Wilhelm Gruissem

Bio: Wilhelm Gruissem is an academic researcher from National Chung Hsing University. The author has contributed to research in topics: Gene & Arabidopsis. The author has an hindex of 94, co-authored 325 publications receiving 32048 citations. Previous affiliations of Wilhelm Gruissem include University of Zurich & University of Colorado Boulder.

GENEVESTIGATOR. Arabidopsis Microarray Database and Analysis Toolbox

Abstract: High-throughput gene expression analysis has become a frequent and powerful research tool in biology. At present, however, few software applications have been developed for biologists to query large microarray gene expression databases using a Web-browser interface. We present GENEVESTIGATOR, a database and Web-browser data mining interface for Affymetrix GeneChip data. Users can query the database to retrieve the expression patterns of individual genes throughout chosen environmental conditions, growth stages, or organs. Reversely, mining tools allow users to identify genes specifically expressed during selected stresses, growth stages, or in particular organs. Using GENEVESTIGATOR, the gene expression profiles of more than 22,000 Arabidopsis genes can be obtained, including those of 10,600 currently uncharacterized genes. The objective of this software application is to direct gene functional discovery and design of new experiments by providing plant biologists with contextual information on the expression of genes. The database and analysis toolbox is available as a community resource at https://www.genevestigator.ethz.ch.

Genevestigator v3: a reference expression database for the meta-analysis of transcriptomes.

Abstract: The Web-based software tool Genevestigator provides powerful tools for biologists to explore gene expression across a wide variety of biological contexts. Its first releases, however, were limited by the scaling ability of the system architecture, multiorganism data storage and analysis capability, and availability of computationally intensive analysis methods. Genevestigator V3 is a novel meta-analysis system resulting from new algorithmic and software development using a client/server architecture, large-scale manual curation and quality control of microarray data for several organisms, and curation of pathway data for mouse and Arabidopsis. In addition to improved querying features, Genevestigator V3 provides new tools to analyze the expression of genes in many different contexts, to identify biomarker genes, to cluster genes into expression modules, and to model expression responses in the context of metabolic and regulatory networks. Being a reference expression database with user-friendly tools, Genevestigator V3 facilitates discovery research and hypothesis validation.

Fruits: A Developmental Perspective.

Abstract: Embryonic development in many angiosperms occurs concomitantly with the development of the ovary into a specialized organ, the fruit, which provides a suitable environment for seed maturation and often a mechanism for the dispersa1 of mature seeds, as Darwin observed. Despite centuries of intensive genetic selection of agriculturally valuable fruit, we still lack most information about how fruits develop, how this development is coordinated with embryonic development and seed formation, and the molecular, cellular, and physiological events that control fruit growth and differentiation. The last 10 years have seen a rapid surge of information on one commercially important aspect of fruit development, fruit ripening, including the genetic control of temporal events during the ripening phase (Theologis, 1992; Theologis et al., 1992). However, fewer advances have been made on temporal and spatial controls of fruit set and growth, although from the agricultura1 point of view, these aspects are of equally critical importance. We will provide a perspective on the molecular, cellular, and physiological mechanisms that must be considered as integral parts of the fruit developmental process. The discussion below will illustrate that fruit development is a potentially useful system to learn more about complex regulatory mechanisms that control the division, growth, and differentiation of plant cells.

A systematic comparison and evaluation of biclustering methods for gene expression data

Abstract: Motivation: In recent years, there have been various efforts to overcome the limitations of standard clustering approaches for the analysis of gene expression data by grouping genes and samples simultaneously. The underlying concept, which is often referred to as biclustering, allows to identify sets of genes sharing compatible expression patterns across subsets of samples, and its usefulness has been demonstrated for different organisms and datasets. Several biclustering methods have been proposed in the literature; however, it is not clear how the different techniques compare with each other with respect to the biological relevance of the clusters as well as with other characteristics such as robustness and sensitivity to noise. Accordingly, no guidelines concerning the choice of the biclustering method are currently available. Results: First, this paper provides a methodology for comparing and validating biclustering methods that includes a simple binary reference model. Although this model captures the essential features of most biclustering approaches, it is still simple enough to exactly determine all optimal groupings; to this end, we propose a fast divide-and-conquer algorithm (Bimax). Second, we evaluate the performance of five salient biclustering algorithms together with the reference model and a hierarchical clustering method on various synthetic and real datasets for Saccharomyces cerevisiae and Arabidopsis thaliana. The comparison reveals that (1) biclustering in general has advantages over a conventional hierarchical clustering approach, (2) there are considerable performance differences between the tested methods and (3) already the simple reference model delivers relevant patterns within all considered settings. Availability: The datasets used, the outcomes of the biclustering algorithms and the Bimax implementation for the reference model are available at http://www.tik.ee.ethz.ch/sop/bimax Contact: bleuler@tik.ee.ethz.ch Supplementary information: Supplementary data are available at http://www.tik.ee.ethz.ch/sop/bimax

