Functional genomic analysis of the rates of protein evolution

Abstract

The evolutionary rates of proteins vary over several orders of magnitude. Recent work suggests that analysis of large data sets of evolutionary rates in conjunction with the results from high-throughput functional genomic experiments can identify the factors that cause proteins to evolve at such dramatically different rates. To this end, we estimated the evolutionary rates of >3,000 proteins in four species of the yeast genus Saccharomyces and investigated their relationship with levels of expression and protein dispensability. Each protein's dispensability was estimated by the growth rate of mutants deficient for the protein. Our analyses of these improved evolutionary and functional genomic data sets yield three main results. First, dispensability and expression have independent, significant effects on the rate of protein evolution. Second, measurements of expression levels in the laboratory can be used to filter data sets of dispensability estimates, removing variates that are unlikely to reflect real biological effects. Third, structural equation models show that although we may reasonably infer that dispensability and expression have significant effects on protein evolutionary rate, we cannot yet accurately estimate the relative strengths of these effects.

Fig. 1.

The Spearman rank correlation coefficient, r_dk (a), and partial correlation coefficient, r_dk|x (b), between protein dispensability d (Warringer et al. data set; see Materials and Methods), and evolutionary rate, k, as a function of mRNA abundance quartile. Expression quartile is shown on the x axis. Correlation coefficient values are indicated by stars, and P values are indicated by diamonds. The points are joined only for visual clarity.

Fig. 2.

Structural equation model partitioning statistical variation caused by uncertain measurement from variation caused by the stochastic biological process of sequence evolution. Capital letters represent true values of protein dispensability (D), expression (X), and evolutionary rate (K). The causal relationships among these variables are shown as solid arrows. Dashed arrows represent the introduction of variance caused by inaccurate measurement, leading to observed values of dispensability (d), expression (x), and evolutionary rate (k). The inaccuracy of measurement may be the result of several variables, including differences between laboratory and wild conditions, variance brought on by technical errors, etc.