Untargeted Metabolomics of Tomato Plants after Root-Knot Nematode

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Untargeted Metabolomics of Tomato Plants after Root-knot Nematode Infestation Kodjo Eloh, Nicola Sasanelli, Andrea Maxia, and Pierluigi Caboni J. Agric. Food Chem., Just Accepted Manuscript • DOI: 10.1021/acs.jafc.6b02181 • Publication Date (Web): 07 Jul 2016 Downloaded from http://pubs.acs.org on July 11, 2016

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Journal of Agricultural and Food Chemistry

Untargeted Metabolomics of Tomato Plants after Root-knot Nematode Infestation Kodjo Eloha, Nicola Sasanellib, Andrea Maxiaa, and Pierluigi Cabonia,*

a

Department of Life and Environmental Sciences, University of Cagliari, via Ospedale 72, 09124

Cagliari, Italy b

Institute for Sustainable Plant Protection, C.N.R., via G. Amendola 122/D, 70126 Bari, Italy

Corresponding Author * Phone: +39 070 6758617. Fax: +39 070 6758612 E-mail: [email protected] Running Title: Metabolomics of tomato plants after M. incognita infestation

1 ACS Paragon Plus Environment


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ABSTRACT

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After two months from the infestation of tomato plants with the root-knot nematode (RKN)

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Meloidogyne incognita, we performed a GC-MS untargeted fingerprint analysis for the

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identification of characteristic metabolites and biomarkers. Principal component analysis (PCA),

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and orthogonal partial least squares-discriminant analysis (OPLS-DA) suggested dramatic local

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changes of the plant metabolome. In the case of tomato leaves, β-alanine, phenylalanine,

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melibiose were induced in response to root-knot nematode stimuli, while ribose, glycerol,

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myristic acid and palmitic acid were reduced. For tomato stems, upregulated metabolites were:

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ribose, sucrose, fructose, and glucose while fumaric acid and glycine were downregulated. The

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variation in molecular strategies to the infestation of root-knot nematodes may play an important

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role in how Solanum lycopersicum and other plants adapt to nematode parasitic stress.

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KEYWORDS: plant defense response, metabonomics, plant metabolism, Meloidogyne

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incognita, acylsugars.


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INTRODUCTION

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Soil inhabiting phytoparasitic nematodes of the first trophic level represent the most damaging

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pest usually controlled with synthetic organophosphorus and methylisothiocyanate producing

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nematicides. Among them, the root-knot nematodes (RKN) are widely distributed and

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polyphagous determining a significant impact on agriculture. The site of second stage nematode

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infestation is the plant root and the most considerable symptom is the formation of galls.

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Juveniles of nematodes by their chemoreceptors are attracted by the root exudates of the host

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plants and migrate to the root systems to create their feeding sites in the vascular cylinders.1 The

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second stage juveniles undergo three molts to develop into adults. The globose females remain

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sedentary, producing large egg masses while, although quite rare, males migrate out of the plant

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into the soil.

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Plants may recognize and respond to pathogens by reprogramming the plant cell with energy

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demanding activation of primary metabolic pathways.2 Moreover, plants rely on a rich

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assortment of defense mechanisms by which they can promptly react to a large variety of biotic

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(herbivores, insects, fungi, viruses, microbial pathogens) and abiotic stresses (intense sunlight,

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wind, drought, flood, wildfires). Thus, it is crucial for the plant to respond fast to the stresses

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making the difference between coping or succumbing.

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The systemic acquired resistance (SAR) is considered the first immune plant response3 relying

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upon the recognition of pathogen or microbial by associated molecular patterns (M/PAMP) with

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the use of pattern recognition receptors (PRR) to contain pathogen attack. On the basis of the

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nature of the elicitor and the regulatory pathways, the second plant response is the induced

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systemic resistance (ISR). The latter is activated in response to a non-pathogenic entity thus



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being effective against a broad spectrum of biotic stresses.4 After attack, plant start synthetizing

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several defense secondary metabolites such us phytoalexins, antioxidants, salicylic acid,

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jasmonic acid and ethylene or activating the phenylpropanoid pathway to support lignin

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biosynthesis5 and involving the production of abscisic acid, brassinosteroids and auxins.6

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Additionally, nematodes may be expected to change the biosynthesis of essential nutrients for

