A workflow to increase the detection rate of proteins from unsequenced organisms in high-throughput proteomics experiments

Differences in proteomic profile between two haemocyte types, granulocytes and hyalinocytes, of the flat oyster Ostrea edulis

Haemocytes play a dominant role in shellfish immunity, being considered the main defence effector cells in molluscs. These cells are known to be responsible for many functions, including chemotaxis, cellular recognition, attachment, aggregation, shell repair and nutrient transport and digestion. There are two basic cell types of bivalve haemocytes morphologically distinguishable, hyalinocytes and granulocytes; however, functional differences and specific abilities are poorly understood: granulocytes are believed to be more efficient in killing microorganisms, while hyalinocytes are thought to be more specialised in clotting and wound healing. A proteomic approach was implemented to find qualitative differences in the protein profile between granulocytes and hyalinocytes of the European flat oyster, Ostrea edulis, as a way to evaluate functional differences. Oyster haemolymph cells were differentially separated by Percoll® density gradient centrifugation. Granulocyte and hyalinocyte proteins were separated by 2D-PAGE and their protein profiles were analysed and compared with PD Quest software; the protein spots exclusive for each haemocyte type were excised from gels and analysed by MALDI-TOF/TOF with a combination of mass spectrometry (MS) and MS/MS for sequencing and protein identification. A total of 34 proteins were identified, 20 unique to granulocytes and 14 to hyalinocytes. The results suggested differences between the haemocyte types in signal transduction, apoptosis, oxidation reduction processes, cytoskeleton, phagocytosis and pathogen recognition. These results contribute to identify differential roles of each haemocyte type and to better understand the oyster immunity mechanisms, which should help to fight oyster diseases.

Proteogenomic Analysis Greatly Expands the Identification of Proteins Related to Reproduction in the Apogamous Fern Dryopteris affinis ssp. affinis

Performing proteomic studies on non-model organisms with little or no genomic information is still difficult. However, many specific processes and biochemical pathways occur only in species that are poorly characterized at the genomic level. For example, many plants can reproduce both sexually and asexually, the first one allowing the generation of new genotypes and the latter their fixation. Thus, both modes of reproduction are of great agronomic value. However, the molecular basis of asexual reproduction is not well understood in any plant. In ferns, it combines the production of unreduced spores (diplospory) and the formation of sporophytes from somatic cells (apogamy). To set the basis to study these processes, we performed transcriptomics by next-generation sequencing (NGS) and shotgun proteomics by tandem mass spectrometry in the apogamous fern D. affinis ssp. affinis. For protein identification we used the public viridiplantae database (VPDB) to identify orthologous proteins from other plant species and new transcriptomics data to generate a "species-specific transcriptome database" (SSTDB). In total 1,397 protein clusters with 5,865 unique peptide sequences were identified (13 decoy proteins out of 1,410, protFDR 0.93% on protein cluster level). We show that using the SSTDB for protein identification increases the number of identified peptides almost four times compared to using only the publically available VPDB. We identified homologs of proteins involved in reproduction of higher plants, including proteins with a potential role in apogamy. With the increasing availability of genomic data from non-model species, similar proteogenomics approaches will improve the sensitivity in protein identification for species only distantly related to models.

Fourteen years of plant proteomics reflected in Proteomics: moving from model species and 2DE-based approaches to orphan species and gel-free platforms

In this article, the topic of plant proteomics is reviewed based on related papers published in the journal Proteomics since publication of the first issue in 2001. In total, around 300 original papers and 41 reviews published in Proteomics between 2000 and 2014 have been surveyed. Our main objective for this review is to help bridge the gap between plant biologists and proteomics technologists, two often very separate groups. Over the past years a number of reviews on plant proteomics have been published . To avoid repetition we have focused on more recent literature published after 2010, and have chosen to rather make continuous reference to older publications. The use of the latest proteomics techniques and their integration with other approaches in the "systems biology" direction are discussed more in detail. Finally we comment on the recent history, state of the art, and future directions of plant proteomics, using publications in Proteomics to illustrate the progress in the field. The review is organized into two major blocks, the first devoted to provide an overview of experimental systems (plants, plant organs, biological processes) and the second one to the methodology.

