How Stress Affects Your Budget-Stress Impacts on Starch Metabolism.

Starch is a polysaccharide that is stored to be used in different timescales. Transitory starch is used during nighttime when photosynthesis is unavailable. Long-term starch is stored to support vegetative or reproductive growth, reproduction, or stress responses. Starch is not just a reserve of energy for most plants but also has many other roles, such as promoting rapid stomatal opening, making osmoprotectants, cryoprotectants, scavengers of free radicals and signals, and reverting embolised vessels. Biotic and abiotic stress vary according to their nature, strength, duration, developmental stage of the plant, time of the day, and how gradually they develop. The impact of stress on starch metabolism depends on many factors: how the stress impacts the rate of photosynthesis, the affected organs, how the stress impacts carbon allocation, and the energy requirements involved in response to stress. Under abiotic stresses, starch degradation is usually activated, but starch accumulation may also be observed when growth is inhibited more than photosynthesis. Under biotic stresses, starch is usually accumulated, but the molecular mechanisms involved are largely unknown. In this mini-review, we explore what has been learned about starch metabolism and plant stress responses and discuss the current obstacles to fully understanding their interactions.

Flux profiling of photosynthetic carbon metabolism in intact plants.

Flux analysis has been carried out in plants for decades, but technical innovations are now enabling it to be carried out in photosynthetic tissues in a more precise fashion with respect to the number of metabolites measured. Here we describe a protocol, using gas chromatography (GC)- and liquid chromatography (LC)-mass spectrometry (MS), to resolve intracellular fluxes of the central carbon metabolism in illuminated intact Arabidopsis thaliana rosettes using the time course of the unlabeled fractions in 40 major constituents of the metabolome after switching to (13)CO2. We additionally simplify modeling assumptions, specifically to cope with the presence of multiple cellular compartments. We summarize all steps in this 8-10-week-long process, including setting up the chamber; harvesting; liquid extraction and subsequent handling of sample plant material to chemical derivatization procedures such as silylation and methoxymation (necessary for gas chromatography only); choosing instrumentation settings and evaluating the resultant chromatogram in terms of both unlabeled and labeled peaks. Furthermore, we describe how quantitative insights can be gained by estimating both benchmark and previously unknown fluxes from collected data sets.

Arabidopsis plants acclimate to water deficit at low cost through changes of carbon usage: an integrated perspective using growth, metabolite, enzyme, and gene expression analysis.

Growth and carbon (C) fluxes are severely altered in plants exposed to soil water deficit. Correspondingly, it has been suggested that plants under water deficit suffer from C shortage. In this study, we test this hypothesis in Arabidopsis (Arabidopsis thaliana) by providing an overview of the responses of growth, C balance, metabolites, enzymes of the central metabolism, and a set of sugar-responsive genes to a sustained soil water deficit. The results show that under drought, rosette relative expansion rate is decreased more than photosynthesis, leading to a more positive C balance, while root growth is promoted. Several soluble metabolites accumulate in response to soil water deficit, with K(+) and organic acids as the main contributors to osmotic adjustment. Osmotic adjustment costs only a small percentage of the daily photosynthetic C fixation. All C metabolites measured (not only starch and sugars but also organic acids and amino acids) show a diurnal turnover that often increased under water deficit, suggesting that these metabolites are readily available for being metabolized in situ or exported to roots. On the basis of 30 enzyme activities, no in-depth reprogramming of C metabolism was observed. Water deficit induces a shift of the expression level of a set of sugar-responsive genes that is indicative of increased, rather than decreased, C availability. These results converge to show that the differential impact of soil water deficit on photosynthesis and rosette expansion results in an increased availability of C for the roots, an increased turnover of C metabolites, and a low-cost C-based osmotic adjustment, and these responses are performed without major reformatting of the primary metabolism machinery.

Robin: an intuitive wizard application for R-based expression microarray quality assessment and analysis.

The wide application of high-throughput transcriptomics using microarrays has generated a plethora of technical platforms, data repositories, and sophisticated statistical analysis methods, leaving the individual scientist with the problem of choosing the appropriate approach to address a biological question. Several software applications that provide a rich environment for microarray analysis and data storage are available (e.g. GeneSpring, EMMA2), but these are mostly commercial or require an advanced informatics infrastructure. There is a need for a noncommercial, easy-to-use graphical application that aids the lab researcher to find the proper method to analyze microarray data, without this requiring expert understanding of the complex underlying statistics, or programming skills. We have developed Robin, a Java-based graphical wizard application that harnesses the advanced statistical analysis functions of the R/BioConductor project. Robin implements streamlined workflows that guide the user through all steps of two-color, single-color, or Affymetrix microarray analysis. It provides functions for thorough quality assessment of the data and automatically generates warnings to notify the user of potential outliers, low-quality chips, or low statistical power. The results are generated in a standard format that allows ready use with both specialized analysis tools like MapMan and PageMan and generic spreadsheet applications. To further improve user friendliness, Robin includes both integrated help and comprehensive external documentation. To demonstrate the statistical power and ease of use of the workflows in Robin, we present a case study in which we apply Robin to analyze a two-color microarray experiment comparing gene expression in tomato (Solanum lycopersicum) leaves, flowers, and roots.