Purification data from one HMS-PCI study, labeled Ho [6], was acquired from the original publication. Ho et al. [6] also infer a HC data set by removal of suspect promiscuous prey proteins and this was downloaded from IntAct. Purification data from two TAP investigations [5,8] were acquired from their original publications. The raw data set of Gavin et al. [5], labeled Gavin, used matrix-assisted laser desorption/ionization-time-of-flight mass spectroscopy (MALDI- TOF MS) to identify co-purifying proteins. A corresponding HC data set was inferred in their study by first determining ‘socio- affinity’ scores for each pair of proteins followed by an iterative clustering procedure that was refined using a curated set of protein complexes [46]. Two raw data sets were taken from the study of Krogan et al. [8]. The first, labeled Krogan-TOF, used MALDI- TOF MS to identify co-purifying proteins, while the second, labeled Krogan-LCMS, used liquid chromatography tandem mass spectrometry (LCMS) for protein identifications. Two types of HC data sets from the study by Krogan et al. [8] were downloaded from (http://tap.med.utoronto.ca/downloads.php). Both of their HC data sets were inferred by first removing 44 nonspecific contaminants and nearly all cytoplasmic ribosomal subunits from the raw data. The first HC data set, labeled Krogan-INT, contains the remaining interactions that were identified by both detection methods. The second, labeled Krogan-CORE, was derived using machine-learning algorithms trained on curated protein complex- es in the MIPS reference database [47]. Yeast cellular protein and mRNA abundances during normal aerobic growth were taken from three investigations, where each used a different measurement methodology: WB [42], FC [43], and GE analysis [44]. Essential yeast proteins were obtained from the Saccharomyces Genome Deletion Project (http:// www-sequence.stanford.edu/group/yeast_deletion_project/Essential_ ORFs.txt) and the Munich Information Center for Protein Sequences (MIPS) (ftp://ftpmips.gsf.de/yeast/catalogues/gene_ disruption). Only proteins annotated as essential in both datasets were considered to be essential here. Escherichia coli Data Raw TAP-determined PPI networks from two investigations, labeled Butland [10] and Arifuzzaman [11], were acquired from their original publications. Gene expression measurements during normal aerobic growth were taken from three studies [48–50]. Computational Analyses Correlations between protein degree and abundance were evaluated by determining Pearson and Spearman rank correlation coefficients for log(degree) vs. log2(abundance). In every case the two coefficients were very similar, with Spearman’s correlation coefficient generally being slightly smaller in magnitude. Corre- lation analyses were performed for pairs of individual data sets (PPI network vs. abundance measurement set) with no averaging of data, i.e., a protein was included as a separate data entity if both its degree and abundance were known. To illustrate the general trends in the correlations, or lack of, we generated plots of log(degree) vs. ,log2(abundance)., where the latter quantity was determined by averaging log2(abundance) values, for a particular abundance measurement set, of proteins having the same degree. To gauge the possibility of artificial correlations arising from irregular abundance distributions of proteins in the PPI data sets, we computed abundance distributions for proteins grouped by degree. These are illustrated via color maps (Figures 1, 2, 3). Correlations between essentiality and degree were evaluated by calculating P-values for two-sample Kolmogorov-Smirnov (KS) test for differences between degree distributions of essential and Abundance in Protein Networks nonessential proteins. Comparable P-values were determined to test for correlations between protein abundance and essentiality. The enrichment of essential-essential protein interactions in a network was evaluated by computing a P-value, via calculation of a Z-score and assuming a normal distribution, for the difference between the actual number and the average obtained from randomly selecting proteins to be essential. However, when randomly selecting proteins to be considered essential, we ensured that the total number and the degree distribution of the chosen essential proteins matched those of the actual essential proteins. This ensures that artificial differences arising from changes in degree distributions of essential proteins are not observed whilst simultaneously conserving the network structure. Results were deduced from 1000 simulations. Results Influence of Protein Abundance upon Degree in Raw PPI Data Sets Experimental PPI data sets are derived from techniques that attempt to detect the presence of protein associations in a cellular environment. Therefore, an investigation into any influences the cellular concentration of each protein has on the detected interactions should be based on protein abundance measurements. Recent determinations of yeast cellular protein abundances during normal growth were achieved by tagging open reading frames (ORFs), thereby allowing expressions from their natural chromo- somal locations, and measuring absolute levels by WB [42] and FC [43] techniques. We utilized protein abundance measurement data sets from both of these studies. Although it is the proteins that are overwhelmingly responsible for the various cellular functions, expression levels of their precursors, mRNA molecules, provide considerable insights into the internal states of a cell. In fact, measurement of mRNA expression levels by microarray experi- ments [51–53] is far simpler and more widespread than measurement of protein abundances. Accordingly, there have been an enormous number of GE studies that have determined yeast mRNA abundances during normal growth. The findings reported here utilized the GE measurements of Holstege et al. [44]; however, it should be stressed that comparable results were obtained when using GE measurements from three more-recent studies [54–56]. The previously noted trend between degree and abundance [35] was based on the data of Holstege et al. [44]. As noted above, six raw yeast PPI data sets were investigated here encompassing three diverse HT technologies: TAP (three data sets), HMS-PCI (one data set), and Y2H (two data sets). We have also investigated two raw TAP data sets for E. coli and used GE measurements from three studies [48–50]. Table 1 shows the correlations between yeast protein degree and abundance, for each of the WB, FC, and GE measurements. We find that all three raw TAP PPI data sets contain statistically significant correlations between protein degree and abundance. The Pearson correlation coefficients for test of linear relationship between log values, lying between 0.23 and 0.33, are similar but not large and all corresponding Spearman rank correlation coefficients are very close in value. However, all P-values are less than 0.0001, suggesting that the relationships are significant, i.e., they do not represent random events. We stress that these correlations are obtained without any averaging of data, i.e., each protein’s degree and abundance is included as a single data entity. Merging the Gavin and Krogan-TOF data sets produces a PPI network that contains very similar degree/abundance correlations to the individual data sets. Figure 1A, showing all data points, illustrates the degree/abundance relationship in the raw TAP PLoS ONE | www.plosone.org 3 June 2009 | Volume 4 | Issue 6 | e5815