Application of de novo sequencing tools to study abiogenic peptide formations by tandem mass spectrometry. The case of homo-peptides from glutamic acid complicated by substitutions of hydrogen by sodium or potassium atoms

RATIONALE

Peptides and proteins are among the most important components of living systems. Different attempts have been made to experimentally model the formation of peptides from amino acid monomers in investigation of the origin of life. Detailed characterization of peptides formed under various conditions in such reactions is very important for understanding processes of abiogenic peptide formation.

METHODS

We used liquid chromatography coupled with tandem mass spectrometry (MS/MS) for an accurate study of homo-peptides formed in a model reaction: glutamic acid oligomerization catalyzed by 1,1'-carbonyldiimidazole in aqueous solution with 1 M of sodium or potassium chloride and without any salts. We used de novo sequencing software for peptide identification. In addition we propose an approach that uses more spectral information for de novo sequencing then standard methods.

RESULTS

Peptides up to 9 amino acids long were found in the experiments with KCl, while in experiments with NaCl and without salts only peptides of up to 7 amino acids were detected. Due to high salt concentrations in samples a high number of singly charged peptide ions with up to 4 substitutions of hydrogen atoms by sodium or potassium atoms were observed. De novo sequencing software provided correct identifications even for peptide ions with substitutions.

CONCLUSIONS

Multiple substitutions of hydrogen by alkali metal atoms in peptide ions strongly change their fragmentation patterns. Proposed approach for de novo sequencing was found very effective, even for ions with substitutions. So, it may be useful in more complicated cases like sequencing abiogenic peptides consisting of different amino acids.

Differential plant proteome analysis by isobaric tags for relative and absolute quantitation (iTRAQ)

Protein relative quantitation is one of the main targets in many proteomic experiments. Among the range of techniques available for both top-down and bottom-up approaches, isobaric tags for relative and absolute quantitation (iTRAQ) have gained positions within the top-rank techniques used for this purpose in the recent years. Briefly, each iTRAQ reagent consists of three different components: a reporter group (with a variable mass in the range of 114-117 amu), a balance group, and an amino-reactive group. The isobaric nature of iTRAQ-labeled peptides adds a signal to every peptide in the sample which is detectable in both MS and MS/MS spectra, thus enhancing the sensitivity of detection. During MS/MS, the reporter groups are released as singly charged ions with m/z ratios ranking from 114 to 117 amu, visible in the low mass region of MS/MS spectra. The iTRAQ technology can be used to analyze up to four different samples using the 4-plex kit (reporter groups 114-115 amu) or can be scaled up to eight different samples using the 8-plex kit (reporter groups 113-121 amu). In this chapter, we focus on the experimental procedures typically using 4-plex labeling, including tips leading to successful application of iTRAQ technology for the analysis of plant protein mixtures.

Protein identification and quantification by data-independent acquisition and multi-parallel collision-induced dissociation mass spectrometry (MS(E)) in the chloroplast stroma proteome

We report here a systematic evaluation of a multiplex mass spectrometry method coupled with ion mobility separation (HD-MS(E)) for the identification and quantification of proteins in the chloroplast stroma. We show that this method allows the robust quantification of reference proteins in mixtures, and it detects concentration differences with high sensitivity when three replicas are performed. Applied to the analysis of the chloroplast stroma proteome, HD-MS(E) identified and quantified many chloroplast proteins that were not previously identified in large-scale proteome analyses, suggesting HD-MS(E) as a suitable complementary tool for discovery proteomics. We find that HD-MS(E) tends to underestimate protein abundances at concentrations above 25fmol, which is likely due to ion transmission loss and detector saturation. This limitation can be circumvented by omitting the ion mobility separation step in the HD-MS(E) workflow. The robustness of protein quantification is influenced by the selection of peptides and their intensity distribution, therefore critical scrutiny of quantification results is required. Based on the HD-MS(E) quantification of chloroplast stroma proteins we performed a meta-analysis and compared published quantitative data with our results, using a parts per million normalization scheme. Important pathways in the chloroplast stroma show quantitative stability against different experimental conditions and quantification strategies.

