Translational plant proteomics: A perspective

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Available online at www.sciencedirect.com

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Review

Translational plant proteomics: A perspective☆ Ganesh Kumar Agrawala,⁎, Romina Pedreschib , Bronwyn J. Barklac , Laurence Veronique Bindschedler d , Rainer Cramer d , Abhijit Sarkar e , Jenny Renaut f , Dominique Job g , Randeep Rakwala, h, i,⁎⁎ a

Research Laboratory for Biotechnology and Biochemistry (RLABB), GPO Box 13265, Kathmandu, Nepal Food and Biobased Research, Wageningen University and Research Centre, P.O. Box 17, 6700 AA Wageningen, The Netherlands c Instituto de Biotecnología, Universidad Nacional Autónoma de Mexico, A.P. 510-3 Col. Miraval Cuernavaca, Morelos 62250, Mexico d Department of Chemistry, University of Reading, Reading RG6 6AD, United Kingdom e CSIR-SRF, Laboratory of Air Pollution and Global Climate Change, Department of Botany, Banaras Hindu University, Varanasi 221005, UP, India f Centre de Recherche Public-Gabriel Lippmann, Department of Environment and Agrobiotechnologies (EVA), Belvaux, GD, Luxembourg g CNRS/UCBL/INSA/Bayer CropScience Joint Laboratory, UMR 5240, Bayer CropScience, 14-20 rue Pierre BAIZET, F-69263, Lyon Cedex, France h Graduate School of Life and Environmental Sciences, University of Tsukuba, 1-1-1 Tennoudai, Tsukuba 305-8572, Ibaraki, Japan i Department of Anatomy I, School of Medicine, Showa University, 1-5-8 Hatanodai, Shinagawa, Tokyo 142-8555, Japan b

AR TIC LE I N FO

ABS TR ACT

Available online 9 April 2012

Translational proteomics is an emerging sub-discipline of the proteomics field in the biological sciences. Translational plant proteomics aims to integrate knowledge from basic sciences to

Keywords:

translate it into field applications to solve issues related but not limited to the recreational and

Biomarker

economic values of plants, food security and safety, and energy sustainability. In this review,

Biodiversity

we highlight the substantial progress reached in plant proteomics during the past decade

Education awareness

which has paved the way for translational plant proteomics. Increasing proteomics knowledge

Food

in plants is not limited to model and non-model plants, proteogenomics, crop improvement,

Nutrition

and food analysis, safety, and nutrition but to many more potential applications. Given the

Stress

wealth of information generated and to some extent applied, there is the need for more efficient and broader channels to freely disseminate the information to the scientific community. This article is part of a Special Issue entitled: Translational Proteomics. © 2012 Elsevier B.V. All rights reserved.

Contents 1. 2. 3.

Introduction: From plant proteomics to translational plant proteomics . . . . . . . . . . . . . . . . . . . . . . . . . 4589 Translational plant proteomics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4589 Information transfer between model and non-model plants to advance knowledge in plant biology . . . . . . . . . 4590

☆

This article is part of a Special Issue entitled: Translational Proteomics. ⁎ Correspondence to: G.K. Agrawal, Research Laboratory for Biotechnology and Biochemistry (RLABB), GPO Box 13265, Kathmandu, Nepal. Tel.: +81 29 853 4653; fax: +81 29 853 6614. ⁎⁎ Correspondence to: R. Rakwal, Graduate School of Life and Environmental Sciences, University of Tsukuba, 1-1-1 Tennoudai, Tsukuba 305-8572, Ibaraki, Japan. E-mail addresses: [email protected] (G.K. Agrawal), [email protected] (R. Rakwal). 1874-3919/$ – see front matter © 2012 Elsevier B.V. All rights reserved. doi:10.1016/j.jprot.2012.03.055

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4.

Proteogenomics to annotate genomes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1. The past and present of plant genomes and their annotation . . . . . . . . . . . . . . . . . . . 4.2. Structural and functional gene annotation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3. The proteogenomics workflow . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.4. The future of plant genomes and their annotation . . . . . . . . . . . . . . . . . . . . . . . . . 5. Biodiversity screening . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6. Tolerance to biotic and abiotic factors for crop improvement . . . . . . . . . . . . . . . . . . . . . . . 7. Food analysis, safety, and nutrition (human health) . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.1. Food composition and quality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.2. Food safety . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.3. Food authenticity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.4. Nutrition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.5. The perspective of food science and technology . . . . . . . . . . . . . . . . . . . . . . . . . . . 8. Energy sustainability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9. Global action plan on plant proteomics (GA3P) in facilitating the transfer of knowledge and discoveries 10. Concluding remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

1. Introduction: From plant proteomics to translational plant proteomics Tremendous progress in plant proteomics has been made since 2000 when few proteomics reports were published and plant proteomics was in its infancy. Today's progress is unquestionable and can be evidenced by the availability of several books solely devoted to plant proteomics [1–5] and several special issues on plant proteomics published by the journals Proteomics (http://onlinelibrary.wiley.com/doi/10.1002/pmic.v11.9/issuetoc), Phytochemistry (http://www.sciencedirect.com/science/journal/ 00319422/72/10), and Journal of Proteomics (http://www.science direct.com/science/journal/18743919/74/8). Moreover, there are several review series in plant proteomics [6, and references therein], rice proteomics [7,8, and references therein], and plant phosphoproteomics [9, and references therein] available to date. These book reviews and special issues describe the progress made in plant proteomics but also highlight the need for open discussion to generate visionary ideas in plant proteomics. Progress includes but it is not limited to: (i) the provision of qualitative and quantitative plant proteomics techniques (e.g., sample preparation and fractionation to gel-based and gel-free approaches, peptide analysis and confident protein assignment, database development and bioinformatics tools, and the process of analyzing and mining the data to address the biological questions of interest), establishing a targeted and global proteome, or mapping the post-translational modifications (PTMs); and (ii) the expansion of proteomics sub-disciplines [1] from expression proteomics to functional, structural, and to translational proteomics. Progress in plant proteomics is the input for “translational plant proteomics”, which is the topic of discussion of this review. The main aims of this review are: (i) to define translational plant proteomics and (ii) to show the progress made in some plant biology related research areas. We also envisage global engagement of the plant proteomics scientific community in support of translational plant proteomics.

