressed proteins were tested for association with tuber starch content, starch yield and processing quality.

RESULTS

Pronounced natural variation of CIS was detected in tubers of a population of 40 tetraploid potato cultivars. Significant differences in protein expression were detected between CIS-tolerant and CIS-sensitive cultivars before the onset as well as during cold storage. Identifiable differential proteins corresponded to protease inhibitors, patatins, heat shock proteins, lipoxygenase, phospholipase A1 and leucine aminopeptidase (Lap). Association mapping based on single nucleotide polymorphisms supported a role of Lap in the natural variation of the quantitative traits tuber starch and sugar content.

CONCLUSIONS

The combination of comparative proteomics and association genetics led to the discovery of novel candidate genes for influencing the natural variation of quantitative traits in potato tubers. One such gene was a leucine aminopeptidase not considered so far to play a role in starch sugar interconversion. Novel SNP's diagnostic for increased tuber starch content, starch yield and chip quality were identified, which are useful for selecting improved potato processing cultivars.

Novel candidate genes influencing natural variation in potato tuber cold sweetening identified by comparative proteomics and association mapping

BACKGROUND

Higher plants evolved various strategies to adapt to chilling conditions. Among other transcriptional and metabolic responses to cold temperatures plants accumulate a range of solutes including sugars. The accumulation of the reducing sugars glucose and fructose in mature potato tubers during exposure to cold temperatures is referred to as cold induced sweetening (CIS). The molecular basis of CIS in potato tubers is of interest not only in basic research on plant adaptation to environmental stress but also in applied research, since high amounts of reducing sugars affect negatively the quality of processed food products such as potato chips. CIS-tolerance varies considerably among potato cultivars. Our objective was to identify by an unbiased approach genes and cellular processes influencing natural variation of tuber sugar content before and during cold storage in potato cultivars used in breeding programs. We compared by two-dimensional polyacrylamide gel electrophoresis the tuber proteomes of cultivars highly diverse for CIS. DNA polymorphisms in genomic sequences encoding differentially expressed proteins were tested for association with tuber starch content, starch yield and processing quality.

RESULTS

Pronounced natural variation of CIS was detected in tubers of a population of 40 tetraploid potato cultivars. Significant differences in protein expression were detected between CIS-tolerant and CIS-sensitive cultivars before the onset as well as during cold storage. Identifiable differential proteins corresponded to protease inhibitors, patatins, heat shock proteins, lipoxygenase, phospholipase A1 and leucine aminopeptidase (Lap). Association mapping based on single nucleotide polymorphisms supported a role of Lap in the natural variation of the quantitative traits tuber starch and sugar content.

CONCLUSIONS

The combination of comparative proteomics and association genetics led to the discovery of novel candidate genes for influencing the natural variation of quantitative traits in potato tubers. One such gene was a leucine aminopeptidase not considered so far to play a role in starch sugar interconversion. Novel SNP's diagnostic for increased tuber starch content, starch yield and chip quality were identified, which are useful for selecting improved potato processing cultivars.

Granger causality in integrated GC-MS and LC-MS metabolomics data reveals the interface of primary and secondary metabolism

Metabolomics has emerged as a key technique of modern life sciences in recent years. Two major techniques for metabolomics in the last 10 years are gas chromatography coupled to mass spectrometry (GC-MS) and liquid chromatography coupled to mass spectrometry (LC-MS). Each platform has a specific performance detecting subsets of metabolites. GC-MS in combination with derivatisation has a preference for small polar metabolites covering primary metabolism. In contrast, reversed phase LC-MS covers large hydrophobic metabolites predominant in secondary metabolism. Here, we present an integrative metabolomics platform providing a mean to reveal the interaction of primary and secondary metabolism in plants and other organisms. The strategy combines GC-MS and LC-MS analysis of the same sample, a novel alignment tool MetMAX and a statistical toolbox COVAIN for data integration and linkage of Granger Causality with metabolic modelling. For metabolic modelling we have implemented the combined GC-LC-MS metabolomics data covariance matrix and a stoichiometric matrix of the underlying biochemical reaction network. The changes in biochemical regulation are expressed as differential Jacobian matrices. Applying the Granger causality, a subset of secondary metabolites was detected with significant correlations to primary metabolites such as sugars and amino acids. These metabolic subsets were compiled into a stoichiometric matrix N. Using N the inverse calculation of a differential Jacobian J from metabolomics data was possible. Key points of regulation at the interface of primary and secondary metabolism were identified.

