Dude, Where's My Phenotype? Dealing with Redundancy in Signaling Networks

In the post-genomic era, our understanding of signal transduction networks necessarily entails the use of genetics analysis. Nowhere is this revealed more clearly than the use of publicly available knockout collections, which are plundered daily by plant re- searchers in search of mutants to test their latest signaling fantasies (Rhee et al., 2003). The lust for knockouts underscores the power of defined geno- types to dissect signal transduction, yet knockout lines often lead to frustration, as many mutants have no obvious phenotype. To the initiated geneticist, this fact is far from surprising since many signaling compo- nents are functionally redundant. The trivial explana- tion for functional redundancy stems from the rich sequence redundancy that eukaryotic genomes are built from—systematic analysis of Saccharomyces cerevisiae deletion strains suggests that about one- quarter of functional redundancy can be explained by compensation by duplicate genes (Gu et al., 2003). While sequence redundancy explains some func- tional redundancy, a ''deeper'' explanation stems from the pesky ability of networks to buffer the effects of perturbations in neighboring nodes and related pathways. This property, sometimes described as homeostasis, is difficult to predict from first princi- ples (unlike the redundancy caused by sequence redundancy). This problem does not mean traditional genetic analysis is waning. It simply means we need to squeeze more out of the classic equation Phenotype 5 Genotype 1 Environment. On one side of the equa- tion, Phenotype is being refined through improve- ments in molecular analysis. In this context, this means that phenotype is in the eye of the beholder and that global transcript profiling of a mutant with ''no ob- servable phenotype'' can yield enough information to place a gene into a signaling pathway or reveal the compensatory changes in related pathways that enable buffering. Again, we turn to S. cerevisiae for hard numbers—here greater than 95% of 300 gene deletions strains examined by whole-genome transcript profil- ing displayed effects on the transcription of at least one other gene besides the deleted gene (Hughes