Metabolic and physiological changes induced by plant growth regulators and plant growth promoting rhizobacteria and their impact on drought tolerance in Cicer arietinum L.

Metabolic and physiological changes induced by plant growth regulators and plant growth promoting... Plant growth regulators (PGRs) and plant growth promoting rhizobacteria (PGPRs) play an important role in mitigating abiotic stresses. However, little is known about the parallel OPENACCESS changes in physiological processes coupled with metabolic changes induced by PGRs and Citation: Khan N, Bano A, Babar MA (2019) PGPRs that help to cope with drought stress in chickpeas. The present investigation was Metabolic and physiological changes induced by plant growth regulators and plant growth carried out to study the integrative effects of PGRs and PGPRs on the physiological and promoting rhizobacteria and their impact on metabolic changes, and their association with drought tolerance in two chickpea genotypes. drought tolerance in Cicer arietinum L.. PLoS ONE Inoculated seeds of two chickpea genotypes, Punjab Noor-2009 (drought sensitive) and 14(3): e0213040. https://doi.org/10.1371/journal. 93127 (drought tolerance), were planted in greenhouse condition at the University of Flor- pone.0213040 ida. Prior to sowing, seeds of two chickpea varieties were soaked for 3 h in 24 h old cultures Editor: Prasanta K. Subudhi, Louisiana State of PGPRs (Bacillus subtilis, Bacillus thuringiensis, and Bacillus megaterium), whereas, University College of Agriculture, UNITED STATES some of the seeds were soaked in distilled water for the same period of time and were Received: December 25, 2018 treated as control. Plant growth regulators, salicylic acid (SA) and putrescine (Put), were Accepted: February 13, 2019 applied on 25 days old seedlings just prior to the induction of drought stress. Drought stress Published: March 4, 2019 was imposed by withholding the supply of water on 25-day-old seedlings (at the three-leaf Copyright: This is an open access article, free of all stage) and continued for the next 25 days until the soil water content reached 14%. Ultra- copyright, and may be freely reproduced, high-performance liquid chromatography-high resolution mass spectrometry (UPLC- distributed, transmitted, modified, built upon, or HRMS) analysis concomitant with physiological parameters were carried out in chickpea otherwise used by anyone for any lawful purpose. leaves at two-time points i.e. 14 and 25 d after imposition of drought stress. The results The work is made available under the Creative Commons CC0 public domain dedication. showed that both genotypes, treated with PGRs and PGPRs (consortium), performed signif- icantly better under drought condition through enhanced leaf relative water content (RWC), Data Availability Statement: All the data generated or analysed during this study are included in this greater biomass of shoot and root, higher Fv/FM ratio and higher accumulation of protein, article and its supplementary information files and sugar and phenolic compounds. The sensitive genotype was more responsive than tolerant all the data described in this manuscript is fully one. The results revealed that the accumulation of succinate, leucine, disaccharide, saccha- available without any restrictions. ric acid and glyceric acid was consistently higher in both genotypes at both time points due Funding: This project was supported by to PGRs and PGPRs treatment. Significant accumulation of malonate, 5-oxo-L-proline, and department of Agronomy, College of Agricultural trans-cinnamate occurred at both time points only in the tolerant genotype following the con- and Life Sciences, University of Florida, USA and Higher Education Commission, Pakistan. The sortium treatment. Aminoacyl-tRNA, primary and secondary metabolite biosynthesis, amino funder had no role in study design, data collection PLOS ONE | https://doi.org/10.1371/journal.pone.0213040 March 4, 2019 1 / 21 Metabolic and physiological changes induced by PGPR and PGRs and analysis, decision to publish, or preparation of acid metabolism or synthesis pathways, and energy cycle were significantly altered due to the manuscript. PGRs and PGPRs treatment. It is inferred that changes in different physiological and meta- Competing interests: The authors have declared bolic parameters induced by PGRs and PGPRs treatment could confer drought tolerance in that no competing interests exist. chickpeas. Introduction Chickpea (Cicer arietinum L.) is a legume belongs in the family) Fabaceae, subfamily Faboi- deae. It is one of the most widely consumed pulse legumes and ranking third after peas and soybean, and also covers a total of 15% of the world’s pulse productions [1]. It is an impor- tant source of protein, carbohydrate, B-group vitamins, and different minerals [2]. It is con- sidered an important source of cheap protein with high energy and nutritional values [3, 4]. Drought stress is the most prevalent environmental factor that limits growth, survival, and productivity of chickpeas [5]. The yield of chickpeas can be reduced from 15 to 60% due to the drought stress. Moreover, the global climate change including high temperature stress and unpredictable rainfall pattern coupled with the increasing world population is creating immense pressure on our capacity for sustainable food production including chickpea pro- duction. Drought affects seed germination and seedling establishment in the field, however, genotypes vary in their capacity to tolerate drought stress. Drought also causes a substantial reduction in crop productivity through negatively impacting plant growth, physiology, nutri- ent and water relations, photosynthesis, and assimilate partitioning [6–8]. To cope with such challenges and develop stress resilient chickpea varieties for future climate change condition, understanding the effects of drought on physiological, morphological and biochemical pro- cesses, and their relationship to the adaptation mechanisms is crucial [9]. Plant growth regulators (PGRs) or hormones are chemical substances that profoundly influence the growth and differentiation of plant cells, tissues, and organs. They function as chemical messengers for intercellular communication [10]. They have been found to improve tolerance of plants against the damages caused by abiotic stresses. However, limited researches have been led to examine the possible benefits of exogenous application of PGRs under water stress conditions [11], particularly in chickpeas. Plant hormones interact with complex signal- ling networks to balance the responses to developmental and environmental signals, and thus limit defense-associated fitness costs [12]. Empirical evidence suggests that PGRs, such as, sali- cylic acid (SA) signalling mechanism positively regulates plant defense against biotrophic pathogens, which requires living tissue to complete their life cycle [13, 14]. SA evidenced to provide tolerance in plants against different abiotic stresses, such as heat, salinity, heavy metal toxicity, and drought [15]. Results have also demonstrated that SA improved tolerance of chickpea seedlings to drought stress [16, 17] and also mitigated the adverse effects of Lead and Mercury on membrane damage [18]. Putrescine (Put) also plays a positive role in reducing the adverse effects of abiotic stresses on plants through its acid neutralizing and cell wall stabilizing capabilities [19]. Put has demonstrated the capability to improve tolerance against drought, oxidative, salinity and chilling stresses in different plant species [20–22]. Beside its role in tol- erance, PGRs also influence different developmental processes in plants [23]. Plant growth promoting rhizobacteria (PGPRs) playan important role in increasing crop yields by facilitating plant growth through different mechanisms [24]. PGPRs affect plant growth positively through the production of phytohormones, increased phosphorus availabil- ity, and expansion of plant root systems to uptake more water and nutrients. In addition, PLOS ONE | https://doi.org/10.1371/journal.pone.0213040 March 4, 2019 2 / 21 Metabolic and physiological changes induced by PGPR and PGRs PGPRs also affect enzymatic activities such as ACC-deaminase, production of rhizobiotoxine to reduce the adverse effects of ethylene and enhance nodulation and fixation of atmospheric nitrogen [25]. PGPRs greatly affect soil characteristics as well and play a vital role in transform- ing the poor quality land into the cultivable land [26]. However, the successful utilization of PGPRs is dependent on its survival in soil, the compatibility with the crop on which it is inocu- lated, the interaction ability with indigenous microflora in the soil, and with the environmental factors [24]. In addition, some PGPRs may possess some more specific plant growth-promot- ing traits, such as heavy metal detoxifying activities, salinity tolerance, and biological control of phytopathogens and insects [27]. Different species of PGPRs, such as Bacillus, can be found in the agricultural fields that can promote the crop health in different ways. Some of these species directly stimulate plant growth either through enhancing acquisition of nutrients or through stimulating host plant’s defense against insect and pathogen infection [28]. The genetics, biochemistry, and ecology of Bacillus subtilis, a species of Bacillus, has been described by different authors [29]. This strain produces indole accetic acid (IAA), siderophore, phytase, organic acid, 1-aminocyclopropane- 1-carboxylate (ACC) deaminase, cyanogens, lytic enzymes, oxalate oxidase, and solubilized various sources of organic and inorganic phosphates as well as potassium and zinc. Bacillus subtilis stimulates production of phytohormones involved in metabolism and growth develop- ment mechanisms [30]. Bacillus thuringiensis has been used as an effective bioinsecticide because it produces the proteins Cry and Cyt, which are highly toxic to insects [31]. More recent studies suggest that B. thuringiensis can be used as a biological control agent to suppress plant disease, and to promote plant growth, seed germination and shoot elongation [32]. Bacil- lus megaterium influences plant growth and development by producing phytohormones such as auxins, gibberellins, and cytokinins [33]. Both PGPRs and PGRs exert beneficial effects on plant growth when applied alone, how- ever, their combined applications were much more effective than PGPRs and/or PGRs used alone to mitigate drought stress in chickpea and wheat. Addition of PGRs to PGPRs inoculated plants assisted in osmoregulation and ameliorated oxidative stresses and induced new pro- teins, and significantly enhanced the leaf chlorophyll and sugar content. Combined application of PGRs and PGPRs decreased lipid peroxidation more effectively and increased the leaf area. The relative water content in leaves, and root fresh and dry weight were also higher in com- bined treatment of PGPRs and PGRs. The nutrient content of rhizosphere soil of PGPRs and PGRs treated plants was also enhanced significantly as compared to single application of PGPRs and PGRs. It is inferred from our previous studies that PGPRs and PGRs work syner- gistically to promote growth of plants under moisture and nutrient deficit condition [17, 34]. A metabolome is a complete set of metabolites produced by an organism in its lifetime. The metabolites play key roles in the biochemical processes of organisms [35]. Metabolomics is one of the fastest growing technologies to understand biochemical changes associated with stress tolerance in plants. This technology is utilized in plant research and includes metabolic fingerprinting, profiling and targeted analysis. Metabolomics is used as an important tool to understand the environmental responses in plants [36]. Empirical evidence suggests that meta- bolic components are linked to high-temperature stress tolerance in corn [37], in cool season grass [38], and in wheat [39]; drought tolerance in wheat [40, 41], in chickpeas [42], and in rice [43]. Plants can modify their physiology to adapt to different environmental conditions through metabolic changes [44, 45]. Our previous study clearly demonstrated that the combi- nation of PGRs and PGPRs was more effective in ameliorating drought stress in chickpea com- pared to PGPRs or PGRs alone [17, 42]. In our previous study, we also presented altered metabolic states in two chickpea genotypes, Punjab Noor-2009 (drought sensitive) and 93127 (drought tolerance), under drought stress conditions [34]. Though the combination of PGRs PLOS ONE | https://doi.org/10.1371/journal.pone.0213040 March 4, 2019 3 / 21 Metabolic and physiological changes induced by PGPR and PGRs and PGPRs can effectively contribute to the drought tolerance, no known information is avail- able on the complex metabolic regulation associated with PGRs and PGPRs, and their effect on drought stress tolerance in chickpeas. Metabolic profiling can help us to understand the biochemical mechanism involved with PGRs and PGPRs induced drought tolerance in chick- peas and can contribute in developing stress resilient variety development for future food secu- rity. Therefore, the present study was carried out to investigate the combined effects of PGRs/ PGPRs on the metabolic profiling of two different chickpea genotypes, contrasting for drought tolerance, grown under drought condition. This study also investigated the relationship between altered metabolic levels with different physiological traits under drought stress condition. Materials and methods The experiment was conducted under greenhouse