BIOLOGICAL SIGNIFICANCE

Our analysis establishes MS(E)-based Hi3 quantification as a tool for the absolute quantification of proteins in the chloroplast stroma. The meta-analysis performed with a parts per million normalization scheme shows that quantitative proteomics data acquired in different labs and with different quantification strategies yield comparable results for some metabolic pathways, while others show a higher variability. Our data therefore indicate that such meta-analyses allow distinguishing robust from fine-controlled metabolic pathways.

Crop seed oil bodies: from challenges in protein identification to an emerging picture of the oil body proteome

Oleaginous seeds store lipids in specialized structures called oil bodies (OBs). These organelles consist of a core of neutral lipids bound by proteins embedded in a phospholipid monolayer. OB proteins are well conserved in plants and have long been grouped into only two categories: structural proteins or enzymes. Recent work, however, which identified other classes of proteins associated with OBs, clearly shows that this classification is obsolete. Proteomics-mediated OB protein identification is facilitated in plants for which the genome is sequenced and annotated. However, it is not clear whether this knowledge can be dependably transposed to less well-characterized plants, including the well-established commercial sources of seed oil as well as the many others being proposed as novel sources for biodiesel, especially in Africa and Asia. Toward an update of the current data available on OB proteins this review discusses (i) the specific difficulties for proteomic studies of organelles; (ii) a 2012 census of the proteins found in seed OBs from various crops; (iii) the oleosin composition of OBs and their role in organelle stability; (iv) PTM of OB proteins as an emerging field of investigation; and finally we describe the emerging model of the OB proteome from oilseed crops.

Proteomics of nonmodel plant species

Until recently, large scale proteomic investigations in the plant field have only been possible for a few model species for which the whole genome sequence had been fully determined. In contrast, for many other species with a strong economic interest as sources of human food and animal feed, as well as industrial and pharmacological molecules, little was known about their genome sequence and identifying the proteome in these species was still considered challenging. However, progress has been made as a result of several recent advances in proteomics tools, e.g. in MS technology and data search programs, and the increasing availability of genomic and cDNA sequences from various species. Moreover, next-generation sequencing technologies now make it possible to rapidly determine, at a reasonable cost, the genome or RNA sequence of species not currently considered as models, thus considerably expanding the plant sequence databases. This review will show how these advances make it possible to identify a large set of proteins, even for species for which few sequences are currently available.

Overcoming species boundaries in peptide identification with Bayesian information criterion-driven error-tolerant peptide search (BICEPS)

Currently, the reliable identification of peptides and proteins is only feasible when thoroughly annotated sequence databases are available. Although sequencing capacities continue to grow, many organisms remain without reliable, fully annotated reference genomes required for proteomic analyses. Standard database search algorithms fail to identify peptides that are not exactly contained in a protein database. De novo searches are generally hindered by their restricted reliability, and current error-tolerant search strategies are limited by global, heuristic tradeoffs between database and spectral information. We propose a Bayesian information criterion-driven error-tolerant peptide search (BICEPS) and offer an open source implementation based on this statistical criterion to automatically balance the information of each single spectrum and the database, while limiting the run time. We show that BICEPS performs as well as current database search algorithms when such algorithms are applied to sequenced organisms, whereas BICEPS only uses a remotely related organism database. For instance, we use a chicken instead of a human database corresponding to an evolutionary distance of more than 300 million years (International Chicken Genome Sequencing Consortium (2004) Sequence and comparative analysis of the chicken genome provide unique perspectives on vertebrate evolution. Nature 432, 695–716). We demonstrate the successful application to cross-species proteomics with a 33% increase in the number of identified proteins for a filarial nematode sample of Litomosoides sigmodontis.