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their own diet, and thus new metabolic pathways may be induced within the host plants.7

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Changes in the metabolic profiles of a living organism as a response to a genetic modification,

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external stimulus and/or stressor can be studied with a metabolomics approach.8 Measuring the

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level of polar metabolites, may give an accurate picture of the physiological status of a cell/tissue

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or biofluid. GC-MS is a hyphenated analytical technique widely used in plant metabolomics.9, 10

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The first metabolomics approach to study rice metabolite modifications during infestation by the

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brown planthopper was reported in 1974 by Cagampang et al.11

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In the context of our research on the biological control of root-knot nematodes,12-16 we report a

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GC-MS untargeted analysis to characterize low-molecular-weight polar plant metabolites

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variation that occurs in tomato plants after two months of infestation with Meloidogyne incognita

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(Kofoid et White) Chitw. As a final point, to identify the metabolic pathways altered by

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nematode infestation, pathway analysis of the identified potential biomarkers was performed.


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MATERIALS AND METHODS

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Chemicals. Analytical standards were obtained from Sigma-Aldrich (Milano, Italy). Methanol

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was of high performance liquid chromatography grade.

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Nematode inoculum: A race 3 M. incognita population was reared in sandy soil in 5 liter pots

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for two months on tomato (Solanum lycopersicum L.) cv. Rutgers in a glasshouse at 25 ± 2°C at

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the Institute for Sustainable Plant Protection (CNR) in Bari (Apulia region, Italy). Nematode

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inocula for the trial consisted of M. incognita eggs that were extracted from tomato roots with a

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1% sodium hypochlorite aqueous solution (Hussey and Barker’s method).17 Eggs released from

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the roots were collected on a 25 µm pore-size sieve and were counted under an optical

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microscope with nematode counting slides.

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Thirty-two tomato seeds (cv. Rutgers) were sown and after emergence, 45 days-old tomato

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seedlings were transplanted in clay pots filled with sandy soil (pH 7.2; sand > 99%; silt < 1 %;

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clay < 1% and organic matter = 0.75%) for the experiment. After a week, sixteen tomato plants

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were inoculated with nematodes (10000 eggs and juveniles/pot) and the plant material was

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collected after two months from each plant. Plants not inoculated served as control (n=16).

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Sample extraction. 1 g of fresh plant material, leaves or stem, were chopped and extracted with

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10 mL of methanol using Falcon tubes. Samples were sonicated for 30 min and after 24 h were

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filtered using cotton and centrifuged at 4000 rpm at 25 °C for 10 min. The liquid supernatant,

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consisting of a pool of low molecular weight polar metabolites, was filtered using nylon syringe

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filters 0.45 µm (Thermo Scientific, Rockwood TN).

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GC-MS analysis. 200 μL of the methanol plant extract were dried overnight in amber vials

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under a gentle nitrogen stream. Samples were derivatized using a solution of methoxamine 5 ACS Paragon Plus Environment


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chloride dissolved in pyridine at 10 mg/mL. After 17h, 80 µL of N-methyl-N-(trimethylsilyl)

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trifluoroacetamide (MSTFA) were added. After 1 h, 50 µL of hexane containing 5 mg/L of

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2,2,3,3-d4-succinic acid as internal standard were added. Four replications were done for every

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sample and the experiment was repeated twice at different times. Derivatized samples were

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analyzed by a Hewlett Packard 6850 gas chromatograph, 5973 mass selective detector, and

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7683B series injector (Agilent Technologies, Palo Alto, CA). Helium flow was the carrier gas at

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the flow of 1 mL/min. One µL samples was injected splitless and resolved on a 30 m × 0.25 mm

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× 0.25 µm DB-5MS column (Agilent Technologies, Palo Alto, CA). The temperatures for the

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inlet, interface, and ion source were 250, 250 and 230 °C, respectively. The oven temperature

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was programmed as follows: from 50 to 230 °C (5 °C/min in 36 min) and kept at this

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temperature for 2 min. Electron impact (70 eV) mass spectra were recorded from m/z 50 to 550.

