1056 Nucleic Acids Research, 2005, Vol. 33, No. 3 Figure 4. CAI profiles for the b and b0 subunits of the DNA-directed RNA polymerase (COG0085 and COG0086, respectively). The normalized CAI values for 46 genomes are shown here. For each organism, the normalization was based on the mean and standard deviation of all its proteins that appear in the COG database. The two organisms not represented were left out due to a large difference in the CAI values of their paralogs (see Materials and Methods). The connecting lines in the plot are for illustration only. Figure 5. Correlation coefficients between neighboring proteins in the histidine biosynthesis pathway of g-proteobacteria. Consecutive proteins within the pathway are shown, with the corresponding COG name in parentheses. Next to the arrows, Spearman correlation coefficients between the consecutive proteins are shown, and the number of organisms over which the correlation was calculated is shown in parentheses. HisB and HisI are each composed of two domains, and are thus each represented by two COGs. The correlation between COG0140 and COG0139 is marked ‘nr’, since the g-proteobacteria proteins within these two COGs are identical. COG0118 and COG0107 form a heterodimer, and the Spearman correlation coefficient for these proteins, over seven organisms, is one. The correlation coefficient of the preceding protein (COG0106) with both of these proteins is identical, as are the correlation coefficients of the successive protein (COG0131) with both of these proteins. normalized CAI values is very high (Figure 4, rs = 0.91, over 46 organisms). (iii) The histidine biosynthesis pathway. The histidine bio- synthesis pathway is very conserved, and the genes encoding the pathway’s proteins undergo regulation at several stages. This is due to the high metabolic cost of the pathway (36). Indeed, we found that the CAI proﬁles of the genes encoding the consecutive steps of the path- way within g -proteobacteria are correlated, as shown in Figure 5. Most of these proteins appear in all seven of the g -proteobacteria in our data. Therefore, to assess the signiﬁcance of these correlation coefﬁcients, we com- pared them with the median of the correlation coefﬁcients calculated for all possible pairs of pathway proteins in our dataset that appear in all seven g -proteobacteria (not just consecutive proteins), which was 0.107. The correlation coefﬁcients within the pathway are noticeably all much higher (P = 0.03 using the Wilcoxon test to compare the correlation coefﬁcients calculated over seven organisms to this median). CONCLUSION In this study, we showed that predicted protein abundance levels of functionally related proteins co-evolve, and that this phenomenon is widespread, occurring over many groups of unicellular organisms. Ideally, it would have been best to explore this phenomenon by analyzing experimental protein levels. However, in the absence of this type of data, we dem- onstrate that putative abundance levels, represented by the CAI, are informative and provide useful insight. The correlation between predicted abundance levels was demonstrated for two types of functional relations between proteins: interacting proteins and neighboring proteins in metabolic pathways. We suggest that the identiﬁcation of such correlations between CAI values across organisms can be used for the inference of functional associations between proteins. This approach can then be integrated with other predictive methods for better prediction of such associations. SUPPLEMENTARY MATERIAL Supplementary Material is available at NAR Online. ACKNOWLEDGEMENTS We thank Norman Grover for useful discussions. We also thank the members of the laboratory, and especially Yael Altuvia, Ruth Hershberg, Esti Yeger-Lotem and Galit Lipsitz for their helpful comments. This study was supported by the Israeli Science Foundation, administered by the Israeli Academy of Sciences and Humanities. Funding to pay the Open Access publication charges for this article was provided by the Israeli Science Foundation. REFERENCES 1. Grigoriev,A. (2001) A relationship between gene expression and protein interactions on the proteome scale: analysis of the bacteriophage T7 and the yeast Saccharomyces cerevisiae. Nucleic Acids Res., 29, 3513–3519. 2. Jansen,R., Greenbaum,D. and Gerstein,M. (2002) Relating whole-genome expression data with protein–protein interactions. Genome Res., 12, 37–46.