Genome-wide investigation of light and carbon signaling interactions in Arabidopsis
1 Department of Biology, New York University, New York, NY 10003, USA
2 Current address: Center for Bioinformatics, University of Pennsylvania, 423 Guardian Drive, Philadelphia, PA 19104, USA
Genome Biology 2004, 5:R10 doi:10.1186/gb-2004-5-2-r10Published: 27 January 2004
Light and carbon are two essential signals influencing plant growth and development. Little is known about how carbon and light signaling pathways intersect or influence one another to affect gene expression.
Microarrays are used to investigate carbon and light signaling interactions at a genome-wide level in Arabidopsis thaliana. A classification system, 'InterAct Class', is used to classify genes on the basis of their expression profiles. InterAct classes and the genes within them are placed into theoretical models describing interactions between carbon and light signaling. Within InterAct classes there are genes regulated by carbon (201 genes), light (77 genes) or through carbon and light interactions (1,247 genes). We determined whether genes involved in specific biological processes are over-represented in the population of genes regulated by carbon and/or light signaling. Of 29 primary functional categories identified by the Munich Information Center for Protein Sequences, five show over-representation of genes regulated by carbon and/or light. Metabolism has the highest representation of genes regulated by carbon and light interactions and includes the secondary functional categories of carbon-containing-compound/carbohydrate metabolism, amino-acid metabolism, lipid metabolism, fatty-acid metabolism and isoprenoid metabolism. Genes that share a similar InterAct class expression profile and are involved in the same biological process are used to identify putative cis elements possibly involved in responses to both carbon and light signals.
The work presented here represents a method to organize and classify microarray datasets, enabling one to investigate signaling interactions and to identify putative cis elements in silico through the analysis of genes that share a similar expression profile and biological function.