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Metabolite profiling of Mexican lime (Citrus aurantifolia) leaves during the progression of witches’ broom disease
Phytochemistry Letters 13 (2015) 290–296
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Phytochemistry Letters journal homepage: www.elsevier.com/locate/phytol
Short communication
Metabolite profiling of Mexican lime (Citrus aurantifolia) leaves during the progression of witches’ broom disease Saeed Mollayia , Reza Zadalib , Mohsen Farzanehc, Alireza Ghassempourd,* a
Department of Chemistry, Faculty of Science, Azarbaijan Shahid Madani University, Tabriz, Iran Department of Pharmacognosy, Faculty of Pharmacy, Tehran University of Medical Sciences, Tehran, Iran c Department of Agriculture, Medicinal Plants and Drugs Research Institute, Shahid Beheshti University, G.C. Evin, Tehran, Iran d Department of Phytochemistry, Medicinal Plants and Drugs Research Institute, Shahid Beheshti University, G.C. Evin, Tehran, Iran b
A R T I C L E I N F O
A B S T R A C T
Article history: Received 28 April 2015 Received in revised form 21 June 2015 Accepted 6 July 2015 Available online xxx
Witches’ broom disease of Mexican lime (WBDL), caused by ‘Candidatus: Phytoplasma aurantifolia’, is a big threat to lime production in southern Iran. In this research work, metabolite profiling of Mexican lime was monitored during WBDL progression (6 months) by gas chromatography–mass spectroscopy (GC– MS), to study the effect of Ca. P. aurantifolia infection on lime, as well as to distinguish infected from healthy limes. The principal components analysis (PCA) plot revealed a clear distinction between the leaf metabolite profiles of healthy and infected plants during the progression of WBDL. Results showed that among the 40 different metabolites, including amino acids, organic acids, sugars and sugar alcohols, and fatty acids, only 13 metabolites (such as proline, arginine, glutamate, salicylate, citrate, fructose, inositol and some TCA cycle intermediate) increased significantly after 30 days of inoculation with Ca. P. aurantifolia. In addition, the concentration ratio of some compounds were monitored, (especially the ratio of proline to arginine, alanine, fructose, citrate, 2-oxoglutarat, and succinate), which could act as considerable indicators to diagnose the infected lime at the early stages of the WBDL progression. Our results provide the first metabolome view on the molecular basis of the infection process and identify metabolites that could help inhibit the effects of the pathogen. ã 2015 Phytochemical Society of Europe. Published by Elsevier B.V. All rights reserved.
Keywords: Lime Witches’ broom disease Metabolite profiling Disease diagnosis
1. Introduction The devastating effect of Candidatus Phytoplasma aurantifolia, the causative agent of witches’ broom disease of lime (WBDL), resulted to an economic loss (more than 70% up to now) for some Mexican lime producing countries such as the United Arab Emirates, Oman and Iran (Garnier et al., 1991; Salehi et al., 1997). Phytoplasmainfected plants could be diagnosed by methods such as polymerase chain reaction (PCR), using polyclonal antibodies as well as symptom expression (Boldicke, 2013). Up to now, genomics, transcriptomics, and proteomics studies of the Mexican Lime tree infected with WBDL were done by scientists (Zamharir and Salekdeh, 2013). The genomics study indicated a number of genes that might be involved in the interaction of Mexican lime trees with “Ca. P. aurantifolia” (Mardi et al., 2011; Zamharir et al., 2011). Ehya et al. (2013) detected miRNA families that are expressed differently upon infection with phytoplasma species. Most of the miRNAs had variants with small sequence variations (isomiRs), which are
* Corresponding author. Fax: +98 2122431598. E-mail address: [email protected] (A. Ghassempour).
expressed differently in response to pathogen infection. Proteomic analysis of the Mexican lime tree response to “Ca. P. aurantifolia” infection was done using 2-DE-MS (Taheri et al., 2011). The results indicated that among the 800 detected proteins, 55 proteins showed a significant response to the disease. These proteins are related to those of oxidative stress defence, photosynthesis, metabolism, and the stress response, (Taheri et al., 2011). In another study, shotgun proteomic analysis of the Mexican Lime tree showed that 448 proteins changed significantly in response to phytoplasma infection. These 448 proteins were involved in stress response, metabolism, growth and development, signal transduction, photosynthesis, cell cycle, and cell wall organization (Monavarfeshani et al., 2013). Metabolomics is a variable tool for analyzing and monitoring the regulation of global plant metabolism in response to stress (Nakabayashi and Saito, 2013; Balmer et al., 2013). Analysis of the metabolic profiles of healthy and Phytophthora cinnamomiinoculated root tissue of Lupinus angustifolius L. was done to discover potentially bioactive phytochemicals of plant roots against P. cinnamomi (Gunning et al., 2013). In many metabolomics studies, principal components analysis (PCA) is often used for data analysis and is a powerful tool to obtain
http://dx.doi.org/10.1016/j.phytol.2015.07.010 1874-3900/ ã 2015 Phytochemical Society of Europe. Published by Elsevier B.V. All rights reserved.
S. Mollayi et al. / Phytochemistry Letters 13 (2015) 290–296
more information about plant defenses and to monitor the intensity and progression of the infection processes. Ibanez et al. (2010) showed that PCA is a useful technique for diagnosis of healthy Nicotiana tabacum from P. nicotianae—infected plant. To the best of the authors’ knowledge of studies on the metabolite profiling of lime, only some flavonoids and essential oil composition of acid lime leaves infected with Ca. P.aurantifolia have been investigated (Mollayi et al., 2015; Al-Yahyai et al., 2014). However, there is no information on the metabolic profiles of Mexican lime during WBDL progress. Therefore, in this present study, the metabolite profiling of Mexican lime was investigated during the progression of WBDL to better understand the responses of lime to Ca. P. aurantifolia infection and introduce metabolites as biomarkers for diagnosis of the infected plant in the early stages of WBDL progression. The metabolite profile of this study, along with genomics, transcriptomics, and proteomics results, may help to elucidate the molecular basis of the infection process that could be targeted to increase plant resistance and inhibit the growth and reproduction of the pathogen. 2. Results 2.1. Assessment of metabolite profiles by PCA
Component 2 (27%) 0.5 0
1.0
The infection of plants was verified by PCR, during the progression of WBDL (data not shown). In the first step, the principle component analysis (PCA) we used in order to evaluate whether metabolite profiling discriminates between healthy and infected plants during progression of WBDL. The PCA result indicated that principal component (1) and principal component, (2) representing healthy and infected plant, explained a 71% and 27% of the samples variance, respectively for the Mexican lime sample plot. The plot reveals that the healthy lime clustered separately from the infected lime (Fig. 1). In addition, metabolite profiling of infected lime changed during the progression of WBDL and the data clustered into three main classes
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(initial day: cluster A; from 30 until 90 days: cluster B; from 120 until 180 days: cluster C). 2.2. Metabolic variations induced by P. aurantifolia infection In this study, among 120 resolved peaks, a total of 40 metabolites were identified (Table 1). The Student’s t-test helped us to identify only 13 metabolites which showed significant differences during the progression of WBDL. Although, a total of 13 amino acids were detected in the hydrophilic extract, only four amino acids (proline, arginine, glutamate and alanine) significantly increased during progression of WBDL, especially after the 30th day of inoculation with Ca. P. aurantifolia. The levels of proline and glutamate peaked at the 120th day after inoculation and then declined, whereas maximum levels of alanine and arginine were observed 90 days after inoculation with Phytoplasma (Table 2). However, the concentration of three amino acids (tryptophan, phenylalanine and valine) were too low for reliable quantification. Furthermore, a total of 12 sugars and sugar alcohols were identified in this work. There were no significant differences in raffinose concentrations, during the progression of WBDL. By contrast, the amounts of inositol, fructose, fructose-6P and ribose-5P significantly differed. Maximum levels of these sugars were achieved after the 60th day and kept constant until the 120th day and then decreased (Table 2). The amount of glucose, glucose 6-P, sucrose, mannitol, glycerol and ribose were too low for quantification analysis. Although, some organic acids such as lactate, citrate, isocitrate, 2-oxoglutarate, succinate, fumarate, malate, glycolate, benzoate and salicylate were identified in the hydrophilic extract of the leaf of the Mexican lime, the amount of citrate, 2-oxoglutarate, succinate, benzoate and salicylate changed significantly, during the different stages of WBDL progression. The content of citrate, 2oxoglutarate, succinate, benzoate and salicylate significantly increased at the 30th day after inoculation with Ca. P. aurantifolia which it followed by a maximum amount at the 90–120th day and then declined gradually (Table 2). Other compounds were not quantified because of their low amount. Also, fatty acids (steric acid, butyric acid, oleate and beta-sitosterol) were not quantified because of their low amount. The variation of metabolites during the progression of WBDL are shown in Fig. 2 and Table 2. The ratio of changed metabolites in healthy and infected plants at the 30th day after inoculation with Phytoplasma is shown in Table 3. The results indicated that there was a significant difference (more than 2-fold) between healthy and infected plants in some cases such as the ratios of proline to arginine, glutamate, alanine, inositol, fructose, citrate, 2-oxoglutarate, and succinate content; the ratios of ribose-5P to arginine and alanine; the ratio of citrate to benzoate, as well as the ratio of 2-oxoglutarate to benzoate
-1.0
-0.5
Table 1 Compilation of the metabolites identified and quantified by gas chromatography mass spectrometry in hydrophilic extracts of Mexican lime.
