Anna Amtmann

Rank products: a simple, yet powerful, new method to detect differentially regulated genes in replicated microarray experiments ☆

One of the main objectives in the analysis of microarray experiments is the identification of genes that are differentially expressed under two experimental conditions. This task is complicated by the noisiness of the data and the large number of genes that are examined simultaneously. Here, we present a novel technique for identifying differentially expressed genes that does not originate from a sophisticated statistical model but rather from an analysis of biological reasoning. The new technique, which is based on calculating rank products (RP) from replicate experiments, is fast and simple. At the same time, it provides a straightforward and statistically stringent way to determine the significance level for each gene and allows for the flexible control of the false‐detection rate and familywise error rate in the multiple testing situation of a microarray experiment. We use the RP technique on three biological data sets and show that in each case it performs more reliably and consistently than the non‐parametric t‐test variant implemented in Tusher et al.s significance analysis of microarrays (SAM). We also show that the RP results are reliable in highly noisy data. An analysis of the physiological function of the identified genes indicates that the RP approach is powerful for identifying biologically relevant expression changes. In addition, using RP can lead to a sharp reduction in the number of replicate experiments needed to obtain reproducible results.

The Potassium-Dependent Transcriptome of Arabidopsis Reveals a Prominent Role of Jasmonic Acid in Nutrient Signaling

Full genome microarrays were used to assess transcriptional responses of Arabidopsis seedlings to changing external supply of the essential macronutrient potassium (K+). Rank product statistics and iterative group analysis were employed to identify differentially regulated genes and statistically significant coregulated sets of functionally related genes. The most prominent response was found for genes linked to the phytohormone jasmonic acid (JA). Transcript levels for the JA biosynthetic enzymes lipoxygenase, allene oxide synthase, and allene oxide cyclase were strongly increased during K+ starvation and quickly decreased after K+ resupply. A large number of well-known JA responsive genes showed the same expression profile, including genes involved in storage of amino acids (VSP), glucosinolate production (CYP79), polyamine biosynthesis (ADC2), and defense (PDF1.2). Our findings highlight a novel role of JA in nutrient signaling and stress management through a variety of physiological processes such as nutrient storage, recycling, and reallocation. Other highly significant K+-responsive genes discovered in our study encoded cell wall proteins (e.g. extensins and arabinogalactans) and ion transporters (e.g. the high-affinity K+ transporter HAK5 and the nitrate transporter NRT2.1) as well as proteins with a putative role in Ca2+ signaling (e.g. calmodulins). On the basis of our results, we propose candidate genes involved in K+ perception and signaling as well as a network of molecular processes underlying plant adaptation to K+ deficiency.

The effect of potassium nutrition on pest and disease resistance in plants

Providing a fast growing world population with sufficient food while preserving ecological and energy resources of our planet is one of the biggest challenges in this century. Optimized management of chemical fertilizers and pesticides will be essential for achieving sustainability of intensive farming and requires both empirical data from field trials and advanced fundamental understanding of the molecular processes controlling plant growth. Genes involved in plant responses to nutrient deficiency and pathogen/herbivore attack have been identified, but we are lacking information about the cross-talk between signalling pathways when plants are exposed to a combination of abiotic and biotic stress factors. The focus of this review is on the relationship between the potassium status of plants and their susceptibility to pathogens and herbivorous insects. We combine field evidence on potassium-disease interaction with existing knowledge on metabolic and physiological factors that could explain such interaction, and present new data on metabolite profiles and hormonal pathways from the model plant Arabidopsis thaliana. The latter provides evidence that facilitated entry and development of pathogens or insects in(to) potassium-deficient plants as a result of physical and metabolic changes is counteracted by an increased defence. A genetic approach should now be applied to establish a causal relationship between disease susceptibility on the one hand and individual enzymatic and signal components on the other. Once identified, these can be used to design agricultural strategies that support the nutritional status of the crops while exploiting their inherent potential for defence.

