Community detection in empirical kinase networks identifies new members of signaling pathways

Abstract

AbstractPhosphoproteomics allows one to measure the activity of kinases that drive the fluxes of signal transduction pathways involved in biological processes such as immune function, senescence and growth. However, deriving knowledge of signaling network circuitry from these data is challenging due to a scarcity of phosphorylation sites that define kinase-kinase relationships. To address this issue, we previously identified around 6,000 phosphorylation sites markers of kinase-kinase relationships (that may be conceptualised as network edges), from which empirical cell-model-specific weighted kinase networks may be reconstructed. Here, we assess whether the application of community detection algorithms to such networks can identify new components linked to canonical signaling pathways.Phosphoproteomics data from acute myeloid leukaemia (AML) cells treated separately with PI3K, ATK, MEK and ERK inhibitors were used to reconstruct individual kinase networks. In each network, we applied the community detection method modularity maximization and selected the community containing the main target of the inhibitor the cells were treated with. These analyses returned communities that contained known canonical signaling components. Interestingly, in addition to canonical PI3K/AKT/MTOR members, the community assignments returned TTK (also known as MPS1) as a likely component of PI3K/AKT/mTOR signaling. We confirmed this observation with wet-lab laboratory experiments showing that TTK phosphorylation was decreased in AML cells treated with AKT and MTOR inhibitors. This study illustrates the application of community detection algorithms to the analysis of empirical kinase networks to uncover new members linked to canonical signaling pathways.Author summaryKinases are key enzymes that regulate the transduction of extracellular signals from cell surface receptors to changes in gene expression via a set of kinase-kinase interactions and signalling cascades. Inhibiting hyperactive kinases is a viable therapeutic strategy to treat different cancer types. Unfortunately, kinase signalling networks are robust to external perturbations, thus allowing tumour cells to orchestrate mechanisms that compensate for inhibition of specific kinases. Therefore, there is a need to better understand kinase network structure and to identify new therapeutic targets. Here, we reconstructed kinase networks from phosphoproteomics data, and compared the activity of its kinase interactions in acute myeloid leukaemia (AML) cells. We then tested community detection algorithms to identify kinase components associated to PI3K/AKT/MTOR signalling, a paradigmatic oncogenic signalling cascade. We found that TTK was usually grouped with networks derived for PI3K, AKT and MTOR kinases. Wet-lab experiments confirmed that TTK is likely to act downstream of AKT and MTOR. We thus show that our methods can be used to identify potential new members of canonical kinase signalling cascades.