Phytochemical composition of Potentilla anserina L. analyzed by an integrative GC-MS and LC-MS metabolomics platform

Potentilla anserina L. (Rosaceae) is known for its beneficial effects of prevention of pre-menstrual syndrome (PMS). For this reason P. anserina is processed into many food supplements and pharmaceutical preparations. Here we analyzed hydroalcoholic reference extracts and compared them with various extracts of different pharmacies using an integrative metabolomics platform comprising GC-MS and LC-MS analysis and software toolboxes for data alignment (MetMAX Beta 1.0) and multivariate statistical analysis (COVAIN 1.0). Multivariate statistics of the integrated GC-MS and LC-MS data showed strong differences between the different plant extract formulations. Different groups of compounds such as chlorogenic acid, kaempferol 3-O-rutinoside, acacetin 7-O-rutinoside, and genistein were reported for the first time in this species. The typical fragmentation pathway of the isoflavone genistein confirmed the identification of this active compound that was present with different abundances in all the extracts analyzed. As a result we have revealed that different extraction procedures from different vendors produce different chemical compositions, e.g. different genistein concentrations. Consequently, the treatment may have different effects. The integrative metabolomics platform provides the highest resolution of the phytochemical composition and a mean to define subtle differences in plant extract formulations.

Using ProtMAX to create high-mass-accuracy precursor alignments from label-free quantitative mass spectrometry data generated in shotgun proteomics experiments

Recently, new software tools have been developed for improved protein quantification using mass spectrometry (MS) data. However, there are still limitations especially in high-sample-throughput quantification methods, and most of these relate to extensive computational calculations. The mass accuracy precursor alignment (MAPA) strategy has been shown to be a robust method for relative protein quantification. Its major advantages are high resolution, sensitivity and sample throughput. Its accuracy is data dependent and thus best suited for precursor mass-to-charge precision of ∼1 p.p.m. This protocol describes how to use a software tool (ProtMAX) that allows for the automated alignment of precursors from up to several hundred MS runs within minutes without computational restrictions. It comprises features for 'ion intensity count' and 'target search' of a distinct set of peptides. This procedure also includes the recommended MS settings for complex quantitative MAPA analysis using ProtMAX (http://www.univie.ac.at/mosys/software.html).

Mitochondria are an early target of oxidative modifications in senescing legume nodules

Legume nodule senescence is a poorly understood process involving a decrease in N(2) fixation and an increase in proteolytic activity. Some physiological changes during nodule aging have been reported, but scarce information is available at the subcellular level. Biochemical, immunological and proteomic approaches were used to provide insight into the effects of aging on the mitochondria and cytosol of nodule host cells. In the mitochondria, the oxidative modification of lipids and proteins was associated with a marked decline in glutathione, a reduced capacity to regenerate ascorbate, and upregulation of alternative oxidase and manganese superoxide dismutase. In the cytosol, there were consistent reductions in the protein concentrations of carbon metabolism enzymes, inhibition of protein synthesis and increase in serine proteinase activity, disorganization of cytoskeleton, and a sharp reduction of cytosolic proteins, but no detectable accumulation of oxidized molecules. We conclude that nodule mitochondria are an early target of oxidative modifications and a likely source of redox signals. Alternative oxidase and manganese superoxide dismutase may play important roles in controlling ROS concentrations and the redox state of mitochondria. The finding that specific methionine residues of a cytosolic glutamine synthetase isoform are sulfoxidized suggests a regulatory role of this enzyme in senescing nodules.