Investigating the deep supercooling ability of an Alaskan beetle, Cucujus clavipes puniceus, via high throughput proteomics

Cucujus clavipes puniceus is a freeze avoiding beetle capable of surviving the long, extremely cold winters of the Interior of Alaska. Previous studies showed that some individuals typically supercool to mean values of approximately -40 °C, with some individuals supercooling to as low as -58 °C, but these non-deep supercooling (NDSC) individuals eventually freeze if temperatures drop below this. However, other larvae, especially if exposed to very cold temperatures, supercool even further. These deep supercooling (DSC) individuals do not freeze even if cooled to -100 °C. In addition, the body water of the DSC larvae vitrifies (turns to a glass) at glass transition temperatures of -58 to -70 °C. This study examines the proteomes of DSC and NDSC larvae to assess proteins that may contribute to or inhibit the DSC trait. Using high throughput proteomics, we identified 138 proteins and 513 Gene Ontology categories in the DSC group and 104 proteins and 573 GO categories in the NDSC group. GO categories enriched in DSC include alcohol metabolic process, cellular component morphogenesis, monosaccharide metabolic process, regulation of biological quality, extracellular region, structural molecule activity, and antioxidant activity. Proteins unique to DSC include alpha casein precursor, alpha-actinin, vimentin, tropomyosin, beta-lactoglobulin, immunoglobulins, tubulin, cuticle proteins and endothelins.

Plant organelle proteomics: collaborating for optimal cell function

Organelle proteomics describes the study of proteins present in organelle at a particular instance during the whole period of their life cycle in a cell. Organelles are specialized membrane bound structures within a cell that function by interacting with cytosolic and luminal soluble proteins making the protein composition of each organelle dynamic. Depending on organism, the total number of organelles within a cell varies, indicating their evolution with respect to protein number and function. For example, one of the striking differences between plant and animal cells is the plastids in plants. Organelles have their own proteins, and few organelles like mitochondria and chloroplast have their own genome to synthesize proteins for specific function and also require nuclear-encoded proteins. Enormous work has been performed on animal organelle proteomics. However, plant organelle proteomics has seen limited work mainly due to: (i) inter-plant and inter-tissue complexity, (ii) difficulties in isolation of subcellular compartments, and (iii) their enrichment and purity. Despite these concerns, the field of organelle proteomics is growing in plants, such as Arabidopsis, rice and maize. The available data are beginning to help better understand organelles and their distinct and/or overlapping functions in different plant tissues, organs or cell types, and more importantly, how protein components of organelles behave during development and with surrounding environments. Studies on organelles have provided a few good reviews, but none of them are comprehensive. Here, we present a comprehensive review on plant organelle proteomics starting from the significance of organelle in cells, to organelle isolation, to protein identification and to biology and beyond. To put together such a systematic, in-depth review and to translate acquired knowledge in a proper and adequate form, we join minds to provide discussion and viewpoints on the collaborative nature of organelles in cell, their proper function and evolution.

iTRAQ-based analysis of changes in the cassava root proteome reveals pathways associated with post-harvest physiological deterioration

The short storage life of harvested cassava roots is an important constraint that limits the full potential of cassava as a commercial food crop in developing countries. We investigated the molecular changes during physiological deterioration of cassava root after harvesting using isobaric tags for relative and absolute quantification (iTRAQ) of proteins in soluble and non-soluble fractions prepared during a 96 h post-harvest time course. Combining bioinformatic approaches to reduce information redundancy for unsequenced or partially sequenced plant species, we established a comprehensive proteome map of the cassava root and identified quantitatively regulated proteins. Up-regulation of several key proteins confirmed that physiological deterioration of cassava root after harvesting is an active process, with 67 and 170 proteins, respectively, being up-regulated early and later after harvesting. This included regulated proteins that had not previously been associated with physiological deterioration after harvesting, such as linamarase, glutamic acid-rich protein, hydroxycinnamoyl transferase, glycine-rich RNA binding protein, β-1,3-glucanase, pectin methylesterase, maturase K, dehydroascorbate reductase, allene oxide cyclase, and proteins involved in signal pathways. To confirm the regulation of these proteins, activity assays were performed for selected enzymes. Together, our results show that physiological deterioration after harvesting is a highly regulated complex process involving proteins that are potential candidates for biotechnology approaches to reduce such deterioration.

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.

Challenges and solutions for the identification of membrane proteins in non-model plants

The workhorse for proteomics in non-model plants is classical two-dimensional electrophoresis, a combination of iso-electric focusing and SDS-PAGE. However, membrane proteins with multiple membrane spanning domains are hardly detected on classical 2-DE gels because of their low abundance and poor solubility in aqueous media. In the current review, solutions that have been proposed to handle these two problems in non-model plants are discussed. An overview of alternative techniques developed for membrane proteomics is provided together with a comparison of their strong and weak points. Subsequently, strengths and weaknesses of the different techniques and methods to evaluate the identification of membrane proteins are discussed. Finally, an overview of recent plant membrane proteome studies is provided with the used separation technique and the number of identified membrane proteins listed.

