CoPPIs Algorithm: A Tool to Unravel Protein Cooperative Strategies in Pathophysiological Conditions

We present here the Co-expressed Protein-Protein Interactions (CoPPIs) algorithm. In addition to minimizing correlation-causality imbalance and contextualizing PPIs to the investigated systems, it combines PPIs and protein co-expression networks to identify differentially correlated functional modules. To test CoPPIs, we processed a set of proteomic profiles from different brain areas of controls and subjects affected by idiopathic Parkinsons disease or carrying a GBA1 mutation. Its robustness was supported by the extraction of functional modules, related to translation and mitochondria, whose involvement in PD pathogenesis is well documented. Furthermore, the selection of hubs and bottlenecks from the weighted PPI networks provided molecular clues consistent with the PD pathophysiology. Of note, like quantification, the CoPPIs algorithm revealed less variations when comparing disease groups than when comparing diseased and controls. However, correlation and quantification results showed low overlap, suggesting the complementarity of these measures. An observation that opens the way to a new investigation strategy that takes into account not only protein expression, but also the level of coordination among proteins that cooperate to perform a given function.