Analysis Of The Micropropagation Biology Essay

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Over the years, sugar has been considered an essential component of the plant in vitro culture medium. However, in addition to its nutritional role, sugar regulates many important metabolic processes associated with plant growth and development (signalling functions). At the cellular level, sugars are essential for intermediary and respiratory metabolism and are the substrate for the synthesis of complex carbohydrates such as starch and cellulose. In addition, sugars supply the precursors for amino acid and fatty acid biosynthesis and essentially all other metabolites present in plants. Many plant species benefit from the presence of sugar in the culture medium. For instance, enhanced growth and photosynthesis was observed in tobacco (Tichá et al., 1998), sugar beet (Kovtun and Daie, 1995), potato (Cournac et al., 1991) and sea oats (Valero-Aracama et al., 2007). In addition, supplementation of sugar to the culture media increases sucrose and starch reserves in micropropagated plantlets a condition which may favour ex vitro acclimatization and accelerate physiological adjustments (Pospisilova et al., 1999). With Alocasia amazonica plantlets Jo et al. (2009) found that, increasing sucrose concentration in the culture medium led to increases in foliar starch and sugars content and decreases in water potential during in in vitro plantlets. During ex vitro acclimatization, shoot length, root length, leaf number and root number, were found to be better when plants grown with 3% sucrose. Although the supply of sugar in the medium has many positive effects, it also has negative ones. For instance, exogenous sugar reduces growth, photosynthesis, expression of photosynthetic genes (Ehness et al., 1997; Hdider and Desjardins, 1994; Kozai et al., 1995). Hdider and Desjardins (1994) observed that, high sugar concentration in the culture medium was causing feedback inhibition of photosynthesis by reducing the quantity and activation of Rubisco in cultured strawberry and potato plantlets. In addition, Gaspar et al. (2002) reported that, the sucrose added to the medium hindered chlorophyll synthesis, Calvin cycle and photosynthesis, as a result, disturbed carbon metabolism in in vitro cultured plantlets. According to these authors, the sugars added in the culture medium are causing an important stress to the plantlets, most likely due to their osmotic effect. Recently, Desjardins et al. (2007) suggested that in vitro conditions are stressful for plant growth and that exogenous sugar is the principal cause of plant stress during in vitro culture.

The traditional metabolic analysis techniques are restricted to the measurement of a limited number of metabolites. Thus, only a partial interpretation of a physiological phenomenon can be obtained. The ability of metabolomics technology to simultaneously identify and quantify a large number of metabolites allows a good understanding of plant metabolism. Indeed, metabolomics is a powerful technique and maybe is the best and most direct technique to understand the plant physiology by gaining a more global picture of the biochemical composition. In a seminal work, Roessner et al. (2000) used GC-MS to obtain a comprehensive metabolic profile of a singles extracts of soil or in vitro grown potato tubers. About 77 metabolites of various biochemical groups were detected and quantified at once. This analytical method proved to be powerful and allowed the simultaneous analysis of a large set of metabolite and revealed major differences between the tubers from different origin. Using the same technique, Jeong et al. (2004) showed that 64 metabolites accumulated differentially during the transition from sink to source of quacking aspen leaves two-thirds of which showed more than 4-fold changes in relative abundance. In this case, the metabolic profiling of three leaf stages yielded distinct biochemical phenotypes.

The aim of this paper is thus to use metabolomics to obtain a comprehensive view of metabolic differences between photoautotrophic and photomixotrophic plantlets during in vitro culture and to assess how the metabolites change after the transfer of these plantlets to ex vitro conditions. This approach will enable us identify in a comprenhensive manner the metabolic pathways which have been affected by the presence of sugar in the culture medium, by the tissue culture conditions and by subsequent ex vitro conditions. This approach will also uncover a metabolic signature for each treatment.