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Translational plant proteomics

“Translation” is derived from the Latin word translatio, which means to bring across. Before the term translational proteomics was coined, “translational research” had already been a familiar term, particularly in connection with medical research. Thus, the Translational Research Working Group (TRWG) has defined it as: “translational research transforms scientific discoveries arising from laboratory, clinical, or population studies into clinical applications to reduce cancer incidence, morbidity, and mortality” (see http://www.cancer.gov/researchandfunding/ trwg/TRWG-definition-and-TR-continuum; accessed October 26, 2011). “Translational proteomics” focuses on the translation of basic proteomics science and is defined by Rice et al. as “the process and platforms that facilitate the delivery of applications derived from proteomics analysis” [10]. Translational proteomics research can be further defined as “a meaningful way to think and conduct proteomics research with the main objective of delivering fruitful applications to solve societal issues.” As Sir Peter Medawar once said: “No branch of science can be called truly mature until it has developed some form of predictive capacity”. This quote perfectly describes translational proteomics. Translational plant proteomics can thus be defined as “applying the outcome of any discovery or technological development in plant proteomics to solve issues related but not limited to the recreational and economic values of plants, food security and safety, energy sustainability, and human health”. Fig. 1 depicts translational plant proteomics taking its input from the field of plant proteomics, which is typically underpinned by the two pillars of gel-based and gel-free proteomics in conjunction with mass spectrometry (MS) and bioinformatics. Translational plant proteomics is certainly driven by advances in plant proteomics but can also enrich plant proteomics. In the following sections different areas within translational plant proteomics are discussed: information transfer between model and non-model plants, proteogenomics,

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• Food Analysis, Safety, & Nutritio n • Crop Improvement • Biodiversity • Proteogenomics-Genome Revisit • Model to Non-model & vice-versa

& Bioinformatics

Gel-Free Proteomics

PLANT PROTEOMICS

Gel-Based Proteomics

Bottom Up Approach

Translational Plant Proteomics

Sample Preparation & Fractionation Fig. 1 – Translational proteomics in plants. Proteomics is evolving as a technology to plant proteomics and now to translational plant proteomics in exploiting the discoveries into applications and enriching the existing knowledge of plant biology.

biodiversity, crop improvement, and food analysis, safety, and nutrition.

3. Information transfer between model and non-model plants to advance knowledge in plant biology Model plants (or reference plants) have been assigned based on their important characteristic features including small genome size, short life cycle, ease of use and cultivation, usefulness in addressing the biological question/s of interest, and the availability of sufficient sample material to conduct the research of interest. In the 21st century, the availability of genome sequences has become a critical feature for a plant to be called a model plant. Within translational plant proteomics, model plants typically have sequenced genomes while nonmodel plants have their genome rarely sequenced. Thus, model plant proteomics (e.g., of Arabidopsis and rice) is far more advanced than non-model plant proteomics [see 1,5,7,8,11,12]. Knowledge gained earlier from the model plants of Arabidopsis and rice has been transferred to develop the proteomes of non-model plants by applying and/or optimizing techniques and strategies used in the model plants. Today, proteomes of many economically important non-model plants are available including banana, papaya, sugarbeet, and sugarcane, which has been largely due to the transfer of proteomics data and technology obtained from model plants. The reverse has also been seen where proteomics knowledge obtained from non-model plants was utilized for the analysis and development of model proteomes; progress in evolutionary proteomics is one example. There are a few excellent reviews that comprehensively cover the transfer of knowledge (such as techniques) between model and non-model plants [13–19].

There are also a number of areas in plant biology that can be taken as excellent examples including seed biology [20,21], organelle proteomics [22], stress proteomics in plants (see Section 6), protein phosphorylation [9, and references therein], and trees [23]. Knowledge transfer between model and nonmodel plant proteomes has revealed that each plant needs to be uniquely/independently studied as many proteins and modifications are highly plant-specific.

4.

Proteogenomics to annotate genomes

4.1. The past and present of plant genomes and their annotation The implementation of third generation DNA sequencing has accelerated genome-wide sequencing of plant species. Many major food crop genomes are now available after the first plant genomes were completed more than a decade ago — the model plant Arabidopsis thaliana in 2000 [24] and rice in 2002 [25,26] (http://www.plantgdb.org/OsGDB/) (map-based sequence annotation in 2005 [27], http://rice.plantbiology.msu. edu/). Examples are maize [28], sorghum [29], soybean [30], potato [31], and domesticated apple [32], to name but a few [33,34]. The list is increasing rapidly as many more plant genomes of economic importance are being sequenced. However, plant genome sequencing and annotation are challenging as plant genome sizes are typically large, containing a substantial proportion of repetitive transposable elements. For instance, the maize genome is constituted of 2.3 billion base pairs [28]. In addition, polyploidy is yet another challenging genome trait common to numerous, if not most of the major crops, such as wheat, potato, tomato, oil seed rape, Brassica sp., banana, and strawberry. For these, independent sequencing of each of the wild-type haplotypes is required [35].

4.2.