The proteomics of heavy metal hyperaccumulation by plants

Hyperaccumulators are distinguished from non-hyperaccumulators on the basis of their capacity to extract heavy metal ions from the soil, their more efficient root-to-shoot translocation of these ions and their greater ability to detoxify and sequester heavy metals in the shoot. The understanding of the mechanisms underlying metal ion accumulation has progressed beyond the relevant biochemistry and physiology to encompass the genetic and molecular regulatory systems which differentiate hyperaccumulators from non-hyperaccumulators. This paper reviews the literature surrounding the application of proteomics technology to plant metal hyperaccumulation, in particular involving the elements As, Cd, Cu, Ni, Pb and Zn. The hyperaccumulation process across a number of unrelated plant species appears to be associated with proteins involved in energy metabolism, the oxidative stress response and abiotic and biotic stress. The relevance of transducers of the metal stress response to the phenomenon of hyperaccumulation is summarized. Proteomic data complement the more voluminous genomic and transcriptomic data sets in providing a more nuanced picture of the process, and should therefore help in the identification of the major genetic determinants of the hyperaccumulation phenomenon.

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.

Identification of metabolites related to mechanisms of resistance in barley against Fusarium graminearum, based on mass spectrometry

Fusarium head blight (FHB) is an economically important disease of the family Triticeae, as, apart from yield reduction it also causes quality deterioration by producing mycotoxins. Host resistance is the most promising way to control the disease. Metabolic profiling was applied to identify resistance related (RR) metabolites against Fusarium graminearum in five FHB-resistant genotypes ('Chevron', 'H5277-44', 'H5277-164', 'M92-513' and 'M122') relative to one FHB-susceptible genotype ('Stander'). The disease severity was assessed in greenhouse to group the genotypes based on FHB-resistance. The disease was quantified as the proportion of diseased spikelets (PSD) and the area under the disease progress curve (AUDPC). Spikelets were collected at 72 h post inoculation. Metabolites were extracted into an aqueous solution of methanol and analyzed using a LC-hybrid-MS system. Metabolite abundances were subjected to a resistant versus susceptible pair-wise analysis, using a t test. Resistance related (RR) metabolites, both constitutive (RRC) and induced (RRI), were identified amongst metabolites whose levels were significantly higher in resistant genotype than in susceptible. Among 1,430 RR metabolites, 115 were putatively identified. These RR metabolites belonged to different chemical groups: fatty acids: linolenic acid; phenylpropanoids: p-coumaric, sinapic acid; flavonoids: naringenin, kaempferol glucoside, catechol glucoside. In addition, resistance indicator metabolites, such as deoxynivalenol (DON) and DON-3-O-glucoside (D3G) were also detected. The amount of total DON synthesized converted to D3G (PDC) was the greatest in resistant genotype 'Chevron' (PDC = 0.76). The role of the resistance-related and resistance-indicator metabolites on plant defense, and their use as potential biomarkers to screen barley genotypes for FHB resistance is discussed.

Green systems biology - From single genomes, proteomes and metabolomes to ecosystems research and biotechnology

