Proteomics

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chlorophyll fluorescence (Fv/Fm) ratios and chlorophyll content. (Kim et al., 2011). The same authors also found higher Fv/Fm ratios under heat stress (42 ◦C).
Environmental and Experimental Botany 103 (2014) 117–127

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Environmental and Experimental Botany journal homepage: www.elsevier.com/locate/envexpbot

Proteomics: State of the art to study Mediterranean woody species under stress Carla Pinheiro a,b,∗ , Leonor Guerra-Guimarães c , Teresa S. David d , Ana Vieira c a

Instituto de Tecnologia Química e Biológica, Av. da República – EAN, 2780-157 Oeiras, Portugal Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa, 2829-516 Caparica, Portugal CIFC-Biotrop/IICT – Instituto de Investigac¸ão Científica Tropical, Quinta do Marquês, 2784-505 Oeiras, Portugal d Instituto Nacional de Investigac¸ão Agrária e Veterinária IP, Quinta do Marquês, Av. da República, 2780-159 Oeiras, Portugal b c

a r t i c l e

i n f o

Keywords: Abiotic stress Blast2GO analysis Woody plant proteomics Systemic review

a b s t r a c t Mediterranean woody species are vulnerable to multiple stresses that negatively affect plant survival and productivity. Drought and heat are increasing threats to the agricultural and forestry systems, making it urgently necessary to determine the mechanisms of plant adaptation and survival. Proteomics allows for the characterisation of a large number of proteins in a given tissue/organ, providing an integrated picture of the molecular events involved in stress responses. In this paper, we have evaluated the contribution of proteomics for the identification of stress-responsive proteins and tolerance/adaptation mechanisms in woody plants of agronomic importance in the Mediterranean basin. A systematic review was performed (Web of Knowledge, 5th March 2013) on the relevant genera for this region: Quercus sp., Pinus sp., Eucalyptus sp., Vitis sp., Olive sp., and Citrus sp. The term Rosaceae was also included in the search due to the relevance of fruit tree crops of this family in the Mediterranean region. This systematic review highlighted the lack of extensive and comprehensive proteomic analyses for Mediterranean plants under stress. The approach retrieved 19 and 38 papers concerning the assessment of abiotic and biotic stresses, respectively, at the proteome level, and 20 and 46 papers, respectively, concerning analyses at the transcriptomics level. With regard to abiotic stress, gel-based proteomic methodologies (15 papers) enabled the identification and quantification of 395 stress-responsive proteins. These results revealed metabolic adjustments to stress, with major alterations in carbon, nitrogen, and amino acid metabolisms. The most consistently represented stress-responsive proteins were RuBisCO, RuBisCO activase, heat shock proteins, chlorophyll a/b binding protein, and proteins from the oxygen-evolving complex. We concluded that gel-based proteomics revealed key proteins and metabolic pathways important for the ability of plants to adjustment to environmental fluctuations. The integration of this information with physiological, agronomic, and technological performance (e.g. survival, productivity, and food and technological quality) is essential for the sustainable development of the Mediterranean regions. © 2014 Elsevier B.V. All rights reserved.

1. Introduction The Mediterranean basin is one of the richest regions in the world in terms of plant diversity (Myers et al., 2000) and is also one of the areas most vulnerable to abiotic and biotic stresses. The Mediterranean climate is characterised by dry and hot summers, rainy and mild winters, with high inter- and intra-annual rainfall variability. Drought coupled with extreme temperatures is an abiotic stress that most adversely affects the survival, growth, development, and productivity of Mediterranean woody species (Mooney, 1983; Ogaya and Penuelas, 2003). These environmental

∗ Corresponding author at: Instituto de Tecnologia Química e Biológica, Av. da República – EAN, 2780-157 Oeiras, Portugal. Tel.: +351 214469656. E-mail address: [email protected] (C. Pinheiro). 0098-8472/$ – see front matter © 2014 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.envexpbot.2014.01.010

constraints are predicted to become even more severe in the near future because of global climate change (Planton et al., 2012; Sanchez-Gomez et al., 2011). Mediterranean forests are the repository of a great bulk of terrestrial biodiversity and they support multiple wood and non-wood industries (Scarascia-Mugnozza et al., 2000). Pine trees are an important source of wood and nuts. Eucalyptus species are the basis for the pulp and paper industry due to their fast-growth. Oak woodlands are natural ecosystems with productive (i.e. cork, wood, cattle, and hunting) and environmental (i.e. biodiversity, soil protection, and water regulation) value. Fruit woody crops (e.g. Olive sp., Vitis sp., Citrus sp.) make up the basis of the Mediterranean diet and many also represent critical regional food products that are in worldwide demand (Sofo et al., 2012). Therefore, stress is a threat to food and feed productions and endangers forest ecosystems (David et al., 2007; Mantri et al., 2012; Sofo et al.,