Structural and functional gene annotation

In functional genomics analyses, DNA sequences are not comprehensively informative per se. Gene models and functional annotation of such gene models help to elucidate biological processes. The structure of eukaryotic genes is constituted by discontinuous coding regions (exons) interrupted by non-coding regions (introns). Structural annotation (bioinformatics translation) of a genome is to find the coordinates of the proteinencoding genes. Alternative bioinformatics approaches are used in conjunction to generate open reading frame (ORF) gene models, e.g., in silico prediction of protein-encoding genes. Gene structure features such as the start and end of a coding region or the splice junctions between exons and introns are predicted ab initio using several gene finder algorithms (such as Eugene, FGENESH, Augustus, Genscan or GeneMark) [36,37]. However, such prediction tools are not highly reliable and gene models are often inaccurate, in particular if the analysis is performed using gene finder algorithms trained on a different template species. Therefore gene modeling is improved by using machine learning based prediction programs fed with a training set of ESTs or messenger RNA (mRNA; transcript) sequences from the species being annotated [38] (Fig. 2). The larger the training set,

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mRNA information

Genome information Genome (DNA) sequencing

mRNA / cDNA sequencing using different tissues, treatments, time points

Genome assembly Genome draft template

ESTs or other transcriptomics data Identification of ORFs using gene finder programs

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Protein information Large scale shotgun proteomics using different tissues, treatments, time points Identification of peptides des using protein search engine and genomic DNA contigs database or assembled ESTs database Mapping of peptides to genome Grouping of peptides to existing gene models

Validation, modification and / or creation of new gene models Re-annotation of gene models (similarity searches, GO, PFAM, structure prediction, etc…)

Generation of an improved ORF database

Fig. 2 – A schematic depiction of the general workflow for genome annotation, involving proteogenomics analysis. Proteins are fractionated, and peptides are generated by digestion and separated by liquid chromatography. Peptides are then analyzed by high-accuracy MS and MS/MS and identified by matching them to a DNA genomic contig database translated into 6 frames prior to mapping the peptides back onto the genome. Genomic contigs can be directly downloaded and used by search engines such as Mascot. Other search engines such as Sequest require the translation of the genomic contigs into six (6) frames. Mapped peptides are then grouped into proteins. The obtained peptides as well as transcripts and expressed sequence tag (EST) sequences are used to validate in silico designed gene models and help the manual (re-)annotation/curation. In absence of a completely sequenced genome, extensive transcript and EST sequences can serve as a database for protein identification in proteogenomics. The whole process can be iterative.

the more accurate the gene models might become. Therefore, massive sequencing of transcripts similar to recently published work, describing over 20,000 uniquely assembled cDNA sequences of barley [39] and maize [40], is desirable. However, it is often possible to annotate the genome using established gene prediction tools, which have been “trained” for related species. In any case, it is best practice to validate the choice of the reference organism selected for the structural annotation of the genome of a particular species by preliminary annotation of a small set of genes. At a second stage, structural annotation can be improved by manual intervention using the BLAST sequence alignment tool for comparing and matching the predicted gene models to known genes or proteins of similar sequence. Again, cross species information is being used. Finally, a more definitive validation of gene prediction can be achieved by protein expression analysis. If the genome of the species of interest is sequenced, large-scale shotgun proteomics can be employed for the detection and characterization (e.g., sequencing) of expressed proteins at the proteomewide level. Such an approach is commonly called proteogenomics. As there can be a significant discrepancy between the level of transcripts and proteins expressed, partly due to the existence of non-coding mRNAs, proteogenomics analysis is the ultimate proof for a gene model. Analogous to the information

provided by transcript sequencing proving the existence of mRNAs, peptide sequence information generated by MS-based proteomics shows the existence of proteins. Such peptide information can also be used for the discovery of unpredicted ORFs [reviewed in 38,41]. This is even true for the wellcharacterized and annotated genomes of model organisms such as fly, human, mouse [42], Arabidopsis [43], and rice. The power of proteogenomics to re-annotate the genome is exemplified by the identification of novel ORFs (13% of the total ORF set) in an in-depth proteogenomics study in Arabidopsis [43], which were previously undetected by ab initio gene prediction or transcriptomics data. Other proteogenomics studies have been described for rice [44] and pathogenic fungi of wheat and barley [45,46].

4.3.

The proteogenomics workflow

There are several reviews that describe the proteogenomics approach [16,36,38,47–49]. Fig. 2 summarizes the proteogenomics approach in the context of a wider genome annotation workflow in combination with other methods from the fields of genomics, transcriptomics, and bioinformatics. Although proteogenomics approaches are extremely valuable in validating and discovering new ORFs, as described in the section above, such approaches remain challenging for the following

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reasons. Even in the best case scenarios large-scale proteomics studies are typically limited to very low proteome coverage, let alone the low sequence coverage of individual proteins identified by a few peptide sequences. In the case of green plant tissue, the problem of high dynamic range of protein concentration with over-representation of chloroplastic proteins, in particular of ribulose 1,5-bisphosphate carboxylase/ oxygenase (RuBisCO) is another challenge to overcome. For an in-depth proteogenomics study, proteomes from different tissues, ages and physiological states are required. In addition, several alternative protein and peptide fractionation techniques are often employed as they substantially increase the number of proteins identified [44], including gel-based and gel-free separation methods. As the goal is to validate predicted ORFs and to discover new ones, peptides are identified against genomic databases rather than against protein sequence databases [47–49]. Inclusion of the six frames and the non-coding DNA sequences increases the search space dramatically. Fortunately, modern mass analyzers provide high speed, greater sequencing depth, and high mass accuracy, which can alleviate some of these effects by providing high-confidence data. In addition to genomic databases, the use of databases of predicted ORFs can be helpful — obviously only to associate peptide groups to proteins of the well-predicted ORFs. Protein identification using a proteogenomics workflow is also feasible with poorly annotated genomes and non-assembled genomes. Thus, it is perfectly suitable to use an imperfect or partial genomic database for the identification of proteins by MS [22,45,47,50]. As a consequence, the proteogenomics approach is particularly powerful to annotate short or species-specific uncharacterized ORFs. This is quite important for most plant proteomics investigations – not only for annotation-driven proteogenomics studies – as plant protein sequences in databases such as UniProtKB are well underrepresented in comparison to sequences from other kingdoms. For instance, there are only 31,498 entries for “viridiplantae” out of total of 531,473 entries, representing less than 10% of the total protein entries in UniProtKB (http://www.uniprot.org/program/plants/ statistics — accessed in July 2011). Less than half (10,617) of the predicted Arabidopsis ORFs and just only over 2000 predicted rice proteins are represented in the UniProtKB database. Tools suitable for non-experts in bioinformatics such as the OryaPG-DB have recently become available for mapping peptides onto the genome and grouping them into proteins [44,51,52]. Castellana and coworkers [50] have even developed a bioinformatics tool (GenoMS) that allows de novo protein sequencing of whole proteins such as human antibodies with hypervariable sequence regions, using incomplete or poorly sequenced genomes.