condition at the Department of Agronomy, University of Florida, Gainesville, Florida in May, 2016. Seeds of two chickpea genotypes, Pun- jab Noor-2009 (drought sensitive) and 93127 (drought tolerance), were obtained from Ayub Agriculture Research Institute, Faisalabad, Pakistan. Initially, chickpea seeds were washed in 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. Some of the washed seeds were soaked for 3 h, prior to sowing, in 24 h old cul- tures of Bacillus subtilis, Bacillus thuringiensis, and Bacillus megaterium, whereas, some of the washed seeds were sown without any treatment (drought control). The PGRs, salicylic acid (SA) and putrescine (Put) were sprayed (150 mg/L) on 25 days old seedlings of chickpea inoc- ulated with PGPRs. Seeds were grown in pots (5 seeds/pot) measuring 30 × 40 cm and filled with 2,000 g of Metro-Mix 360 soil mixture. The pots were well watered (twice per week) throughout until drought stress was applied, and each pot contained 5 plants. Water was applied until the soil mix 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. The green house condition was maintained at 26 and 19 ± 1 ˚C (day and night temperatures) with 70 ± 2% relative humidity and 11 and 13 hours day and night lengths, respectively. Drought stress was imposed on 25-day-old plants (at the 3-leaf stage) by with- holding water supply for the next 25 days until the soil water content reached 14%. Leaf tissue samples were collected twice; at 14 days (first-time point) and 25 days (second-time point) after drought stress initiated for metabolomics analysis and different physiological trait estimation. The present experiment comprised 2 different treatments; plants under drought stress treated with 3 PGPRs (Bacillus subtilis, Bacillus thuringiensis, and Bacillus megaterium) and 2 PGRs (salicylic acid and putrescine) designated as “consortium”, and plants under drought stress without PGPRs and PGRs treatment and designated as “drought”. The experiment was laid out in a completely randomized block design with 6 replications. Method of inoculation The Luria Bertani (LB) broth was inoculated with fresh (24 h old) bacterial culture. The inocu- lated LB broth incubated in a shaker for 24 h at 27 ˚C followed by centrifugation at 10000 rpm (10 min). The pellet was mixed with distilled water and the optical density (OD) (at 660 nm) was adjusted to 1. The colony was then soaked in broth with isolates. The seeds were soaked in broth for 3 h prior to sowing. PLOS ONE | https://doi.org/10.1371/journal.pone.0213040 March 4, 2019 4 / 21 Metabolic and physiological changes induced by PGPR and PGRs Leaf chlorophyll content and chlorophyll fluorescence Leaf chlorophyll content was determine by using SPAD chlorophyll meter (SPAD-502 plus. serial No. 20001472 made by Konica Minolta, Japan). 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). Chlorophyll fluorescence and chlorophyll content were measured on 3 leaflets in each plant and 5 plants per pot (a total of 15 readings) and averaged. The average value of 15 readings was considered as a single repli- cation, and 6 replicated values/variety were used for statistical analysis and comparison of treatment means, and significant testing at P < .05 level. Relative water content (RWC) The relative water content (RWC) of leaves for each treatment was calculated according to the formula of Weatherly [46]. RWC ¼ ½ðfresh weight of leaves dry weight of leavesÞ=ðturgid weight of leaves dry weight of leavesÞ�� 100: Leaf protein content The Lowery et al. [47] method was used for the estimation of protein content in leaves of chickpea. In detail, leaf tissue was ground in a phosphate buffer (1 mL) and centrifuged for 10 min. The supernatant was transferred to a tube and distilled water was added for a final vol- ume of 1 mL. Reagents C and D were added to the supernatant and were mixed by shaking. The sample was then incubated at room temperature for 30 min and the absorbance at 650 nm of each sample was determined along with the absorbance of different concentrations of bovine serum albumin (BSA). Protein concentration was calculated as: Protein concentration mg=g ¼ K value� Dilution factor� Absorbance=sample wt: K value = 19.6, Dilution factor = 2, Wt. of sample = 0.1 g Sugar estimation Sugar content was estimated as outlined by Dube et al. [48]. Fresh leaf tissue was ground in 10 mL of distilled water and centrifuged at 3000 rpm for 5 min. The supernatant (0.1 mL) was mixed with phenol (1 mL; 80%) and concentrated H SO (5 mL). Absorbance was recorded at 2 4 420 nm of wavelength. The concentration of the unidentified sample was considered with ref- erence to the standard curve made by using glucose: Sugar concentration mg=g ¼ K value� Dilution factor� Absorbance=Sample Wt: K value = 20, Dilution factor = 10, Wt. of sample = 0.5 g Total phenolic content The total phenolic content of the extract was determined by the Folin—Ciocalteu method [49]. Briefly, crude extract (200 μL) of 3 mL was made with distilled water, mixed thoroughly with Folin—Ciocalteu reagent (0.5 mL) for 3 min, followed by the addition of 2 mL of 20% (w/v) sodium carbonate. The mixture was kept in dark for one hour, and absorbance was measured at 650 nm. The total phenolic content was calculated from the calibration curve, and the results were expressed as mg of gallic acid equivalent per g dry weight (GAE/g). PLOS ONE | https://doi.org/10.1371/journal.pone.0213040 March 4, 2019 5 / 21 Metabolic and physiological changes induced by PGPR and PGRs Shoot and root dry weights estimation Shoots and roots of the same 5 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 root and shoot dry weights were measured after 25 days of drought stress imposition [42]. Leaf tissue collection and sample preparation for metabolites Leaf samples from the consortium and drought-stressed plants were collected at midday for metabolic profiling. Leaves were harvested at 14 days (time point 1) and at 25 days (time point 2) after the imposition of drought stress. The sampled leaf tissues were frozen in liquid nitro- gen immediately after collection and stored at -80 ˚C. Tissue samples were lyophilized for 72 hours and ground using a TissueLyser. Lyophilized powder (30 mg) was used for ultrahigh performance liquid chromatography-high resolution mass spectrometry (UPLC-HRMS) based metabolite profiling following the protocol of Lisec et al. [50]. In brief, freeze-dried leaf tissues were weighed (30 mg) into a clean Eppendorf tube, fol- lowed by the addition of internal standards (20 μL) to each sample. Methanol (750 μL) and ammonium acetate (10 mM, 750 μL) was added to each sample and vortexed for 1 min at room temperature. Centrifugation (17000 G, 10 m) was done after all the samples were ultra- sonicated for 20 min at room temperature. The supernatant (> 1 mL) was transferred to a 1.5 mL Eppendorf tube, followed by a 50 μL transfer of supernatant to an Eppendorf tube. The supernatant was dried down after adding 50 μL of injection of standard solution. Samples were then vortexed for 30 sec and put at 4 ˚C for 10 min, centrifuged at 20,000 rpm for 10 min, and the supernatant was transferred into an LC-vial. UPLC—HRMS analysis Untargeted metabolic profiling was performed on an ultrahigh performance liquid chroma- tography-high resolution mass spectrometry (Model: Thermo Ultimate 3000 UPLC and Thermo QExactive mass spectrometer) platform at the University of Florida Southeast Center for Integrated Metabolomics (SECIM). All samples were analyzed in positive and negative heated electrospray ionization with a mass resolution of 70,000 at m/z 200 as separate injec- tions. 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 70–1000 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 minutes. Probe (HESI probe) temperature was maintained at 350˚C for both positive and negative run with a spray voltage of 3500 V and a capillary temperature of 320 ˚C [42]. Metabolite data analysis. Data tables with metabolite peaks (mz/rt) at 2-time points for consortium with drought stress and control drought stress treatment were formatted as comma separated values (.csv) files and uploaded to the MetaboAnalyst 3.0 server (http:// www.metaboanalyst.ca) [51]. Metabolite data generated by UPLC-HRMS were checked for data integrity and normalized using MetaboAnalyst’s normalization protocols (selecting nor- malization by the sum, log transformation, and auto-scaling) to shrink any possible variance and to improve the performance for downstream statistical analysis. Univariate analysis (t-test and one way ANOVA) was applied to calculate the statistical sig- nificance of the metabolites between 2 group means (consortium/drought). We applied multi- variate methods, supervised method-Partial Least Squares Discriminant Analysis (PLS-DA) and unsupervised method-Hierarchical clustering with a heat map, for the comprehensive data analysis as it takes all the variables into consideration, for example, a heat map was PLOS ONE | https://doi.org/10.1371/journal.pone.0213040 March 4, 2019 6 / 21 Metabolic and physiological changes induced by PGPR and PGRs generated based on the Pearson distance measure and the Ward clustering algorithm, showing the top 25 metabolites for consortium versus drought treatments by PLS-DA VIP (variable importance in projection) score using a significance level of P� 0.05, and post-hoc analysis of Fisher’s LSD. The samples were arranged according to their sampling time points (time point 1 and 2) in all two groups. The important metabolites were identified by using 2 different methods separately: PLS-DA and SAM (Significant Analysis of Metabolites) [42, 52]. The pathway analysis was performed using MetaboAnalyst for the identified important metabolites using Arabidopsis thaliana pathway libraries. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway database (http://www.genome.ad.jp/kegg/pathway.html) was also used for the metabolites that were not found in the Arabidopsis pathway libraries. Data analysis for biochemical characters. The data analysis was carried out by using soft- ware Statistics, version. 8.1. An ANOVA was performed to determine the effect of treatments and error associated with the experiment. A total number of six replicates were used for each treatment. To identify significant differences among treatments, a mean comparison of traits was carried out by using protected LSD (p = 0.05) test where the error mean square was used to estimate the standard error of differences between mean. Results Chlorophyll content, chlorophyll fluorescence and relative water content (RWC) of two chickpea genotypes Under drought condition, the tolerant genotype (93127) demonstrated higher chlorophyll content than the sensitive genotype (Punjab Noor-2009) (Table 1). The application of PGRs in combination with PGPRs (consortium) increased chlorophyll content in both genotypes, but the sensitive genotype was more responsive than tolerant genotype. The Fv/Fm ratio is an indi- cator of photosystem II damage in plants. The lower value indicates more damage to the pho- tosystem. The sensitive genotype showed higher damage to the photosystem due to drought stress than the tolerant one. Both varieties showed responsiveness to the consortium treatment, but the sensitive genotype was more responsive than the tolerant variety. Regarding RWC, we also observed a similar trend and the sensitive genotype showed a higher increase in RWC compared to the tolerant genotype due to consortium treatment (Table 1). In summary, the consortium treatment increased chlorophyll content, Fv/Fm, and RWC in both genotypes compared to their drought treatment, but the increase was more in the sensitive genotype than Table 1. Chlorophyll content and chlorophyll florescence (Fv/Fm ratio) of two chickpea genotypes under consortium and drought condition after 25 days of stress imposition. Chickpea Spad Chlorophyll Chlorophyll RWC (%) Protein (μg/g) Sugar (mg/g) Phenolics (mg Shoot dry wt. Root dry wt. genotype Content Florescence (Fv/ GAE/g) (g) (g) Fm) Cons. Dro Cons. Dro. Cons. Dro. Cons. Dro. Cons. Dro. Cons. Dro. Cons. Dro. Cons. Dro. Punjab 35.6 16.4 0.727 0.317 64 27 1.6 1.2 1.8 0.7 3.1 1.7 10.9 3.11 2.41 0.94 a b a b a b a b a b a b a b a b Noor-2009 ±0.011 ±0.01 ±0.014 ±0.015 ±0.02 ±0.07 ±0.003 ±0.016 ±0.014 ±0.001 ±0.012 ±0.013 ±0.15 ±0.1 ±0.06 ±0.03 (Sensitive genotype, G1) 93127 46.1 34.4 0.843 0.643 79 55 1.7 1.5 2.3 1.5 3.7 2.6 12.27 6.33 2.4 1.51 a b a b a b a b a b a b a b a b (Tolerant ±0.021 ±0.017 ±0.007 ±0.03 ±0.011 ±0.01 ±0.015 ±0.023 ±0.019 ±0.015 ±0.08 ±0.016 ±0.28 ±0.21 ±0.08 ±0.02 genotype, G2) Cons-Consortium (Mean±SE); Dro-Drought (Mean±SE); Different letters (i.e. a and b) indicate significant differences (P< .05) among treatments. https://doi.org/10.1371/journal.pone.0213040.t001 PLOS ONE | https://doi.org/10.1371/journal.pone.0213040 March 4, 2019 7 / 21 Metabolic and physiological changes induced by PGPR and PGRs the tolerant genotype. Consortium treatment helped to maintain the water balance in cell and to reduce damage to the photosystem in chickpeas. Protein, sugar and phenolics content in the leaves of two chickpea genotypes The tolerant genotype showed a higher value for protein and phenolic compound under drought condition compared to the sensitive genotype (Table 1). However, the sugar content was higher in the sensitive genotype than the tolerant genotype under drought condition. Con- sortium treatment induced higher protein, sugar, and phenolic compound accumulation in both genotypes, however, the increase was more in the sensitive genotype than the tolerant one. Shoot and root