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2012). Understanding how plants acclimate to different stressors can contribute to mitigating the impact of stress. Depending on the degree of species-specific plasticity, as well as stress intensity and progression rate, plants undergo a series of physiological and biochemical adjustments to avoid or tolerate different stresses (Macedo, 2012). Current state of the art proteomics may help unravel the mechanisms activated by Mediterranean plants in order to cope with and recover from adverse environmental events. Proteomics is a powerful tool for studying stress tolerance, enabling the identification and quantification of stress tolerance-associated proteins (Abril et al., 2011; Agrawal et al., 2012), thus providing the missing link between the transcriptome and the metabolome (Afroz et al., 2011). Two dimensional polyacrylamide gel electrophoresis (2-DE) and mass spectrometry (MS) are still the most widely used analytical techniques for the profiling, identification, and quantitative assessment of protein expression in plant species, particularly when little is known about their genome sequence (Abril et al., 2011; Afroz et al., 2011). Understanding the dynamics of stress tolerance-associated proteins expression, and gaining insight into their function is essential for breeding and/or engineering stress-tolerant crops with novel traits through marker selection and transgenic strategies (Agrawal et al., 2012). In the Mediterranean context important issues that need to be addressed are: 1. The impact of drought, heat, and salinity on plant growth and survival, as well as fruit quantity and quality (Rowley and Mockler, 2011). Responses at the level of leaf shedding (Ogaya and Penuelas, 2006) and modification of root architecture (Vilagrosa et al., 2012) can provide useful information about genotype plasticity and adaptation to the environment. Additionally, the impact on wood and cork growth and quality need to be evaluated. 2. The impact of air pollutants, which originate from anthropogenic activities or forest fires, on seed germination capacity and plant regeneration, growth, and ability to cope with other stresses (Fares et al., 2013; Vallejo et al., 2012). 3. Multiple stresses effects, namely the study of the interactions between abiotic and biotic stresses (Syvertsen and GarciaSanchez, 2014). The mechanisms underlying responses to environmental fluctuations and understanding how such mechanisms relate to plant performance will provide powerful tools for agricultural and forest management, as well as for genotype selection. In this report, we review the current literature concerning the effects of abiotic stresses on Mediterranean woody crops and trees at the proteome level. Our goal was to list the stress-responsive proteins for functional annotation, thereby revealing metabolic pathways associated with stress, and to compile quantitative data for meta-analysis (Gurevitch and Hedges, 1999). It should be noted that the data in the pertinent literature are fragmented and, therefore, a comprehensive understanding of the mechanisms activated or deactivated during stressful conditions is needed (Cramer et al., 2011; Pinheiro and Chaves, 2011). 2. Criteria for the systematic review Bibliographic data available from the Web of Knowledge (http://apps.webofknowledge.com, 5th March 2013) were considered for the systematic review of proteomic information. We selected specific genera (Quercus sp., Vitis sp., Eucalyptus sp., Olive sp., Citrus sp., Pinus sp. and Prunus sp.), that were relevant to social, environmental, and economic factors. The term “Rosacea” was also included in the search since it includes many fruit trees.

Additionally, for comparison purposes, the search term “Arabidopsis” was also included. The bibliographic review was complemented with a parallel search for related transcriptomic data using the same search terms. This survey identified less than 450 papers that used proteomics methodologies in the study of Mediterranean woody species, which represents only 6–7% of papers available for Arabidopsis (Table 1). This number was further reduced by considering only peer reviewed original research articles and with the removal of duplicate records. However, when stress related studies were considered, the lack of proteomic data on Mediterranean woody species is fully revealed: there were only 19 papers on abiotic stress and 38 on biotic stress. A similar dearth of research was found with regard to the use of transcriptomic methodologies: there were only 20 and 46 papers that addressed abiotic and biotic stresses (Table 1), respectively. Only two papers reported both protein and transcript data (Grimplet et al., 2009; Jellouli et al., 2010). In two other cases, protein and mRNA data extracted from the same sets of experiments were published as two separate papers: Citrus (Tanou et al., 2009, 2012) and Quercus (Sergeant et al., 2011; Spiess et al., 2012). In this report, an “abiotic-stress” paper was defined as original research on the effects of stress on the plant proteome, and in which stress-responsive proteins were identified and the stress effects on protein abundance was quantified. By applying this filter, a total of 15 proteomics papers on abiotic stress were considered for inclusion in the quantitative analysis, thus revealing the genuine scarcity of available data.