4.4.

The future of plant genomes and their annotation

A. thaliana was the first plant model that was sequenced, selected for its small size, fast seed-to-seed generation, its small genome and the possibility to easily transform it genetically. These features made it an ideal model system for molecular laboratory studies. Unfortunately, Arabidopsis is phylogenetically only distantly related to most of the cultivated crop species, thus compromising its use for identifying proteins from cultivated crop species by sequence homology

searching. Although the rice genome has been available since 2002 as the first monocot genome sequenced, rice is still quite distant from the other major cereal crops such as barley and wheat. Despite the increase in sequencing power with the constant arrival of new DNA sequencing technologies, barley and wheat genomes are large and are at the moment only partially available in public databases. However, Brachypodium is now becoming the laboratory model of choice for temperate cereal crops. Like Arabidopsis, this grass has a small genome size and a 4-month seed-to-seed life cycle. Nevertheless, the number of studies using Brachypodium as reference organism is still very modest in comparison to Arabidopsis. Moreover, although the use of plant models has been proven to be powerful for advancing fundamental molecular plant research, plants are notorious for their biodiversity, i.e. for their complex and diversified secondary metabolism, of which a substantial amount is species-specific. Likewise, comparative plant genomic studies have shown that a large proportion of predicted genes are species-specific, thus justifying the intensive effort and resources employed to complete the genome sequencing and annotation of commercially valuable crops. Nevertheless, since proteins are mostly conserved between related species, studying non-model plants by proteomic means, even if those are lacking genome or sufficient EST sequence information for protein identification [13,53], is to some extent still possible as shown for banana [15,18,54]. Analytical software supporting error tolerance sequence database searches for the analysis of tandem mass spectra of peptides allows the identification of peptides diverging slightly in sequence, thus enabling the identification of peptides with one amino acid variation to closely related peptides from proteins of different species. However, for many plant species which are phylogenetically distant from sequenced plant models or crops, the above approach is less than ideal and genomes are rarely or only partially sequenced. In these non-model plants, generating database templates for proteomics studies by massive sequencing of ESTs using the high-throughput but imperfect pyrosequencing techniques can be a practical solution [13,17].

5.

Biodiversity screening

Why biodiversity? Biodiversity is essential in preserving the capacity of organisms to survive by adapting to changing environments. Moreover, biodiversity is not a simple term as it works at different levels, the ecosystem, the species in the ecosystem, and the genetic diversity reflected in a species genome, proteome, and metabolome. The Convention on Biological Diversity (CBD) defines biodiversity as “the variability among living organisms from all sources, inter alia, terrestrial, marine, and other aquatic ecosystems and the ecological complexes of which they are part; this includes diversity within species, between species, and of ecosystems” [55]. Moreover, biodiversity constitutes an excellent resource for seeking genes, proteins and metabolites of interest. The growing information of plant genomes enables proteomics to provide a new way to study biodiversity and to understand our ecosystem. Other than gene-based analysis, biodiversity screening can be applied at the proteomics level to screen out novel proteins and peptides from plants and crops with a targeted aim to understand their

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phenotype and ecological and/or economic importance in the changing environment. Mostly, the medicinal value of plants (drugs based on naturally occurring proteins) is used as a rationale to preserve biodiversity. The approach can also be applied to genetically modified (GM) crops and how they are affecting the genetic make-up of farmland biodiversity. Thus, in order to identify, preserve, and utilize the beneficial products of plant species, systematic proteomics approaches developed for model plants will have to be employed, alone or in combination with other omics approaches. However, it must be remembered that proteomics is not that new (as we would like to imagine) in analyzing plant/crop biodiversity. For instance, even before the word “proteomics” was coined it had been used for differentiating cold-tolerant and cold-sensitive cultivars. Virginia Walbot and Matthias Hahn published in 1989 a paper showing the effects of coldtreatment on protein synthesis and mRNA levels in rice leaves [56]. The authors in that study used one-dimensional gel electrophoresis (1-DGE) and RuBisCO large and small subunits (LSU and SSU, respectively) to differentiate the cold-sensitive Indica rice varieties Wag-wag and Peta from the cold-tolerant rice Japonica varieties Calmochi-101 and Ta-Mao-Tao. More recently, peanuts from the US mini-core collection were analyzed for changes in leaf protein profiles during reproductive stage growth under water-deficit stress [57]. With the objectives to unravel molecular mechanisms conferring water-deficit stress tolerance in peanuts and identify stresstolerant genotypes, the authors used a variety of morphological, physiological, and proteomics techniques. The results of 1-DGE and two-dimensional gel electrophoresis (2-DGE) analysis allowed for the association of physiologically significant candidate proteins with water-deficit stress tolerance mechanisms [57]. There are many more examples spanning the time since Walbot and Hahn showed the importance of the abundant protein RuBisCO in discriminating cold-tolerant genotypes of rice, to the present day when proteomics is routinely utilized to study cultivar differences in plants from model to non-models in normal and abnormal growth conditions. Natural variation of plants is as old as the history of plant biology itself, and like any other technique proteomics tools are bound to be utilized for investigating traits that are essential to breed better plants or crops. Again, a good example comes from Arabidopsis, where 2-DGE in combination with peptide mass fingerprinting was employed to investigate the natural variation in the proteome among eight A. thaliana ecotypes [58]. The results led Chevalier and co-authors to suggest that proteomics comparison can help differentiate among the physiological status of ecotypes, and importantly that proteomics can be a powerful technique to mine the biodiversity between ecotypes of a single plant species [58].

