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Nuclear Magnetic Resonance Metabolomics of Iron Deficiency in Soybean Leaves Marta R. M. Lima,†,‡ Sílvia O. Diaz,§ Inês Lamego,§ Michael A. Grusak,‡ Marta W. Vasconcelos,*,† and Ana M. Gil*,§ †

CBQF − Centro de Biotecnologia e Química Fina − Laboratório Associado, Escola Superior de Biotecnologia, Universidade Católica Portuguesa/Porto, Rua Dr. António Bernardino Almeida, 4200-072 Porto, Portugal ‡ USDA-ARS Children’s Nutrition Research Center, Department of Pediatrics, Baylor College of Medicine, 1100 Bates Street, Houston, Texas 77030, United States § CICECO−Departamento de Química, Universidade de Aveiro, Campus Universitário de Santiago, 3810-193 Aveiro, Portugal S Supporting Information *

ABSTRACT: Iron (Fe) deficiency is an important agricultural concern that leads to lower yields and crop quality. A better understanding of the condition at the metabolome level could contribute to the design of strategies to ameliorate Fe-deficiency problems. Fe-sufficient and Fe-deficient soybean leaf extracts and whole leaves were analyzed by liquid 1H nuclear magnetic resonance (NMR) and high-resolution magic-angle spinning NMR spectroscopy, respectively. Overall, 30 compounds were measurable and identifiable (comprising amino and organic acids, fatty acids, carbohydrates, alcohols, polyphenols, and others), along with 22 additional spin systems (still unassigned). Thus, metabolite differences between treatment conditions could be evaluated for different compound families simultaneously. Statistically relevant metabolite changes upon Fe deficiency included higher levels of alanine, asparagine/aspartate, threonine, valine, GABA, acetate, choline, ethanolamine, hypoxanthine, trigonelline, and polyphenols and lower levels of citrate, malate, ethanol, methanol, chlorogenate, and 3-methyl-2-oxovalerate. The data indicate that the main metabolic impacts of Fe deficiency in soybean include enhanced tricarboxylic acid cycle activity, enhanced activation of oxidative stress protection mechanisms and enhanced amino acid accumulation. Metabolites showing accumulation differences in Fe-starved but visually asymptomatic leaves could serve as biomarkers for early detection of Fe-deficiency stress. KEYWORDS: Chlorosis, Fe deficiency, Glycine max (soybean), nuclear magnetic resonance (NMR), high-resolution magic-angle spinning (HRMAS), metabolomics, multivariate analysis

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INTRODUCTION

With very few exceptions, iron (Fe) is an essential nutrient for virtually all living organisms, and soybean, like all plants, requires iron because it plays a role in several important physiological processes such as chlorophyll biosynthesis, respiration and photosynthesis. Although Fe is the fourth most abundant element in the earth’s crust, its limited solubility makes it poorly bioavailable for plants,4 thus hindering its availability for plant uptake, particularly in calcareous soils, and leading to Fe-deficiency chlorosis (IDC).5 IDC symptoms generally

Soybean (Glycine max L.) is one of the most economically important crops worldwide, with the Food and Agriculture Organization statistics for 2012 showing an annual production of 253 million metric tons of soybean and ranking it as one of the world’s top commodity products. Soybean is a good source of protein, fiber, micronutrients, vitamins, isoflavonoids1 and oil, which can be used in the food and biodiesel industries. In recent years, there has been a decrease in the value of soybean as oilseed, which has led to the enhancement of soybean cultivation for forage, providing high-quality grazing, hay or silage.2,3 © 2014 American Chemical Society

Received: March 17, 2014 Published: April 17, 2014 3075

dx.doi.org/10.1021/pr500279f | J. Proteome Res. 2014, 13, 3075−3087

Journal of Proteome Research

Article

case of soybean, the metabolic response to Fe deficiency has been investigated only through targeted spectrophotometric studies of root exudates.16 Furthermore, response in other soybean plant parts, namely from the aerial part, remains to be investigated. Because often expensive remediation strategies are usually applied solely on the basis of leaf visual symptom scoring23 and because chlorosis symptoms resulting from other micronutrient deficiencies are sometimes visually indistinguishable from IDC, the discovery of adequate biomarkers of soybean Fe deficiency could help to assess Fe nutritional status more accurately and to improve remediation efficiency. This article describes an untargeted NMR metabolomic study of Fe-sufficient and Fe-deficient soybean leaves. To this end, leaf methanolic extracts and whole leaves were analyzed, respectively, using liquid-state 1H NMR and high-resolution magic-angle spinning (HRMAS) NMR spectroscopies. The compositional complementarity of extracts and whole leaves is discussed, and multivariate statistical analysis was employed to identify and characterize relevant metabolite variations between the two conditions (Fe sufficiency and Fe deficiency).

