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. 2018 Apr 15;42(1):115–132. doi: 10.1111/pce.13195

UPLC‐HRMS‐based untargeted metabolic profiling reveals changes in chickpea (Cicer arietinum) metabolome following long‐term drought stress

Naeem Khan 1, Asghari Bano 1,2, Mohammad Atikur Rahman 3, Bala Rathinasabapathi 4, Md Ali Babar 3,✉
PMCID: PMC7379973  PMID: 29532945

Abstract

Genetic improvement for drought tolerance in chickpea requires a solid understanding of biochemical processes involved with different physiological mechanisms. The objective of this study is to demonstrate genetic variations in altered metabolic levels in chickpea varieties (tolerant and sensitive) grown under contrasting water regimes through ultrahigh‐performance liquid chromatography/high‐resolution mass spectrometry‐based untargeted metabolomic profiling. Chickpea plants were exposed to drought stress at the 3‐leaf stage for 25 days, and the leaves were harvested at 14 and 25 days after the imposition of drought stress. Stress produced significant reduction in chlorophyll content, F v/F m, relative water content, and shoot and root dry weight. Twenty known metabolites were identified as most important by 2 different methods including significant analysis of metabolites and partial least squares discriminant analysis. The most pronounced increase in accumulation due to drought stress was demonstrated for allantoin, l‐proline, l‐arginine, l‐histidine, l‐isoleucine, and tryptophan. Metabolites that showed a decreased level of accumulation under drought conditions were choline, phenylalanine, gamma‐aminobutyric acid, alanine, phenylalanine, tyrosine, glucosamine, guanine, and aspartic acid. Aminoacyl‐tRNA and plant secondary metabolite biosynthesis and amino acid metabolism or synthesis pathways were involved in producing genetic variation under drought conditions. Metabolic changes in light of drought conditions highlighted pools of metabolites that affect the metabolic and physiological adjustment in chickpea that reduced drought impacts.

Keywords: drought, metabolic pathway, metabolites, UPLC‐HRMS analysis, water stress

Short abstract

Drought stress is one of the major problems in chickpea‐growing areas. Though drought stress changes biochemical mechanisms in plants, however, little is known about the complex metabolic regulation for genetic improvement in chickpea under drought stress environments. This study was conducted to identify changes at different metabolites in two chickpea varieties contrasting for drought tolerance under drought and control conditions. This study also demonstrates the metabolic pathways potentially involved in drought tolerance mechanisms in chickpea.

1. INTRODUCTION

Chickpea (Cicer arietinum) is the fourth largest grown legume crop in the world, which is grown in an area of 10.2 million hectares with an average production of 11 million metric tons. Chickpea significantly contributes to world food security by providing calories and dietary protein for millions of people (Varshney et al., 2013). More than 80% of chickpea is grown under rainfed conditions where unpredictable rainfall pattern or drought stress is the most common limiting factor that affects its growth and productivity. Moreover, drought conditions also affect symbiotic association of nodulating bacteria that contribute 45% of nitrogen needed for agriculture (Babalola, 2010). In addition, the climate change scenario, especially progressive drought stress induced by either declining rainfall or higher water stress allied with hot climate, will cause significant damage to crop production in the future (Dai, 2013; Lobell et al., 2014; Ruane et al., 2014). The major effects of drought include reduction in cell elongation, cell division, stem enlargement, and decrease in root growth (Farooq, Wahid, Kobayashi, Fujita, & Basra, 2009; Y. Li, Ye, Wang, & Yan, 2009). In addition, drought stress interrupts photosynthesis and increases translocation of carbohydrates, accelerates phasic development, and boosts senescence (Kubota & Kozai, 1992; Yang, Zhang, Huang, Zhu, & Wang, 2000; Yang, Zhang, Wang, Zhu, & Wang, 2001). Genetic improvement is the most efficient and sustainable way to reduce drought stress effect and develop genetically superior germ plasm that adapts to future climate change conditions. However, genetic improvement requires a solid understanding of the biochemical mechanisms controlling different traits. The analyses of biologically important molecules are necessary to understand which molecules are influencing stress tolerance mechanisms in chickpea plants.

Plants produce an array of biochemical compounds including hundreds of metabolites under stress conditions. Plant metabolites play essential roles in growth, cell integrity, energy storage, cell signalling, membrane formation and scaffolding, cellular replenishment, and whole‐plant resource allocation, as well as roles in plant development and stress responses (Wen et al., 2015). Plants can modify their physiology to adapt to different conditions through metabolic changes (Khan, Ali, Shahid, & Kharabian‐Masouleh, 2017; Kim, Choi, & Verpoorte, 2011). Plants have various metabolic adaptation mechanisms to defend against the negative effects of stress, which can play a crucial role in the adaptive mechanisms in plants. Changes in the metabolic level in an organism are likely to be correlated with the phenotype as metabolites are the end products of the biological system. Empirical evidence suggests that metabolic components are linked to high‐temperature stress tolerance in corn (Chen, 2010) and in cool‐season grass (Su, Bremer, Jeannotte, Welti, & Yang, 2009) as well as to cold and freezing tolerance in Arabidopsis (Welti et al., 2002). Additionally, recent studies have demonstrated altered levels of metabolites between drought‐stressed and well‐watered plants (Bowne et al., 2012; Hong‐Bo et al., 2006; Obata et al., 2015; Witt et al., 2012). The increased level of branched chain amino acids (leucine, isoleucine, and valine) was reported under drought conditions in tolerant cultivars of wheat and barley (Bowne et al., 2012; Krugman et al., 2011; Rontein, Basset, & Hanson, 2002). Differing metabolite expression was associated with high‐temperature stress tolerance in maize (Chen, 2010) and in cool‐season grasses (Su et al., 2009). Maize plants under high‐temperature stress showed increases in metabolites such as tryptophan, serine, threonine, beta‐alanine, proline, glutamate, myoinositol, and urea (Obata et al., 2015). In addition to this, certain metabolites showed a negative correlation (threonine, valine, trehalose, and glycerol) or a positive correlation (fumarate, succinate, and raffinose) with grain yield and that change in metabolite level was consistent between greenhouse plants and field‐grown plants (Obata et al., 2015). Approaches to profile metabolites are receiving increasing attention from the plant biologist community. Metabolic quantitative trait locus identification revealed that heterosis is mainly under the control of epistasis in Arabidopsis, whereas in tomato, the underlying genetic variation was explained mainly by primary metabolites (Lisec et al., 2009; Schauer et al., 2006). Riedelsheimer et al. (2012) found a strong correlation between metabolites and agronomic traits in field‐evaluated hybrid progeny in corn. They built whole‐genome and metabolic prediction models with accuracies ranging from 0.72 to 0.81 for single‐nucleotide polymorphisms and from 0.60 to 0.80 for metabolites.

Little is known about the complex metabolic regulation for drought stress tolerance in chickpea. Thus, using the metabolic phenotype to study differential expression of contrasting varieties for complex traits might deepen our understanding of the metabolic pathways and expedite genetic gains in chickpea productivity. Metabolomics allows the parallel assessment of a large number of metabolites in a biological sample. Recent metabolomics studies have been of great value in phenotyping and diagnostics in crop breeding (Fernie & Schauer, 2009). Recent progress in mass spectrometry with advanced data processing technology allows simultaneous measurement of hundreds of chemically different metabolite species in a single sample of plant tissue. These advances have made it possible to investigate more thoroughly the regulation of metabolic networks and to study their influence on complex traits. The applicability of this technology has been demonstrated in tomato, Arabidopsis, and potato through discovery of biomarkers associated with traits of interest (Lisec et al., 2009; Rowe, Hansen, Halkier, & Kliebenstein, 2008; Schauer et al., 2006). Thus, metabolomics represents an important and exciting new addition for genetic improvement of plants. Metabolomics could be a powerful selection tool to establish the association between phenotype and genotype, leading to a better understanding of the genetic basis of plant responses to stress.