6. Tolerance to biotic and abiotic factors for crop improvement Plant performance can be severely affected by both biotic and abiotic stress conditions in the environment and this is a particular concern in agriculture where stress-related alterations in plant development, growth and productivity can translate into huge economic losses. Understanding and more

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importantly mitigating the effects of stress on plants are essential if agriculture is to keep pace with the ever increasing demand for food [59]. Stress can be classified into two broad categories: (i) biotic stress which encompasses damage done to plants by other living organisms such as bacteria, viruses, fungi, parasites, insects and even animals and other plants; and (ii) abiotic stress, covering extremes in temperature, light, water supply and solute levels [60]. Literally being rooted to one place, plants must rely on physiological and metabolic mechanisms to obtain the phenotypic plasticity required to withstand the adverse biotic and abiotic growth conditions with which they are faced on a daily basis. Proteomics, and in particular quantitative proteomics, is emerging as a powerful field of stress tolerance research, allowing the rapid identification and quantification of stressand tolerance-related proteins. There are excellent reviews that summarize biotic and abiotic stress proteomics and the readers are referred to those reviews [61–64]. Understanding the dynamics of expression and PTMs of these proteins, and gaining direct insight into their function can provide essential information that can be rapidly applied to engineer stresstolerant crops with novel traits through biomarker selection and transgenic strategies. In order to rapidly disseminate information from different studies, species, and stresses, and drive technological applications related to these findings, it is necessary to collate the information into collective searchable database platforms to ensure that the findings are available to a wide audience. The majority of plant proteomics resources are focused on the model non-crop plant A. thaliana and much of these have been recently amassed into a single portal, Multinational Arabidopsis Steering Committee for Proteomics (MASCP; http:// www.masc-proteomics.org; [65]). Only a limited amount of stress proteomics data and/or experiments are contained in MASCP, including links to small datasets from woundinduced proteins from Arabidopsis leaves [66]. A reliable and comprehensive database for other Arabidopsis stress proteomics is lacking. However, there has been tremendous progress in establishing rice proteomes and related databases in response to biotic and abiotic stresses [reviewed in 7,8]. The first tropical crop to have its own stress proteomics database is banana (http://www.pdata.ua.ac.be/musa/). The database contains 2D-PAGE gels of protein samples collected from osmotically stressed banana meristematic tissue and protein identification data for 138 identified proteins that were up or downregulated under osmotic stress [14]. Despite the immense potential of proteomics to advance stress-tolerance crop breeding strategies, it is still an area in the discovery mode. This is primarily due to the relatively new development of the field of plant stress proteomics and its recent application to crop plants, notwithstanding the small number of fully sequenced crop plant genomes. While there are many current informative reviews addressing what needs to be done [67–71], there are as yet no reported success stories whereby the knowledge gained directly from proteomics studies has been applied to improve crop stress tolerance. Plant responses to stress are complex and invariably involve changes in multiple molecular pathways. Validating these associations between proteins and pathways can be hugely challenging. This is not to imply that it will not be possible in

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the near future; validation of potential protein candidates can take several years and requires independent approaches, such as microarrays, extensive literature searches, and systemlevel analysis to identify candidate proteins. Furthermore not all proteins that are identified will be suitable for application. Not in any way challenging the large amount of excellent research done in human/animal sciences, we can draw parallels to medical proteomics and look at the success of disease biomarker discovery in that field which is much larger, better funded, and with a considerably longer history than plant proteomics. One can see that thousands of putative disease biomarkers have been identified through both genomics and proteomics approaches, yet only about 1% of those have been successfully selected for clinical use [72]. It will require collaborative research efforts, combined with standardized approaches, and robust data analysis, but the next decade should see the implementation of knowledge gained from plant proteomics studies to crop improvement in the field.

7. Food analysis, safety, and nutrition (human health) Proteins in foods are not only important from a nutritional point of view but from a technological point of view since they are responsible for major changes (e.g., viscosity consistency, flavors, etc.) in food products and closely related to food quality, safety and nutrition. Staple food crops such as maize, wheat and rice deserve special attention since they represent 60% of the world's food energy intake. With advances in coverage of the deep proteome [73], there is the possibility to unravel the unknown protein composition of these major crops that might be translated to improvements in quality, storage, and food processing conditions. The food industry so far has taken advantage to some extent of the proteomics-driven research mainly in the areas of quality and authentication, safety and nutritional assessment, and to a minor degree in process optimization and monitoring [74]. This section will focus on translational proteomics of plant-based foods (Fig. 3).

7.1.

