Transcription regulatory networks in Caenorhabditis elegans inferred through reverse-engineering of gene expression profiles constitute biological hypotheses for metazoan development.

Differential gene expression governs the development, function and pathology of multicellular organisms. Transcription regulatory networks study differential gene expression at a systems level by mapping the interactions between regulatory proteins and target genes. While microarray transcription profiles are the most abundant data for gene expression, it remains challenging to correctly infer the underlying transcription regulatory networks. The reverse-engineering algorithm LeMoNe (learning module networks) uses gene expression profiles to extract ensemble transcription regulatory networks of coexpression modules and their prioritized regulators. Here we apply LeMoNe to a compendium of microarray studies of the worm Caenorhabditis elegans. We obtain 248 modules with a regulation program for 5020 genes and 426 regulators and a total of 24 012 predicted transcription regulatory interactions. Through GO enrichment analysis, comparison with the gene-gene association network WormNet and integration of other biological data, we show that LeMoNe identifies functionally coherent coexpression modules and prioritizes regulators that relate to similar biological processes as the module genes. Furthermore, we can predict new functional relationships for uncharacterized genes and regulators. Based on modules involved in molting, meiosis and oogenesis, ciliated sensory neurons and mitochondrial metabolism, we illustrate the value of LeMoNe as a biological hypothesis generator for differential gene expression in greater detail. In conclusion, through reverse-engineering of C. elegans expression data, we obtained transcription regulatory networks that can provide further insight into metazoan development.

Unraveling transcriptional control in Arabidopsis using cis-regulatory elements and coexpression networks.

Analysis of gene expression data generated by high-throughput microarray transcript profiling experiments has demonstrated that genes with an overall similar expression pattern are often enriched for similar functions. This guilt-by-association principle can be applied to define modular gene programs, identify cis-regulatory elements, or predict gene functions for unknown genes based on their coexpression neighborhood. We evaluated the potential to use Gene Ontology (GO) enrichment of a gene's coexpression neighborhood as a tool to predict its function but found overall low sensitivity scores (13%-34%). This indicates that for many functional categories, coexpression alone performs poorly to infer known biological gene functions. However, integration of cis-regulatory elements shows that 46% of the gene coexpression neighborhoods are enriched for one or more motifs, providing a valuable complementary source to functionally annotate genes. Through the integration of coexpression data, GO annotations, and a set of known cis-regulatory elements combined with a novel set of evolutionarily conserved plant motifs, we could link many genes and motifs to specific biological functions. Application of our coexpression framework extended with cis-regulatory element analysis on transcriptome data from the cell cycle-related transcription factor OBP1 yielded several coexpressed modules associated with specific cis-regulatory elements. Moreover, our analysis strongly suggests a feed-forward regulatory interaction between OBP1 and the E2F pathway. The ATCOECIS resource (http://bioinformatics.psb.ugent.be/ATCOECIS/) makes it possible to query coexpression data and GO and cis-regulatory element annotations and to submit user-defined gene sets for motif analysis, providing an access point to unravel the regulatory code underlying transcriptional control in Arabidopsis (Arabidopsis thaliana).