dry weights The tolerant genotype produced higher root and shoot dry weight than the sensitive genotype under drought condition (Table 1). Both genotypes showed increased root and shoot dry weight accumulation due to consortium treatment, and the increase was higher in the sensitive genotype than the tolerant one. In general, consortium treatment helped the plant to maintain growth by accumulating higher biomass under drought condition. Metabolic profile of chickpea genotypes induced by consortium and drought treatments The untargeted UPLC-HRMS global metabolomics analysis was performed for profiling of leaf metabolites for 2 different chickpea genotypes under 2 contrasting treatments at 2 different sampling time points (14 and 25 days after stress imposition). UPLC-HRMS analysis detected a total of 178 known metabolite peaks out of which 53 were found to be significant (S1 Table). Metabolites were highly reproducible among the six analyzed biological replications at the 2 different time points. PLS-DA and 2D loading plot results. We performed a supervised clustering method, Partial Least Squares-Discriminant Analysis (PLS-DA), for both the genotypes and for consor- tium versus drought treatments at 2 different time points. PLS-components (PCs) analysis revealed that component 1 explained 51% and 36.2% of the total variation of the sensitive and tolerant genotypes, respectively, under consortium versus drought treatment (Fig 1), while the second component explained 15.9% and 17.9% variation for sensitive and tolerant genotypes, respectively, for the same treatment (Figs 1 and 2). The 2D scores plots between PC1 and PC2 showed 2 different groups associated with the consortium and drought samples at 2 different sampling points (Fig 1), suggesting a clear distinction in the metabolite accumulation under two different conditions. The separation between the consortium and the drought treatments is suggesting the dominant role of PGRs and PGPRs in modulating drought tolerance in treated plants. Analysis of variance and heat map. A total of 53 metabolites were identified through a multi-factorial ANOVA which were significantly altered in 2 genotypes across 2 different time points and treatments. Among different groups of metabolites, amino acids, sugars, sugar alco- hol, organic acids, polyamines, nitrogenous compounds and polyphenols and other organic compounds were significantly accumulated in the leaves of plants treated with PGRs + PGPRs under drought stress. Amino acid: leucine; organic compounds: succinate, lactic acid, phenyl- pyruvate, trans-cinnamate, 2-aminophenol, and malonate; sugar acid: glyceric acid; sugar: disaccharides; chemical compounds: saccharic acid, syringic acid; and ammonium compound: PLOS ONE | https://doi.org/10.1371/journal.pone.0213040 March 4, 2019 8 / 21 Metabolic and physiological changes induced by PGPR and PGRs Fig 1. Partial least square discriminant analysis (PLS-DA) and 2D Scores loading plot for the chickpea Punjab Noor-2009 (G1) and 93127 (G2) leaves at 2 time points under consortium and water deficit treatments. Metabolites at consortium and drought treatments did not overlap indicating an altered state of metabolite levels in the chickpea leaves. Sampling time points are thereby demonstrating its effect over time and proofing in the leaves of chickpea plants. https://doi.org/10.1371/journal.pone.0213040.g001 PLOS ONE | https://doi.org/10.1371/journal.pone.0213040 March 4, 2019 9 / 21 Metabolic and physiological changes induced by PGPR and PGRs Fig 2. (A & B). Heatmap [A: consortium vs water-deficit (G1) and B: consortium vs water-deficit (G2)] illustrating (distance measure: Pearson; Clustering algorithm: Ward) of the performed partial least square discriminant analysis (PLS-DA) showing levels of key metabolite. Metabolite feature areas were normalized and range-scaled across all experimental samples at 2 different time points. https://doi.org/10.1371/journal.pone.0213040.g002 L-carnitine showed increased levels of accumulation in the leaves of plants treated with PGRs and PGPRs consortium. However, compounds like 5-oxo-proline tryptophan, 4-coumorate, sugar alcohol, salicylate, and phosphocholine were highly accumulated in the leaves of untreated plants grown under stress condition. Significantly different metabolites (identified through ANOVA) were analyzed by hierar- chical clustering with a heat map in order to visualize the effect of PGRs + PGPRs consortium on metabolomics expression over uninoculated drought stress plants. The heat map was gener- ated for consortium versus drought treatments for both the sensitive and tolerant genotypes which indicates a clear distinction between metabolites at consortium and drought treatments. The first cluster of heat map was represented by metabolites accumulated at higher levels in the leaves of PGPRs + PGRs treated (consortium) plants including carnitine, glyceric acid, phenylpyruvate, succinate, lactic acid, leucine, D-saccharic acid, isocytosine and coumarate whereas, tryptophan, sugar alcohol, N-butylbezalsulfomide and malonate were abundantly present in leaves of untreated sensitive genotype grown under drought stress (Fig 2A). Accu- mulation of L-carnitine, salicylate, succinate, and malonate occurred only at the first time point in the leaves of PGRs + PGPRs treated plants of tolerant genotype whereas, isocytosine, trans-cinnamate, syringic acid, phosphocholine, and phenylpyruvate were only accumulated at second time point. The consortium of PGRs and PGPRs also significantly enhanced the accumulation of glyceric acid, disaccharide, saccharic acid, aminophenol and 5-oxo-L-proline at both time points in the tolerant genotype (Fig 2B). PLOS ONE | https://doi.org/10.1371/journal.pone.0213040 March 4, 2019 10 / 21 Metabolic and physiological changes induced by PGPR and PGRs Table 2. Important metabolites with their compound ID (KEGG ID/PubChem CID and molecular formula, identified through partial least square discrepant anal- ysis (PLS-DA) and significant analysis of metabolites (SAM) across genotypes and treatments. S.No. Important Metabolites KEGG ID PubChem CID Molecular Formula SAM (d-value) PLS-DA VIP score (variance For component 1) 1 5-oxo-L-proline C01877 107541 C H NO 6.2298 0.10057 5 6 3 2 Azelaic acid C08261 2266 C H O 5.9834 0.4252 9 16 4 3 Glyceric acid C00258 439194 C H O 7.7543 0.73388 3 6 4 4 Succinate C00042 1110 C H O 9.4668 1.4221 4 6 4 5 L-(+)-Lactic Acid C00186 107689 C H O 4.961 1.3168 3 6 3 6 Phenylpyruvate C00166 997 C H O 3.854 1.2303 9 8 3 7 Choline C00114 305 C H NO 5.936 - 5 14 8 Tryptophan-NH3 C00078 6305 C H N O 3.112 1.4373 11 12 2 2 9 L-Leucine 13C6 C00123 6106 C H NO -4.189 1.1682 6 13 2 10 Caffeine-D3 C07481 2519 C H N O -4.532 1.224 8 10 4 2 11 2-Hydroxyphenylalanine C00082 91482 C H NO 2.524 0.5669 9 11 3 12 Syringic Acid C10833 10742 C H O 5.265 0.86745 9 10 5 13 Trans-cinnamate C00423 444539 C H O 4.341 0.62645 9 8 2 14 D-Saccharic acid C00818 33037 C H O 3.265 1.1957 6 10 8 15 Triethyl phosphate - 6535 C H O P 1.474 1.0598 6 15 4 16 L-Carnitine C00318 2724480 C H NO 7.284 1.1691 7 15 3 17 2-Aminophenol C01987 23035081 C H NO 2.193 0.75572 6 7 18 N-Butylbenzenesulfonamide - 19241 C H NO S 1.743 1.0977 10 15 2 19 Isocytosine - 66950 C H N O 4.983 0.74784 4 5 3 20 4-Coumarate C00811 637542 C9H8O3 8.974 1.1368 21 Malonate C00383 867 C H O 5.846 1.0699 3 4 4 22 Salicylate C07588 10253 C H O 3.957 0.70754 7 6 3 23 C5-Sugar alcohol - - - 3.654 0.92692 24 Disaccharide C00089 5988 C H O 2.884 0.76615 12 22 11 25 Phosphocholine C00588 1014 C H NO P+ 2.631 0.38416 5 15 4 KEGG = Kyoto Encyclopedia of Genes and Genomes. https://doi.org/10.1371/journal.pone.0213040.t002 Profiling of leaf metabolites. Two statistical methods, PLS-DA and SAM, were carried out for the identification of the most important metabolites across genotypes and treatments (Table 2). The PLS-DA analysis identified 25 most important metabolites based on the VIP score using a 5-component model. Similarly, the most important metabolites were also identi- fied by significant analysis of metabolites (SAM) with the delta value of 1.5, false discord rate (FDR) of 0.002 with less than one (0.25) false positive. Overall, the identified metabolites were quite similar across the 2 methods. The top most important 25 metabolites which were identi- fied by these 2 methods are shown in Table 2. These important metabolites included different amino acids, sugars, sugar alcohol, amines, organic acids, fatty acids and other intermediate compounds. The sensitive genotype significantly accumulated succinate, leucine, carnitine, lactic acid, glyceric acid, phenylpyruvate, isocytosine, and saccharic acid when treated with PGRs and PGPRs consortium (Fig 3). Contrary to that, compounds like malonate, disaccha- ride, and trans-cinnamate were highly accumulated in tolerant genotype under PGRs and PGPRs treatment. Drought stress caused significant accumulation of tryptophan, salicylate, and sugar alcohol in the leaves of untreated drought plants compare to the consortium in both tolerant and sensitive genotypes (Fig 3). More metabolites were significantly altered in sensi- tive genotype due to consortium treatment than tolerant genotype. PLOS ONE | https://doi.org/10.1371/journal.pone.0213040 March 4, 2019 11 / 21 Metabolic and physiological changes induced by PGPR and PGRs Fig 3. Significantly different levels of selected metabolites (ANOVA, P� .05, Tukey’s honest significant difference) in the leaves of two chickpea varieties under consortium and drought conditions at 2 time points (14 and 25 days). G1: drought-sensitive chickpea genotype (Punjab Noor-2009); G2: drought-tolerant chickpea genotype (93127). Error bars represent standard errors of the mean (n = 6) at each time point. Cons = consortium, D = drought. Different letters indicate significant differences (P < .05) among treatments (consortium vs drought) for a genotype for mz/rt peak in a particular time point. https://doi.org/10.1371/journal.pone.0213040.g003 Identification of corresponding biological pathways. All the metabolites significantly affected by PGRs + PGPRs treatment under drought stress were mapped to the biological path- ways involved in the KEGG online database, which was assigned to 22 different pathways/ metabolism in either treatment. Table 3 is showing the metabolites involved in each pathway, the number of hits, and FDR of the pathway. As expected, the significantly altered metabolites were involved in a number of different pathways. These include Phenylalanine, tyrosine and tryptophan biosynthesis, Phenylpropanoid biosynthesis, Glucosinolate biosynthesis, Tropane, piperidine and pyridine alkaloid biosynthesis, Citrate cycle (TCA cycle), Aminoacyl-tRNA biosynthesis, Ubiquinone and terpenoid-quinone biosynthesis, Glycolysis or Gluconeogenesis, Valine, leucine and isoleucine degradation, and Valine, leucine, and isoleucine biosynthesis. In addition, 11 primary metabolisms such as the Phenylalanine metabolism, Ascorbate and alda- rate metabolism, Glycerolipid metabolism, Propanoate metabolism, Glyoxylate and dicarboxy- late metabolism, Tyrosine metabolism, Pyruvate metabolism, Butanoate metabolism, Alanine, aspartate and glutamate metabolism, Glycerophospholipid metabolism, Tryptophan metabo- lism and Glycine, serine and threonine metabolism were also significantly altered due to con- sortium and drought treatments. Discussion Drought stress is one of the major constraints for agricultural productivity throughout the world. Approximately, 40% of the agricultural lands are located in the arid and semi-arid regions of the world [53] where plants suffer frequently from drought stress. Both plant hor- mones (PGRs) and PGPRs respond to environmental stresses and impart tolerance to plant against the stresses [54]. PGRs and PGPRs consortium plays a significant role in the alleviation of drought stress in plants by maintaining water budget of the plant and by producing PLOS ONE | https://doi.org/10.1371/journal.pone.0213040 March 4, 2019 12 / 21 Metabolic and physiological changes induced by PGPR and PGRs Table 3. Pathway names, total metabolites involved in that pathways, metabolites significantly accumulated in present study (hits), and false discord rate (FDR). Pathway name Total Hits FDR Phenylalanine metabolism 11 3 2.2225E-5 Phenylalanine, tyrosine and tryptophan biosynthesis 22 2 0.000108 Glycerophospholipid metabolism 25 2 0.000109 Phenylpropanoid biosynthesis 31 2 0.00369 Glucosinolate biosynthesis 8 1 0.00387 Tropane, piperidine and pyridine alkaloid biosynthesis 10 1 0.00525 Ascorbate and aldarate metabolism 14 1 0.00574 Glycerolipid metabolism 14 1 0.00656 Propanoate metabolism 14 1 0.00793 Glyoxylate and dicarboxylate metabolism 17 1 0.00999 Tyrosine metabolism 18 1 0.01001 Pyruvate metabolism 20 1 0.01436 Butanoate metabolism 20 1 0.01446 Citrate cycle (TCA cycle) 20 1 0.01563 Alanine, aspartate and glutamate metabolism 21 1 0.01612 Aminoacyl-tRNA biosynthesis 67 2 0.01801 Ubiquinone and other terpenoid-quinone biosynthesis 22 1 0.01823 Glycolysis or Gluconeogenesis 25 1 0.01893 Tryptophan metabolism 25 1 0.0221 Valine, leucine and isoleucine biosynthesis 26 1 0.0251 Glycine, serine and threonine metabolism 29 1 0.0325 Valine, leucine and isoleucine degradation 34 1 0.0471 https://doi.org/10.1371/journal.pone.0213040.t003 metabolites as observed during the present investigation. In the sensitive genotype, the amelio- rating effect of PGRs and PGPRs consortium was noteworthy. Chlorophyll content, chloro- phyll fluorescence, RWC, and root and shoot biomass accumulation were greater than 2 fold due to PGRs and PGPRs