We have developed an approach using Bayesian networks to predict protein-protein interactions genome-wide in yeast. Our method naturally weights and combines into reliable predictions genomic features only weakly associated with interaction (e.g., messenger RNAcoexpression, coessentiality, and colocalization). In addition to de novo predictions, it can integrate often noisy, experimental interaction data sets. We observe that at given levels of sensitivity, our predictions are more accurate than the existing high-throughput experimental data sets. We validate our predictions with TAP (tandem affinity purification) tagging experiments. Our analysis, which gives a comprehensive view of yeast interactions, is available at genecensus.org/intint .

Proteins function mainly through interactions, especially with DNA and other proteins. While some large-scale interaction networks are now available for a number of model organisms, their experimental generation remains difficult. Consequently, interolog mapping-the transfer of interaction annotation from one organism to another using comparative genomics-is of significant value. Here we quantitatively assess the degree to which interologs can be reliably transferred between species as a function of the sequence similarity of the corresponding interacting proteins. Using interaction information from Saccharomyces cerevisiae, Caenorhabditis elegans, Drosophila melanogaster, and Helicobacter pylori, we find that protein-protein interactions can be transferred when a pair of proteins has a joint sequence identity >80% or a joint E-value <10 −70 . (These "joint" quantities are the geometric means of the identities or E-values for the two pairs of interacting proteins.) We generalize our interolog analysis to protein-DNA binding, finding such interactions are conserved at specific thresholds between 30% and 60% sequence identity depending on the protein family. Furthermore, we introduce the concept of a "regulog"-a conserved regulatory relationship between proteins across different species. We map interologs and regulogs from yeast to a number of genomes with limited experimental annotation (e.g., Arabidopsis thaliana) and make these available through an online database at http://interolog.gersteinlab.org. Specifically, we are able to transfer ∼90,000 potential protein-protein interactions to the worm. We test a number of these in two-hybrid experiments and are able to verify 45 overlaps, which we show to be statistically significant.

Similar to mammalian excitotoxic cell death, necrotic-like cell death (NCD) in Caenorhabditis elegans can be initiated by hyperactive ion channels. Here we investigate the requirements for genes that execute and regulate programmed cell death (PCD) in necrotic-like neuronal death caused by a toxic MEC-4 channel. Neither the kinetics of necrosis onset nor the total number of necrotic corpses generated is altered by any C. elegans mutation known to block PCD, which provides genetic evidence that the activating mechanisms for NCD and apoptotic cell death are distinct. In contrast, all previously reported ced genes required for phagocytotic removal of apoptotic corpses, as well as ced-12, a new engulfment gene we have identified, are required for efficient elimination of corpses generated by distinct necrosis-inducing stimuli. Our results show that a common set of genes acts to eliminate cell corpses irrespective of the mode of cell death, and provide the first identification of the C. elegans genes that are required for orderly removal of necrotic cells. As phagocytotic mechanisms seem to be conserved from nematodes to humans, our findings indicate that injured necrotic cells in higher organisms might also be eliminated before lysis through a controlled process of corpse removal, a hypothesis that has significant therapeutic implications.