Manipulating Large-Scale Arabidopsis Microarray Expression Data: Identifying Dominant Expression Patterns and Biological Process Enrichment
ABSTRACT A series of large-scale Arabidopsis thaliana microarray expression experiments profiling genome-wide expression across different developmental stages, cell types, and environmental conditions have resulted in tremendous amounts of gene expression data. This gene expression is the output of complex transcriptional regulatory networks and provides a starting point for identifying the dominant transcriptional regulatory modules acting within the plant. Highly co-expressed groups of genes are likely to be regulated by similar transcription factors. Therefore, finding these co-expressed groups can reduce the dimensionality of complex expression data into a set of dominant transcriptional regulatory modules. Determining the biological significance of these patterns is an informatics challenge and has required the development of new methods. Using these new methods we can begin to understand the biological information contained within large-scale expression data sets.
- SourceAvailable from: Nobutoshi Yamaguchi
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- "Of the 769 genes bound by LFY and AP1, a total of 196 (26%) were differentially expressed after steroid activation of LFY-GR and of AP1-GR (P-value < 10 −15 , í µí¼ 2 test; Fig. 1C, Table S5). To examine the likely functions of the 196 putative LFY and AP1 target genes, we tested for GO term enrichment among them using CHIPENRICH (Orlando et al. 2009). Significantly enriched (FDR < 0.001) GO terms included 'regulation of transcription', 'flower development' and 'stamen development', as expected (Fig. 2). "
ABSTRACT: Two key regulators of the switch to flower formation and of flower patterning in Arabidopsis are the plant specific helix-turn-helix transcription factor LEAFY (LFY) and the MADS-box transcription factor APETALA1 (AP1). The interactions between these two transcriptional regulators are complex. AP1 is both a direct target of LFY and can act in parallel with LFY. Available genetic and molecular evidence suggests LFY and AP1 together orchestrate the switch to flower formation and early events during flower morphogenesis by altering transcriptional programs. However, very little is known about target genes regulated by both transcription factors. Here we performed a meta-analysis of public datasets to identify genes that are likely to be regulated by both LFY and AP1. Our analyses uncovered known and novel direct LFY and AP1 targets with a role in the control of onset of flower formation. It also identified additional families of proteins and regulatory pathways that may be under transcriptional control by both transcription factors. In particular several of these genes are linked to response to hormones, to transport and to development. Finally, we show that the gibberellin catabolism enzyme ELA1, which was recently shown to be important for the timing of the switch to flower formation, is positively feedback regulated by AP1. Our study contributes to the elucidation of the regulatory network that leads to formation of a vital plant organ system, the flower. This article is protected by copyright. All rights reserved.Physiologia Plantarum 06/2015; 155(1). DOI:10.1111/ppl.12357 · 3.14 Impact Factor
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- "Branch length distribution of the HCL tree and the figure of merit (FOM) of iterative K-means clustering runs were used to gauge the expected number of clusters (Multiple Experiment Viewer). A Fuzzy K-means clustering search for dominant expression patterns was executed employing the R script by Orlando and co-workers for the manipulation of large-scale Arabidopsis microarray data sets (Orlando et al, 2009 "
ABSTRACT: In plants, changes in local auxin concentrations can trigger a range of developmental processes as distinct tissues respond differently to the same auxin stimulus. However, little is known about how auxin is interpreted by individual cell types. We performed a transcriptomic analysis of responses to auxin within four distinct tissues of the Arabidopsis thaliana root and demonstrate that different cell types show competence for discrete responses. The majority of auxin-responsive genes displayed a spatial bias in their induction or repression. The novel data set was used to examine how auxin influences tissue-specific transcriptional regulation of cell-identity markers. Additionally, the data were used in combination with spatial expression maps of the root to plot a transcriptomic auxin-response gradient across the apical and basal meristem. The readout revealed a strong correlation for thousands of genes between the relative response to auxin and expression along the longitudinal axis of the root. This data set and comparative analysis provide a transcriptome-level spatial breakdown of the response to auxin within an organ where this hormone mediates many aspects of development.Molecular Systems Biology 09/2013; 9(1):688. DOI:10.1038/msb.2013.40 · 10.87 Impact Factor
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- "Organisms respond to external cues (biological perturbations) by synchronized changes in the expression levels of multiple genes, which together integrate into specific phenotypic outputs. The development of microarray technology, together with the development of a variety of bioinformatics approaches, has enabled the analysis of the simultaneous response of gene networks to various developmental, physiological, or external cues at the systems biology level (Loraine, 2009; Orlando et al., 2009; Sreenivasulu et al., 2010). As sessile organisms, plants adjust to environmental stresses through highly compound changes in gene expression programs. "
ABSTRACT: The expression pattern of any pair of genes may be negatively correlated, positively correlated, or not correlated at all in response to different stresses and even different progression stages of the stress. This makes it difficult to identify such relationships by classical statistical tools such as the Pearson correlation coefficient. Hence, dedicated bioinformatics approaches that are able to identify groups of cues in which there is a positive or negative expression correlation between pairs or groups of genes are called for. We herein introduce and discuss a bioinformatics approach, termed Gene Coordination, that is devoted to the identification of specific or multiple cues in which there is a positive or negative coordination between pairs of genes and can further incorporate additional coordinated genes to form large coordinated gene networks. We demonstrate the utility of this approach by providing a case study in which we were able to discover distinct expression behavior of the energy-associated gene network in response to distinct biotic and abiotic stresses. This bioinformatics approach is suitable to a broad range of studies that compare treatments versus controls, such as effects of various cues, or expression changes between a mutant and the control wild-type genotype.The Plant Cell 04/2011; 23(4):1264-71. DOI:10.1105/tpc.110.082867 · 9.34 Impact Factor