The objective of this study is to demonstrate the differential accumulation of metabolites in chickpea leaves under drought stress conditions in different periods and the involvement of those metabolites in different pathways in relation to drought tolerance in chickpea. To achieve this objective, we employed a nontargeted global ultrahigh‐performance liquid chromatography/high‐resolution mass spectrometry (UPLC‐HRMS) for the first time to identify metabolites from the leaf tissue of irrigated and drought‐stressed chickpea plants. The UPLC‐HRMS technique is sensitive and potentially more robust than nuclear magnetic resonance and gas chromatography–mass spectrometry (GC‐MS) technology as it can identify metabolites in low concentration and has better capacity to control the false discovery rate.

2. MATERIALS AND METHODS

2.1. Plant materials and growth conditions

The experiment was conducted at a controlled greenhouse condition in the Department of Agronomy, University of Florida, in Gainesville, Florida. Drought‐sensitive chickpea (C. arietinum) variety Punjab Noor‐2009 (G1) and drought‐tolerant variety 93127 (G2) were used for the study (Khan & Bano, 2016). The seeds of those varieties were obtained from Ayub Agricultural Research Institute Faisalabad, Pakistan. Chickpea seeds were washed in 100% distilled water followed by surface sterilization with 95% ethanol for 2–3 min and then soaked in 10% Clorox with concomitant shaking. The seeds were subsequently washed in autoclaved distilled water (Khan & Bano, 2016). Seeds were grown in pots filled with 2,000 g of Metro‐Mix® 360 soil mixture. The pots were well watered (typically two times per week) throughout until drought stress was applied, and each pot contained five plants. Water was applied until the substrate was completely wet and the water started to seep out through the holes at the bottom of the pot. A teaspoon of Osmocote (15N–9P–12K) was applied one time after germination. Experiment was laid out in a completely randomized block design with six (biological) replications. During the experiment, the greenhouse condition was maintained at 26 and 19 ± 1 °C (day and night temperatures, respectively) with 70 ± 2% relative humidity and day and night lengths (11 and 13 hr, respectively). Drought stress was imposed on 25‐day‐old plants (three‐leaf stage) by withholding water supply for 25 days until the soil water content reached 4%. A set of well‐watered plants served as a control. Chlorophyll content, chlorophyll fluorescence (F v/F m ratio), shoot and root dry weight, and relative water content (RWC) were estimated after 14 and 25 days of the drought condition. Leaf tissues were harvested for metabolomics analysis also after 14 and 25 days of the drought condition.

2.2. Physiological characterization

Data were collected on physiological traits known to be affected by the drought condition. Chlorophyll fluorescence (the ratio of variable, F v, to maximum fluorescence, F m) and SPAD chlorophyll content were used as an indirect method to assess photosystem II efficiency (Kadir, Von Weihe, & Al‐Khatib, 2007; Ristic, Bukovnik, & Prasad, 2007) and chlorophyll damages due to stress. Both traits were measured at 14 and 25 days after imposition of drought stress following the methods described by Talukder (2014). Chlorophyll fluorescence was measured on intact leaves of the abaxial surface (third leaf) after 30 min of dark adaptation with a pulse modular fluorometer (Model OS5‐FL, Opti‐Sciences, Hudson, NH) in both the control and drought‐stressed leaves. Estimates of leaf chlorophyll content were taken using a SPAD chlorophyll meter (Model 502, Spectrum Technologies, Plainfield, IL) in both the control and drought‐stressed leaves. Chlorophyll fluorescence and chlorophyll content were measured at three leaflets in each plant and five plants per pot (a total of 15 readings) and averaged. The average value of 15 readings was considered as a single replication, and six replicated values/variety were used for statistical analysis and comparison of treatment means, and significant testing at P < .05 level. The two chickpea varieties demonstrated differences from each other for different morphological and phenotypic traits under drought conditions (Khan & Bano, 2017).

Leaf RWC was determined with fully expanded leaves after 14 and 25 days of drought stress imposition using the method described by Barrs and Weatherley (1962). Leaves were clipped and weighed (fresh weight, FW) and placed in a Ziploc bag filled with distilled water to ensure both sides of leaves fully absorb water. They were soaked in water for 24 hr at room temperature and then weighed immediately after excess moisture was removed with paper towels (turgid weight, TW). The leaves were then dried in an oven at 60 °C for 72 hr to determine dry weight (DW). Leaf RWC was calculated as (FW − DW)/(TW − DW) × 100.

2.3. Measures of shoot and root dry weights

Shoots of five plants per replication were cut at the base and dried at 60 °C for 72 hr, and dry weight was taken by using an electronic scale. The roots of the same plants with soil were isolated from the pots, washed carefully to separate roots, dried at 60 °C for 72 hr, and weighed using an electronic scale. The root and shoot dry weights were measured after 25 days of drought stress imposition.

2.4. Antioxidant enzyme extraction

Fresh leaves (0.5 g) were ground in 5 ml of 50‐mm phosphate buffer and placed in an ice bath. The supernatant was taken after centrifuging the homogenate for 20 min at 18,928 g at 4 °C, which was used for assays of enzyme activities as outlined by Verma and Dubey (2003).

2.5. Leaf tissue collection and sample preparation

Leaf tissues were collected from control and drought plants at midday after 14 and 25 days of drought stress. Leaf blades were collected from six individual pots (biological replicates) (Bais, Moon‐Quanbeck, Nikolau, & Dickerson, 2011), frozen in liquid nitrogen immediately after collection, and stored at −80 °C. Tissue samples were lyophilized for 72 hr and ground using a TissueLyser. Lyophilized powder (30 mg) was used for nontargeted global UPLC‐HRMS‐based metabolite profiling. Freeze‐dried ground leaf tissues (30 mg) were spiked with a mixture of internal standards (20 μl) into a clean Eppendorf tube. Methanol (750 μl) and ammonium acetate (10 mm, 750 μl) were added to each sample and vortexed for 1 min at room temperature. Centrifugation (at 17,000 g for 10 min) of all samples was done after ultrasonication for 20 min at room temperature. Supernatant (>1 ml) was transferred to 1.5‐ml tube, followed by a 50‐μl transfer of supernatant to an Eppendorf tube. The supernatant was dried down and was reconstituted with 50 μl of injection standard solution. Samples were then vortexed (30 s) and put at 4 °C for 10 min and centrifuged at 20,000 rpm for 10 min, and the supernatant was transferred into an LC vial.

2.6. UPLC‐HRMS analysis

Untargeted metabolomics profiling was performed on a UPLC‐HRMS (Model: Thermo Ultimate 3000 UPLC and Thermo Q Exactive mass spectrometer). All samples were analysed in positive and negative heated electrospray ionization with a mass resolution of 70,000 at m/z 200 as separate injections. Chromatographic separation was attained on an ACE Excel 2 C18‐PFP100 × 2.1 mm, a 2‐μm particle size column with Mobile Phase A as 0.1% formic acid in water and Mobile Phase B as acetonitrile, at a flow rate of 350 μl/min with a run time of 16.8 min, mass resolution of 35,000 at m/z 200, and mass range of 70–1,000 m/z. Injection volume was 4 μl for negative ion mode and 2 μl for positive ion mode. The total run time per sample was 20.5 min. Probe (HESI probe) temperature was maintained at 350 °C for both positive and negative runs with a spray voltage of 3,500 V and capillary temperature of 320 °C.