3. Data extraction for meta-analysis Proteomic studies on Mediterranean woody species under abiotic stress are available for drought (n = 10), salinity (n = 4), acid rain (n = 1), and low temperature (n = 1). No data are available for high temperature, UV-radiation, or multiple stress effects on the plant proteome. The papers that met the criteria defined in this review (Table 2) used 2-DE-based proteomics, and mostly involved analysis of leaves. Meta-analysis requires quantitative data (i.e. average values, standard deviation, and sample size) (Gurevitch and Hedges, 1999; Walker et al., 2008); it was only possible to extract these data from five papers (Bedon et al., 2012; Echevarria-Zomeno et al., 2009; Jellouli et al., 2010; Macarisin et al., 2009; Tanou et al., 2009). In all the other cases, authors were contacted and asked whether they would make their data available. Several authors replied but only two sent the necessary quantitative information (Jorge et al., 2006; Renaut et al., 2008). Thus, the limited number of studies available did not support the application of meta-analysis methodologies. Alternatively, information on proteins identified as abiotic stress-responsive was collected from the 15 available papers (Table 2). Most of the papers documented studies on leaves/needles under drought stress (seven papers), except for work on Vitis, for which other organs were also analysed (Table 2; Supplemental table S1). The number of identified protein spots per paper ranges considerably (i.e. from 1 to 77 spots; Table 2), mainly as a result of the following: the primary objective of the analysis, methodological limitations at the time of the investigation (e.g. genome availability), different mass spectrometry techniques (i.e. peptide mass fingerprinting versus tandem mass spectrometry), and available budget for mass spectrometry (MS). Our methodological approach compiled 494 proteins, corresponding to 356 unique UniProtKB accessions. These were the stress-responsive proteins for which we then carried out further functional categorisation on the basis of biological processes, molecular function(s), and the cellular components/organelles with which they are associated.

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Table 1 Criteria for the systematic review on the proteomic and transcriptomic data available for Mediterreanean woody species and Arabidopsis. Data was collected at the web of knowledge (5th March 2013) with no temporal restrictions. Search terms

No. of papers

Papers to considera

Topic = ((((‘proteom*’) AND (‘olive’ OR ‘olea’)))) Topic = ((((‘proteom*’) AND (‘vitis’ OR ‘grapevine’)))) Topic = ((((‘proteom*’) AND (‘eucalyptus’)))) Topic = ((((‘proteom*’) AND (‘pinus’ OR ‘pine’)))) Topic = ((((‘proteom*’) AND (‘oak’ OR ‘quercus’)))) Topic = ((((‘proteom*’) AND (‘citrus’)))) Topic = ((((‘proteom*’) AND (‘prunus’)))) Topic = ((((‘proteom*’) AND (‘rosaceae’)))) Topic = ((((‘proteom*’) AND (‘Arabidopsis’))))

57 116 14 83 26 62 58 16 >6500

53 105 11 78 23 50 48 15

Topic = ((((‘transcriptom*’) AND (‘olive’ OR ‘olea’)))) Topic = ((((‘transcriptom*’) AND (‘vitis’ OR ‘grapevine’)))) Topic = ((((‘transcriptom*’) AND (‘eucalyptus’)))) Topic = ((((‘transcriptom*’) AND (‘pinus’ OR ‘pine’)))) Topic = ((((‘transcriptom*’) AND (‘oak’ OR ‘quercus’)))) Topic = ((((‘transcriptom*’) AND (‘citrus’)))) Topic = ((((‘transcriptom*’) AND (‘prunus’)))) Topic = ((((‘transcriptom*’) AND (‘rosaceae’)))) Topic = ((((‘transcriptom*’) AND (‘Arabidopsis’)))) a b

32 105 34 93 14 65 44 20 >7000

Abiotic stress

Biotic stress

Development

Not relatedb

– 4 2 4 3 3 2 1

2 19 – 11 – 5 1 –

9 36 2 22 7 12 19 8

42 46 7 41 13 29 26 6



19

38

115

210

28 95 32 92 13 49 36 16

2 6 4 2 1 3 0 2

1 18 1 13 1 11 1 –

5 32 7 27 1 12 10 3

20 39 20 50 10 23 25 11



20

46

97

198

After removal of duplicate records, posters and abstracts in journal supplements. Review papers were also removed. Papers analysing other effects than stress and development.

4. Functional categorisations using Blast2GO The functional categorisation through a similarity search (Blast2GO) (Gotz et al., 2008) for abiotic stress was performed using the data available in the following papers: (Bedon et al., 2012; Costa et al., 1999; Echevarria-Zomeno et al., 2009; Grimplet et al., 2009; He et al., 2007, 2012; Jellouli et al., 2008, 2010; Jorge et al., 2006; Macarisin et al., 2009; Renaut et al., 2008; Sergeant et al., 2011; Tanou et al., 2009; Vincent et al., 2007; Wang et al., 2013). Identified protein spots with UniProtKB accession and gi numbers were used to collect the sequence information from the NCBI database (http://blast.ncbi.nlm.nih.gov), with the final dataset consisting of 487 sequences. The initial Blastp and Blastn searches were performed against the NCBI non-redundant database with a minimum expectation value of 10−3 . The functional annotation was carried out using the Blast2GO default parameters and complemented with

the information available at the InterPro database for functional domains. By applying this methodology, it was possible to associate the most consistently identified stress-responsive proteins with biological processes and the molecular functions in which they are involved in, and with the cellular components in which they are predicted to occur (Gotz et al., 2008). For comparison purposes (abiotic versus biotic stress), this approach was also carried out for proteins described as being involved in the response to biotic stress (Supplemental Tables S2 and S3). The functional annotation was performed using data from the following papers: (Basha et al., 2010; Campos et al., 2009; Cantu et al., 2008; Diaz-Vivancos et al., 2006; Ekramoddoullah et al., 2006; Garavaglia et al., 2010; Giribaldi et al., 2011; Margaria et al., 2013; Margaria and Palmano, 2011; Marsh et al., 2010; Maserti et al., 2011; Melo-Braga et al., 2012; Milli et al., 2012; Palmieri et al., 2012; Pasquier et al., 2013; Purcino et al., 2007; Smith et al., 2006; Spagnolo et al., 2012; Taheri et al.,