Food composition and quality

The knowledge often gained for various crops in terms of protein composition using proteomics has been translated to potential improvements in industrial applications. Food industry has a basic rule: good quality in, good quality out. Thus, to obtain a high-quality food product, the quality of the raw material must be high. Knowledge obtained from proteomics can be translated into direct problem solving for the postharvest industry of horticultural crops. Physiological disorders that result in huge economic losses can be detected at a very early stage in production [75–78]. A proteomics-based approach has been employed to find biomarkers associated with optimal harvest maturity [79], thus assuring postharvest quality. Analysis of the postharvest withering process in grapes is a key for obtaining high quality wines. An effort has been undertaken through gel-based proteomics analysis of this process in order to support quality improvement [80]. The wine industry has gained meaningful information from proteomics research and readily translated this into improvements of the process. Besides understanding of the ripening process of grape that has a direct impact on wine quality [80], wine spoilage and thus quality can be tracked by gel-based proteomics means [81]. Champagne wine's quality is associated with the foaming properties and loss of foaming properties is associated to the loss of proteins [82]. Understanding the ripening processes and postharvest physiology during storage (low temperature, modified atmosphere) has not only a direct impact on food quality but on the optimization of the technological processes involved, given that a series of abiotic stresses is applied (low/high temperature, modified atmosphere). For such scenarios, plenty of proteomics applications have been carried out [76,83–85]. For example, a recent proteomics study on the application of heat treatment on peach fruit indicated that all the proteins identified as being differentially expressed were involved in the regulation of peach fruit development and ripening, implying that this treatment could be used to improve fruit quality and shelf-life [85]. The cereal industry has also benefited from proteomics research. For instance, based on 2-DGE fingerprinting,

Food Proteomics

Process Optimization & monitoring

Quality & Traceability

• Early detection of problems

• Composition & Analysis

• Correct & Take action

• Authenticity

• Understanding processing

• Quality raw materials

effects

• Tracking

Safety Assessment • Allergens, toxins & Foodborne pathogen detection

• Controversial foods • Impact of new technologies

Nutritional Assessment • Health biomarkers • Plant based bioactives • Impact of food processing on nutritional aspects

• Sanitation assessment

Fig. 3 – Food proteomics. Opportunities of translational proteomics driven research in different areas in food science and technology: (1) process optimization and monitoring, (2) quality and traceability, (3) safety assessment and (4) nutritional assessment. The different mentioned areas are inter-connected.

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selection of appropriate durum wheat cultivar for pasta making was feasible [86]. Flour quality is highly correlated with protein composition and functionality, thus proteomics has been a useful platform to identify protein markers to select suitable cultivars for flour [87]. Barley cultivar and the level of protein modification during malting are associated to beer quality. The construction of a beer proteome map is a useful tool for detection and potential manipulation of beer proteins related to quality [88]. Unraveling the low-abundant proteome including that of beer has now become feasible [89].

7.2.

Food safety

Food allergens are a constant threat to allergic consumers. According to EU legislation, most of the food allergens in food products must be declared if they are part of the ingredient list or if their presence might be expected due to processing contamination (e.g., cereals containing gluten such as wheat, rye, barley, oats, etc.; peanuts, soybeans, different nuts such as almonds, hazelnuts, etc.; celery, mustard, lupin and sesame seeds) [90]. The translation of proteomics research in the field of food allergens is quite extensive because development of sensitive detection/quantification methods is crucial for allergen diagnosis, therapy, and risk assessment and for reinforcing current legislation on the subject. Commonly, a combination of 2-DGE with immunoblotting of IgE reactive proteins using allergic patients' sera has been the approach taken to characterize the allergenicity of certain food proteins [91,92]. Shotgun proteomics approaches have been performed for characterization and detection of several food allergens [93,94]. The generated information is key for targeted approaches such as selective reaction monitoring (SRM) where allergens are not only detected at trace levels but also quantified [95,96]. A multiallergen detection method for the simultaneous detection of seven allergenic foods (five of plant origin) in bread based on the targeted SRM approach has been recently reported [95]. For confirmation, especially when liability issues are raised, SRM is recommended as the food allergen detection/quantification method of choice [97]. Food-borne pathogens constitute a constant hazard. Consumer trends toward healthier and more natural foods are driving common food preservation techniques (e.g., sterilization, freezing) into milder preservation techniques, imposing new challenges for the food industry in terms of food safety. Matrix-assisted laser desorption/ionization-time-of-flight (MALDI-TOF) MS and electrospray ionization-tandem mass spectrometry (ESI-MS/MS) are two powerful proteomics platforms currently used for routine identification of different microorganism species and in many cases strains [98–100]. Compared to traditional microbiology tests that are tedious and time consuming, MS proteomics and bioinformatics tools are used for the characterization and detection of pathogenic microorganisms and toxins [98]. Additional tandem MS information of the tryptic peptides allows sequence identification. In addition, MS instrumentation can be automated and there is the possibility of multiplexing for rapid screening [98]. Understanding the mechanisms of action as well as the mechanisms of stress resistance of food-borne bacteria is crucial for the development and design of effective food preservation techniques [101,102].

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The use of GM foods in Europe is still highly controversial. To assess the same food composition of GMO (genetically modified organism) foods with its counterpart, the principle of ‘substantial equivalence’ is applied [92]. However, the applied targeted approach, in which key nutrients are tested, is not enough to account for unintended effects. Therefore, other omics platforms are being used. Measuring changes at the transcriptome level is not necessarily translated into changes at the food composition level. Thus, metabolomics has been the platform of choice but it has shown limits for safety assessment [103]. Instead, the proteome can deliver further information in terms of safety assessment since many proteins can behave hazardously (e.g., toxins, allergens, antinutrients, etc.). However, proteomics and the other omics have failed so far in delivering translated science despite all the investments in research work [103]. The large numbers of proteomics studies in this area are quite controversial mainly because of the lack of reproducibility, the mixed environmental effects not properly dealt with and the far from total coverage of the proteome.

7.3.

