Skip to content

Advertisement

  • Research article
  • Open Access

Root proteomic and metabolic analyses reveal specific responses to drought stress in differently tolerant grapevine rootstocks

BMC Plant Biology201818:126

https://doi.org/10.1186/s12870-018-1343-0

  • Received: 4 December 2017
  • Accepted: 6 June 2018
  • Published:

Abstract

Background

Roots play a central role in plant response to water stress (WS). They are involved in its perception and signalling to the leaf as well as in allowing the plant to adapt to maintaining an adequate water balance. Only a few studies have investigated the molecular/biochemical responses to WS in roots of perennial plants, such as grapevine. This study compares two grapevine rootstock genotypes (i.e. 101.14 and M4) with different tolerance to WS, evaluating the responses at proteomic and metabolite levels.

Results

WS induced changes in the abundance of several proteins in both genotypes (17 and 22% of the detected proteins in 101.14 and M4, respectively). The proteomic analysis revealed changes in many metabolic pathways that fitted well with the metabolite data. M4 showed metabolic responses which were potentially able to counteract the WS effects, such as the drop in cell turgor, increased oxidative stress and loss of cell structure integrity/functionality. However, in 101.14 it was evident that the roots were suffering more severely from these effects. We found that many proteins classified as active in energy metabolism, hormone metabolism, protein, secondary metabolism and stress functional classes showed particular differences between the two rootstocks.

Conclusion

The proteomic/metabolite comparative analysis carried out provides new information on the possible biochemical and molecular strategies adopted by grapevine roots to counteract WS. Although further work is needed to define in detail the role(s) of the proteins and metabolites that characterize WS response, this study, involving the M4 rootstock genotype, highlights that osmotic responses, modulations of C metabolism, mitochondrial functionality and some specific responses to stress occurring in the roots play a primary role in Vitis spp. tolerance to this type of abiotic stress.

Keywords

  • Drought
  • Grapevine
  • Proteomics
  • Rootstock
  • Water stress

Background

Among the environmental problems related to viticulture, drought is one of the major factors that negatively affect grape production [1]. However, a significant part of the land devoted to viticulture, such as the Mediterranean regions of Europe, is located in areas characterized by a seasonal drought coinciding with the grapevine ripening period, thus affecting both yield and fruit quality [25]. Moreover, provisional studies on climate changes indicate that in these areas the availability of water will diminish in the coming years [4, 6].

Under drought, the grapevine leaf exhibits deep changes at molecular, biochemical, physiological and morphological levels which are useful to improve water use efficiency (WUE) through the activation of adaptive responses [2, 713]. Furthermore, it is clear that the strategy adopted in these adverse conditions can be somewhat different between grapevine cultivars (isohydric or anisohydric behaviours), besides being affected by pedo-climatic conditions [3, 9]. Among the typical immediate responses observed in the leaf organ under water deficit, there is progressive stomatal closure to counteract the untenable water loss [2, 7, 10]. This response, mediated by both hydraulic and chemical signalling, has direct and severe effects on photosynthesis and therefore on biomass production [2, 12, 14, 15]. Moreover, the reduction in water content leads to a loss of turgor with a consequent reduction of plant growth [7, 16]. In this condition, other typical responses in leaf tissues are osmotic adjustment, activation of ROS-scavenging mechanisms, changes in cell wall elasticity and metabolic acclimation [7, 9, 17, 18].

The recent literature reinforces the central role in the WS response of the roots, which take part in stress perception, adaptation and signaling to the aerial system, as well as their role in the uptake and transport of water towards the leaves [9, 14, 1921]. In this context, many studies have highlighted that a positive plant response to WS is strictly dependent on the roots’ capabilities of sustaining growth (i.e., changing the depth and density of the root system) and of maintaining/increasing the root hydraulic conductance [10, 13, 1923]. Regarding this last aspect, the woody roots of perennial plants like grapevine, despite their low hydraulic conductance, can significantly contribute to water absorption from the soil [24].

Many studies indicate that the tolerance to WS of the root depends on its capability of maintaining adequate symplastic osmotic potential, cell wall protein composition, carbohydrate metabolism and the metabolic pathways involved in the oxidative stress response [2528]. In agreement with this picture, two recent studies have also shown that in grapevine roots these responses play a central role in WS tolerance [29, 30].

In recent years, proteomic approaches have been undertaken to study WS responses. Some of this work focused attention on the roots of herbaceous species, such as soybean, wild watermelon, wheat, rapeseed and sugarcane [31]. These studies reported proteome changes linked to different biochemical responses, such as an increase in energy demand, transport activities, and the appearance or increase in the levels of proteins known to have protective roles under these stress conditions. In grapevine, the proteomic approach has been used to investigate the effects of WS in shoot and fruit tissues [18, 32, 33]. To our knowledge, to date no proteomic investigation has studied water stress responses in the roots of this perennial species.

Previously, the molecular, biochemical and physiological responses to WS of the novel rootstock genotype named M4 [(V. vinifera x V. berlandieri) x V. berlandieri cv. Resseguier no. 1] have been evaluated [29, 30]. The comparison with the genotype 101.14 (V. riparia x V. rupestris) highlighted the greater tolerance to WS of M4, as shown by photosynthetic parameters as well as by the analysis of molecular and biochemical responses in both leaf and root [29, 30].

The present study focused attention on the changes in root proteomes which occur in M4 and 101.14 rootstock genotypes under WS. The analyses were performed on samples obtained from the same experiments described by Meggio et al. and Corso et al. [29, 30], so allowing a high level of confidence between the proteomic results and the physiological, biochemical and molecular ones. The analyses were carried out to evaluate the proteomic changes induced by harsh WS conditions (i.e. reduction of the field soil capacity down to 30%). Moreover, a metabolomic analysis was performed to obtain further information on the effects induced by WS on the main biochemical pathways, such as glycolysis and related sugar metabolism, the Krebs cycle and amino acid metabolisms. The results showed many differences between the two genotypes, so revealing specific traits linked to a low (101.14) or a high (M4) capability to tolerate WS conditions.

Results

Mass spectrum interpretation allowed us to identify and quantify a total of 972 and 788 unique proteins for the 101.14 and the M4 genotypes, respectively. The technical parameters concerning peptide validation and protein identification reported in (Additional file 1: Table S1) indicated the good reliability of the approach adopted.

The results obtained by the comparison within the two experimental conditions experienced by each genotype (i.e., control versus water-stressed plants) are reported in Tables 1 and 2 and (Additional file 2: Table S2 A and B).
Table 1

Proteins showing significant changes in responses to WS in the 101.14 genotype

n.

Name (f.c.)

Accession

Δ:WS/C

Carbon and energy metabolism (1, 2, 3, 4, 5, 6, 7, 8, 9, 25)

 97

unnamed protein product - sucrose synthase 2-like (2)

CBI35298.3

new

 397

fumarate hydratase 1, mitochondrial (8)

XP_002273033.1

new

 595

alpha-1,4 glucan phosphorylase L isozyme, chloroplastic/amyloplastic-like (2)

XP_002279075.2

39.40

 559

aldehyde dehydrogenase family 7 member A1 (5, 8, 20)

XP_002278093.1

6.14

 938

ATP-citrate synthase alpha chain protein 2 isoform 2 (8)

XP_003633614.1

5.55

 222

glucose-6-phosphate 1-dehydrogenase, cytoplasmic isoform (7)

XP_002266527.1

3.56

 368

sucrose synthase 2 (2)

XP_002271896.1

2.86

 818

1,4-alpha-glucan-branching enzyme-like (2)

XP_002284841.2

2.43

 443

triosephosphate isomerase, chloroplastic-like isoform 1 (1)

XP_002274871.1

−2.12

 432

enolase 1, chloroplastic-like (4)

XP_002274334.1

−2.18

 269

fructokinase-2 (2)

XP_002268097.1

−2.38

 158

pyruvate dehydrogenase E1 component subunit beta, mitochondrial-like isoform 1 (8)

XP_002264210.1

−2.53

 350

dihydrolipoyllysine-residue acetyltransferase component of pyruvate dehydrogenase complex, mitochondrial-like isoform 1 (8)

XP_002271286.1

−2.94

 698

dihydrolipoyllysine-residue acetyltransferase component of pyruvate dehydrogenase complex, mitochondrial-like (8, 11)

XP_002282287.1

−3.24

 307

pyrophosphate--fructose 6-phosphate 1-phosphotransferase subunit beta-like (4)

XP_002269934.2

−3.40

 622

pyruvate kinase isozyme A, chloroplastic isoform 1 (4, 11)

XP_002279975.1

−3.71

 507

dihydrolipoyl dehydrogenase (8, 11, 21)

XP_002276853.1

−4.56

 301

pyruvate dehydrogenase E1 component subunit beta (1, 8, 11)

XP_002269441.1

−8.46

 236

glucose-6-phosphate 1-dehydrogenase, chloroplastic (7, 30)

XP_002266930.1

−25.91

 280

succinate dehydrogenase [ubiquinone] iron-sulfur subunit 1, mitochondrial (8, 29)

XP_002268523.1

d.

 551

carbonic anhydrase, chloroplastic (8, 16)

XP_002277957.1

d.

Cell Wall (10)

 273

UDP-glucose 4-epimerase GEPI48 (10)

XP_002268294.2

4.34

 282

beta-xylosidase/alpha-L-arabinofuranosidase 2-like (10, 33)

XP_002268626.2

−2.68

 620

probable UDP-arabinopyranose mutase 5 (10)

XP_002279911.1

−2.27

 423

probable xyloglucan endotransglucosylase/hydrolase protein 30-like (10)

XP_002273975.1

−3.28

 702

probable rhamnose biosynthetic enzyme 1 (10)

XP_002282339.1

−5.93

Lipid Metabolism (11)

 248

long chain acyl-CoA synthetase 8 (11)

XP_002267417.1

new

 312

glyoxysomal fatty acid beta-oxidation multifunctional protein MFP-a (11)

XP_002270067.1

50.99

 271

phospholipase D alpha 1 (11, 27)

XP_002268195.1

2.46

 564

biotin carboxyl carrier protein of acetyl-CoA carboxylase-like (11)

XP_002278151.2

−2.86

 183

3-oxoacyl-[acyl-carrier-protein] synthase I, chloroplastic-like (11)

XP_002265207.1

−3.09

 942

phospholipase C 4-like isoform 2 (11)

XP_003633883.1

−3.12

 219

biotin carboxylase 1, chloroplastic-like (11)

XP_002266489.1

−3.58

 268

3-oxoacyl-[acyl-carrier-protein] reductase, chloroplastic (11, 26)

XP_002268080.1

−3.71

 775

acyl-CoA-binding domain-containing protein 4-like isoform 1 (11)

XP_002284019.1

−4.32

 958

flavoprotein wrbA isoform 2 (11)

XP_003634692.1

d.

N and amino acid metabolism (12, 13)

 3

S-adenosylmethionine synthase 2 (13, 15)

A7NVX9.1

−2.13

 929

glyoxylate reductase isoform 2 (1, 13, 26)

XP_003632860.1

−2.34

 971

phosphoserine aminotransferase, chloroplastic-like (13, 27)

XP_003635669.1

−2.36

 843

ferredoxin--nitrite reductase, chloroplastic (12)

XP_002285208.1

−2.65

 518

serine hydroxymethyltransferase, mitochondrial (1, 13, 25)

XP_002277146.1

−2.92

 318

bifunctional 3-dehydroquinate dehydratase/shikimate dehydrogenase, chloroplastic (13)

XP_002270188.1

−4.84

 206

adenosylhomocysteinase isoform 1 (13)

XP_002266154.1

−5.43

 321

bifunctional 3-dehydroquinate dehydratase/shikimate dehydrogenase, chloroplastic-like (13)

XP_002270232.1

−6.15

 212

probable S-adenosylmethionine-dependent methyltransferase At5g37990-like (13, 17)

XP_002266288.2

−7.51

 342

methionine S-methyltransferase-like (13)

XP_002270977.1

−10.04

 687

ornithine carbamoyltransferase, chloroplastic (13)

XP_002281919.1

−10.33

 186

alanine aminotransferase 2 (13)

XP_002265294.2

−10.94

Secondary metabolism (16)

 576

2-C-methyl-D-erythritol 2,4-cyclodiphosphate synthase, chloroplastic-like (16)

XP_002278406.1

new

 392

aldo-keto reductase family 4 member C9-like (3, 16)

XP_002272909.1

−2.10

 136

3-isopropylmalate dehydratase small subunit (16)

XP_002263405.1

−2.26

 549

isopentenyl-diphosphate Delta-isomerase I-like (16)

XP_002277935.1

−2.59

 717

1-deoxy-D-xylulose 5-phosphate reductoisomerase, chloroplastic (16)

XP_002282761.1

−2.76

 836

4-hydroxy-3-methylbut-2-en-1-yl diphosphate synthase (16)

XP_002285130.1

−3.06

 960

bifunctional dihydroflavonol 4-reductase/flavanone 4-reductase-like (16, 26, 29)

XP_003634871.1

−6.69

 106

chalcone--flavonone isomerase 1 (16, 27)

P51117.1

d.

 373

protein SRG1 (16)

XP_002272119.1

d.

 525

zeta-carotene desaturase, chloroplastic/chromoplastic (16)

XP_002277348.2

d.

 557

REF/SRPP-like protein At1g67360-like (16)

XP_002278036.1

d.

 584

carotenoid 9,10(9′,10′)-cleavage dioxygenase 1-like (16, 17)

XP_002278628.1

d.

Hormone metabolism (17)

 617

auxin-repressed 12.5 kDa protein isoform 1 (17, 27, 33)

XP_002279836.1

new

 591

auxin-induced protein PCNT115 isoform 1 (17, 26)

XP_002278850.1

2.93

 148

linoleate 13S-lipoxygenase 2–1, chloroplastic (17)

XP_002263854.1

2.55

 457

HVA22-like protein a (17)

XP_002275428.1

2.29

 828

gibberellin 20 oxidase 3-like (16, 17, 26)

XP_002284983.1

−2.15

 767

probable indole-3-acetic acid-amido synthetase GH3.1 (17)

XP_002283886.1

−2.23

 825

gibberellin 20 oxidase 3 (16, 17, 26)

XP_002284968.1

−7.11

 54

hypothetical protein VITISV_007808 (gibberellin 3-beta-dioxygenase 1) (17, 26)

CAN66061.1

−14.90

 897

1-aminocyclopropane-1-carboxylate oxidase 5 (17)

XP_002285881.1

d.