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The resulting data was elaborated using MSD ChemStation. Raw data files were exported into

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the Automatic Mass spectral Deconvolution and Identification System (AMDIS 2.1) for spectral

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deconvolution18 and database search against the NIST Mass Spectral Database (2.0 a) and Golm

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metabolome database.19 Confirmation of sample components was performed by: (a) comparison

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of their relative retention times and mass fragmentation with those of pure standards; and (b)

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computer matching against NIST, as well as retention indices as calculated according to Kovats,

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for alkanes C9-C36 compared with those reported-by Adams.20

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Multivariate analysis. To investigate metabolite features we applied a principal component

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analysis (PCA) using the SIMCA-P software (version 13.0, Umetrics, Umea, Sweden) with the

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data matrix (64X50 and 32X47) obtained after GC-MS analysis. GC-MS chromatographic data

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was normalized by scaling each sample-vector to unit vector by using the simply sum of

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chromatographic area values.21 When a variable showed an abnormal distribution, it was 6 ACS Paragon Plus Environment

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discarded using a logarithmic transformation validated from the Skewness test; statistical test

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was provided. Prior to analysis, data arrays were subjected to centering through the centering

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unit variance. The matrices obtained were subjected to multivariate analysis using SIMCA-P.

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The following analysis were made: PCA and a partial least squares discriminant analysis (PLS-

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DA) and its orthogonal extension (OPLS-DA). The quality of the model has been validated on

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the basis of the parameters R2X (change in X explained by the model), R2Y (the total of Y

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explained) and Q2 sum parameter in cross-validation.21

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Metabolic pathway analysis. Metabolite Log2 fold changes were calculated using Excel

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software. The significant differences were determined using the Log2 fold change value (> 0.4).

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Finally, to identify the metabolic pathways altered by the RKN infestation, pathway analysis of

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the identified potential biomarkers was performed using MetaboAnalyst 3.023 based on the

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pathway library of Arabidopsis thaliana (L.) Heynh.



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RESULTS AND DISCUSSION

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Gas chromatography coupled to mass spectrometry unsupervised metabolite profiling was

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developed to try to understand the dynamics of the S. lycopersicum response to the infestation of

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the root knot nematode M. incognita. Two months after RKN infestation, different plant parts,

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i.e. stem and leaves, were extracted with methanol and after derivatization submitted to GC-MS

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fingerprinting metabolomics analysis comparing levels of each metabolite to the equivalent

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control. Representative GC-MS chromatograms of tomato leaves and stems are reported in

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Figure 1. A total of 50 low molecular weight polar metabolites were detected of which 8 were

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unknowns (Table 1).

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Macroscopic foliar symptoms of nematode infestation of roots generally involve stunting and

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general unthriftiness, premature wilting and leaf chlorosis.24 On the other hand, no macroscopic

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changes were observable on the plant material used for this study (Figure 2). From a

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metabolomics point of view, RKN infestation appears to cause a metabolic response, mainly of

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primary metabolism and different from leaves and sink tissues. Statistical significant polar

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metabolites were studied using SIMCA-P. We first performed a PCA to examine interrelation

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between groups, clustering and outlier diagnostics among the samples. One outlier corresponding

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to a tomato leave control sample was discarded because it is outside of the Hotteling’s T2 area

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and after a DmodX test implemented by SIMCA-P. After this step, a PLS-DA was performed to

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maximize the difference of metabolic profiles between treated and control samples and allowing

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metabolite recognition. The following step of the statistical analysis was to perform a supervised

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OPLS-DA with the goal to separate samples in two clusters and identify biomarkers between the


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control and treated groups (Figures 3 and 4). Validation parameters for the two OPLS-DA were

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R2Y= 0.91 and Q2Y= 0.84 for stems while for leaves R2Y= 0.97 and Q2Y= 0.89.

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Discriminant metabolites reported in Table 2 were selected from the VIP-plot (selecting VIP>1).

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Metabolic pathway analysis was performed by using the web platform MetaboAnalyst (ver.

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3.0),23 combining the topology with a powerful pathway enrichment analysis. The changes in the

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metabolite or potential biomarkers suggested that seven biochemical pathways were altered by

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nematode infestation. Considering the limited number of metabolites reported in this study, the

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most altered pathways were β-alanine metabolism, phenylalanine metabolism, pantothenate and

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CoA biosynthesis, glycine, serine and threonine metabolism, galactose and sucrose metabolism

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(Figure 5).

