Kurt Fellenberg

Correspondence analysis applied to microarray data.

Correspondence analysis is an explorative computational method for the study of associations between variables. Much like principal component analysis, it displays a low-dimensional projection of the data, e.g., into a plane. It does this, though, for two variables simultaneously, thus revealing associations between them. Here, we demonstrate the applicability of correspondence analysis to and high value for the analysis of microarray data, displaying associations between genes and experiments. To introduce the method, we show its application to the well-known Saccharomyces cerevisiae cell-cycle synchronization data by Spellman et al. [Spellman, P. T., Sherlock, G., Zhang, M. Q., Iyer, V. R., Anders, K., Eisen, M. B., Brown, P. O., Botstein, D. & Futcher, B. (1998) Mol. Biol. Cell 9, 3273–3297], allowing for comparison with their visualization of this data set. Furthermore, we apply correspondence analysis to a non-time-series data set of our own, thus supporting its general applicability to microarray data of different complexity, underlying structure, and experimental strategy (both two-channel fluorescence-tag and radioactive labeling).

Monitoring the Switch from Housekeeping to Pathogen Defense Metabolism in Arabidopsis thaliana Using cDNA Arrays

Plants respond to pathogen attack by deploying several defense reactions. Some rely on the activation of preformed components, whereas others depend on changes in transcriptional activity. Using cDNA arrays comprising 13,000 unique expressed sequence tags, changes in the transcriptome ofArabidopsis thaliana were monitored after attempted infection with the bacterial plant pathogen Pseudomonas syringae pv. tomato carrying the avirulence geneavrRpt2. Sampling at four time points during the first 24 h after infiltration revealed significant changes in the steady state transcript levels of ∼650 genes within 10 min and a massive shift in gene expression patterns by 7 h involving ∼2,000 genes representing many cellular processes. This shift from housekeeping to defense metabolism results from changes in regulatory and signaling circuits and from an increased demand for energy and biosynthetic capacity in plants fighting off a pathogenic attack. Concentrating our detailed analysis on the genes encoding enzymes in glycolysis, the Krebs cycle, the pentose phosphate pathway, the biosynthesis of aromatic amino acids, phenylpropanoids, and ethylene, we observed interesting differential regulation patterns. Furthermore, our data showed potentially important changes in areas of metabolism, such as the glyoxylate metabolism, hitherto not suspected to be components of plant defense.

Processing and quality control of DNA array hybridization data

MOTIVATIONnThe technology of hybridization to DNA arrays is used to obtain the expression levels of many different genes simultaneously. It enables searching for genes that are expressed specifically under certain conditions. However, the technology produces large amounts of data demanding computational methods for their analysis. It is necessary to find ways to compare data from different experiments and to consider the quality and reproducibility of the data.nnnRESULTSnData analyzed in this paper have been generated by hybridization of radioactively labeled targets to DNA arrays spotted on nylon membranes. We introduce methods to compare the intensity values of several hybridization experiments. This is essential to find differentially expressed genes or to do pattern analysis. We also discuss possibilities for quality control of the acquired data.nnnAVAILABILITYnhttp://www.dkfz.de/tbinnnCONTACTnM.Vingron@dkfz-heidelberg.de

Characterization of Antirrhinum Petal Development and Identification of Target Genes of the Class B MADS Box Gene DEFICIENS

The class B MADS box transcription factors DEFICIENS (DEF) and GLOBOSA (GLO) of Antirrhinum majus together control the organogenesis of petals and stamens. Toward an understanding of how the downstream molecular mechanisms controlled by DEF contribute to petal organogenesis, we conducted expression profiling experiments using macroarrays comprising >11,600 annotated Antirrhinum unigenes. First, four late petal developmental stages were compared with sepals. More than 500 ESTs were identified that comprise a large number of stage-specifically regulated genes and reveal a highly dynamic transcriptional regulation. For identification of DEF target genes that might be directly controlled by DEF, we took advantage of the temperature-sensitive def-101 mutant. To enhance the sensitivity of the profiling experiments, one petal developmental stage was selected, characterized by increased transcriptome changes that reflect the onset of cell elongation processes replacing cell division processes. Upon reduction of the DEF function, 49 upregulated and 52 downregulated petal target genes were recovered. Eight target genes were further characterized in detail by RT-PCR and in situ studies. Expression of genes responding rapidly toward an altered DEF activity is confined to different petal tissues, demonstrating the complexity of the DEF function regulating diverse basic processes throughout petal morphogenesis.

Gene expression profiling identifies novel key players involved in the cytotoxic effect of Artesunate on pancreatic cancer cells

Pancreatic cancer is one of the most aggressive human malignancies, with an extremely poor prognosis. The paucity of curative therapies has translated into an overall 5-year survival rate of less than 5%, underscoring a desperate need for new therapeutic options. Artesunate (ART), clinically used as anti-malarial agent, has recently revealed remarkable anti-tumor activity. However, the mechanisms underlying those activities in pancreatic cancer are not yet known. Here we evaluated the anti-tumor activity of Artesunate and the possible underlying mechanisms in pancreatic cancer. MiaPaCa-2 (poorly differentiated) and BxPC-3 (moderately differentiated) pancreatic cancer cell lines were treated with Artesunate and the effect was monitored by a tetrazolium-based assay (MTS) for evaluating cell viability and by flow cytometry and caspase 3/7 activation for apoptosis evaluation. In addition cDNA arrays were used to identify differentially expressed genes. The microarray data were then validated by RT-PCR and Western blotting. Moreover, pathways associated with these expression changes were identified using the Ingenuity Pathway Analysis. The expression analysis identified a common set of genes that were regulated by Artesunate in pancreatic cancer. Our results provide the first in vitro evidence for the therapeutic utility of Artesunate in pancreatic cancer. Moreover, we identified Artesunate as a novel topoisomerase IIalpha inhibitor that inhibits pancreatic cancer growth through modulation of multiple signaling pathways. The present analysis is a starting point for the generation of hypotheses on candidate genes and for a more detailed dissection of the functional role of individual genes for the activity of Artesunate in tumor cells.