Plants have shaped our human life form from the outset. With the emerging recognition of world population feeding, global climate change and limited energy resources with fossil fuels, the relevance of plant biology and biotechnology is becoming dramatically important. One key issue is to improve plant productivity and abiotic/biotic stress resistance in agriculture due to restricted land area and increasing environmental pressures. Another aspect is the development of CO(2)-neutral plant resources for fiber/biomass and biofuels: a transition from first generation plants like sugar cane, maize and other important nutritional crops to second and third generation energy crops such as Miscanthus and trees for lignocellulose and algae for biomass and feed, hydrogen and lipid production. At the same time we have to conserve and protect natural diversity and species richness as a foundation of our life on earth. Here, biodiversity banks are discussed as a foundation of current and future plant breeding research. Consequently, it can be anticipated that plant biology and ecology will have more indispensable future roles in all socio-economic aspects of our life than ever before. We therefore need an in-depth understanding of the physiology of single plant species for practical applications as well as the translation of this knowledge into complex natural as well as anthropogenic ecosystems. Latest developments in biological and bioanalytical research will lead into a paradigm shift towards trying to understand organisms at a systems level and in their ecosystemic context: (i) shotgun and next-generation genome sequencing, gene reconstruction and annotation, (ii) genome-scale molecular analysis using OMICS technologies and (iii) computer-assisted analysis, modeling and interpretation of biological data. Systems biology combines these molecular data, genetic evolution, environmental cues and species interaction with the understanding, modeling and prediction of active biochemical networks up to whole species populations. This process relies on the development of new technologies for the analysis of molecular data, especially genomics, metabolomics and proteomics data. The ambitious aim of these non-targeted 'omic' technologies is to extend our understanding beyond the analysis of separated parts of the system, in contrast to traditional reductionistic hypothesis-driven approaches. The consequent integration of genotyping, pheno/morphotyping and the analysis of the molecular phenotype using metabolomics, proteomics and transcriptomics will reveal a novel understanding of plant metabolism and its interaction with the environment. The analysis of single model systems - plants, fungi, animals and bacteria - will finally emerge in the analysis of populations of plants and other organisms and their adaptation to the ecological niche. In parallel, this novel understanding of ecophysiology will translate into knowledge-based approaches in crop plant biotechnology and marker- or genome-assisted breeding approaches. In this review the foundations of green systems biology are described and applications in ecosystems research are presented. Knowledge exchange of ecosystems research and green biotechnology merging into green systems biology is anticipated based on the principles of natural variation, biodiversity and the genotype-phenotype environment relationship as the fundamental drivers of ecology and evolution.

Recent progress in liquid chromatography-based separation and label-free quantitative plant proteomics

Recent innovations in liquid chromatography-mass spectrometry (LC-MS)-based methods have facilitated quantitative and functional proteomic analyses of large numbers of proteins derived from complex samples without any need for protein or peptide labelling. Regardless of its great potential, the application of these proteomics techniques to plant science started only recently. Here we present an overview of label-free quantitative proteomics features and their employment for analysing plants. Recent methods used for quantitative protein analyses by MS techniques are summarized and major challenges associated with label-free LC-MS-based approaches, including sample preparation, peptide separation, quantification and kinetic studies, are discussed. Database search algorithms and specific aspects regarding protein identification of non-sequenced organisms are also addressed. So far, label-free LC-MS in plant science has been used to establish cellular or subcellular proteome maps, characterize plant-pathogen interactions or stress defence reactions, and for profiling protein patterns during developmental processes. Improvements in both, analytical platforms (separation technology and bioinformatics/statistical analysis) and high throughput nucleotide sequencing technologies will enhance the power of this method.

Next generation functional proteomics in non-model plants: A survey on techniques and applications for the analysis of protein complexes and post-translational modifications

The congruent development of computational technology, bioinformatics and analytical instrumentation makes proteomics ready for the next leap. Present-day state of the art proteomics grew from a descriptive method towards a full stake holder in systems biology. High throughput and genome wide studies are now made at the functional level. These include quantitative aspects, functional aspects with respect to protein interactions as well as post translational modifications and advanced computational methods that aid in predicting protein function and mapping these functionalities across the species border. In this review an overview is given of the current status of these aspects in plant studies with special attention to non-genomic model plants.

Enolases: storage compounds in seeds? Evidence from a proteomic comparison of zygotic and somatic embryos of Cyclamen persicum Mill