Table 2 Number proteins identified as abiotic stress responsive (drought, salinity, acid rain and low temperature) through proteomic analysis (for detailed information refer to supplemental table S1). Genera

No. of papers

Stressor

Organ

Reference

No. of spots identified

No. of unique UniProtKB accessions

Eucalyptus Malus

1 1

Drought Drought Drought

1

Acid rain

3

Drought

2

Drought

3

Salinity

1 1

Salinity Low temperature

Bedon et al. (2012) Macarisin et al. (2009) Costa et al. (1999) He et al. (2007) He et al. (2012) Wang et al. (2013) Echevarria-Zomeno et al. (2009) Jorge et al. (2006) Sergeant et al. (2011) Grimplet et al. (2009) Grimplet et al. (2009) Vincent et al. (2007) Vincent et al. (2007) Jellouli et al. (2010) Jellouli et al. (2008) Jellouli et al. (2008) Tanou et al. (2009) Renaut et al. (2008)

58 77 27 13 6 38 10 18 33 7 14 32 28 1 1 1 74 56

38 71 35

3

Leaves Leaves Needles Needles Seedlings Needles Leaves Leaves Leaves Berry pulp Berry skin Shoots Shoots Roots Leaves Stems Leaves Bark

Pinus

Quercus

Vitis

Citrus Prunus

Average no. of spots per paper, 27. Median no. of spots per paper, 23. Min no. of spots per paper, 1. Max no. of spots per paper, 77.

No. of unique accessions considering all papers

32 50

51

28

38 52

356

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2011; Wang et al., 2006; Yang et al., 2011; Yao et al., 2012; Zamany et al., 2012). The final dataset consisted of 1239 proteins.

5. Abiotic stress-responsive proteins: functional annotation The functional annotation for the abiotic stress-responsive proteins (Fig. 1) illustrates the: • Most representative biological processes – three categories are highly represented (>15%): metabolic processes, cellular processes and response to stimulus. • Most representative molecular functions – two categories dominated (>40%): catalytic activity and binding activity. These two categories largely overlap, with 73.0–92.5% of the proteins in the binding category also being present in the catalytic activity category. • Most representative cellular components – four categories were represented >10%: cell, organelle, membrane, and extracellular region. The relatively high number of proteins with predicted membrane localisation (n = 211) does not support the view that 2DE based proteomics is not suitable for the analysis of membrane proteomes.

“Catalytic activity” and “binding activity” were the most represented stress-responsive categories (41% and 44%, respectively), thus indicating that metabolic adjustments occurred under stress (Fig. 1B). A more detailed analysis of these proteins (Fig. 2A; Supplemental Table S4) showed that of the 293 proteins in the “catalytic activity” category, 232 have an associated Enzyme Commission number (E.C. number), corresponding to 74 distinct enzymes (KEGG, Kyoto Encyclopedia of Genes and Genomes) (Kanehisa et al., 2012). Based on the functional classifications of the identified proteins, the most represented pathway was carbon metabolism, comprising carbon fixation in photosynthetic organisms (n = 90), glyoxylate and dicarboxylate metabolism (n = 51), pentose phosphate pathway (n = 17), respiration (n = 67, comprising glycolysis/gluconeogenesis, the citrate cycle, pyruvate metabolism, and oxidative phosphorylation), fructose and mannose metabolism (n = 22), and starch and sucrose metabolism (n = 13). Other pathways that were strongly represented included amino acid and nitrogen metabolism (n = 51), glutathione metabolism (n = 14), phenylpropanoid metabolism and lignification (n = 11). For the “binding activity” category (n = 314), we found that 243 proteins grouped in this category were also in the “catalytic activity” category, which reflects the fact that an enzymatic reaction involves substrate binding. Concerning the remaining 71 proteins, we found that some of these were enzymatic activity regulators, such as RuBisCO activase (n = 19), and others were identified as being involved in protein folding and stability, such as chaperonins (n = 10), or in cell cycle control (actin and actin binding, n = 4; Fig. 2B). Proteins involved in photochemical reactions were also represented and included chlorophyll a/b-binding protein (n = 4) and proteins of the oxygen evolving complex (n = 4). Within the binding category, we also found stress-responsive auxin binding proteins (n = 6), which highlighted hormone mediated responses, and stress-responsive proteins associated with the regulation of transcription (n = 1), as well as mRNA processing and translation (n = 7). However, not all proteins with apparently similar functions appeared in this category. Two transcription factors, which were described as atypical and likely unable to bind DNA, were identified as “nucleic acid binding transcription factor” (bHLH150, n = 2), not as “binding activity”.