Stress (20)

 132

22.0 kDa heat shock protein (20)

XP_002263376.1

new

 436

auxin-binding protein ABP19a-like (20)

XP_002274457.1

new

 513

putative germin-like protein 2–1 (12, 20, 27, 30, 34)

XP_002277055.1

new

 636

18.2 kDa class I heat shock protein (20, 29)

XP_002280353.1

new

 900

MLP-like protein 28-like (20)

XP_003631204.1

new

 652

18.2 kDa class I heat shock protein (20, 29)

XP_002280821.1

21.56

 810

MLP-like protein 34 (20)

XP_002284578.1

2.92

 349

universal stress protein A-like protein (20, 27, 33)

XP_002271154.1

2.14

 152

stress-related protein-like (16)

XP_002263944.1

2.12

 798

germin-like protein subfamily 1 member 17 (12, 20, 27, 34)

XP_002284436.1

−2.50

 677

chitinase 2 (20, 21)

XP_002281729.1

−3.28

 566

germin-like protein 9–3 (15, 20)

XP_002278170.1

−4.10

 44

hypothetical protein VITISV_005677 (germin-like protein 9–3) (15, 20)

CAN61171.1

−14.62

 64

hypothetical protein VITISV_005471 (germin-like protein 1) (12, 20, 27, 34)

CAN71140.1

d.

 237

putative germin-like protein 2–1 (12, 20, 27, 30, 34)

XP_002266984.1

d.

 240

putative germin-like protein 2–1 (12, 20, 27, 30, 34)

XP_002267172.1

d.

 867

pathogen-related protein (20)

XP_002285489.1

d.

Redox (21)

 205

glutathione S-transferase DHAR3, chloroplastic (21)

XP_002266106.1

new

 482

glutaredoxin (21)

XP_002276266.1

3.29

 754

peroxiredoxin-2E, chloroplastic (21)

XP_002283652.1

−2.01

 334

catalase isozyme 1 isoform 1 (21)

XP_002270703.2

−3.41

Miscellaneous enzyme families (26)

 845

probable glutathione S-transferase (26, 28, 33)

XP_002285214.1

new

 36

hypothetical protein VITISV_041925 - carboxymethylenebutenolidase (26)

CAN60148.1

2.41

 633

peroxidase 3 (20, 26)

XP_002280274.1

2.27

 23

glutathione S-transferase 5 (11, 16, 26)

ABW34390.1

2.11

 445

(+)-neomenthol dehydrogenase-like isoform 2 (26)

XP_002274970.2

−2.01

 396

minor allergen Alt a 7-like (11, 26, 27)

XP_002273030.1

−2.16

 427

probable inactive purple acid phosphatase 1-like (26, 27)

XP_002274118.2

−2.29

 338

epoxide hydrolase 2 (26)

XP_002270883.2

−2.29

 286

probable glutathione S-transferase (26)

XP_002268911.1

−2.45

 840

NADP-dependent alkenal double bond reductase P1 (26, 34)

XP_002285167.1

−47.43

DNA/RNA (27, 28)

 281

proactivator polypeptide-like 1 isoform 1 (28)

XP_002268581.1

new

 623

ribonuclease 3 (27)

XP_002280078.1

new

 755

putative DNA repair protein RAD23–3 isoform 1 (28, 29)

XP_002283656.1

−2.49

 164

KH domain-containing protein At4g18375 isoform 1 (27)

XP_002264417.1

−5.48

Protein (29)

 99

unnamed protein product (pseudouridine synthase) (29)

CBI39540.3

new

 759

outer envelope pore protein 16, chloroplastic (29)

XP_002283749.1

new

 803

eukaryotic translation initiation factor 3 subunit E (29)

XP_002284533.1

new

 829

vesicle-fusing ATPase-like (29)

XP_002284987.1

new

 252

protease Do-like 1, chloroplastic-like (29)

XP_002267510.2

7.32

 154

aspartic proteinase nepenthesin-1 (27, 29)

XP_002263964.1

4.86

 924

40S ribosomal protein S15a-like isoform 2 (29)

XP_003632608.1

4.08

 794

N-carbamoyl-L-amino acid hydrolase-like (29)

XP_002284376.1

3.95

 196

26S proteasome non-ATPase regulatory subunit 1-like (29)

XP_002265758.2

2.39

 279

serine carboxypeptidase-like 18 (29)

XP_002268517.1

−2.00

 774

acylamino-acid-releasing enzyme-like isoform 1 (29)

XP_002284013.2

−2.25

 768

aspartic proteinase nepenthesin-1-like (27, 29)

XP_002283889.2

−2.31

 641

uncharacterized protein LOC100259133 (m.: 29, 33)

XP_002280454.1

−2.58

 50

hypothetical protein VITISV_017087 (serine carboxypeptidase II-3-like) (29)

CAN63486.1

−2.86

 651

pyrrolidone-carboxylate peptidase isoform 4 (29)

XP_002280794.1

−2.92

 722

protein transport protein Sec24-like At3g07100-like (29)

XP_002282857.1

−3.18

 701

serine carboxypeptidase II-3-like (29)

XP_002282331.1

−3.30

 65

hypothetical protein VITISV_003230 (m.: 29)

CAN71580.1

−3.78

 679

cucumisin-like (29)

XP_002281790.2

−4.22

 522

cucumisin-like (29)

XP_002277242.2

−5.96

 689

serine carboxypeptidase II-3-like (29)

XP_002281988.1

−6.70

 311

probable serine/threonine-protein kinase At5g41260 (29)

XP_002270065.1

−12.57

 720

serine carboxypeptidase-like 45-like (29)

XP_002282852.1

−15.26

 58

hypothetical protein VITISV_026357 (m.: 29, 30, 33)

CAN68006.1

−51.68

 470

cucumisin-like (29)

XP_002275807.1

−106.41

 303

uncharacterized protein LOC100254416 (pathogenesis-related protein 17) (29)

XP_002269470.1

d.

 721

subtilisin-like protease-like (29, 30)

XP_002282856.1

d.

Cell / signaling / development (30, 31, 33)

 140

70 kDa peptidyl-prolyl isomerase (29, 31)

XP_002263566.2

new

 357

actin-depolymerizing factor 10 (31)

XP_002271495.1

new

 744

transmembrane emp24 domain-containing protein A (31)

XP_002283487.1

new

 163

uncharacterized protein LOC100255239 (calcium ion binding protein) (30)

XP_002264359.1

5.11

 799

glutelin type-A 1 (28, 33)

XP_002284459.1

4.50

 415

calnexin homolog 1 (30)

XP_002273708.1

4.47

 644

uncharacterized protein LOC100266227 (Late embryogenesis abundant protein Lea14-A) (33)

XP_002280489.1

4.09

 934

VAMP-like protein YKT61-like (31)

XP_003633163.1

3.26

 740

oxysterol-binding protein-related protein 3C (31)

XP_002283434.1

−2.07

 676

uncharacterized protein HI_0488 (phosphatase YqaB) (33)

XP_002281714.1

−2.18

 332

coatomer subunit epsilon-1 isoform 1 (31)

XP_002270662.1

−2.35

 599

nitrogen regulatory protein P-II homolog (30)

XP_002279289.1

−2.60

 907

uncharacterized protein LOC100854676 (m.: 30)

XP_003631533.1

−3.20

 735

DAG protein, chloroplastic isoform 1 (33)

XP_002283211.1

−3.83

 245

tubulin beta-1 chain isoform 1 (31)

XP_002267304.1

−4.10

 374

golgin candidate 6-like (29, 31)

XP_002272168.1

−4.22

 560

PRA1 family protein B4-like (30, 31)

XP_002278095.1

d.

Transport (34)

 704

pyrophosphate-energized vacuolar membrane proton pump 1 (34)

XP_002282358.1

2.76

 870

uncharacterized protein LOC100240897 (m.: 34)

XP_002285517.1

2.29

 548

aquaporin TIP2–3 (34)

XP_002277904.2

−5.27

Others (15, 18, 23, 24)

 86

unnamed protein product (DJ-1 family protein) (18)

CBI20205.3

new

 516

ferritin-3, chloroplastic (15)

XP_002277114.1

6.49

 731

NAD-dependent dihydropyrimidine dehydrogenase subunit PreA (23)

XP_002283095.1

5.41

 700

ectonucleotide pyrophosphatase/phosphodiesterase family member 3 (23)

XP_002282308.1

4.52

 95

unnamed protein product (nucleoside diphosphate kinase) (23)

CBI34488.3

3.07

 639

6,7-dimethyl-8-ribityllumazine synthase, chloroplastic-like (18)

XP_002280427.1

2.58

 492

guanine deaminase (23)

XP_002276494.1

−2.41

 123

nucleoside diphosphate kinase 2, chloroplastic isoform 1 (23)

XP_002263177.1

−3.31

 894

biotin--protein ligase (18)

XP_002285834.1

−4.28

 535

probable carboxylesterase 15 (24)

XP_002277507.1

−7.50

 90

unnamed protein product (soluble inorganic pyrophosphatase) (23)

CBI25065.3

d.

 161

selT-like protein (15)

XP_002264265.1

d.

Hypothetical / Unknown function (35)

 76

unknown protein (35)

CAQ58595.1

new

 85

unnamed protein product (Protein tolB) (35)

CBI18981.3

new

 230

protein LURP-one-related 15 (35)

XP_002266795.1

new

 902

probable nucleoredoxin 1-like (35)

XP_003631263.1

2.99

 127

uncharacterized protein At5g48480 (35)

XP_002263284.1

2.81

 387

pre-mRNA-processing factor 39-like (35)

XP_002272685.1

−2.36

 420

uncharacterized protein LOC100242710 (35)

XP_002273917.1

−2.44

 361

putative phosphatidylglycerol/phosphatidylinositol transfer protein DDB_G0282179 isoform 1 (35)

XP_002271535.1

−3.07

 841

putative clathrin assembly protein At2g25430-like (35)

XP_002285168.1

−3.35

 199

uncharacterized protein LOC100265424 (35)

XP_002265851.1

−4.35

 232

uncharacterized protein LOC100253185 (35)

XP_002266892.1

−4.68

 412

S-norcoclaurine synthase (35)

XP_002273566.1

−4.92

 48

hypothetical protein VITISV_010154 (35)

CAN62850.1

−6.76

 553

transmembrane protein 111 (35)

XP_002277989.1

−7.13

 184

NADPH:quinone oxidoreductase (35)

XP_002265225.1

−16.72

 82

unnamed protein product (metal ion binding protein, putative) (35)

CBI17463.3

d.

 582

clavaminate synthase-like protein At3g21360 (35)

XP_002278552.1

d.

 890

uncharacterized protein LOC100254028 (35)

XP_002285734.1

d.

Numbers reported in brackets refer to bin code (i.e. major functional categories). n.: identification number. f.c.: bin code of functional categories. Name: for proteins without a name in brackets are indicated the results from BLAST alignment against NCBI Viridiplantae database; m.: classification obtained through grape/Arabidopsis or grape/potato matching by BLASTp algorithm (E.value < 1020). Δ: fold changes in WS plants with respect to the Control ones (up: %(SI)WS/%(SI)C, down: - %(SI)C/%(SI)WS). new: not present in C; d.: disappeared, not present in WS

Table 2

Proteins showing significant changes in responses to WS in the M4 genotype

n.

Name (f.c.)

Accession

Δ:WS/C

Carbon and energy metabolism (1, 2, 3, 4, 5, 6, 7, 8, 9, 25)

 474

alpha-1,4 glucan phosphorylase L isozyme, chloroplastic/amyloplastic-like (2)

XP_002279075.2

new

 542

isocitrate dehydrogenase [NAD] catalytic subunit 5, mitochondrial-like (8)

XP_002281826.1

14.46

 231

succinate-semialdehyde dehydrogenase (acetylating)-like (5, 26)

XP_002268625.1

7.33

 481

2-oxoglutarate dehydrogenase, mitochondrial-like (8)

XP_002279332.2

6.00

 757

ATP-citrate synthase alpha chain protein 2 isoform 2 (8)

XP_003633614.1

4.05

 463

formate dehydrogenase, mitochondrial (25)

XP_002278444.1

3.05

 695

phosphoenolpyruvate carboxylase, housekeeping isozyme isoform 1 (4)

XP_002285441.1

2.84

 446

aldehyde dehydrogenase family 7 member A1 (5, 8, 20)

XP_002278093.1

2.34

 251

L-idonate 5-dehydrogenase (3, 5, 26)

XP_002269895.1

2.23

 626

citrate synthase, glyoxysomal (6)

XP_002284064.1

2.20

 556

UTP--glucose-1-phosphate uridylyltransferase isoform 1(4)

XP_002282276.1

2.20

 187

glucose-6-phosphate 1-dehydrogenase, cytoplasmic isoform (7)

XP_002266527.1

2.16

 655

phosphoglucomutase, cytoplasmic (4)

XP_002284729.1

2.01

 374

pyruvate decarboxylase isozyme 1 (5)

XP_002275486.1

−2.01

 718

glucose-6-phosphate isomerase isoform 1 (4)

XP_002285696.1

−2.12

 313

pyruvate decarboxylase isozyme 2 (5)

XP_002272615.1

−2.33

 116

ferredoxin--NADP reductase, root isozyme, chloroplastic-like (7)

XP_002263658.2

−2.40

 354

enolase 1, chloroplastic-like (4)

XP_002274334.1

−2.63

 269

D-threo-aldose 1-dehydrogenase (2)

XP_002270562.1

−2.65

 92

L-idonate 5-dehydrogenase (3, 5)

Q1PSI9.2

−2.93

 413

dihydrolipoyl dehydrogenase (8, 11, 21)

XP_002276853.1

−3.52

 495

pyruvate kinase isozyme A, chloroplastic isoform 1 (4, 11)

XP_002279975.1

−3.79

 557

dihydrolipoyllysine-residue acetyltransferase component of pyruvate dehydrogenase complex, mitochondrial-like (8, 11)

XP_002282287.1

−4.11

 194

glucose-6-phosphate 1-dehydrogenase, chloroplastic (7, 30)

XP_002266930.1

−19.38

Cell Wall (10)

 357

probable xyloglucan endotransglucosylase/hydrolase protein B (10)

XP_002274520.1

11.89

 222

UDP-glucose 4-epimerase GEPI48 (10)

XP_002268294.2

7.31

 34

hypothetical protein VITISV_001144 (m.: 10)

CAN61024.1

3.39

 129

beta-xylosidase/alpha-L-arabinofuranosidase 2-like (10, 33)

XP_002264183.2

−2.07

 561

probable rhamnose biosynthetic enzyme 1 (10)

XP_002282339.1

−2.12

 772

UDP-sugar pyrophospharylase isoform 2 (10)

XP_003634394.1

−2.37

 508

pectinesterase 2 (10)

XP_002280446.1

−3.32

 12

acetyl-CoA carboxylase carboxyltransferase beta subunit (10, 11, 29)

ABE47543.1

d.

 345

probable xyloglucan endotransglucosylase/hydrolase protein 30-like (10)

XP_002273975.1

d.