Challenges in proteome analyses of tropical plants

Genome sequencing of various organisms allow global analysis of gene expression, providing numerous clues on the biological function and involvement in the biological processes studied. Proteomics is a branch of molecular biology and biotechnology that has undergone considerable development in the post-genomic era. Despite the recent significant advancements in proteomics techniques, still there is much to be improved. Due to peculiarities to the plant kingdom, proteomics approaches require adaptations, so as to improve efficiency and accuracy of results in plants. Data generated by proteomics can substantially contribute to the understanding and monitoring of plant physiological events and development of biotechnological strategies. Especially for tropical species, challenges are even greater, in the light of the abundance of secondary metabolites, as well as of the lack of complete genome sequences. This review discusses current topics in proteomics concerning challenges and perspectives, with emphasis on the proteomics of tropical plant species.

OVNIp: an open source application facilitating the interpretation, the validation and the edition of proteomics data generated by MS analyses and de novo sequencing

Several academic software are available to help the validation and reporting of proteomics data generated by MS analyses. However, to our knowledge, none of them have been conceived to meet the particular needs generated by the study of organisms whose genomes are not sequenced. In that context, we have developed OVNIp, an open-source application which facilitates the whole process of proteomics results interpretation. One of its unique attributes is its capacity to compile multiple results (from several search engines and/or several databank searches) with a resolution of conflicting interpretations. Moreover, OVNIp enables automated exploitation of de novo sequences generated from unassigned MS/MS spectra leading to higher sequence coverage and enhancing confidence in the identified proteins. The exploitation of these additional spectra might also identify novel proteins through a MS-BLAST search, which can be easily ran from the OVNIp interface. Beyond this primary scope, OVNIp can also benefit to users who look for a simple standalone application to both visualize and confirm MS/MS result interpretations through a simple graphical interface and generate reports according to user-defined forms which may integrate the prerequisites for publication. Sources, documentation and a stable release for Windows are available at http://wwwappli.nantes.inra.fr:8180/OVNIp.

A survey of computational methods and error rate estimation procedures for peptide and protein identification in shotgun proteomics

This manuscript provides a comprehensive review of the peptide and protein identification process using tandem mass spectrometry (MS/MS) data generated in shotgun proteomic experiments. The commonly used methods for assigning peptide sequences to MS/MS spectra are critically discussed and compared, from basic strategies to advanced multi-stage approaches. A particular attention is paid to the problem of false-positive identifications. Existing statistical approaches for assessing the significance of peptide to spectrum matches are surveyed, ranging from single-spectrum approaches such as expectation values to global error rate estimation procedures such as false discovery rates and posterior probabilities. The importance of using auxiliary discriminant information (mass accuracy, peptide separation coordinates, digestion properties, and etc.) is discussed, and advanced computational approaches for joint modeling of multiple sources of information are presented. This review also includes a detailed analysis of the issues affecting the interpretation of data at the protein level, including the amplification of error rates when going from peptide to protein level, and the ambiguities in inferring the identifies of sample proteins in the presence of shared peptides. Commonly used methods for computing protein-level confidence scores are discussed in detail. The review concludes with a discussion of several outstanding computational issues.

Plant-extract-induced changes in the proteome of the soil-borne pathogenic fungus Thielaviopsis basicola

Thielaviopsis basicola is a hemibiotroph fungus that causes black root rot disease in diverse plants with significant impact on cotton production in Australia. To elucidate how T. basicola growth and proteome are influenced by interactions with natural sources, this fungus was cultured in the presence of root extracts from non-host (wheat, hairy vetch) and susceptible host (cotton, lupin) plants. We found that T. basicola growth was significantly favored in the presence of host extracts, while hierarchical clustering analysis of 2-DE protein profiles of T. basicola showed plant species had a larger effect on the proteome than host/non-host status. Analysis by LC-MS/MS of unique and differentially expressed spots and identification using cross-species similarity searching and de novo sequencing allowed successful identification of 41 spots. These proteins were principally involved in primary metabolism with smaller numbers implicated in other diverse functions. Identification of several "morpho" proteins suggested morphological differences that were further microscopically investigated. Identification of several highly expressed spots suggested that vitamin B(6) is important in the T. basicola response to components present in hairy vetch extract, and finally, three spots, induced in the presence of lupin extract, may correspond to malic enzyme and be involved in lipid accumulation.

