Measurements of drought tolerance of chickpea genotypes
In the present study, the drought tolerance of chickpea genotypes was measured as a function of yield performance in control (irrigated) and drought (rainfed) stressed conditions. Plants grown in rainfed desert areas were assumed to experience drought stress mainly due to (i) limited rainfall and (ii) soil composition and structure in comparison to the irrigated control where the soil is more fertile and which has relatively more rainfall. However, among chickpea genotypes, desi type is reported to be more tolerant to drought stress than the kabuli genotypes15,16. Farooq et al., reported that desi genotypes were found more tolerant to drought stress due to greater accumulation of osmolytes than kabuli genotypes17.
Our results also validate these findings and revealed that the kabuli genotypes displayed relatively greater yield reduction when subjected to drought stress under natural field conditions in comparison to their respective irrigated controls as compared to desi genotypes (Table 1; Supplementary Material, Fig. 1).
Besides, analysis of variance also showed that variations in grain yield caused by drought stress in Kabuli genotypes were more pronounced in approximately 73% of total variations; however, the figure was 58% in desi genotypes. Similarly, genotype variations in kabuli genotypes were lesser—i.e. 19% of total variations—than desi genotypes i.e. 26% (Table 2), which indicates that Desi chickpea has more diversity in it when grain yield was accounted for in stressed and non-stressed conditions. These results clearly indicate that desi genotypes acquired better drought tolerance from mother nature than kabuli genotypes.
Metabolite analysis of desi type of chickpea
After alignment of the data of desi chickpea plants, a total of 414 metabolites were detected and filtered by frequency, fold change and probability. T-test was applied to determine the significant difference between rainfed and irrigated control samples at probability 0.05 and fold change > 1.5 and a list of 19 metabolites was found to be significantly different on these parameters. Among them, 11 compounds were found to be up-regulated and 8 were down-regulated (Table 3). Up-regulated metabolites were including sugars; d-fructose, allose, α –d-glucopyranoside, and fucose; two sugar alcohols inositol, myo-inositol; and other compounds such as malic acid, l-proline, ethylamine, butane 1,2,3-triol. While four acids including threonic acid, gluconic acid, malonic acid, oxalic acid; two sugar alcohols including xylitol, erythritol; and a sugar arabinofuranose were down-regulated.
PCA was generated which showed a notable trend of separation for the two groups i.e. the irrigated control plants and the plants grown under rainfed conditions (Fig. 1A). Each sample in this PCA score is represented by a single point. The variance of the first three components on X, Y, and Z were found to be 57.9%, 7.28%, and 6.37%, respectively. The bar chart was drawn on the basis of normalized average intensities of 19 metabolites showing an up-regulation of 11 metabolites (bar above the baseline of 0) and downregulation of 8 metabolites (bar below the baseline of 0) in rainfed samples as compared to irrigated control (Fig. 1B).
Supervised Partial Least Square Discriminant Analysis (PLS-DA) was performed and a model was built in order to classify samples into discrete classes. The dataset was divided into two equal groups: one part was used for training, and the other part for testing. Thus, a confusion matrix was generated. The results of the confusion matrix are provided in the supplementary information (Table S2). Plot obtained by PLS-DA score (Fig. 2A), showing a clear separation trend between the rainfed conditions and irrigated control plants. Sensitivity and specificity were also measured from the constructed model, and was found to be 100%.
Metabolite analysis of kabuli type of chickpea
A total of 260 metabolites were detected from the kabuli chickpea samples and a list of 13 metabolites was found to be significantly different between rainfed and irrigated control group using t-test at the probability of 0.05 and fold change > 1.5. Mannofuranoside, arabino-hexose-2-ulose, maltose, beta-D-glactofuranoside, sucrose, trehalose and oxalic acid were found to be down-regulated, while three organic acids, malic acid, threonic acid, malonic acid, and a sugar d-fructose were up-regulated (Table 4). PCA model was generated on kabuli chickpea samples, which showed a notable difference between irrigated control plants and the plants grown under rainfed conditions. The variance of the first three components on X, Y, and Z were found to be 55.93%, 13.6%, and 10.95%, respectively, (Fig. 3A). The bar chart was built on the basis of normalized average intensities of 13 metabolites, showing an up-regulation of 5 metabolites (bar above the baseline of 0) and downregulation of 8 metabolites (bar below the baseline of 0) in rainfed samples as compared to irrigated control as shown in Fig. 3B.
A prediction model of water-stressed versus control plants was built by using thirteen significantly important metabolites of kabuli genotype of chickpea. PLS-DA score plots showed a clear separation trend between the plants grown under rainfed, and irrigated control conditions (Fig. 2B). Sensitivity and specificity were also measured from the constructed model which was found to be 94.11% and 100% respectively, while the overall accuracy of the model was 97.14%, (Supplementary Material Table S3). On the basis of observed up- and down-regulated metabolites, oxalic acid, threonic acid, d-Fructose, malic acid, and malonic acid were found to be common between the desi and kabuli genotypes.
In order to find out relevant metabolic pathways using p-values and fold change of each metabolite, an online available software ChemRICH was used18. Identified metabolites were used to generate the pathways from KEGG metabolic pathway database (Supplementary Material, Figs. S3–S5). In desi chickpea genotypes, three metabolite clusters with significant impact were obtained (Supplementary Material Table S4), including dicarboxylic acid cluster with four altered metabolites; threonic acid, malonic acid, malic acid and oxalic acid. Sugar alcohols cluster with four altered metabolites including xylitol, erythritol, inositol and butane-1,2,3-triol. Hexose cluster including four monosaccharides, α-d-Glucopyranoside, d-fructose, allose and fucose. The key compounds of the above mentioned clusters were threonic acid, xylitol and α-d-Glucopyranoside respectively. (Fig. 4A). From the sugar alcohol cluster inositol was found to be involved in inositol phosphate metabolism which plays an important role in diverse cellular functions, such as cell growth, cell migration, apoptosis, endocytosis, and cell differentiation. As in the desi genotypes, inositol was found to be up-regulated in the plants grown in the rainfed condition as compared to irrigated control plants, this is clearly showing that the inositol phosphate metabolism was perturbed in the rainfaid plants. Because inositol serves as an osmoprotectant, we can state that inositol phosphate metabolism served as a defense strategy in desi chickpea genotypes against limited water stress. From the monosaccharides cluster, D-fructose was found to be involved in the starch and sucrose metabolism.
In kabuli chickpea genotypes, metabolite clusters with significant impact were of monosaccharides including d-fructose, mannofuranoside, Arabino-hexos-2-ulose and β-d-Galactofuranoside. Disaccharides cluster including three compounds; trehalose, sucrose and maltose. Dicarboxylic acid cluster includes four metabolites namely malic acid, malonic acid, threonic acid and oxalic acid. The key compounds of these clusters were mannofuranoside, maltose, and oxalic acid respectively (Fig. 4B). From sugar clusters of monosaccharides and disaccharides d-fructose, trehalose, sucrose, and maltose were found to be involved in starch and sucrose metabolism. Sucrose is the end product of photosynthesis, serves in the production of energy, and the synthesis of complex carbohydrate. Among the 4 metabolites involved in starch and sucrose metabolism, d-fructose also indirectly influenced the amino sugar and nucleotide sugar metabolism.