Somatic embryogenesis is well established for the economic relevant ornamental crop Cyclamen and thus could supplement the elaborate propagation via seeds. However, the use of somatic embryogenesis for commercial large scale propagation is still limited due to physiological disorders and asynchronous development within emerged embryos. To overcome these problems, profound knowledge of the physiological processes in Cyclamen embryogenesis is essential. Thus, the proteomes of somatic and zygotic embryos were characterised in a comparative approach. Protein separation via two dimensional IEF-SDS PAGE led to a resolution of more than 1,000 protein spots/gel. Overall, 246 proteins were of differential abundance in the two tissues compared. Mass spectrometry analysis of the 300 most abundant protein spots resulted in the identification of 247 proteins, which represent 90 distinct protein species. Fifty-five percent of the 247 proteins belong to only three physiological categories: glycolysis, protein folding and stress response. The latter physiological process was especially predominant in the somatic embryos. Remarkably, the glycolytic enzyme enolase was the protein most frequently detected and thus is supposed to play an important role in Cyclamen embryogenesis. Data are presented that indicate involvement of "small enolases" as storage proteins in Cyclamen. A digital reference map was established via a novel software tool for the web-based presentation of proteome data linked to KEGG and ExPasy protein-databases and both were made publicly available online.

Spectral counting robust on high mass accuracy mass spectrometers

Mass spectrometry is central to shotgun proteomics, an application that seeks to quantify as much of the total protein complement of a biological sample as possible. The high mass accuracy, resolution, capacity and scan rate of modern mass spectrometers have greatly facilitated this endeavor. The sum of MS to MS/MS transitions in tandem mass spectrometry, the spectral count (SC), of a peptide has been shown to be a reliable estimate of its relative abundance. However, when using SCs, optimal MS configurations are crucial in order to maximize the number of low abundant proteins quantified while keeping the estimates for the highly abundant proteins within the linear dynamic range.In this study, LC/MS/MS analysis was performed using an LTQ-OrbiTrap on a sample containing many highly abundant proteins. Tuning the LTQ-OrbiTrap mass spectrometer to minimize redundant MS/MS acquisition and to maximize resolution of the proteome by accurately measured m/z ratios resulted in an appreciable increase in quantified low abundant proteins. An exclusion duration of 90 s and an exclusion width of 10 ppm were found best of those tested. The spectral count of individual proteins was found to be highly reproducible and protein abundance ratios were not affected by the different settings that were applied. We conclude that on a high mass accuracy instrument spectral counting is a robust measure of protein abundance even for samples containing many highly abundant proteins and that tuning dynamic exclusion parameters appreciably improves the number of proteins that can be reliably quantified.

Discovering plant metabolic biomarkers for phenotype prediction using an untargeted approach

Biomarkers are used to predict phenotypical properties before these features become apparent and, therefore, are valuable tools for both fundamental and applied research. Diagnostic biomarkers have been discovered in medicine many decades ago and are now commonly applied. While this is routine in the field of medicine, it is of surprise that in agriculture this approach has never been investigated. Up to now, the prediction of phenotypes in plants was based on growing plants and assaying the organs of interest in a time intensive process. For the first time, we demonstrate in this study the application of metabolomics to predict agronomic important phenotypes of a crop plant that was grown in different environments. Our procedure consists of established techniques to screen untargeted for a large amount of metabolites in parallel, in combination with machine learning methods. By using this combination of metabolomics and biomathematical tools metabolites were identified that can be used as biomarkers to improve the prediction of traits. The predictive metabolites can be selected and used subsequently to develop fast, targeted and low-cost diagnostic biomarker assays that can be implemented in breeding programs or quality assessment analysis. The identified metabolic biomarkers allow for the prediction of crop product quality. Furthermore, marker-assisted selection can benefit from the discovery of metabolic biomarkers when other molecular markers come to its limitation. The described marker selection method was developed for potato tubers, but is generally applicable to any crop and trait as it functions independently of genomic information.

Metabolomics unravel contrasting effects of biodiversity on the performance of individual plant species