Food authenticity

Food adulteration (e.g., replacement of certain ingredients by cheaper ones) is not an uncommon practice. Thus, the availability of specific and sensitive protein markers of the substitutes would allow food authentication [92] and help to reduce frauds. In this research field, many proteomics studies can be directly translated into practical use; for example, identification of the presence of cheaper substitutes for certain coffee varieties through specific protein markers [104]. The beverage industry, nowadays, claims the introduction of plant and fruit extracts in the formulation of certain food products. Thus, in this case, the identification of protein markers specific to the fruit or plant extract that is claimed being used in the formulation is a way to assess the genuineness of the products. These are good examples of translational proteomics for food authenticity [105,106]. Methods to assess the geographical origin of products are key to prevent illegal adulteration. Assessment of geographical origin is one of the main requirements for the certification of wine authenticity, grape variety, wine age and technology for production and proteomics can provide the biomarkers for such purposes [107]. Assessment of the floral origin of honey has been performed using protein markers [108]. Assessment of production origin in the case of conventional versus organically grown products through different platforms including proteomics has failed so far to deliver strong results.

7.4.

Nutrition

This section will focus on health biomarkers and plant-based bioactives. Proteins are not only one of the three macronutrients, but they are protagonists of many cellular processes, participating in cell signaling and immune responses, acting as bioactive components exerting functions such as growth factors, anti-hypertensive agents, antimicrobials, modifiers of food intake or immune regulators [109]. Modern nutrition aims to promote health by preventing or delaying the onset of diseases, by optimizing performance and by assessing risks [110]. This is evaluated by studying the interaction of the food

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proteome, host and microbial proteomes. Thus, proteomics translates scientific knowledge into discovery and quantification of biomarkers to assess predisposition, exposure, efficacy and characterization and quantification of food bioactives [110]. Proteomics also tries to understand how our genome is expressed in response to the diet and translates it into deliverables such as bioactives and biomarkers, information on nutritional bioavailability and bioefficacy [110,111]. Bioactive peptides are released during either digestion by host enzymes and microbial enzymes, food processing or ripening [110]. Bioactive peptides from plant sources such as wheat, maize, soy, rice, mushrooms, pumpkin, and sorghum have been reported [112]. Particularly bioactive peptides from soy have sparked attention. Lunasin, Bowman–Birk inhibitor, lectin and beta-conglycinin are some of the bioactive proteins and peptides in soy besides the phytochemicals. The antioxidant properties of the proteolytically released peptides from soy have been investigated [113] and this might be translated in the near future into applications to treat oxidative-stress related disorders [110]. Lupin is another crop containing high amounts of storage proteins (alpha and beta-conglutins) and these proteins seem to exert bioactive effects [114]. Certainly, current approaches based on either in vitro or in silico would allow the discovery and identification of bioactive peptides from many more food sources.

7.5.

The perspective of food science and technology

Food science and technology is one of the fields that have benefited the most from the advances in current proteomics techniques. It is also one of the fields that have incorporated a substantial amount of knowledge gained through proteomics platforms and translated it into solutions for real scenario problems in food quality, safety, and nutrition, and ultimately human health. However, there is still the need to translate knowledge generated through proteomics in food process optimization and monitoring. Proteomics could be used throughout the food processing steps to track the history of allergens, adjust processing steps and even predict shelf life. In the area of nutrition, given the still limited number of sequenced plant based genomes, there is the possibility to study little-known crops with potentially high amounts of unique bioactives. Microbial proteomics knowledge can be incorporated and implemented for food and plant/facility sanitation and safety assessment.

8.

example is the African grain plant sorghum, already being used as food and feed, which is gaining attention as a promising energy crop [118,119]. Other than the fact that it is able to grow in a wide range of geographical areas requiring much less of fertilizer and water compared to other cereal crops, the sweetstem varieties of sorghum yield readily soluble fermentable sugars in the stalk juice [120]. Therefore, the stems can be used for biofuel production. Proteomics studies in this direction have been initiated by Bongani Kaiser Ndimba and colleagues at the University of Western Cape (South Africa), where an in vitro system of suspension cultured cells has been established to comprehensively map and characterize the sorghum suspension cultured cell secretome [121,122]. Another developing example is a non-domesticated shrub of an oilseed crop, namely Jatropha curcas L. Jatropha is recently gaining attention for its oil that can be converted into biodiesel, and like sorghum, it can be easily cultivated in arid and semi-arid regions, including wastelands [123, and references therein]. Currently, proteomics analysis in Jatropha is mainly confined to oil body characterization for understanding oil biogenesis [124–126]. Identified proteins could be exploited for their suitability as markers in phylogenetic and molecular breeding studies [123, and references therein]. These and upcoming proteomics studies will be very helpful for utilization of crop plants as a source of sustainable production of biofuels.

Energy sustainability

Undoubtedly, the role of biofuels will pose a concern when food security is considered. Biofuels are liquid gaseous fuels primarily derived from plant biomass. Different factors ranging from the changing climate, rising fuel prices, and to a general awareness among the public for using renewable energy sources had generated interest in plants/crops as sources for biofuels (ethanol and diesel) [for review see 115–117]. Among crops, maize, sugarcane, and rapeseed are major sources of “green energy”. However, the use of these food crops for biofuel is controversial ‘vis-a-vis’ food security. Yet, continuous efforts are made to search for suitable alternative crop sources for biofuels. One