Lipid Metabolism (11)

 253

putative esterase HI_1161 isoform 1 (11)

XP_002269958.1

new

 219

phospholipase D alpha 1 (11, 27)

XP_002268195.1

2.44

 152

3-oxoacyl-[acyl-carrier-protein] synthase I, chloroplastic-like (11)

XP_002265207.1

−2.23

 451

biotin carboxyl carrier protein of acetyl-CoA carboxylase-like (11)

XP_002278151.2

−2.64

 216

3-oxoacyl-[acyl-carrier-protein] reductase, chloroplastic (11, 26)

XP_002268080.1

−3.72

 737

3-oxoacyl-[acyl-carrier-protein] synthase 3 A, chloroplastic-like (11)

XP_003631438.1

−5.47

 623

acyl-CoA-binding domain-containing protein 4-like isoform 1 (11)

XP_002284019.1

−5.81

 648

probable linoleate 9S-lipoxygenase 5 (11)

XP_002284535.2

−7.06

 414

biotin carboxyl carrier protein of acetyl-CoA carboxylase 1, chloroplastic (11, 18)

XP_002276955.2

d.

N and amino acid metabolism (12, 13)

 516

arginase-like (13)

XP_002280690.2

new

 210

alanine--glyoxylate aminotransferase 2 homolog 2, mitochondrial (13, 19)

XP_002267787.1

5.42

 178

methylmalonate-semialdehyde dehydrogenase [acylating], mitochondrial (13)

XP_002266390.1

5.29

 170

adenosylhomocysteinase isoform 1 (13)

XP_002266154.1

2.99

 787

phosphoserine aminotransferase, chloroplastic-like (13, 27)

XP_003635669.1

−2.04

 763

3-phosphoshikimate 1-carboxyvinyltransferase, chloroplastic-like (13)

XP_003633923.1

−3.63

 428

bifunctional 3-dehydroquinate dehydratase/shikimate dehydrogenase, chloroplastic-like (13)

XP_002277395.2

−4.68

 25

glutamine synthetase (12)

CAA63982.1

−8.64

 177

probable S-adenosylmethionine-dependent methyltransferase At5g37990-like (13, 17)

XP_002266288.2

−30.63

 259

hydroxyacylglutathione hydrolase 3, mitochondrial-like (13)

XP_002270140.1

d.

Secondary metabolism (16)

 230

anthocyanidin 5,3-O-glucosyltransferase (16, 26)

XP_002268560.1

new

 766

caffeic acid 3-O-methyltransferase 1-like isoform 1 (16, 26)

XP_003634161.1

new

 678

probable NAD(P)H-dependent oxidoreductase 1 (3, 16)

XP_002285219.1

12.08

 90

chalcone-flavonone isomerase 1 (16, 27)

P51117.1

3.07

 206

anthocyanidin 5,3-O-glucosyltransferase-like (16, 26)

XP_002267573.1

2.44

 135

stilbene synthase 1 (16)

XP_002264455.1

2.32

 497

chalcone--flavonone isomerase-like isoform 1 (16)

XP_002280158.1

−2.09

 427

probable cinnamyl alcohol dehydrogenase 1-like (16)

XP_002277375.1

−2.88

 692

probable cinnamyl alcohol dehydrogenase 1 (16)

XP_002285406.1

−3.02

 574

1-deoxy-D-xylulose 5-phosphate reductoisomerase, chloroplastic (16)

XP_002282761.1

−6.95

 247

isoeugenol synthase 1 (16)

XP_002269639.1

d.

 670

4-hydroxy-3-methylbut-2-en-1-yl diphosphate synthase (16)

XP_002285130.1

d.

Hormone metabolism (17)

 471

auxin-induced protein PCNT115 isoform 1 (17, 26)

XP_002278850.1

18.40

 121

linoleate 13S-lipoxygenase 2–1, chloroplastic (17)

XP_002263854.1

3.85

 515

gibberellin 3-beta-dioxygenase 3 (17, 26)

XP_002280670.1

d.

 662

gibberellin 20 oxidase 3-like (16, 17, 26)

XP_002284983.1

d.

Stress (20)

 124

stress-related protein-like (16)

XP_002263944.1

new

 715

disease resistance response protein 206 (20)

XP_002285676.1

new

 581

osmotin-like protein OSM34 (20)

XP_002282917.1

3.47

 653

MLP-like protein 34 (20)

XP_002284578.1

7.22

 525

osmotin-like protein (20)

XP_002281193.1

5.06

 91

basic endochitinase (20)

P51613.1

2.93

 687

topless-related protein 4-like isoform 1 (20, 33)

XP_002285341.2

2.45

 10

chitinase class I basic (20)

ABD64684.1

2.38

 172

putative germin-like protein 2–1 (12, 20, 27, 30, 34)

XP_002266227.1

2.38

 254

endoplasmin homolog (20)

XP_002270014.2

2.33

 649

MLP-like protein 28-like isoform 1 (20)

XP_002284538.1

2.17

 652

MLP-like protein 28 (20)

XP_002284570.1

2.10

 359

major allergen Pru av. 1 (20, 27, 34)

XP_002274617.1

2.08

 780

heat shock cognate protein 80-like (20)

XP_003635036.1

2.01

 346

major allergen Pru ar 1 (20, 27, 34)

XP_002273982.1

−2.45

 643

germin-like protein subfamily 1 member 17 (20, 27, 34)

XP_002284436.1

−2.82

 334

germin-like protein 9–3 (15, 20)

XP_002273554.1

−3.62

 195

putative germin-like protein 2–1 (12, 20, 27, 30, 34)

XP_002266984.1

−4.25

 453

germin-like protein 9–3 (15, 20)

XP_002278170.1

−4.76

 537

chitinase 2 (20)

XP_002281729.1

−4.80

 102

pathogen-related protein (20)

XP_002263121.1

−7.46

 74

unnamed protein product (m.: 11, 20)

CBI28159.3

d.

 583

putative endo-1,3(4)-beta-glucanase 2-like (20)

XP_002282971.1

d.

Redox (21)

 8

catalase (21)

AAL83720.1

2.91

 714

glutathione reductase, cytosolic (21)

XP_002285672.1

2.70

 607

peroxiredoxin-2E, chloroplastic (21)

XP_002283652.1

−2.02

Miscellaneous enzyme families (26)

 193

glutathione transferase GST 23-like isoform 1 (26, 28, 33)

XP_002266900.1

8.62

 777

probable glutathione S-transferase parC-like isoform 2 (26)

XP_003634746.1

4.78

 15

glutathione S-transferase (26)

ABL84692.1

2.08

 287

tropinone reductase 1-like (26)

XP_002271432.1

−2.14

 274

epoxide hydrolase 2 (26)

XP_002270883.2

−2.40

 323

glutathione S-transferase zeta class-like isoform 1 (26)

XP_002273077.1

−2.59

 238

glutathione S-transferase U9 (26)

XP_002269118.1

−2.93

 236

probable glutathione S-transferase (26)

XP_002268911.1

−3.06

 674

NADP-dependent alkenal double bond reductase P1 (26, 34)

XP_002285167.1

−3.38

 417

l-Ala-D/L-Glu epimerase (26)

XP_002277056.1

−3.45

 412

UDP-glycosyltransferase 85A1-like (17, 26, 29)

XP_002276823.1

−3.84

 197

momilactone A synthase (26)

XP_002267041.1

−4.51

 320

momilactone A synthase-like (26)

XP_002272981.1

−5.53

 127

glutathione transferase GST 23 (26)

XP_002264054.1

−7.67

 536

glutathione S-transferase U7 isoform 1 (26, 28, 33)

XP_002281654.1

−9.51

 512

probable glutathione S-transferase (26, 28, 33)

XP_002280532.1

−16.45

 527

UDP-glycosyltransferase 83A1 (26)

XP_002281262.1

d.

 748

epoxide hydrolase 2-like (26)

XP_003632381.1

d.

DNA/RNA (27, 28)

 207

DEAD-box ATP-dependent RNA helicase 37-like (27)

XP_002267581.2

31.87

 235

DEAD-box ATP-dependent RNA helicase 56 (27, 28)

XP_002268833.1

11.78

 148

transcription factor BTF3 (27, 34)

XP_002265041.1

4.42

 676

polyadenylate-binding protein 2 (27)

XP_002285190.1

−2.62

 502

ribonuclease UK114-like (27)

XP_002280251.1

−2.90

 39

hypothetical protein VITISV_017556 (m.: 27)

CAN66609.1

−3.96

 205

uncharacterized protein LOC100253093 (m.: 27, 29, 34)

XP_002267536.1

−5.72

 42

hypothetical protein VITISV_020351 (probable ADP-ribosylation factor GTPase-activating protein AGD11) (27)

CAN67438.1

d.

 133

KH domain-containing protein At4g18375 isoform 1 (27)

XP_002264417.1

d.

 240

poly(rC)-binding protein 3-like (27)

XP_002269249.1

d.

Protein (29)

 180

miraculin (20, 29, 31)

XP_002266430.1

11.55

 651

26S proteasome non-ATPase regulatory subunit 14 (29)

XP_002284566.1

4.76

 387

probable protein phosphatase 2C 58 (29)

XP_002275890.1

3.85

 137

26S proteasome non-ATPase regulatory subunit 4 (29)

XP_002264558.1

2.95

 352

60S ribosomal protein L10a-1-like (29)

XP_002274218.2

2.73

 696

60S ribosomal protein L23-like (29)

XP_002285443.1

2.69

 213

uncharacterized protein At2g37660, chloroplastic (m.: 29)

XP_002267965.1

2.55

 190

elongation factor 2-like isoform 1 (29)

XP_002266780.1

2.40

 266

60S ribosomal protein L11–1-like (29)

XP_002270266.1

2.34

 303

serine carboxypeptidase-like 18 (29)

XP_002272116.1

2.27

 604

probable glutamate carboxypeptidase 2-like isoform 1 (27, 29)

XP_002283565.2

2.20

 57

hypothetical protein VITISV_003812 (60S acidic ribosomal protein P0) (29)

CAN80537.1

2.16

 543

heme-binding protein 2-like (19, 29)

XP_002281829.1

2.10

 538

cucumisin-like (29)

XP_002281790.2

−2.07

 559

serine carboxypeptidase II-3-like (29)

XP_002282331.1

−2.38

 572

ATP-dependent Clp protease proteolytic subunit 4, chloroplastic (29)

XP_002282652.1

−2.43

 169

60S acidic ribosomal protein P2B isoform 1 (29)

XP_002266030.1

−2.60

 2

40S ribosomal protein SA (29)

A5BUU4.1

−3.26

 501

eukaryotic translation initiation factor 3 subunit M (29)

XP_002280247.1

−4.72

 633

uncharacterized protein LOC100262703 (dipeptidyl-peptidase 5) (29)

XP_002284264.1

−4.78

 616

ADP-ribosylation factor-like protein 5 (29)

XP_002283837.1

−4.88

 702

probable protein phosphatase 2C 60 (29)

XP_002285549.1

−4.97

 232

protein transport protein SEC23-like (29)

XP_002268633.2

−17.96

 518

pyrrolidone-carboxylate peptidase isoform 4 (29)

XP_002280794.1

d.

 577

subtilisin-like protease (29)

XP_002282841.1

d.

 578

protein transport protein Sec24-like At3g07100-like (29)

XP_002282857.1

d.

 620

aspartic proteinase nepenthesin-1-like (29)

XP_002283889.2

d.

Cell / signaling / development (30, 31, 33)

 114

70 kDa peptidyl-prolyl isomerase (31)

XP_002263566.2

new

 250

11S globulin seed storage protein 2 (28, 33)

XP_002269868.1

50.05

 94

peptidyl-prolyl cis-trans isomerase H (28, 33)

XP_002262773.1

25.35

 490

PRA1 family protein F2-like (30, 31)

XP_002279772.1

13.62

 741

coatomer subunit gamma-2-like (31)

XP_003631645.1

5.84

 644

glutelin type-A 1 (28, 33)

XP_002284459.1

5.53

 685

probable plastid-lipid-associated protein 6, chloroplastic (31)

XP_002285326.1

2.21

 45

hypothetical protein VITISV_0120489 (plastid lipid-associated protein) (31)

CAN69132.1

2.15

 391

tubulin alpha chain (31)

XP_002275973.1

−2.05

 503

syntaxin-71 (27, 31)

XP_002280272.1

−3.77

 459

PITH domain-containing protein At3g04780 (33)

XP_002278320.1

d.

Transport (34)

 296

V-type proton ATPase subunit H-like (34)

XP_002271887.1

5.21

 365

V-type proton ATPase subunit E (29, 34)

XP_002274995.1

3.68

 358

probable aquaporin PIP2–5 (34)

XP_002274555.1

−3.94

Others (15, 18, 23, 24)

 418

ferritin-3, chloroplastic (15)

XP_002277114.1

new

 196

selenium-binding protein 1 (15)

XP_002267004.1

10.44

 143

soluble inorganic pyrophosphatase-like (23)

XP_002264695.2

2.20

 576

nicotinate phosphoribosyltransferase-like (18, 23)

XP_002282786.1

−6.86

 433

probable carboxylesterase 15 (24)

XP_002277507.1

−9.08

 332

uracil phosphoribosyltransferase (23)

XP_002273489.1

d.

Hypothetical / Unknown function (35)

 108

uncharacterized protein At5g48480 (35)

XP_002263284.1

20.80

 733

probable nucleoredoxin 1-like (35)

XP_003631263.1

8.46

 582

elicitor-responsive protein 3 (35)

XP_002282926.1

4.99

 80

unnamed protein product (35)

CBI34823.3

4.73

 771

uncharacterized protein LOC100854733 (35)

XP_003634361.1

3.22

 98

CBS domain-containing protein CBSX3, mitochondrial isoform 1 (35)

XP_002262902.1

2.02

 580

uncharacterized protein LOC100259086 (35)

XP_002282908.1

−2.08

 281

nodal modulator 1 (35)

XP_002271147.1

−2.24

 443

transmembrane protein 111 (35)

XP_002277989.1

−2.43

 343

uncharacterized protein LOC100242710 (35)

XP_002273917.1

−3.60

 164

uncharacterized protein LOC100265424 (D-alanine--D-alanine ligase family protein) (35)

XP_002265851.1

−3.64

 335

S-norcoclaurine synthase (35)

XP_002273566.1

−4.27

 48

hypothetical protein VITISV_002394 (35)

CAN70694.1

−4.82

 138

non-lysosomal glucosylceramidase (35)

XP_002264575.2

−5.50

 466

clavaminate synthase-like protein At3g21360 (35)

XP_002278552.1

−6.68

 40

hypothetical protein VITISV_001156 (35)

CAN67361.1

d.

 547

GDT1-like protein 4 (35)

XP_002281939.1

d.

