Baldeep Kular

Evidence for a Direct Link between Glutathione Biosynthesis and Stress Defense Gene Expression in Arabidopsis

The mutant regulator of APX2 1-1 (rax1-1) was identified in Arabidopsis thaliana that constitutively expressed normally photooxidative stress-inducible ASCORBATE PEROXIDASE2 (APX2) and had ≥50% lowered foliar glutathione levels. Mapping revealed that rax1-1 is an allele of γ-GLUTAMYLCYSTEINE SYNTHETASE 1 (GSH1), which encodes chloroplastic γ-glutamylcysteine synthetase, the controlling step of glutathione biosynthesis. By comparison of rax1-1 with the GSH1 mutant cadmium hypersensitive 2, the expression of 32 stress-responsive genes was shown to be responsive to changed glutathione metabolism. Under photo-oxidative stress conditions, the expression of a wider set of defense-related genes was altered in the mutants. In wild-type plants, glutathione metabolism may play a key role in determining the degree of expression of defense genes controlled by several signaling pathways both before and during stress. This control may reflect the physiological state of the plant at the time of the onset of an environmental challenge and suggests that changes in glutathione metabolism may be one means of integrating the function of several signaling pathways.

Elevated Glutathione Biosynthetic Capacity in the Chloroplasts of Transgenic Tobacco Plants Paradoxically Causes Increased Oxidative Stress

Glutathione (GSH), a major antioxidant in most aerobic organisms, is perceived to be particularly important in plant chloroplasts because it helps to protect the photosynthetic apparatus from oxidative damage. In transgenic tobacco plants overexpressing a chloroplast-targeted γ-glutamylcysteine synthetase (γ-ECS), foliar levels of GSH were raised threefold. Paradoxically, increased GSH biosynthetic capacity in the chloroplast resulted in greatly enhanced oxidative stress, which was manifested as light intensity–dependent chlorosis or necrosis. This phenotype was associated with foliar pools of both GSH and γ-glutamylcysteine (the immediate precursor to GSH) being in a more oxidized state. Further manipulations of both the content and redox state of the foliar thiol pools were achieved using hybrid transgenic plants with enhanced glutathione synthetase or glutathione reductase activity in addition to elevated levels of γ-ECS. Given the results of these experiments, we suggest that γ-ECS–transformed plants suffered continuous oxidative damage caused by a failure of the redox-sensing process in the chloroplast.

Changes in oxidative processes and components of the antioxidant system during tomato fruit ripening

Abstract. Analysis of the oxidative processes taking place during fruit ripening in a salad tomato variety (Lycopersicon esculentum Mill. cv. Ailsa Craig) revealed changes in oxidative and antioxidative parameters. Hydrogen peroxide content, lipid peroxidation and protein oxidation were measured as indices of oxidative processes and all were found to increase at the breaker stage. The levels of the aqueous-phase antioxidants, glutathione and ascorbate, increased during the ripening process and these increases were associated with significant changes in their redox status, becoming more reduced as ripening progressed. Changes in the activities of superoxide dismutase, catalase and the enzymes involved in the ascorbate-glutathione cycle during ripening indicated that the antioxidative system plays a fundamental role in the ripening of tomato fruits.

Starch turnover in developing oilseed embryos

*Starch accumulates early during embryo development in Arabidopsis and oilseed rape, then disappears during oil accumulation. Little is known about the nature and importance of starch metabolism in oilseed embryos. *Histochemical and quantitative measures of starch location and content were made on developing seeds and embryos from wild-type Arabidopsis plants, and from mutants lacking enzymes of starch synthesis and degradation with established roles in leaf starch turnover. Feeding experiments with [(14)C]sucrose were used to measure the rate of starch synthesis in oilseed rape embryos within intact siliques. *The patterns of starch turnover in the developing embryo are spatially and temporally complex. Accumulation is associated with zones of cell division. Study of mutant plants reveals a major role in starch turnover for glucan, water dikinase (absent from the sex1 mutant) and isoforms of beta-amylase (absent from various bam mutants). Starch is synthesized throughout the period of its accumulation and loss in embryos within intact siliques of oilseed rape. *We suggest that starch turnover is functionally linked to cell division and differentiation rather than to developmental or storage functions specific to embryos. The pathways of embryo starch metabolism are similar in several respects to those in Arabidopsis leaves.

Toward Supportive Data Collection Tools for Plant Metabolomics

Over recent years, a number of initiatives have proposed standard reporting guidelines for functional genomics experiments. Associated with these are data models that may be used as the basis of the design of software tools that store and transmit experiment data in standard formats. Central to the success of such data handling tools is their usability. Successful data handling tools are expected to yield benefits in time saving and in quality assurance. Here, we describe the collection of datasets that conform to the recently proposed data model for plant metabolomics known as ArMet (architecture for metabolomics) and illustrate a number of approaches to robust data collection that have been developed in collaboration between software engineers and biologists. These examples also serve to validate ArMet from the data collection perspective by demonstrating that a range of software tools, supporting data recording and data upload to central databases, can be built using the data model as the basis of their design.

Enhancement of Plant Metabolite Fingerprinting by Machine Learning

Metabolite fingerprinting of Arabidopsis (Arabidopsis thaliana) mutants with known or predicted metabolic lesions was performed by 1H-nuclear magnetic resonance, Fourier transform infrared, and flow injection electrospray-mass spectrometry. Fingerprinting enabled processing of five times more plants than conventional chromatographic profiling and was competitive for discriminating mutants, other than those affected in only low-abundance metabolites. Despite their rapidity and complexity, fingerprints yielded metabolomic insights (e.g. that effects of single lesions were usually not confined to individual pathways). Among fingerprint techniques, 1H-nuclear magnetic resonance discriminated the most mutant phenotypes from the wild type and Fourier transform infrared discriminated the fewest. To maximize information from fingerprints, data analysis was crucial. One-third of distinctive phenotypes might have been overlooked had data models been confined to principal component analysis score plots. Among several methods tested, machine learning (ML) algorithms, namely support vector machine or random forest (RF) classifiers, were unsurpassed for phenotype discrimination. Support vector machines were often the best performing classifiers, but RFs yielded some particularly informative measures. First, RFs estimated margins between mutant phenotypes, whose relations could then be visualized by Sammon mapping or hierarchical clustering. Second, RFs provided importance scores for the features within fingerprints that discriminated mutants. These scores correlated with analysis of variance F values (as did Kruskal-Wallis tests, true- and false-positive measures, mutual information, and the Relief feature selection algorithm). ML classifiers, as models trained on one data set to predict another, were ideal for focused metabolomic queries, such as the distinctiveness and consistency of mutant phenotypes. Accessible software for use of ML in plant physiology is highlighted.