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Recently, we have developed a quantitative shotgun proteomics strategy called mass accuracy precursor alignment (MAPA). The MAPA algorithm uses high mass accuracy to bin mass-to-charge (m/z) ratios of precursor ions from LC-MS analyses, determines their intensities, and extracts a quantitative sample versus m/z ratio data alignment matrix from a multitude of samples. Here, we introduce a novel feature of this algorithm that allows the extraction and alignment of proteotypic peptide precursor ions or any other target peptide from complex shotgun proteomics data for accurate quantification of unique proteins. This strategy circumvents the problem of confusing the quantification of proteins due to indistinguishable protein isoforms by a typical shotgun proteomics approach. We applied this strategy to a comparison of control and heat-treated tomato pollen grains at two developmental stages, post-meiotic and mature. Pollen is a temperature-sensitive tissue involved in the reproductive cycle of plants and plays a major role in fruit setting and yield. By LC-MS-based shotgun proteomics, we identified more than 2000 proteins in total for all different tissues. By applying the targeted MAPA data-processing strategy, 51 unique proteins were identified as heat-treatment-responsive protein candidates. The potential function of the identified candidates in a specific developmental stage is discussed.

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

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.

Pollen development in angiosperms is one of the most important processes controlling plant reproduction and thus productivity. At the same time, pollen development is highly sensitive to environmental fluctuations, including temperature, drought, and nutrition. Therefore, pollen biology is a major focus in applied studies and breeding approaches for improving plant productivity in a globally changing climate. The most accessible developmental stages of pollen are the mature pollen and the pollen tubes, and these are thus most frequently analyzed. To reveal a complete quantitative proteome map, we additionally addressed the very early stages, analyzing eight stages of tobacco pollen development: diploid microsporocytes, meiosis, tetrads, microspores, polarized microspores, bipolar pollen, desiccated pollen, and pollen tubes. A protocol for the isolation of the early stages was established. Proteins were extracted and analyzed by means of a new gel LC-MS fractionation protocol. In total, 3817 protein groups were identified. Quantitative analysis was performed based on peptide count. Exceedingly stage-specific differential protein regulation was observed during the conversion from the sporophytic to the gametophytic proteome. A map of highly specialized functionality for the different stages could be revealed from the metabolic activity and pronounced differentiation of proteasomal and ribosomal protein complex composition up to protective mechanisms such as high levels of heat shock proteins in the very early stages of development.

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.

Tomato is a globally important crop grown and consumed worldwide. Its reproductive activity is highly sensitive to environmental fluctuations, for instance temperature and drought. Here, pollen development is one of the most decisive processes. The present study aims for the identification of cell-specific proteins during pollen developmental stages of tomato. We have setup a protocol for stage-specific pollen isolation including microsporocytes (pollen mother cells), tetrads, microspores, polarized microspores, and mature pollen. Proteins were extracted using phenol and prefractionated using SDS-PAGE followed by protein digestion, peptide extraction, and desalting. Identification and quantification of proteins were performed using nanoHPLC coupled to LTQ-Orbitrap-MS. In total, 1821 proteins were identified. Most of these proteins were classified based on their homology and designated functions of orthologs. Cluster and principal components analysis revealed stage-specific proteins and demonstrated that pollen development of tomato is a highly controlled sequential process at the proteome level. Intermediate stages such as tetrad and polarized microspore are clearly distinguished by different functionality compared to other stages. From the predicted functions, energy-related proteins are increased during the later stages of development, which indicates that pollen germination depends upon presynthesized proteins in mature pollen. In contrast, heat stress-related proteins are highly abundant in very early developmental stages, suggesting a dominant role in stress protection. Taken together, the data provide a first cell-specific protein reference set for tomato pollen development from pollen mother cells to the mature pollen and give evidence for developmentally controlled processes that might help to prepare the cells for specific developmental programs and environmental stresses.

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.

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.

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).

Mitogen-activated protein kinase (MPK) cascades are important for eukaryotic signal transduction. They convert extracellular stimuli (e.g. some hormones, growth factors, cytokines, microbe- or damage-associated molecular patterns) into intracellular responses while at the same time amplifying the transmitting signal. By doing so, they ensure proper performance, and eventually survival, of a given organism, for example in times of stress. MPK cascades function via reversible phosphorylation of cascade components MEKKs, MEKs, and MPKs. In plants the identity of most MPK substrates remained elusive until now. Here, we provide a robust and powerful approach to identify and quantify, with high selectivity, site-specific phosphorylation of MPK substrate candidates in the model plant Arabidopsis thaliana. Our approach represents a two-step chromatography combining phosphoprotein enrichment using Al(OH)(3)-based metal oxide affinity chromatography, tryptic digest of enriched phosphoproteins, and TiO(2)-based metal oxide affinity chromatography to enrich phosphopeptides from complex protein samples. When applied to transgenic conditional gain-of-function Arabidopsis plants supporting in planta activation of MPKs, the approach allows direct measurement and quantification ex vivo of site-specific phosphorylation of several reported and many yet unknown putative MPK substrates in just a single experiment.