Whole genome analysis of a wine yeast strain

Saccharomyces cerevisiae strains frequently exhibit rather specific phenotypic features needed for adaptation to a special environment. Wine yeast strains are able to ferment musts, for example, while other industrial or laboratory strains fail to do so. The genetic differences that characterize wine yeast strains are poorly understood, however. As a first search of genetic differences between wine and laboratory strains, we performed DNA-array analyses on the typical wine yeast strain T73 and the standard laboratory background in S288c. Our analysis shows that even under normal conditions, logarithmic growth in YPD medium, the two strains have expression patterns that differ significantly in more than 40 genes. Subsequent studies indicated that these differences correlate with small changes in promoter regions or variations in gene copy number. Blotting copy numbers vs. transcript levels produced patterns, which were specific for the individual strains and could be used for a characterization of unknown samples.

Small trypanosome RNA-binding proteins TbUBP1 and TbUBP2 influence expression of F-box protein mRNAs in bloodstream trypanosomes.

ABSTRACT In the African trypanosome Trypanosoma brucei nearly all control of gene expression is posttranscriptional; sequences in the 3′-untranslated regions of mRNAs determine the steady-state mRNA levels by regulation of RNA turnover. Here we investigate the roles of two related proteins, TbUBP1 and TbUBP2, containing a single RNA recognition motif, in trypanosome gene expression. TbUBP1 and TbUBP2 are in the cytoplasm and nucleus, comprise ca. 0.1% of the total protein, and are not associated with polysomes or RNA degradation enzymes. Overexpression of TbUBP2 upregulated the levels of several mRNAs potentially involved in cell division, including the CFB1 mRNA, which encodes a protein with a cyclin F-box domain. CFB1 regulation was mediated by the 3′-untranslated region and involved stabilization of the mRNA. Depletion of TbUBP2 and TbUBP1 inhibited growth and downregulated expression of the cyclin F box protein gene CFB2; trans splicing was unaffected. The results of pull-down assays indicated that all tested mRNAs were bound to TbUBP2 or TbUBP1, with some preference for CFB1. We suggest that TbUBP1 and TbUBP2 may be relatively nonspecific RNA-binding proteins and that specific effects of overexpression or depletion could depend on competition between various different proteins for RNA binding.

Microarray data representation, annotation and storage.

Management and analysis of the huge amounts of data produced by microarray experiments is becoming one of the major bottlenecks in the utilization of this high-throughput technology. We describe the basic design of a microarray gene expression database to help microarray users and their informatics teams to set up their information services. We describe two data models--a simpler one called ArrayExpressB and the complete model ArrayExpressC, and discuss some implementation issues. For latest developments see http: wwwebi.ac.uk/arrayexpress

Integration of GO annotations in Correspondence Analysis: facilitating the interpretation of microarray data

MOTIVATIONnThe functional interpretation of microarray datasets still represents a time-consuming and challenging task. Up to now functional categories that are relevant for one or more experimental context(s) have been commonly extracted from a set of regulated genes and presented in long lists.nnnRESULTSnTo facilitate interpretation, we integrated Gene Ontology (GO) annotations into Correspondence Analysis to display genes, experimental conditions and gene-annotations in a single plot. The position of the annotations in these plots can be directly used for the functional interpretation of clusters of genes or experimental conditions without the need for comparing long lists of annotations. Correspondence Analysis is not limited in the number of experimental conditions that can be compared simultaneously, allowing an easy identification of characterizing annotations even in complex experimental settings. Due to the rapidly increasing amount of annotation data available, we apply an annotation filter. Hereby the number of displayed annotations can be significantly reduced to a set of descriptive ones, further enhancing the interpretability of the plot. We validated the method on transcription data from Saccharomyces cerevisiae and human pancreatic adenocarcinomas.nnnAVAILABILITYnThe M-CHiPS software is accessible for collaborators at http://www.mchips.org

Systematic interpretation of microarray data using experiment annotations

BackgroundUp to now, microarray data are mostly assessed in context with only one or few parameters characterizing the experimental conditions under study. More explicit experiment annotations, however, are highly useful for interpreting microarray data, when available in a statistically accessible format.ResultsWe provide means to preprocess these additional data, and to extract relevant traits corresponding to the transcription patterns under study. We found correspondence analysis particularly well-suited for mapping such extracted traits. It visualizes associations both among and between the traits, the hereby annotated experiments, and the genes, revealing how they are all interrelated. Here, we apply our methods to the systematic interpretation of radioactive (single channel) and two-channel data, stemming from model organisms such as yeast and drosophila up to complex human cancer samples. Inclusion of technical parameters allows for identification of artifacts and flaws in experimental design.ConclusionBiological and clinical traits can act as landmarks in transcription space, systematically mapping the variance of large datasets from the predominant changes down toward intricate details.