consortium under drought stress. The effect of PGRs and PGPRs treatment was more pronounced in the sensitive genotype compared to the tolerant genotype. Previous studies demonstrated that the drought-induced reduction in chlorophyll content and chlorophyll fluorescence led to a decrease in photosynthesis and overall plant growth [55, 56]. The combined treatment significantly enhanced the chlorophyll fluorescence and chlorophyll content values in the PGRs/PGPRs treated plants in both genotypes. The application of PGRs significantly enhanced the root and shoot growth, and dry matter production in different plants [42, 57]. Similarly, PGPRs are known to enhance the root growth and uptake of miner- als and water, thus promote the growth of the whole plant which in turn has a positive impact on plant dry matter content in wheat [58] and in chickpea [59]. Hassanzadeh et al. [60] reported that decrease in RWC is related to the decrease in chlorophyll content and leaf fresh weight in sesame genotypes, however, the tolerant genotypes maintained higher RWC under stress condition and thus showed higher affinity for chlorophyll content and leaf fresh weight. Our results demonstrated that PGRs combined with PGPRs helped both genotypes to main- tain an efficient photosystem with improved water budget resulting in improved growth and productivity under drought stress condition. Significant increase in the leaf protein content in both genotypes was evident after being treated with PGRs and PGPRs in comparison to untreated plants grown under stress condi- tion. A correlation between increased protein levels and their involvement in ROS scavenging PLOS ONE | https://doi.org/10.1371/journal.pone.0213040 March 4, 2019 13 / 21 Metabolic and physiological changes induced by PGPR and PGRs and oxidative stress metabolism have been demonstrated in plants under stress [61]. The higher protein accumulation was associated with significant increase in Fv/Fm and chlorophyll content due to PGRs and PGPRs treatment under drought stress condition. The increased level of proteins due to PGRs and PGPRs might have assisted plants to mitigate ROS effect [62, 63]. Under stress conditions, the leaf sugar content decreases significantly which is an indication of rapid senescence due to stress. PGRs in combination with PGPRs effectively increased the leaf sugar content under drought condition in both genotypes. Soluble sugars have been dem- onstrated to play an important role in responses to biotic and abiotic stresses [64]. Sugar sig- nalling pathways interact with stress pathways into a complex network in plants to modulate metabolic responses [65]. Soluble sugars may either act directly as negative signals or as modu- lators of plant sensitivity and thus, they can also play important roles in cell responses to stress-induced remote signals [66]. Under stress conditions, a decrease in dry matter accumu- lation and depletion of sugar was correlated in the plant [67]. Present findings demonstrated an increased dry matter accumulation due to PGRs and PGPRs treatment under drought con- dition which could potentially be attributed to the better cellular osmotic balance (demon- strated by RWC) in photosynthetic organs, and thus helped to maintain higher photosynthetic rate and growth. PGRs and PGPRs treatments have also increased the production of phenolic compounds. These molecules have been described as markers for abiotic stress tolerance in plants [68] and they have been proclaimed to be involved in oxidative stress caused by ROS [69]. UPLC-HRMS based untargeted metabolic profiling in the leaves of two chickpea genotypes was performed to understand the effect of PGRs and PGPRs on metabolic changes to adjust drought stress condition. The increased accumulations of different metabolites were previ- ously reported under drought condition in different plant species [42, 70, 71]. It has been noted that the accumulation of leucine, succinate, lactic acid, and glyceric acid was higher in the sensitive genotype at both time points when treated with PGRs + PGPRs consortium. Leu- cine and other amino acids are known to play a variety of different roles in plants, especially under stress condition and impart drought tolerance [72]. Previously, we have reported the increased level of amino acids and organic acids in tolerant variety grown under drought con- dition as compared to control plants [42]. Coupled with different amino acids, our study also demonstrated an increased level of total protein accumulation due to PGRs and PGPR treat- ment. The increased level of different amino acids and protein were associated with water bal- ance, intact photosynthetic structure, and high biomass accumulation in chickpeas plants treated with PGRs + PGPR consortium in our study. Significant accumulation of L-carnitine, trans-cinnamate, succinate and syringic acid occurred at the first time point, whereas, saccharic acid, isocytosine, hydroxyphenylalanine and phenylpyruvate showed significant accumulation in the leaves of PGRs + PGPRs treated sensitive genotype at the second time point. L-carnitine regulates the level of acyl-CoA and CoA in the mitochondrion and cytosol and involvs in the regulation of water resorption and photosynthesis [73]. Plants produce a moiety of organic compounds in response to a variety of environmental stimuli which have key ecological functions and involved in interactions with biotic and abiotic stresses. These compounds are responsible for osmoregulation in both plants and animals. Cinnamate and coumarate are widely distributed in the plant kingdom and play a key role in plant defence, growth and plant-insect interactions [74]. Succinate act as a pri- mary intermediate in ATP pathway of Kreb cycle and play a vital role in energy production and regulation of mitochondrial TCA cycle [75]. Excess of succinate in plants under stress results in more ATP production in mitochondria [76–78]. The elevated level of succinate found in PGRs and PGPRs treated plants demonstrated better tolerance to drought stress and PLOS ONE | https://doi.org/10.1371/journal.pone.0213040 March 4, 2019 14 / 21 Metabolic and physiological changes induced by PGPR and PGRs this could potentially be attributed to the efficient TCA cycle that produces more energy under water-limited conditions. It was evident that all these compounds were involved in dif- ferent biologically significant activities, such as osmoregulation, photosynthesis, energy pro- duction or defense activity in the plant. Their biological functions correlate with our study through osmoregulation or increased photosynthetic efficiency or through higher biomass accumulation in chickpea plants treated with PGRs and PGPRs under drought stress conditions. The present study has demonstrated enhanced accumulation of disaccharide, saccharic acid, glyceric acid, aminophenol, and 5-oxo-L-proline at both time points in the tolerant genotype when treated with PGRs and PGPRs consortium. These sugars are responsible for osmotic adjustment by detoxifying reactive oxygen species and stabilize the quaternary struc- ture of protein under water scarcity [79]. Accumulation of sugars and their derivatives lead to drought tolerance in wheat, maize, Arabidopsis, chickpea, millet and rye [42, 80, 81]. Higher accumulation of the total sugar content was also evidenced in our study due to PGRs and PGPRs treatment which was due to increased photosynthetic activity. Higher accumula- tion of sugar alcohol was noted in drought-tolerant genotype treated with PGRs + PGPRs. Sugar alcohols also play a significant role in stress tolerance by inducing osmotic adjustment through accumulation of a compatible solute or the transitory storage of carbon reserves [82]. The untreated genotypes exhibited a higher accumulation of salicylate and tryptophan when exposed to long-term drought stress. In plants, exogenous application of salicylates affected many physiological and biochemical processes such as seed germination, seedling establishment, thermogenesis cell growth, senescence, stomatal responses, thermotolerance and nodulation [83–85]. Reduced accumulation of salicylates due to PGRs and PGPR consor- tium treatment could potentially be attributed to a reduced rate of senescence and thus prolong the photoassimilation. Tryptophan plays a major role in the regulation of plant devel- opment and defense responses [86]. Tryptophan is the precursors of different secondary metabolites including indoleacetate, lipid precursor, and lignin in the Shikimate pathway, which plays a vital role in stress tolerance [87]. The link between different metabolic pathways and associated metabolites was stimulated using MetaboAnalyst. Twenty five metabolic pathways were significantly altered using the Kyoto Encyclopedia of Genes and Genomes database (KEGG) and Arabidopsis annotation project database. The Phenylalanine, tyrosine and tryptophan biosynthesis pathway was upre- gulated in the PGRs and PGPRs treated plants. This is an important pathway for the synthesis of essential aromatic amino acids. These aromatic amino acids not only serve as part of protein biosynthesis but also involved in the synthesis of other important secondary metabolites that play key roles in plant growth and development [88, 89]. Aminoacyl-tRNA biosynthesis and citrate cycle was also altered in the present study due to PGRs and PGPRs treatment. Aminoa- cyl-tRNA biosynthesis is a group of twenty different enzymes that establish the rules of genetic code. It had been reported earlier that the disrupted metabolic conditions is associated to a specific aminoacyl-tRNA synthetase [90]. The aminoacyl-tRNA synthetases catalyse the bind- ing of amino acids to their specific tRNA and thus play a key role in translation and in gene expression. Citrate cycle play a key role in producing ATP and providing carbon skeletons for a variety of biosynthetic processes in both heterotrophic and photosynthetic tissues [91]. Gly- cine, serine and threonine metabolism play an important role during signalling process and in plant stress responses [92]. Glycine and Serine are two interconvertible amino acids that play significant role in C1 metabolism. Whereas, Serine has a central role in the metabolism and signalling, and involved in plant homeostasis. PLOS ONE | https://doi.org/10.1371/journal.pone.0213040 March 4, 2019 15 / 21 Metabolic and physiological changes induced by PGPR and PGRs Conclusion Different amino acids, sugars, sugar alcohol, amines, organic acids, fatty acids and other inter- mediate compounds were changed significantly due to PGRs and PGPRs treatment. Similar to physiological responses, sensitive genotype also showed altered levels of more metabolites than tolerant genotype. The accumulation of succinate, leucine, disaccharide, saccharic aid and gly- ceric acid was significantly higher in both genotypes in both time points due to PGRs and PGPRs treatment. As these metabolite levels were constantly higher in both genotypes and at different time points, demonstrating their roles in monitoring biochemical pathways related to drought tolerance. Significant accumulation of malonate, 5-oxo-L-proline, and trans-cinna- mate occurred at both time points only in the tolerant genotype due to the consortium treat- ment. On the contrary, lactic acid, L-carnitine, isocytosine, and phenylpyruvate were accumulated significantly in sensitive genotypes at both times. These results indicate that the higher accumulation of these metabolites could possibly associated only with the tolerance mechanism in sensitive genotype. These data provide information that may, with further investigation, help to understand the biochemical pathway underlying drought stress tolerance in chickpea induced by PGRs and PGPRs treatment. Supporting information S1 Table. List of top 53 significant metabolites identified in the study with their compound type, identifier (KEGG ID/PubChem CID ), molecular formula, P-value and false discov- ery rate (FDR), mass-to-charge ratio (m/z), and retention time (RT). (KEGG = Kyoto Ency- clopedia of Genes and Genomes). (DOCX) Acknowledgments We acknowledge the help of South Eastern Center for Integrative Metabolomics (SECIM) for providing the greenhouse and laboratory facility for conducting the experiment and metabolo- mics analysis and acknowledge the help of Dr. Fredy Altpeter to allow us to use lyophilizer and TissueLyser. Author Contributions Conceptualization: Asghari Bano. Data curation: Naeem Khan, MD Ali Babar. Formal analysis: Naeem Khan, MD Ali Babar. Funding acquisition: MD Ali Babar. Investigation: MD Ali Babar. Methodology: Naeem Khan. Project administration: Asghari Bano, MD Ali Babar. Resources: MD Ali Babar. Software: Naeem Khan, MD Ali Babar. Supervision: Asghari Bano. Visualization: MD Ali Babar. PLOS ONE | https://doi.org/10.1371/journal.pone.0213040 March 4, 2019 16 / 21 Metabolic and physiological changes induced by PGPR and PGRs Writing – original draft: Naeem Khan. Writing – review & editing: Asghari Bano, MD Ali Babar. References 1. Montenegro JB, Fidalgo JA, Gabella VM. Response of chickpea (Cicer arietinum L.) yield to zinc, boron and molybdenum application under pot conditions. Span J Agric Res. 2010; 3:797–807. 2. Williams PC, Singh U. Quality screening and evaluation in pulse breeding. InWorld crops: Cool season food legumes 1988 (pp. 445–457). Springer, Dordrecht. 3. El-Karamany MF, Bahr AA. 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Metabolic and physiological changes induced by plant growth regulators and plant growth promoting rhizobacteria and their impact on drought tolerance in Cicer arietinum L.