-0.5
-1.0
Days
0
0 Component 1(71%) 30
60
0.5
1.0
90 120 150 180
Healthy Infected Fig. 1. Principal component analysis (PCA) scores plot of metabolic profiles in healthy and infected Mexican lime during progression of WBDL.
Amino acids
Sugars and sugar alcohols
Organic acids
Fatty acids
Alanine Phenyl alanine Tryptophan Arginine Proline Glutamate Valine leucine Isoleucine Tyrosine Threonine Orthinine Glutamine
Glucose Glucose-6P Raffinose Fructose Fructose-6P Ribose Ribose-5P Inositol Mannitol Glycerol Sucrose Pyruvate
Citrate Lactate Salicylate Benzoate Malate Glycolate Succinate Fumarate 2-oxoglutarate Isocitrate
Beta-sitosterol Steric acid Butyric acid Oleate
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Table 2 Amount of changing metabolites (mg/g) during progression of WBDL. Metabolite groups
Days after inoculation with phytoplasma 0
30
60
90
120
150
180
Amino acids
Proline Arginine Glutamate Alanine
2.32 3.41 1.82 2.64
0.11d 0.15d 0.13e 0.12d
8.62 5.05 3.15 3.51
0.7b 0.3c 0.2d 0.3c
12.01 6.33 6.01 5.23
0.9a 0.3b 0.8c 0.8b
13.41 7.01 7.41 7.13
1.1a 0.3a 1.2b 0.9a
13.82 5.16 7.11 2.74
1.0a 0.4b 0.8a 0.12d
4.01 2.14 1.16 0.98
0.2c 0.2e 0.1f 0.1e
1.21 0.11e Trace Trace Trace
Sugars and Sugar alcohols
Inositol Fructose Fructose-6P Ribose-5P
5.11 3.82 4.11 2.11
0.3e 0.11c 0.23e 0.11d
7.51 5.33 7.62 6.26
0.9d 0.6b 0.7d 0.7c
14.01 5.89 9.15 10.16
1.2b 0.4b 0.8c 0.9b
16.65 6.44 15.61 13.14
1.3a 0.7ab 1.2b 1.1a
17.11 7.51 18.03 14.22
1.0a 0.5a 1.0a 0.9a
9.61 4.55 4.00 1.72
0.9c 0.2bc 0.2e 0.1e
7.26 0.7d 3.99 0.14c Trace 1.11 0.10f
Organic acids
Citrate 2-Oxoglutarate Succinate Benzoate Salicylate
3.01 3.27 2.84 1.89 4.15
0.14c 0.13d 0.11e 0.11d 0.14e
5.11 5.16 5.26 3.38 8.78
0.4b 0.5c 0.5c 0.2c 0.6c
5.77 6.11 7.75 11.89 12.17
0.5b 0.5b 0.6b 0.9b 0.9b
8.19 7.27 8.92 13.16 15.35
0.7a 0.5a 0.7a 1.0a 1.1a
5.16 5.86 4.28 3.00 5.66
0.3b 0.4c 0.2d 0.2c 0.7d
2.04 2.16 2.11 1.73 2.87
0.2d 0.2e 0.3e 0.2d 0.2f
Trace Trace Trace 1.33 0.15f Trace
Data are means obtained from five independent repetitions, each with at least five samples SD. According to the results of the LSD test, treatments that showed significant differences with each other at P < 0.05 are indicated by different letters.
content. In addition, the ratios of proline to the mentioned metabolites were monitored after 60, 90, 120, 150 and 180 days of inoculation with Phytoplasma (Table 4). However, the ratios of proline to arginine, alanine, fructose, citrate, 2-oxoglutarate, and succinate contents in leaves of infected plants were considerably higher (more than two times) than those in healthy plants at the 60th, 90th, 120th and 150th days after inoculation. 3. Discussion Recognition of the presence of micro-organisms by plant is the first step to activate plant defense responses. However, both pathogenic ad non-pathogenic micro-organisms cause the activation of ion fluxes, phosphorylation/dephosphorylation of proteins, and the production of signaling molecules such as salicylic acid, jasmonic acid, ethylene, and reactive oxygen species (ROS) in plant tissues. This activation leads to regulation of gene expression and induce plant defense responses such as cell wall strengthening and the accumulation of phytoalexins and pathogenesis related (PR) proteins (Dangl and Jones, 2001; Garcia-Brugger et al., 2006). In this study, infection of the grafted plants with phytoplasma was confirmed by PCR, after 30, 60, 90, 120, 150 and 180 days of innoculation, respectively. In addition, metabolite profiling of the hydrophilic extracts of Mexican lime leaves by GC–MS showed 120 resolved peaks and approximately 30% of them could be identified as discrete metabolites with known chemical structure (Table 1). However, the content of 13 metabolites, namely proline, arginine, glutamate, alanine, ribose-5P, fructose, fructose-6P, inositol, benzoate, salicylate, citrate, 2-oxoglutarate and succinate were significantly increased in the infected leaves. One of the major areas of research in plant disease is the introduction of biomarkers for diagnosis of infected plants. Cevallos-Cevallos et al. (2009) reported that natural compounds such as hesperidin, naringenin, and quercetin present in the leaves could be used as biomarkers to diagnose diseases in citrus trees. In another study, several biomarkers such as gallic and azelaic acids, arabitol, ribitol, 4-amino butanoic acid, 1-O-methyl-glucopyranoside, and several fatty acids were tested for the monitoring of infection in grapes at the early infection stage in the vineyard (Agudelo-Romero et al., 2015). So, in the present study, the changed metabolites peaks were determined in GC–MS results and then their ratio was calculated before introducing a biomarker to diagnose the infected lime (Table 3). The results suggest that by estimating the amount of some metabolites such as proline,
arginine, glutamate, salicylate, citrate, fructose, inositol and some TCA cycle intermediates in the leaves of lime, it is possible to diagnose the infected lime tree. In addition, the ratios of proline to arginine, alanine, fructose, citrate, 2-oxoglutarate, and succinate can be the best biomarkers for diagnosis of infected plants at an early stage of the disease progression. Proline can be involved in cell-wall reinforcement in response to abiotic and biotic stress conditions in the plant through the accumulation of proline-rich proteins and hydroxyproline-rich glycoproteins, which are important structural components of plant cell walls (Cassab, 1998). Indeed, it is well known that proline accumulation is related to plant susceptibility to pathogen infection, as plant defenses (Haudecoeur et al., 2009). Arginine is a key amino acid for the synthesis of polyamines (PAs) which are implicated in plant disease resistance (Walden et al., 1997; Martin-Tanguy, 2001; Walters, 2003; Deeb et al., 2010). PAs were conjugated with phenolic compounds such as cinnamic acids and produced hydroxycinnamic acid during pathogeninduced defense response. Therefore, increase in the amount of arginine is a likely consequence of P. aurantifolia infection in Mexican lime in the synthesis of PAs that could be conjugated with phenolic compounds in order to defend response against infection. Glutamate plays an important role in plant amino acid metabolism, and provides both the C skeleton and a-amino group for biosynthesis of amino acids such as g-aminobutyric acid (GABA), arginine, and proline, which play key roles in plant defense (Forde and Lea 2007; Galili et al., 2001). Thus, it seems that glutamate biosynthesis and/or accumulation in the leaves of infected Mexican lime (Table 2) reflects an activation of plant defense systems against phytoplasma infection. Although the precise function of stress-induced alanine in the cell is still unknown, it has been proposed that the accumulation of alanine in the plant may result to an increased resistance to the pathogen (Monselise et al., 2003). In plants, proline is synthesized mainly from glutamate, which is reduced to glutamate-semialdehyde (GSA) by the pyrroline-5carboxylate synthetase (P5CS) enzyme, and spontaneously converted to pyrroline-5-carboxylate (P5C). P5C reductase (P5CR) further reduces the P5C intermediate to proline. These processes require NADPH for the reduction of glutamate to P5C and P5C to proline, and generate NADP+ (Orcutt and Nilsen, 2000; Armengaud et al., 2004) (Fig. 3). NADP+ can be used further as electron acceptor and convert glucose-6P to 6-phosphogluconolactone using glucose-6-phosphate Dehydrogenase (G-6P-DH) in the pentose
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Fig. 2. Mapping of the identified metabolites of Mexican lime during progression of WBDL. Data are means obtained from 5 independent repetitions. A significant change in the metabolites was assessed at P < 0.05 through a student’s t-test and was showed with colors. X = area percent of treatment to control.