Identification of a novel Plasmopara halstedii elicitor protein combining de novo peptide sequencing algorithms and RACE-PCR

Background

Often high-quality MS/MS spectra of tryptic peptides do not match to any database entry because of only partially sequenced genomes and therefore, protein identification requires de novo peptide sequencing. To achieve protein identification of the economically important but still unsequenced plant pathogenic oomycete Plasmopara halstedii, we first evaluated the performance of three different de novo peptide sequencing algorithms applied to a protein digests of standard proteins using a quadrupole TOF (QStar Pulsar i).

Results

The performance order of the algorithms was PEAKS online > PepNovo > CompNovo. In summary, PEAKS online correctly predicted 45% of measured peptides for a protein test data set.

All three de novo peptide sequencing algorithms were used to identify MS/MS spectra of tryptic peptides of an unknown 57 kDa protein of P. halstedii. We found ten de novo sequenced peptides that showed homology to a Phytophthora infestans protein, a closely related organism of P. halstedii. Employing a second complementary approach, verification of peptide prediction and protein identification was performed by creation of degenerate primers for RACE-PCR and led to an ORF of 1,589 bp for a hypothetical phosphoenolpyruvate carboxykinase.

Conclusions

Our study demonstrated that identification of proteins within minute amounts of sample material improved significantly by combining sensitive LC-MS methods with different de novo peptide sequencing algorithms. In addition, this is the first study that verified protein prediction from MS data by also employing a second complementary approach, in which RACE-PCR led to identification of a novel elicitor protein in P. halstedii.

Cross species proteomics

Proteomics has advanced in leaps and bounds over the past couple of decades. However, the continuing dependency of mass spectrometry-based protein identification on the searching of spectra against protein sequence databases limits many proteomics experiments. If there is no sequenced genome for a given species, then cross species proteomics is required, attempting to identify proteins across the species boundary, typically using the sequenced genome of a closely related species. Unlike sequence searching for homologues, the proteomics equivalent is confounded by small differences in amino acid sequences, leading to large differences in peptide masses; this renders mass matching of peptides and their product ions difficult. Therefore, the phylogenetic distance between the two species and the attendant level of conservation between the homologous proteins play a huge part in determining the extent of protein identification that is possible across the species boundary. In this chapter, we review the cross species challenge itself, as well as various approaches taken to deal with it and the success met with in past studies. This is followed by recommendations of best practice and suggestions to researchers facing this challenge as well as a final section predicting developments, which may help improve cross species proteomics in the future.

Proteomics: challenges, techniques and possibilities to overcome biological sample complexity

Proteomics is the large-scale study of the structure and function of proteins in complex biological sample. Such an approach has the potential value to understand the complex nature of the organism. Current proteomic tools allow large-scale, high-throughput analyses for the detection, identification, and functional investigation of proteome. Advances in protein fractionation and labeling techniques have improved protein identification to include the least abundant proteins. In addition, proteomics has been complemented by the analysis of posttranslational modifications and techniques for the quantitative comparison of different proteomes. However, the major limitation of proteomic investigations remains the complexity of biological structures and physiological processes, rendering the path of exploration paved with various difficulties and pitfalls. The quantity of data that is acquired with new techniques places new challenges on data processing and analysis. This article provides a brief overview of currently available proteomic techniques and their applications, followed by detailed description of advantages and technical challenges. Some solutions to circumvent technical difficulties are proposed.

Tools for exploring the proteomosphere

Homology-driven proteomics aims at exploring the proteomes of organisms with unsequenced genomes that, despite rapid genomic sequencing progress, still represent the overwhelming majority of species in the biosphere. Methodologies have been developed to enable automated LC-MS/MS identifications of unknown proteins, which rely on the sequence similarity between the fragmented peptides and reference database sequences from phylogenetically related species. However, because full sequences of matched proteins are not available and matching specificity is reduced, estimating protein abundances should become the obligatory element of homology-driven proteomics pipelines to circumvent the interpretation bias towards proteins from evolutionary conserved families.

