Reconstruction of the temporal signaling network in salmonella infected human cells


Thesis Type: Postgraduate

Institution Of The Thesis: Orta Doğu Teknik Üniversitesi, Graduate School of Informatics, Medical Informatics, Turkey

Approval Date: 2016

Student: GÜNGÖR BUDAK

Co-Consultant: NURCAN TUNÇBAĞ, YEŞİM AYDIN SON

Abstract:

Salmonella enterica is a bacterial pathogen whose mechanism of infection is usually through food sources. The pathogen proteins are translocated into the host cells to change the host signaling mechanisms either by activating or inhibiting the host proteins. In order to obtain a more complete view of the biological processes and the signaling networks and to reconstruct the temporal signaling network of the human host, we have used two network modeling approaches, the Prize-collecting Steiner Forest (PCSF) approach and the Integer Linear Programming (ILP) based edge inference approach by integrating a published temporal phosphoproteomic dataset of Salmonella-infected human cells and the human interactome. The final temporal signaling network conserves the information about temporality and directionality, while showing hidden entities in the signaling, such as the SNARE binding, mTOR signaling, immune response, cytoskeleton organization, and apoptosis pathways. Although the targets of Salmonella effectors such as CDC42, RHOA, 14-3-3δ, Syntaxin family, Oxysterol-binding proteins were not present in the phosphoproteomic dataset, they were revealed in the reconstructed signaling network. Structural analysis of these targets also revealed binding preferences of their neighbors. The application of such integrated approaches has a high potential to identify the clinical targets in infectious diseases, especially in the Salmonella infections.