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Biomedical subjects

Anna Amtmann

Publications and source records attributed to Anna Amtmann.

7 recordsLinked to original sources
PubMed2005

Vector analysis as a fast and easy method to compare gene expression responses between different experimental backgrounds.

BACKGROUND: Gene expression studies increasingly compare expression responses between different experimental backgrounds (genetic, physiological, or phylogenetic). By focusing on dynamic responses rather than a direct comparison of static expression levels, this type of study allows a finer dissection of primary and secondary regulatory effects in the various backgrounds. Usually, results of such experiments are presented in the form of Venn diagrams, which are intuitive and visually appealing, but lack a statistical foundation. RESULTS: Here we introduce Vector Analysis (VA) as a simple, yet principled, approach to comparing expression responses in different experimental backgrounds. VA enables the automatic assignment of genes to response prototypes and provides statistical significance estimates to eliminate spurious response patterns. The application of VA to a real dataset, comparing nutrient starvation responses in wild type and mutant Arabidopsis plants, reveals that consistent patterns of expression behavior are present in the data and are reliably detected by the algorithm. CONCLUSION: Vector analysis is a flexible, easy-to-use technique to compare gene expression patterns in different experimental backgrounds. It compares favorably with the classical Venn diagram approach and can be implemented manually using spreadsheets, such as Excel, or automatically by using the supplied software.

Algorithms↗
PubMed2004

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 Ca(2+) 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.

Arabidopsis↗
PubMed2004

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.

Acute Disease↗
PubMed2004

Graph-based iterative Group Analysis enhances microarray interpretation.

BACKGROUND: One of the most time-consuming tasks after performing a gene expression experiment is the biological interpretation of the results by identifying physiologically important associations between the differentially expressed genes. A large part of the relevant functional evidence can be represented in the form of graphs, e.g. metabolic and signaling pathways, protein interaction maps, shared GeneOntology annotations, or literature co-citation relations. Such graphs are easily constructed from available genome annotation data. The problem of biological interpretation can then be described as identifying the subgraphs showing the most significant patterns of gene expression. We applied a graph-based extension of our iterative Group Analysis (iGA) approach to obtain a statistically rigorous identification of the subgraphs of interest in any evidence graph. RESULTS: We validated the Graph-based iterative Group Analysis (GiGA) by applying it to the classic yeast diauxic shift experiment of DeRisi et al., using GeneOntology and metabolic network information. GiGA reliably identified and summarized all the biological processes discussed in the original publication. Visualization of the detected subgraphs allowed the convenient exploration of the results. The method also identified several processes that were not presented in the original paper but are of obvious relevance to the yeast starvation response. CONCLUSIONS: GiGA provides a fast and flexible delimitation of the most interesting areas in a microarray experiment, and leads to a considerable speed-up and improvement of the interpretation process.

Algorithms↗
PubMed2004

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

BACKGROUND: The 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. RESULTS: Our 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. CONCLUSIONS: By 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.

Animals↗
PubMed2003

Transcriptome analysis of root transporters reveals participation of multiple gene families in the response to cation stress.

Plant nutrition critically depends on the activity of membrane transporters that translocate minerals from the soil into the plant and are responsible for their intra- and intercellular distribution. Most plant membrane transporters are encoded by multigene families whose members often exhibit overlapping expression patterns and a high degree of sequence homology. Furthermore, many inorganic nutrients are transported by more than one transporter family. These considerations, coupled with a large number of so-far non-annotated putative transporter genes, hamper our progress in understanding how the activity of specific transporters is integrated into a response to fluctuating conditions. We designed an oligonucleotide microarray representing 1096 Arabidopsis transporter genes and analysed the root transporter transcriptome over a 96-h period with respect to 80 mM NaCl, K+ starvation and Ca2+ starvation. Our data show that cation stress led to changes in transcript level of many genes across most transporter gene families. Analysis of transcriptionally modulated genes across all functional groups of transporters revealed families such as V-type ATPases and aquaporins that responded to all treatments, and families - which included putative non-selective cation channels for the NaCl treatment and metal transporters for Ca2+ starvation conditions - that responded to specific ionic environments. Several gene families including primary pumps, antiporters and aquaporins were analysed in detail with respect to the mRNA levels of different isoforms during ion stress. Cluster analysis allowed identification of distinct expression profiles, and several novel putative regulatory motifs were discovered within sets of co-expressed genes.

Arabidopsis↗