2.7. Data analysis

The raw files were converted to .mzXML using MSConvert (ProteoWizard 3.0). MZmine 2.1 was used to identify features, deisotope, align features, and perform gap filling to fill in any features that may have been missed in the first alignment algorithm. All adducts and complexes were identified and removed from the data set. The data were searched against an internal retention time metabolite library. Data from positive and negative ion modes were separately subjected to statistical analyses. Data tables with metabolite peaks (mz/rt) at two time points under both drought and control conditions for tolerant and sensitive varieties were formatted as comma‐separated values (.csv) files and uploaded to the MetaboAnalyst 3.0 server (http://www.metaboanalyst.ca; Xia, Mandal, Sinelnikov, Broadhurst, & Wishart, 2012). To shrink any possible variance and to improve the performance for downstream statistical analysis, metabolite data generated by UPLC‐HRMS were checked for data integrity and normalized using MetaboAnalyst's normalization protocols (selecting normalization by sum, log transformation, and autoscaling) for statistical analysis.

We applied univariate analysis (t test and one‐way anova) to calculate the statistical significance and fold change of the metabolites between two group means (drought over control). As the multivariate methods take all the variables into consideration, we applied multivariate methods for comprehensive data analysis, for example, a supervised method (partial least squares discriminant analysis [PLS‐DA]) and an unsupervised method (hierarchical clustering with heat map). The supervised method, PLS‐DA, was used to maximize the difference of metabolic profiles between control and drought groups to enable the detection of metabolites existing in the biological samples. The heat map was generated based on the Pearson distance measure and the Ward clustering algorithm, showing top 19 metabolites selected by PLS‐DA with a variable importance in projection (VIP) score using a significance level of P ≤ .05, and post hoc analysis of Fisher's least significant difference. The samples were arranged according to their sampling time points in both control and drought groups. The important metabolites were identified by using two different methods separately: significant analysis of metabolites (SAM) and PLS‐DA.

The pathway analysis was performed using MetaboAnalyst for the identified important metabolites using Arabidopsis thaliana pathway libraries. The Kyoto Encyclopedia of Genes and Genomes pathway database (http://www.genome.ad.jp/kegg/pathway.html) was also used for the metabolites that were not found in the rice and Arabidopsis pathway libraries.

For phenotypic data analysis, we used SAS version 9.1. An anova was performed to determine the effect of treatments and error associated with the experiment with replications and treatments as random effects. To identify significant differences among treatments, a mean comparison was carried out by using protected least significant difference (P < .05) test where error mean square was used to estimate the standard error of differences between means.

3. RESULTS

3.1. Physiological responses

3.1.1. Chlorophyll content and photochemical efficiency (F v/F m)

In general, the chlorophyll content decreased significantly (P < .01) in both the sensitive variety (G1) and tolerant variety (G2) under the drought condition as compared with control (Figure 1) and decreased with the increase in the duration of drought stress. However, the percentage of decrease in the tolerant variety (G2) was less than that in the sensitive variety (G1). Maximum decrease (64%) in chlorophyll content was observed in the sensitive variety at 25 days under the drought condition as compared with control. The drought‐tolerant variety, on the other hand, showed 7% and 16% decrease of chlorophyll compared with control after the 14 and 25 days of stress. The photochemical efficiency of photosystem II (F v/F m) showed variability between varieties and at two time points (14 and 25 days) of stress treatments (Table 1). The F v/F m ratio was decreased for both varieties at both time points under the drought condition, but the decrease was more pronounced (50% and 63%) for the sensitive variety after 14 and 25 days of stress imposition.

Figure 1.

Figure 1

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Chlorophyll content (±SE) in leaves of two chickpea varieties under drought and control conditions at 14 and 25 days after water treatment. G1: drought‐sensitive variety (Punjab Noor‐2009); G2: drought‐tolerant variety (93127). Error bars represent standard errors of the mean (n = 6) at each time point. Different letters indicate significant differences (P < .05) among treatments (drought vs. irrigation) for a genotype in a particular time point

Table 1.

Chlorophyll florescence (F v/F m ratio) of two chickpea varieties under control and drought conditions at 14 and 25 days after stress imposition

Chickpea variety Days after stress imposition Chlorophyll florescence (F v/F m)
Control (M ± SE) Drought stress (M ± SE)
Punjab Noor‐2009 (sensitive variety, G1) 14 0.946 ± 0.015 0.472 ± 0.02
93127 (tolerant variety, G2) 14 0.976 ± 0.008 0.794 ± 0.023
Punjab Noor‐2009 (sensitive variety, G1) 25 0.927 ± 0.012 0.336 ± 0.019
93127 (tolerant variety,G2) 25 0.910 ± 0.007 0.681 ± 0.021
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3.1.2. Relative water content (%)

Drought stress caused significant (P < .01) changes in RWC in both the varieties (Figure 2). The RWC of the tolerant variety (G2) decreased by 27% and 33% under the drought condition compared with the control condition at 14 and 25 days, respectively, but the decrease in RWC was higher for the sensitive variety (G1) at both time points (40% and 65%, respectively) under the drought condition. This result demonstrates that the drought‐sensitive variety was more affected due to drought stress.

Figure 2.

Figure 2

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Relative water content (±SE) of chickpea under drought versus control conditions. G1: drought‐sensitive variety (Punjab Noor‐2009); G2: drought‐tolerant variety (93127). Error bars represent standard errors of the mean (n = 6) at each time point. Different letters indicate significant differences (P < .05) among treatments (drought vs. irrigation) for a genotype in a particular time point

3.1.3. Shoot and root dry weights (g)

Shoot and root dry weights of both sensitive and tolerant varieties were reduced significantly (P < .01) due to drought stress compared with control after 25 days of drought stress imposition (Figure 3). However, the reduction was more for the sensitive variety than for the tolerant variety. Sensitive and tolerant varieties showed 69% and 24% reduction in shoot dry weight under the drought condition compared with control, respectively (Figure 3). Similarly, 77% and 25% reductions in root dry weights were shown by the sensitive and tolerant varieties, respectively, under the drought condition as compared with control.

Figure 3.

Figure 3

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Shoot and root dry weights (±SE) of chickpea under drought versus control conditions after 25 days of drought stress imposition. G1: drought‐sensitive variety (Punjab Noor‐2009); G2: drought‐tolerant variety (93127). Error bars represent standard errors of the mean (n = 6). Different letters indicate significant differences (P < .05) among treatments (drought vs. irrigation) for a genotype

3.1.4. Antioxidant enzyme activity

The antioxidant enzyme activities were significantly enhanced in plants under drought stress conditions (Figure 4). Maximum increase was noted for superoxide dismutase activities in the leaves of stressed plants as compared with control plants in the sensitive (45%) and tolerant varieties (62%), respectively. The tolerant variety showed higher antioxidant activities than the sensitive variety. The increase was significant and 25% higher in tolerant variety as compared with the sensitive variety under stress conditions. Similarly, the tolerant variety also showed significant increase in catalase (24%), ascorbate peroxidase (33%), and peroxidase (27%) activities as compared with the sensitive variety under stress conditions.

Figure 4.

Figure 4

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Antioxidant enzyme content (±SE) in the leaves of drought‐sensitive variety (G1) and drought‐tolerant variety (G2) after 25 days of drought treatment. Data are means of six replicates along with standard error bars. APOX = ascorbate peroxidase, POD = peroxidase, SOD = superoxide dismutase. Different letters indicate significant differences (P < .05) among treatments (drought vs. irrigation) for a variety. Asterisk represents significant difference between sensitive (G1) and tolerant (G2) varieties under the drought condition

3.1.5. Metabolite profiling

The nonbiased UPLC‐HRMS global metabolomics approach detected a total of 691 peaks, of which 175 were identified as known metabolites (178 including duplications) and the remaining peaks were unknown metabolites. Metabolites were highly reproducible among the six analysed biological replications at the two different time points. The identified metabolites included amino acids, organic acids, sugars, polyamines, nitrogenous compounds, and polyphenols.