Numbers reported in brackets refer to bin code (i.e. major functional categories). n.: identification number. f.c.: bin code of functional categories. Name: for proteins without a name in brackets are indicated the results from BLAST alignment against NCBI Viridiplantae database; m.: classification obtained through grape/Arabidopsis or grape/potato matching by BLASTp algorithm (E.value < 1020). Δ: fold changes in WS plants with respect to the Control ones (up: %(SI)WS/%(SI)C, down: - %(SI)C/%(SI)WS). new: not present in C; d.: disappeared, not present in WS

The majority of the identified proteins did not change in abundance under WS, and this was named the “static proteome” (Fig. 1). In detail, the static proteome was 83 and 78% in the 101.14 and the M4 rootstock genotypes, respectively (Fig. 1a and b). Proteins that changed in abundance in WS were 181 and 186 for 101.14 and M4, respectively. Among these, 48 were identified only in 101.14, whereas 64 were found only in M4 (Additional file 3: Table S3 A, B and C). Small differences occurred between proteins that appeared (named “new”, 2 and 1% in 101.14 and M4, respectively) or disappeared (2 and 3% in 101.14 and M4, respectively) in WS. Moreover, 9% of the proteins decreased in abundance in both genotypes, while the accumulated proteins were 4 and 9% in 101.14 and M4, respectively (Fig. 1a and b).
Fig. 1
Fig. 1

Proteomic changes in response to water stress in 101.14 (a) and M4 (b) rootstock genotypes. Proteins that did not show an at least two-fold change between control and WS (Student’s t-test; p < 0.05) were classified as “static proteome”. Proteins that significantly changed were divided into four groups: new, accumulated, depleted and disappeared

Functional classification was made according to the bin hierarchical tree developed by MapMan ontology [34] using a Vitis vinifera mapping file (see Methods for details; Additional file 3: Table S3 A and B for the data). In the control condition, the two rootstock genotypes showed a very similar functional distribution of the identified proteins (Fig. 2a and b for 101.14 and M4, respectively). About 50% of these fell into four functional categories: carbon and energy metabolism, protein, miscellaneous enzyme families and stress. Considering only the proteins that changed significantly in abundance, essentially all the functional categories were affected by WS, but the extent of these changes was very different in the two genotypes (Fig. 2c, d, e and f). Proteins that increased/appeared (64 and 84 for 101.14 and M4, respectively) were represented in all the functional categories. Interestingly, the 101.14 genotype did not show any increase of proteins involved in N and amino acid metabolism. Comparing the responses between the two genotypes, 101.14 showed a greater number of increased/appeared proteins concerned to lipid metabolism, hormone metabolism, miscellaneous enzyme families and others, while for all the remaining functional categories the number of accumulated proteins was generally higher in M4 (Fig. 2c and d for 101.14 and M4, respectively). An opposite response occurred for proteins that decreased/disappeared in WS, which were 117 and 102 for 101.14 and M4, respectively. Indeed, only in four functional categories, i.e., cell wall, stress, miscellaneous enzyme families and DNA/RNA, the number of proteins that decreased in abundance was higher in M4, while for essentially all the remaining categories the higher number was found in 101.14 (Fig. 2e and f for 101.14 and M4, respectively).
Fig. 2
Fig. 2

Functional distribution of identified proteins in the 101.14 (a, c and e) and M4 (b, d and f) rootstock genotypes. a, b distribution of all the proteins identified in the control condition. c and d proteins that increased in abundance/appeared in WS. e and f proteins that decreased in abundance/disappeared in WS

Main metabolic pathways of the primary metabolism

The analysis of the nature of the proteins affected by WS highlighted changes in many metabolic pathways with particular differences between the two genotypes (Tables 1 and 2). For a better visualization, datasets containing all of the proteins identified in each genotype were used to produce heat maps concerning the overview of both the main metabolic pathways (Fig. 3) and those known to be involved in stress responses (Fig. 4).
Fig. 3
Fig. 3

MAPMAN overview of metabolic pathways in WS in root tissue of 101.14 (a) and M4 (b) rootstock genotypes. Green circles: decrease, white circles: no change, red circles: increase in protein abundance in WS respect to the control (see colour scale)

Fig. 4
Fig. 4

MAPMAN overview of stress pathways in WS in root tissue of 101.14 (a) and M4 (b) rootstock genotypes. Green circles: decrease, white circles: no change, red circles: increase in protein abundance in WS respect to the control (see colour scale)

WS led to deep changes in the functional classes of carbon and energy metabolism (Tables 1 and 2; Fig. 3; Additional file 3: Table S3 C). In both rootstock genotypes, enzymes involved in starch mobilization (alpha-1,4 glucan phosphorylase L isozyme) or in the sucrose biosynthetic pathway (phosphoglucomutase, UTP-glucose-1-phosphate uridylyltransferase and glucose-6-phosphate isomerase) were found. On the basis of the observed changes in abundance, M4 showed a greater activation of these pathways. Moreover, an increase in sucrose synthase 2, an enzyme involved in the degradation of this sugar, occurred only in 101.14.

Many of the changing proteins classified in the cell wall functional class decreased in abundance in WS (Tables 1 and 2; Figs. 3 and 4). Nevertheless, UDP-glucose 4-epimerase GEPI48, an enzyme that probably plays a central role in biosynthesis and growth of cell walls [35], rose during WS in both genotypes. Moreover, a xyloglucan endotransglucosylase/hydrolase protein B, an enzyme involved in wall loosening [28], showed an evident enhancement only in M4. In this genotype, the hypothetical protein VITISV_001144 (CAN61024.1), that shows a high similarity with some leucine-rich repeat (LRR) proteins, also surged up under WS.

Many plastidial enzymes, such as triosephosphate isomerase, enolase 1, pyruvate kinase isozyme A, ferredoxin-NADP reductase and glucose-6-phosphate 1-dehydrogenase, decreased in abundance under WS in both genotypes. Unlike the plastid isoform, the abundance of cytosolic glucose-6-phosphate dehydrogenase increased in both genotypes (Tables 1 and 2).

Some units of the pyruvate dehydrogenase complex (dihydrolipoyl dehydrogenase, E1 component subunit beta and dihydrolipoyllysine-residue acetyltransferase) reduced their abundance in WS in both genotypes, although the effect was more evident in 101.14. Moreover, only in M4, there was an increase in phosphoenolpyruvate carboxylase, which participates in replenishing TCA cycle intermediates [36].

Several enzymes of the TCA cycle were affected by WS (Fig. 3). In the 101.14 genotype, an ATP-citrate synthase and a fumarate hydratase increased in abundance, whilst a succinate dehydrogenase iron-sulfur subunit 1 disappeared. In M4, in addition to the same ATP-citrate synthase, an isocitrate dehydrogenase and a 2-oxoglutarate dehydrogenase also showed increases during water stress (Tables 1 and 2).

Among the proteins classified in carbon metabolism there was an aldehyde dehydrogenase family 7 member A1 that increased in abundance in WS in both genotypes (Tables 1 and 2). This protein was previously associated to the osmotic adaptation response to different abiotic stresses or ABA treatment [37, 38].

Only in M4 there was a surge in abundance of a succinate-semialdehyde dehydrogenase (acetylating) and a formate dehydrogenase (mitochondrial). The first of these is known to be involved in the metabolism of γ-aminobutyric acid (GABA) and plays a very important role in the response to reactive oxygen species (ROS), as suggested by the behaviour of the Arabidopsis ssadh mutant that accumulates elevated levels of H2O2 and is sensitive to UV-B light and heat stresses [37]. Similarly, an involvement of mitochondrial formate dehydrogenase under stress conditions, such as drought and cold, was previously suggested to be linked to the requirement to metabolize the increasing levels of formate and to use this alternative substrate to sustain the requirement for reducing power [39].

Among the proteins involved in lipid metabolism, a phospholipase D alpha 1 increased in WS in both genotypes. This enzyme, which produces phosphatidic acids hydrolyzing membrane glycerol-phospholipids, plays an important role in the responses that involve abscisic acid [40]. Many of the other changing proteins classified in this functional class, such as the carboxyl carrier protein of acetyl-CoA carboxylase-like, 3-oxoacyl-[acyl-carrier-protein] synthase I and acyl-CoA-binding domain-containing protein 4-like isoform 1, which are involved in the metabolism of fatty acids, decreased in WS. Only in 101.14, glyoxysomal fatty acid beta-oxidation multifunctional protein MFP-a, involved in fatty acid degradation, showed a very large increase in abundance in WS (Table 1). In this unfavourable condition, 101.14 also induced a long chain acyl-CoA synthetase.

In both genotypes, WS negatively affected many proteins classified in the functional class of N and amino acid metabolism (Fig. 3). Nevertheless, the results revealed that the decrease in abundance of enzymes involved in amino acid (aspartate, serine, glycine, cysteine and aromatic amino acids) biosynthesis was more pronounced in 101.14. Moreover, only in M4 some of these proteins increased in abundance/appeared during WS. Among these was an arginase-like, known to be involved also in proline metabolism [41].

Secondary metabolism and miscellaneous enzyme families

Differences between the two genotypes were detected among the proteins classified in the secondary metabolism functional class (Tables 1 and 2; Fig. 4). In 101.14, 2-C-methyl-D-erythritol 2,4-cyclodiphosphate synthase, involved in the MEP/DOPX pathway [42], appeared in WS, whilst all the other changing proteins of this class decreased. However, in M4 a minor number of decreasing proteins was found, whereas six proteins involved in secondary metabolism rose or appeared in WS. Among these there were enzymes involved in the biosynthesis of flavonoids (chalcone-flavonone isomerase 1; anthocyanidin 5,3-O-glucosyltransferase) and stilbenes (stilbene synthase 1).

Many of the changing proteins classified in the miscellaneous enzyme families were identified as glutathione S-transferases (GST). Among these, only one was referred to the same form and decreased in both genotypes, while the other WS-affected GSTs were assigned to different entries. In M4, three GSTs increased and six decreased in WS. In 101.14 only three GSTs changed their levels. Among the proteins classified in the miscellaneous enzyme category that surged up in WS, a peroxidase 3 was found only in 101.14. Moreover, an NADP-dependent alkenal double bond reductase P1, which catalyzes the reduction of the α,β-unsaturated bond of the reactive carbonyls, playing an important role in the antioxidative defence mechanisms [43], dropped in WS in 101.14, while in M4 the reduction was much lesser severe (− 4643% and − 238% in 101.14 and M4, respectively).

Hormone and redox metabolisms

A few proteins of which the levels varied in WS belong to the hormone metabolism functional class, especially in M4 (Tables 1 and 2; Fig. 4). Among the proteins that increased in both genotypes were the linoleate 13S-lipoxygenase 2–1, involved in the biosynthesis of jasmonic acid [44] and the auxin-induced protein PCNT115 isoform 1. The extent of the observed changes was very different in the two genotypes: + 155 and + 193% in 101.14, and + 285% and + 1740% in M4 respectively. Nevertheless, only in 101.14 was there an increase in an isoform of auxin-repressed 12.5 kDa protein as well as a decrease in indole-3-acetic acid-amido synthetase GH3.1. In both genotypes, WS negatively affected the abundance of the gibberellin 3-beta-dioxygenase and of the gibberellin 20 oxidase 3, both involved in gibberellin biosynthesis [45].

Only in 101.14, there was an increase in HVA22-like protein a, known to be induced by ABA under different abiotic stresses [46], while 1-aminocyclopropane-1-carboxylate oxidase 5, an enzyme that catalyzes the last reaction of ethylene biosynthesis, disappeared.

Similarly, proteomic analysis revealed differences between the two genotypes concerning a few enzymes involved in the redox metabolism (Tables 1 and 2; Fig. 4). In detail, a peroxiredoxin-2E decreased to similar extent in both genotypes, whereas a catalase and a glutathione reductase increased in WS only in M4, and another catalase decreased in stress conditions only in 101.14. In this last genotype the glutathione S-transferase DHAR3, a chloroplastic enzyme involved in the scavenging of ROS [47], also increased.

Nucleic acid and protein metabolisms

Among the proteins relating to DNA/RNA functionalities, the basal transcription factor 3 (BFT3) rose in WS only in M4 (Tables 1 and 2). In this genotype an upturn in two proteins involved in RNA metabolism (DEAD-box ATP-dependent RNA helicase 37-like and DEAD-box ATP-dependent RNA helicase 56) also occurred. At the same time, the appearance of a ribonuclease 3 and of a proactivator polypeptide-like 1 isoform 1 occurred only in 101.14 (Tables 1 and 2).

In WS conditions, some proteins involved in protein synthesis or degradation significantly varied in abundance (Tables 1 and 2; Fig. 4). The results showed up the differences between the two genotypes. Among the proteins that increased, some ribosomal proteins (i.e., a 60S ribosomal protein L10–1-like, a 60S ribosomal protein L11–1-like, a 60S ribosomal protein L23-like and a 60S acidic ribosomal protein P0), as well as the elongation factor 2-like isoform 1, were observed only in M4. Nevertheless, other proteins belonging to this functional class, such as the 60S acidic ribosomal protein P2B isoform 1, the translation initiation factor 3 subunit M and the 40S ribosomal protein SA, decreased in WS in this genotype. In 101.14, WS induced a rise in a 40S ribosomal protein S15a-like isoform 2 and in the translation initiation factor 3 subunit E. In both genotypes, WS affected the abundance of proteins with diverse functions, from protein catabolism to protein maturation. Among these, in both genotypes there was an increase in 26S proteasome non-ATPase regulatory subunits. Moreover, in 101.14 a N-carbamoyl-L-amino acid hydrolase-like, a protease Do-like 1 chloroplastic-like, and an aspartic proteinase nepenthesin-1 increased, whereas in M4 an upturn of a serine carboxypeptidase-like 18 and a probable glutamate carboxypeptidase 2-like isoform 1 took place. Water stress negatively affected in both genotypes other enzymes with similar functions, such as aspartic proteinase nepenthesin-1-like, pyrrolidone-carboxylate peptidase isoform 4, serine carboxypeptidase II-3-like, subtilisin-like protease-like and cucumisin-like. The same trend was observed for a protein transport protein SEC24-like, a subunit of COPII coat vesicles [48]. Only in 101.14, in WS four proteins, identified as a serine carboxypeptidase-like 18, two serine carboxypeptidase II-3-like and a serine carboxypeptidase-like 45-like, decreased. To these enzymes, which belong to a larger class of proteases in plants, both proteolytic and non-proteolytic functions have been attributed [49].