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After nematode infestation, S. lycopersicum leaves metabolites β-alanine and Phe were

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upregulated with a Log2 fold change of 1.89 and 0.71, respectively. β-alanine and Phe are central

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components of the pantothenate biosynthesis and β-alanine metabolism, respectively (Figure 5).

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Ala can be derived from the degradation of polyamines such as spermidine and spermine.25

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Hewezi et al. reported that in Arabidopsis spermidine synthase is targeted by an effector protein

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of the cyst nematode Heterodera schachtii.26 The subsequent degradation of spermidine through

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polyamine oxidase stimulates the induction of the plant antioxidant machinery, protecting the

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nematode feeding structure from reactive oxygen species that are produced as a common

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response of host plants to nematode infestation.27 In leaves, melibiose was found upregulated

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while ribose was downregulated suggesting a possible inhibition of the plant glycosyl

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hydrolase.25 Furthermore, glycerol levels were strongly downregulated in leaves. Levels of

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saturated fatty acids, such as palmitic acid and myristic acid, were reduced in leaves when



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compared to controls. This fact is corroborated by Agarrwal et al. that reported the

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downregulation of saturated fatty acid in rice as a response to the asian rice gall midge

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infestation.28

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On the contrary, in stems soluble sugars such as glucose, ribose, fructose, and sucrose were

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upregulated while melibiose was downregulated suggesting that melibiose is consumed by the

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gylcosyl hydrolase to yield glucose. Notably, modifications of sugar levels involved in the stress

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response pathways such as signaling, osmotic adjustment, and respiration for energy production

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were found by Merewitz et al. in ipt transgenic creeping bentgrass in a drought tolerance

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experiment.29 Soluble sugars such as glucose, fructose, and sucrose are recognized as carbon and

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energy sources,30 and as well as signaling molecules in plants.31 After a comparative

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metabolomics analysis, Quian et al. reported that soluble sugars and glycerol were upregulated in

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Arabidopsis challenged with a bacterial pathogen.32 Sugar signals may also contribute to immune

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responses against pathogens33 and probably function as priming molecules leading to pathogen-

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associated molecular patterns (PAMP)-triggered immunity and effector-triggered immunity in

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plants.34

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Additionally, our metabolic data showed that RKN infestation caused in tomato plants a marked

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reduction of glycine in stems. The latter is consumed by the glycine decarboxylase complex and

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functions in photorespiration producing ammonia. Moreover, fumaric acid was downregulated in

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stems and this fact is in accordance with Guo et al. that reported reduced levels of fumaric acid

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in response to salt and alkali stress in wheat seedlings.35 The stem content of the tricarboxylic

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acid cycle intermediate fumaric acid was strongly reduced, reflecting a likely higher demand for


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reducing equivalents required for defense responses reported in an Arabidopsis model challenged

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by soil-borne fungus Verticillium dahliae Kleb.36

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We also performed a LC-QTOF metabolomics analysis of both plant tissues (data not shown).

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After a supervised statistical analysis OPLS-DA we found that the most upregulated metabolite

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in tomato leaves was an O-acylsugar with a formula C29H48O15 corresponding to m/z = 654.3335

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[M+NH4]+ a non-volatile secondary metabolite reported for S. lycopersicum by Schilmiller et

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al.37, 38 O-acylsugars are specialized metabolites produced by glandular trichromes, an epidermal

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secretory structure that play a crucial defensive role in many plant species.39

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Concluding, a GC-MS metabolomics analysis provides a powerful and reliable approach to study

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levels changes of plant metabolites after RKN infestation. We have measured levels variation of

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different plant metabolites associated with nematode infestation. According to MetaboAnalyst,

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β-alanine metabolism, phenylalanine metabolism, pantothenate and CoA biosynthesis, glycine,

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serine and threonine metabolism and soluble sugars metabolism were found altered in our M.

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incognita infestation model. We are the first to report a GC-MS metabolomics analysis of tomato

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plants infested with the root-knot nematode and our results indicated that metabolomics methods

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have adequate sensitivity and specificity to distinguish infested plant from controls.

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Combination of several -omic techniques (such as metabolomics, lipidomics, proteomics,

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genomics and transcriptomics) will be helpful in understanding the plant-nematodes interaction

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with the goal of improving plant health and growth while reducing nematode infestation.

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Moreover, assuming that we analyzed a partial set of S. lycopersicum plant metabolites, i.e. polar

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low-molecular-weight compounds, and at only one time of infestation, more studies are needed

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to have a complete view of the picture.