Network Analysis of the MVA and MEP Pathways for Isoprenoid Synthesis

Abstract: Isoprenoid biosynthesis is essential for all living organisms, and isoprenoids are also of industrial and agricultural interest. All isoprenoids are derived from prenyl diphosphate (prenyl-PP) precursors. Unlike isoprenoid biosynthesis in other living organisms, prenyl-PP, as the precursor of all isoprenoids in plants, is synthesized by two independent pathways: the mevalonate (MVA) pathway in the cytoplasm and the 2-C-methyl-D-erythritol 4-phosphate (MEP) pathway in plastids. This review focuses on progress in our understanding of how the precursors for isoprenoid biosynthesis are synthesized in the two subcellular compartments, how the underlying pathway gene networks are organized and regulated, and how network perturbations impact each pathway and plant development. Because of the wealth of data on isoprenoid biosynthesis, we emphasize research in Arabidopsis thaliana and compare the synthesis of isoprenoid precursor molecules in this model plant with their synthesis in other prokaryotic and eukaryotic organisms.

Plant cellular and molecular responses to high salinity.

Abstract: ▪ Abstract Plant responses to salinity stress are reviewed with emphasis on molecular mechanisms of signal transduction and on the physiological consequences of altered gene expression that affect biochemical reactions downstream of stress sensing. We make extensive use of comparisons with model organisms, halophytic plants, and yeast, which provide a paradigm for many responses to salinity exhibited by stress-sensitive plants. Among biochemical responses, we emphasize osmolyte biosynthesis and function, water flux control, and membrane transport of ions for maintenance and re-establishment of homeostasis. The advances in understanding the effectiveness of stress responses, and distinctions between pathology and adaptive advantage, are increasingly based on transgenic plant and mutant analyses, in particular the analysis of Arabidopsis mutants defective in elements of stress signal transduction pathways. We summarize evidence for plant stress signaling systems, some of which have components analogous to t...

Plant drought stress: effects, mechanisms and management

Abstract: Scarcity of water is a severe environmental constraint to plant productivity. Drought-induced loss in crop yield probably exceeds losses from all other causes, since both the severity and duration of the stress are critical. Here, we have reviewed the effects of drought stress on the growth, phenology, water and nutrient relations, photosynthesis, assimilate partitioning, and respiration in plants. This article also describes the mechanism of drought resistance in plants on a morphological, physiological and molecular basis. Various management strategies have been proposed to cope with drought stress. Drought stress reduces leaf size, stem extension and root proliferation, disturbs plant water relations and reduces water-use efficiency. Plants display a variety of physiological and biochemical responses at cellular and whole-organism levels towards prevailing drought stress, thus making it a complex phenomenon. CO2 assimilation by leaves is reduced mainly by stomatal closure, membrane damage and disturbed activity of various enzymes, especially those of CO2 fixation and adenosine triphosphate synthesis. Enhanced metabolite flux through the photorespiratory pathway increases the oxidative load on the tissues as both processes generate reactive oxygen species. Injury caused by reactive oxygen species to biological macromolecules under drought stress is among the major deterrents to growth. Plants display a range of mechanisms to withstand drought stress. The major mechanisms include curtailed water loss by increased diffusive resistance, enhanced water uptake with prolific and deep root systems and its efficient use, and smaller and succulent leaves to reduce the transpirational loss. Among the nutrients, potassium ions help in osmotic adjustment; silicon increases root endodermal silicification and improves the cell water balance. Low-molecular-weight osmolytes, including glycinebetaine, proline and other amino acids, organic acids, and polyols, are crucial to sustain cellular functions under drought. Plant growth substances such as salicylic acid, auxins, gibberrellins, cytokinin and abscisic acid modulate the plant responses towards drought. Polyamines, citrulline and several enzymes act as antioxidants and reduce the adverse effects of water deficit. At molecular levels several drought-responsive genes and transcription factors have been identified, such as the dehydration-responsive element-binding gene, aquaporin, late embryogenesis abundant proteins and dehydrins. Plant drought tolerance can be managed by adopting strategies such as mass screening and breeding, marker-assisted selection and exogenous application of hormones and osmoprotectants to seed or growing plants, as well as engineering for drought resistance.

Locating proteins in the cell using TargetP, SignalP and related tools

Abstract: Determining the subcellular localization of a protein is an important first step toward understanding its function. Here, we describe the properties of three well-known N-terminal sequence motifs directing proteins to the secretory pathway, mitochondria and chloroplasts, and sketch a brief history of methods to predict subcellular localization based on these sorting signals and other sequence properties. We then outline how to use a number of internet-accessible tools to arrive at a reliable subcellular localization prediction for eukaryotic and prokaryotic proteins. In particular, we provide detailed step-by-step instructions for the coupled use of the amino-acid sequence-based predictors TargetP, SignalP, ChloroP and TMHMM, which are all hosted at the Center for Biological Sequence Analysis, Technical University of Denmark. In addition, we describe and provide web references to other useful subcellular localization predictors. Finally, we discuss predictive performance measures in general and the performance of TargetP and SignalP in particular.