PLoS ONE, Volume 14 (3) – Mar 4, 2019

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Abstract

Plant growth regulators (PGRs) and plant growth promoting rhizobacteria (PGPRs) play an important role in mitigating abiotic stresses. However, little is known about the parallel OPENACCESS changes in physiological processes coupled with metabolic changes induced by PGRs and Citation: Khan N, Bano A, Babar MA (2019) PGPRs that help to cope with drought stress in chickpeas. The present investigation was Metabolic and physiological changes induced by plant growth regulators and plant growth carried out to study the integrative effects of PGRs and PGPRs on the physiological and promoting rhizobacteria and their impact on metabolic changes, and their association with drought tolerance in two chickpea genotypes. drought tolerance in Cicer arietinum L.. PLoS ONE Inoculated seeds of two chickpea genotypes, Punjab Noor-2009 (drought sensitive) and 14(3): e0213040. https://doi.org/10.1371/journal. 93127 (drought tolerance), were planted in greenhouse condition at the University of Flor- pone.0213040 ida. Prior to sowing, seeds of two chickpea varieties were soaked for 3 h in 24 h old cultures Editor: Prasanta K. Subudhi, Louisiana State of PGPRs (Bacillus subtilis, Bacillus thuringiensis, and Bacillus megaterium), whereas, University College of Agriculture, UNITED STATES some of the seeds were soaked in distilled water for the same period of time and were Received: December 25, 2018 treated as control. Plant growth regulators, salicylic acid (SA) and putrescine (Put), were Accepted: February 13, 2019 applied on 25 days old seedlings just prior to the induction of drought stress. Drought stress Published: March 4, 2019 was imposed by withholding the supply of water on 25-day-old seedlings (at the three-leaf Copyright: This is an open access article, free of all stage) and continued for the next 25 days until the soil water content reached 14%. Ultra- copyright, and may be freely reproduced, high-performance liquid chromatography-high resolution mass spectrometry (UPLC- distributed, transmitted, modified, built upon, or HRMS) analysis concomitant with physiological parameters were carried out in chickpea otherwise used by anyone for any lawful purpose. leaves at two-time points i.e. 14 and 25 d after imposition of drought stress. The results The work is made available under the Creative Commons CC0 public domain dedication. showed that both genotypes, treated with PGRs and PGPRs (consortium), performed signif- icantly better under drought condition through enhanced leaf relative water content (RWC), Data Availability Statement: All the data generated or analysed during this study are included in this greater biomass of shoot and root, higher Fv/FM ratio and higher accumulation of protein, article and its supplementary information files and sugar and phenolic compounds. The sensitive genotype was more responsive than tolerant all the data described in this manuscript is fully one. The results revealed that the accumulation of succinate, leucine, disaccharide, saccha- available without any restrictions. ric acid and glyceric acid was consistently higher in both genotypes at both time points due Funding: This project was supported by to PGRs and PGPRs treatment. Significant accumulation of malonate, 5-oxo-L-proline, and department of Agronomy, College of Agricultural trans-cinnamate occurred at both time points only in the tolerant genotype following the con- and Life Sciences, University of Florida, USA and Higher Education Commission, Pakistan. The sortium treatment. Aminoacyl-tRNA, primary and secondary metabolite biosynthesis, amino funder had no role in study design, data collection PLOS ONE | https://doi.org/10.1371/journal.pone.0213040 March 4, 2019 1 / 21 Metabolic and physiological changes induced by PGPR and PGRs and analysis, decision to publish, or preparation of acid metabolism or synthesis pathways, and energy cycle were significantly altered due to the manuscript. PGRs and PGPRs treatment. It is inferred that changes in different physiological and meta- Competing interests: The authors have declared bolic parameters induced by PGRs and PGPRs treatment could confer drought tolerance in that no competing interests exist. chickpeas. Introduction Chickpea (Cicer arietinum L.) is a legume belongs in the family) Fabaceae, subfamily Faboi- deae. It is one of the most widely consumed pulse legumes and ranking third after peas and soybean, and also covers a total of 15% of the world’s pulse productions [1]. It is an impor- tant source of protein, carbohydrate, B-group vitamins, and different minerals [2]. It is con- sidered an important source of cheap protein with high energy and nutritional values [3, 4]. Drought stress is the most prevalent environmental factor that limits growth, survival, and productivity of chickpeas [5]. The yield of chickpeas can be reduced from 15 to 60% due to the drought stress. Moreover, the global climate change including high temperature stress and unpredictable rainfall pattern coupled with the increasing world population is creating immense pressure on our capacity for sustainable food production including chickpea pro- duction. Drought affects seed germination and seedling establishment in the field, however, genotypes vary in their capacity to tolerate drought stress. Drought also causes a substantial reduction in crop productivity through negatively impacting plant growth, physiology, nutri- ent and water relations, photosynthesis, and assimilate partitioning [6–8]. To cope with such challenges and develop stress resilient chickpea varieties for future climate change condition, understanding the effects of drought on physiological, morphological and biochemical pro- cesses, and their relationship to the adaptation mechanisms is crucial [9]. Plant growth regulators (PGRs) or hormones are chemical substances that profoundly influence the growth and differentiation of plant cells, tissues, and organs. They function as chemical messengers for intercellular communication [10]. They have been found to improve tolerance of plants against the damages caused by abiotic stresses. However, limited researches have been led to examine the possible benefits of exogenous application of PGRs under water stress conditions [11], particularly in chickpeas. Plant hormones interact with complex signal- ling networks to balance the responses to developmental and environmental signals, and thus limit defense-associated fitness costs [12]. Empirical evidence suggests that PGRs, such as, sali- cylic acid (SA) signalling mechanism positively regulates plant defense against biotrophic pathogens, which requires living tissue to complete their life cycle [13, 14]. SA evidenced to provide tolerance in plants against different abiotic stresses, such as heat, salinity, heavy metal toxicity, and drought [15]. Results have also demonstrated that SA improved tolerance of chickpea seedlings to drought stress [16, 17] and also mitigated the adverse effects of Lead and Mercury on membrane damage [18]. Putrescine (Put) also plays a positive role in reducing the adverse effects of abiotic stresses on plants through its acid neutralizing and cell wall stabilizing capabilities [19]. Put has demonstrated the capability to improve tolerance against drought, oxidative, salinity and chilling stresses in different plant species [20–22]. Beside its role in tol- erance, PGRs also influence different developmental processes in plants [23]. Plant growth promoting rhizobacteria (PGPRs) playan important role in increasing crop yields by facilitating plant growth through different mechanisms [24]. PGPRs affect plant growth positively through the production of phytohormones, increased phosphorus availabil- ity, and expansion of plant root systems to uptake more water and nutrients. In addition, PLOS ONE | https://doi.org/10.1371/journal.pone.0213040 March 4, 2019 2 / 21 Metabolic and physiological changes induced by PGPR and PGRs PGPRs also affect enzymatic activities such as ACC-deaminase, production of rhizobiotoxine to reduce the adverse effects of ethylene and enhance nodulation and fixation of atmospheric nitrogen [25]. PGPRs greatly affect soil characteristics as well and play a vital role in transform- ing the poor quality land into the cultivable land [26]. However, the successful utilization of PGPRs is dependent on its survival in soil, the compatibility with the crop on which it is inocu- lated, the interaction ability with indigenous microflora in the soil, and with the environmental factors [24]. In addition, some PGPRs may possess some more specific plant growth-promot- ing traits, such as heavy metal detoxifying activities, salinity tolerance, and biological control of phytopathogens and insects [27]. Different species of PGPRs, such as Bacillus, can be found in the agricultural fields that can promote the crop health in different ways. Some of these species directly stimulate plant growth either through enhancing acquisition of nutrients or through stimulating host plant’s defense against insect and pathogen infection [28]. The genetics, biochemistry, and ecology of Bacillus subtilis, a species of Bacillus, has been described by different authors [29]. This strain produces indole accetic acid (IAA), siderophore, phytase, organic acid, 1-aminocyclopropane- 1-carboxylate (ACC) deaminase, cyanogens, lytic enzymes, oxalate oxidase, and solubilized various sources of organic and inorganic phosphates as well as potassium and zinc. Bacillus subtilis stimulates production of phytohormones involved in metabolism and growth develop- ment mechanisms [30]. Bacillus thuringiensis has been used as an effective bioinsecticide because it produces the proteins Cry and Cyt, which are highly toxic to insects [31]. More recent studies suggest that B. thuringiensis can be used as a biological control agent to suppress plant disease, and to promote plant growth, seed germination and shoot elongation [32]. Bacil- lus megaterium influences plant growth and development by producing phytohormones such as auxins, gibberellins, and cytokinins [33]. Both PGPRs and PGRs exert beneficial effects on plant growth when applied alone, how- ever, their combined applications were much more effective than PGPRs and/or PGRs used alone to mitigate drought stress in chickpea and wheat. Addition of PGRs to PGPRs inoculated plants assisted in osmoregulation and ameliorated oxidative stresses and induced new pro- teins, and significantly enhanced the leaf chlorophyll and sugar content. Combined application of PGRs and PGPRs decreased lipid peroxidation more effectively and increased the leaf area. The relative water content in leaves, and root fresh and dry weight were also higher in com- bined treatment of PGPRs and PGRs. The nutrient content of rhizosphere soil of PGPRs and PGRs treated plants was also enhanced significantly as compared to single application of PGPRs and PGRs. It is inferred from our previous studies that PGPRs and PGRs work syner- gistically to promote growth of plants under moisture and nutrient deficit condition [17, 34]. A metabolome is a complete set of metabolites produced by an organism in its lifetime. The metabolites play key roles in the biochemical processes of organisms [35]. Metabolomics is one of the fastest growing technologies to understand biochemical changes associated with stress tolerance in plants. This technology is utilized in plant research and includes metabolic fingerprinting, profiling and targeted analysis. Metabolomics is used as an important tool to understand the environmental responses in plants [36]. Empirical evidence suggests that meta- bolic components are linked to high-temperature stress tolerance in corn [37], in cool season grass [38], and in wheat [39]; drought tolerance in wheat [40, 41], in chickpeas [42], and in rice [43]. Plants can modify their physiology to adapt to different environmental conditions through metabolic changes [44, 45]. Our previous study clearly demonstrated that the combi- nation of PGRs and PGPRs was more effective in ameliorating drought stress in chickpea com- pared to PGPRs or PGRs alone [17, 42]. In our previous study, we also presented altered metabolic states in two chickpea genotypes, Punjab Noor-2009 (drought sensitive) and 93127 (drought tolerance), under drought stress conditions [34]. Though the combination of PGRs PLOS ONE | https://doi.org/10.1371/journal.pone.0213040 March 4, 2019 3 / 21 Metabolic and physiological changes induced by PGPR and PGRs and PGPRs can effectively contribute to the drought tolerance, no known information is avail- able on the complex metabolic regulation associated with PGRs and PGPRs, and their effect on drought stress tolerance in chickpeas. Metabolic profiling can help us to understand the biochemical mechanism involved with PGRs and PGPRs induced drought tolerance in chick- peas and can contribute in developing stress resilient variety development for future food secu- rity. Therefore, the present study was carried out to investigate the combined effects of PGRs/ PGPRs on the metabolic profiling of two different chickpea genotypes, contrasting for drought tolerance, grown under drought condition. This study also investigated the relationship between altered metabolic levels with different physiological traits under drought stress condition. Materials and methods The experiment was conducted under greenhouse condition at the Department of Agronomy, University of Florida, Gainesville, Florida