[
International Worm Meeting,
2013]
Increasing mounds of data derived from high throughput approaches assaying a huge number of species, tissues and diseases have been generated over the past 2 decades. However, the integration of those data sets to generate useful biological information can be challenging. Here, we introduce two approaches to translate data derived from extensive microarray analysis into a manageable set of candidate genes according to biological relevant questions. In a meta-analysis of microarray based screens for differentially regulated genes under dietary restriction (DR) from 8 independent experimental set ups in C. elegans, we extracted 177 strongly overlapping differentially regulated genes. Among those, the sub class of CUB like proteins was significantly enriched (12,4%). Notably, most of those CUB like proteins have not been linked to DR. In another approach we applied a novel developed bioinformatic tool - the Ortho2Express Matrix (Meinel et al., 2011) - to summarize, compare and interpret gene expression data performed in mouse and C. elegans under DR and ad libidum feeding conditions. We use this tool to combine those gene expression profiles with complex gene family information, derived from sequence similarity. We end up with a list of 18 mouse genes and 24 assigned putative functional C. elegans orthologs that are regulated in both species in the same direction in response to DR. In conclusion, based on data mining we identified a set of genes which likely play a role in the regulation of the DR response and DR related phenomena such as improved stress resistance and long life. We discriminate between genes with already known functions in DR regulation, genes that have been connoted with other functional context and genes of complete unknown function. First functional assays for heat stress resistance under DR with selected pre-evaluated mutant C. elegans strains confirmed our in silico analysis.