phosphate pathway (Fig. 3). This proposed mechanism is in good agreement with the finding that ribose-5P accumulates in the leaf of Mexican lime infected by Ca. P. aurantifolia. In this study, the infected lime leaves contained significantly higher concentration of TCA cycle intermediates (citrate, 2oxoglutarate and succinate) than healthy leaves which indicate an enhanced rate of respiratory metabolism of infected plants similar to the results obtained from infected melon plants by
Cucumber mosaic virus (Shalitin et al., 2002) and infected Jatropha curcas with Jatropha mosaic virus (Sidhu et al., 2010). Sugars act as signal molecules, increasing lignification of cell walls, and induce the synthesis of PR proteins, and consequently, biosynthesis of phenolic compounds which are important components of the defense system (Morkunas and Ratajczak, 2014; Boddu et al., 2006; Pritsch et al., 2000). In addition, Hamzehzarghani et al. (2005) suggested that the constitutive accumulation of sugars in
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Table 3 Ratio of changing metabolites of healthy (H) and infected (I) Mexican lime at 30th day after inoculation with Phytoplasma aurantifolia. Proline Arginine Glutamate Alanine Inositol Fructose Fructose-6P Ribose-5P Citrate 2-Oxoglutarate Succinate Benzoate Salicylate Proline (H) Proline (I) Arginine (H) Arginine (I) Glutamate (H) Glutamate (I) Alanine (H) Alanine (I) Inositol (H) Inositol (I) Fructose (H) Fructose (I) Fructose-6P (H) Fructose-6P (I) Ribose-5P (H) Ribose-5P (I) Citrate (H) Citrate (I) 2-Oxoglutarate (H) 2-Oxoglutarate (I) Succinate (H) Succinate (I) Benzoate (H) Benzoate (I) Salicylate (H) Salicylate (I)
1.0 1.0 1.5 0.6 0.8 0.4 1.1 0.4 2.2 0.9 1.6 0.6 1.8 0.8 0.9 0.7 1.3 0.6 1.4 0.6 1.2 0.6 0.8 0.4 1.8 1.0
0.7 1.6 1.0 1.0 0.5 0.6 0.8 0.7 1.5 1.5 1.1 1.0 1.2 1.5 0.6 1.2 0.9 1.0 0.9 1.0 0.8 1.0 0.6 0.7 1.2 1.7
1.2 2.7 1.8 1.6 1.0 1.0 1.5 1.1 2.8 2.4 2.1 1.7 2.2 2.4 1.1 2.0 1.6 1.6 1.8 1.6 1.6 1.7 1.0 1.0 2.3 2.8
0.9 2.4 1.3 1.4 0.7 0.9 1.0 1.0 1.9 2.1 1.4 1.5 1.5 2.2 0.8 1.8 1.1 1.5 1.2 1.5 1.1 1.5 0.7 1.0 1.6 2.5
0.5 1.1 0.7 1.1 0.3 0.4 0.5 0.4 1.0 1.0 0.7 0.7 0.8 1.0 0.4 0.8 0.6 0.7 0.6 0.7 0.6 0.7 0.4 0.5 0.8 1.1
0.6 1.6 0.9 0.9 0.5 0.6 0.7 0.6 1.3 1.4 1.0 1.0 1.1 1.4 0.6 1.1 0.8 1.0 0.8 1.0 0.7 1.0 0.5 0.6 1.1 1.6
0.6 1.1 0.8 0.7 0.4 0.6 0.6 0.5 1.2 1.0 0.9 0.7 1.0 1.0 0.5 0.8 0.7 0.7 0.8 0.7 0.7 0.7 0.5 0.4 1.0 1.1
1.1 1.4 1.6 0.8 0.9 0.5 1.2 0.6 2.4 1.2 1.8 0.8 1.9 1.2 1.0 1.0 1.4 0.8 1.5 0.8 1.3 0.8 0.9 0.5 2.0 1.4
0.8 1.7 1.1 1.0 0.6 0.6 0.9 0.7 1.7 1.5 1.3 1.0 1.4 1.5 0.7 1.2 1.0 1.0 1.1 1.0 0.9 1.0 0.6 0.7 1.4 1.7
0.7 1.6 1.0 1.0 0.6 0.6 0.8 0.7 1.6 1.5 1.2 1.0 1.2 1.5 0.6 1.2 0.9 1.0 1.0 1.0 0.9 1.0 0.6 0.7 1.3 1.7
0.8 1.7 1.2 1.0 0.6 0.6 0.9 0.7 1.8 1.4 1.3 1.0 1.4 1.4 0.7 1.2 1.0 1.0 1.1 1.0 1.0 1.0 0.7 0.6 1.5 1.7
1.2 2.4 1.8 1.5 1.0 0.9 1.4 1.0 2.7 2.2 2.0 1.6 2.2 2.3 1.1 1.8 1.6 0.5 1.7 0.5 1.5 1.5 1.0 1.0 2.2 1.7
0.6 0.9 0.8 0.6 0.4 0.3 0.6 0.4 1.2 0.8 0.9 0.6 1.0 0.9 0.5 0.7 0.7 0.6 0.8 0.6 0.7 0.6 0.5 0.4 1.0 1.0
Table 4 The ration of proline to changing metabolites during progression of WBDL.
30 (H) 30 (I) 60 (H) 60 (I) 90 (H) 90 (I) 120 (H) 120 (I) 150 (H) 150 (I) 180 (H) 180 (I)
Arginine
Glutamate
Alanine
Inositol
Fructose
Fructose-6P
Ribose-5P
Citrate
2-Oxoglutarate
Succinate
Benzoate
Salicylate
0.7 1.7 0.5 1.9 0.8 1.9 1.5 2.6 1.1 1.7 1.2 1
1.2 2.7 1.0 2.0 1.5 1.8 2.8 1.9 2.1 3.4 2.2 1
0.9 2.4 0.7 2.3 1.0 2.0 1.9 5.0 1.4 4.1 1.5 1
0.5 1.5 0.3 0.8 0.5 0.8 1.0 0.8 0.7 0.4 0.8 0.2
0.6 1.6 0.5 2.0 0.7 2.1 1.3 1.8 1.0 0.9 1.1 0.3
0.6 1.1 0.4 1.3 0.6 0.8 1.2 0.8 0.9 1.0 1.0 1
1.1 1.4 0.9 1.2 1.2 1.0 2.4 1.0 1.8 2.3 1.9 1.1
0.8 1.7 0.6 2.1 0.9 1.6 1.7 2.7 1.3 2.0 1.4 1
0.7 1.6 0.6 2.0 0.8 1.8 1.6 2.4 1.2 1.8 1.2 1
0.8 1.7 0.6 1.2 0.9 1.5 1.8 3.2 1.3 1.9 1.4 1
1.2 2.4 1.0 1. 1.4 1.0 2.7 4.6 2.0 2.3 2.2 0.9
0.6 0.9 0.4 1.0 0.6 0.9 1.2 2.4 0.9 1.4 1.0 1
resistant cultivars may be related to a higher signal perception and transduction capacity, enabling a rapid response to pathogen attack. Therefore, the increase of fructose, fructose-6P, inositol, benzoate and salicylate in this work may relate to plant defense response to pathogen attack PCA is the most common chemometric tool used to assess differences between plant varieties or genetically modified (GM) plants and their non-GM counterparts at the metabolome levels (Kim et al., 2013a,b). In this study, PCA could be used to distinguish between healthy and infected plants of Mexican lime during WBDL progress Finally, these results allow us to hypothesize that when lime trees are infected by Ca. P. aurantifolia, some defense responses are initiated in a rapid and more intensive manner. However, these defense responses are not effective against phytoplasma attack, hence WBDL would develop. Furthermore, the proline content is the best indicator for the diagnosis of the infected plant and monitor of the ratio of proline to arginine, alanine, fructose, citrate, 2-oxoglutarate, and succinate is the best biomarker to diagnose the infected plant at the early stage of WBDL progression.
