Dynamics of endogenous cytokinin pools in tobacco seedlings: a modelling approach

Abstract

Recent advances in cytokinin analysis have made it possible to measure the content of 22 cytokinin metabolites in the tissue of developing tobacco seedlings. Individual types of cytokinins in plants are interconverted to their respective forms by several enzymatic activities (5'-AMP-isopentenyltransferase, adenosine nucleosidase, 5'-nucleotidase, adenosine phosphorylase, adenosine kinase, trans-hydroxylase, zeatin reductase, beta-glucosidase, O-glucosyl transferase, N-glucosyl transferase, cytokinin oxidase). This paper reports modelling and measuring of the dynamics of endogenous cytokinins in tobacco plants grown on media supplemented with isopentenyl adenine (IP), zeatin (Z) and dihydrozeatin riboside (DHZR). Differences in phenotypes generated by the three cytokinins are shown and discussed, and the assumption that substrate concentration drives enzyme kinetics underpinned the construction of a simple mathematical model of cytokinin metabolism in developing seedlings. The model was tested on data obtained from liquid chromatography/tandem mass spectrometry cytokinin measurements on tobacco seedlings grown on Murashige and Skoog agar nutrient medium, and on plants grown in the presence of IP, Z and DHZR. A close match was found between measured and simulated data, especially after a series of iterative parameter searches, in which the parameters were set to obtain the best fit with one of the data sets.

Fig. 1. The CK simulation model. Adenosine 5′‐monophosphate and isopentenyl pyrophosphate are maintained at constant concentrations. The other 15 rectangles represent state variables corresponding to the main CK compounds. Arrows represent their biochemical conversions catalysed by enzymes (black, unidirectional; grey, bidirectional; white, uptake and oxidation). The metabolic network used for construction of the model was adapted from Kaminek (1992) to fit the set of known enzymes and possibilities to measure metabolite concentration experimentally. Enzyme names are used as defined in the list of abbreviations. ipu, isopentenyl adenine uptake; zu, trans‐zeatin uptake, dhzru, dihydrozeatin riboside uptake.

Fig. 2. The effect of isopentenyl adenine, trans‐zeatin and dihydro zeatin riboside on the morphology and development of SR1 tobacco seedlings grown on MS medium. The three rows represent seedlings of different ages (day 2, day 5 and day 12 after germination). The first column (MS) shows control seedlings grown in absence of external CKs. The other columns show seedlings grown on MS medium supplemented with 1 µm isopentenyl adenine (MS+IP), 1 µm trans‐zeatin (MS+Z) and 1 µm dihydrozeatin riboside (MS+DHZR). The displayed seedlings were chosen as representative samples from more than 100 individuals. The scale bar = 1 mm.

Fig. 3. Comparison of data simulated by the mathematical model and experimental measurements. Data shown are for the MS‐grown control (open triangle) and the MS+IP treatment sampled on day 2 (open circles), day 5 (grey circles) and day 12 (closed circles). Simulations were carried out (A) before calibration of the model with equal V_max (= 10) and K_m (= 100) values for all the key enzymes represented in the model and (B) after calibration of the model with data from the MS+IP treatment. Symbols occurring close to the diagonal line (1 : 1) represent a perfect match between simulated and measured data. For completeness of the presentation, metabolites which were below the limit of detection were replaced by a value of 0·01.

Fig. 4. Sensitivity analysis for the MS+IP‐calibrated model. Panels show the goodness‐of‐fit of the calibrated model when individual parameters (A, V_max values and uptake rate ipu; B, K_m values) are varied by a factor of 0·2–3. The flat, more open curves represent parameters with smaller effect on the overall behaviour of the model.

Fig. 5. Graphical representation of V_max and K_m values obtained by calibration of the model using data from the MS+IP treatment. Rectangles represent state variables corresponding to the main CK compounds. Arrows represent their biochemical conversions catalysed by enzymes. Thickness of individual arrows is proportional to the V_max of the represented enzyme. The size of the circle associated with the arrow is inversely proportional to the K_m; small circles therefore represent low affinity for the substrate.

Fig. 6. Comparison of data simulated by the mathematical model and experimental measurements. Data shown are for MS+Z (A) and MS+DHZR (B) treatments sampled on day 2 (open circles), day 5 (grey circles) and day 12 (closed circles). Simulations were carried out after calibration of the model with data from the MS+IP treatment. Symbols occurring close to the diagonal line (1 : 1) represent a perfect match between simulated and measured data. For completeness of the presentation, metabolites which were below the limit of detection were replaced by a value of 0·01.