6. Abiotic versus biotic stress-responsive proteins The functional annotation revealed a very similar distribution between the categories of the stress-responsive proteins, regardless of the type of stressor (abiotic or biotic; Fig. 1; Supplemental figure S1). Furthermore, the KEGG pathway analysis also identified the same metabolic pathways as being the most highly represented: carbon metabolism (35%), nitrogen and amino-acid metabolism (19%), glutathione metabolism (3%), and phenylpropanoid metabolism (3%). The recurrent identification of these pathways suggests that they may well represent universal cellular sensors that are able to integrate and appropriately respond to multiple environmental stimuli. Our analysis indicated that in the top 10 most represented stress-responsive enzymes, there are proteins common to both abiotic and biotic stress: RuBisCO, fructose-bisphosphate aldolase, H+ -ATPase, peroxidase (peroxiredoxin and lactoperoxidase), and malate desidrogenase. However, the detection of the same proteins or protein families has raised some concerns (Petrak et al., 2008; Wang et al., 2009). The dynamic range of protein detection and quantification, as well as identification (i.e. representation in the database), can limit the type of proteins detected by gel-based proteomics. Fractionation and/or enrichment methods allow for the detection of less abundant proteins, but it must be noted that subsequent quantitative analyses can only be consider in relative terms. In addition, the hypothesis that these proteins have an active role in the stress response, as opposed to being a secondary result of stress, must be validated by analysing the effect of a specific change in one or more of these proteins on plant performance (see the following sections). 7. Functional annotation per individual abiotic stress When analysing the available information for each individual stress (i.e. drought, salinity, low temperature, and acid rain; Supplemental Figure S2), the general trend that was identified above was maintained (Fig. 1). However, it is notable the more extensive representation in the literature of drought effects evaluated at the leaf level, highlighted by the predominance of RuBisCO and RuBisCO activase (Supplemental Table S5 and S6). In contrast, alternate results arise when other plant structures are analysed: RuBisCO, a predominant protein in our survey, was not described as stressresponsive in bark (low temperature; one paper; 56 proteins) or grape berry (drought; one paper; 21 proteins). Nonetheless, leaves were the only organ for which it was possible to extract information from several papers (n = 11), and with a high number of protein entries (n = 349). Furthermore, the data concerning leaves are strongly influenced by drought studies (i.e. 8 of 10 papers). Drought stress-responsive proteins represent 68% of the total number of proteins collected from the 15 papers. On the other hand, leaves represent 80% of the drought responsive proteins. 8. Most represented stress-responsive proteins At the protein level, abiotic stress results in major metabolic reorganisation, with catalytic and binding activity categories constituting 79–94% of the stress-responsive proteins when considering each individual stress (Figs. 1 and 2, Supplemental figure S2). The most represented proteins (Supplemental tables S5 and S6) enabled identification of the likely stress response pathways, which were largely dominated by photosynthesis (photochemical and carbon reactions) and its regulation (e.g. RuBisCO assembly by HSP60, and RuBisCO activase). Amino acid, glutathione, and phenylpropanoid metabolism were also highly represented. One question that needed to be addressed was how changes in these metabolic pathways (metabolic adjustments) were integrated at the whole plant (i.e. systems) level, thus affecting plant performance (survival

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Fig. 1. Functional categorization of abiotic stress-responsive proteins by similarity search (Blast2GO). The functional annotation of the 487 proteins was performed using GO terms merged with InterProScan results. The graphics (A) biological process, (B) molecular function, and (C) cellular component, represent level 2 of GO terms. The identity of the stress responsive proteins and details of the biological system and the stressor to which each protein responds is available in Supplemental Table S1.

capacity and production). Since there was no quantitative information, it was not possible to associate a given trend (i.e. up or down regulation) with any particular pathway. Therefore, we searched the literature for loss-of-function and gain-of-function studies concerning these particular enzymes. These enzymes/proteins are related with several pathways and include: 8.1. Respiration (oxidative phosphorylation) • Proton-exporting ATPase – plant H+ -ATPases are encoded by a multigenic family and can be located at the plasma membrane or at the tonoplast. Suppression of one plasma membrane isoform (PMA4) in tobacco resulted in increased salinity tolerance (i.e. germination and growth in the presence of 250 mM NaCl)

(Gevaudant et al., 2007; Zhao et al., 2000). On the other hand, in rice, the increased activity of another plasma membrane isoform (PAM3) was associated with increased sensitivity to salt and osmotic stress (i.e. germination is inhibited). This was caused by the down-regulation of a vacuolar ATPase in rice (OsVHA-A knockdown) (Zhang et al., 2013). The authors found a phenotype characterised by higher stomata density and aperture, and a possible regulation via calmodulin and YDA1 (a MAPK). The plasma membrane ATPases are activated through phosphorylation, e.g. through the regulatory effect of the 14-3-3 proteins (Muniz Garcia et al., 2011; Piette et al., 2011). However, other regulators were identified in potato, and their abundance is modulated by salinity and cold, but not by drought or abscisic acid (Muniz Garcia et al., 2011).

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Fig. 2. Abiotic stress-responsive enzymes (A) and non-enzymatic proteins with binding activity (B) as revealed by Blast2GO analysis. (A) Graphic results from the combined GO terms and KEGG pathways annotation (detailed information is in Supplemental table S4); (B) proteins grouped in the “binding” category but absent from the “catalytic category” (molecular function annotation) were used. For detailed information refer to Supplemental Table S6.