consist of a yellowing or chlorosis of youngest leaves, with a common interveinal appearance. Nonetheless, several nutrient deficiencies lead to leaf chlorosis and, sometimes, Fe deficiency can be mistaken by deficiencies in other nutrients.6 IDC is frequently accompanied by reduced growth, lower yields and quality of crops, and lower Fe concentrations in seeds and other harvested tissues used for human and animal nutrition.5 Soil or foliar fertilization of soybean with Fe has been used to alleviate IDC; however, these strategies involve higher costs and additional environmental concerns,7,8 whereas the breeding of IDC-resistant lines has been a lengthy process that has been met with limited success.9 Nongrass monocots and dicot plants, such as soybean, employ a reduction-based strategy (Strategy I) to acquire Fe from their root environment. At its core, this strategy consists of the reduction of Fe, from Fe(III) to Fe(II), by an Fe reductase belonging to the ferric reductase oxidase (FRO) family followed by the uptake of divalent Fe into the root cells by an iron regulated transporter (IRT).10 In addition, Strategy I plants deploy auxiliary actions such as proton excretion by a plasma membrane H+-ATPase, which lowers the surrounding pH, thus helping inorganic Fe solubilization and exudation of organic compounds such as carboxylates and phenolics, which are thought to play a role in the Fe-acquisition process.7,11 In addition, a particular class of phenolic-type compounds (scopoletins, a coumarin) was recently identified as comprising important Fe-deficiency-elicited compounds.12,13 Other metabolic changes that are involved in Fe uptake include a shift from anabolic to catabolic carbohydrate metabolism, leading to enhanced ATP synthesis, to compensate the higher H+-ATPase activity as well as the production of reducing equivalents to maintain Fe reductase activity. Also, increased activity of phosphoenolpyruvate carboxylase and several tricarboxylic acid (TCA) cycle enzymes, as well as an increase in organic and amino acids (reviewed by Zocchi14), often occurs in response to Fe deficiency. However, not all Strategy I plants activate (or activate to the same extent) all of the metabolic pathways related to the auxiliary actions mentioned above, which can contribute to the different degrees of IDC tolerance among species and even among genotypes.14,15 In this regard, soybean has been considered one of the species in which these supporting responses to Fe deficiency are not activated or are activated only to a small degree.16 Much has been done to characterize the molecular mechanisms11,17 and proteome changes (reviewed by LópezMillán et al.)18 underlying plant response to Fe deficiency; however, only a few studies have characterized the changes induced in the plant’s metabolome. Gas chromatography coupled to mass spectrometry (GC−MS) analysis of Fedeficient sugar beet root tips revealed higher levels of raffinose sugars and metabolites related to glycolysis and the TCA cycle,19 along with similar observations (increased amino acids, carbohydrates and the TCA cycle metabolites) reported for pea, tomato, sugar beet, and peach tree leaf tissue,20,21 whereas carbohydrates and amino acids were decreased in the xylem sap of tomato, lupine, and peach tree plants.21 In this context, nuclear magnetic resonance (NMR) metabolomics, a holistic methodology of high compound resolution and reproducibility, in spite of its lower sensitivity (submicromolar) compared to MS (micromolar), has been used only in one instance, to our knowledge, to study the metabolome of Prunus rootstocks with contrasting tolerance to Fe deficiency.22 This study reported changes in sucrose and in several amino and organic acids in Prunus root tips because of Fe deficiency.22 In the particular

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MATERIALS AND METHODS

Plant Growth, Sample Collection and Processing

Glycine max cv. Williams 82 seeds were germinated on moist paper for 6 days in the dark at 25 °C and then transferred to hydroponics under controlled environmental conditions (16 h light at 25 °C, 8 h dark at 18 °C, 70% relative humidity, 350 μmol/(m2 s) photon flux density, photosynthetic active radiation (PAR)). Twelve plants (per condition) were grown for 14 days in 20 L of a permanently aerated nutrient solution buffered with 1 mM MES (2,4-morpholino-ethane sulfonic acid) at pH 5.5. The nutrient solution consisted of 1.2 mM KNO3, 0.8 mM Ca(NO3)2, 0.3 mM NH4H2PO4, 0.2 mM MgSO4, 25 μM CaCl2, 25 μM H3BO3, 0.5 μM MnSO4, 2 μM ZnSO4, 0.5 μM CuSO4, 0.5 μM H2MoO4, and 0.1 μM NiSO4. Fe-sufficient plants were grown at 20 μM Fe(III)-EDDHA (ethylenediamine-N,N′-bis(2-hydroxyphenylacetic acid)), whereas no Fe was added in the case of Fe-deficient conditions. Solutions were changed weekly. After 14 days, each plant was scored for Fe-deficiency chlorosis (IDC) using a visual symptom scale ranging from 1 to 6: 1-full green; 2-mild chlorosis; 3-full yellow, veins still green; 4-yellow with veinal chlorosis; 5-full chlorosis with necrosis; and 6-tip necrosis and chlorotic axillary shoots developing.24 SPAD (Konica Minolta, model 502-plus) readings were performed daily on all expanded leaves, from day 6 (time when new leaves were adequately expanded to allow readings) to day 14 (except on day 12). At day 14, all plants were collected individually for metabolomic analysis. For each plant, fresh weight was recorded, and all fully or partially open trifoliate leaves of each plant (1−3 trifoliate leaves) were pooled and immediately frozen in liquid N2. Leaf samples were ground in a mortar with liquid N2 and stored at −80 °C until further analysis. Sample Preparation for NMR

Frozen leaf samples were extracted on the basis of the method of Wu et al.25 with some modifications. Briefly, 50 mg of leaf biomass was extracted in 1 mL of solution made up of 800 μL of methanol-d4 plus 200 μL of 90 mM KH2PO4 buffer (pH 6.0) in D2O containing 0.1% trimethyl silane propionic acid sodium salt (TSP) as a chemical shift reference. Samples were shaken, extracted (1 h, 22 °C) and sonicated (30 min). The supernatant (800 μL) was recovered after centrifugation (12 000 rpm, 3076



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3 min) and stored at 4 °C for 7 days until being transferred to 5 mm tubes and analyzed by NMR. For whole-leaf analysis, 20− 40 mg of frozen powdered leaf biomass were thawed immediately before HRMAS analysis and placed into a standard 4 mm MAS rotor, and to it were added 30 μL of sodium buffer (pH 7.2) and 5 μL of TSP 0.25%.

