Principal component analysis of hormone profiling data suggests an important role for cytokinins in regulating leaf growth and senescence of salinized tomato

High throughput analytical methods allow phytohormonal profiling, but the magnitude of the data generated makes it difficult to draw firm conclusions about the physiological roles of different compounds. Principal component analysis (PCA) was used as a mathematical tool to evaluate relationships between physiological and hormonal variables in two experiments with salinised tomato. When tomato plants (cv Boludo F1) were grafted onto a recombinant inbred line (RIL) population derived from a Solanum lycopersicum x S. cheesmaniae cross and grown under moderate salinity (75 mM NaCl) for 100 days under greenhouse conditions, PCA revealed an important role for leaf xylem cytokinins (CKs) in controlling leaf growth and photosystem II efficiency (Fv/Fm) and thus crop productivity under salinity. PCA analysis from a similar experiment, with ungrafted tomato grown under highly saline (100 mM NaCl) conditions, that evaluated the temporal sequence of leaf growth (as relative growth rate, LRGR) and senescence and hormone concentrations, revealed a similar influence of CKs on both processes, since Fv/Fm and LRGR were strongly loaded along the two principal components and placed in the same cluster as leaf trans-zeatin and/or related to other CK-related parameters. The conservative behaviour of the eigen vectors for Fv/Fm and the analyzed phytohormones in different compartments (xylem, leaf and root) between different experiments suggests an important role for CKs in regulating leaf senescence, while CKs and other hormones seem to regulate leaf growth under salinity.Key words: cytokinins, leaf growth, principal component analysis, salinity, senescence, tomatoAn important paradigm of plant growth regulation is that plant roots can sense their environment, alter their metabolism and transmit chemical signals via the xylem to the shoots to regulate shoot physiology.¹ Much work has aimed to substantiate this “chemical signaling hypothesis” by determining the production and distribution of various signals such as the plant hormones ABA, cytokinins, the ethylene precursor ACC and various nutrient ions.² Although this work has largely been “ABA-centric”, in part due to its relative ease of measurement, the advent of high-throughput, multi-analyte physicochemical techniques to quantify plant hormones^3,4 greatly amplifies the information available from analyses of long-distance signaling, and allows us to move away from a priori assumptions as to which hormone(s) might be physiologically relevant to particular processes. Ultimately, interpreting this information is necessary to provide a sound physiological basis to underpin efforts aimed at manipulating long-distance signaling in planta.Full spectrum hormone profiling can potentially assay more variables per sample than the typical number of samples assayed. Principal component analysis (PCA) is a mathematical algorithm that reduces the dimensionality of the data set while retaining most of the inherent variation.⁵ This is achieved by identifying directions, called principal components, along which the variation in the data is maximal. By using few components, each sample can be represented by relatively few numbers instead of values for many variables.⁶ PCA identifies new variables, the principal components, which are linear combinations of the original variables, and may be an appropriate technique to aid understanding of hormone profiling experiments.Recently, we grew a commercial tomato cultivar (cv Boludo F1) grafted onto 100 rootstocks from a population of recombinant inbred lines derived from a Solanum lycopersicum x S. cheesmaniae cross, and exposed the plants to moderate (75 mM NaCl) salinity for 100 days.⁷ The rootstock generated considerable variability in vegetative vigour (assessed as fresh weight of a fully expanded leaf, LFW) and leaf senescence (assessed by the chlorophyll fluorescence parameter Fv/Fm in that leaf). Ionic and hormonal factor(s) putatively regulating these processes (xylem concentrations of Na⁺ and K⁺ and ABA; the cytokinins trans-zeatin, Z, and the storage form trans-zeatin riboside, ZR; and the ethylene precursor 1-aminocyclopropane-1-carboxylic acid, ACC) were analyzed in leaf xylem sap collected 50 days after salinisation in seven graft combinations of contrasting vigour. Since different xylem parameters showed a high degree of autocorrelation, PCA was performed in order to gain further insights about their real contribution to the physiological processes. Xylem K⁺, K⁺/Na⁺, the active cytokinins Z and ZR, its sum (Z + ZR) and ratio (Z/ZR), and especially the ratio between cytokinins and ACC (Z (ZR)/ACC and Z + ZR/ACC) were strong and positively loaded into the first principal component (PC1) determining both LFW and Fv/Fm (Fig. 1A⁷). Although other variables are included in the same cluster at the 95% of confidence level, their strength in PC1 was much weaker (e.g., Na⁺, Z/ABA, ACC). Does this PCA output provide generic information about the hormonal processes regulating leaf growth and Fv/Fm of salinized plants, or is it specific to the particular methodological conditions (duration of salinization, choice of genotypes or plant compartment) imposed by our experimental design?Open in a separate windowFigure 1Two axes of a principal components (PC1, PC2) analysis showing plant productivity trait vectors (leaf fresh