Learning from Evolution: Thellungiella Generates New Knowledge on Essential and Critical Components of Abiotic Stress Tolerance in Plants

Thellungiella salsuginea (halophila) is a close relative of Arabidopsis thaliana but, unlike A. thaliana, it grows well in extreme conditions of cold, salt, and drought as well as nitrogen limitation. Over the last decade, many laboratories have started to use Thellungiella to investigate the physiological, metabolic, and molecular mechanisms of abiotic stress tolerance in plants, and new knowledge has been gained in particular with respect to ion transport and gene expression. The advantage of Thellungiella over other extremophile model plants is that it can be directly compared with Arabidopsis, and therefore generate information on both essential and critical components of stress tolerance. Thellungiella research is supported by a growing body of technical resources comprising physiological and molecular protocols, ecotype collections, expressed sequence tags, cDNA-libraries, microarrays, and a pending genome sequence. This review summarizes the current state of knowledge on Thellungiella and re-evaluates its usefulness as a model for research into plant stress tolerance.

Multilevel Analysis of Primary Metabolism Provides New Insights into the Role of Potassium Nutrition for Glycolysis and Nitrogen Assimilation in Arabidopsis Roots

Potassium (K) is required in large quantities by growing crops, but faced with high fertilizer prices, farmers often neglect K application in favor of nitrogen and phosphorus. As a result, large areas of farmland are now depleted of K. K deficiency affects the metabolite content of crops with negative consequences for nutritional quality, mechanical stability, and pathogen/pest resistance. Known functions of K in solute transport, protein synthesis, and enzyme activation point to a close relationship between K and metabolism, but it is unclear which of these are the most critical ones and should be targeted in biotechnological efforts to improve K usage efficiency. To identify metabolic targets and signaling components of K stress, we adopted a multilevel approach combining transcript profiles with enzyme activities and metabolite profiles of Arabidopsis (Arabidopsis thaliana) plants subjected to low K and K resupply. Roots and shoots were analyzed separately. Our results show that regulation of enzymes at the level of transcripts and proteins is likely to play an important role in plant adaptation to K deficiency by (1) maintaining carbon flux into amino acids and proteins, (2) decreasing negative metabolic charge, and (3) increasing the nitrogen-carbon ratio in amino acids. However, changes in transcripts and enzyme activities do not explain the strong and reversible depletion of pyruvate and accumulation of sugars observed in the roots of low-K plants. We propose that the primary cause of metabolic disorders in low-K plants resides in the direct inhibition of pyruvate kinase activity by low cytoplasmic K in root cells.

EZ-Rhizo: integrated software for the fast and accurate measurement of root system architecture

The root system is essential for the growth and development of plants. In addition to anchoring the plant in the ground, it is the site of uptake of water and minerals from the soil. Plant root systems show an astonishing plasticity in their architecture, which allows for optimal exploitation of diverse soil structures and conditions. The signalling pathways that enable plants to sense and respond to changes in soil conditions, in particular nutrient supply, are a topic of intensive research, and root system architecture (RSA) is an important and obvious phenotypic output. At present, the quantitative description of RSA is labour intensive and time consuming, even using the currently available software, and the lack of a fast RSA measuring tool hampers forward and quantitative genetics studies. Here, we describe EZ-Rhizo: a Windows-integrated and semi-automated computer program designed to detect and quantify multiple RSA parameters from plants growing on a solid support medium. The method is non-invasive, enabling the user to follow RSA development over time. We have successfully applied EZ-Rhizo to evaluate natural variation in RSA across 23 Arabidopsis thaliana accessions, and have identified new RSA determinants as a basis for future quantitative trait locus (QTL) analysis.