in May, 2016. Seeds of two chickpea genotypes, Pun- jab Noor-2009 (drought sensitive) and 93127 (drought tolerance), were obtained from Ayub Agriculture Research Institute, Faisalabad, Pakistan. Initially, chickpea seeds were washed in 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. Some of the washed seeds were soaked for 3 h, prior to sowing, in 24 h old cul- tures of Bacillus subtilis, Bacillus thuringiensis, and Bacillus megaterium, whereas, some of the washed seeds were sown without any treatment (drought control). The PGRs, salicylic acid (SA) and putrescine (Put) were sprayed (150 mg/L) on 25 days old seedlings of chickpea inoc- ulated with PGPRs. Seeds were grown in pots (5 seeds/pot) measuring 30 × 40 cm and filled with 2,000 g of Metro-Mix 360 soil mixture. The pots were well watered (twice per week) throughout until drought stress was applied, and each pot contained 5 plants. Water was applied until the soil mix 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. The green house condition was maintained at 26 and 19 ± 1 ˚C (day and night temperatures) with 70 ± 2% relative humidity and 11 and 13 hours day and night lengths, respectively. Drought stress was imposed on 25-day-old plants (at the 3-leaf stage) by with- holding water supply for the next 25 days until the soil water content reached 14%. Leaf tissue samples were collected twice; at 14 days (first-time point) and 25 days (second-time point) after drought stress initiated for metabolomics analysis and different physiological trait estimation. The present experiment comprised 2 different treatments; plants under drought stress treated with 3 PGPRs (Bacillus subtilis, Bacillus thuringiensis, and Bacillus megaterium) and 2 PGRs (salicylic acid and putrescine) designated as “consortium”, and plants under drought stress without PGPRs and PGRs treatment and designated as “drought”. The experiment was laid out in a completely randomized block design with 6 replications. Method of inoculation The Luria Bertani (LB) broth was inoculated with fresh (24 h old) bacterial culture. The inocu- lated LB broth incubated in a shaker for 24 h at 27 ˚C followed by centrifugation at 10000 rpm (10 min). The pellet was mixed with distilled water and the optical density (OD) (at 660 nm) was adjusted to 1. The colony was then soaked in broth with isolates. The seeds were soaked in broth for 3 h prior to sowing. PLOS ONE | https://doi.org/10.1371/journal.pone.0213040 March 4, 2019 4 / 21 Metabolic and physiological changes induced by PGPR and PGRs Leaf chlorophyll content and chlorophyll fluorescence Leaf chlorophyll content was determine by using SPAD chlorophyll meter (SPAD-502 plus. serial No. 20001472 made by Konica Minolta, Japan). 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). Chlorophyll fluorescence and chlorophyll content were measured on 3 leaflets in each plant and 5 plants per pot (a total of 15 readings) and averaged. The average value of 15 readings was considered as a single repli- cation, and 6 replicated values/variety were used for statistical analysis and comparison of treatment means, and significant testing at P < .05 level. Relative water content (RWC) The relative water content (RWC) of leaves for each treatment was calculated according to the formula of Weatherly [46]. RWC ¼ ½ðfresh weight of leaves dry weight of leavesÞ=ðturgid weight of leaves dry weight of leavesÞ�� 100: Leaf protein content The Lowery et al. [47] method was used for the estimation of protein content in leaves of chickpea. In detail, leaf tissue was ground in a phosphate buffer (1 mL) and centrifuged for 10 min. The supernatant was transferred to a tube and distilled water was added for a final vol- ume of 1 mL. Reagents C and D were added to the supernatant and were mixed by shaking. The sample was then incubated at room temperature for 30 min and the absorbance at 650 nm of each sample was determined along with the absorbance of different concentrations of bovine serum albumin (BSA). Protein concentration was calculated as: Protein concentration mg=g ¼ K value� Dilution factor� Absorbance=sample wt: K value = 19.6, Dilution factor = 2, Wt. of sample = 0.1 g Sugar estimation Sugar content was estimated as outlined by Dube et al. [48]. Fresh leaf tissue was ground in 10 mL of distilled water and centrifuged at 3000 rpm for 5 min. The supernatant (0.1 mL) was mixed with phenol (1 mL; 80%) and concentrated H SO (5 mL). Absorbance was recorded at 2 4 420 nm of wavelength. The concentration of the unidentified sample was considered with ref- erence to the standard curve made by using glucose: Sugar concentration mg=g ¼ K value� Dilution factor� Absorbance=Sample Wt: K value = 20, Dilution factor = 10, Wt. of sample = 0.5 g Total phenolic content The total phenolic content of the extract was determined by the Folin—Ciocalteu method [49]. Briefly, crude extract (200 μL) of 3 mL was made with distilled water, mixed thoroughly with Folin—Ciocalteu reagent (0.5 mL) for 3 min, followed by the addition of 2 mL of 20% (w/v) sodium carbonate. The mixture was kept in dark for one hour, and absorbance was measured at 650 nm. The total phenolic content was calculated from the calibration curve, and the results were expressed as mg of gallic acid equivalent per g dry weight (GAE/g). PLOS ONE | https://doi.org/10.1371/journal.pone.0213040 March 4, 2019 5 / 21 Metabolic and physiological changes induced by PGPR and PGRs Shoot and root dry weights estimation Shoots and roots of the same 5 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 root and shoot dry weights were measured after 25 days of drought stress imposition [42]. Leaf tissue collection and sample preparation for metabolites Leaf samples from the consortium and drought-stressed plants were collected at midday for metabolic profiling. Leaves were harvested at 14 days (time point 1) and at 25 days (time point 2) after the imposition of drought stress. The sampled leaf tissues were frozen in liquid nitro- gen immediately after collection and stored at -80 ˚C. Tissue samples were lyophilized for 72 hours and ground using a TissueLyser. Lyophilized powder (30 mg) was used for ultrahigh performance liquid chromatography-high resolution mass spectrometry (UPLC-HRMS) based metabolite profiling following the protocol of Lisec et al. [50]. In brief, freeze-dried leaf tissues were weighed (30 mg) into a clean Eppendorf tube, fol- lowed by the addition of internal standards (20 μL) to each sample. Methanol (750 μL) and ammonium acetate (10 mM, 750 μL) was added to each sample and vortexed for 1 min at room temperature. Centrifugation (17000 G, 10 m) was done after all the samples were ultra- sonicated for 20 min at room temperature. The supernatant (> 1 mL) was transferred to a 1.5 mL Eppendorf tube, followed by a 50 μL transfer of supernatant to an Eppendorf tube. The supernatant was dried down after adding 50 μL of injection of standard solution. Samples were then vortexed for 30 sec and put at 4 ˚C for 10 min, centrifuged at 20,000 rpm for 10 min, and the supernatant was transferred into an LC-vial. UPLC—HRMS analysis Untargeted metabolic profiling was performed on an ultrahigh performance liquid chroma- tography-high resolution mass spectrometry (Model: Thermo Ultimate 3000 UPLC and Thermo QExactive mass spectrometer) platform at the University of Florida Southeast Center for Integrated Metabolomics (SECIM). All samples were analyzed in positive and negative heated electrospray ionization with a mass resolution of 70,000 at m/z 200 as separate injec- tions. 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 70–1000 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 minutes. Probe (HESI probe) temperature was maintained at 350˚C for both positive and negative run with a spray voltage of 3500 V and a capillary temperature of 320 ˚C [42]. Metabolite data analysis. Data tables with metabolite peaks (mz/rt) at 2-time points for consortium with drought stress and control drought stress treatment were formatted as comma separated values (.csv) files and uploaded to the MetaboAnalyst 3.0 server (http:// www.metaboanalyst.ca) [51]. Metabolite data generated by UPLC-HRMS were checked for data integrity and normalized using MetaboAnalyst’s normalization protocols (selecting nor- malization by the sum, log transformation, and auto-scaling) to shrink any possible variance and to improve the performance for downstream statistical analysis. Univariate analysis (t-test and one way ANOVA) was applied to calculate the statistical sig- nificance of the metabolites between 2 group means (consortium/drought). We applied multi- variate methods, supervised method-Partial Least Squares Discriminant Analysis (PLS-DA) and unsupervised method-Hierarchical clustering with a heat map, for the comprehensive data analysis as it takes all the variables into consideration, for example, a heat map was PLOS ONE | https://doi.org/10.1371/journal.pone.0213040 March 4, 2019 6 / 21 Metabolic and physiological changes induced by PGPR and PGRs generated based on the Pearson distance measure and the Ward clustering algorithm, showing the top 25 metabolites for consortium versus drought treatments by PLS-DA VIP (variable importance in projection) score using a significance level of P� 0.05, and post-hoc analysis of Fisher’s LSD. The samples were arranged according to their sampling time points (time point 1 and 2) in all two groups. The important metabolites were identified by using 2 different methods separately: PLS-DA and SAM (Significant Analysis of Metabolites) [42, 52]. The pathway analysis was performed using MetaboAnalyst for the identified important metabolites using Arabidopsis thaliana pathway libraries. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway database (http://www.genome.ad.jp/kegg/pathway.html) was also used for the metabolites that were not found in the Arabidopsis pathway libraries. Data analysis for biochemical characters. The data analysis was carried out by using soft- ware Statistics, version. 8.1. An ANOVA was performed to determine the effect of treatments and error associated with the experiment. A total number of six replicates were used for each treatment. To identify significant differences among treatments, a mean comparison of traits was carried out by using protected LSD (p = 0.05) test where the error mean square was used to estimate the standard error of differences between mean. Results Chlorophyll content, chlorophyll fluorescence and relative water content (RWC) of two chickpea genotypes Under drought condition, the tolerant genotype (93127) demonstrated higher chlorophyll content than the sensitive genotype (Punjab Noor-2009) (Table 1). The application of PGRs in combination with PGPRs (consortium) increased chlorophyll content in both genotypes, but the sensitive genotype was more responsive than tolerant genotype. The Fv/Fm ratio is an indi- cator of photosystem II damage in plants. The lower value indicates more damage to the pho- tosystem. The sensitive genotype showed higher damage to the photosystem due to drought stress than the tolerant one. Both varieties showed responsiveness to the consortium treatment, but the sensitive genotype was more responsive than the tolerant variety. Regarding RWC, we also observed a similar trend and the sensitive genotype showed a higher increase in RWC compared to the tolerant genotype due to consortium treatment (Table 1). In summary, the consortium treatment increased chlorophyll content, Fv/Fm, and RWC in both genotypes compared to their drought treatment, but the increase was more in the sensitive genotype than Table 1. Chlorophyll content and chlorophyll florescence (Fv/Fm ratio) of two chickpea genotypes under consortium and drought condition after 25 days of stress imposition. Chickpea Spad Chlorophyll Chlorophyll RWC (%) Protein (μg/g) Sugar (mg/g) Phenolics (mg Shoot dry wt. Root dry wt. genotype Content Florescence (Fv/ GAE/g) (g) (g) Fm) Cons. Dro Cons. Dro. Cons. Dro. Cons. Dro. Cons. Dro. Cons. Dro. Cons. Dro. Cons. Dro. Punjab 35.6 16.4 0.727 0.317 64 27 1.6 1.2 1.8 0.7 3.1 1.7 10.9 3.11 2.41 0.94 a b a b a b a b a b a b a b a b Noor-2009 ±0.011 ±0.01 ±0.014 ±0.015 ±0.02 ±0.07 ±0.003 ±0.016 ±0.014 ±0.001 ±0.012 ±0.013 ±0.15 ±0.1 ±0.06 ±0.03 (Sensitive genotype, G1) 93127 46.1 34.4 0.843 0.643 79 55 1.7 1.5 2.3 1.5 3.7 2.6 12.27 6.33 2.4 1.51 a b a b a b a b a b a b a b a b (Tolerant ±0.021 ±0.017 ±0.007 ±0.03 ±0.011 ±0.01 ±0.015 ±0.023 ±0.019 ±0.015 ±0.08 ±0.016 ±0.28 ±0.21 ±0.08 ±0.02 genotype, G2) Cons-Consortium (Mean±SE); Dro-Drought (Mean±SE); Different letters (i.e. a and b) indicate significant differences (P< .05) among treatments. https://doi.org/10.1371/journal.pone.0213040.t001 PLOS ONE | https://doi.org/10.1371/journal.pone.0213040 March 4, 2019 7 / 21 Metabolic and physiological changes induced by PGPR and PGRs the tolerant genotype. Consortium treatment helped to maintain the water balance in cell and to reduce damage to the photosystem in chickpeas. Protein, sugar and phenolics content in the leaves of two chickpea genotypes The tolerant genotype showed a higher value for protein and phenolic compound under drought condition compared to the sensitive genotype (Table 1). However, the sugar content was higher in the sensitive genotype than the tolerant genotype under drought condition. Con- sortium treatment induced higher protein, sugar, and phenolic compound accumulation in both genotypes, however, the increase was more in the sensitive genotype than the tolerant one. Shoot and root