Abundance in Protein Networks Table 1. Pearson correlation coefficients (r) and corresponding P-values for tests of linear association between log(degree) and log2(abundance) in raw yeast PPI data sets. PPI data set TAP Gavin Krogan-TOF Krogan-LCMS Gavin+Krogan-TOFc HMS-PCI Ho Y2H Ito Uetz Ito+Uetzd NPa 2146 2291 3543 2845 1326 2107 929 2403 Western blot (Ghaemmaghami et al.) Flow cytometry (Newman et al.) Gene expression (Holstege et al.) r 0.29 (0.28) 0.24 (0.21) 0.26 (0.21) 0.33 (0.31) 0.26 (0.23) 0.01 (0.04) 20.01 (0.01) 20.01 (0.01) Pb , , , , , 0.5820 0.8470 0.6066 NPa 1585 1523 2314 1925 938 1357 599 1560 r 0.29 (0.26) 0.25 (0.20) 0.28 (0.22) 0.32 (0.27) 0.28 (0.25) 20.05 (20.04) 20.07 (20.06) 20.06 (20.04) Pb , , , , , 0.0646 0.0686 0.0277 NPa 2496 2701 4707 3410 1630 2910 1221 3311 r 0.33 (0.30) 0.23 (0.21) 0.26 (0.20) 0.32 (0.30) 0.18 (0.17) ,0 (0.01) 20.04 (20.03) 20.01 (20.01) Pb , , , , , 0.9212 0.2214 0.5689 Spearman rank correlation coefficients are provided in parentheses. aNumber of proteins in PPI data set having abundance measurements. bThe symbol ‘‘,’’ signifies P,0.0001 for both Pearson and Spearman correlation coefficients. cCombined Gavin and Krogan-TOF interaction data sets. dCombined Ito and Uetz interaction data sets. doi:10.1371/journal.pone.0005815.t001 degree/abundance correlations observed in the TAP and HMS- PCI PPI networks are not the result of skewed abundance distributions, whether total or for individual degrees. It is clear that the Y2H methodology is distinct from the affinity purification methods in that protein expression levels do not influence the observed interactions for the former technique. The findings above are consistent considering that in a Y2H screen both bait and prey proteins are expressed from similar plasmids. A protein expressed from a plasmid is likely to have a different cellular concentration than if it were expressed from its native chromosomal location. Additionally, different proteins expressed from the same plasmid are presumed to have similar expression levels. However, if the latter were not true, then it is possible that the Y2H screen could be influenced by some other abundance factor related to post-transcriptional modification. Investigation of this speculation is not straightforward but may be possible if the translational efficiencies of the proteins can be estimated. Table 2. Pearson and Spearman rank (in parentheses) correlation coefficients (r) for tests of linear association between log(degree) and log2(mRNA expression) in raw E. coli TAP PPI data sets. PPI data set Butland Arifuzzaman Butland+ Arifuzzmanb Covert et al. Kang et al. Salmon et al. NPa r NPa r NPa r 1277 0.46 (0.45) 1278 0.45 (0.42) 856 0.24 (0.21) 2918 0.21 (0.19) 2919 0.21 (0.20) 1764 0.13 (0.11) 3158 0.30 (0.28) 3159 0.30 (0.27) 1904 0.17 (0.13) All P,0.0001 for both Pearson and Spearman correlation coefficients. aNumber of proteins in PPI data set having abundance measurements. bCombined Butland and Arifuzzaman interaction data sets. doi:10.1371/journal.pone.0005815.t002 Influence of Protein Abundance upon Degree in HC PPI Data Sets Some of the yeast PPI studies utilized in this work also inferred HC interaction data sets from their raw data. These HC PPI data sets are meant to contain interactions that are most reproducible or resolute. The methods used to infer HC interactions were varied (see Materials and Methods). Therefore, it is of interest to discover whether these sets contain any relationship between degree and abundance. We find that all the yeast HC PPI networks deduced from raw affinity purification data (TAP and HMS-PCI) contain statistically significant correlations (Table 3). All correlation coefficients are modest, lying between 0.13 and 0.39, and are similar to those observed for the raw TAP and HMS-PCI data sets. However, all P-values are less than 0.0001, again suggesting nontrivial relationships exist between degree and abundance. These results are surprising for the Krogan-CORE, Krogan-INT, and HC Ho data sets, as their inferring methodologies included, as a first step, the removal of promiscuous proteins. Krogan et al. removed 44 nonspecific contaminants and nearly all cytoplasmic ribosomal subunits from the raw data as a preliminary step, while the HC Ho PPI data set is a subset of the raw data in which nonspecifically binding proteins have been subtracted. In spite of these removals, and any further inferring procedures, their HC data sets still contain a degree/abundance relationship, albeit weak. Therefore, the influence of protein cellular abundance is not limited to a small proportion of highly- abundant and promiscuous proteins. Rather, the effect seems subtly ingrained throughout the data. The TAP HC data set of Gavin et al. was inferred by first determining ‘socio-affinity’ scores for each pair of proteins, which quantified the propensity of them to occur together in purifications. However, degree/ abundance correlation coefficients lie between 0.31 and 0.39. In fact, the HC Gavin data set has larger correlation coefficients than the corresponding values for the raw PPI network. These findings for the HC TAP and HMS-PCI data sets might suggest that the degree/abundance relationship is, as discussed earlier, PLoS ONE | www.plosone.org 7 June 2009 | Volume 4 | Issue 6 | e5815