1052 Nucleic Acids Research, 2005, Vol. 33, No. 3 Figure 1. Illustration of protein abundance profiles. The predicted abundance levels of two proteins, each with orthologs in four organisms, are shown. The first is represented by filled triangles, and the second by open circles. The correlation coefficient between these two abundance profiles can be computed. The connecting lines in the plot are for illustration only. due to the higher availability of the corresponding tRNAs, particularly during exponential cell growth. Several measures of codon bias have been developed, including the Codon Adaptation Index (CAI) (17) and the effective Number of Codons (Nc) (25). These measures were shown to correlate with experimentally measured proteins levels and have there- fore been used as predictors of relative protein abundance in the cell (14–16). Each of these measures provides one value of codon bias for a protein encoded within a genome. Therefore, a proﬁle of the codon bias measures, representing the growth- phase protein abundance of a gene in different organisms, can be created. We, therefore, propose to study the conservation of relative protein abundance levels of functionally related proteins by examining whether their corresponding codon bias measures changed in a coordinated fashion through evolution (Figure 1). For interacting proteins, such a correlation has recently been reported, using four species of yeast (26). In this study, we looked at 48 bacterial, archaeal and fungal genomes. We cal- culated the correlation coefﬁcients between the codon bias proﬁles of functionally related proteins of two types, based on data derived from Escherichia coli: a protein–protein interaction dataset and a dataset consisting of proteins that are involved in consecutive steps within pathways. For both types of data, we show that functionally associated proteins tend to have correlated protein abundance proﬁles. MATERIALS AND METHODS Calculation of codon bias measures We used two measures of codon bias, which we calculated using the EMBOSS package (27). (i) The CAI (17) is the most commonly used, and is a measure of the tendency of the coding sequence of a protein to use the codons that are preferentially used by a reference set of highly expressed proteins. We used the ribosomal proteins as this reference set. This measure ranges between 0 and 1, where a higher CAI value indicates a greater level of codon bias, and predicts a higher protein level. (ii) The Nc (25) is a number which ranges between 20 and 61, and measures codon bias by considering the number of various codons used to encode the protein. A lower Nc indicates a greater level of codon bias. This measure was used since it does not require a reference set of abundant proteins. Genome data Sequence data and annotations were extracted from GenBank (http://www.ncbi.nlm.nih.gov). The COG database (Clusters of Orthologous Groups of proteins) (28), was used in order to extract orthologs. A COG is deﬁned as the group of orthologs for a given protein. We applied several ﬁlters to the organisms and proteins within the COG database. (i) When there existed multiple strains, we kept only one genome (we tried to take the more studied strain). (ii) For certain organisms, it has been shown that codon bias is not a good indicator of protein abund- ance, because of the existence of other dominating selective pressures, such as GC content and G+C strand bias (29,30). We, therefore, kept only organisms for which we rejected (using a one-tailed Mann–Whitney test with a P-value cutoff of 0.01) the hypothesis that ribosomal proteins do not have lower Nc values than the rest of the proteins in the genome. For this test, hypothetical and mitochondrial proteins were excluded. (iii) Only proteins with a length of at least 100 amino acids were kept since the codon bias measures are not reliable for short proteins. After these ﬁltering steps, we were left with 48 organisms for the analysis (Supplementary Table 1). Datasets regarding functional associations between proteins Two datasets representing different types of functional asso- ciations were used, both from E.coli: (i) protein–protein interaction data; and (ii) pathway data. Data regarding protein–protein interactions were extracted from the Database of Interacting Proteins (DIP, version 07/04) (31). Homodimers and interactions involving the chaperone GroL were excluded. For a total of 167 E.coli interactions, their interacting pair mates could be assigned to COGs, and these were kept for further analyses (Supplementary Table 2). Pathway data for E.coli were extracted from the EcoCyc database (32), using the PerlCyc interface (33) to the Pathway Tools Software (34). Pairs of neighboring proteins were taken as those conducting consecutive steps within a pathway, or a set of pathways. If one of these proteins was assigned to an additional pathway to which the other protein was not assigned, the pair was discarded. A total of 197 pairs could be assigned to COGs, and were kept for further analyses (Supplementary Table 3). Creation of codon bias profiles In order to exclude any bias that may result from the CAI distribution of any of the organisms, and in order to be able to compare the contribution of different groups of organisms, we repeated the analysis for many subgroups of organisms. The rationale for this approach is clariﬁed further in the ‘Analysis of signiﬁcance’ section below and in the Results and Discus- sion. For each combination of four organisms, the CAI was calculated for each protein within each COG. We chose a