The PLS‐DA was performed for drought (and control) conditions on both drought‐tolerant and drought‐sensitive varieties together at two different time points. The first PLS component (PC1) explained 61.8% of total variation, whereas the second component (PC2) explained 8.9% variation across the data set (Figure 5). The scores plot between PC1 and PC2 revealed two distinct groups associated with the drought and control samples at two different sampling points (Figure 5), suggesting a clear distinction in the metabolite accumulation under two conditions. The sensitive variety (G1) and tolerant variety (G2) samples were separated from each other under both drought and control conditions, with overlap at two time points, especially under the drought condition.

Figure 5.

Figure 5

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Partial least square discriminant analysis and 2D scores loading plot for the Chickpea Punjab Noor‐2009 (G1) and 93127 (G2) under control (well‐watered) and drought treatments at two time points (14 and 25 days). Samples at control and drought treatments did not overlap with each other, indicating an altered state of metabolite levels in the chickpea leaves. G1, sensitive variety; G2, tolerant variety

The effect of water stress treatment on metabolic accumulation becomes evident from hierarchical clustering with heat map (Figure 6). Two major clusters were identified with distinct patterns of altered metabolite abundances. Metabolites that were highly accumulated in plants grown under the drought condition compared with the control condition formed the first cluster including amino acids (e.g., proline, tryptophan, histidine, and isoleucine) and organic acids. On the other hand, metabolites that showed a decreased level of accumulation under the drought condition included putrescine, choline, phenylalanine, gamma‐aminobutyric acid (GABA), and alpha‐ketoglutaric acid, forming the second cluster. Choline, phenylalanine, and putrescine were abundantly found in the sensitive variety, whereas tyrosine, GABA, and alpha‐ketoglutaric acid were abundantly present in the tolerant variety under the control condition. The increased level of proline and tryptophan was evident in both the sensitive and tolerant varieties when plants were exposed to the drought condition. The patterns of metabolite clustering clearly indicate the metabolic changes under different water regimes.

Figure 6.

Figure 6

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Heat map illustrating levels of top metabolites in the leaves of two chickpea varieties according to the partial least square discriminant analysis variable importance in projection scores under control (well‐watered) and drought conditions at two time points (14 and 25 days). The heat map was generated using Pearson and Ward for distance measure and clustering algorithm, respectively. G1: drought‐sensitive chickpea variety (Punjab Noor‐2009); G2: drought‐tolerant chickpea variety (93127)

To identify the important metabolites associated with the drought condition, two statistical models, namely, SAM and PLS‐DA, were carried out (Table 2). The most significantly different compounds were identified by the SAM plot with a delta value of 1.4, false discovery rate of 0.003, and false positive of 0.4. Similarly, the PLS‐DA method identified the most important metabolites based on the VIP score using the five‐component model. The two methods showed quite similar results, identifying the same metabolites. The top most important 20 metabolites (based on fold change values) were identified by two different methods and included different amino acids, sugars, organic acids/compounds, amines, fatty acids, and other compounds (Table 2).

Table 2.

Important metabolites with their compound ID (Kyoto Encyclopedia of Genes and Genomes ID or PubChem CID) and molecular formula, identified through PLS‐DA, random forest, and SAM

S. no. Important metabolites Compound ID Molecular formula SAM (d value) PLS‐DA VIP score (variance for Component 1)
1 Alanine C00041 C3H7NO2 7.4698 0.66574
2 N‐Methyl‐d‐aspartic acid C00123 C5H9NO4 — 0.52234
3 Choline C00114 C5H13NO 4.4954 1.3675
4 BOC‐l‐tyrosine 117439a C14H19NO5 4.4658 1.3622
5 BOC‐d‐phenylalanine 637610a C14H19NO4 4.405 1.3512
6 Adenosine C00212 C10H13N5O4 3.9386 1.2596
7 4‐Aminobutanoate C00334 C4H9NO2 — 0.98778
8 Guanine C00242 C5H5N5O 3.9403 1.2599
9 Allantoin C01551 C4H6N4O3 −4.0799 1.2886
10 N‐Methyl‐l‐glutamate C01046 C6H11NO4 −4.79 1.4179
11 l‐Arginine C00062 C6H14N4O2 — 1.0363
12 l‐Histidine C00135 C6H9N3O2 −5.2754 1.4913
13 l‐Proline C00148 C5H9NO2 −4.41 1.352
14 N‐Acetyl putrescine C02714 C6H14N2O 3.6282 1.1916
15 Glutathione disulphide C00127 C20H32N6O12S2 — 0.67311
16 Alpha‐ketoglutaric acid C00026 C5H6O5 4.2727 1.3264
17 l‐Isoleucine C00407 C6H13NO2 — 1.22
18 Tryptophan C00078 C11H12N2O2 −5.0264 1.455
19 Isocitric acid C00311 C6H8O7 4.5211 1.3721
20 Glucosamine C00329 C6H13NO5 0.00098 0.64819
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Note. PLS‐DA = partial least square discriminant analysis; SAM = significant analysis of metabolites; VIP = variable importance in projection.

a

PubChem CID.

Among different groups of metabolites, many amino acids were significantly accumulated in the leaves of plants grown in the drought condition. Proline, l‐arginine, l‐histidine, l‐isoleucine, allantoin, tyrosine, and tryptophan showed increased levels of accumulation in the leaves of drought‐stressed plants (Table 3). However, compounds such as GABA, adenosine, alanine, alpha‐ketoglutaric acid, phenylalanine, choline, glucosamine, guanine, and aspartic acid were highly accumulated in the leaves of plants grown under the control condition but decreased under the drought condition. Higher accumulation of l‐arginine occurred in the sensitive variety when plants were exposed to the drought condition; however, the level of l‐arginine was decreased in the tolerant variety. On the contrary, allantoin and tyrosine were highly accumulated in the tolerant variety compared with the sensitive variety under the drought condition. Isoleucine was accumulated under the drought condition in the tolerant variety at both time points (14 and 25 days), but accumulation occurred only at 14 days after stress in the sensitive variety. The levels of isocitric acid and alpha‐ketoglutaric acid were reduced in the tolerant variety at 25 days under the drought condition (Table 3).

Table 3.

Fold changes of important metabolites identified by partial least square discriminant analysis and significant analysis of metabolites