4. Materials and methods 4.1. Materials The reference standards for metabolite studies were obtained from Sigma (St Louis, MO, USA). All extraction solvents were purchased from Merck (Darmstadt, Germany) with high purity. 4.2. Plant material and inoculation Twenty healthy 1-year-old Mexican lime trees grown in a greenhouse were arranged on a greenhouse bench. Specimens of Mexican lime trees infected with witches’ broom disease were grafted to half of them randomly and were covered for 30 days with plastic bags to increase their humidity. All trees were kept under natural light conditions at a temperature of 25–28 C. Diagnoses of WBDL in the trees were done based on polymerase chain reaction (PCR) using P1/ WB3 primers (Zreik et al., 1995). The experiment was conducted using a randomized complete block design (RCBD). Randomly, leaves of five trees infected by Ca.
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295
Cytosterol
Pentose phosphate phaswaye
Glycolysis Glocose
Mitochonderia Proline
Glocose-6P
Proline P5CR
NADP+
Glocose-6P
G-6P-DH
NADPH P-5-C
P-5-C
6-phosphogluconolactone NADP+
GSA
GSA
NADPH
Fructose-6P
Ribulose-5P
P5CS Glutamate
Ribose-5P
Glutamate
Oxoglutarate Erythrose-4P
Phosphoenol pyrovate (PEP)
Shikimate pathwaye
Pyruvate
TCA Cycle TCA Cycle
Fig. 3. Biosynthesis of proline and related to pentose phosphate pathway.
P. aurantifolia, as treatment and five healthy trees without Ca. P. aurantifolia inoculation, as control were sampled every month and used for further analysis. 4.3. Sample preparation Leaves of Mexican lime were homogenized under liquid nitrogen and about 20 mg fresh material was applied during the extraction procedure. A slightly modified water/chloroform/ methanol mixture was used to extract water soluble metabolites. After phase separation, the polar phase was dried out in a vacuum centrifuge and derivatized in two steps: methoxyamination (methoxyamin hydrochloride dissolved in pyridine) to suppress keto–enol tautomerism, followed by trimethylsilylation using MSTFA (N-methyl-N-(trimethylsilyl) trifluoroacetamide) to derivatize polar functional groups. The total quantity derivatized was 100 mL. Standards were dissolved in methanol or water, diluted to various concentrations, dried out and derivatized according to plant material (Zorb et al., 2006). 4.4. GC–MS and mass spectra analyses Sample volumes of 1 mL were analyzed by GC–MS (Agilent 19,091S-433 Gas-chromatograph coupled with a quadropole MS detector, Agilent Technologies Inc.) derivatized metabolites were evaporated at 250 C in splitl-less mode and separated on a
15 m 0.25 mm DB5-MS capillary column with 0.25 mm coating (Varian, Darmstadt, Germany). Helium carrier gas flow was adjusted to 1.5 mL/min. The interface and ion source temperatures were set to 250 C. The oven temperature was kept constant for 3 min at 80 C and subsequently raised to 320 C at 10 C/min and was kept constant for 4 min. Mass spectra were recorded at 4 scans/s with a scanning range of 50–550 m/z. Metabolites were identified by comparison to purified standards and the NIST 2005 database (NIST, Gaithersburg, MD). Relative levels of selected metabolites were determined automatically by integrating the peak areas of selective ions using the processing setup implemented in the Xcalibur 1.4 software (Thermo Electron, Dreieich, Germany). Relative response ratios were calculated by normalizing the respective peak is as to the peak area of the internal standard. 4.5. Statistical analysis The experiment was carried out using a randomized complete design (RCD) considering five replications for each sample. Statistical analysis was performed using Student’s t-test and the differences between samples were expressed as significant at the level of p 0.05. A principal component analysis (PCA) model was constructed to investigate differentiation between the groups. The PCA was typically performed after log10 transformation of the data in a metabolite response matrix.
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Acknowledgements The authors gratefully acknowledge the Shahid Beheshti University, G.C. and Tarbiat Modares University for financial support of this study. References Agudelo-Romero, P., Erban, A., Rego, C., Carbonell-Bejerano, P., Nascimento, T., Sousa, L., Martinez-Zapater, J., Kopka, J., Fortes, A.F., 2015. Transcriptome and metabolome reprogramming in Vitis vinifera cv. Trincadeira berries upon infection with Botrytis cinerea. J. Exp. Bot. doi:http://dx.doi.org/10.1093/jxb/ eru517. Al-Yahyai, R., Al-Subhi, A., Al-Sabahi, J., Al-Said, F., Al-Wahaibi, K., Al-Sadi, A.M., 2014. Chemical composition of acid lime leaves infected with Candidatus Phytoplasma aurantifolia. Agric. Sci. 5 (1), 66–70. Armengaud, P., Thiery, L., Buhot, N., March, G., Savouré, A., 2004. Transcriptional regulation of proline biosynthesis in Medicago truncatula reveals developmental and environmental specific features. Physiol. Plant 120, 442–450. Balmer, D., Flors, V., Glauser, G., Mauch-Mani, B., 2013. Metabolomics of cereals under biotic stress: current knowledge and techniques. Front. Plant Sci. 4 82. Boddu, J., Cho, S., Kruger, W.M., Muehlbauer, G.J., 2006. Transcriptome analysis of the barley Fusarium graminearum interaction. Mol. Plant Microbe Interact. 19, 407–417. Boldicke, T., 2013. Polyclonal antibodies for detection of Witches’ broom disease of lime. Iran. J. Biotechnol. 11 (3), 144. Cassab, G.I., 1998. Plant cell wall proteins. Ann. Rev. Plant Biol. 49, 281–309. Cevallos-Cevallos, J.M., Rouseff, R., Reyes-De-Corcuera, J.I., 2009. Untargeted metabolite analysis of healthy and Huanglongbing-infected orange leaves by CE-DAD. Electrophoresis 30, 1–8. Dangl, J.L., Jones, J.D., 2001. Plant pathogens and integrated defence responses to infection. Nature 411, 826–833. Deeb, F., Van der Weele, C.M., Wolniak, S.M., 2010. Spermidine is a morphogenetic determinant for cell fate specification in the male gametophyte of the water fern Marsilea vestita. Plant Cell. 22, 3678–3691. Ehya, F., Monavarfeshani, A., Fard, E.M., Farsad, L.K., Nekouei, M.K., Mardi, M., Salekdeh, G.H., 2013. Phytoplasma-responsive microRNAs modulate hormonal, nutritional, and stress signalling pathways in Mexican lime trees. PLoS One 8 (6), 1–10. Forde, B.G., Lea, P.J., 2007. Glutamate in plants: metabolism, regulation, and signalling. J. Exp. Bot. 58, 2339–2358. Galili, G., Tang, G.L., Zhu, X.H., Gakiere, B., 2001. Lysine catabolism: a stress and development super-regulated metabolic pathway. Curr. Opin. Plant Biol. 4, 261– 266. Garcia-Brugger, A., Lamotte, O., Vandelle, E., Bourque, S., Lecourieux, D., Poinssot, B., Wendehenne, D., Pugin, A., 2006. Early signaling events induced by elicitors of plant defenses. Mol. Plant Microbe Interact. 19, 711–724. Garnier, M., Zreik, L., Bove, J.M., 1991. Witches’ broom, a lethal mycoplasma disease of lime trees in the Sultanate of Oman and the United Arab Emirates. Plant Dis. 75, 546–551. Gunning, T.K., Conlan, X.A., Parker, R.M., Dyson, G.A., Adams, M.J., Barnett, N.W., Cahill, D.M., 2013. Profiling of secondary metabolites in blue lupin inoculated with Phytophthora cinnamomi following phosphite treatment. Funct. Plant Biol. 40 (11), 1089–1097. Hamzehzarghani, H., Kushalappa, A.C., Dion, Y., Rioux, S., Comeau, A., Yaylayan, V., Marshall, W.D., Mather, D.E., 2005. Metabolic profiling and factor analysis to discriminate quantitative resistance in wheat cultivars against fusarium head blight. Physiol. Mol. Plant Pathol. 66, 119–133. Haudecoeur, E., Planamentre, S., Cirou, A., Tannieres, M., Shelp, B.J., Morera, S., Faure, D., 2009. Proline antagonizes GABA-induced quenching of quorum-sensing in Agrobacterium tumefaciens. Proc. Natl. Acad. Sci. U. S. A. 106, 14587–14592. Ibanez, A.J., Scharte, J., Bones, P., Pirkl, A., Meldau, S., Baldwin, I., Weis, E., Dreisewerd, K., Hillenkamp, F., 2010. Rapid metabolic profiling of Nicotiana tabacum defence responses against Phytophthora nicotianae using direct