Other cell functions

Among the proteins belonging to the cell/signalling/development functional classes (Tables 1 and 2), a 70 kDa peptidyl-prolyl isomerase was identified. This protein, which appeared in WS in both genotypes, was previously described as changing its cellular localization under heat stress, according to its involvement in preserving cell functionality under abiotic stress [50]. The evident increase in the level of a peptidyl-prolyl cis-trans isomerase H taking place during WS was exclusive to M4. In the same condition, only in 101.14 the appearance of a transmembrane emp24 domain-containing protein and the upsurge of a VAMP-like protein YKT61-like (both involved in vesicle-mediated transport) were observed. Interestingly, two proteins of the PRA1 protein family (small transmembrane proteins controlling vesicle trafficking; Kamei et al. [51]) showed a mirror behaviour in the two genotypes: one disappeared in 101.14 (PRA1 family protein B4-like) while the other increased in M4 (PRA1 family protein F2-like). Moreover, a coatomer subunit gamma-2-like, a COP protein involved in the ER/Golgi network [52], increased only in M4. By contrast, the coatomer subunit epsilon-1 isoform 1 decreased in 101.14.

Water stress also affected a few cytoskeletal proteins (Tables 1 and 2). In particular, in 101.14 an actin-depolymerizing factor 10 was detected only in stressed roots, while in the same experimental condition a tubulin beta-1 chain isoform 1 decreased. Moreover, a tubulin alpha chain decreased in M4.

Water stress also affected the abundance of a few storage proteins (Tables 1 and 2). In detail, an upturn of a glutelin type-A 1 occurred in both genotypes, while only in M4 there was a consistent rise of an 11S globulin seed storage protein 2. Aquaporin TIP2.3 and PIP2–5 decreased in abundance in 101.14 and M4, respectively.

Among the proteins belonging to the transport functional class (Tables 1 and 2), WS induced in 101.14 the increase of a pyrophosphate-energized vacuolar membrane proton pump 1, whereas two subunits (H-like and E) of the V-type proton ATPase were positively affected by WS in M4.

Proteins involved in stress responses

Water stress affected the abundance of 17 and 23 proteins belonging to the stress functional class in 101.14 and M4, respectively (Tables 1 and 2; Fig. 4). Some of these were identified as germin-like proteins (GLP), a group of proteins with heterogeneous functions belonging to the cupin superfamily known to change their levels in different biotic and abiotic stress conditions [53]. In both genotypes, two GLPs were positively affected by WS, while the other ones were found to decrease in this adverse condition, though to a major extent in 101.14.

Similar considerations also applied to both the stress-related and the major latex protein (MLP) like proteins. Although further work is necessary to clarify the specific function(s) of MLPs, emerging evidence suggests their role in improving the tolerance to stress conditions [5456]. In detail, in 101.14 the rise of two stress-related protein-like and of two MLP-like proteins was observed. In M4 the same stress-related protein-like and MLP-like protein 34 appeared, and two other MLP-like proteins increased in abundance.

Two osmotin like-proteins, which play an important role in osmotic adjustment to tolerate WS conditions [57, 58], rose in abundance in WS only in M4. In the same genotype, WS also positively affected a disease resistance response protein 206, a chitinase class I basic, a basic endochitinase, an endoplasmin homolog, a topless-related protein 4-like isoform 1, and a pathogen-related protein. Nevertheless, only in 101.14, WS induced the appearance or a great increase in three heat-shock proteins.

Metabolic analysis

The changes of metabolite contents induced by WS are visualized in Fig. 5. Many metabolites appeared to be affected by the stress condition. Overall, amino acids, sugars and sugar alcohols were positively affected by WS, whilst almost all organic acids decreased under the unfavourable condition. Some of the changes were different in the two genotypes.
Fig. 5
Fig. 5

Effect of WS on metabolite levels in the root tissue of 101.14 and M4 rootstock genotypes. The clustering analysis was performed with PermutMatrix graphical interface after Z-score normalization of the values. Pearson’s distance and Ward’s algorithm were used for the analysis. Each coloured cell represents the value of the normalized WS/Control ratio, according to the colour scale at the bottom of the figure. Red increased levels, green: decreased levels. Black: identified metabolites not showing significant variation at the Student’s t-test (p-value < 0.05)

The analysis revealed in both genotypes a quite similar increasing trend, during WS, for some sugars, such as raffinose, galactose, fructose and sucrose. The increase of the last two metabolites was higher in M4. However, WS induced a greater increase in maltose in M4 and higher levels of iso-maltose in 101.14.

WS raised the levels of some sugar alcohols, such as inositol, galactol, mannitol and arabitol in both genotypes, but they increased to a higher extent in M4. Moreover, an upturn of erythritol occurred only in this last genotype.

Under WS, the levels of many metabolites of the Krebs cycle were negatively affected in both genotypes while, among the amino acids, similar increases in tyrosine, isoleucine, phenylalanine glutamine and glutamate took place. However, a greater increase in leucine was observed in M4. Only in 101.14 was an upsurge of alanine detected, whereas particular increments in valine, serine, methionine, threonine, and proline were measured in M4. Under WS, an increase of GABA level occurred in both genotypes, but to a major extent in 101.14.

Discussion

According to previous studies conducted in the same experimental conditions [29, 30], proteomic analysis strengthens the crucial role of roots in the plant responses to water stress (WS) and allows some particular traits of WS tolerance in a perennial plant species such as grapevine to come to light. The physiological measurements previously performed by Meggio and co-workers, revealed different capabilities of 101.14 and M4 rootstock genotypes to sustain CO2 assimilation rate (An) and stomatal conductance (gs) [29]. In both genotypes there occurred a progressive decrease of gs and An during the first days of stress, while afterwards 101.14 showed a further decrease whereas M4 partially recovered to 20 and 40% of the control condition for gs and An, respectively. This anisohydric behaviour also appeared linked to a better ability of roots to sustain loss of water [29]. When the field capacity was reduced to 30%, the roots of M4 showed, indeed, a greater capacity to adjust its osmolality adequately as well as to maintain cell integrity, as suggested by the protein and ion contents and by the recovery observed after re-watering [29].

The present work shows that the water-limiting condition induced significant changes in the whole root proteome (i.e., 17 and 22% of the quantified proteins in 101.14 and M4, respectively) and this result appeared to be well related to the capability of the genotype to respond more (M4) or less (101.14) positively to this stress condition. Whereas the functional distribution of the identified proteins in the control condition was quite similar in the two genotypes, important differences were found among the proteins that changed in WS (Fig. 2). The differences observed between the two genotypes were also consistent with the trends of the main metabolites (Fig. 5). Taken together, M4 showed positive metabolic responses potentially able to counteract the WS effects, whilst in 101.14 the suffering status of the roots became evident.

Root growth and osmotic adjustment

An important feature observed in plants exposed to water-limiting conditions is their ability to maintain primary root elongation, a process that is strictly dependent on several different responses. Crucial aspects for sustaining cell growth are to guarantee an adequate cell wall extensibility as well as to maintain water uptake, which depends heavily on an adjustment of the solute potential [28, 59].

Proteomic analysis showed that WS induced in both genotypes changes in abundance of some proteins involved in the biosynthesis and expansion of cell walls, as well as higher levels of typical osmoprotective compounds (i.e., amino acids, raffinose and some sugar alcohols). M4 showed a better capability to respond to the adverse condition, as indicated by the increase in specific proteins involved in cell wall loosening (i.e. xyloglucan endotransglucosylase/hydrolase protein B). Moreover, even if both genotypes showed an increase in compatible solutes, this response was greater in M4, as suggested by the increased levels of proline and of many sugar alcohols. Recently, the physiological relevance of this last class of compounds in the response to WS was investigated in fruit and leaves of grapevine, where WS induced the synthesis of these osmoprotective solutes, especially in the fruit mesocarp [60]. Our study shows that WS also evoked the synthesis of some polyols, such as mannitol, inositol, galactinol, and erythritol, in the roots. Overall, the higher polyol levels detected in the tolerant genotype M4 confirmed the importance of these compounds in improving WS response suggested by previous works [60, 61]. Furthermore, other metabolites, such as amino acids, appeared to contribute to the osmotic adjustment evoked by WS with a more massive response in M4 (see below). In addition, it is interesting to observe that only in M4 was there found an increase in two osmotin like-proteins, a class of proteins previously found to increase WS tolerance [57, 58]. Taken together, our study highlights a greater capability of M4 to activate molecular and biochemical processes useful to sustain the osmotic adjustment needed for root growth in a very severe WS condition.

Even though previous works pointed out the central role of aquaporins in the water stress response, the present study found decreases of aquaporin TIP2–3 and PIP2–5 in 101.14 and M4, respectively. This ostensible discrepancy could be explained by observing that our study analysed the whole root organ, while the water channel play an important role only in the young roots [19, 24, 62]. At the same time, we should consider that these proteins show diurnal changes in expression [62]. The proteomic analysis was performed on roots sampled 2 h after the start of the light period, a moment in which the daily increase of aquaporins may not yet be evident. Nevertheless, in woody root systems, the older suberized root portion can contribute significantly to water uptake and this role could depend upon the suberisation process occurring in this part of the root organ [24]. Proteomic analysis highlighted that only in 101.14 was there found an increase of a long chain acyl-CoA synthetase, an enzyme involved in suberin biosynthesis [63].

Root growth and hormones

Hormones have a central role in the plant responses to environmental stimuli. Beyond ABA, which plays a substantial role in water stress, other hormones such as auxin, gibberellins (GAs) and jasmonate (JA) are involved in the responses to this abiotic stress ([64] and references therein). Our analysis revealed changes in the abundance of proteins involved in hormone metabolism, which overall suggests a decrease in GAs biosynthesis and an increase in that of JA in both genotypes under stress. These possible changes appear consistent with the reduction in root growth and with the typical responses to stress conditions [65, 66]. Moreover, the proteomic analysis revealed an increase in abundance of an auxin-induced protein PCNT115 isoform 1, which was considerably higher in M4 than in 101.14. This result fits well with the concomitant appearance of an auxin-repressed 12.5 kDa protein and the decrease in an indole-3-acetic acid-amido synthase GH3.1 that occurred only in 101.14. Recently, a central role of auxin-induced protein PCNT115 in the formation of new adventitious roots in chrysanthemum cuttings was proposed [67]. This finding sustains the idea that this protein could play the same role in the root of the grapevine plant.

Carbohydrate metabolism and plastidial functionality

Water stress deeply affected carbohydrate metabolism: a higher abundance of enzymes involved in the pathways of starch breakdown and sucrose synthesis was observed. This greater use of root storage starch could be a consequence of the reduction of sugar provision from the leaf organ, a result of the fall in net CO2 assimilation previously described by Meggio et al. [29]. Nevertheless, these changes at the root level were more evident in M4, even while this genotype maintained photosynthetic activity under more severe WS conditions [29]. Previously, Regier and co-workers found that a crucial characteristic of Poli, a black poplar clone tolerant to WS, was its capability to maintain the photosynthetic rate as well as to improve adequately the usage of carbon skeletons in the root [27]. In agreement with these results, M4 showed a similar behaviour in response to WS.

Nonphotosynthetic plastids are the sites of primary pathways, such as those involved in the synthesis of starch and fatty acids and in nitrogen assimilation [68]. Proteomic analysis suggests a clear reduction in these processes in WS. Some of the plastidial enzymes involved in the production of reducing power (i.e., glucose-6-phosphate 1-dehydrogenase and ferredoxin-NADP reductase) or in plastidial glycolysis (i.e., triosephosphate isomerase and enolase) decreased in abundance in both genotypes. Moreover, some enzymes involved in nitrogen assimilation (i.e., ferredoxin-nitrite reductase and glutamine synthetase in 101.14 and M4, respectively) also decreased in WS.

In WS, a parallel reduction in the abundance of several enzymes involved in fatty acid biosynthesis occurred. Although this effect was observed in both genotypes, the concomitant increase of the glyoxysomal fatty acid beta-oxidation multifunctional protein MFP-a, evident only in 101.14, may suggest that in this genotype lipid catabolism was higher than in M4, possibly due to a different strategy to sustain energetic requirements (i.e., lipid respiration).

Taken together, these data are consistent with a reduction in plastidial functionality that could be directly due to a reduction in abundance of specific enzymes while at the same time there could be an inability to sustain the demand for reducing power by the anabolic processes [68]. According with a shift in the metabolic ways of sustaining the request of NADPH, an increase in cytosolic glucose-6-phosphate dehydrogenase occurred in both genotypes.

Mitochondrial functionality

Proteomic analysis revealed severe changes in mitochondrial functionality. Indeed, WS induced a fall of the intermediates of the tricarboxylic acid (TCA) cycle in both genotypes. At the same time, a few enzymes of this pathway, such as ATP-citrate synthase and a fumarate hydratase, increased in abundance, while others, such as components of the pyruvate dehydrogenase complex, were adversely affected by WS. This last effect was more evident in 101.14, where many of the components of this enzyme complex decreased under the stress. Conversely, only in M4, other enzymes of the TCA cycle, such as isocitrate dehydrogenase and 2-oxoglutarate dehydrogenase, as well as the anaplerotic enzyme PEP carboxylase, increased in WS, suggesting that this genotype was generally able to maintain a better functionality of the TCA cycle in WS. According to other work [39], this hypothesis also seems to be supported by the increase in mitochondrial formate dehydrogenase observed in M4.

Under stress conditions, a concomitant increase in the levels of several amino acids occurred. This result fits well with the central role of the TCA cycle in providing carbon skeletons for amino acid biosynthesis [69]. Indeed, it could be observed that the higher activation of the TCA cycle hypothesized in M4 is associated with a higher accumulation of specific amino acids, such as valine, threonine, serine, proline, and methionine. Nevertheless, WS negatively affected a larger number of enzymes involved in amino acid metabolism in 101.14 than in M4. In this view, it may be considered that the increase in amino acid levels detected in the stress condition could be, at least partly, a consequence of an increase in protein degradation. This event could be ascribable to general cell damage and/or could represent a specific response evoked by the requirement for alternative substrates for respiration [70, 71].

Although the energy demand is expected to decrease as a consequence of a reduction in root growth and ion uptake, two events frequently affected when WS becomes severe, the maintenance of an adequate mitochondrial respiration is fundamental to sustain the cell functionality ([72] and references therein). Our study reveals that a deficiency in respiratory substrates could occur and, at the same time, only in 101.14 the mitochondrial electron chain (i.e., disappearance of the succinate dehydrogenase [ubiquinone] iron-sulfur subunit 1, mitochondrial) was negatively affected by WS. Overall, the data suggest that, whereas in 101.14 a reduction in the functionality of respiratory machinery emerged, M4 showed a better capability for sustaining the demand for energy.