S. no. Compounds Fold change P
1 Alanine 0.36492 1.52E−19
2 N‐Methyl‐d‐aspartic acid 0.34825 0.020197
3 Choline 0.63339 4.46E−09
4 BOC‐l‐tyrosine 1.55282 5.47E−09
5 BOC‐d‐phenylalanine 0.60043 8.29E−09
6 Adenosine 0.33917 3.75E−08
7 4‐Aminobutanoate (gamma‐aminobutyric acid) 0.59135 0.000123
8 Guanine 0.13727 0.042377
9 Allantoin 3.8455 7.30E−08
10 N‐Methyl‐l‐glutamate 3.613 5.61E−10
11 l‐Arginine 3.564 0.0178
12 l‐Histidine 2.3746 1.63E−11
13 l‐Proline 2.1467 8.03E−09
14 N‐Acetyl putrescine 0.40561 1.54E−09
15 Di‐tert‐butylbenzaldehyde 4.2729 4.40E−08
16 Alpha‐ketoglutaric acid 0.38964 1.80E−09
17 l‐Isoleucine 2.0514 5.84E−07
18 Tryptophan 3.395 5.34E−10
19 Isocitric acid 0.40098 3.84E−10
20 Glucosamine 0.39251 0.000283
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It was evident from Table 2 that tryptophan was the most contributory of metabolites in the PLS‐DA model with a VIP score of 1.455 and a mean decrease accuracy of 0.005654, followed by choline and BOC‐l‐tyrosine (1.36), whereas N‐methyl‐d‐aspartic acid had the lowest VIP score (0.52234) with a mean decrease accuracy of 0.003807, followed by 4‐aminobutanoate (0.987) with a mean decrease accuracy of 0.00018. The results revealed that tryptophan, proline, and histidine were highly accumulated in the leaves of plants under the drought condition at two time points and hence can be considered as responding to the drought condition. Similarly, adenosine, alanine, and choline were significantly accumulated in the leaves of plants grown under the control condition. The maximum increase (4.279) in fold change was recorded in 3,5‐di‐tert‐butylbenzaldehyde, followed by allantoin > N‐methyl‐l‐glutamate > l‐arginine > tryptophan (3.845, 3.613, 3.5674, and 3.395; Table 3), whereas the maximum decrease in fold change was recorded in guanine followed by riboflavin < adenosine < N‐methyl‐d‐aspartic acid < alanine < alpha‐ketoglutaric acid (Tables 3 and S1).

Figure 7 presents mz/rt peak values of the most important metabolites for sensitive and tolerant varieties at 14 and 25 days after drought stress initiated. In general, the peak values of choline, guanine, GABA, adenosine, phenylalanine, aspartic acid, and guanosine decreased due to drought stress in both varieties. This indicates that the accumulation of these metabolites was reduced over time due to drought stress. Glutathione disulphide accumulation was increased after drought stress imposition but reduced due to a longer period of drought stress. Arginine also showed a similar trend for tolerant lines; however, sensitive lines showed continuous accumulation in long‐term drought stress (25 days of drought stress). The peak values of allantoin, histidine, l‐proline, tyrosine, isoleucine, and tryptophan increased in both the tolerant and sensitive varieties. However, the peak values were consistently higher in the tolerant variety, which indicates the higher accumulation of these metabolites in tolerant varieties compared with sensitive varieties under the drought stress condition. The peak for isocitric acid showed an increase at both time points for the sensitive variety, whereas it showed an increase only at 25 days after drought stress for the tolerant variety. The mz/rt peak values were significantly different between tolerant and sensitive varieties under drought conditions for allantoin, histidine, l‐proline, tyrosine, isoleucine, tryptophan, arginine, phenylalanine, glutamine, and glucosamine.

Figure 7.

Figure 7

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Significantly different levels of selected metabolites (anova, P ≤ .05, Tukey's honestly significant difference) in the leaves of two chickpea varieties under control and drought conditions at two time points (14 and 25 days). G1: drought‐sensitive chickpea variety (Punjab Noor‐2009); G2: drought‐tolerant chickpea variety (93127). Error bars represent standard errors of the mean (n = 6) at each time point. C = control, D = drought. Different letters indicate significant differences (P < .05) among treatments (drought vs. irrigation) for a genotype for mz/rt peak in a particular time point. Asterisk represents significant difference in mz/rt peak between sensitive (G1) and tolerant (G2) varieties under the drought condition at a specific time point of sampling

3.1.6. Metabolic pathway analysis

The pathway analysis of MetaboAnalyst3 was performed on significantly different known metabolites using A. thaliana as the pathway libraries. Seventeen different pathways were identified where these metabolites are involved in different steps (Table 4), of which five are biosynthetic pathways (aminoacyl‐tRNA biosynthesis; indole alkaloid biosynthesis; glucosinolate biosynthesis; phenylalanine, tyrosine, and tryptophan biosynthesis; and valine, leucine, and isoleucine biosynthesis), and the remaining are involved with primary metabolism.

Table 4.

Pathway names, total metabolites involved in that pathways, metabolites significantly accumulated in present study (hits), and false discovery rate (FDR)

Pathway name Total Hits FDR
Aminoacyl‐tRNA biosynthesis 67 6 0.0073
Glycine, serine, and threonine metabolism 30 2 0.0032
Arginine and proline metabolism 38 2 0.0009
Indole alkaloid biosynthesis 7 1 0.0054
Glucosinolate biosynthesis 54 2 0.0093
Purine metabolism 61 2 0.0036
Histidine metabolism 16 1 0.0028
Selenoamino acid metabolism 19 1 0.0063
Phenylalanine, tyrosine, and tryptophan biosynthesis 21 1 0.0079
Carbon fixation in photosynthetic organisms 21 1 0.0022
Alanine, aspartate, and glutamate metabolism 22 1 0.0047
Glycerophospholipid metabolism 25 1 0.0024
Valine, leucine, and isoleucine biosynthesis 26 1 0.0019
Glutathione metabolism 26 1 0.0013
Tryptophan metabolism 27 1 0.0081
Valine, leucine, and isoleucine degradation 34 1 0.0062
Amino sugar and nucleotide sugar metabolism 41 1 0.00921
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4. DISCUSSION

Drought adversely affects plant morphology and physiology and hence results in reduced growth. Drought causes reduction in photosynthesis and photochemical efficiency, which affects photosynthetic machinery, reduces leaf expansion, enhances senescence of new leaves, and results in decreased productivity (Farooq et al., 2009). To cope with the drought stress, the plant must develop a specific tolerance mechanism. Different varieties have shown different abilities to maintain growth and productivity by acclimating to stress conditions through specific tolerance mechanisms. Genotypic variability in different attributes related to photosynthesis, RWC, and root and shoot dry weight are good indicators of water stress or drought monitoring. Our results demonstrated that in the sensitive variety, even short‐term exposure (14 days) to the drought condition resulted in a significant decrease in the chlorophyll production but the tolerant variety sustained, and a lower decrease was recorded. The photochemical efficiency (F v/F m) of both varieties was significantly affected by drought stress. However, the sensitive variety showed greater F v/F m reduction than the tolerant variety. Some other studies also demonstrated a significant reduction of photosynthetic capacity and chlorophyll content due to stress condition (Basu, Ramegowda, Kumar, & Pereira, 2016; Cielniak, Filek, & Cielniak, 2006; J. Li et al., 2017; Luo, Zhang, & Zhang, 2016; Ullah, Sun, Yang, & Zhang, 2017; Zhou et al., 2017). Our results are aligned with other reported studies and demonstrated that the experimental condition separated the expression of these traits between the tolerant and sensitive varieties.

Drought‐induced reduction in plant dry weight had also been studied previously in different crops (Boutraa, Akhkha, Al‐Shoaibi, & Alhejeli, 2010; Munns, 2002; Salma, Khatun, Bhuiyan, Yasmin, & Khan, 2016). We have found a significant reduction in chickpea dry weights when grown under the drought condition in both the drought‐tolerant and drought‐sensitive varieties. It is worth noting that the percentage of decrease in dry weights was significantly higher for the sensitive variety. Earlier studies demonstrated significant reduction in shoot dry weights when grown under drought conditions in chickpea (Pang et al., 2016) and in corn (Anjum et al., 2017). Silvente, Sobolev, and Lara (2012) attributed the decrease in dry weight under drought in the sensitive soybean variety to the depletion of sucrose in the leaves of this variety. However, Reddy, Chaitanya, and Vivekanandan (2004) reported that water stress inhibited dry matter production due to limitation of photosynthesis. Our data also demonstrated a reduction of photosynthetic capacity (demonstrated by reducing chlorophyll content and F v/F m values), which might have ultimately reduced shoot dry weight in our study.