Most legume species establish a symbiotic association with soil bacteria. The plant accommodates the differentiated rhizobia in specialized organs, the root nodules. In this environment, the microsymbiont reduces atmospheric nitrogen (N) making it available for plant metabolism. Symbiotic N-fixation is driven by the respiration of the host photosynthates and thus constitutes an additional carbon sink for the plant. Molecular phenotypes of symbiotic and non-symbiotic Medicago truncatula are identified. The implication of nodule symbiosis on plant abiotic stress response mechanisms is not well understood. In this study, we exposed nodulated and non-symbiotic N-fertilized plants to salt and drought conditions. We assessed the stress effects with proteomic and metabolomic methods and found a nutritionally regulated phenotypic plasticity pivotal for a differential stress adjustment strategy.

Protein identification and proteome mapping mostly rely on the combination of tandem mass spectrometry and sequence database searching. Despite constant improvements achieved in instrumentation, search algorithms, and genome annotations, little effort has been invested in estimating the impact of different genome annotation releases on the final results of a proteome study. We have used a large dataset of mass spectra obtained using an Orbitrap LTQ XL instrument, covering different growth situations of the model species Chlamydomonas reinhardtii. More than one million spectra were analyzed employing the SEQUEST algorithm and four different databases corresponding to the major Chlamydomonas genome assemblies. In total more than 3000 proteins and about 11,000 peptides were identified. 238 proteins were exclusively detected in assembly 3.0 in contrast to 1222 missing proteins only detectable in other databases. The comparison of the results demonstrates that the database selection affects not only the number of identified proteins but also label free quantitation and the biological interpretation of the results. Lists of protein accessions exclusively assigned to individual C. reinhardtii genome assemblies and annotations are provided as a resource for proteogenomic studies., (Copyright © 2012 Elsevier B.V. All rights reserved.)

The ProMEX database is one of the main collection of annotated tryptic peptides in plant proteomics. The main objective of the ProMEX database is to provide experimental MS/MS-based information for cell type-specific or sub-cellular proteomes in Arabidopsis thaliana, Medicago truncatula, Chlamydomonas reinhardtii, Lotus japonicus, Lotus corniculatus, Phaseolus vulgaris, Lycopersicon esculentum, Solanum tuberosum, Nicotiana tabacum, Glycine max, Zea mays, Bradyrhizobium japonicum, and Sinorhizobium meliloti. Direct links at the protein level to the most relevant databases are present in ProMEX. Furthermore, the spectral sequence information are linked to their respective pathways and can be viewed in pathway maps.

Mass Accuracy Precursor Alignment is a fast and flexible method for comparative proteome analysis that allows the comparison of unprecedented numbers of shotgun proteomics analyses on a personal computer in a matter of hours. We compared 183 LC-MS analyses and more than 2 million MS/MS spectra and could define and separate the proteomic phenotypes of field grown tubers of 12 tetraploid cultivars of the crop plant Solanum tuberosum. Protein isoforms of patatin as well as other major gene families such as lipoxygenase and cysteine protease inhibitor that regulate tuber development were found to be the primary source of variability between the cultivars. This suggests that differentially expressed protein isoforms modulate genotype specific tuber development and the plant phenotype. We properly assigned the measured abundance of tryptic peptides to different protein isoforms that share extensive stretches of primary structure and thus inferred their abundance. Peptides unique to different protein isoforms were used to classify the remaining peptides assigned to the entire subset of isoforms based on a common abundance profile using multivariate statistical procedures. We identified nearly 4000 proteins which we used for quantitative functional annotation making this the most extensive study of the tuber proteome to date.

