Dissection of Genetic Pathways in C. elegans - Caenorhabditis Elegans: Molecular Genetics and Development - page 138

factor LAG-1. To identify transcriptional target genes of LIN-12/Notch that antag-
onizes LIN-3/LET-23 signaling, Yo o et al. (2004) utilized computational programs
to determine genes whose promoter regions contain clusters of the binding sites for
LAG-1. One hundred sixty three genes were identified, two of which were previously
reported to, respectively, antagonize or interact with the LIN-3/LET-23 signaling
pathway during vulval development. By comparing the 5 0 regulatory regions of these
two genes, they deduced two additional motifs that they postulated conferred tissue
specificity of the 2 fated vulval cells. Through searching for genomic regions
containing these motifs in the vicinity of the LAG-1 binding site clusters, they
identified 10 candidate LIN-12 target genes that might act to antagonize LIN-3/
LET-23 signaling in the 2 vulval cells. Of these 10 genes, five were experimentally
verified as novel negative regulators of LIN-3/LET-23 signaling as the depletion of
their activity using RNAi resulted in the increased activity of the LIN-3/LET-23
pathway in the 2 vulval cells. Importantly, in no case did elimination of activity of
any of these genes individually disrupt 2 fate specification, suggesting that they
function redundantly to inhibit LIN-3/LET-23 activity in the vulval cells. This work
powerfully underscores the usefulness of functional genomic approaches, such as
bioinformatic analysis, in identifying gene regulatory networks and in circumvent-
ing genetic functional redundancy.
C. Using Systems Biology Approaches to Identify Components of Pathways
Systems biology approaches biological questions from the holistic and system-
wide perspective rather than by a reductionist ' s gene-by-gene method. Though
sometimes confusing in its definition, systems biology is emerging as an important
approach for identifying and understanding gene networks in biology. All of the
previously discussed genomic approaches are tools utilized in systems biology.
These functional genomic approaches produce a large number of heterogeneous
datasets on a genome-wide scale, such as gene expression data (transcriptome),
protein-protein interaction data (interactome), and RNAi phenotypic data (phe-
nome). Computational integration and systematic analysis of these datasets can
reveal meaningful correlations that point to functionally associated components
and lead to the generation of testable models that may assemble a more comprehen-
sive picture of how gene regulatory networks control a particular biological process.
Evidence of correlations between any two types of datasets - transcriptome,
interactome, and phenome - obtained from studies in yeast and worm has suggested
that interacting genes appear to share similar expression, protein-protein interac-
tion, and phenotypic profiles ( Boulton et al., 2002; Ge et al., 2003; Jansen et al.,
2002; Jeong et al., 2001; Kamath et al., 2003; Li et al., 2004; Oltvai and Barabasi,
2002; Piano et al., 2002; Walhout et al., 2002 ). Correlations across all these three
types of data have been utilized to study and model the mechanisms underlying early
embryogenesis in C. elegans ( Gunsalus et al., 2005 ). By integrating coexpression,
protein-protein interaction, and phenotypic similarity datasets, Gunsalus et al.
(2005) generated a network of 661 genes involved in early embryogenesis, including