Leaf RWC is an indicator of water status in plants under the drought condition and reflects the balance between water supply to the leaf tissue and transpiration rate. RWC has been used to identify stress‐tolerant varieties in different crops (Rampino, Pataleo, Gerardi, & Perotta, 2006; Soltys‐Kalina, Plich, Strzelczyk‐Żyta, Śliwka, & Marczewski, 2016). Significant correlations between RWC and relative growth rate, photosynthesis rate, chlorophyll, and proline contents have been reported in Aeluropus lagopoide (Mohsenzadeh, Malboobi, Razavi, Farrahi‐Aschtiani, & Razavi, 2006). Though RWC was reduced due to drought stress in our study, reduction in the tolerant variety (93127) was significantly lower than in the sensitive variety (Punjab Noor‐2009). In addition, the tolerant line showed less damage to chlorophyll content and photosynthetic capacity and also reduced loss of dry weight and increased proline level compared with the sensitive variety, which is in agreement with the findings of Mohsenzadeh et al. (2006).

The increase of antioxidant enzyme activities is known to occur under water deficit, salinity, low temperature, heavy metal exposure, and UV radiations (Sharma & Dietz, 2006). These enzymes form an antioxidant defence system in plants to mitigate oxidative damage. Under drought stress, cells of plants are suffered from the accretion of reactive oxygen species (ROS), which is responsible for oxidative damage and cell death. The ROS molecules are found in various subcellular compartments. The stability among the creation and reclamation of ROS is influenced by enhanced antioxidant enzyme activities (Caverzan, Casassola, & Brammer, 2016). Both verities showed increased level of ROS enzymes; however, the tolerant variety demonstrated a higher level than the sensitive variety. The observed increase in antioxidant enzyme activities in the tolerant variety enhanced ROS detoxification mechanism. Matsumura, Tabayashi, Kamagata, Souma, and Saruyama (2002) reported an increase in Cat gene expression under stress that improved tolerance to stress in transgenic rice by reducing the adverse effects of hydrogen peroxide (H2O2). Significant increase in the activities of superoxide dismutase, peroxidase, ascorbate peroxidase and catalase was also reported previously in other plant species under stress environments (Mafakheri, Siosemardeh, Bahramnejad, Struik, & Sohrabi, 2010; Pompelli et al., 2010; W. B. Wang et al., 2009).

In order to understand changes in metabolites in chickpea under the drought condition, we carried out a metabolomic profiling analysis in two different chickpea varieties (drought sensitive and tolerant) as levels of metabolite accumulation during stress at different growth stages can provide more specific and accurate indication of stress tolerance. This is the first report on chickpea to demonstrate comprehensive differences in metabolic profiling in tolerant and sensitive chickpea varieties over an extended period of drought condition. For this purpose, we used a global UPLC‐HRMS analysis to classify metabolites linked with drought tolerance in the chickpea crop. The study demonstrated variations in the metabolite levels in chickpea leaves of plants grown under drought and control conditions. Imposition of drought stress had considerably increased the accumulation of amino acids including proline, tryptophan, tyrosine, histidine, and isoleucine at both time points. The increased accumulation of amino acids under drought stress was reported previously in wheat (Bowne et al., 2012; Rahman et al., 2017), in soybean (Silvente et al., 2012), in grape vine (Hochberg et al., 2013), and in Phaseolus vulgaris (Sassi, Aydi, Hessini, Gonzalez, & Arrese‐Igor, 2010). The expanded level of amino acids is considered to enhance stress resilience in plants by influencing various physiological mechanisms, for example, adjustment to osmotic changes, ROS detoxification, and regulation of the intracellular pH level (Krasensky & Jonak, 2012). Regardless of the variety (tolerant or sensitive), drought stress conditions enhanced accumulation of those metabolites, but their relative intensity was higher in the tolerant variety (Figure 7) at different time points. These results suggest the biochemical pathways involving those metabolites potentially related to drought tolerance in chickpea. Drought stress leads to the reduction in protein synthesis and an increase in hydrolysis of proteins, endorsing surge in soluble nitrogen compounds such as free amino acids (Farooq et al., 2009; Krasensky & Jonak, 2012). Proline, highly accumulated due to water stress, not only acts as an osmolyte during drought but also acts as a signalling molecule and provides defence against oxidative damage (Cheng et al., 2015; Gagné‐Bourque, Bertrand, Claessens, Aliferis, & Jabaji, 2016; Hayat et al., 2012). In cell cytoplasm, it not only stabilizes protein structure but also helps in maintaining cell pH and redox status under severe stress condition by reducing the amount of singlet oxygen present, which causes lipid peroxidation of thylakoid membranes (Alia & Mohanty, 1997; Hammad & Ali, 2014; Hayat et al., 2012; Szabados & Savouré, 2010). As in our study, proline was also highly accumulated in soybean under the drought stress condition (Das, Rushton, & Rohila, 2017), which clearly shows the potential for an advanced tolerance response provided by proline during adverse environmental conditions. Under the drought condition, a high accumulation of proline might function as a sink for excess products that are needed for the maintenance of photosynthetic and respiratory processes (Kishor et al., 2005). Tripathi et al. (2016) also found similar results for amino acid metabolism where metabolomic alterations during drought stress were monitored in soybean. Both varieties showed damage to photosynthetic capacity in the present study; however, the damage was significantly lower in the tolerant variety (16%) as compared with the sensitive variety (64%). This could potentially be contributed by the strong accumulation of proline, which helped in protecting the photosystem and maintaining redox balance in photosynthetic membranes.

Increased level of aromatic amino acids, such as l‐tryptophan and tyrosine, was exhibited in the current study, which was also found in wheat (Bowne et al., 2012; Rahman et al., 2017) and in soybean (Das et al., 2017) under drought conditions. The highly abundant aromatic amino acids potentially acted as an alternate source of energy supply and provided stress tolerance in chickpea. Under various stress conditions, the plant metabolic response is regulated by convergent signal transduction associated with energy production. These aromatic amino acids are the precursors of different secondary metabolites including indole acetate, lipid precursor, and lignin in the shikimate pathway, which plays a vital role in stress tolerance (Maeda & Dudareva, 2012; Suguiyama, Silva, Meirelles, Centeno, & Braga, 2014). In addition of acting as alternate source for energy under stress conditions, L‐tryptophan has been demonstrated to play roles in transportation of ions, opening of stomata, reducing ROS and act as an osmolyte (Rai, Yadav, & Gade, 2009; Ford, Cassin, & Bacic, 2011). Tryptophan and ROS are synthesized in photosynthetic organs like chloroplast, and tryptophan is the target of oxidation. As tryptophan is a target for oxidation, free proteins may provide a buffer between ROS and protein (Bowne et al., 2012). Ford et al. (2011) demonstrated a correlation between increased protein levels and their involvement in ROS scavenging and oxidative stress metabolism. Photosynthetic machinery generally generates different ROS, such as singlet oxygen, superoxide, and peroxide. However, under stress conditioning, the production of ROS could be increased to such a level that can inhibit the production of D1 protein, which is essential for the repair of damage in photosystem II (Allakhverdiev & Murata, 2004; Nishiyama et al., 2001). In addition, ROS can oxidase different proteins including D1 protein (Silva et al., 2003). Higher tryptophan accumulation in our study has contributed to a ROS scavenging mechanism, which is evidenced by higher ROS scavenging enzyme activities in drought stress compared with control, thus ultimately reducing the damage to the photosystem and the reduction in growth in chickpea plants. The tryptophan histidine biosynthesis pathway is integrated with a number of other metabolic pathways. In our study, drought‐tolerant variety showed very strong accumulation of histidine after 25 days of drought stress (based on mz/rt peak). Zemanová, Pavlík, Pavlíková, and Tlustoš (2014) demonstrated that Noccaea caerulescens accumulated 10‐fold higher histidine than Arabidopsis halleri under cadmium stress. Their observations indicated that histidine may be involved in Cd resistance and accumulation by reducing oxidative damage. Histidine might have played a similar role in reducing oxidative stress in our study, which helped in maintaining membrane structure and water status in the plant (demonstrated by RWC).

