CO-EXPRESSION AMONG CONSTITUENTS OF A MOTIF IN THE PROTEIN–PROTEIN INTERACTION NETWORK

Abstract

Almost all cellular functions are the results of well-coordinated interactions between various proteins. A more connected hub or motif in the interaction network is expected to be more important, and any perturbation in this motif would be more damaging to the smooth performance of the related functions. Thus, some coherent robustness of these hubs has to be derived. Here, we provide the global evidence that interaction hubs obtain their robustness against uneven protein concentrations through co-expression of the constituents, and that the degree of co-expression correlates strongly with the complexity of the embedded motif. We calculated the gene expression correlations between the proteins embedded in 3-, 4-, 5-, and 6-node interaction motifs of increasing complexities, and compared them to those between proteins from random motifs of similar complexities. We find that as the connectedness of these motifs increases, there is higher co-expression between the constituent proteins. For example, when the expression correlation is 0.7, the kernel density of the correlation increases from 0.152 for 4-node motifs with three edges to 0.403 for 4-node cliques. This implies that the robustness of the interaction system emerges from a proportionate synchronicity among the constituents of the motif via co-expression. We further show that such biological coherence via co-expression of component proteins can be reinforced by integrating conservation data in the analysis. For example, with addition of evolutionary information from other genomes, the ratio of kernel density for interaction and random data in the case of 5- and 6-node cliques in yeast increases from 37.8 to 123 and 98.4 to 1300, respectively, given that the expression correlation is 0.8. Our results show that genes whose products are involved in motifs have transcription and translation properties that minimize the noise in final protein concentrations, compared to random sets of genes.