version 11.5 software. Relevant peaks were identified through PLS-DA loadings colored by variable importance to the projection (VIP). In spite of the low sample numbers available (Fe-deficient samples n = 10 (for both extracts and whole leaves); Fe-sufficient samples n = 12 (extracts) and n = 8 (whole leaves, because of limited machine availability for HRMAS analysis)), PLS-DA model validation by Monte Carlo cross-validation (MCCV) was attempted (500 runs), recovering Q2 values, sensitivity (sens), specificity (spec) and classification rates (CR). The relevant peaks were integrated (Amix 3.9.14, BrukerBioSpin) and the Wilcoxon test was used to evaluate the statistical relevance of variations (p < 0.05). Variations (%) were calculated as the percent increase or decrease in Fe-deficient leaves (all IDC scores included) relative to Fe-sufficient leaves, and effect size values (a measure of variation that takes into account group standard deviation) were calculated according to Berben et al.,32 including correction for a small sample size. Statistical tests and boxplots were carried out using R-statistical software version 3.0.2 and Plotrix package. Prism version 6.00 (GraphPad Software, Inc.) was used to obtain bar plots. STOCSY and VIP-colored loading plots were produced using MATLAB version 7.12.0 (The MathWorks, Inc.). Assignment of metabolites to metabolic pathways was assisted by the KEGG database (http://www.genome.jp/kegg) and the Plant Metabolic Network (http://www.plantcyc.org).

1

H NMR Spectroscopy

All NMR spectra were recorded on a Bruker DRX-500 spectrometer operating at a proton frequency of 500.13 MHz. For extracts, 1D 1H spectra were acquired at 300 K using the “noesypr1d” (RD−90°−t1−90°−tm−90°−acquire) pulse sequence with a 100 ms mixing time (tm), a fixed 24 ms t1 delay, and water presaturation performed during the relaxation delay and mixing time. The acquired spectra consisted of 256 transients, with 32 k complex data points, an 8012.820 Hz spectral width (SW), and a 2 s relaxation delay. Prior to Fourier transformation (FT), the free induction decays (FIDs) were zero-filled to 64 k points and multiplied by a 0.3 Hz exponential line-broadening function. The 1D spectra were manually phased and baseline corrected, and the chemical shifts were referenced internally to the alanine signal at 1.48 ppm (found preferable to the TSP signal, as this showed evidence of interaction with sample components, with a probable impact on signal chemical shift). For whole-leaf samples, rotors were spun at 4 kHz and spectra were recorded at 277 K. Standard 1D spectra (“noesypr1d”) were acquired with 256 scans, a SW of 6510.417 Hz, 32 k data points and a 2.5 s relaxation delay. Spectra were processed as described above for extracts. Two-dimensional NMR experiments were run for selected samples to aid peak assignment. For extracts, total correlation spectroscopy (TOCSY) 1H−1H spectra were recorded in phase-sensitive mode using States-TPPI (time proportional phase incrementation) detection in t1 with a MLEV17 spin lock pulse sequence.26 Forty eight FIDs with 2 k complex data points per increment and a total of 128 increments were acquired with a SW of 8012.820 and 8002.119 Hz (for the F1 and F2 dimensions, respectively), an 80 ms mixing time and a 2 s relaxation delay. Heteronuclear single quantum coherence (HSQC) 1H−13C spectra were acquired with inverse detection, 13C decoupling during acquisition, recording 48 FIDs with 2 k complex data points per increment to a total of 180 increments, with a SW of 8012.820 and 21739.131 Hz for the 1H and 13C dimensions, respectively, and a 1.5 s relaxation delay. Spectra were manually phased in both dimensions, baseline corrected, and referenced internally to the alanine resonance at 1.48 ppm (1H) and to the methanol methyl resonance at 51.56 ppm (13C). For whole leaves, TOCSY 1H−1H spectra were recorded with 100 FIDs, 8 k complex data points per increment, a total of 128 increments acquired with a SW of 6009.614 Hz (F1) and 6510.417 Hz (F2), a 70 ms mixing time and a 2 s relaxation delay. For both extract and whole-leaf spectra, NMR peak assignments were carried out using 2D NMR and compound databases (Bruker Biorefcode database and human metabolome database, HMDB27), along with statistical total correlation spectroscopy (STOCSY)28 and literature.29−31

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RESULTS

Fresh Weight, SPAD Readings, and IDC Visual Scoring

In order to determine the impact of Fe-deficiency treatment on overall plant growth, the fresh weight of Fe-deficient and Fe-sufficient shoots and roots was analyzed. On day 14, Fe-deficient plants showed lower fresh weight (p < 0.05) compared to that of sufficient plants, with shoot and root weights that were 41 and 39% lower, respectively (Figure 1a). In order to monitor the efficacy of the low-Fe treatment, SPAD readings were recorded daily on all expanded leaves of each plant from days 6 to 14 (no measurements were obtained on day 12) (Figure 1b). With the exception of day 7, all plants grown under Fe-deficient conditions showed significantly lower SPAD readings (p < 0.05). Also, in Fe-deficient plants, there was an overall reduction in SPAD values as a function of time until day 14. No visual changes were noticed between leaves of Fe-deficient and Fe-sufficient plants until day 6 in hydroponic culture (data not shown); however, after this day, differences in growth and appearance could be clearly observed. Thus, the lower SPAD values paralleled a concomitant increase in the severity of IDC visual symptoms. The appearance of plants at sampling time (day 14) is shown in Figure 2. All Fe-sufficient plants were scored 1-full green, whereas visual symptoms of plants growing with Fe-deficient conditions ranged from 1-full green (two plants), 2-mild chlorosis (four plants), and 3-full yellow, veins still green (two plants) to 5-full chlorosis with necrosis (one plant) and 6-tip necrosis and chlorotic axillary shoots developing (three plants). Within the latter three plants, two were in a more severe symptomatic stage, and the trifoliate leaves fell off the day prior to sampling. For that reason, only leaves corresponding to the remaining 10 Fe-deficient plants were analyzed by NMR spectroscopy.