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AUTHOR INFORMATION

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Corresponding Author

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* Phone: +39 070 6758617. Fax: +39 070 6758612

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E-mail: [email protected]

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ACKNOWLEDGMENTS

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We are grateful to dr. Martina Demuru for helpful suggestions.

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ABBREVIATIONS USED

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GC-MS, gas chromatography-mass spetrometry; LC-QTOF-MS, liquid chromatography

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quadrupole time of flight mass spec trometry; PCA, principal component analysis; PLS-DA,

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partial least squares discriminant analysis; OPLS-DA, orthogonal projections to latent structures

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discriminant analysis; RKN, root-knot nematode.


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metabolism of nematode-infected Arabidopsis thaliana plants. J. Exp. Bot. 2014, 65, 201-

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212.


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Moghaddam, M. R. B.; Van den Ende, W., Sugars and plant innate immunity. J. Exp. Bot. 2012, 63(11), 3989-98.

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Guo, R.; Yang, Z.; Li, F.; Yan, C.; Zhong, X.; Liu, Q.; Xia, X.; Li, H.; Zhao, L.,

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Comparative metabolic responses and adaptive strategies of wheat (Triticum aestivum) to

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salt and alkali stress. BMC Plant Biol. 2015, 15, 1.

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Buhtz, A.; Witzel, K.; Strehmel, N.; Ziegler, J.; Abel, S.; Grosch, R., Perturbations in the

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primary metabolism of tomato and Arabidopsis thaliana plants infected with the soil-

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Borne fungus Verticillium dahliae. PloS one 2015, 10, e0138242.

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Schilmiller, A.; Shi, F.; Kim, J.; Charbonneau, A. L.; Holmes, D.; Daniel Jones, A.; Last,

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R. L., Mass spectrometry screening reveals widespread diversity in trichome specialized

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metabolites of tomato chromosomal substitution lines. Plant J. 2010, 62, 391-403.

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Schilmiller, A. L.; Charbonneau, A. L.; Last, R. L., Identification of a BAHD

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acetyltransferase that produces protective acyl sugars in tomato trichomes. P Natl. Acad.

311

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314

analyses of branched-chain fatty acid and acyl sugar production in Solanum pennellii and

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Nicotiana benthamiana. Plant physiol.2008, 148, 1830-1846.



FIGURE CAPTIONS

316 317

Figure 1: GC-MS TIC Chromatograms of Solanum lycopersicum cv. Rutgers plant extracts. (A)

318

leaves of uninfested plants, (B) leaves of M. incognita infested plants, (C) stems of uninfested

319

plants and (D) stems of M. incognita infested plants, analysis.

320

Figure 2: Tomato plants: (A) infested and (B) not infested with M. incognita.

321

Figure 3: OPLS-DA of the not infested (black) vs infested leaves (white): (A) score plot (B)

322

permutations analysis (C) multivariate statistic parameters.

323

Figure 4: PLS-DA of the not infested (black) vs infested stems (white): (A) score plot (B)

324

permutations analysis (C) multivariate statistic parameters.

325

Figure 5: Pathway analysis showing changes in the metabolism of tomato plant after root-knot

326

nematode infestation.

327


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Page 19 of 28

328


Table 1. GC-MS analysis of tomato plant after root-knot nematode infestation. Cmpda m/zb LRIc Cmpda m/zb LRIc lactic acid (TMS)2

117

alanine (TMS)2

116

2-hexanoic acid (TMS)

171

3-hydroxypropanoic acid (TMS)2

177

3-hydroxy-2methylpropanoic acid (TMS)2

117

valine (TMS)2

144

973 unknown 3

103

1617

1005 ribose

103

1629

1024 glutamine (TMS)3

156

1632

citric acid (TMS)4

273

1043

1691 myristic acid (TMS)

117

1056

1813

Fructose (MEOX) 1098 (TMS)5

103 1832

phosphoric acid (TMS)3

299

1148 galactose

331

1838

glycerol (TMS)3

205

1149 glucose

117

1847

isoleucine (TMS)2

158

1167 unknown 4

204

1851

proline (TMS)2

142

1170 tyrosine (TMS)3

218

1871

glycine (TMS)3

174

1177 palmitic acid (TMS)