infrared laser desorption ionization mass spectrometry and principal component analysis. Plant Methods 6 (1) 16. Kim, J.K., Choi, S.R., Lee, J., Park, S.Y., Song, S.Y., Na, J., Kim, S.W., Kim, S.J., Nou, I.S., Lee, Y.H., Park, S.U., Kim, H.R., 2013a. Metabolic diffetentiation of diamondback moth (Plutella xylostella (L.)) resistance in cabbage (Brassica oleracea L. ssp. capitata). J. Agric. Food Chem. 61, 11222–11230. Kim, J.K., Park, S.Y., Lee, S.M., Lim, S.H., Kim, H.J., Oh, S.D., Yeo, Y., Cho, H.S., Ha, S.H., 2013b. Unintended polar metabolite profiling of carotenoid-biofortified transgenic rice reveals substantial equivalence to its non-transgenic counterpart. Plant Biotechnol. Rep. 7, 121–128. Mardi, M., Nekouei, M.K., Farsad, L.K., Ehya, F., Shabani, M., Shafiee, M., Tabatabaei, M., Safarnejad, M.R., Jouzani, G.S., Salekdeh, G.H., 2011. Witches’ broom disease of Mexican lime trees: disaster to be addressed before it will be too late. Bull. Insectol. 64 (Suppl), S205–S206. Martin-Tanguy, J., 2001. Metabolism and function of polyamines in plants: recent development (new approaches). Plant Growth Regul. 34, 135–148. Mollayi, S., Ghanati, T., Ghassempour, A., 2015. Validation of a HPLC method for the quantification of flavonoids in Mexican Lime (Citrus Aurantifolia) during the Progression of Witches’ Broom Disease. J Chem. Pharm. Res. 7 (4), 1597–1603. Monavarfeshani, A., Nekouei, M.K., Mirzaei, M., Haynes, P.A., Sarhadi, E., Mardi, M., Amirkhani, A., Salekdeh, G.H., 2013. Shotgun proteomic analysis of the Mexican lime tree infected with Candidatus Phytoplasma aurantifolia. J. Proteome Res. 12, 785–795. Monselise, E., Parola, A.H., Kost, D., 2003. Low-frequency electromagnetic fields induce a stress effect upon higher plants, as evident by the universal stress signal, alanine. Biochem. Biophys. Res. Commun. 302, 427–434. Morkunas, I., Ratajczak, L., 2014. The role of sugar signaling in plant defense responses against fungal pathogens. Acta Physiol. Plant. 36, 1607–1619. Nakabayashi, R., Saito, K., 2013. Metabolomics for unknown plant metabolites. Anal. Bioanal. Chem. doi:http://dx.doi.org/10.1007/s00216-013-6869-2. Orcutt, D.M., Nilsen, E.T., 2000. The Physiology of Plants Under Stress, Soil and Biotic Factors. John Wiley, New York. Pritsch, C., Muehlbauer, G.J., Bushnell, W.R., Somers, D.A., Vance, C.P., 2000. Fungal development and induction of defence response genes during early infection of wheat spikes by Fusarium graminearum. Mol. Plant Microbe Interact. 13, 159– 169. Salehi, M., Izadpanah, K., Rahimian, H., 1997. Witches’ broom disease of lime in Sistan-Baluchistan. Iran. J. Plant Pathol. 33, 76. Shalitin, D., Wang, Y., Omid, A., Gal-On, A., Wolf, S., 2002. Cucumber mosaic virus movement protein affects sugar metabolism and transport in tobacco and melon plants. Plant Cell Environ. 25, 989–997. Sidhu, O.P., Annarao, S., Pathre, U., Snehi, S., Raj, S., Roy, R., Tuli, R., Khetrapal, C., 2010. Metabolic and histopathological alterations of Jatropha mosaic begomovirus-infected Jatropha curcas L. by HR-MAS NMR spectroscopy and magnetic resonance imaging. Planta 232, 85–93. Taheri, F., Nematzadeh, G., Zamharir, M.G., Nekouei, M.K., Naghavi, M., Mardi, M., Salekdeh, G.H., 2011. Proteomic analysis of the Mexican lime tree response to‘Candidatus Phytoplasma aurantifolia’infection. Mol. BioSyst. 7, 3028–3035. Walden, R., Cordeiro, A., Tiburcio, A.F., 1997. Polyamines: small molecules triggering pathways in plant growth and development. Plant Physiol. 113, 1009–1013. Walters, D.R., 2003. Resistance to plant pathogens: possible roles for free polyamines and polyamine catabolism. New Phytol. 159, 109–115. Zamharir, M.G., Mardi, M., Alavi, S.M., Hasanzadeh, N., Nekouei, M.K., Zamanizadeh, H.R., Alizadeh, A., Salekdeh, G.H., 2011. Identification of genes differentially expressed during interaction of Mexican lime tree infected with Candidatus Phytoplasma aurantifolia. BMC Microbiol. 12, 1–9. Zamharir, M.G., Salekdeh, G.H., 2013. Molecular response of Mexican lime tree to “Candidatus Phytoplasma aurantifolia” infection: an overview. Afr. J. Microbiol. Res. 7 (51), 5766–5770. Zreik, L., Carle, P., Bové, J.M., Garnier, M., 1995. Characterization of the mycoplasmalike organism associated with witches’ broom disease of lime and proposition of a Candidatus taxon for the organism, Candidatus Phytoplasma aurantifolia. Inter. J. Syst. Bacteriol. 45, 449–453. Zorb, C., Langenka-Mper, G., Niehaus, K., Betsche, T., Barsch, A., 2006. Metabolite profiling of wheat grains (Triticum aestivum L.) from organic and conventional agriculture. J. Agric. Food Chem. 54, 8301–8306.
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Phytochemistry Letters journal homepage: www.elsevier.com/locate/phytol
Short communication
Metabolite profiling of Mexican lime (Citrus aurantifolia) leaves during the progression of witches’ broom disease Saeed Mollayia , Reza Zadalib , Mohsen Farzanehc, Alireza Ghassempourd,* a
Department of Chemistry, Faculty of Science, Azarbaijan Shahid Madani University, Tabriz, Iran Department of Pharmacognosy, Faculty of Pharmacy, Tehran University of Medical Sciences, Tehran, Iran c Department of Agriculture, Medicinal Plants and Drugs Research Institute, Shahid Beheshti University, G.C. Evin, Tehran, Iran d Department of Phytochemistry, Medicinal Plants and Drugs Research Institute, Shahid Beheshti University, G.C. Evin, Tehran, Iran b
A R T I C L E I N F O
A B S T R A C T
Article history: Received 28 April 2015 Received in revised form 21 June 2015 Accepted 6 July 2015 Available online xxx
Witches’ broom disease of Mexican lime (WBDL), caused by ‘Candidatus: Phytoplasma aurantifolia’, is a big threat to lime production in southern Iran. In this research work, metabolite profiling of Mexican lime was monitored during WBDL progression (6 months) by gas chromatography–mass spectroscopy (GC– MS), to study the effect of Ca. P. aurantifolia infection on lime, as well as to distinguish infected from healthy limes. The principal components analysis (PCA) plot revealed a clear distinction between the leaf metabolite profiles of healthy and infected plants during the progression of WBDL. Results showed that among the 40 different metabolites, including amino acids, organic acids, sugars and sugar alcohols, and fatty acids, only 13 metabolites (such as proline, arginine, glutamate, salicylate, citrate, fructose, inositol and some TCA cycle intermediate) increased significantly after 30 days of inoculation with Ca. P. aurantifolia. In addition, the concentration ratio of some compounds were monitored, (especially the ratio of proline to arginine, alanine, fructose, citrate, 2-oxoglutarat, and succinate), which could act as considerable indicators to diagnose the infected lime at the early stages of the WBDL progression. Our results provide the first metabolome view on the molecular basis of the infection process and identify metabolites that could help inhibit the effects of the pathogen. ã 2015 Phytochemical Society of Europe. Published by Elsevier B.V. All rights reserved.