Statistical Analysis

Data matrices of integrated regions of 0.005 ppm width (excluding the water region at 4.70−4.85 and 4.90−5.10 ppm for extracts and whole leaves, respectively) were normalized to total intensity, centered or unit variance (UV) scaled, and analyzed by principal component analysis (PCA) and partial least-squares discriminant analysis (PLS-DA) using SIMCA-P

NMR Spectroscopy of Leaf Extracts and Intact Leaves

Representative 1H NMR spectra of methanolic extracts obtained at day 14 are shown in Figure 3a,b, respectively, 3077



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from Fe-sufficient and Fe-deficient leaves, whereas Figure 3c,d shows the 1H HRMAS NMR spectra registered for the corresponding whole leaves. It should be noted that 1H HRMAS spectra reflect the more mobile moieties in the plant tissue (i.e. mostly small and medium Mw compounds), with macromolecules and macromolecule-bound metabolites expected to be too dynamically restricted to produce significant signal in the spectrum. Therefore, signal intensities in the HRMAS spectrum reflect not only metabolite concentrations but also their relative mobility, so quantitative analysis must be handled cautiously. Considering Fe-sufficient conditions first, it can be seen that the extract spectrum shows contributions from lipids (resonances 5 and 6 in Figure 3a), along with many amino and organic acids, methanol (resonance 15), a residual amount of sugars (overlapped with a prominent unassigned resonance at 3.60 ppm, resonance 16), chlorogenate, hypoxanthine, and trigonelline (resonances 17−19 in the 6−10 ppm region). The corresponding 1H HRMAS spectrum of Fe-sufficient leaves (Figure 3c) reflects residual contributions from lipids (probably because of their dynamic restriction in the cellular medium) and aliphatic amino acids, with important contributions from γ-aminobutyrate (GABA), citrate, succinate, and malate (resonances 8 and 10−12). The sugar region (3−6 ppm) shows a profile distinct to that of extracts (with an apparent predominance of glucose in leaves), as does the aromatic region (6−10 ppm). Table 1 lists all the metabolites measured and identified in soybean leaves and shows that inclusion of both sample types (extracts and whole leaves) is useful in providing a fuller picture of the metabolome: acetate, fatty acids, ethanolamine, and chlorogenate were detected only in extracts, whereas five out of the 10 amino acids found, citrate, fumarate, glucose, 3-methyl-2-oxovalerate, polyphenols and p-coumarate

Figure 1. (a) Shoot and root fresh weight of Fe-sufficient and Fedeficient plants after 14 days in hydroponic culture. Values are the mean ± standard error (n = 12); all values are statistically significant compared to that of the control (t test, p < 0.05). (b) SPAD values registered for all open leaves of Fe-sufficient and Fe-deficient plants from days 6 to 14 in hydroponic culture (no measurement on day 12). Values are the mean ± standard error (n = 12); all values are statistically significant compared to that of the control except for day 7 (t test, p < 0.05). * indicates statistically significant differences.

Figure 2. Appearance of Glycine max cv. Williams 82 plants growing under Fe-sufficient (a) and Fe-deficient (b) conditions for 14 days. Enlargements representative of different IDC visual scores are shown. 3078



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Figure 3. 1H NMR spectra of methanolic extracts of (a) Fe-sufficient and (b) Fe-deficient (IDC score 6) soybean leaves and 1H HRMAS NMR spectra of whole (c) Fe-sufficient and (d) Fe-deficient (IDC score 6) soybean leaves. Insets refer to the 6−9.25 ppm region. Arrows indicate visual changes induced by Fe deficiency and some assignments are indicated: 1, leucine; 2, isoleucine; 3, valine; 4, ethanol; 5, tridecanoate; 6, fatty acids; 7, alanine; 8, γ-aminobutyrate (GABA); 9, acetate; 10, malate; 11, succinate; 12, citrate; 13, asparagine; 14, choline; 15, methanol; 16, unassigned 3 (3.60, 3.94 and 5.25 ppm); 17, hypoxanthine; 18, chlorogenate; 19, trigonelline; 20, fumarate; 21, tyrosine; 22, p-coumarate; 23, histidine; 24, phenylalanine; 25, unassigned 14 (1.36 ppm); 26, unassigned 16 (3.13 ppm); 27, unassigned 18 (4.01 ppm); 28, β-glucose; and 29, threonine.

Un 3, Un 6, and Un 12 (Table 1), and the broad underlying features at 6−10 ppm indicate the presence of polyphenolic species. Furthermore, Fe-deficient whole leaves (Figure 3d) confirmed the changes in ethanol (lower levels) and asparagine (higher levels) previously seen in extracts and revealed additional changes, namely, lower levels of malate (resonance 10), citrate (resonance 12), Un 16 (resonance 26), choline (resonance 14), and methanol (resonance 15) and higher levels of threonine (resonance 29) and polyphenols (expressed by the strong broad resonances at 7.02 and 7.77 ppm). It is notable that although a markedly reduced level of glucose was apparent in the spectra shown in Figure 3c,d, this change turned out not to be representative of the whole sample group. A more thorough assessment of the relevant changes between sample groups was carried out through multivariate

were observed only in whole leaves (see the metabolites indicated by footnotes d and e in Table 1). Metabolomic Changes Induced by Fe Deficiency