8.2. Amino acid metabolism • Homocysteine methylase – in yeast, loss-of-function mutants showed increased sensitivity to salinity (Narita et al., 2004). • Choline dehydrogenase – in tobacco plants, over-expressing this enzyme, which results in glycine betaine accumulation, reduced the impact of salinity, but the photosynthetic capacity was not significantly affected and the growth rate was less inhibited (Holmstrom et al., 2000). The over-expression of both choline dehydrogenase and a vacuolar H+ -ATPPase has also been related to drought tolerance in maize (Wei et al., 2011). These authors also found that for the double transgenic exposed to drought,

there was a positive outcome in measures of yield (i.e. higher kernel number per row and heavier 100-grain weight), in addition to higher biomass and root/shoot ratio. 8.3. Redox regulation (glutathione metabolism) • Peroxiredoxin – the suppression of 2-cysteine peroxiredoxin in Arabidopsis resulted in lower photosynthetic capacity (i.e. the photochemical reactions were strongly affected) (Baier and Dietz, 1999). On the other hand, the over-expression of a rice stress-responsive 2-cysteine peroxiredoxin (OsTPX) in yeast led to higher stress tolerance, as measured by cell viability upon

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exposure to 20 mM H2 O2 or heat (55 ◦ C) (Kim et al., 2013). A similar finding was made in tobacco plants by over-expressing an Arabidopsis 2-cysteine peroxiredoxin (At2-Cys Prx). Under oxidative stress caused by methyl viologen (a compound generating reactive oxygen species), the plants maintained higher maximal chlorophyll fluorescence (Fv/Fm) ratios and chlorophyll content (Kim et al., 2011). The same authors also found higher Fv/Fm ratios under heat stress (42 ◦ C). • Glutathione transferase – Arabidopsis and potato plants overexpressing glutathione transferase were shown to perform better under stress. In Arabidopsis, the over-expression of OsGSTL2 from rice showed enhanced germination rate and root length when exposed to different stressors, such as NaCl, mannitol, or heavy metals (Kumar et al., 2013). Tobacco lines over-expressing this enzyme retained a higher Fv/Fm ratio and seedling length when exposed to cold or NaCl (Le Martret et al., 2011). When exposed to oxidative stress (i.e. methyl viologen), the over-expressing plants exhibited enhanced chlorophyll content (Le Martret et al., 2011). Carbon metabolism – for each of the following, we were unable to find any report concerning phenotype, cell viability, or stress tolerance for plants or cells over-expressing or suppressing the enzymes. We were, however, able to find data on the growth effects under control conditions. • Triose-phosphate isomerase – an Arabidopsis mutant with this enzyme (pdtpi) down-regulated exhibited very reduced growth and a paler phenotype (Chen and Thelen, 2010). Furthermore, the authors showed that chloroplast ultra-structure was affected and that a comparable phenotype was observed in non-transformed plants grown in glycerol or mannitol. • Fructose-bisphosphate aldolase – this enzyme is involved in glycolysis and gluconeogenesis in the cytoplasm, and in the Calvin cycle in plastids. Down-regulation of this enzyme in rice resulted in a severe reduction of root length (Konishi et al., 2004), and lower photosynthetic activity in Arabidopsis (Strand et al., 2000) and potato plants (Kossmann et al., 1994). • Transketolase – tobacco lines in which transketolase activity was reduced below 40% relative to wild-type through down-regulation at the level of transcription exhibited lower photosynthesis (Henkes et al., 2001). • Ribulose-bisphosphate carboxylase – any constraint causing stomata closure results in lower leaf conductance to CO2 and, therefore, has the ability to severely impair photosynthesis. In such cases, RuBisCO can be deactivated, providing a signal to regulate its abundance. RuBisCO abundance can be regulated by the following: (1) increased turnover so that amino-acids can be used as building blocks for other proteins; or (2) decreased synthesis. RuBisCO abundance is the net result of the relative rate of biosynthesis and degradation (i.e. the level of gene expression, mRNA stability and translation, post-translational modifications, assembly, folding, and degradation) (Parry et al., 2008). In the current survey, we found that CPN-60, which is an HSP60 chaperonin involved in RuBisCO assembly, was among the most represented proteins (Supplemental Table S6), emphasising the impact of stress on post-translation modification/modification machinery. RuBisCO activase, which induces conformational changes through carbamylation of the lysine residues by which RuBisCO activity is regulated, was also strongly represented (Supplemental Table S6). These findings indicate that deactivation of RuBisCO has consequences on protein stability. It was been shown that tobacco plants expressing low levels of RuBisCO activase (i.e. less than 15% of the wild-type) exhibit a lower photosynthetic rate (Hammond et al., 1998). However, rice mutants over-expressing RuBisCO activase, with a concomitant higher RuBisCO activation state, do not exhibit higher photosynthetic activity (Fukayama