Protein metabolism

Changes in both protein catabolism (i.e., increase in abundance of regulatory proteins of the 26S proteasome and of proteases) and synthesis (i.e., changes in abundance of ribosomal proteins and of translation factors) occurred. Nevertheless, the changes in other proteins, such as transport protein SEC24-like, proteins belonging to PRA1 family, coatomer subunits, proteases and other ribosomal proteins, highlighted the occurrence of broad changes in overall protein metabolism. Moreover, only in M4, an increase in basal transcription factor 3 (BFT3), involved in transcription initiation, translational regulation and protein localization and known to be modulated under stress conditions ([73] and references therein), was found. Taken together, the results suggest that in 101.14, protein catabolism prevails, whereas M4 is characterized by a prevalence of protein synthesis and by a greater capability to maintain the vesicle traffic functionality. In this view, a very interesting difference between the two genotypes is the higher number of ribosomal proteins identified and positively affected by WS in M4. This result encourages further studies to deepen knowledge about the changes induced by WS in the cytosolic ribosomal proteome(s) under both the quantitative and the qualitative points of view ([74] and references therein).

Secondary metabolism and oxidative stress responses

Many differences between the two genotypes concerned proteins involved in secondary metabolism. The main result consisted in an increase of enzymes involved in the synthesis of flavonoid and stilbene compounds, which occurred only in M4. These data are in agreement with the previous transcriptomic study on 101.14 and M4 genotypes grown in the same WS experimental conditions, contributing to reinforce the conclusion that the capability to synthesize larger amounts of antioxidant compounds, such as flavonoids and stilbenes (i.e. resveratrol), enhances the tolerance to WS in M4 [30]. Furthermore, it is interesting to observe that only in M4 was there found an increase of typical ROS scavenging enzymes, such as catalase and glutathione reductase. In other words, the greater tolerance of this genotype appears also to be linked to the ability to activate mechanisms capable of better counteracting the oxidative stress occurring in WS conditions [17, 26, 30, 75].

Stress-related proteins

Water stress induced changes in several stress-related proteins that were quite different in the two genotypes. M4, when compared to 101.14, showed a larger number of stress-related proteins belonging to Bet v I family, such as MLP proteins that increased in WS. The possible role played by these proteins, in the response to abiotic and biotic stresses is emerging [55, 7678]. For example, the central role of MLP43 in the modulation of the ABA response to drought conditions has been recently highlighted [55]. Although further work is necessary, it may be hypothesized that the higher tolerance of M4 to WS is also related to the presence of specific MLP-like proteins.

Moreover, the rise of chitinases in the root observed under drought was related to a response useful to reduce the risk of infection in drought-weakened plants. [79, 80]. Our study shows an increase in two chitinases only in the M4 genotype.

Almost all of the stress-related proteins of the germin subfamily decreased in WS in both genotypes. Further work is needed to explain this result, considering that GLPs are a very heterogeneous class whose members show very different characteristics, such as oxalate oxidase activity, superoxide dismutase activity and other unclarified roles involved in the photoperiodic and abiotic stress responses [81, 82].

As previously observed in typical stress conditions [83], an increase in a few heat-shock proteins occurred in both genotypes. The changes observed in 101.14 and in M4 involved different members of this protein family, reaffirming peculiarities in the strategy and/or different abilities of each genotype to respond to WS conditions. Moreover, it is interesting to observe that the three small heat-shock proteins (sHSPs) which appeared or dramatically increased in the 101.14 genotype have a Hsp20/α-crystallin domain, which characterized some sHSPs strongly induced by heat and oxidative stress [84].

Conclusion

This study provides new information about the responses to WS in soil growth conditions of the roots of a woody plant, i.e., grapevine. Comparative analysis of two genotypes with different tolerance to this abiotic stress highlighted specific traits of the strategy adopted to counteract it (Fig. 6). The proteomic/metabolomic analyses strengthen the view that a crucial aspect is the capability to activate and to sustain the metabolic pathways involved in the protection of the cell from hazardous events, like a drop in cell turgor, increased oxidative stress and loss of cell structural integrity. Within this framework, the root has to sustain metabolic activities in a condition of reduced carbon skeleton availability, which derives from the reduction in plant photosynthetic performance. In other words, the root must optimize the availability of metabolic energy and sustain as much as possible root functionality and growth. A crucial aspect in the modulation of root responses is the hormonal balance, which controls these physiological/biochemical processes.
Fig. 6
Fig. 6

Schematic overview of the responses of 101.14 and M4 induced by WS. The physiological evaluation is reported according to Meggio et al. [29]. Boxes summarize some of the particular responses as well as some of the proteins involved

An important aspect emerging from this study is that the responses of the root to WS depend on its ability to guarantee mitochondrial functionality, essential for both respiration and anabolic processes. In this view, the activation of alternative pathways capable of sustaining the TCA cycle and the production of reducing power appear to be distinctive responses of the tolerant genotype. Moreover, the observed increases in several stress-related proteins, somewhat different in the two genotypes studied, confirm the multifaceted and very important role of these proteins in the responses to WS.

Methods

Root material and water-deficit treatment

Two-year-old grapevines (genus Vitis) of rootstocks 101.14 Millardet et de Grasset (V. riparia x V. rupestris) and M4 [(V. vinifera x V. berlandieri) x V. berlandieri cv. Resseguier no. 1] were grown in pots filled with a sand-peat mixture (7:3 in volume) using experimental conditions previously described by Meggio and co-workers [29].

Plants of each genotype (3 plants for each replicate) used as control (C) were maintained at 80% of soil field capacity while in pots of plants subjected to water stress (WS) the water supply was progressively reduced until down to 30% of field capacity. In order to maintain the established soil water content (SWC), an adequate quantity of water was added twice a day, at 8:00 h and at 18:00 h. After 10 days, starting at 9:00 h (2 h after the start of the light period), plants were sampled immediately after in vivo measurements [29]. Root samples were obtained harvesting the whole root system. The soil was removed from roots by a gentle shaking action. After that, the whole root system was rinsed twice in distilled water, immediately blotted with paper towels, weighed and then frozen in liquid nitrogen and stored at − 80 °C until use.

Protein extraction

Frozen powdered samples (1 g) of four replicates for each experimental conditions were finely powdered in liquid nitrogen using a pestle and mortar, then to each was added 5% (w/w) of polyvinylpolypyrrolidone, and the total protein fraction was extracted as described by Prinsi et al. [85]. Protein samples were then dissolved in SDS-buffer [150 mM Tris-HCl pH 6.8, 10% (w/w) glycerol, 2% (w/w) Sodium Dodecyl Sulphate (SDS), 2% (v/v) 2-mercaptoethanol] and incubated at 95 °C for 5 min. The sample was centrifuged at 10,000 g for 10 min and the supernatant stored at − 80 °C until further use. The protein concentration was determined by the 2-D Quant Kit (GE Healthcare).

One-Dimensional Gel Electrophoresis (1-DE) and tryptic digestion

The SDS-PAGE (SDS - PolyAcrylamide Gel Electrophoresis) was performed as described by Laemmli [86] in a Hoefer™ SE 600 Ruby Vertical system. Each protein sample, was purified in 10% acrylamide gel (10% T, 2.6% C, 180x160x1 mm). Analytical running was conducted at 200 V at 20 °C until the bromophenol blue line ran off. Proteins were stained using the colloidal Coomassie Brilliant Blue G-250 procedure, as previously described by Neuhoff and co-workers [87]. Electrophoresis was monitored using the Full-Range Rainbow Markers (Mr 12,000–225,000) (GE Healthcare).

The blank portions of the gels as well as the regions above 150 KDa or below 12 KDa were removed from the gel, obtaining a line of about 12 cm for each sample. Then each line was cut in 15 regular slices (8x10x1 mm). In the further analysis each slice was treated as an independent sample. In-gel digestion of the slices was performed according to Prinsi and co-workers [88] with the only refinements consisting in the additionally cutting of the slice into 8 portions and in the volume adjustment, in order to assure that the gel pieces were completely immersed in the treatment solutions. The extracted peptides were finally dissolved in 10 μl of 0.1% (v/v) of formic acid (FA).

Protein mass spectrometry analysis

All mass spectrometry experiments were conducted on an Agilent 6520 Q-TOF mass spectrometer equipped with an HPLC Chip Cube source driven by a 1200 series nano/capillary LC system (Agilent Technologies). Both systems were controlled by MassHunter Workstation Acquisition (version B.02.01, B2116.20; Agilent Technologies). The chip consisted of a 40-nL trap column and a 75 μm × 150-mm analytical column (Zorbax SB, C18, 300 Å). Peptides were loaded onto the trap column at 4 μL min− 1 in 5% (v/v) acetonitrile and 0.1% (v/v) FA. The chip was then switched to separation, and peptides were eluted into the mass spectrometer during a 43-min acetonitrile gradient (from 5 to 50% v/v) in 0.1% (v/v) FA at 0.4 μl min− 1. The mass spectrometer ran in positive ion mode and MS scans were acquired over a range from 300 to 3000 mass-to-charge ratio (m/z) at 4 spectra s− 1. Precursor ions were selected for auto-MS/MS with a maximum of 4 precursors per cycle and active exclusion set at 2 spectra and released after 0.1 min.

Analysis of MS/MS spectra were performed by Spectrum Mill MS Proteomics Workbench (Rev A.03.03.084; Agilent Technologies). Carbamidomethylation of cysteines was set as fixed modification while variable modification was oxidation of methionines. Trypsin was selected as enzyme for digestion, accepting 2 missed cleavages per peptide. The search was conducted against the subset of Vitis vinifera protein sequences (77,487 entries) downloaded from the National Center for Biotechnology Information (http://0-www.ncbi.nlm.nih.gov.brum.beds.ac.uk/). The database was concatenated with the reverse one. The threshold used for peptide identification was Spectrum Mill score ≥ 9, SPI% ≥ 70%, difference between forward and reverse scores ≥2 and error mass shift comprised between ±10 ppm. Peptide quantification was obtained as the Spectrum Intensity (SI) of the precursor (MH+). Protein quantification was obtained summing the SI of all identified peptides and normalized as the % respect the sum of all validated proteins in the sample (%(SI), summing all valid peptides in the 15 slices of each lane). Obviously, within each sample some redundant entries were found. If one entry was repeated in two vertically adjacent slices, the two single quantifications were summed to reduce the interference of the cut procedure. In all other cases, the entries were treated as independent form of the same protein (among them, only forms representing at least the 80% of the total protein were considered for quantitative purposes). Only proteins showing at least a two-fold change in their %(SI) (Student’s t-test, p < 0.05) were considered subjected to a significant change in abundance.

MapMan visualization

The proteomic data were visualized in figures reporting schematic metabolism pathways that were produced using the MapMan software [34]. The MapMan software (Version 3.6.0RC1) for local application was downloaded from http://mapman.gabipd.org/ Web Site. Mapping file, kindly provided by Živa Ramšak (see acknowledgement), was prepared starting from Vitis vinifera protein sequences present in NCBI and using the BLASTp algorithm to match grapevine proteins with Arabidopsis and potato ones. The matching grape/Arabidopsis or grape/potato were kept only if E-value of the BLASTp match was lower than 10− 20. When a grapevine sequence did not have any hits in either TAIR9 or potato ITAG, then it was automatically placed into 35.2 (not assigned/unknown).

Metabolite analysis

Metabolites were extracted considering the polar fraction derived from 150 mg of frozen powder, according to the protocol by Lisec and coworkers [89] with some modifications, as previously described [90]. The analyses were performed on three replicates through a GC-MS approach using the instrument comprising the gas chromatograph 7890 and the single-quadrupole spectrometer 5975 (Agilent Technologies).

Chromatograms and spectra were evaluated through the software MetaboliteDetector version 2.0.6 beta [91]. After the conversion of Agilent D files to netCDF, the chromatograms were aligned according to the elution of the C8-C40 Alcane Mixture and compound spectra were isolated through deconvolution [peak threshold: 10; minimum peak height: 2, number of bins per scan: 10; deconvolution width (scans): 8.0]. Metabolites were identified matching spectral and retention index (RI) information to a library containing information about the entries of the GMD 20111121VAR5 ALK MSL provided by the Golm Metabolome Database [92], setting the cutoff score to 0.90 and the max RI difference to 15. Identified metabolites were quantified integrating the peak area of the ions normalized by the one of ribitol.

Sucrose level was estimated by colorimetric procedure. Briefly, sucrose was extracted as previously described in Prinsi et al. [88] and then estimated from the difference between total and reducing sugars that were determined according to Nelson method [93].

Student’s t-test (p-value < 0.05) was performed through Statistica software v 8.0 (StatSoft Inc., Tulsa) to determine significant differences between means of stressed and well-watered samples.

The results were visualized through the two-way hierarchical clustering methodology using the software PermutMatrix [94, 95]. For this purpose, the data were converted into a binary matrix replacing the values that did not show significant differences by zero. Pearson’s distance and Ward’s algorithm were used for the analysis.

Abbreviations

ABA: 

Abscisic acid

FA: 

Formic acid

GABA: 

γ-Aminobutyric acid

GMD: 

Golm Metabolome Database

GST: 

Glutathione S-transferases

NCBI: 

National center for biotechnology information

nLC-nESI-MS/MS: 

Nanoflow liquid chromatography-mass spectrometry

PAGE: 

PolyAcrylamide Gel Electrophoresis

ROS: 

Reactive Oxygen Species

SDS: 

Sodium dodecyl sulfate

SI: 

Spectrum Intensity

WS: 

Water stress

Declarations

Acknowledgements

The authors thank Živa Ramšak (Department of Biotechnology and Systems Biology, National Institute of Biology, 1000 Ljubljana, Slovenia), member of GoMapMan staff, http://www.gomapman.org/, for providing mapping file of Vitis vinifera. The authors thank Dr. Silvia Morgutti (University of Milan) and Dr. Lesley Currah for polishing the English in the manuscript.

Funding

This work was supported by “AGER- SERRES Project”, grant n° 2010-2105.

Availability of data and materials

The data set supporting results from this study can be found within both the article and Additional files 1, 2, 3, and 4.

Authors’ contributions

BP contributed to the conception of the study, conceived the experimental design, carried out protein extraction, SDS-PAGE, protein characterization by LC-ESI-MS/MS, analyzed the MS data and participated in writing the manuscript. ASN performed the metabolomic analyses. LE contributed to the conception of study, coordinated the experiments, determined sucrose, wrote and edited the manuscript. OF and AS participated in the manuscript revision. All authors read and approved the final manuscript.