dry weights The tolerant genotype produced higher root and shoot dry weight than the sensitive genotype under drought condition (Table 1). Both genotypes showed increased root and shoot dry weight accumulation due to consortium treatment, and the increase was higher in the sensitive genotype than the tolerant one. In general, consortium treatment helped the plant to maintain growth by accumulating higher biomass under drought condition. Metabolic profile of chickpea genotypes induced by consortium and drought treatments The untargeted UPLC-HRMS global metabolomics analysis was performed for profiling of leaf metabolites for 2 different chickpea genotypes under 2 contrasting treatments at 2 different sampling time points (14 and 25 days after stress imposition). UPLC-HRMS analysis detected a total of 178 known metabolite peaks out of which 53 were found to be significant (S1 Table). Metabolites were highly reproducible among the six analyzed biological replications at the 2 different time points. PLS-DA and 2D loading plot results. We performed a supervised clustering method, Partial Least Squares-Discriminant Analysis (PLS-DA), for both the genotypes and for consor- tium versus drought treatments at 2 different time points. PLS-components (PCs) analysis revealed that component 1 explained 51% and 36.2% of the total variation of the sensitive and tolerant genotypes, respectively, under consortium versus drought treatment (Fig 1), while the second component explained 15.9% and 17.9% variation for sensitive and tolerant genotypes, respectively, for the same treatment (Figs 1 and 2). The 2D scores plots between PC1 and PC2 showed 2 different groups associated with the consortium and drought samples at 2 different sampling points (Fig 1), suggesting a clear distinction in the metabolite accumulation under two different conditions. The separation between the consortium and the drought treatments is suggesting the dominant role of PGRs and PGPRs in modulating drought tolerance in treated plants. Analysis of variance and heat map. A total of 53 metabolites were identified through a multi-factorial ANOVA which were significantly altered in 2 genotypes across 2 different time points and treatments. Among different groups of metabolites, amino acids, sugars, sugar alco- hol, organic acids, polyamines, nitrogenous compounds and polyphenols and other organic compounds were significantly accumulated in the leaves of plants treated with PGRs + PGPRs under drought stress. Amino acid: leucine; organic compounds: succinate, lactic acid, phenyl- pyruvate, trans-cinnamate, 2-aminophenol, and malonate; sugar acid: glyceric acid; sugar: disaccharides; chemical compounds: saccharic acid, syringic acid; and ammonium compound: PLOS ONE | https://doi.org/10.1371/journal.pone.0213040 March 4, 2019 8 / 21 Metabolic and physiological changes induced by PGPR and PGRs Fig 1. Partial least square discriminant analysis (PLS-DA) and 2D Scores loading plot for the chickpea Punjab Noor-2009 (G1) and 93127 (G2) leaves at 2 time points under consortium and water deficit treatments. Metabolites at consortium and drought treatments did not overlap indicating an altered state of metabolite levels in the chickpea leaves. Sampling time points are thereby demonstrating its effect over time and proofing in the leaves of chickpea plants. https://doi.org/10.1371/journal.pone.0213040.g001 PLOS ONE | https://doi.org/10.1371/journal.pone.0213040 March 4, 2019 9 / 21 Metabolic and physiological changes induced by PGPR and PGRs Fig 2. (A & B). Heatmap [A: consortium vs water-deficit (G1) and B: consortium vs water-deficit (G2)] illustrating (distance measure: Pearson; Clustering algorithm: Ward) of the performed partial least square discriminant analysis (PLS-DA) showing levels of key metabolite. Metabolite feature areas were normalized and range-scaled across all experimental samples at 2 different time points. https://doi.org/10.1371/journal.pone.0213040.g002 L-carnitine showed increased levels of accumulation in the leaves of plants treated with PGRs and PGPRs consortium. However, compounds like 5-oxo-proline tryptophan, 4-coumorate, sugar alcohol, salicylate, and phosphocholine were highly accumulated in the leaves of untreated plants grown under stress condition. Significantly different metabolites (identified through ANOVA) were analyzed by hierar- chical clustering with a heat map in order to visualize the effect of PGRs + PGPRs consortium on metabolomics expression over uninoculated drought stress plants. The heat map was gener- ated for consortium versus drought treatments for both the sensitive and tolerant genotypes which indicates a clear distinction between metabolites at consortium and drought treatments. The first cluster of heat map was represented by metabolites accumulated at higher levels in the leaves of PGPRs + PGRs treated (consortium) plants including carnitine, glyceric acid, phenylpyruvate, succinate, lactic acid, leucine, D-saccharic acid, isocytosine and coumarate whereas, tryptophan, sugar alcohol, N-butylbezalsulfomide and malonate were abundantly present in leaves of untreated sensitive genotype grown under drought stress (Fig 2A). Accu- mulation of L-carnitine, salicylate, succinate, and malonate occurred only at the first time point in the leaves of PGRs + PGPRs treated plants of tolerant genotype whereas, isocytosine, trans-cinnamate, syringic acid, phosphocholine, and phenylpyruvate were only accumulated at second time point. The consortium of PGRs and PGPRs also significantly enhanced the accumulation of glyceric acid, disaccharide, saccharic acid, aminophenol and 5-oxo-L-proline at both time points in the tolerant genotype (Fig 2B). PLOS ONE | https://doi.org/10.1371/journal.pone.0213040 March 4, 2019 10 / 21 Metabolic and physiological changes induced by PGPR and PGRs Table 2. Important metabolites with their compound ID (KEGG ID/PubChem CID and molecular formula, identified through partial least square discrepant anal- ysis (PLS-DA) and significant analysis of metabolites (SAM) across genotypes and treatments. S.No. Important Metabolites KEGG ID PubChem CID Molecular Formula SAM (d-value) PLS-DA VIP score (variance For component 1) 1 5-oxo-L-proline C01877 107541 C H NO 6.2298 0.10057 5 6 3 2 Azelaic acid C08261 2266 C H O 5.9834 0.4252 9 16 4 3 Glyceric acid C00258 439194 C H O 7.7543 0.73388 3 6 4 4 Succinate C00042 1110 C H O 9.4668 1.4221 4 6 4 5 L-(+)-Lactic Acid C00186 107689 C H O 4.961 1.3168 3 6 3 6 Phenylpyruvate C00166 997 C H O 3.854 1.2303 9 8 3 7 Choline C00114 305 C H NO 5.936 - 5 14 8 Tryptophan-NH3 C00078 6305 C H N O 3.112 1.4373 11 12 2 2 9 L-Leucine 13C6 C00123 6106 C H NO -4.189 1.1682 6 13 2 10 Caffeine-D3 C07481 2519 C H N O -4.532 1.224 8 10 4 2 11 2-Hydroxyphenylalanine C00082 91482 C H NO 2.524 0.5669 9 11 3 12 Syringic Acid C10833 10742 C H O 5.265 0.86745 9 10 5 13 Trans-cinnamate C00423 444539 C H O 4.341 0.62645 9 8 2 14 D-Saccharic acid C00818 33037 C H O 3.265 1.1957 6 10 8 15 Triethyl phosphate - 6535 C H O P 1.474 1.0598 6 15 4 16 L-Carnitine C00318 2724480 C H NO 7.284 1.1691 7 15 3 17 2-Aminophenol C01987 23035081 C H NO 2.193 0.75572 6 7 18 N-Butylbenzenesulfonamide - 19241 C H NO S 1.743 1.0977 10 15 2 19 Isocytosine - 66950 C H N O 4.983 0.74784 4 5 3 20 4-Coumarate C00811 637542 C9H8O3 8.974 1.1368 21 Malonate C00383 867 C H O 5.846 1.0699 3 4 4 22 Salicylate C07588 10253 C H O 3.957 0.70754 7 6 3 23 C5-Sugar alcohol - - - 3.654 0.92692 24 Disaccharide C00089 5988 C H O 2.884 0.76615 12 22 11 25 Phosphocholine C00588 1014 C H NO P+ 2.631 0.38416 5 15 4 KEGG = Kyoto Encyclopedia of Genes and Genomes. https://doi.org/10.1371/journal.pone.0213040.t002 Profiling of leaf metabolites. Two statistical methods, PLS-DA and SAM, were carried out for the identification of the most important metabolites across genotypes and treatments (Table 2). The PLS-DA analysis identified 25 most important metabolites based on the VIP score using a 5-component model. Similarly, the most important metabolites were also identi- fied by significant analysis of metabolites (SAM) with the delta value of 1.5, false discord rate (FDR) of 0.002 with less than one (0.25) false positive. Overall, the identified metabolites were quite similar across the 2 methods. The top most important 25 metabolites which were identi- fied by these 2 methods are shown in Table 2. These important metabolites included different amino acids, sugars, sugar alcohol, amines, organic acids, fatty acids and other intermediate compounds. The sensitive genotype significantly accumulated succinate, leucine, carnitine, lactic acid, glyceric acid, phenylpyruvate, isocytosine, and saccharic acid when treated with PGRs and PGPRs consortium (Fig 3). Contrary to that, compounds like malonate, disaccha- ride, and trans-cinnamate were highly accumulated in tolerant genotype under PGRs and PGPRs treatment. Drought stress caused significant accumulation of tryptophan, salicylate, and sugar alcohol in the leaves of untreated drought plants compare to the consortium in both tolerant and sensitive genotypes (Fig 3). More metabolites were significantly altered in sensi- tive genotype due to consortium treatment than tolerant genotype. PLOS ONE | https://doi.org/10.1371/journal.pone.0213040 March 4, 2019 11 / 21 Metabolic and physiological changes induced by PGPR and PGRs Fig 3. Significantly different levels of selected metabolites (ANOVA, P� .05, Tukey’s honest significant difference) in the leaves of two chickpea varieties under consortium and drought conditions at 2 time points (14 and 25 days). G1: drought-sensitive chickpea genotype (Punjab Noor-2009); G2: drought-tolerant chickpea genotype (93127). Error bars represent standard errors of the mean (n = 6) at each time point. Cons = consortium, D = drought. Different letters indicate significant differences (P < .05) among treatments (consortium vs drought) for a genotype for mz/rt peak in a particular time point. https://doi.org/10.1371/journal.pone.0213040.g003 Identification of corresponding biological pathways. All the metabolites significantly affected by PGRs + PGPRs treatment under drought stress were mapped to the biological path- ways involved in the KEGG online database, which was assigned to 22 different pathways/ metabolism in either treatment. Table 3 is showing the metabolites involved in each pathway, the number of hits, and FDR of the pathway. As expected, the significantly altered metabolites were involved in a number of different pathways. These include Phenylalanine, tyrosine and tryptophan biosynthesis, Phenylpropanoid biosynthesis, Glucosinolate biosynthesis, Tropane, piperidine and pyridine alkaloid biosynthesis, Citrate cycle (TCA cycle), Aminoacyl-tRNA biosynthesis, Ubiquinone and terpenoid-quinone biosynthesis, Glycolysis or Gluconeogenesis, Valine, leucine and isoleucine degradation, and Valine, leucine, and isoleucine biosynthesis. In addition, 11 primary metabolisms such as the Phenylalanine metabolism, Ascorbate and alda- rate metabolism, Glycerolipid metabolism, Propanoate metabolism, Glyoxylate and dicarboxy- late metabolism, Tyrosine metabolism, Pyruvate metabolism, Butanoate metabolism, Alanine, aspartate and glutamate metabolism, Glycerophospholipid metabolism, Tryptophan metabo- lism and Glycine, serine and threonine metabolism were also significantly altered due to con- sortium and drought treatments. Discussion Drought stress is one of the major constraints for agricultural productivity throughout the world. Approximately, 40% of the agricultural lands are located in the arid and semi-arid regions of the world [53] where plants suffer frequently from drought stress. Both plant hor- mones (PGRs) and PGPRs respond to environmental stresses and impart tolerance to plant against the stresses [54]. PGRs and PGPRs consortium plays a significant role in the alleviation of drought stress in plants by maintaining water budget of the plant and by producing PLOS ONE | https://doi.org/10.1371/journal.pone.0213040 March 4, 2019 12 / 21 Metabolic and physiological changes induced by PGPR and PGRs Table 3. Pathway names, total metabolites involved in that pathways, metabolites significantly accumulated in present study (hits), and false discord rate (FDR). Pathway name Total Hits FDR Phenylalanine metabolism 11 3 2.2225E-5 Phenylalanine, tyrosine and tryptophan biosynthesis 22 2 0.000108 Glycerophospholipid metabolism 25 2 0.000109 Phenylpropanoid biosynthesis 31 2 0.00369 Glucosinolate biosynthesis 8 1 0.00387 Tropane, piperidine and pyridine alkaloid biosynthesis 10 1 0.00525 Ascorbate and aldarate metabolism 14 1 0.00574 Glycerolipid metabolism 14 1 0.00656 Propanoate metabolism 14 1 0.00793 Glyoxylate and dicarboxylate metabolism 17 1 0.00999 Tyrosine metabolism 18 1 0.01001 Pyruvate metabolism 20 1 0.01436 Butanoate metabolism 20 1 0.01446 Citrate cycle (TCA cycle) 20 1 0.01563 Alanine, aspartate and glutamate metabolism 21 1 0.01612 Aminoacyl-tRNA biosynthesis 67 2 0.01801 Ubiquinone and other terpenoid-quinone biosynthesis 22 1 0.01823 Glycolysis or Gluconeogenesis 25 1 0.01893 Tryptophan metabolism 25 1 0.0221 Valine, leucine and isoleucine biosynthesis 26 1 0.0251 Glycine, serine and threonine metabolism 29 1 0.0325 Valine, leucine and isoleucine degradation 34 1 0.0471 https://doi.org/10.1371/journal.pone.0213040.t003 metabolites as observed during the present investigation. In the sensitive genotype, the amelio- rating effect of PGRs and PGPRs consortium was noteworthy. Chlorophyll content, chloro- phyll fluorescence, RWC, and