Keywords: Lime Witches’ broom disease Metabolite profiling Disease diagnosis
1. Introduction The devastating effect of Candidatus Phytoplasma aurantifolia, the causative agent of witches’ broom disease of lime (WBDL), resulted to an economic loss (more than 70% up to now) for some Mexican lime producing countries such as the United Arab Emirates, Oman and Iran (Garnier et al., 1991; Salehi et al., 1997). Phytoplasmainfected plants could be diagnosed by methods such as polymerase chain reaction (PCR), using polyclonal antibodies as well as symptom expression (Boldicke, 2013). Up to now, genomics, transcriptomics, and proteomics studies of the Mexican Lime tree infected with WBDL were done by scientists (Zamharir and Salekdeh, 2013). The genomics study indicated a number of genes that might be involved in the interaction of Mexican lime trees with “Ca. P. aurantifolia” (Mardi et al., 2011; Zamharir et al., 2011). Ehya et al. (2013) detected miRNA families that are expressed differently upon infection with phytoplasma species. Most of the miRNAs had variants with small sequence variations (isomiRs), which are
* Corresponding author. Fax: +98 2122431598. E-mail address: [email protected] (A. Ghassempour).
expressed differently in response to pathogen infection. Proteomic analysis of the Mexican lime tree response to “Ca. P. aurantifolia” infection was done using 2-DE-MS (Taheri et al., 2011). The results indicated that among the 800 detected proteins, 55 proteins showed a significant response to the disease. These proteins are related to those of oxidative stress defence, photosynthesis, metabolism, and the stress response, (Taheri et al., 2011). In another study, shotgun proteomic analysis of the Mexican Lime tree showed that 448 proteins changed significantly in response to phytoplasma infection. These 448 proteins were involved in stress response, metabolism, growth and development, signal transduction, photosynthesis, cell cycle, and cell wall organization (Monavarfeshani et al., 2013). Metabolomics is a variable tool for analyzing and monitoring the regulation of global plant metabolism in response to stress (Nakabayashi and Saito, 2013; Balmer et al., 2013). Analysis of the metabolic profiles of healthy and Phytophthora cinnamomiinoculated root tissue of Lupinus angustifolius L. was done to discover potentially bioactive phytochemicals of plant roots against P. cinnamomi (Gunning et al., 2013). In many metabolomics studies, principal components analysis (PCA) is often used for data analysis and is a powerful tool to obtain
http://dx.doi.org/10.1016/j.phytol.2015.07.010 1874-3900/ ã 2015 Phytochemical Society of Europe. Published by Elsevier B.V. All rights reserved.
S. Mollayi et al. / Phytochemistry Letters 13 (2015) 290–296
more information about plant defenses and to monitor the intensity and progression of the infection processes. Ibanez et al. (2010) showed that PCA is a useful technique for diagnosis of healthy Nicotiana tabacum from P. nicotianae—infected plant. To the best of the authors’ knowledge of studies on the metabolite profiling of lime, only some flavonoids and essential oil composition of acid lime leaves infected with Ca. P.aurantifolia have been investigated (Mollayi et al., 2015; Al-Yahyai et al., 2014). However, there is no information on the metabolic profiles of Mexican lime during WBDL progress. Therefore, in this present study, the metabolite profiling of Mexican lime was investigated during the progression of WBDL to better understand the responses of lime to Ca. P. aurantifolia infection and introduce metabolites as biomarkers for diagnosis of the infected plant in the early stages of WBDL progression. The metabolite profile of this study, along with genomics, transcriptomics, and proteomics results, may help to elucidate the molecular basis of the infection process that could be targeted to increase plant resistance and inhibit the growth and reproduction of the pathogen. 2. Results 2.1. Assessment of metabolite profiles by PCA
Component 2 (27%) 0.5 0
1.0
The infection of plants was verified by PCR, during the progression of WBDL (data not shown). In the first step, the principle component analysis (PCA) we used in order to evaluate whether metabolite profiling discriminates between healthy and infected plants during progression of WBDL. The PCA result indicated that principal component (1) and principal component, (2) representing healthy and infected plant, explained a 71% and 27% of the samples variance, respectively for the Mexican lime sample plot. The plot reveals that the healthy lime clustered separately from the infected lime (Fig. 1). In addition, metabolite profiling of infected lime changed during the progression of WBDL and the data clustered into three main classes
291
(initial day: cluster A; from 30 until 90 days: cluster B; from 120 until 180 days: cluster C). 2.2. Metabolic variations induced by P. aurantifolia infection In this study, among 120 resolved peaks, a total of 40 metabolites were identified (Table 1). The Student’s t-test helped us to identify only 13 metabolites which showed significant differences during the progression of WBDL. Although, a total of 13 amino acids were detected in the hydrophilic extract, only four amino acids (proline, arginine, glutamate and alanine) significantly increased during progression of WBDL, especially after the 30th day of inoculation with Ca. P. aurantifolia. The levels of proline and glutamate peaked at the 120th day after inoculation and then declined, whereas maximum levels of alanine and arginine were observed 90 days after inoculation with Phytoplasma (Table 2). However, the concentration of three amino acids (tryptophan, phenylalanine and valine) were too low for reliable quantification. Furthermore, a total of 12 sugars and sugar alcohols were identified in this work. There were no significant differences in raffinose concentrations, during the progression of WBDL. By contrast, the amounts of inositol, fructose, fructose-6P and ribose-5P significantly differed. Maximum levels of these sugars were achieved after the 60th day and kept constant until the 120th day and then decreased (Table 2). The amount of glucose, glucose 6-P, sucrose, mannitol, glycerol and ribose were too low for quantification analysis. Although, some organic acids such as lactate, citrate, isocitrate, 2-oxoglutarate, succinate, fumarate, malate, glycolate, benzoate and salicylate were identified in the hydrophilic extract of the leaf of the Mexican lime, the amount of citrate, 2-oxoglutarate, succinate, benzoate and salicylate changed significantly, during the different stages of WBDL progression. The content of citrate, 2oxoglutarate, succinate, benzoate and salicylate significantly increased at the 30th day after inoculation with Ca. P. aurantifolia which it followed by a maximum amount at the 90–120th day and then declined gradually (Table 2). Other compounds were not quantified because of their low amount. Also, fatty acids (steric acid, butyric acid, oleate and beta-sitosterol) were not quantified because of their low amount. The variation of metabolites during the progression of WBDL are shown in Fig. 2 and Table 2. The ratio of changed metabolites in healthy and infected plants at the 30th day after inoculation with Phytoplasma is shown in Table 3. The results indicated that there was a significant difference (more than 2-fold) between healthy and infected plants in some cases such as the ratios of proline to arginine, glutamate, alanine, inositol, fructose, citrate, 2-oxoglutarate, and succinate content; the ratios of ribose-5P to arginine and alanine; the ratio of citrate to benzoate, as well as the ratio of 2-oxoglutarate to benzoate
-1.0
-0.5
Table 1 Compilation of the metabolites identified and quantified by gas chromatography mass spectrometry in hydrophilic extracts of Mexican lime.
-0.5
-1.0
Days
0
0 Component 1(71%) 30
60
0.5
1.0
90 120 150 180
Healthy Infected Fig. 1. Principal component analysis (PCA) scores plot of metabolic profiles in healthy and infected Mexican lime during progression of WBDL.
Amino acids
Sugars and sugar alcohols
Organic acids
Fatty acids
Alanine Phenyl alanine Tryptophan Arginine Proline Glutamate Valine leucine Isoleucine Tyrosine Threonine Orthinine Glutamine
Glucose Glucose-6P Raffinose Fructose Fructose-6P Ribose Ribose-5P Inositol Mannitol Glycerol Sucrose Pyruvate
Citrate Lactate Salicylate Benzoate Malate Glycolate Succinate Fumarate 2-oxoglutarate Isocitrate
Beta-sitosterol Steric acid Butyric acid Oleate
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Table 2 Amount of changing metabolites (mg/g) during progression of WBDL. Metabolite groups
Days after inoculation with phytoplasma 0
30
60
90
120
150
180
Amino acids
Proline Arginine Glutamate Alanine
2.32 3.41 1.82 2.64
0.11d 0.15d 0.13e 0.12d
8.62 5.05 3.15 3.51
0.7b 0.3c 0.2d 0.3c
12.01 6.33 6.01 5.23
0.9a 0.3b 0.8c 0.8b
13.41 7.01 7.41 7.13
1.1a 0.3a 1.2b 0.9a
13.82 5.16 7.11 2.74
1.0a 0.4b 0.8a 0.12d
4.01 2.14 1.16 0.98
0.2c 0.2e 0.1f 0.1e
1.21 0.11e Trace Trace Trace
Sugars and Sugar alcohols
Inositol Fructose Fructose-6P Ribose-5P
5.11 3.82 4.11 2.11
0.3e 0.11c 0.23e 0.11d
7.51 5.33 7.62 6.26
0.9d 0.6b 0.7d 0.7c
14.01 5.89 9.15 10.16
1.2b 0.4b 0.8c 0.9b
16.65 6.44 15.61 13.14
1.3a 0.7ab 1.2b 1.1a
17.11 7.51 18.03 14.22
1.0a 0.5a 1.0a 0.9a
9.61 4.55 4.00 1.72
0.9c 0.2bc 0.2e 0.1e
7.26 0.7d 3.99 0.14c Trace 1.11 0.10f
Organic acids
Citrate 2-Oxoglutarate Succinate Benzoate Salicylate
3.01 3.27 2.84 1.89 4.15
0.14c 0.13d 0.11e 0.11d 0.14e
5.11 5.16 5.26 3.38 8.78
0.4b 0.5c 0.5c 0.2c 0.6c
5.77 6.11 7.75 11.89 12.17
0.5b 0.5b 0.6b 0.9b 0.9b
8.19 7.27 8.92 13.16 15.35
0.7a 0.5a 0.7a 1.0a 1.1a
5.16 5.86 4.28 3.00 5.66
0.3b 0.4c 0.2d 0.2c 0.7d
2.04 2.16 2.11 1.73 2.87
0.2d 0.2e 0.3e 0.2d 0.2f
Trace Trace Trace 1.33 0.15f Trace
Data are means obtained from five independent repetitions, each with at least five samples SD. According to the results of the LSD test, treatments that showed significant differences with each other at P < 0.05 are indicated by different letters.
