Inference of Predictive Phospho-regulatory Networks from LC-MS/MS Phosphoproteomics Data.

In the field of transcriptomics data the automated inference of predictive gene regulatory networks from high-throughput data is a common approach for the identification of novel genes with potential therapeutic value. Sophisticated methods have been developed that extensively make use of diverse sources of prior-knowledge to obtain biologically relevant hypotheses. Transferring such concepts to the field of phosphoproteomics data has the potential to reveal new insights into phosphorylation-related signaling mechanisms. In this study we conceptually adapt the TILAR network inference algorithm for the inference of a phospho-regulatory network. Therefore, we use published phosphoproteomics data of WP1193 treated and IL6-stimulated glioblastoma stem cells under normoxic and hypoxic condition. Peptides corresponding to 21 differentially phosphorylated proteins were used for network inference. Topological analysis of the phospho-regulatory network suggests lamin B2 (LMNB2) and spectrin, beta, non-erythrocytic 1 (SPTBN1) as potential hub-proteins associated with the alteration of phosphorylation under the observed conditions. Altogether, our results show that inference of phospho-regulatory networks can aid in the understanding of complex molecular mechanisms and cellular processes of biological systems.