Regulon organization of Arabidopsis

<p>Abstract</p> <p>Background</p> <p>Despite the mounting research on Arabidopsis transcriptome and the powerful tools to explore biology of this model plant, the organization of expression of Arabidopsis genome is only partially understood. Here, we create a coexpression network from a 22,746 Affymetrix probes dataset derived from 963 microarray chips that query the transcriptome in response to a wide variety of environmentally, genetically, and developmentally induced perturbations.</p> <p>Results</p> <p>Markov chain graph clustering of the coexpression network delineates 998 regulons ranging from one to 1623 genes in size. To assess the significance of the clustering results, the statistical over-representation of GO terms is averaged over this set of regulons and compared to the analogous values for 100 randomly-generated sets of clusters. The set of regulons derived from the experimental data scores significantly better than any of the randomly-generated sets. Most regulons correspond to identifiable biological processes and include a combination of genes encoding related developmental, metabolic pathway, and regulatory functions. In addition, nearly 3000 genes of unknown molecular function or process are assigned to a regulon. Only five regulons contain plastomic genes; four of these are exclusively plastomic. In contrast, expression of the mitochondrial genome is highly integrated with that of nuclear genes; each of the seven regulons containing mitochondrial genes also incorporates nuclear genes. The network of regulons reveals a higher-level organization, with dense local neighborhoods articulated for photosynthetic function, genetic information processing, and stress response.</p> <p>Conclusion</p> <p>This analysis creates a framework for generation of experimentally testable hypotheses, gives insight into the concerted functions of Arabidopsis at the transcript level, and provides a test bed for comparative systems analysis.</p

Dissecting the dynamics of dysregulation of cellular processes in mouse mammary gland tumor

<p>Abstract</p> <p>Background</p> <p>Elucidating the sequence of molecular events underlying breast cancer formation is of enormous value for understanding this disease and for design of an effective treatment. Gene expression measurements have enabled the study of transcriptome-wide changes involved in tumorigenesis. This usually occurs through identification of differentially expressed genes or pathways.</p> <p>Results</p> <p>We propose a novel approach that is able to delineate new cancer-related cellular processes and the nature of their involvement in tumorigenesis. First, we define modules as densely interconnected and functionally enriched areas of a Protein Interaction Network. Second, 'differential expression' and 'differential co-expression' analyses are applied to the genes in these network modules, allowing for identification of processes that are up- or down-regulated, as well as processes disrupted (low co-expression) or invoked (high co-expression) in different tumor stages. Finally, we propose a strategy to identify regulatory miRNAs potentially responsible for the observed changes in module activities. We demonstrate the potential of this analysis on expression data from a mouse model of mammary gland tumor, monitored over three stages of tumorigenesis. Network modules enriched in adhesion and metabolic processes were found to be inactivated in tumor cells through the combination of dysregulation and down-regulation, whereas the activation of the integrin complex and immune system response modules is achieved through increased co-regulation and up-regulation. Additionally, we confirmed a known miRNA involved in mammary gland tumorigenesis, and present several new candidates for this function.</p> <p>Conclusions</p> <p>Understanding complex diseases requires studying them by integrative approaches that combine data sources and different analysis methods. The integration of methods and data sources proposed here yields a sensitive tool, able to pinpoint new processes with a role in cancer, dissect modulation of their activity and detect the varying assignments of genes to functional modules over the course of a disease.</p

Articulation of three core metabolic processes in Arabidopsis: Fatty acid biosynthesis, leucine catabolism and starch metabolism

<p>Abstract</p> <p>Background</p> <p>Elucidating metabolic network structures and functions in multicellular organisms is an emerging goal of functional genomics. We describe the co-expression network of three core metabolic processes in the genetic model plant <it>Arabidopsis thaliana</it>: fatty acid biosynthesis, starch metabolism and amino acid (leucine) catabolism.</p> <p>Results</p> <p>These co-expression networks form modules populated by genes coding for enzymes that represent the reactions generally considered to define each pathway. However, the modules also incorporate a wider set of genes that encode transporters, cofactor biosynthetic enzymes, precursor-producing enzymes, and regulatory molecules. We tested experimentally the hypothesis that one of the genes tightly co-expressed with starch metabolism module, a putative kinase AtPERK10, will have a role in this process. Indeed, knockout lines of AtPERK10 have an altered starch accumulation. In addition, the co-expression data define a novel hierarchical transcript-level structure associated with catabolism, in which genes performing smaller, more specific tasks appear to be recruited into higher-order modules with a broader catabolic function.</p> <p>Conclusion</p> <p>Each of these core metabolic pathways is structured as a module of co-expressed transcripts that co-accumulate over a wide range of environmental and genetic perturbations and developmental stages, and represent an expanded set of macromolecules associated with the common task of supporting the functionality of each metabolic pathway. As experimentally demonstrated, co-expression analysis can provide a rich approach towards understanding gene function.</p