weight, LFW or relative growth rate, LRGR, and Fv/Fm, indicated by arrows) and the position of various hormonal and ionic variables (denoted by abbreviations) for the long-term grafting experiment which analysed xylem variables (A), and the medium-term experiments with ungrafted plants that analyzed leaf (B) and root (C) variables. Arrows indicate eigen vectors representing the strength (given by the length of the vector) and direction of the trait correlation relative to the first two principal components (PC1, PC2). The circles enclose those variables that fall into the same cluster (95% confidence level). Abbreviations for the variables are given thus: ABA, abscisic acid; AC , 1-aminocyclopropane-1-carboxylic acid; IAA , indole-3-acetic acid; Z, trans-zeatin; ZR, trans-zeatin riboside.To answer this question, PCA was performed on data obtained from a similar experiment where a single genotype of tomato (cv Moneymaker) was exposed to high (100 mM NaCl) salinity for 22 days under hydroponic conditions in a controlled environment chamber.^8,9 Although the same ionic and hormonal variables (including the auxin indole-3-acetic acid, IAA) were assayed in both roots and leaves, xylem ion concentrations were not quantified. Therefore PCA was conducted using leaf relative growth rate (LRGR) as an indicator of vegetative vigour (due to the temporal variation of leaf fresh weight in young plants) and senescence (Fv/Fm), and hormonal and ionic variables measured in leaves (Fig. 1B) and roots (Fig. 1C).Both physiological variables (LRGR and Fv/Fm) were significantly loaded into the two major principal components (PC1 and PC2) explaining more than 90% of the variance, and in which most of the leaf and root ionic and hormonal parameters were strongly associated in three clusters (enclosed within the circles, Fig. 1B and C). As in the PCA from the grafting experiment (Fig. 1A), LRGR and Fv/Fm, were also placed in the same cluster as the cytokinin Z and the ionic variables K⁺ and K⁺/Na⁺ in the leaf (Fig. 1B), although some dissociation between them was observed, probably due to the temporal dynamics of these variables in the same plant organ. Moreover, most of the other CK-related variables (ZR, Z + ZR, Z + ZR/ACC, Z + ZR/ABA) were associated in a distinct cluster that was also strongly loaded along with Fv/Fm parameter in PC1 (Fig. 1B). When the root data were considered (Fig. 1C), all these CK-related (with the exception of Z) and ionic variables were placed in the same cluster as Fv/Fm and loaded into PC1 explaining 71% of variance.Interestingly, the position and value of the eigen vector defining Fv/Fm remained highly conservative and positively associated with hormonal (ZR, Z + ZR, Z + ZR/ACC) and ionic (K⁺, K⁺/Na⁺) variables independent of the experimental design and organ analyzed. However, the position of the vector for LRGR was more variable since its relationship with PC2 and some hormonal parameters (e.g., IAA, IAA/Z + ZR) was positive in the leaves and negative in the roots. Particular attention should be paid to leaf Z concentration since it was highly related to both Fv/Fm and leaf growth vectors in both experiments (Fig. 1A and B), while this hormone was placed in an opposite (to Fv/Fm) or orthogonal (to LRGR) vector in the roots (Fig. 1C). Only two of the hormones analyzed showed differential responses to salinity in leaves and roots suggesting an important role in biomass partitioning: Z and IAA decreased in leaves and increased in roots.⁸ Similar responses of both roots and leaves (for the other hormones) may indicate the importance of root-to-shoot signaling in maintaining hormone homeostasis in particular organs and integrating the whole plant response to the stress, but reciprocal grafting experiments with hormone-content or -sensitivity mutants are required to test this hypothesis for each compound.¹⁰ Additionally, PCA also revealed a conserved position for some hormonal parameters such as ACC and ABA tissue concentrations (Fig. 1B and C) and also the ratio ACC/ABA in both tissues and leaf xylem (Fig. 1A–C), which were always placed in clusters opposed to Fv/Fm (ACC, ABA) and leaf growth (ACC/ABA), supporting a role of these hormones in negatively regulating salt-induced leaf senescence⁹ and growth.⁷The relatively conservative geometric positions of some ionic and hormonal variables, and their co-occurrence with physiological variables of interest, suggest that PCA was relatively insensitive to methodological issues such as duration of salinisation, choice of genotypes or plant compartment. Moreover, similar PCAs for both tissue (Fig. 1B and C) and xylem (Fig. 1A) samples indicate the adequacy of the latter (with its considerably decreased sample preparation time) to infer physiological relationships. Particularly noteworthy was the clustering of cytokinin-related variables with the chlorophyll fluorescence parameter Fv/Fm in both experiments and organs, as well as the leaf Z concentration and growth, which highlighted the potential physiological importance of cytokinins in a way that was not so apparent from inspection of a typical correlation matrix which analyses two variables at a time. Accordingly, we have instigated experiments manipulating the cytokinin status of salinised plants by selectively overexpressing the ipt gene. That these plants grew better, and showed delayed senescence, under salinity (Ghanem ME, unpublished results) supports our use of PCA as a tool to explore temporal^8,9 and genetic⁷ correlations.