The effect of Fe-deficient conditions on the composition of soybean leaves becomes clear both in the spectra of extracts (Figure 3b) and whole leaves (Figure 3d). Extract spectra show that the condition impacts all regions of the spectra, with relatively higher levels of valine (resonance 3, overlapped with leucine and isoleucine), asparagine (resonances 13), Un 3 (resonances 16), hypoxantine (resonance 17), and trigonelline (resonances 19), in tandem with lower levels of ethanol (resonance 4), fatty acids (resonances 5 and 6), methanol (resonance 15), and chlorogenate (resonances 18). In addition, the sugar anomeric region (5 to 6 ppm) shows additional complexity because of contributions from unassigned compounds 3079



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Table 1. Metabolites and Unassigned Spin Systems Observed in the 1H NMR Spectra of Soybean Extracts and Whole Leaves assignment Amino Acids alanineb asparagineb; asparagine/aspartated γ-aminobutyrateb histidined isoleucineb leucineb phenylalanined threonined tyrosined valineb Organic Acids acetatec citrated fumarated malateb succinateb Fatty Acids fatty acidsc,e tridecanoatec,f Carbohydrates sucroseb,e α-glucosed β-glucosed

δHppm (multiplicity/J Hz, δCppm)a

assignment

δHppm (multiplicity/J Hz, δCppm)a

Other Compounds ethanolb ethanolaminec methanolb cholineb

1.48 (d/7.3), 3.78 (q) 2.76 (dd), 2.95 (dd), 3.88 (dd); 2.86 (d), 2.91 (dd) 1.90 (m/7.5), 2.29 (t/7.5), 2.99 (t/7.7) 7.23 (br) 1.01 (d/7.2) 0.90−0.97 7.31 (d), 7.37 (d), 7.41 (m) 1.33 (d/6.6), 3.58 (d), 4.9 (m) 6.90 (d), 7.19 (d) 1.01 (d), 1.06 (d); 0.99 (d/7.0), 1.04 (d/7.0), 3.62 (d/4.3)

1.18 (t/7.4), 3.65 (q) 3.08 (t), 3.74 3.31 (s), 3.35 (s) 3.21 (s, 57.40), 3.53 (dd), 4.07 (m); 3.20 (s), 3.52 (dd), 4.06 (m) 3-methyl-2-oxovalerated,f 1.09 (d/6.4) polyphenolsd,e 7.02 (br), 7.77 (br) trigonellineb,e 4.45 (s), 8.08 (m), 8.85 (m), 9.16 (s); 4.44 (s), 8.07 (m), 8.84 (m), 9.14 (br) hypoxanthinec 8.19 (s), 8.22 (s) chlorogenatec 6.37 (d), 6.94 (d), 7.62 (d) p-coumarated 6.40 (d), 6.94 (m), 7.36 (d), 7.54 (m) unassigned spin unassigned spin systems in whole systems in leaves spectra extracts spectra

1.92 (s, 26.37) 2.52 (d), 2.68 (d) 6.52 (s) 2.40, (dd), 2.71 (dd), 4.26 (dd); 2.38 (dd), 2.68 (dd), 4.31 (dd) 2.40 (s, 36.46); 2.41 (s)

Un Un Un Un Un Un Un Un Un Un Un Un Un

1.32, 1.35, 1.60 (m), 1.90 (dd), 2.05 (m), 2.79 (m), 5.35 (m) 0.86 (t), 1.27, 1.53 (t), 2.16 (t) 3.40 (t), 3.63 (s), 3.78 (m), 4.14 (d), 5.40 (d); 4.23 (d), 5.41 (d) 3.41 (m), 3.54 (dd), 3.72 (t), 3.77 (t), 3.83 (m), 5.24 (d) 3.24 (dd), 3.41 (m), 3.47 (m), 3.50 (t), 3.73 (dd), 4.65 (d)

1 2 3e 4 5 6 7 8 9 10e 11 12 13

1.10 (m) 3.16 (m), 3.35 (s), 4.50e 3.60 (s), 3.94 (m), 5.25 (m), 5.93(br)e 3.71 (m) 3.93 (m, 75.68)e 5.06 (d), 5.00, 5.85, 6.23e 6.32 (d) 6.91(d), 7.37e 7.53 (d), 6.84e 7.63 (m), 7.66 (dd), 7.69 (d)e 7.82 (d), 7.39e 7.96 (d), 5.80 (d)e 8.24 (s)

Un Un Un Un Un Un Un Un Un Un

14 15 16 17 3 18 19 20 21 22

1.36 2.44 3.13 3.34 3.60 4.01 4.16 4.28 4.61 4.78

(s) (t) (s) (t) (s) (m) (m) (d) (m) (br)

a

Designations and numbers in italic correspond observations in the 1H HRMAS spectra (whole leaves). Spin multiplicity designations: s, singlet; d, doublet; t, triplet; dd, doublet of doublets; br, broad; m, complex multiplet; and Un i: unassigned resonance i. bMetabolites detected in both extracts and whole leaves spectra. cMetabolites detected only in extracts. dMetabolites detected only in leaves. eSpin systems identified with STOCSY. f Tentative assignment.