117

1876

succinic acid (TMS)2

75

1186 inositol (TMS)6

217

2033

glyceric acid (TMS)3

189

1197 tryptophan (TMS)3

202

2051

uracil (TMS)2

241

1205 unknown 5

117

2217

fumaric acid (TMS)2

245

stearic acid (TMS)

117

2220

serine (TMS)3

204

1219 melibiose (TMS)8

204

2234

threonine (TMS)3

218

1225 unknown 6

204

2263

unknown 1

228

β-alanine (TMS)3

glycerol myristate (TMS)2

343

1247

1-monopalmitoyl glycerol (TMS)2

371

1413

174

2393 2409

malic acid (TMS)3

233

1415 sucrose (TMS)8

361

2604

aspartic acid (TMS)3

232

1446 unknown 7

217

2625

pyroglutamic acid (TMS)2

156

1462 2-monostearin (TMS)2 129

2640



Page 20 of 28

γ-aminobutyric acid

174

1464 1-monostearin (TMS)2 399

2689

glutamic acid (TMS)3

246

1468 maltose (TMS)8

85

2807

phenylalanine (TMS)2

218

1612 unknown 8

217

3000

329 330

a

MEOX, methoxime derivative; TMS, trimethylsilyl derivative. b Masses shown are those of the

331

ion(s) selected for identification and quantitation of individual derivatized metabolites. c Values

332

shown are linear retention indices based on linear interpolation of retention times between

333

adjacent alkane retention standards.


Page 21 of 28

334 335 336


Table 2. Log2(fold-change) of discriminant plant metabolites after root-knot nematode infestation. cmpd classes sugars

amino acids

fatty acids dicarboxylic acids

metabolites sucrose fructose ribose melibiose glycerol glucose β-alanine glycine phenylalanine myristic acid palmitic acid fumaric acid

leaves

-1.08 0.60 -0.58

stem 1.41 1.31 0.74 -0.50 0.40

1.89 -0.85 0.71 -0.88 -0.68 -1.47

337



Abundance

A

B

C

D

Time (min)

338 22 ACS Paragon Plus Environment

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Page 23 of 28


339

Figure 1: GC-MS TIC Chromatograms of Solanum lycopersicum cv. Rutgers plant extracts. (A)

340

leaves of uninfested plants, (B) leaves of M. incognita infested plants, (C) stems of uninfested

341

plants and (D) stems of M. incognita infested plants, analysis.

342

343



344 345

Figure 2: Tomato plants: (A) infested and (B) not infested with M. incognita.


Page 24 of 28

Page 25 of 28


346

(B) Validate Model $M2.DA(1) Intercepts: R2=(0.0, 0,33), Q2=(0.0, -0,69)

(A)

(C) 2

2

R = 0.97 Q =0.89

347 348 349

Figure 3: OPLS-DA of the not infested (black) vs infested leaves (white): (A) score plot (B) permutations analysis (C) multivariate statistic parameters.



Page 26 of 28

(A) (B) Validate Model $M3.DA(1) Intercepts: R2=(0.0, 0,638),Q2=(0.0, -

R2

matrice tomato alice NUOVAstem.M3 (OPLS-DA): Validate Model $M3.DA(1) Intercepts: R2=(0.0, 0,638), Q2=(0.0, -0,931)

Q2

1 0,5 0 -0,5 -1 -1,5 -2 -2,5 -3 -0,2

0

0,2

0,4 0,6 500 permutations 1 components

0,8

1

(C) 2

2

Comp[1+2]

Comp[1+1]

Comp[1+0]

R = 0.91 Q =0.84

350 351 352

Figure 4: PLS-DA of the not infested (black) vs infested stems (white): (A) score plot (B) permutations analysis (C) multivariate statistic parameters.


Page 27 of 28


353

-log(p)

Galactose metabolism

Starch and sucrose metabolism

Phenylalanine metabolism β-alanine metabolism Citrate cycle (TCA cycle) Pantothenate and CoA biosynthesis Glycine, serine and threonine metabolism

Pathway impact 354 355 356

Figure 5: Pathway analysis showing changes in the metabolism of tomato plant after root-knot nematode infestation.



357 358

359 360

TABLE OF CONTENTS GRAPHICS

78.4x47.5 mm


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Untargeted Metabolomics of Tomato Plants after Root-Knot Nematode

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