9. Global action plan on plant proteomics (GA3P) in facilitating the transfer of knowledge and discoveries Historically, global organizations have demonstrated a role in transferring the research knowledge directly to the field applications. One of the famous examples can be found in the vision of Consultative Group on International Agricultural Research (CGIAR) that established a unique worldwide network of agricultural research centers coordinating and collaborating activities toward the improvement of global agriculture. The first two institutes established by CGIAR were the International Maize and Wheat Improvement Center (CIMMYT; http://www. cimmyt.org/) and the International Rice Research Institute (IRRI; http://www.irri.org/). CIMMYT helped to bring about the first Green Revolution innovations of the late 20th century that reduced the fraction of the world's hunger from half to less than a sixth. IRRI presents a good example for transferring the discoveries obtained via screening of the natural population of rice and creating new cultivars tolerant to a wide range of biotic and abiotic stresses, increased yield, and seed quality. Generated resources are further helping the scientific community to enrich the knowledge on plant biology. Organizations related to plant proteomics are also in the process of facilitating the transfer of the acquired knowledge and discoveries to the scientific community and into field applications. To bring these data to a single platform is a goal we all share. The first of such initiation was the establishment of MASCP in the year 2000 [34]. Its most notable achievement was the recent establishment of a GATOR portal, where the Arabidopsis researcher is able to search for any AGI code or lists of AGI codes in all proteomics databases assembled by MASCP [65]. One aim behind the MASCP Gator was to translate

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the Arabidopsis knowledge also to other plants. It is worth noting that the Plant Proteomics in Europe (EuPP) group under the European Proteomics Association (EuPA), has also been a popular and successful initiative. The EuPP is involved in building up expertise in plant proteomics through an integrated network of European scientists to i) share and develop tools for fundamental and applied research in plants, and ii) utilize the generated information for monitoring the environment and food quality (http://www.cost.eu/domains_actions/ fa/Actions/FA0603). The next major effort appearing on the horizon was the establishment of a global initiative on plant proteomics termed the International Plant Proteomics Organization (INPPO; http://www.inppo.com), to “properly organize, preserve, and disseminate collected information on plant proteomics” [12]. To quote Wolfram Weckwerth, chairman of the MASCP, “INPPO is an excellent example of a non-profit, open-source initiative. Interestingly, it is merely based on the initiative of scientists without any funding, comparable to the early stages of MASC and the corresponding subcommittees or other communities” [34]. At INPPO, the research subcommittee is considering the development of a comprehensive and userfriendly database for cereal and legume crops, which will be a major milestone in the field of translational proteomics, once completed (http://www.inppo.com/researchcom.jsp). Considering how much education and training can contribute to the future of (translated) proteomics, the education committees of the Human Proteome Organization (HUPO) and the European Proteomics Association (EuPA) together with their national counterparts have recently initiated an International Proteomics Tutorial Programme (IPTP) [for details see, 127]. This ambitious program involves the leading proteomics journal, Journal of Proteome Research, Journal of Proteomics, Molecular and Cellular Proteomics, and Proteomics, and aims to instruct Masters/PhD level students beginning their careers in core techniques and the basics of proteomics research using articles and slides (http://www.proteomicstutorials. org/Proteomics_Tutorials/Welcome.html). The INPPO is closely following the IPTP and looking forward to mutually enriching the plant proteomics based tutorials currently under discussion with the INPPO subcommittee of Education Outreach (http://www.inppo.com/eduoutreach.jsp).

10.

Concluding remarks

Over the past decade, the information transfer between model and non-model proteomes, proteogenomics for the annotation of genomes, biodiversity screening, crop improvement against biotic and abiotic stresses, and food science and technology are among the research areas that have highly benefited from the rapid developments in plant proteomics. Current knowledge of gene structure, organization, and evolution of a plant genome is the result of advances in proteogenomics. Food science and technology has incorporated a substantial amount of knowledge, gained through proteomics platforms for routine analysis and other applications related to food quality, safety, and nutrition. The enrichment of basic knowledge, one dimension of translational plant proteomics, has certainly taken place. In contrast, very little or no progress has been made in the other

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dimension of translational plant proteomics, which is the field applications of the discoveries. Although hundreds of identified proteins have been reported as potential biomarkers for monitoring disease in plants and seed/food safety and quality, those candidate biomarkers still need to be rigorously validated. Moreover, the biological function of the majority of these biomarkers remains to be experimentally dissected. Nevertheless, given the tremendous achievements at least in basic science of plant biology, it could be stated that translational plant proteomics has an imminent prominent future. Plant proteomics platforms have generated a huge amount of protein data. Techniques have been optimized from sample preparation to the identification of proteins and their bioinformatics analysis. Equipment and bioinformatics tools have been updated and encompassed with new breakthroughs in technology. Mass spectrometry instruments are sensitive enough to identify rare proteins with high accuracy and to meet the criteria being imposed for safe food by regulatory agencies. However, it should be noted that mass spectrometers are high-end instruments and require highly experienced technicians or researchers as well as substantial maintenance. Thus, it is highly unlikely for such expensive equipment to be found in many institutions in the decade to come. Proteomics core facilities seem to be the more likely alternative. To conclude, translational plant proteomics has already existed for a long time although not explicitly by name. The technology and resources are certainly promising to further advance translational plant proteomics. It only remains to be seen as to what extent and when solutions to the big challenges in today's world can be found. Are we going to be able to meet the needs of a growing world population in terms of food security and sustainability?

Acknowledgments Authors acknowledge the INPPO platform for this initiative in bringing together scientists of different disciplines in constructing this review and translating their knowledge and experience to the global community including scientific. BJB acknowledges DGAPA, UNAM (Grant #212410) for funding the proteomics research in the laboratory. LVB and RC acknowledge the support by the BBSRC (grant BB/H001948/1). RR acknowledges the great support of Professors Yoshihiro Shiraiwa (Provost, Graduate School of Life and Environmental Sciences, University of Tsukuba) and Seiji Shioda and Dr. Tetsuo Ogawa (Department of Anatomy I, Showa University School of Medicine) in promoting interdisciplinary research and unselfish encouragement.

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