data in order for complementary information to be obtained (Figure 4e). This enabled the metabolites responsible for extract separation to be identified and Table 2 lists the changes that were found to be statistically relevant (p < 0.05). In addition to these, qualitative observations were noted regarding fatty acids and polyphenols, which exhibited lower and higher levels, respectively, in deficient plants. A similar approach was followed for the 1H HRMAS spectra of whole leaves, confirming group separation in PCA and, particularly, in PLS-DA scores (Figure 5a,b). These results provided a corresponding list of statistically relevant metabolite variations (Table 3) obtained through the inspection of the loadings (Figure 5c). Qualitative changes comprised apparent, nonstatistically significant, lower levels of choline and methanol. Again, it became clear that the use of liquid and solid NMR techniques provides complementary information on metabolite variations, as expressed by Tables 2 and 3 and the boxplots in Supporting Information Figure S1. The apparent absence of some metabolites (e.g., choline and methanol) in whole leaves (Table 3), viewed by HRMAS, may reflect their distribution in macromolecule-bound environments, thus not enabling an adequate quantitative determination. Overall, evaluation of Fe-deficient plants through extract and whole-leaf NMR analysis showed (1) increased levels of alanine, asparagine, threonine, valine, GABA, acetate, choline, ethanolamine, hypoxanthine, trigonelline and polyphenols and

analysis of the data. A strong separation between groups is seen by PCA of the extract spectra (Figure 4a), where Fe-sufficient extracts (in black) cluster together in positive PC1 values and Fe-deficient samples (in red) group in negative PC1 (explaining 81.5% of data variability). A trajectory may be suggested for the latter group, which is seemingly dependent on symptom severity: the first three stages (1−3 IDC scores) are in close proximity to each other (with the exception of an outlier with IDC of 3) and stages 5 and 6 are displaced toward negative PC1/PC2 values. Interestingly, samples labeled 1 (no visual symptoms) are placed in different PCA quadrants depending on the growth conditions (Fe sufficiency or deficiency), showing that metabolic changes taking place before visual changes are noted, are detectable by NMR. The outlier sample circled in Figure 4a was found to have higher methanol content than the remaining Fe-deficient plants (which generally showed lower levels compared to that of the controls). Removal of the methanol resonance from the data produced a second PCA plot (Figure 4b) where group separation is clear only after stage 2 of IDC, showing the importance of methanol in differentiating early deficiency stage plants. PLS-DA confirmed the above observations and provided robust models regardless of whether the methanol resonance was included (Figure 4c; Q2 0.83, 100% sens, 92% spec, 95% CR) or not (Figure 4d; Q2 0.66, 100% sens, 97% spec, 98% CR). Inspection of PLS-DA loadings was carried out considering both centered and univariate scaled 3080



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changes occurring in the leaves are, therefore, a reflection of the plants response to the lack of Fe. The metabolites found to change, through NMR, may be putatively placed into particular metabolic pathways, thus leading to a set of proposed metabolic alterations induced by Fe deficiency (Figure 6). The condition seems to affect multiple pathways, with the occurrence of several catabolic processes, as described in detail below. Although the range of Fe-deficiency symptoms presented by the plants facilitated the demonstration of gradual metabolic changes from the least to the most symptomatic leaves (Figures 4 and 5), additional significant changes in metabolites might have been identified if samples were symptomatically more homogeneous. Changes in Amino Acids

Fe-deficient soybean leaves had higher levels of alanine, valine, asparagine, aspartate, threonine and GABA (Tables 2 and 3 and Figure 6). A lower level of 3-methyl-2-oxovalerate was also detected, possibly related to the higher level of threonine (Figure 6), thus suggesting a reduction in amino acid degradation processes that maintained a pool of free amino acids in soybean leaves. Indeed, besides being the building blocks for protein synthesis, amino acids are also important in many metabolic networks that control growth and adaptation to environmental stresses.38 Their biosynthesis is regulated by a complex network involving both nitrogen (N) and carbon (C) metabolic pathways, such as nitrate reduction occurring alongside carbon oxidation. Fe deficiency induces several modifications concerning N and C metabolism, and changes in amino acid biosynthesis have been linked to Fe deficiency in cucumber,39 where Fe-deficient roots showed decreased amino acid levels, whereas increased levels were detected in leaves and xylem sap. Such results also showed that amino acid alterations are tissue-specific, as confirmed by high amino acid levels in Fe-deficient Prunus root tips22 and soybean root exudates16 but low levels in xylem sap of deficient tomato and lupine plants.21 Furthermore, the specific amino acid profile characteristic of Fe deficiency differs according to plant species, as reports are not completely consistent at this level. Regarding leaves, increases in amino acids reported for Fe-deficient pea, sugar beet, tomato, and peach tree20,21 are consistent with the results hereby reported for soybean leaves. In the specific case of GABA, higher levels of its precursor, glutamate, have been found previously in Fe-deficient tobacco40 and cucumber39 leaves. Even though we could not detect glutamate, it is possible that it is converting rapidly into GABA, leading to higher levels in deficient leaves. It is thought that increased amino acid levels in Fe chlorotic/ senescent leaves (consequently with lower photosynthetic activity) reflect increased nitrogen recycling processes, with amino acids being used as carbon source in anaplerotic reactions.21,41 Also, higher levels of leaf amino acids may be due to increased export from the roots.39 Our results could, in addition, be explained by a decrease in protein synthesis in leaves, with lower amino acid utilization rate and consequent accumulation. Indeed, photosynthetic CO2 assimilation is the carbon source for protein synthesis,42 and, as our Fe deprived plants were chlorotic and stunted (Figures 1 and 2), this may have lowered protein synthesis rates.

Figure 4. Scatter plots of PCA (a, b) and PLS-DA (c, d) scores obtained for the full 1H NMR extract spectra (a, c) and for the same spectra after removal of the methanol peak (3.31 ppm) (b, d, centered data). Fe-sufficient (black) and Fe-deficient (red) samples are labeled with an IDC visual score: 1-full green; 2-mild chlorosis; 3-full yellow, veins still green; 4-yellow with veinal chlorosis; 5-full chlorosis with necrosis; and 6-tip necrosis and chlorotic axillary shoots developing. The circle identifies an outlier sample. VIP-colored PLS-DA loadings (e) corresponding to the PLS-DA model in panel d (unit variance scaled data).

