Determinants of protein abundance and translation efficiency in S. cerevisiae.

Tamir Tuller,Martin Kupiec,Eytan Ruppin,Mark B Gerstein

doi:10.1371/journal.pcbi.0030248

Abstract

The translation efficiency of most Saccharomyces cerevisiae genes remains fairly constant across poor and rich growth media. This observation has led us to revisit the available data and to examine the potential utility of a protein abundance predictor in reinterpreting existing mRNA expression data. Our predictor is based on large-scale data of mRNA levels, the tRNA adaptation index, and the evolutionary rate. It attains a correlation of 0.76 with experimentally determined protein abundance levels on unseen data and successfully cross-predicts protein abundance levels in another yeast species (Schizosaccharomyces pombe). The predicted abundance levels of proteins in known S. cerevisiae complexes, and of interacting proteins, are significantly more coherent than their corresponding mRNA expression levels. Analysis of gene expression measurement experiments using the predicted protein abundance levels yields new insights that are not readily discernable when clustering the corresponding mRNA expression levels. Comparing protein abundance levels across poor and rich media, we find a general trend for homeostatic regulation where transcription and translation change in a reciprocal manner. This phenomenon is more prominent near origins of replications. Our analysis shows that in parallel to the adaptation occurring at the tRNA level via the codon bias, proteins do undergo a complementary adaptation at the amino acid level to further increase their abundance.

Highlights

DNA microarrays are commonly used to measure the expression levels of large numbers of genes simultaneously [1]
DNA microarrays measuring gene expression levels have been a mainstay of systems biology research, but since proteins are more direct mediators of cellular processes, protein abundance levels are likely to be a better indicator of the cellular state
A solid protein abundance prediction tool is invaluable for advancing our understanding of cellular processes; this study presents a further step in this direction

Summary

Introduction

DNA microarrays are commonly used to measure the expression levels of large numbers of genes simultaneously [1]. Since proteins are the direct mediators of cellular processes, the abundance level of each protein is likely to be a better indicator of the cellular state than its corresponding mRNA expression level. Genome-wide technologies to detect protein abundance are still lagging behind those that measure mRNA, and only few studies that measure protein abundance on a large scale are currently available [2,3,4,5,6]. The relationship between mRNA and protein abundance levels has been studied by several groups. Genes with similar mRNA levels may have very different protein abundance levels [7]. A more recent study, combining three technologies for measuring mRNA expression, has yielded correlation levels of about 0.7 with protein abundance [9]. Several studies have aimed at correlating protein abundance to various other features of proteins, such as their codon bias, molecular weight, stop codon identity, and more [3,4,10,11] These investigations and other previous proteomic studies [12,13,14] were usually based on small- to medium-scale measurements

Methods

Results

Conclusion