The increased level of tyrosine under drought stress and its possible role in tolerance have been studied by different authors (Bowne et al., 2012; Rahman et al., 2017; Yao, Wang, Cheng, & Wu, 2013). Our study highlights the increase of amino acid tyrosine (mz/rt peak) in the tolerant variety under the drought condition. Since the last decade, extensive research has been carried out to study the role of protein tyrosine on plant physiology and on biotic and abiotic stress tolerance (Mata‐Pérez et al., 2016), and it was found to be associated with drought tolerance in plants (Hammad & Ali, 2014; Shankar, Agrawal, Sharma, Pandey, & Pandey, 2015). Witt et al. (2012) reported an increased level of tryptophan and phenylalanine in corn under drought conditions. However, the phenylalanine level was decreased under drought compared with the control condition in our study. An increased level of branched amino acids has been reported in wheat and Arabidopsis under drought conditions (Bowne et al., 2012; Joshi & Jander, 2009; Rahman et al., 2017) and also has been regulated at the transcriptional level in Arabidopsis (Urano et al., 2009). Our study also demonstrated increased level of isoleucine in the tolerant variety under the drought condition, whereas the level of the same metabolite was decreased in the sensitive variety. Taylor, Heazlewood, Day, and Millar (2004) reported that branched chain amino acids are a source of alternate energy when Arabidopsis plants were under sugar starvation, which is a common phenomenon under drought stress conditions. This supports the increased level of isoleucine in the tolerant variety over different time points in our study.

This present study has demonstrated enhanced accumulation of arginine in the sensitive variety under the drought condition. Arginine is a vital amino acid in plants that acts as a precursor in the synthesis of other amino acids including some polyamines and provides a medium for the transportation of nitrogen in plants under stressful environmental conditions (Brauc et al., 2012; Flores et al., 2008). It has been reported earlier that the expression of genes involved in the biosynthesis of arginine increased whereas those enzymes involved in the breakdown of arginine decreased during stress. Besides this, enzyme arginine decarboxylase that is involved in arginine metabolism is known for its synergistic effect on polyamine concentration and thus enhances plant tolerance to various abiotic stresses in Arabidopsis, rice, tomato, and tobacco (Q. Wang et al., 2011; Wimalasekera, Tebartz, & Scherer, 2011). Peremarti, Bassie, Christou, and Capell (2009) showed that overexpression of arginine decarboxylase in transgenic rice plants leads to increased tolerance to water stress. These results show that the variations in amino acid concentrations induced by abiotic stresses may be connected with the altered expression of genes that encode for enzymes intricate with plant metabolism (Krasensky & Jonak, 2012).

For plant development and metabolism, nucleotide metabolism is one of the essential components that affect many metabolic pathways. Nucleotides are an essential building block for nucleic acid. For the synthesis of carbohydrate, lipids, peptides, and secondary metabolites, nucleic acid provides nucleic acids that serve as an ultimate energy source (Stasolla, Katahira, Thorpe, & Ashihara, 2003). Enhanced accumulation of purine compounds such as allantoin was also recorded for the tolerant variety, but not for the sensitive variety in our study. Allantoin, an intermediate in purine catabolism, was reported to accumulate in plants during stress (Takagi et al., 2016). Silvente et al. (2012) separated sensitive and tolerant soybean genotypes on the basis of accumulation of allantoin, pinitol, and citric acid. Allantoin plays a vital role in plant survival under stress condition and has a significant role in plant metabolism, plant signalling, bioenergetics, and heredity. Allantoin may function in stress defence by reducing ROS accumulation and death of plant cells (Brychkova, Alikulov, Fluhr, & Sagi, 2008). Besides its role in scavenging ROS, allantoin may also trigger stress responses by increasing levels of the stress‐induced hormone abscisic acid (ABA; Watanabe et al., 2014). ABA has essential roles in developmental and adaptive responses to numerous environmental stresses, because of its synergistic and antagonistic interactions with other plant hormones, notably with salicylic acid, jasmonic acid, and ethylene (De Vleesschauwer, Gheysen, & Höfte, 2013; Kohli, Sreenivasulu, Lakshmanan, & Kumar, 2013). Takagi et al. (2016) confirmed an increased expression of ABA‐related genes and also revealed altered expression of genes involved in jasmonic acid responses in an allantoin mutant in Arabidopsis. Their findings suggest a possible connection of purine catabolism with stress hormone homeostasis and signal and highlight the potential importance of allantoin in these interactions. Our metabolic profiling indicates a down‐regulation of purine compounds, guanine and adenosine, under drought conditions compared with control in both drought‐tolerant and drought‐sensitive varieties, which ultimately affects overall purine biosynthesis and nucleic acid biosynthesis. The key amino acids glutamate and aspartate for de novo biosynthesis of purine pathway were down‐regulated in both sensitive and tolerant chickpea varieties. These results are the opposite of findings in soybean under drought conditions where purine nucleotides and those amino acids were up‐regulated (Das et al., 2017). However, the same authors reported down‐regulating those metabolites in soybean under high‐temperature stress conditions (Das et al., 2017).

The results of this study did not show any enhanced accumulation of sugar derivatives. The accumulation of soluble sugars or sugar derivatives acts as osmoprotectant under water stress conditions (Sanchez, Manzanares, Andres, Tenorio, & Ayerbe, 1998). Lower accumulation of glucose and sucrose is potentially due to reduced photosynthetic capacity, as demonstrated by lower chlorophyll fluorescence and SPAD chlorophyll content of the drought‐stressed leaves at the grain filling stage (Ahmed et al., 2015; Akhkha, Tahar, Hazem, Parvaiz, & Piotr, 2013). Our results were in agreement with soybean studied under drought conditions where most of the soluble sugar and sugar derivatives were down‐regulated under water stress conditions (Silvente et al., 2012). The lower accumulation of sugars was further accompanied by reduced levels of organic acids or compounds including isocitric acid and alpha‐ketoglutaric acid, which are involved in Krebs cycle in leaves of stressed plants. A decreased level of organic acids or compounds was reported in wheat (Bowne et al., 2012) and soybean (Silvente et al., 2012) under drought stress in tolerant genotypes and in drought‐sensitive bean varieties (Sassi et al., 2010).

We recorded decreased levels of choline and glutathione disulphide GABA due to drought stress in both tolerant and sensitive lines. GABA, a vital nonprotein amino acid, exhibits many physiological roles and is involved in stress tolerance during environmental stresses by maintaining cytoplasmic pH, metabolism, and osmoregulation (Barbosa, Singh, Cherry, & Locy, 2010; Z. Li, Yu, Peng, & Huang, 2016). Earlier studies also verified that GABA levels of plants quickly increased when exposed to different environmental stresses (Kinnersley & Turano, 2000). Zhou et al. (2017) reported the synergistic effects of GABA on the regulation of metabolomic pathways under stress conditions.