(2) decreased levels of citrate, malate, ethanol, methanol, chlorogenate and 3-methyl-2-oxovalerate. Furthermore, many additional statistically relevant changes were noted in both extracts and whole leaves but are, at this stage, left unassigned (Tables 2 and 3 and Supporting Information Figure S1). This demonstrates the remarkable wealth of metabolic information obtainable through NMR spectra and the need for further assignment strategies (namely, through LC−NMR or targeted extract analysis).

■

DISCUSSION Soybean is especially prone to IDC, particularly at a young developmental stage. IDC-affected plants usually show yellowing of the upper leaves, interveinal chlorosis, and stunted growth.13,33 The lower fresh weight observed in shoots and roots (Figure 1a) and the lower SPAD values obtained for Fedeficient plants from day 6 onward (Figure 1b) confirmed that the Fe-deficiency treatment was efficient at generating IDC symptoms. SPAD values are often used as indicators of the chlorophyll concentration and chlorosis degree in many plant species,34−37 including soybean.33 The concomitant metabolite

Changes in Organic Acids

Soybean Fe-deficient leaves showed higher levels of acetate and lower levels of citrate and malate (Tables 2 and 3 and Figure 6). 3081



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Table 2. Metabolite Variations Observed at Day 14 in the 1H NMR Spectra of MeOH Extracts of Fe-Deficient Soybean Leaves Compared to That of Fe-Sufficient Leaves Fe-deficient compared to Fe-sufficient leaves δ/ppm (multiplicity)a

compound Amino Acids alanine asparagine γ-aminobutyrate valine Organic Acids acetate Other Compounds choline ethanol ethanolamine methanole hypoxanthinee chlorogenate trigonelline Unassigned Resonances Un 1 Un 2 Un 3 Un 4 Un 6 Un 7 Un 8 Un 9 Un 10 Un 11 Un 12 Un 13

1.48 2.76 1.90 1.01

variation (%)b

(d), 3.78 (q) (dd), 2.95 (dd), 3.88 (dd) (m), 2.99 (t), 2.99 (t) (d), 1.06 (d)

40 293 54 66

1.92 (s) 3.21 1.18 3.08 3.31 8.19 6.37 4.45

56

(s), 3.53 (dd), 4.07 (m) (t), 3.65 (q) (t), 3.74 (s) (s), 8.22 (s) (d), 6.94 (m), 7.62 (d) (s)

1.10 (m) 3.16 (m), 3.35 (s), 4.50 3.60 (s), 3.94 (m), 5.25 (m), 5.93 (br) 3.71 (m) 5.06 (d), 5.00, 5.85, 6.23 6.32 (d) 6.91(d), 7.37 7.53 (d), 6.84 7.63 (m), 7.66 (dd), 7.69 (d) 7.82 (d), 7.39 7.96 (d), 5.80 (d) 8.24 (s)

p valuec

effect sized

10−2 10−3 10−5 10−3

1.04 1.09 2.99 0.81

2.96 × 10−3

1.27

2.95 2.65 1.24 8.28

× × × ×

27 −17 32 −33 28 −17 29

1.24 1.52 8.61 3.00 2.79 1.44 6.72

× × × × × × ×

10−5 10−3 10−5 10−04 10−2 10−2 10−4

2.37 −1.50 3.71 −2.57 1.02 −1.16 1.85

−24 −15 75 22 −43 −23 52 −16 −37 −31 62 68

4.16 1.87 8.69 2.47 8.34 1.31 4.50 1.55 8.65 8.30 1.71 1.27

× × × × × × × × × × × ×

10−4 10−3 10−5 10−4 10−5 10−2 10−3 10−2 10−5 10−4 10−4 10−2

−2.12 −1.70 2.29 2.25 −3.38 −1.19 1.01 −1.20 −2.91 −1.67 2.13 1.21

a

Chemical shifts of spin system signals; values underlined refer to those used for integration. Spin multiplicity designations: s, singlet; d, doublet; t, triplet; dd, doublet of doublets; br, broad; m, complex multiplet; and Un i: unassigned resonance i. bVariations (%) were calculated as the percent increase or decrease in the Fe-deficient leaves group (all IDC scores included) relative to that of the Fe-sufficient leaves group. cOnly p values < 0.05 (95% significance) are shown. dEffect size values were calculated according to Berben et al.32 including correction for a small sample size. eVariation associated for the first time with Fe deficiency.

Changes in Secondary Metabolism

Most studies have reported increases in total organic acids, and it is believed that these compounds (in particular citrate) form complexes with Fe, enabling its translocation from roots to leaves.43 Increases in citrate and malate have been reported in deficient pea leaves, but only in an Fe deficiency tolerant variety,20 whereas lower levels of malate were observed in leaves of kiwifruit and leaf apoplastic fluid of pear trees.44,45 In Fe-deficient tomato, sugar beet, and peach leaves, higher levels of succinate, citrate, and fumarate were seen, along with increased levels of oxoglutarate, aconitate, citrate, and malate in xylem sap.21 In soybean, citrate has been seen to increase in root exudates.16 The lower levels of TCA organic acids registered here for Fe-deficient soybean leaves could relate to TCA enhancement to produce amino acids, consistent with the higher levels of the latter (Figure 6). Furthermore, we hypothesize that acetate in Fe-deficient soybean leaves is being produced via fermentation, because the higher levels of acetate are accompanied by lower levels of ethanol (Figure 6). Anaerobic metabolism has been associated with Fe deficiency.15,18 In fact, anaerobic metabolism has been shown to be activated in Fe-deficient Arabidopsis thaliana, specifically through the induction of lactate dehydrogenase, pyruvate decarboxylase, and alcohol dehydrogenase.46