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et al., 2012). In fact, these authors detected lower amounts of RuBisCO in the youngest leaves of transgenic plants and, more importantly, observed a slight decrease in growth and yield. While the carbamylation, which is promoted by RuBisCO activase, protects RuBisCO from proteolysis (Khan et al., 1999), other post-transcriptional mechanisms also affect RuBisCO stability. Similarly, the activity of RuBisCO activase is regulated by post translational modifications (e.g. reduction/oxidation via thioredoxin) (Moreno and Spreitzer, 1999). Therefore, it could be relevant to the consistency of crop yields to improve RuBisCO stability under stress conditions. Two steps seem to be important to keep RuBisCO stable: (1) decrease the threshold of CO2 concentration in the chloroplast that can trigger deactivation (Galmes et al., 2011); and (2) decrease the susceptibility of the regulatory cysteine residues to oxidation that causes conformational changes and target RuBisCO for proteolytic degradation (Moreno and Spreitzer, 1999). RuBisCO mutants for the residue C172S are less susceptible to proteolysis and to degradation under osmotic or oxidative stress (Moreno and Spreitzer, 1999). 8.4. Phenylpropanoid biosynthesis • Caffeate O-methyltransferase – this enzyme is involved in aromatic compound metabolism and phenylpropanoid biosynthesis, as well as lignin byosinthesis, which impacts cell wall properties. In maize, an antisense mutant line, COMT-AS, displays lower activity of this enzyme and an altered lignin content and composition (Piquemal et al., 2002). However, in Arabidopsis, mutants of this enzyme (Atom1) show no phenotypical differences in rosette growth under control conditions (Moinuddin et al., 2010). 9. Regulation of the most represented proteins Quantitative alterations in protein levels can be attributed to variations in mRNA expression (typically 30–85%) and/or to posttranscriptional and post-translation regulation mechanisms (the remaining 15–70%) (Abreu et al., 2009). Post-translational modifications impact activity, binding, stability, folding, and intracellular localisation (Jensen, 2004; Navrot et al., 2011). For at least four of these proteins there are information about post-translational modifications, which we describe here: (1) Fructose-bisphosphate aldolase is redox-state regulated via glutathionylation (van der Linde et al., 2011). Furthermore, 2Cys peroxiredoxin, which belongs to the same class as one of the other most represented enzymes in this survey, can modulate the activity of a cytoplasmatic isoform of fructose-bisphosphate aldolase (Caporaletti et al., 2007). (2) RuBisCO can be carbamylated and oxidised, which alters its stability and susceptibility to proteolysis (Khan et al., 1999; Moreno and Spreitzer, 1999). (3) RuBisCO activase is redox-state regulated via the reductase/thioreductase ferredoxin/ferredoxin-thioredoxin system, and is closely related to critical photochemical reactions (Schurmann and Buchanan, 2008). (4) H+ -ATPase is activated through phosphorylation (Muniz Garcia et al., 2011), and the process can be mediated by the auxin pathway in a post-transcriptional manner (Takahashi et al., 2012). However, our survey also highlighted the auxin-binding proteins ABP19b and ABP20. These are subunits of the same protein, and the ABP19b subunit is a probable receptor for auxin and involved in the signalling pathway (Ohmiya et al., 1998). Protein abundance can be transcriptionally regulated by de novo gene expression. Quite erroneously, it is often thought that

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protein abundance is mainly dependent on the transcription of the corresponding mRNA, and gene expression data is often seemingly interpreted as equivalent to protein abundance in the sample. However, it is known that protein abundance and mRNA expression levels actually correlate to very variable extents (Abreu et al., 2009; Greenbaum et al., 2003; Laurent et al., 2010). Alternatively, protein-per-mRNA ratios can provide useful information concerning the activity of regulatory mechanisms other than gene expression (Abreu et al., 2009). One example of such regulation is the inhibition of the ribosome loading and, thus, translation, as has been detected in Arabidopsis leaves under drought conditions (Kawaguchi and Bailey-Serres, 2005). To fully enable such comparisons, data from the same samples must be available for both protein and mRNA levels. In the current analysis, only one data set was sufficient to enable such a direct comparison. In Vitis, the PR-10 stress profile at the protein level was compared to the profile at the mRNA level (Jellouli et al., 2010). In only one of the four conditions tested did the two profiles agree. While there was mRNA information for the other three datasets, they had the following problems: (1) protein and mRNA extractions were from samples grown in different years (Grimplet et al., 2009); (2) the stress-responsive proteins were not analysed by RT-PCR (Tanou et al., 2009, 2012); and 3) the comparsion between the protein and mRNA data was indirect, since no information concering gi number was available (Sergeant et al., 2011; Spiess et al., 2012). Nevertheless, there was a possible overlap between mRNA and protein data for three protein classes, with two of them being highly represented in our survey: HSP and auxin-binding proteins.