Plant material preparation for metabolite analysis was described by Roessner et al. (2000). Briefly, for metabolites measurement, 15 plants from 15 individual test tubes were measured for each treatment. The fourth and fifth leaves of each plant were collected at the middle of the day and frozen in liquid nitrogen, and they were then stored at -80°C until samples preparation. The fourth and fifth leaf samples of ex vitro plantlets (after 8d) were formed under in vitro conditions and developed during ex vitro stage, however the leaf samples which have been taken after 16 d of the acclimatization stage were formed and developed during the ex vitro stage. 100 mg of frozen leaf tissue was ground to a fine powder by mortar and pestle with liquid nitrogen and extracted with 1.4 mL of methanol. 50 µL of ribitol was added as an internal standard (20 mg of ribitol/10 mL H2O) to the samples to correct for the loss of analytes during sample preparation or sample injection. Metabolites were extracted from the sample by incubation for 15 min at 70°C, one volume of water was added to the mixture which was then centrifuged at 2200g, and dried in a speed-vacuum. The residue was redissolved and derivatized for 90 min at 30°C (in 80 µL of 20 mg mL-1 methoxyamine hydrochloride in pyridine) followed by a 30-min treatment at 37°C with 8 µL of MSTFA (N-methyl-N-[trimethylsilyl]trifluoroacetamide). Before trimethylsilylation, 40 µL of a retention time standard mixture was added, 3.7% [w/v] for hepatonic acid, nonanoic acid, undecanoic acid, and tridecanoic acid; 7.4% [w/v] for pentadecanic acid, nonadeanoic acid and tricosanoic acid, 22.2% [w/v] for heptacosanoic acid; 55.5% [w/v] for hentriacontanoic acid dissolved in 50 mg/5mL-1 tetrahydrofuran). One µL volumes of sample were injected with a splitless mode.

GC-MS Analysis

Samples were injected with a HP 7683 series automatic sampler separated on a 6890 plus series GC fitted with a split/splitless injector port and identified with HP model 5973 mass selective detector. GC was performed on a 30m SPB-50 0.25mm ø column and 0.25µm layer thickness (Superlco, Bellfonte, CA). The injector temperature was 250°C. The carrier gas was helium at a flow-rate 1 ml/min. Oven temperature was maintained for five min at a isothermal temperature of 70°C, followed by a stepwise 5°C min-1 rise temperature until the oven temperature reached 310°C, after which the temperature was maintained for an additional period of 1 min. The system was then temperature equilibrated for 6 min at 70°C before the next injection. Mass spectra were recorded at 2.69 scan/sec with the range of 50 to 600 m/z. The chromatographic system was controlled and validated by the ChemStation software (Agilent Technologies). Perfluorotributylamine (PFTBA), with m/z of 69, 219, and 502, was used for autotuning.

The mass spectra were deconvoluted, and peaks were assigned identities using the automated mass spectral deconvolution and identification program (AMDIS) and the National Institute of Standards and Technology (NIST library, version 2005). Peaks that were at least 75 (out of 100) match factor were automatically or manualy assigned identities compared with the NIST library. We confirmed peaks identification by using authentic standards (100 compounds) and Q_MSRI_ID library from Max Planck Institute of Molecular Plant Physiology, Golm, Germany (website:

Statistical analysis of Data

The area of every metabolite peak was divided by peak area of the internal standard, ribitol present in the same chromatogram, to correct for recovery differences. Log10 transformation was performed on all data for statistical analysis. ANOVA was performed on data using SAS software (version 9.1; SAS Institute inc., NC, 2003). Protected LSD was used to perform a multiple comparison among treatment means. Principal component analysis (PCA) was performed on normalized datasets with SAS software. PCA was used to account for the contribution of replicates or specific metabolites to build the treatments clustering.

Application of principal component analysis to metabolite data set

By applying principal component analysis (PCA) to our data set, two distinct clusters were clearly observable between in vitro PAT and PMT conditions, largely separating along the second component axis, and two others distinctive clusters were noticeably separated during the acclimatization stage (Fig. 4.8). PCA was used also to estimate the contribution of individual metabolites to the clustering of the leaf tissues (Fig. 4.9). Metabolites located close to the zero cut off axes contributed in a relatively small manner to the variance, while a number of more distantly distributed metabolites contributed to the separation of autotrophic from mixotrophic leaves clusters.