Significantly higher levels of polyphenols were noted for Fedeficient leaves, which may be associated with the observed lower level of chlorogenate (Figure 6), indicating that the metabolism is flowing toward secondary metabolite production. Increases in phenolics in response to Fe deficiency have been reported in roots and root exudates of several plants, including soybean,16,47,48 whereas in leaves, polyphenols and flavonoids have been shown to be increased in lettuce.49 These phenolic compounds are thought to play a role in Fe(III) reduction and chelation, thus helping to mobilize Fe and to protect the plant against oxidative damage resulting from Fe deficiency. Chlorogenate (5-caffeoylquinic acid), formed from p-coumarate (detected here in whole leaves, Table 1), is one of the most abundant and widespread soluble phenolics in vascular plants,50,51 and it has a role in oxidative stress protection. It is possible that the significantly lower chlorogenate levels found in extracts of Fedeficient leaves are due to its use in the synthesis of polyphenols and/or to a diversion of its biosynthetic precursors toward the synthesis of other Fe-deficiency-inducible compounds. Changes in Nucleoside/Nucleotide Metabolism

Soybean Fe-deficient leaves exhibited higher levels of trigonelline and hypoxanthine (Table 2 and Figure 6). Trigonelline is a 3082



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response to oxidative stress, so it could be accumulating in Fe-deficient soybean leaves in response to oxidative damage resulting from Fe deficiency. However, it is also possible that trigonelline works as a storage/transport compound supporting NAD synthesis.52 Pyridine nucleotides were shown to increase in Fe-deficient pear leaves,45 whereas the pyridine nucleotide pool did not change in tomato leaves, although changes in the ratios of NAD(H)/NAD+ and NADPH/NADP+ were noted.15 Additionally, the increased levels of hypoxanthine may relate to perturbation of purine catabolism (Figure 6) or a relation to its role in the salvage pathway of purines.53 Changes in Other Compounds

Fe-deficient leaves also showed significant accumulation of choline and ethanolamine and decreases in methanol (Tables 2 and 3 and Figure 6) and fatty acids (although the latter variation was not statistically relevant). An increase in ethanolamine has been shown in Fe-deficient tomato leaves,21 and changes in choline were reported in Prunus root tips, although choline concentrations differed more between species than between Fe treatments.22 Choline is the precursor of glycine betaine, a metabolite that has been shown to increase in response to abiotic stress and whose overexpression in plants increases their tolerance to stress.54,55 Most interestingly, glycine betaine has been shown to have a protective action on the photosynthetic apparatus under stress.56−58 Therefore, it is possible that leaf metabolism in Fe-deficient soybean is being directed toward the production of these compounds in order to cope with the micronutrient deficiency. We also propose that the higher levels of ethanolamine and choline may be associated with changes in lipid metabolism, as these two compounds are components of phospholipids. A deregulation in lipid

Figure 5. Scatter plots of PCA (a) and PLS-DA (b) scores obtained for the 1H HRMAS NMR spectra of whole soybean leaves (centered data). Fe-sufficient (black) and Fe-deficient (red) samples labeled with an IDC visual score: 1-full green; 2-mild chlorosis; 3-full yellow, veins still green; 4-yellow with veinal chlorosis; 5-full chlorosis with necrosis; and 6-tip necrosis and chlorotic axillary shoots developing. (c) VIP-colored PLS-DA loadings obtained for whole soybean leaves (UV scaled data).

metabolite derived from the pyridine nucleotide cycle, whose function is thought to be the detoxification of excess nicotinate produced from NAD.52 Trigonelline has also been suggested to be involved in the signal transduction chain linked to the plant’s

Table 3. Metabolite Variations Observed at Day 14 in the 1H HRMAS NMR Spectra of Whole Soybean Fe-Deficient Leaves Compared to That of Fe-Sufficient Leaves Fe-deficient compared to Fe-sufficient leaves δ/ppm (multiplicity)a

variation (%)b

p valuec

2.86 (d), 2.91 (dd) 1.33 (d), 3.58 (d), 4.90 (m)

215 22

2.05 × 10−2 1.45 × 10−2

0.97 1.24

2.52 (d), 2.68 (d) 2.38 (dd), 2.68 (dd), 4.31 (dd)

−43 −39

4.39 × 10−3 5.11 × 10−3

−1.50 −1.57

1.09 (d) 7.02 (br), 7.77 (br)

−23 205

1.84 × 10−2 8.55 × 10−3

−1.49 1.07

1.36 2.44 3.13 3.34 3.60 4.01 4.16 4.28 4.61 4.78

30 20 −52 36 34 28 −25 −22 −42 −52

2.06 2.66 5.48 6.22 3.06 4.39 2.06 1.13 3.06 1.10

10−3 10−2 10−4 10−3 10−3 10−3 10−3 10−2 10−3 10−2

1.16 1.07 −1.96 1.23 1.27 1.41 −1.86 −1.56 −1.54 −1.19

compound Amino Acids asparagine/aspartate threonine Organic Acids citrate malate Other Compounds 3-methyl-2-oxovaleratee,f polyphenols Unassigned Resonances Un 14 Un 15 Un 16 Un 17 Un 18 Un 19 Un 20 Un 21 Un 22 Un 23

(s) (t) (s) (t) (s) (m) (m) (d) (m) (br)

× × × × × × × × × ×

effect sizeb

a

Chemical shifts of spin system signals; values underlined refer to those used for integration. Spin multiplicity designations: s, singlet; d, doublet; t, triplet; dd, doublet of doublets; br, broad; m, complex multiplet; and Un i: unassigned resonance i. Numbering follows that in Table 2. bVariations (%) were calculated as the percent increase or decrease in the Fe-deficient leaves group (all IDC scores included) relative to that of the Fe-sufficient leaves group. cOnly p values

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