10. Relating protein profiles with plant performance under field conditions Alterations of the protein profiles in response to stress must be validated in order to establish effective correlations with stress tolerance, plant performance, and productivity. While this could be achieved through physiological evaluation, information on the physiological status and/or the plant performance is commonly scarce. Furthermore, performance under field conditions is even scarcer. Of the 15 papers considered as “abiotic stress”, seven did not provide physiological data. Basic characterisation was presented by several authors: leaf water potential at predawn (He et al., 2007, 2012; Jorge et al., 2006); and stem water or relative water content (Grimplet et al., 2009; Vincent et al., 2007; Echevarria-Zomeno et al., 2009; Macarisin et al., 2009). Some authors presented growth parameters (Vincent et al., 2007; Bedon et al., 2012), while stomatal conductance and photosynthetic rate were presented in only one paper (He et al., 2012). Insufficient stress characterisation at the plant level impaired the comparison and integration of data from multiple sources, thus limiting the information that could be extracted from these proteomics experiments. An approach combining physiological and metabolic aspects of plant stress tolerance is required to increase our working knowledge about stress at the gene and protein levels (Kosova et al., 2011; Perez-Clemente et al., 2013). In addition to stress kinetics (i.e. the rate of progression and intensity at the plant level), phenotyping for plant survival and productivity is crucial (Pinheiro and Chaves, 2011). Assuming that a phenotype, such as higher tolerance and stable yield under changing environments, and a quantitative assessment of the proteome, such as variations in protein abundance, were available, how could the critical information concerning the most appropriate/sought plant responses be extracted? A user friendly tool would be most effective within the proteomics community if it enabled the search for a term (e.g. gi number, accession number, and EC number) and returned

integrated information about the roles/features/etc. of the regulatory network(s) (e.g. transcription factors, miRNAs, translation factors, splicing, and protein–protein interactions), together with their spatial (i.e. organ, cell, organelle) and temporal activation during the stress response.

11. Conclusions This systematic review confirmed a serious lack of comprehensive proteomic analyses for Mediterranean plants, but also highlighted some of the major research achievements for Mediterranean woody plant proteomics to date. In spite of the scarcity of data (only 15 papers), the proteomic studies on abiotic stress responses of Mediterranean woody species have identified numerous proteins involved in a diverse array of biological processes: photosynthesis (e.g. electron transport proteins, carbon fixation, and photorespiration enzymes), respiration (e.g. glycolysis and oxidative phosphorylation), carbohydrate and nitrogen metabolism, and cell wall modification. This likely arises, at least in part, due to the predominance of using analyses of leaves to evaluate the stress response. Nonetheless, as these studies are done cross species, this would seem to indicate a universal response. In the Mediterranean ecosystems, plants with predominant drought-avoidance strategies (e.g. deep-rooted perennials) coexist with drought-tolerant sclerophylls (Chaves et al., 2002). The plant responses include adaptations to injury and death. Under intense and long lasting stress, woody plants even discard leaves (Limousin et al., 2010; Sergeant et al., 2011). What then is the overall gain in studying leaves? It would be most informative to know the impact at the proteome level of the different strategies, and which mechanisms are correlated with each strategy. At the leaf level, the stress stimuli, which is generated in the leaf itself or elsewhere in the plant, usually provokes a negative effect on carbon assimilation and growth. Soil drought and salinity are initially sensed by roots (Chaves et al., 2009; Duque et al., 2013) and trigger cellular signal transduction pathways leading to molecular and metabolic changes (Komatsu and Hossain, 2013). Since it is the integrated response at the whole plant level that dictates survival and persistence under environmental stress (Chaves et al., 2002), it is important to focus on both the root and leaf proteomes (Komatsu and Hossain, 2013). The lack of data concerning roots in Mediterranean woody species may thus limit a full and effective understanding of the processes involved in stress responses, and additional efforts to gather such data should be a clear priority for future research. As the climate is predicted to be even more extreme in the future (IPCC, 2007; Planton et al., 2012), only plants with sufficient phenotypic plasticity will cope successfully. Critical, fundamental information concerning stress tolerance, adaptation, and survival mechanisms are needed with considerable urgency. Gel-based proteomics has revealed key proteins and metabolic pathways involved in plant adjustments to fluctuating environments. However, whether or not stress-responsive proteins correlate with stress tolerance is uncertain, since data combining physiological and metabolic aspects of plant stress tolerance, plant survival, and productivity are scarce in the literature (Kosova et al., 2011; PerezClemente et al., 2013; Pinheiro and Chaves, 2011). Such coupled molecular and functional assessments are absolutely critical to dissecting and understanding key molecular mechanisms. Gel-based proteomics provide the necessary depth of analysis and by combining proteomics with other analyses, such as plant physiology, mRNA, and bioinformatics, it will be possible to address the major questions initially raised in this work. Therefore, research on yield stability and quality maintenance, within the framework of sustainable agriculture, is a challenge for the proteomics community, and has clear and substantial ecological, social, and economic impacts.

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Acknowledgements This work was supported by Fundac¸ão para a Ciência e Tecnologia through the strategic project PEst-OE/EQB/LA0004/2011 and the project FCT-PTDC/AGR-GPL/109990/2009. The authors acknowledge Cândido Pinto Ricardo (ITQB) and Jens R. Coorssen (University of Western Sydney) for critically reviewing the manuscript.

Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j.envexpbot. 2014.01.010.

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