In spite of evidence for positive diversity-productivity relationships increasing plant diversity has highly variable effects on the performance of individual plant species, but the mechanisms behind these differential responses are far from being understood. To gain deeper insights into the physiological responses of individual plant species to increasing plant diversity we performed systematic untargeted metabolite profiling on a number of herbs derived from a grassland biodiversity experiment (Jena Experiment). The Jena Experiment comprises plots of varying species number (1, 2, 4, 8, 16 and 60) and number and composition of functional groups (1 to 4; grasses, legumes, tall herbs, small herbs). In this study the metabolomes of two tall-growing herbs (legume: Medicago x varia; non-legume: Knautia arvensis) and three small-growing herbs (legume: Lotus corniculatus; non-legumes: Bellis perennis, Leontodon autumnalis) in plant communities of increasing diversity were analyzed. For metabolite profiling we combined gas chromatography coupled to time-of-flight mass spectrometry (GC-TOF-MS) and UPLC coupled to FT-ICR-MS (LC-FT-MS) analyses from the same sample. This resulted in several thousands of detected m/z-features. ANOVA and multivariate statistical analysis revealed 139 significantly changed metabolites (30 by GC-TOF-MS and 109 by LC-FT-MS). The small-statured plants L. autumnalis, B. perennis and L. corniculatus showed metabolic response signatures to increasing plant diversity and species richness in contrast to tall-statured plants. Key-metabolites indicated C- and N-limitation for the non-leguminous small-statured species B. perennis and L. autumnalis, while the metabolic signature of the small-statured legume L. corniculatus indicated facilitation by other legumes. Thus, metabolomic analysis provided evidence for negative effects of resource competition on the investigated small-statured herbs that might mechanistically explain their decreasing performance with increasing plant diversity. In contrast, taller species often becoming dominant in mixed plant communities did not show modified metabolite profiles in response to altered resource availability with increasing plant diversity. Taken together, our study demonstrates that metabolite profiling is a strong diagnostic tool to assess individual metabolic phenotypes in response to plant diversity and ecophysiological adjustment.

Functional analysis of proteins and protein species using shotgun proteomics and linear mathematics

Covalent post-translational modification of proteins is the primary modulator of protein function in the cell. It greatly expands the functional potential of the proteome compared to the genome. In the past few years shotgun proteomics-based research, where the proteome is digested into peptides prior to mass spectrometric analysis has been prolific in this area. It has determined the kinetics of tens of thousands of sites of covalent modification on an equally large number of proteins under various biological conditions and uncovered a transiently active regulatory network that extends into diverse branches of cellular physiology. In this review, we discuss this work in light of the concept of protein speciation, which emphasizes the entire post-translationally modified molecule and its interactions and not just the modification site as the functional entity. Sometimes, particularly when considering complex multisite modification, all of the modified molecular species involved in the investigated condition, the protein species must be completely resolved for full understanding. We present a mathematical technique that delivers a good approximation for shotgun proteomics data.

Comparative analysis of phytohormone-responsive phosphoproteins in Arabidopsis thaliana using TiO2-phosphopeptide enrichment and mass accuracy precursor alignment

Protein phosphorylation/dephosphorylation is a central post-translational modification in plant hormone signaling, but little is known about its extent and function. Although pertinent protein kinases and phosphatases have been predicted and identified for a variety of hormone responses, classical biochemical approaches have so far revealed only a few candidate proteins and even fewer phosphorylation sites. Here we performed a global quantitative analysis of the Arabidopsis phosphoproteome in response to a time course of treatments with various plant hormones using phosphopeptide enrichment and subsequent mass accuracy precursor alignment (MAPA). The use of three time points, 1, 3 and 6 h, in combination with five phytohormone treatments, abscisic acid (ABA), indole-3-acetic acid (IAA), gibberellic acid (GA), jasmonic acid (JA) and kinetin, resulted in 324,000 precursor ions from 54 LC-Orbitrap-MS analyses quantified and aligned in a data matrix with the dimension of 6000 x 54 using the ProtMax algorithm. To dissect the phytohormone responses, multivariate principal/independent components analysis was performed. In total, 152 phosphopeptides were identified as differentially regulated; these phosphopeptides are involved in a wide variety of signaling pathways. New phosphorylation sites were identified for ABA response element binding factors that showed a specific increase in response to ABA. New phosphorylation sites were also found for RLKs and auxin transporters. We found that different hormones regulate distinct amino acid residues of members of the same protein families. In contrast, tyrosine phosphorylation of the G alpha subunit appeared to be a common response for multiple hormones, demonstrating global cross-talk among hormone signaling pathways.