Interaction networks of all acetylated proteins with the top five clusters of proteins associated with ribosome (red), aminoacyl-tRNA biosynthesis (blue), RNA transport (light blue), ribosome biogenesis in eukaryotes (light green) and oxidative phosphorylation (green).
In both populations similar clusters of proteins associated with BMI or insulin were identified.
An overview of all clusters of proteins and interactions between the single proteins selected by RF is displayed in figure 4.
There are 4,844 clusters of proteins present in at least four of the genome, in which 3,795 single-copy gene clusters were found.
The proteins that are selected by RF can subsequently be analyzed by clustering methods, offering the opportunity to identify clusters of proteins that are associated with different health outcomes.
Using this approach, three clusters of proteins associated with BMI could be identified; cluster 1 and 3 were positively associated with BMI while cluster 2 was negatively associated with BMI.
The genes unique to C. sakazakii strains were in two separate clusters of proteins involved in pilus assembly (ESA_02540 ESA_02542 and ESA_02796 ESA_02799), pilin FimA proteins (ESA_02541, ESA_02542, ESA_02796 and ESA_02799), porin PapC (ESA_02797) and the chaperone PapD (ESA_02798).
To compare the association of the identified clusters of proteins and BMI to the association of single traditional biomarkers and BMI regression analysis was performed using the statistical package PASW (version 17.0; SPSS, Chicago, IL).
Using a walk trap community algorithm and permutation approaches (with n = 1000), we were able to extract clusters of proteins from the network (Details are available in Methods S1).
To identify transcriptionally enriched modules within the protein network, we further analyzed this large interactome for clusters of proteins that interact and also are modulated by BER intermediate accumulation; five subnetworks were identified (Fig. S1).
These observations suggest that that the structural differences in functional regions discussed above appear to be mainly responsible for the branching in structure-based phylogeny (Figure 2) resulting in different clusters of proteins with different functions.