content. In addition, the ratios of proline to the mentioned metabolites were monitored after 60, 90, 120, 150 and 180 days of inoculation with Phytoplasma (Table 4). However, the ratios of proline to arginine, alanine, fructose, citrate, 2-oxoglutarate, and succinate contents in leaves of infected plants were considerably higher (more than two times) than those in healthy plants at the 60th, 90th, 120th and 150th days after inoculation. 3. Discussion Recognition of the presence of micro-organisms by plant is the first step to activate plant defense responses. However, both pathogenic ad non-pathogenic micro-organisms cause the activation of ion fluxes, phosphorylation/dephosphorylation of proteins, and the production of signaling molecules such as salicylic acid, jasmonic acid, ethylene, and reactive oxygen species (ROS) in plant tissues. This activation leads to regulation of gene expression and induce plant defense responses such as cell wall strengthening and the accumulation of phytoalexins and pathogenesis related (PR) proteins (Dangl and Jones, 2001; Garcia-Brugger et al., 2006). In this study, infection of the grafted plants with phytoplasma was confirmed by PCR, after 30, 60, 90, 120, 150 and 180 days of innoculation, respectively. In addition, metabolite profiling of the hydrophilic extracts of Mexican lime leaves by GC–MS showed 120 resolved peaks and approximately 30% of them could be identified as discrete metabolites with known chemical structure (Table 1). However, the content of 13 metabolites, namely proline, arginine, glutamate, alanine, ribose-5P, fructose, fructose-6P, inositol, benzoate, salicylate, citrate, 2-oxoglutarate and succinate were significantly increased in the infected leaves. One of the major areas of research in plant disease is the introduction of biomarkers for diagnosis of infected plants. Cevallos-Cevallos et al. (2009) reported that natural compounds such as hesperidin, naringenin, and quercetin present in the leaves could be used as biomarkers to diagnose diseases in citrus trees. In another study, several biomarkers such as gallic and azelaic acids, arabitol, ribitol, 4-amino butanoic acid, 1-O-methyl-glucopyranoside, and several fatty acids were tested for the monitoring of infection in grapes at the early infection stage in the vineyard (Agudelo-Romero et al., 2015). So, in the present study, the changed metabolites peaks were determined in GC–MS results and then their ratio was calculated before introducing a biomarker to diagnose the infected lime (Table 3). The results suggest that by estimating the amount of some metabolites such as proline,
arginine, glutamate, salicylate, citrate, fructose, inositol and some TCA cycle intermediates in the leaves of lime, it is possible to diagnose the infected lime tree. In addition, the ratios of proline to arginine, alanine, fructose, citrate, 2-oxoglutarate, and succinate can be the best biomarkers for diagnosis of infected plants at an early stage of the disease progression. Proline can be involved in cell-wall reinforcement in response to abiotic and biotic stress conditions in the plant through the accumulation of proline-rich proteins and hydroxyproline-rich glycoproteins, which are important structural components of plant cell walls (Cassab, 1998). Indeed, it is well known that proline accumulation is related to plant susceptibility to pathogen infection, as plant defenses (Haudecoeur et al., 2009). Arginine is a key amino acid for the synthesis of polyamines (PAs) which are implicated in plant disease resistance (Walden et al., 1997; Martin-Tanguy, 2001; Walters, 2003; Deeb et al., 2010). PAs were conjugated with phenolic compounds such as cinnamic acids and produced hydroxycinnamic acid during pathogeninduced defense response. Therefore, increase in the amount of arginine is a likely consequence of P. aurantifolia infection in Mexican lime in the synthesis of PAs that could be conjugated with phenolic compounds in order to defend response against infection. Glutamate plays an important role in plant amino acid metabolism, and provides both the C skeleton and a-amino group for biosynthesis of amino acids such as g-aminobutyric acid (GABA), arginine, and proline, which play key roles in plant defense (Forde and Lea 2007; Galili et al., 2001). Thus, it seems that glutamate biosynthesis and/or accumulation in the leaves of infected Mexican lime (Table 2) reflects an activation of plant defense systems against phytoplasma infection. Although the precise function of stress-induced alanine in the cell is still unknown, it has been proposed that the accumulation of alanine in the plant may result to an increased resistance to the pathogen (Monselise et al., 2003). In plants, proline is synthesized mainly from glutamate, which is reduced to glutamate-semialdehyde (GSA) by the pyrroline-5carboxylate synthetase (P5CS) enzyme, and spontaneously converted to pyrroline-5-carboxylate (P5C). P5C reductase (P5CR) further reduces the P5C intermediate to proline. These processes require NADPH for the reduction of glutamate to P5C and P5C to proline, and generate NADP+ (Orcutt and Nilsen, 2000; Armengaud et al., 2004) (Fig. 3). NADP+ can be used further as electron acceptor and convert glucose-6P to 6-phosphogluconolactone using glucose-6-phosphate Dehydrogenase (G-6P-DH) in the pentose
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Fig. 2. Mapping of the identified metabolites of Mexican lime during progression of WBDL. Data are means obtained from 5 independent repetitions. A significant change in the metabolites was assessed at P < 0.05 through a student’s t-test and was showed with colors. X = area percent of treatment to control.
phosphate pathway (Fig. 3). This proposed mechanism is in good agreement with the finding that ribose-5P accumulates in the leaf of Mexican lime infected by Ca. P. aurantifolia. In this study, the infected lime leaves contained significantly higher concentration of TCA cycle intermediates (citrate, 2oxoglutarate and succinate) than healthy leaves which indicate an enhanced rate of respiratory metabolism of infected plants similar to the results obtained from infected melon plants by
Cucumber mosaic virus (Shalitin et al., 2002) and infected Jatropha curcas with Jatropha mosaic virus (Sidhu et al., 2010). Sugars act as signal molecules, increasing lignification of cell walls, and induce the synthesis of PR proteins, and consequently, biosynthesis of phenolic compounds which are important components of the defense system (Morkunas and Ratajczak, 2014; Boddu et al., 2006; Pritsch et al., 2000). In addition, Hamzehzarghani et al. (2005) suggested that the constitutive accumulation of sugars in
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Table 3 Ratio of changing metabolites of healthy (H) and infected (I) Mexican lime at 30th day after inoculation with Phytoplasma aurantifolia. Proline Arginine Glutamate Alanine Inositol Fructose Fructose-6P Ribose-5P Citrate 2-Oxoglutarate Succinate Benzoate Salicylate Proline (H) Proline (I) Arginine (H) Arginine (I) Glutamate (H) Glutamate (I) Alanine (H) Alanine (I) Inositol (H) Inositol (I) Fructose (H) Fructose (I) Fructose-6P (H) Fructose-6P (I) Ribose-5P (H) Ribose-5P (I) Citrate (H) Citrate (I) 2-Oxoglutarate (H) 2-Oxoglutarate (I) Succinate (H) Succinate (I) Benzoate (H) Benzoate (I) Salicylate (H) Salicylate (I)
1.0 1.0 1.5 0.6 0.8 0.4 1.1 0.4 2.2 0.9 1.6 0.6 1.8 0.8 0.9 0.7 1.3 0.6 1.4 0.6 1.2 0.6 0.8 0.4 1.8 1.0
0.7 1.6 1.0 1.0 0.5 0.6 0.8 0.7 1.5 1.5 1.1 1.0 1.2 1.5 0.6 1.2 0.9 1.0 0.9 1.0 0.8 1.0 0.6 0.7 1.2 1.7
1.2 2.7 1.8 1.6 1.0 1.0 1.5 1.1 2.8 2.4 2.1 1.7 2.2 2.4 1.1 2.0 1.6 1.6 1.8 1.6 1.6 1.7 1.0 1.0 2.3 2.8
0.9 2.4 1.3 1.4 0.7 0.9 1.0 1.0 1.9 2.1 1.4 1.5 1.5 2.2 0.8 1.8 1.1 1.5 1.2 1.5 1.1 1.5 0.7 1.0 1.6 2.5
0.5 1.1 0.7 1.1 0.3 0.4 0.5 0.4 1.0 1.0 0.7 0.7 0.8 1.0 0.4 0.8 0.6 0.7 0.6 0.7 0.6 0.7 0.4 0.5 0.8 1.1
0.6 1.6 0.9 0.9 0.5 0.6 0.7 0.6 1.3 1.4 1.0 1.0 1.1 1.4 0.6 1.1 0.8 1.0 0.8 1.0 0.7 1.0 0.5 0.6 1.1 1.6
0.6 1.1 0.8 0.7 0.4 0.6 0.6 0.5 1.2 1.0 0.9 0.7 1.0 1.0 0.5 0.8 0.7 0.7 0.8 0.7 0.7 0.7 0.5 0.4 1.0 1.1
1.1 1.4 1.6 0.8 0.9 0.5 1.2 0.6 2.4 1.2 1.8 0.8 1.9 1.2 1.0 1.0 1.4 0.8 1.5 0.8 1.3 0.8 0.9 0.5 2.0 1.4
0.8 1.7 1.1 1.0 0.6 0.6 0.9 0.7 1.7 1.5 1.3 1.0 1.4 1.5 0.7 1.2 1.0 1.0 1.1 1.0 0.9 1.0 0.6 0.7 1.4 1.7
0.7 1.6 1.0 1.0 0.6 0.6 0.8 0.7 1.6 1.5 1.2 1.0 1.2 1.5 0.6 1.2 0.9 1.0 1.0 1.0 0.9 1.0 0.6 0.7 1.3 1.7
0.8 1.7 1.2 1.0 0.6 0.6 0.9 0.7 1.8 1.4 1.3 1.0 1.4 1.4 0.7 1.2 1.0 1.0 1.1 1.0 1.0 1.0 0.7 0.6 1.5 1.7
1.2 2.4 1.8 1.5 1.0 0.9 1.4 1.0 2.7 2.2 2.0 1.6 2.2 2.3 1.1 1.8 1.6 0.5 1.7 0.5 1.5 1.5 1.0 1.0 2.2 1.7
0.6 0.9 0.8 0.6 0.4 0.3 0.6 0.4 1.2 0.8 0.9 0.6 1.0 0.9 0.5 0.7 0.7 0.6 0.8 0.6 0.7 0.6 0.5 0.4 1.0 1.0
Table 4 The ration of proline to changing metabolites during progression of WBDL.