Inferring gene networks: dream or nightmare?: Part 1: challenges 1 and 3

Inferring gene networks is a daunting task. We here describe several algorithms we used in the Dialogue for Reverse Engineering Assessments and Methods (DREAM2) Reverse Engineering Competition 2007: an algorithm based on first-order partial correlation for discovering BCL6 targets in Challenge 1 and an algorithm using nonlinear optimization with winning performance in Challenge 3. After the gold standards for the challenges were released, the performance of alternative variants of the algorithms could be evaluated. The DREAM competition taught us some strong lessons. Amazingly, simpler methods performed in general better than more advanced, theoretically motivated approaches. Also, the challenges strongly showed that inferring gene networks requires controlled experimentation using a well-defined experimental design. Analyzing data obtained through merging many unrelated datasets indeed resulted in weak performances of all algorithms, while algorithms that explicitly took the experimental design into account performed best

Articulation of three core metabolic processes in Arabidopsis: Fatty acid biosynthesis, leucine catabolism and starch metabolism-1

N are compared: , 0.5; , 0.6; and , 0.7. With increasing correlation threshold, the within-pathway links emerge from noisy inter-pathway connections. Node colors represent the metabolic function assigned to each gene (blue: fatty acid synthesis, green: starch metabolism, red: leucine catabolism, yellow: transport or cofactor synthesis, white: acetyl-CoA generation) [see Additional file for gene names]. The networks layouts were produced by GraphExplore software. The most densely crowded nodes indicate genes with the highest co-expression. Within each of these three co-expression networks the number of links between genes from the same metabolic pathway is significantly larger than in randomly generated networks with similar link structure. Isolated nodes not shown.<p><b>Copyright information:</b></p><p>Taken from "Articulation of three core metabolic processes in Arabidopsis: Fatty acid biosynthesis, leucine catabolism and starch metabolism"</p><p>http://www.biomedcentral.com/1471-2229/8/76</p><p>BMC Plant Biology 2008;8():76-76.</p><p>Published online 11 Jul 2008</p><p>PMCID:PMC2483283.</p><p></p

Articulation of three core metabolic processes in Arabidopsis: Fatty acid biosynthesis, leucine catabolism and starch metabolism-6

Pression correlates with the expression of starch metabolism genes. The mutant plants have 13% more starch than WT plant (p-value = 0.045). is in Col-0 background. Col-0 is a wild type sibling. EOL: samples taken at the end of the light phase.<p><b>Copyright information:</b></p><p>Taken from "Articulation of three core metabolic processes in Arabidopsis: Fatty acid biosynthesis, leucine catabolism and starch metabolism"</p><p>http://www.biomedcentral.com/1471-2229/8/76</p><p>BMC Plant Biology 2008;8():76-76.</p><p>Published online 11 Jul 2008</p><p>PMCID:PMC2483283.</p><p></p

Articulation of three core metabolic processes in Arabidopsis: Fatty acid biosynthesis, leucine catabolism and starch metabolism-9

N are compared: , 0.5; , 0.6; and , 0.7. With increasing correlation threshold, the within-pathway links emerge from noisy inter-pathway connections. Node colors represent the metabolic function assigned to each gene (blue: fatty acid synthesis, green: starch metabolism, red: leucine catabolism, yellow: transport or cofactor synthesis, white: acetyl-CoA generation) [see Additional file for gene names]. The networks layouts were produced by GraphExplore software. The most densely crowded nodes indicate genes with the highest co-expression. Within each of these three co-expression networks the number of links between genes from the same metabolic pathway is significantly larger than in randomly generated networks with similar link structure. Isolated nodes not shown.<p><b>Copyright information:</b></p><p>Taken from "Articulation of three core metabolic processes in Arabidopsis: Fatty acid biosynthesis, leucine catabolism and starch metabolism"</p><p>http://www.biomedcentral.com/1471-2229/8/76</p><p>BMC Plant Biology 2008;8():76-76.</p><p>Published online 11 Jul 2008</p><p>PMCID:PMC2483283.</p><p></p

Articulation of three core metabolic processes in Arabidopsis: Fatty acid biosynthesis, leucine catabolism and starch metabolism-4

font. , branched-chain amino acid aminotransferase; , branched-chain alpha-keto acid dehydrogenase; , isovaleryl-CoA dehydrogenase; , methylcrotonyl-CoA carboxylase; , methylglutaconyl-CoA hydratase (enoyl-CoA hydratase); , hydroxymethylglutaryl-CoA lyase. The candidate genes for enzymes catalyzing two terminal reactions have not been identified.<p><b>Copyright information:</b></p><p>Taken from "Articulation of three core metabolic processes in Arabidopsis: Fatty acid biosynthesis, leucine catabolism and starch metabolism"</p><p>http://www.biomedcentral.com/1471-2229/8/76</p><p>BMC Plant Biology 2008;8():76-76.</p><p>Published online 11 Jul 2008</p><p>PMCID:PMC2483283.</p><p></p