Proteomics has become a critical tool in the functional understanding of plant processes at the molecular level. Proteomics-based studies have also contributed to the ever-expanding array of data in modern biology, with many generating Web portals and online resources that contain incrementally expanding and updated information. Many of these resources reflect specialist research areas with significant and novel information that is not currently captured by centralized repositories. The Arabidopsis (Arabidopsis thaliana) community is well served by a number of online proteomics resources that hold an abundance of functional information. These sites can be difficult to locate among a multitude of online resources. Furthermore, they can be difficult to navigate in order to identify specific features of interest without significant technical knowledge. Recently, members of the Arabidopsis proteomics community involved in developing many of these resources decided to develop a summary aggregation portal that is capable of retrieving proteomics data from a series of online resources on the fly. The Web portal is known as the MASCP Gator and can be accessed at the following address: http://gator.masc-proteomics.org/. Significantly, proteomics data displayed at this site retrieve information from the data repositories upon each request. This means that information is always up to date and displays the latest data sets. The site also provides hyperlinks back to the source information hosted at each of the curated databases to facilitate more in-depth analysis of the primary data.

In the era of fast genome sequencing a critical goal is to develop genome-wide quantitative molecular approaches. Here, we present a metaproteogenomic strategy to integrate proteomics and metabolomics data for systems level analysis in the recently sequenced unicellular green algae Chlamydomonas reinhardtii. To achieve a representative proteome coverage we analysed different growth conditions with protein prefractionation and shotgun proteomics. For protein identification, different genome annotations as well as new gene model predictions with stringent peptide filter criteria were used. An overlapping proteome coverage of 25%, consistent for all databases, was determined. The data are stored in a public mass spectral reference database ProMEX (http://www.promexdb.org/home.shtml). A set of proteotypic peptides comprising Calvin cycle, photosynthetic apparatus, starch synthesis, glycolysis, TCA cycle, carbon concentrating mechanisms (CCM) and other pathways was selected from this database for targeted proteomics (Mass Western). Rapid subcellular fractionation in combination with targeted proteomics allowed for measuring subcellular protein concentrations in attomole per 1000 cells. From the same samples metabolite concentrations and metabolic fluxes by stable isotope incorporation were analyzed. Differences were found in the growth-dependent crosstalk of chloroplastidic and mitochondrial metabolism. A Mass Western survey of all detectable carbonic anhydrases partially involved in carbon-concentrating mechanism (CCM) revealed highest internal cell concentrations for a specific low-CO2-inducible mitochondrial CAH isoform. This indicates its role as one of the strongest CO2-responsive proteins in the crosstalk of air-adapted mixotrophic chloroplast and mitochondrial metabolism in Chlamydomonas reinhardtii.

A wide range of research areas in molecular biology and medical biochemistry require a reliable enzyme classification system, e.g., drug design, metabolic network reconstruction and system biology. When research scientists in the above mentioned areas wish to unambiguously refer to an enzyme and its function, the EC number introduced by the Nomenclature Committee of the International Union of Biochemistry and Molecular Biology (IUBMB) is used. However, each and every one of these applications is critically dependent upon the consistency and reliability of the underlying data for success. We have developed tools for the validation of the EC number classification scheme. In this paper, we present validated data of 3788 enzymatic reactions including 229 sub-subclasses of the EC classification system. Over 80% agreement was found between our assignment and the EC classification. For 61 (i.e., only 2.5%) reactions we found that their assignment was inconsistent with the rules of the nomenclature committee; they have to be transferred to other sub-subclasses. We demonstrate that our validation results can be used to initiate corrections and improvements to the EC number classification scheme.

Background: Enzymes are classified in a numerical classification scheme introduced by the Nomenclature Committee of the IUBMB based on the overall reaction chemistry. Due to the manifold of enzymatic reactions the system has become highly complex. Assignment of enzymes to the enzyme classes requires a detailed knowledge of the system and manual analysis. Frequently rearrangements and deletions of enzymes and sub-subclasses are necessary., Results: We use the Dugundji-Ugi model for coding of biochemical reactions which is based on electron shift patterns occurring during reactions. Changes of the bonds or of non-bonded valence electrons are expressed by reaction matrices. Our program calculates reaction matrices automatically on the sole basis of substrate and product chemical structures based on a new strategy for maximal common substructure determination, which allows an accurate atom mapping of the substrate and product atoms. The system has been tested for a large set of enzymatic reactions including all sub-subclasses of the EC classification system. Altogether 147 different representative reaction operators were found in the classified enzymes, 121 of which are unique with respect to an EC sub-subclass. The other 26 comprise groups of enzymes with very similar reactions, being identical with respect to the bonds formed and broken., Conclusion: The analysis and comparison of enzymatic reactions according to their electron shift patterns is defining enzyme groups characterised by unique reaction cores. Our results demonstrate the applicability of the Dugundji-Ugi model as a reasonable pre-classification system allowing an objective and rational view on biochemical reactions., Availability: The program to generate reaction matrix descriptors is available upon request.

