Alvarez-Ponce David, Feyertag Felix, Chakraborty Sandip
Department of Biology, University of Nevada, Reno.
Genome Biol Evol. 2017 Jun 1;9(6):1742-1756. doi: 10.1093/gbe/evx117.
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.
任何生物体的蛋白质进化速率各不相同。人们已经确定了一长串影响蛋白质进化速率的因素。然而，每个因素在决定蛋白质进化速率方面的相对重要性仍未得到解决。普遍的观点是，进化速率主要由基因表达决定，而其他因素（如网络中心性）即使有影响，也只是微不足道的。然而，这种观点很大程度上是基于对酵母的分析，而且由于不同因素往往相互关联，以及现有功能基因组学数据集质量相对较差，准确衡量蛋白质进化速率决定因素的重要性变得很复杂。在这里，我们使用相关性分析、偏相关性分析和主成分回归分析来衡量几个因素对人类蛋白质进化速率变异性的贡献。为此，我们分析了整个人类蛋白质 - 蛋白质相互作用数据集以及人类信号转导网络——一个通过人工整理获得的质量极高的网络数据集，预计几乎不存在假阳性。与普遍观点相反，我们观察到网络中心性（以物理和非物理相互作用的数量、中介中心性和接近中心性来衡量）对蛋白质进化速率有相当大的影响。令人惊讶的是，根据一些分析，中心性对蛋白质进化速率的影响似乎与基因表达的影响相当，甚至更显著。我们的观察结果似乎不受潜在混杂因素以及相互作用组数据集的局限性（偏差和误差）的影响。