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
Dipartimento di Scienze Agrarie e Ambientali - Produzione, Territorio, Agroenergia (DiSAA), Università degli Studi di Milano, Via Celoria, 2, 20133 Milano, Italy

References

  1. Chaves MM, Santos TP, Souza CR, Ortuño MF, Rodrigues ML, Lopes CM, Maroco JP, Pereira JS. Deficit irrigation in grapevine improves water-use efficiency while controlling vigour and production quality. Ann Appl Biol. 2007;150:237–52. https://doi.org/10.1111/j.1744-7348.2006.00123.x.View ArticleGoogle Scholar
  2. Flexas JJ, Galmés J, Gallé A, Gulías J, Pou A, Ribas-Carbo M, Tomás M, Medrano H. Improving water use efficiency in grapevines: potential physiological targets for biotechnological improvement. Aust J Grape Wine R. 2010;16:106–21. https://doi.org/10.1111/j.1755-0238.2009.00057.x.View ArticleGoogle Scholar
  3. Chaves MM, Zarrouk O, Francisco R, Costa JM, Santos T, Regalado AP, Rodrigues ML, Lopes CM. Grapevine under deficit irrigation: hints from physiological and molecular data. Ann Bot. 2010;105:661–76. https://doi.org/10.1093/aob/mcq030.View ArticlePubMedPubMed CentralGoogle Scholar
  4. Schultz HR, Stoll M. Some critical issues in environmental physiology of grapevines: future challenges and current limitations. Aust J Grape Wine. 2010;16:4–24. https://doi.org/10.1111/j.1755-0238.2009.00074.x.View ArticleGoogle Scholar
  5. Castellarin SD, Matthews MA, Di Gaspero G, Gambetta GA. Water deficits accelerate ripening and induce changes in gene expression regulating flavonoid biosynthesis in grape berries. Planta. 2007;227:101–12. https://doi.org/10.1007/s00425-007-0598-8.View ArticlePubMedGoogle Scholar
  6. Jones GV, White MA, Cooper OR, Storchmann K. Climate change and global wine quality. Clim Chang. 2005;73:319–43. https://doi.org/10.1007/s10584-005-4704-2.View ArticleGoogle Scholar
  7. Chaves M, Maroco JP, Pereira JS. Understanding plant responses to drought – from genes to the whole plant. Funct Plant Biol. 2003;30:239–64. https://doi.org/10.1071/FP02076.View ArticleGoogle Scholar
  8. Cramer GR. Abiotic stress and plant responses from the whole vine to the genes. Aust J Grape Wine R. 2010;16:86–93. https://doi.org/10.1111/j.1755-0238.2009.00058.x.View ArticleGoogle Scholar
  9. Lovisolo C, Perrone I, Carra A, Ferrandino A, Flexas J, Medrano H, Schubert A. Drought-induced changes in development and function of grapevine (Vitis spp.) organs and in their hydraulic and non-hydraulic interactions at the whole-plant level: a physiological and molecular update. Funct Plant Biol. 2010;37:98–16. https://doi.org/10.1071/FP09191.View ArticleGoogle Scholar
  10. Skirycz A, Inzé D. More from less: plant growth under limited water. Curr Opin Biotechnol. 2010;21:197–203. https://doi.org/10.1016/j.copbio.2010.03.002.View ArticlePubMedGoogle Scholar
  11. Munns R. Plant adaptation to salt and water stress: differences and commonalities. Adv Bot Res. 2011;57:1–32. https://doi.org/10.1016/B978-0-12-387692-8.00001-1. View ArticleGoogle Scholar
  12. Kantar M, Lucas SJ, Budak H. Drought stress molecular genetics and genomics approaches. Adv Bot Res. 2011;57:445–93. https://doi.org/10.1016/B978-0-12-387692-8.00013-8. View ArticleGoogle Scholar
  13. Serra I, Strever A, Myburgh PA, Deloire A. Review: the interaction between rootstocks and cultivars (Vitis vinifera L.) to enhance drought tolerance in grapevine. Aust J Grape Wine R. 2014;20:1–14. https://doi.org/10.1111/ajgw.12054.View ArticleGoogle Scholar
  14. Schachtman DP, Goodger JQD. Chemical root to shoot signalling under drought. Trends Plant Sci. 2008;13:281–7. https://doi.org/10.1016/j.tplants.2008.04.003.View ArticlePubMedGoogle Scholar
  15. Osakabe Y, Osakabe K, Shinozaki K, Lam-Son PT. Response of plants to water stress. Front Plant Sci. 2014;5:3. https://doi.org/10.3389/fpls.2014.00086.View ArticleGoogle Scholar
  16. Neumann PM. Recent advances in understanding the regulation of whole-plant growth inhibition by salinity, drought and colloid stress. Adv Bot Res. 2011;57:33–48. https://doi.org/10.1016/B978-0-12-387692-8.00002-3.View ArticleGoogle Scholar
  17. Miller G, Suzuki N, Ciftci_Yilmaz S, Mittler R. Reactive oxygen species homeostasis and signalling during drought and salinity. Plant Cell Environ. 2010;33:453–67. https://doi.org/10.1111/j.1365-3040.2009.02041.x. View ArticlePubMedGoogle Scholar
  18. Cramer GR, Van Sluyter SC, Hopper DW, Pascovici D, Keighley T, Haynes PA. Proteomic analysis indicates massive changes in metabolism prior to the inhibition of growth and photosynthesis of grapevine (Vitis vinifera L.) in response to water deficit. BMC Plant Biol. 2013;13:49. https://doi.org/10.1186/1471-2229-13-49.View ArticlePubMedPubMed CentralGoogle Scholar
  19. Gambetta GA, Manuck CM, Drucker ST, Shaghasi T, Fort K, Matthews MA, Walker MA, McElron AJ. The relationship between root hydraulics and scion vigour across Vitis rootstocks: what role do root aquaporins play? J Exp Bot. 2012;63:6445–55. https://doi.org/10.1093/jxb/ers312.View ArticlePubMedPubMed CentralGoogle Scholar
  20. Aroca R, Porcel R, Ruiz-Lozano JM. Regulation of root water uptake under abiotic stress conditions. J Exp Bot. 2011;66:5739–52. https://doi.org/10.1093/jxb/err266.View ArticleGoogle Scholar
  21. Alasina MM, Smart DR, Bauerle T, de Herralde F, Biel C, Stockert C, Negron C, Save R. Seasonal changes of whole root system conductance by a drought-tolerant grape root system. J Exp Bot. 2011;62:99–109. https://doi.org/10.1093/jxb/erq247.View ArticleGoogle Scholar
  22. Comas LH, Baurle TL, Eissenstat DM. Biological and environmental factors controlling root dynamics and function: effects of root ageing and soil moisture. Aust J Grape Wine R. 2010;16:131–17. https://doi.org/10.1111/j.1755-0238.2009.00078.x.View ArticleGoogle Scholar
  23. Vadez V. Root hydraulics: the forgotten side of roots in drought adaptation. Field Crop Res. 2014;165:15–24. https://doi.org/10.1016/j.fcr.2014.03.017.View ArticleGoogle Scholar
  24. Gambetta GA, Fei J, Rost TL, Knipfer T, Matthews MA, Shackel KA, Walker MA, McElrone AJ. Water uptake along the length of grapevine fine roots: developmental anatomy, tissue specific aquaporin expression, and pathways of water transport. Plant Physiol. 2013;163:1254–65. https://doi.org/10.1104/pp.113.221283
  25. Wu Y, Cosgrove DJ. Adaptation of roots to low water potentials by changes in cell wall extensibility and cell wall proteins. J Exp Bot. 2000;51:1543–53. https://doi.org/10.1093/jexbot/51.350.1543.View ArticlePubMedGoogle Scholar
  26. Ober ES, Sharp RE. Regulation of root growth responses to water deficit. In: Jenks MA, Hasegawa PM, Jain SM, editors. Advances in molecular breeding toward drought and salt tolerant crops; 2007. p. 33–53. ISBN: 978-1-4020-5578-2.View ArticleGoogle Scholar
  27. Regier N, Streb S, Cocozza C, Schaub M, Cherubini P, Zeeman SC, Frey B. Drought tolerance of two black poplar (Populus nigra L.) clones: contribution of carbohydrates and oxidative stress defence. Plant Cell Environ. 2009;32:1724–36. https://doi.org/10.1111/j.1365-3040.2009.02030.x.View ArticlePubMedGoogle Scholar
  28. Yamaguchi M, Sharp RE. Complexity and coordination of root growth at low water potentials: recent advances from transcriptomic and proteomic analyses. Plant Cell Environ. 2010;33:590–603. https://doi.org/10.1111/j.1365-3040.2009.02064.x.View ArticlePubMedGoogle Scholar
  29. Meggio F, Prinsi B, Negri AS, Simone Di Lorenzo G, Lucchini G, Pitacco A, Failla O, Scienza A, Cocucci M, Espen L. Different biochemical and physiological responses of two grapevine rootstock genotypes to drought and salt treatments. Aust J Grape Wine R. 2014;20:310–23. https://doi.org/10.1111/ajgw.12071.View ArticleGoogle Scholar
  30. Corso M, Vannozzi A, Maza E, Vitulo N, Meggio F, Pitacco A, Telatin A, D’Angelo M, Feltrin E, Negri AS, Prinsi B, Valle G, Ramina A, Bouzayen M, Bonghi C, Lucchin M. Comprehensive transcript profiling of two grapevine rootstock genotypes contrasting in drought susceptibility links the phenylpropanoid pathway to enhanced tolerance. J Exp Bot. 2015;66:43–57. https://doi.org/10.1093/jxb/erv274.View ArticleGoogle Scholar
  31. Ghosh D, Xu J. Abiotic stress responses in plant roots: a proteomics perspective. Front Plant Sci. 2014;5:6. https://doi.org/10.3389/fpls.2014.00006.View ArticlePubMedPubMed CentralGoogle Scholar
  32. Vincent D, Ergül A, Bohlman MC, Tattersall EAR, Tillett RL, Wheatley MT, Woolsey R, Quilici DR, Joets J, Schlauch K, Schooley DA, Cushman JC, Cramer GR. Proteomic analysis reveals differences between Vitis vinifera L. cv. Chardonnay and cv. Cabernet Sauvignon and their responses to water deficit and salinity. J Exp Bot. 2007;58:1873–92. https://doi.org/10.1093/jxb/erm012.View ArticlePubMedGoogle Scholar
  33. Grimplet J, Wheatley MD, Jouira HB, Deluc LG, Cramer GR, Cushman JC. Proteomic and selected metabolite analysis of grape berry tissues under well-watered and water-deficit stress conditions. Proteomics. 2009;9:2503–28. https://doi.org/10.1002/pmic.200800158.View ArticlePubMedPubMed CentralGoogle Scholar
  34. Thimm O, Bläsing O, Gibon A, Nagel A, Meyer S, Krüger P, Selbing J, Müller LA, Rhee SEY, Stitt M. MAPMAN: a user-driven tool to display genomics data sets onto diagrams of metabolic pathways and other biological processes. Plant J. 2004;37:914–39. https://doi.org/10.1111/j.1365-313X.2004.02016.x.View ArticlePubMedGoogle Scholar
  35. Rösti J, Barton CJ, Albrecht S, Dupree P, Pauly M, Findlay K, Roberts K, Seifert GJ. UDP-glucose 4-epimerase isoforms UGE2 and UGE4 cooperate in providing UDP-galactose for cell wall biosynthesis and growth of Arabidopsis thaliana. Plant Cell. 2007;19:1565–79. https://doi.org/10.1105/tpc.106.049619.View ArticlePubMedPubMed CentralGoogle Scholar
  36. Plaxton WC. The organization and regulation of plant glycolisis. Annu Rev Plant Physiol Plant Mol Biol. 1996;47:185–214. https://doi.org/10.1146/annurev.arplant.47.1.185.View ArticlePubMedGoogle Scholar
  37. Kirch HH, Bartels D, Wei J, Schnable PS, Wood AJ. The ALDH gene superfamily of Arabidopsis. Trends Plant Sci. 2004;9:371–7. https://doi.org/10.1016/j.tplants.2004.06.004.View ArticlePubMedGoogle Scholar
  38. Kavar T, Maras M, Kidrič M, Šuštar-Vozlič J, Meglic V. Identification of genes involved in the response of leaves of Phaseolus vulgaris to drought stress. Mol Breed. 2008;21:159–72. https://doi.org/10.1007/s11032-007-9116-8.View ArticleGoogle Scholar
  39. McDonald AE, Vanlerberghe GC. The organization and control of plant mitochondrial metabolism. In: Planxoton WC, McManus MT, editors. Control of primary metabolism in plants. Annual plant reviews; 2006. p. 290–324. ISBN: 978-1-405e1-3096-7.View ArticleGoogle Scholar
  40. Zhang W, Qin C, Zhao J, Wang X. Phospholipase Dα1-derived phosphatidic acid interacts with ABI1 phosphatase 2C and regulates abscisic acid signalling. PNAS. 2004;101:9508–13. https://doi.org/10.1073/pnas.0402112101.View ArticlePubMedPubMed CentralGoogle Scholar
  41. Winter G, Todd CD, Trovato M, Forlani G, Funck D. Physiological implications of arginine metabolism in plants. Front Plant Sci. 2015;6:534. https://doi.org/10.3389/fpls.2015.00534
  42. González-Cabanelas D, Wright LP, Paetz C, Onkokesung N, Gershenzon J, Rodríguez-Concepción M, Phillips MA. The diversion of 2-C-methyl-D-erythritol-2,4-cyclodiphosphate from the 2-C methyl-D-erythritol 4-phosphate pathway to hemiterpene glycosides mediates stress responses in Arabidopsis thaliana. Plant J. 2015;82:122–37. https://doi.org/10.1111/tpj.12798.View ArticlePubMedGoogle Scholar
  43. Mano J, Belles-Boix E, Babiychuk E, Inzé D, Torii Y, Hiraoka E, Takimoto K, Slooten L, Asada K, Kushnir S. Protection against fhotooxidative injury of tobacco leaves by 2-alkenal reductase. Detoxication of lipid peroxide-derived reactive carbonyls. Plant Physiol. 2005;139:1773–83. https://doi.org/10.1104/pp.105.070391.View ArticlePubMedPubMed CentralGoogle Scholar
  44. Turner JG, Ellis C, Devoto A. The Jasmonate signal pathway. Plant Cell. 2002;14:S153–64. https://doi.org/10.1105/tpc.000679.View ArticlePubMedPubMed CentralGoogle Scholar
  45. Hedden P, Thomas G. Gibberellin biosynthesis and its regulation. Biochem J. 2012;444:11–25. https://doi.org/10.1042/BJ20120245.View ArticlePubMedGoogle Scholar
  46. Chen C-N, Chu C-C, Zentella R, Pan S-M, Ho DT-H. AtHVA22 gene family in Arabidopsis: phylogenetic relationship, ABA and stress regulation, and tissue-specific expression. Plant Mol Biol. 2002;49:633–44. https://doi.org/10.1023/A:1015593715144. PubMedView ArticleGoogle Scholar