Effects of N,P,K and S on metabolism: new knowledge gained from multi-level analysis

Deficiency of mineral nutrients such as nitrate, phosphate, potassium and sulphate strongly affects the type and amount of metabolites produced by crops with knock-on effects on nutritional quality of the crop, its processing properties and disease resistance. Owing to the multitude of biochemical reactions underlying metabolism and the high degree of connectivity between biochemical pathways, predicting the output of the metabolic network in response to a change in nutrient input is an enormous challenge. Recently several studies have taken a systemic approach monitoring the response of plants to withdrawal and/or re-supply of mineral nutrients at the level of transcripts, metabolites and enzyme activities. These multi-level studies have provided important new insight into how plants re-prioritise different metabolic pathways during nutrient shortage and how they integrate metabolism with growth. On the basis of the obtained information we can formulate specific hypotheses about the causal relationships between changes in individual transcripts, proteins and metabolites.

Iterative Group Analysis (iGA): A simple tool to enhance sensitivity and facilitate interpretation of microarray experiments

BackgroundThe biological interpretation of even a simple microarray experiment can be a challenging and highly complex task. Here we present a new method (Iterative Group Analysis) to facilitate, improve, and accelerate this process.ResultsOur Iterative Group Analysis approach (iGA) uses elementary statistics to identify those functional classes of genes that are significantly changed in an experiment and at the same time determines which of the class members are most likely to be differentially expressed. iGA does not require that all members of a class change and is therefore robust against imperfect class assignments, which can be derived from public sources (e.g. GeneOntologies) or automated processes (e.g. key word extraction from gene names).In contrast to previous non-iterative approaches, iGA does not depend on the availability of fixed lists of differentially expressed genes, and thus can be used to increase the sensitivity of gene detection especially in very noisy or small data sets. In the extreme, iGA can even produce statistically meaningful results without any experimental replication.The automated functional annotation provided by iGA greatly reduces the complexity of microarray results and facilitates the interpretation process. In addition, iGA can be used as a fast and efficient tool for the platform-independent comparison of a microarray experiment to the vast number of published results, automatically highlighting shared genes of potential interest.ConclusionsBy applying iGA to a wide variety of data from diverse organisms and platforms we show that this approach enhances and accelerates the interpretation of microarray experiments.

Nutrient Sensing and Signalling in Plants: Potassium and Phosphorus

Abstract Potassium and phosphorus are important macronutrients for crops but are often deficient in the field. Very little is known about how plants sense fluctuations in K and P and how information about K and P availability is integrated at the whole plant level into physiological and metabolic adaptations. This chapter reviews recent advances in discovering molecular responses of plants to K and P deficiency by microarray experiments. These studies provide us not only with a comprehensive picture of adaptive mechanisms, but also with a large number of transcriptional markers that can be used to identify upstream components of K and P signalling pathways. On the basis of the available information we discuss putative receptors and signals involved in the sensing and integration of K and P status both at the cellular and at the whole plant level. These involve membrane potential, voltage‐dependent ion channels, intracellular Ca and pH, and transcription factors, as well as hormones and metabolites for systemic signalling. Genetic screens of reporter lines for transcriptional markers and metabolome analysis of K- and P-deficient plants are likely to further advance our knowledge in this area in the near future.

Regulation of macronutrient transport

In addition to light, water and CO(2), plants require a number of mineral nutrients, in particular the macronutrients nitrogen, sulphur, phosphorus, magnesium, calcium and potassium. After uptake from the soil by the root system they are either immediately assimilated into organic compounds or distributed within the plant for usage in different tissues. A good understanding of how the transport of macronutrients into and between plant cells is adjusted to different environmental conditions is essential to achieve an increase of nutrient usage efficiency and nutritional value in crops. Here, we review the current state of knowledge regarding the regulation of macronutrient transport, taking both a physiological and a mechanistic approach. We first describe how nutrient transport is linked to environmental and internal cues such as nutrient, carbon and water availability via hormonal, metabolic and physical signals. We then present information on the molecular mechanisms for regulation of transport proteins, including voltage gating, auto-inhibition, interaction with other proteins, oligomerization and trafficking. Combining of evidence for different nutrients, signals and regulatory levels creates an opportunity for making new connections within a large body of data, and thus contributes to an integrative understanding of nutrient transport.