root and shoot biomass accumulation were greater than 2 fold due to PGRs and PGPRs consortium under drought stress. The effect of PGRs and PGPRs treatment was more pronounced in the sensitive genotype compared to the tolerant genotype. Previous studies demonstrated that the drought-induced reduction in chlorophyll content and chlorophyll fluorescence led to a decrease in photosynthesis and overall plant growth [55, 56]. The combined treatment significantly enhanced the chlorophyll fluorescence and chlorophyll content values in the PGRs/PGPRs treated plants in both genotypes. The application of PGRs significantly enhanced the root and shoot growth, and dry matter production in different plants [42, 57]. Similarly, PGPRs are known to enhance the root growth and uptake of miner- als and water, thus promote the growth of the whole plant which in turn has a positive impact on plant dry matter content in wheat [58] and in chickpea [59]. Hassanzadeh et al. [60] reported that decrease in RWC is related to the decrease in chlorophyll content and leaf fresh weight in sesame genotypes, however, the tolerant genotypes maintained higher RWC under stress condition and thus showed higher affinity for chlorophyll content and leaf fresh weight. Our results demonstrated that PGRs combined with PGPRs helped both genotypes to main- tain an efficient photosystem with improved water budget resulting in improved growth and productivity under drought stress condition. Significant increase in the leaf protein content in both genotypes was evident after being treated with PGRs and PGPRs in comparison to untreated plants grown under stress condi- tion. A correlation between increased protein levels and their involvement in ROS scavenging PLOS ONE | https://doi.org/10.1371/journal.pone.0213040 March 4, 2019 13 / 21 Metabolic and physiological changes induced by PGPR and PGRs and oxidative stress metabolism have been demonstrated in plants under stress [61]. The higher protein accumulation was associated with significant increase in Fv/Fm and chlorophyll content due to PGRs and PGPRs treatment under drought stress condition. The increased level of proteins due to PGRs and PGPRs might have assisted plants to mitigate ROS effect [62, 63]. Under stress conditions, the leaf sugar content decreases significantly which is an indication of rapid senescence due to stress. PGRs in combination with PGPRs effectively increased the leaf sugar content under drought condition in both genotypes. Soluble sugars have been dem- onstrated to play an important role in responses to biotic and abiotic stresses [64]. Sugar sig- nalling pathways interact with stress pathways into a complex network in plants to modulate metabolic responses [65]. Soluble sugars may either act directly as negative signals or as modu- lators of plant sensitivity and thus, they can also play important roles in cell responses to stress-induced remote signals [66]. Under stress conditions, a decrease in dry matter accumu- lation and depletion of sugar was correlated in the plant [67]. Present findings demonstrated an increased dry matter accumulation due to PGRs and PGPRs treatment under drought con- dition which could potentially be attributed to the better cellular osmotic balance (demon- strated by RWC) in photosynthetic organs, and thus helped to maintain higher photosynthetic rate and growth. PGRs and PGPRs treatments have also increased the production of phenolic compounds. These molecules have been described as markers for abiotic stress tolerance in plants [68] and they have been proclaimed to be involved in oxidative stress caused by ROS [69]. UPLC-HRMS based untargeted metabolic profiling in the leaves of two chickpea genotypes was performed to understand the effect of PGRs and PGPRs on metabolic changes to adjust drought stress condition. The increased accumulations of different metabolites were previ- ously reported under drought condition in different plant species [42, 70, 71]. It has been noted that the accumulation of leucine, succinate, lactic acid, and glyceric acid was higher in the sensitive genotype at both time points when treated with PGRs + PGPRs consortium. Leu- cine and other amino acids are known to play a variety of different roles in plants, especially under stress condition and impart drought tolerance [72]. Previously, we have reported the increased level of amino acids and organic acids in tolerant variety grown under drought con- dition as compared to control plants [42]. Coupled with different amino acids, our study also demonstrated an increased level of total protein accumulation due to PGRs and PGPR treat- ment. The increased level of different amino acids and protein were associated with water bal- ance, intact photosynthetic structure, and high biomass accumulation in chickpeas plants treated with PGRs + PGPR consortium in our study. Significant accumulation of L-carnitine, trans-cinnamate, succinate and syringic acid occurred at the first time point, whereas, saccharic acid, isocytosine, hydroxyphenylalanine and phenylpyruvate showed significant accumulation in the leaves of PGRs + PGPRs treated sensitive genotype at the second time point. L-carnitine regulates the level of acyl-CoA and CoA in the mitochondrion and cytosol and involvs in the regulation of water resorption and photosynthesis [73]. Plants produce a moiety of organic compounds in response to a variety of environmental stimuli which have key ecological functions and involved in interactions with biotic and abiotic stresses. These compounds are responsible for osmoregulation in both plants and animals. Cinnamate and coumarate are widely distributed in the plant kingdom and play a key role in plant defence, growth and plant-insect interactions [74]. Succinate act as a pri- mary intermediate in ATP pathway of Kreb cycle and play a vital role in energy production and regulation of mitochondrial TCA cycle [75]. Excess of succinate in plants under stress results in more ATP production in mitochondria [76–78]. The elevated level of succinate found in PGRs and PGPRs treated plants demonstrated better tolerance to drought stress and PLOS ONE | https://doi.org/10.1371/journal.pone.0213040 March 4, 2019 14 / 21 Metabolic and physiological changes induced by PGPR and PGRs this could potentially be attributed to the efficient TCA cycle that produces more energy under water-limited conditions. It was evident that all these compounds were involved in dif- ferent biologically significant activities, such as osmoregulation, photosynthesis, energy pro- duction or defense activity in the plant. Their biological functions correlate with our study through osmoregulation or increased photosynthetic efficiency or through higher biomass accumulation in chickpea plants treated with PGRs and PGPRs under drought stress conditions. The present study has demonstrated enhanced accumulation of disaccharide, saccharic acid, glyceric acid, aminophenol, and 5-oxo-L-proline at both time points in the tolerant genotype when treated with PGRs and PGPRs consortium. These sugars are responsible for osmotic adjustment by detoxifying reactive oxygen species and stabilize the quaternary struc- ture of protein under water scarcity [79]. Accumulation of sugars and their derivatives lead to drought tolerance in wheat, maize, Arabidopsis, chickpea, millet and rye [42, 80, 81]. Higher accumulation of the total sugar content was also evidenced in our study due to PGRs and PGPRs treatment which was due to increased photosynthetic activity. Higher accumula- tion of sugar alcohol was noted in drought-tolerant genotype treated with PGRs + PGPRs. Sugar alcohols also play a significant role in stress tolerance by inducing osmotic adjustment through accumulation of a compatible solute or the transitory storage of carbon reserves [82]. The untreated genotypes exhibited a higher accumulation of salicylate and tryptophan when exposed to long-term drought stress. In plants, exogenous application of salicylates affected many physiological and biochemical processes such as seed germination, seedling establishment, thermogenesis cell growth, senescence, stomatal responses, thermotolerance and nodulation [83–85]. Reduced accumulation of salicylates due to PGRs and PGPR consor- tium treatment could potentially be attributed to a reduced rate of senescence and thus prolong the photoassimilation. Tryptophan plays a major role in the regulation of plant devel- opment and defense responses [86]. Tryptophan is the precursors of different secondary metabolites including indoleacetate, lipid precursor, and lignin in the Shikimate pathway, which plays a vital role in stress tolerance [87]. The link between different metabolic pathways and associated metabolites was stimulated using MetaboAnalyst. Twenty five metabolic pathways were significantly altered using the Kyoto Encyclopedia of Genes and Genomes database (KEGG) and Arabidopsis annotation project database. The Phenylalanine, tyrosine and tryptophan biosynthesis pathway was upre- gulated in the PGRs and PGPRs treated plants. This is an important pathway for the synthesis of essential aromatic amino acids. These aromatic amino acids not only serve as part of protein biosynthesis but also involved in the synthesis of other important secondary metabolites that play key roles in plant growth and development [88, 89]. Aminoacyl-tRNA biosynthesis and citrate cycle was also altered in the present study due to PGRs and PGPRs treatment. Aminoa- cyl-tRNA biosynthesis is a group of twenty different enzymes that establish the rules of genetic code. It had been reported earlier that the disrupted metabolic conditions is associated to a specific aminoacyl-tRNA synthetase [90]. The aminoacyl-tRNA synthetases catalyse the bind- ing of amino acids to their specific tRNA and thus play a key role in translation and in gene expression. Citrate cycle play a key role in producing ATP and providing carbon skeletons for a variety of biosynthetic processes in both heterotrophic and photosynthetic tissues [91]. Gly- cine, serine and threonine metabolism play an important role during signalling process and in plant stress responses [92]. Glycine and Serine are two interconvertible amino acids that play significant role in C1 metabolism. Whereas, Serine has a central role in the metabolism and signalling, and involved in plant homeostasis. PLOS ONE | https://doi.org/10.1371/journal.pone.0213040 March 4, 2019 15 / 21 Metabolic and physiological changes induced by PGPR and PGRs Conclusion Different amino acids, sugars, sugar alcohol, amines, organic acids, fatty acids and other inter- mediate compounds were changed significantly due to PGRs and PGPRs treatment. Similar to physiological responses, sensitive genotype also showed altered levels of more metabolites than tolerant genotype. The accumulation of succinate, leucine, disaccharide, saccharic aid and gly- ceric acid was significantly higher in both genotypes in both time points due to PGRs and PGPRs treatment. As these metabolite levels were constantly higher in both genotypes and at different time points, demonstrating their roles in monitoring biochemical pathways related to drought tolerance. Significant accumulation of malonate, 5-oxo-L-proline, and trans-cinna- mate occurred at both time points only in the tolerant genotype due to the consortium treat- ment. On the contrary, lactic acid, L-carnitine, isocytosine, and phenylpyruvate were accumulated significantly in sensitive genotypes at both times. These results indicate that the higher accumulation of these metabolites could possibly associated only with the tolerance mechanism in sensitive genotype. These data provide information that may, with further investigation, help to understand the biochemical pathway underlying drought stress tolerance in chickpea induced by PGRs and PGPRs treatment. Supporting information S1 Table. List of top 53 significant metabolites identified in the study with their compound type, identifier (KEGG ID/PubChem CID ), molecular formula, P-value and false discov- ery rate (FDR), mass-to-charge ratio (m/z), and retention time (RT). (KEGG = Kyoto Ency- clopedia of Genes and Genomes). (DOCX) Acknowledgments We acknowledge the help of South Eastern Center for Integrative Metabolomics (SECIM) for providing the greenhouse and laboratory facility for conducting the experiment and metabolo- mics analysis and acknowledge the help of Dr. Fredy Altpeter to allow us to use lyophilizer and TissueLyser. Author Contributions Conceptualization: Asghari Bano. Data curation: Naeem Khan, MD Ali Babar. Formal analysis: Naeem Khan, MD Ali Babar. Funding acquisition: MD Ali Babar. Investigation: MD Ali Babar. Methodology: Naeem Khan. Project administration: Asghari Bano, MD Ali Babar. Resources: MD Ali Babar. Software: Naeem Khan, MD Ali Babar. Supervision: Asghari Bano. Visualization: MD Ali Babar. PLOS ONE | https://doi.org/10.1371/journal.pone.0213040 March 4, 2019 16 / 21 Metabolic and physiological changes induced by PGPR and PGRs Writing – original draft: Naeem Khan. Writing – review & editing: Asghari Bano, MD Ali Babar. References 1. Montenegro JB, Fidalgo JA, Gabella VM. Response of chickpea (Cicer arietinum L.) yield to zinc, boron and molybdenum application under pot conditions. Span J Agric Res. 2010; 3:797–807. 2. Williams PC, Singh U. Quality screening and evaluation in pulse breeding. InWorld crops: Cool season food legumes 1988 (pp. 445–457). Springer, Dordrecht. 3. El-Karamany MF, Bahr AA. 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