The use of delayed ion extraction in MALDI time-of-flight mass spectrometry distorts the linear relationship between m/z and the square of the ion flight time (t2) with the consequence that, if a mass accuracy of 10 ppm or better is to be obtained, the calibrant signals have to fall close to the analyte signals. If this is not possible, systematic errors arise. To eliminate these, a higher-order calibration function and thus several calibrant signals are required. For internal calibration, however, this approach is limited by signal suppression effects and the increasing chance of the calibrant signals overlapping with analyte signals. If instead the calibrants are prepared separately, this problem is replaced by an other; i.e., the ion flight times are dependent on the sample plate position. For this reason, even if the calibrants are placed close to the sample, the mass accuracy is not improved when a higher-order calibration function is applied. We have studied this phenomenon and found that the relative errors, which result when moving from one sample to the next, are directly proportional to m/z. Based on this observation, we developed a two-step calibration method, that overcomes said limitations. The first step is an external calibration with a high-order polynomial function used for the determination of the relation between m/z and t2, and the second step is a first-order internal correction for sample position-dependent errors. Applying this method, for instance, to a mass spectrum of a mixture of 18 peptides from a tryptic digest of a recombinant protein resulted in an average mass error of 1.0 ppm with a standard deviation of 3.5 ppm. When instead using a conventional two-point internal calibration, the average relative error was 2.2 ppm with a standard deviation of 15 ppm. The new method is described and its performance is demonstrated with examples relevant to proteome research.

A new strategy for identifying proteins by MALDI-TOF-MS peptide mapping is reported. In contrast to current approaches, the strategy does not rely on a good relative or absolute mass accuracy as the criterion that discriminates false positive results. The protein sequence database is first searched for all proteins that match a minimum five of the submitted masses within the maximum expected relative errors when the default or externally determined calibration constants are used, for instance, +/-500 ppm. Typically, this search retrieves many thousand candidate sequences. Assuming initially that each of these is the correct protein, the relative errors of the matching peptide masses are calculated for each candidate sequence. Linear regression analysis is then performed of the calculated relative errors as a function of m/z for each candidate sequence, and the standard deviation to the regression is used to distinguish the correct sequence among the candidates. We show that this parameter is independent of whether the mass spectrometric data were internally or externally calibrated. The result is a search engine that renders internal spectrum calibration unnecessary and adapts to the quality of the raw data without user interference. This is made possible by a dynamic scoring algorithm, which takes into account the number of matching peptide masses, the percentage of the protein's sequence covered by these peptides and, as new parameter, the determined standard deviation. The lower the standard deviation, the less cleavage peptides are required for identification and vice versa. Performance of the new strategy is demonstrated and discussed. All necessary computing has been implemented in a computer program, free access to which is provided in the Internet.

We describe the technical feasibility and methodology to characterize a protein by a minimal set of structural information generated by matrix assisted laser desorption/ionization (MALDI)-mass spectrometry, termed a "minimal protein Identifier" (MPI). MPIs can be determined for proteins from two-dimensional gels and recombinant proteins and can be used to compare and identify proteins from these sources.

The large-gel two-dimensional electrophoresis (2-DE) technique, developed by Klose and co-workers over the past 25 years, provides the resolving power necessary to separate crude proteome extracts of higher eukaryotes. Matrix assisted laser desorption/ionization-time of flight-mass spectrometry (MALDI-TOF-MS) provides the sample throughput necessary to identify thousands of different protein species in an adequate time period. Spot excision, in situ proteolysis, and extraction of the cleavage products from the gel matrix, peptide purification and concentration as well as the mass spectrometric sample preparation are the crucial steps that interface the two analytical techniques. Today, these routines and not the mass spectrometric instrumentation determine how many protein digests can be analyzed per day per instrument. The present paper focuses on this analytical interface and reports on an integrated protocol and technology developed in our laboratory. Automated identification of proteins in sequence databases by mass spectrometric peptide mapping requires a powerful search engine that makes full use of the information contained in the experimental data, and scores the search results accordingly. This challenge is heading a second part of the paper.

A new strategy for identifying proteins in sequence data-bases by MALDI-MS peptide mapping is reported. The strategy corrects for systematic deviations of determined peptide molecular masses using information contained in the opened database and thereby renders unnecessary internal spectrum calibration. As a result, data acquisition is simplified and less error prone. Performance of the new strategy is demonstrated by identification of a set of recombinant, human cDNA expression products as well as native proteins isolated from crude mouse brain extracts by 2-D electrophoresis. Using one set of calibration constants for the mass spectrometric analyses, 20 proteins were identified without applying any molecular weight restrictions, which was not possible without data correction. A sequence database search program has been written that performs all necessary calculations automatically, access to which will be provided to the scientific community in the Internet.