We used MetaboAnalyst to determine the link between the metabolites and metabolic pathways with their biosynthetic and catabolic pathways using the Kyoto Encyclopedia of Genes and Genomes database and Arabidopsis annotation project database. Our study demonstrated that biosynthesis of some amino acids such as arginine, phenylalanine, tyrosine and tryptophan, and isoleucine were elevated under drought conditions. These amino acids are produced through the shikimate pathway. Though regulation of the pathway is still not totally understood, the involvement of three enzymes, namely, chorismate mutase (phenylalanine and tyrosine synthesis), tryptophan synthase (tryptophan biosynthesis), and arogenate dehydratase (phenylalanine biosynthesis), has so far been experimentally evident in the regulation of their synthesis (Maeda & Dudareva, 2012; Tzin & Galili, 2010). Phenylalanine, tryptophan, and tyrosine are considered the three central molecules in plant metabolism (Galili & Höfgen, 2002). They are synthesized from the precursor chorismate that originates from the shikimate pathway. The shikimate pathway plays a fundamental role in plant reproduction, development, pest defence, and environmental stresses (Maeda & Dudareva, 2012). In plants, the biosynthetic pathway of these aromatic amino acids serves as a precursor for the synthesis of plant hormones, namely, auxin and salicylates, and for aromatic secondary metabolites including indole acetate, lipid precursor, and lignin, which play a critical role in stress tolerance (Herrmann & Weaver, 1999; Maeda & Dudareva, 2012; Suguiyama et al., 2014; Vogt, 2010). Drought stress reduced the level of glycine, serine, and threonine in our experiment. This pathway is very crucial for the synthesis of four key amino acids, namely, lysine, threonine, methionine, and isoleucine (Azevedo, Lancien, & Lea, 2006; Hildebrandt, Nesi, Araújo, & Braun, 2015). Arginine and proline metabolism is one of the unique pathways for the biosynthesis of amino acids and the regulatory mechanisms controlling proline metabolism, and intercellular and intracellular transport and connections of proline to other metabolic pathways that are associated with in vivo functions of proline metabolism (Verslues & Sharma, 2010). Gene expression profiling for the pathway further suggests that N‐acetylglutamate kinase, N‐acetylglutamate synthase, N‐acetylornithine deacetylase, and N‐acetylornithine:glutamate acetyltransferase are regulated in coordination in reply to change in arginine demand during plant growth and developmental stages (Slocum, 2005). Less and Galili (2008) reported that for several amino acids, abiotic stress changed transcription of the catabolism genes but there was very little effect on the expression of those genes encoding for biosynthetic enzymes. Notably, exemptions to this were the linked pathways of proline and arginine metabolism, where abiotic stress triggered wide transcriptional regulation of the biosynthetic enzymes. Proline metabolism between various compartments of cell is of critical importance but not fully understood. The intercellular and intracellular transport of proline, differences in proline metabolism within tissues, and the linkage of proline metabolism to other metabolic pathways are all significant for both to understand proline metabolism in a basic sense and to know how it may be beneficial for plant development in an applied sense. Upon exposure to stress, the proline metabolic pathway decreases mitochondrial oxidative phosphorylation and yields of ATP for repair of stress‐induced damage (Hare & Cress, 1997; Hare, Cress, & Van Staden, 1998). The up‐ and down‐regulated metabolomic pathways of this study in the leaves of two different chickpea varieties combined with their related main metabolic pathways and biosynthetic processes define a wide model of water stress responses in the chickpea plant. It has been reported earlier that the glucosinolate biosynthesis pathway is controlled by two transcriptional factors (R2R3‐Myb), which are responsible for the synthesis of glucosinolates in Arabidopsis. Myb28 and Myb29 are transcriptional factor genes that are commonly involved in the regulation of this pathway (Hirai et al., 2007). Pathways controlling threonine, methionine, isoleucine, and leucine metabolism are effectively interconnected in crop plants. It has been shown that threonine deaminase and methionine gamma lyase maintain the equilibrium of isoleucine in a variety of plants under different environmental conditions (Joshi & Jander, 2009). Valine, leucine, and isoleucine are coordinately regulated because all of them share four common enzymes in their biosynthetic pathway. Isoleucine belongs to the aspartate‐derived pathway, whereas leucine and valine belong to pyruvate pathways. These amino acids are important in symbiotic N fixation in leguminous plants and are essential for the development of bacteroids (Prell et al., 2009).

5. CONCLUSIONS

We used a nontargeted UPLC‐HRMS global metabolomics method and multivariate analysis for identifying metabolites from the leaf tissue of the drought‐stressed and well‐watered chickpea plants at two different time points. We have reported an increased or decreased level of different metabolites in two different varieties at different time points and their involvement in different metabolic pathways and in drought tolerance mechanisms. Metabolites proline, l‐arginine, l‐histidine, l‐isoleucine, and tryptophan showed increased levels in the tolerant line after drought stress induction, which was likely the indication of acclimation in response to drought stress. Increased accumulation of proline and tryptophan is common in other crops and is not surprising, but increased levels of l‐arginine, l‐histidine, and l‐isoleucine and decreased levels of choline, phenylalanine, and alpha‐ketoglutaric acid in response to drought stress have not been widely reported and may serve as metabolite biomarkers for screening for greater drought stress tolerance in chickpea to develop climate‐resilient chickpea varieties. The most pronounced decrease in the accumulation level was demonstrated by GABA, adenosine, alpha‐ketoglutaric acid, choline, guanine, and aspartic acid. As these metabolite levels were constantly higher or lower in both sensitive and tolerant varieties and at different time points, this suggests their role in monitoring biochemical pathways related to drought tolerance. Although some of these newly identified metabolites are promising as biomarkers for improving drought stress tolerance in chickpea, their correlation to drought tolerance in chickpea and other pulses requires further investigation. Our results demonstrated that metabolites, allantoin, proline, tryptophan, histidine, tyrosine, isoleucine, and arginine were affected differentially in the varieties when long‐term drought stress was exposed. These data provide information that may, with further investigation, help to understand the biochemical pathway underlying stress tolerance in chickpea.

AUTHOR CONTRIBUTION

N. K. carried out greenhouse and laboratory experiments. A. B. and M. A. B. designed the project. M. A. R. and N. K. performed data analysis. M. A. B. designed the experiment. N. K., M. A. R., and M. A. B. wrote the manuscript. M. A. B. and B. R. edited the manuscript.

CONFLICT OF INTEREST

The authors declare no conflict of interest.

Supporting information

Table S1:

List of top 49 significant metabolites identified in the study with their identifier (KEGG ID/PubChem CID*), molecular formula (MF), mass‐to‐charge ratio (m/z), retention time (RT), P value, false discovery rate (FDR), and fold change (drought/control, WD/C). KEGG = Kyoto Encyclopedia of Genes and Genomes.

Click here for additional data file. (21.1KB, docx)

ACKNOWLEDGMENTS

This project was supported by Dean's research intuitive, Institute of Food and Agricultural Sciences, College of Agricultural and Life Sciences, University of Florida, USA and Higher Education Commission, Pakistan. We also acknowledge the support of the Department of Agronomy and South Eastern Center for Integrative Metabolomics (SECIM) for providing the greenhouse and laboratory facility for conducting the experiment and metabolomics analysis.

Khan N, Bano A, Rahman MA, Rathinasabapathi B, Babar MA. UPLC‐HRMS‐based untargeted metabolic profiling reveals changes in chickpea (Cicer arietinum) metabolome following long‐term drought stress. Plant Cell Environ. 2019;42:115–132. 10.1111/pce.13195

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Associated Data

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Supplementary Materials

Table S1:

List of top 49 significant metabolites identified in the study with their identifier (KEGG ID/PubChem CID*), molecular formula (MF), mass‐to‐charge ratio (m/z), retention time (RT), P value, false discovery rate (FDR), and fold change (drought/control, WD/C). KEGG = Kyoto Encyclopedia of Genes and Genomes.

Click here for additional data file. (21.1KB, docx)

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