Plant proteomics: concepts, applications, and novel strategies for data interpretation

Proteomics is an essential source of information about biological systems because it generates knowledge about the concentrations, interactions, functions, and catalytic activities of proteins, which are the major structural and functional determinants of cells. In the last few years significant technology development has taken place both at the level of data analysis software and mass spectrometry hardware. Conceptual progress in proteomics has made possible the analysis of entire proteomes at previously unprecedented density and accuracy. New concepts have emerged that comprise quantitative analyses of full proteomes, database-independent protein identification strategies, targeted quantitative proteomics approaches with proteotypic peptides and the systematic analysis of an increasing number of posttranslational modifications at high temporal and spatial resolution. Although plant proteomics is making progress, there are still several analytical challenges that await experimental and conceptual solutions. With this review I will highlight the current status of plant proteomics and put it into the context of the aforementioned conceptual progress in the field, illustrate some of the plant-specific challenges and present my view on the great opportunities for plant systems biology offered by proteomics.

Protein identification pipeline for the homology-driven proteomics

Homology-driven proteomics is a major tool to characterize proteomes of organisms with unsequenced genomes. This paper addresses practical aspects of automated homology-driven protein identifications by LC-MS/MS on a hybrid LTQ Orbitrap mass spectrometer. All essential software elements supporting the presented pipeline are either hosted at the publicly accessible web server, or are available for free download.

Proteome analysis of non-model plants: a challenging but powerful approach

Biological research has focused in the past on model organisms and most of the functional genomics studies in the field of plant sciences are still performed on model species or species that are characterized to a great extent. However, numerous non-model plants are essential as food, feed, or energy resource. Some features and processes are unique to these plant species or families and cannot be approached via a model plant. The power of all proteomic and transcriptomic methods, that is, high-throughput identification of candidate gene products, tends to be lost in non-model species due to the lack of genomic information or due to the sequence divergence to a related model organism. Nevertheless, a proteomics approach has a great potential to study non-model species. This work reviews non-model plants from a proteomic angle and provides an outline of the problems encountered when initiating the proteome analysis of a non-model organism. The review tackles problems associated with (i) sample preparation, (ii) the analysis and interpretation of a complex data set, (iii) the protein identification via MS, and (iv) data management and integration. We will illustrate the power of 2DE for non-model plants in combination with multivariate data analysis and MS/MS identification and will evaluate possible alternatives.

Proteomics with a pinch of salt: a cyanobacterial perspective

Cyanobacteria are ancient life forms and have adapted to a variety of extreme environments, including high salinity. Biochemical, physiological and genetic studies have contributed to uncovering their underlying survival mechanisms, and as recent studies demonstrate, proteomics has the potential to increase our overall understanding further. To date, most salt-related cyanobacterial proteomic studies have utilised gel electrophoresis with the model organism Synechocystis sp. PCC6803. Moreover, focus has been on 2-4% w/v NaCl concentrations within different cellular compartments. Under these conditions, Synechocystis sp. PCC6803 was found to respond and adapt to salt stress through synthesis of general and specific stress proteins, altering the protein composition of extracellular layers, and re-directing control of complex central intermediary pathways. Post-transcriptional control was also predicted through non-correlating transcript level data and identification of protein isoforms.In this paper, we also review technical developments with emphasis on improving the quality and quantity of proteomic data and overcoming the detrimental effects of salt on sample preparation and analysis. Developments in gel-free methods include protein and peptide fractionation workflows, which can increase coverage of the proteome (20% in Synechocystis sp. PCC6803). Quantitative techniques have also improved in accuracy, resulting in confidence in quantitation approaching or even surpassing that seen in transcriptomic techniques (better than 1.5-fold in differential expression). Furthermore, in vivo metabolic labelling and de novo protein sequencing software have improved the ability to apply proteomics to unsequenced environmental isolates. The example used in this review is a cyanobacterium isolated from a Saharan salt lake.