, 2008 and Oldham et al., 2008). However, to date, this method has never been applied to proteomics data. Because the semiquantitative data provided by AP-MS provides a good proxy for relative protein abundance, we applied WGCNA to our proteomic data set. We call this adapted application of the method to protein analysis, Weighted Correlation Network Analysis (still MAPK Inhibitor Library supplier abbreviated as WGCNA). Briefly, after selecting proteins present in at least three samples (n = 411), the pairwise

correlation coefficients between one protein and every other detected protein were computed, weighted using a power function (Zhang and Horvath, 2005 and Langfelder and Horvath, 2008), and used to determine the topological overlap, a measure of connection strength or “neighborhood sharing” in the Trichostatin A network. A pair of nodes in a network is said to have high topological overlap if they are both strongly connected to the same group of nodes. In WGCNA networks, genes with high topological overlap have been found to have an increased chance of being part of the same tissue, cell type,

or biological pathway. Our analyses of the fl-Htt interactome produced eight clusters of highly correlated proteins, or modules, with each including 22–145 proteins (Figure 4A; Table S10). Based on the convention of WGCNA (Zhang and Horvath, 2005), the modules were named with

different colors (red, yellow, blue, cyan, pink, green, navy, and brown). To investigate the biological underpinning of the WGCNA modules, we addressed whether each module could have differential correlation strength with the central protein in our Non-specific serine/threonine protein kinase interactome, fl-Htt. We computed a Module Eigenprotein (MP) for each module, which is defined as the most representative protein member (i.e., a weighted summary) among all proteins in the module. We then calculated each MP’s correlation with fl-Htt (Figure 4B and Table S11). The relationship between module membership (MM, defined as the correlation between each protein in the network and MP) and fl-Htt levels was determined (Figures S2A–S2H). Both measures pointed to one module (red) as the most correlated to fl-Htt across samples, with five other modules (yellow, blue, cyan, pink, and green) also highly significantly correlated with fl-Htt. Importantly, the red module (comprised of 62 proteins, where 19 were previously known Htt interactors) includes Htt itself, thus giving further support that the proteins assigned to this module may have important biological relationships with Htt (Table S12).