30 (H) 30 (I) 60 (H) 60 (I) 90 (H) 90 (I) 120 (H) 120 (I) 150 (H) 150 (I) 180 (H) 180 (I)
Arginine
Glutamate
Alanine
Inositol
Fructose
Fructose-6P
Ribose-5P
Citrate
2-Oxoglutarate
Succinate
Benzoate
Salicylate
0.7 1.7 0.5 1.9 0.8 1.9 1.5 2.6 1.1 1.7 1.2 1
1.2 2.7 1.0 2.0 1.5 1.8 2.8 1.9 2.1 3.4 2.2 1
0.9 2.4 0.7 2.3 1.0 2.0 1.9 5.0 1.4 4.1 1.5 1
0.5 1.5 0.3 0.8 0.5 0.8 1.0 0.8 0.7 0.4 0.8 0.2
0.6 1.6 0.5 2.0 0.7 2.1 1.3 1.8 1.0 0.9 1.1 0.3
0.6 1.1 0.4 1.3 0.6 0.8 1.2 0.8 0.9 1.0 1.0 1
1.1 1.4 0.9 1.2 1.2 1.0 2.4 1.0 1.8 2.3 1.9 1.1
0.8 1.7 0.6 2.1 0.9 1.6 1.7 2.7 1.3 2.0 1.4 1
0.7 1.6 0.6 2.0 0.8 1.8 1.6 2.4 1.2 1.8 1.2 1
0.8 1.7 0.6 1.2 0.9 1.5 1.8 3.2 1.3 1.9 1.4 1
1.2 2.4 1.0 1. 1.4 1.0 2.7 4.6 2.0 2.3 2.2 0.9
0.6 0.9 0.4 1.0 0.6 0.9 1.2 2.4 0.9 1.4 1.0 1
resistant cultivars may be related to a higher signal perception and transduction capacity, enabling a rapid response to pathogen attack. Therefore, the increase of fructose, fructose-6P, inositol, benzoate and salicylate in this work may relate to plant defense response to pathogen attack PCA is the most common chemometric tool used to assess differences between plant varieties or genetically modified (GM) plants and their non-GM counterparts at the metabolome levels (Kim et al., 2013a,b). In this study, PCA could be used to distinguish between healthy and infected plants of Mexican lime during WBDL progress Finally, these results allow us to hypothesize that when lime trees are infected by Ca. P. aurantifolia, some defense responses are initiated in a rapid and more intensive manner. However, these defense responses are not effective against phytoplasma attack, hence WBDL would develop. Furthermore, the proline content is the best indicator for the diagnosis of the infected plant and monitor of the ratio of proline to arginine, alanine, fructose, citrate, 2-oxoglutarate, and succinate is the best biomarker to diagnose the infected plant at the early stage of WBDL progression.
4. Materials and methods 4.1. Materials The reference standards for metabolite studies were obtained from Sigma (St Louis, MO, USA). All extraction solvents were purchased from Merck (Darmstadt, Germany) with high purity. 4.2. Plant material and inoculation Twenty healthy 1-year-old Mexican lime trees grown in a greenhouse were arranged on a greenhouse bench. Specimens of Mexican lime trees infected with witches’ broom disease were grafted to half of them randomly and were covered for 30 days with plastic bags to increase their humidity. All trees were kept under natural light conditions at a temperature of 25–28 C. Diagnoses of WBDL in the trees were done based on polymerase chain reaction (PCR) using P1/ WB3 primers (Zreik et al., 1995). The experiment was conducted using a randomized complete block design (RCBD). Randomly, leaves of five trees infected by Ca.
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Cytosterol
Pentose phosphate phaswaye
Glycolysis Glocose
Mitochonderia Proline
Glocose-6P
Proline P5CR
NADP+
Glocose-6P
G-6P-DH
NADPH P-5-C
P-5-C
6-phosphogluconolactone NADP+
GSA
GSA
NADPH
Fructose-6P
Ribulose-5P
P5CS Glutamate
Ribose-5P
Glutamate
Oxoglutarate Erythrose-4P
Phosphoenol pyrovate (PEP)
Shikimate pathwaye
Pyruvate
TCA Cycle TCA Cycle
Fig. 3. Biosynthesis of proline and related to pentose phosphate pathway.
P. aurantifolia, as treatment and five healthy trees without Ca. P. aurantifolia inoculation, as control were sampled every month and used for further analysis. 4.3. Sample preparation Leaves of Mexican lime were homogenized under liquid nitrogen and about 20 mg fresh material was applied during the extraction procedure. A slightly modified water/chloroform/ methanol mixture was used to extract water soluble metabolites. After phase separation, the polar phase was dried out in a vacuum centrifuge and derivatized in two steps: methoxyamination (methoxyamin hydrochloride dissolved in pyridine) to suppress keto–enol tautomerism, followed by trimethylsilylation using MSTFA (N-methyl-N-(trimethylsilyl) trifluoroacetamide) to derivatize polar functional groups. The total quantity derivatized was 100 mL. Standards were dissolved in methanol or water, diluted to various concentrations, dried out and derivatized according to plant material (Zorb et al., 2006). 4.4. GC–MS and mass spectra analyses Sample volumes of 1 mL were analyzed by GC–MS (Agilent 19,091S-433 Gas-chromatograph coupled with a quadropole MS detector, Agilent Technologies Inc.) derivatized metabolites were evaporated at 250 C in splitl-less mode and separated on a
15 m 0.25 mm DB5-MS capillary column with 0.25 mm coating (Varian, Darmstadt, Germany). Helium carrier gas flow was adjusted to 1.5 mL/min. The interface and ion source temperatures were set to 250 C. The oven temperature was kept constant for 3 min at 80 C and subsequently raised to 320 C at 10 C/min and was kept constant for 4 min. Mass spectra were recorded at 4 scans/s with a scanning range of 50–550 m/z. Metabolites were identified by comparison to purified standards and the NIST 2005 database (NIST, Gaithersburg, MD). Relative levels of selected metabolites were determined automatically by integrating the peak areas of selective ions using the processing setup implemented in the Xcalibur 1.4 software (Thermo Electron, Dreieich, Germany). Relative response ratios were calculated by normalizing the respective peak is as to the peak area of the internal standard. 4.5. Statistical analysis The experiment was carried out using a randomized complete design (RCD) considering five replications for each sample. Statistical analysis was performed using Student’s t-test and the differences between samples were expressed as significant at the level of p 0.05. A principal component analysis (PCA) model was constructed to investigate differentiation between the groups. The PCA was typically performed after log10 transformation of the data in a metabolite response matrix.
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