  47. Dixon DP, Davis BG, Edwards R. Functional divergence in the glutathione transferase superfamily in plants. J Biol Chem. 2002;277:30859–69. https://doi.org/10.1074/jbc.M202919200.View ArticlePubMedGoogle Scholar
  48. Miller EA, Beilharz TH, Malkus PN, Lee MCS, Hamamoto S, Orci SR. Multiple cargo binding sites on the COPII subunit Sec24p ensure capture of diverse membrane proteins into transport vesicles. Cell. 2003;114:497–509. https://doi.org/10.1016/S0092-8674(03)00609-3.View ArticlePubMedGoogle Scholar
  49. Schaller A. A cut above the rest: the regulatory function of plant proteases. Planta. 2004;220:183–97. https://doi.org/10.1007/s00425-004-1407-2.View ArticlePubMedGoogle Scholar
  50. Dwivedi RS, Breiman A, Herman EM. Differential distribution of the cognate and heat stress induced isoforms of high Mr cis-trans prolyl peptidyl isomerase (FKBP) in the cytoplasm and nucleoplasm. J Exp Bot. 2003;54:2679–89. https://doi.org/10.1093/jxb/erg307.View ArticlePubMedGoogle Scholar
  51. Kamei CLA, Boruc J, Vandepoele K, Van den Daele H, Maes S, Russinova E, Inzé D, De Veylder L. The PRA1 gene family in Arabidopsis. Plant Physiol. 2008;147:1735–49. https://doi.org/10.1104/pp.108.122226.View ArticleGoogle Scholar
  52. Marti L, Fornaciari S, Renna L, Stefano G, Brandizzi F. COPII-mediated traffic in plants. Trends Plant Sci. 2010;15:522–8. https://doi.org/10.1016/j.tplants.2010.05.010.View ArticlePubMedGoogle Scholar
  53. Dunwell JM, Gibbings JG, Mahmood T, Naqvi SMS. Germin and germin-like proteins: evolution, structure, and function. Crit Rev Plant Sci. 2008;27:342–75. https://doi.org/10.1080/07352680802333938. View ArticleGoogle Scholar
  54. Kim HS, Yu Y, Snesrud EC, Moy LP, Linfor LD, Haas BJ, Nierman WC, Quackenbus J. Transcriptional divergence of the duplicated oxidative stress-responsive genes in the Arabidopsis genome. Plant J. 2005;41:212–20. https://doi.org/10.1111/j.1365-313X.2004.02295.x. View ArticleGoogle Scholar
  55. Chen JY, Dai XF. Cloning and characterization of the Gossypium hirsutum major latex protein gene and functional analysis in Arabidopsis thaliana. Planta. 2010;231:861–73. https://doi.org/10.1007/s00425-009-1092-2. View ArticlePubMedGoogle Scholar
  56. Wang Y, Li Y, Chen X, Ye T, Zhong B, Liu R, Wu Y, Chan Z. Major latex protein-like protein 43 (MLP43) functions as a positive regulator during abscisic acid responses and confers drought tolerance in Arabidopsis thaliana. J Exp Bot. 2016;67:421–34. https://doi.org/10.1093/jxb/erv477.View ArticlePubMedGoogle Scholar
  57. Barthakur S, Babu V, Bansal KC. Over-expression of osmotin induces proline accumulation and confers tolerance to osmotic stress in transgenic tobacco. J Plant Biochem Biotechnol. 2001;10:31–7. https://doi.org/10.1007/BF03263103. View ArticleGoogle Scholar
  58. Kumar SA, Kumari PH, Kumar GS, Mohanalatha C, KaviKishor PB. Osmotin: a plant sentinel and a possible agonist of mammalian adiponectin. Front Plant Sci. 2015;6:163. https://doi.org/10.3389/fpls.2015.00163.View ArticleGoogle Scholar
  59. Bartels D, Sunkar R. Drought and salt tolerance in plants. Crit Rev Plant Sci. 2005;24:23–58. https://doi.org/10.1080/07352680590910410.View ArticleGoogle Scholar
  60. Conde A, Regalado A, Rodrigues D, Costa JM, Blumwald E, Chaves MM, Gerós H. Polyols in grape berry: transport and metabolic adjustments as a physiological strategy for water-deficit stress tolerance in grapevine. J Exp Bot. 2015;66:889–906. https://doi.org/10.1093/jxb/eru446.View ArticlePubMedGoogle Scholar
  61. Wang W, Vinocur B, Altman A. Plant responses to drought, salinity and extreme temperatures: towards genetic engineering for stress tolerance. Planta. 2003;218:1–14. https://doi.org/10.1007/s00425-003-1105-5. View ArticlePubMedGoogle Scholar
  62. Vandeleur RK, Mayo G, Shelden MC, Gilliham M, Kaiser BN, Tyerman SD. The role of plasma membrane intrinsic protein aquaporins in water transport through roots: diurnal and drought stress responses reveal different strategies between isohydric and anisohydric cultivars of grapevine. Plant Physiol. 2009;149:445–60. https://doi.org/10.1104/pp.108.128645.View ArticlePubMedPubMed CentralGoogle Scholar
  63. Vishwanath SJ, Delude C, Domergue F, Rowland O. Suberin: biosynthesis, regulation, and polymer assembly of a protective extracellular barrier. Plant Cell Rep. 2015;34:573–86. https://doi.org/10.1007/s00299-014-1727-z.View ArticlePubMedGoogle Scholar
  64. Peleg Z, Blumwald E. Hormone balance and abiotic stress tolerance in crop plants. Curr Opin Plant Biol. 2011;14:290–5. https://doi.org/10.1016/j.pbi.2011.02.001.View ArticlePubMedGoogle Scholar
  65. Tanimoto E. Regulation of root growth by plant hormones-roles for auxin and gibberellin. Crit Rev Plant Sci. 2013;24:249–65. https://doi.org/10.1080/07352680500196108. View ArticleGoogle Scholar
  66. Wasternack C. Jasmonates: an update on biosynthesis, signal transduction and action in plant stress response, growth and development. Ann Bot. 2007;100:681–97. https://doi.org/10.1093/aob/mcm079.View ArticlePubMedPubMed CentralGoogle Scholar
  67. Liu R, Chen S, Jiang J, Zhu L, Zheng C, Han S, Gu J, Sun J, Li H, Wang H, Song A, Chen F. Proteomic changes in the base of chrysanthemum cuttings during adventitious root formation. BMC Genomics. 2013;14:919. https://doi.org/10.1186/1471-2164-14-919.View ArticlePubMedPubMed CentralGoogle Scholar
  68. Neuhaus HE, Emes MJ. Nonphotosynthetic metabolism in plastids. Annu Rev Plant Physiol Plant Mol Biol. 2000;51:111–40. https://doi.org/10.1146/annurev.arplant.51.1.111.View ArticlePubMedGoogle Scholar
  69. Millar AH, Siedow JN, Day D. Respiration and photorespiration. In: Buchanan BB, Gruissem W, Jones RL, editors. Biochemistry & molecular biology of plants; 2015. p. 610–55. ISBN: 9780470714218.Google Scholar
  70. Araújo WL, Tohge T, Ishizaki K, Leaver CJ, Fernie AR. Protein degradation - an alternative respiratory substrate for stressed plants. Trends Plant Sci. 2011;16:489–98. https://doi.org/10.1016/j.tplants.2011.05.008.PubMedView ArticleGoogle Scholar
  71. Brunner I, Herzog C, Dawes MA, Arend M, Sperisen C. How tree roots respond to drought. Front Plant Sci. 2015;6:547. https://doi.org/10.3389/fpls.2015.00547.View ArticlePubMedPubMed CentralGoogle Scholar
  72. Atkin OK, Macherel D. The crucial role of plant mitochondria in orchestrating drought tolerance. Ann Bot. 2009;103:581–97. https://doi.org/10.1093/aob/mcn094.View ArticlePubMedGoogle Scholar
  73. Wang Y, Zhang X, Lu S, Wang M, Wang L, Wang W, Cao F, Chen H, Wang J, Zhang J, Tu J. Inhibition of a basal transcription factor 3-like gene Osj10gBTF3 in rice results in significant plant miniaturization and typical pollen abortion. Plant Cell Physiol. 2012;53:2073–89. https://doi.org/10.1093/pcp/pcs146.View ArticlePubMedGoogle Scholar
  74. Carrol AJ. The Arabidopsis cytosolic ribosomal proteome: from form to function. Front Plant Sci. 2013;4:32. https://doi.org/10.3389/fpls.2013.00032.View ArticleGoogle Scholar
  75. Kaushik D, Aryadeep R. Reactive oxygen species (ROS) and response of antioxidants as ROS-scavengers during environmental stress in plants. Front Plant Sci. 2014;2:53. https://doi.org/10.3389/fenvs.2014.00053.View ArticleGoogle Scholar
  76. Liu JJ, Ekramoddoullah AKM. The family 10 of plant pathogenesis-related proteins: their structure, regulation, and function in response to biotic and abiotic stresses. Physiol Mol Plant Pathol. 2006;68:3–13. https://doi.org/10.1016/j.pmpp.2006.06.004.View ArticleGoogle Scholar
  77. Radauer C, Lackner P, Breiteneder H. The Bet v 1 fold: an ancient, versatile scaffold for binding of large, hydrophobic ligands. BMC Evol Biol. 2008;8:286. https://doi.org/10.1186/1471-2148-8-286.View ArticlePubMedPubMed CentralGoogle Scholar
  78. Inui H, Sawada M, Goto J, Yamazaki K, Kodama N, Tsuruta H, Eun H. A major latex-like protein is a key factor in crop contamination by persistent organic pollutants. Plant Physiol. 2013;161:2128–35. https://doi.org/10.1104/pp.112.213645.View ArticlePubMedPubMed CentralGoogle Scholar
  79. Lorenz WW, Sun F, Liang C, Kolychev D, Wang H, Zhao X, Cordonnier-Pratt MM, Pratt LH, Dean GFD. Water stress-responsive genes in loblolly pine (Pinus taeda) roots identified by analyses of expressed sequence tag libraries. Tree Physiol. 2006;26:1–16. https://doi.org/10.1093/treephys/26.1.1. View ArticlePubMedGoogle Scholar
  80. Gregorová Z, Kováčik J, Klejdus B, Maglovski M, Kuna R, Hauptvogel P, Matušíková I. Drought-induced responses of physiology, metabolites, and PR proteins in Triticum aestivum. J Agric Food Chem. 2015;63:8125–33. https://doi.org/10.1021/acs.jafc.5b02951.View ArticlePubMedGoogle Scholar
  81. Davidson RM, Reeves PA, Manosalva PM, Leach JN. Germins: a diverse protein family important for crop improvement. Plant Sci. 2009;177:499–510. https://doi.org/10.1016/j.plantsci.2009.08.012.View ArticleGoogle Scholar
  82. Lu M, Han YP, Gao JG, Wang XJ, Li WB. Identification and analysis of the germin-like gene family in soybean. BMC Genomics. 2010;11:620. https://doi.org/10.1186/1471-2164-11-620.View ArticlePubMedPubMed CentralGoogle Scholar
  83. Timperio AM, Egidi MG, Zolla L. Proteomics applied on plant abiotic stresses: role of heat shock proteins (HSP). J Proteome. 2008;71:391–411. https://doi.org/10.1016/j.jprot.2008.07.005.View ArticleGoogle Scholar
  84. Ma C, Haslbeck M, Babujee L, Jahn O, Reumann S. Identification and characterization of a stress-inducible and constitutive small heat-shock protein targeted to the matrix of plant peroxisomes. Plant Physiol. 2006;141:47–60. https://doi.org/10.1104/pp.105.073841.View ArticlePubMedPubMed CentralGoogle Scholar
  85. Prinsi B, Negri AS, Quattrocchio FM, Koes RE, Espen L. Proteomics of red and white corolla limbs in petunia reveals a novel function of the anthocyanin regulator ANTHOCYANIN1 in determining flower longevity. J Proteome. 2016;131:38–47. https://doi.org/10.1016/j.jprot.2015.10.008.View ArticleGoogle Scholar
  86. Laemmli UK. Cleavage of structural proteins during the assembly of the head of bacteriophage. T4. Nature. 1970;227:680–5. https://doi.org/10.1038/227680a0.View ArticlePubMedGoogle Scholar
  87. Neuhoff V, Arold N, Taube D, Ehrhardt W. Improved staining of proteins in polyacrylamide gels including isoelectric focusing gels with clear background at nanogram sensitivity using Coomassie Brilliant Blue G-250 and R-250. Electrophoresis. 1988;9:255–62. https://doi.org/10.1002/elps.1150090603.View ArticlePubMedGoogle Scholar
  88. Prinsi B, Negri AS, Pesaresi P, Cocucci M, Espen L. Evaluation of protein pattern changes in roots and leaves of Zea mays plants in response to nitrate availability by two-dimensional gel electrophoresis analysis. BMC Plant Biol. 2009;9:113. https://doi.org/10.1186/1471-2229-9-113.View ArticlePubMedPubMed CentralGoogle Scholar
  89. Lisec J, Schauer N, Kopka J, Willmitzer L, Fernie AR. Gas chromatography mass spectrometry-based metabolite profiling in plants. Nat Protoc. 2006;1:387–96. https://doi.org/10.1038/nprot.2006.59.View ArticlePubMedGoogle Scholar
  90. Negri AS, Prinsi B, Failla O, Scienza A, Espen L. Proteomic and metabolic traits of grape exocarp to explain different anthocyanin concentrations of the cultivars. Front Plant Sci. 2015;6:603. https://doi.org/10.3389/fpls.2015.00603.PubMedPubMed CentralView ArticleGoogle Scholar
  91. Hiller K, Hangebrauk J, Jäger C, Spura J, Schreiber K, Schomburg D. MetaboliteDetector: comprehensive analysis tool for targeted and nontargeted gc/ms based metabolome analysis. Anal Chem. 2009;81:3429–39. https://doi.org/10.1021/ac802689c.View ArticlePubMedGoogle Scholar
  92. Hummel J, Selbig J, Walther D, Kopka J. The Golm metabolome database: a database for GC-MS based metabolite profiling. In: Nielsen J, Jewett MC, editors. Topics in current genetics, Vol. 18: metabolomics; 2009. p. 75–95. https://doi.org/10.1007/4735_2007_0229. View ArticleGoogle Scholar
  93. Nelson NA. A photometric adaptation of the Somogy method for the determination of glucose. JBC. 1944;153:375–84. http://www.jbc.org/content/153/2/375.short#ref-list-1 Google Scholar
  94. Caraux G, Pinloche S. PermutMatrix: a graphical environment to arrange gene expression profiles in optimal linear order. Bioinformatics. 2005;21:1280–1. https://doi.org/10.1093/bioinformatics/bti141.View ArticlePubMedGoogle Scholar
  95. Meunier B, Dumas E, Piec I, Béchet D, Hébraud M, Hocquette JF. Assessment of hierarchical clustering methodologies for proteomic data mining. J Proteome Res. 2007;6:358–66. https://doi.org/10.1021/pr060343h.View ArticlePubMedGoogle Scholar

Copyright

© The Author(s). 2018

Advertisement