Position Matters: Network Centrality Considerably Impacts Rates of Protein Evolution in the Human Protein-Protein Interaction Network

Abstract

The proteins of any organism evolve at disparate rates. A long list of factors affecting rates of protein evolution have been identified. However, the relative importance of each factor in determining rates of protein evolution remains unresolved. The prevailing view is that evolutionary rates are dominantly determined by gene expression, and that other factors such as network centrality have only a marginal effect, if any. However, this view is largely based on analyses in yeasts, and accurately measuring the importance of the determinants of rates of protein evolution is complicated by the fact that the different factors are often correlated with each other, and by the relatively poor quality of available functional genomics data sets. Here, we use correlation, partial correlation and principal component regression analyses to measure the contributions of several factors to the variability of the rates of evolution of human proteins. For this purpose, we analyzed the entire human protein-protein interaction data set and the human signal transduction network-a network data set of exceptionally high quality, obtained by manual curation, which is expected to be virtually free from false positives. In contrast with the prevailing view, we observe that network centrality (measured as the number of physical and nonphysical interactions, betweenness, and closeness) has a considerable impact on rates of protein evolution. Surprisingly, the impact of centrality on rates of protein evolution seems to be comparable, or even superior according to some analyses, to that of gene expression. Our observations seem to be independent of potentially confounding factors and from the limitations (biases and errors) of interactomic data sets.

Keywords: dN/dS; network centrality; protein–protein interactions; rates of evolution.

Fig. 1.

—Relationship between rates of protein evolution and a number of factors in the human protein–protein interaction network. In panel A, proteins with >500 interactions are not shown. In panel B, proteins with a betweenness higher than 0.1 are not shown. In panels A–C, E–H, and J–N, lines represent regression lines. In panels E and F, outliers are not represented.

Fig. 2.

—Principal component regression analysis on the human protein–protein interaction network. For each principal component (PC), the size of the bar represents the percent of the variability of the response variable explained by the PC. The composition of each component is represented in colors. Network parameters are highlighted within black boxes. This analysis was restricted to the 11,593 genes for which data were available for all variables.

Fig. 3.

—Relationship between rates of protein evolution and a number of factors in the human signal transduction network. In panels A–D, G–J, and L–P, lines represent regression lines. In panels E and F, outliers are not represented.

Fig. 4.

—Principal component regression analysis on the human signal transduction network. For each principal component (PC), the size of the bar represents the percent of the variability of the response variable explained by the PC. The composition of each component is represented in colors. Network parameters are highlighted within black boxes. This analysis was restricted to the 1,254 genes for which data were available for all variables.

Fig. 5.

—Principal component regression analysis on the yeast, fly, and worm protein–protein interaction networks. For each principal component (PC), the size of the bar represents the percent of the variability of the response variable explained by the PC. The composition of each component is represented in colors. Network parameters are highlighted within black boxes. PCR analyses were restricted to the genes for which data were available for all variables (2,429 for yeast, 3,880 for fly, and 608 for worm).