Thomas Melendy

Polyomavirus (PyV) Large T-antigen (LT) is the major viral regulatory protein that targets numerous cellular pathways for cellular transformation and viral replication. LT directly recruits the cellular replication factors involved in initiation of viral DNA replication through mutual interactions between LT, DNA polymerase alpha-primase (Polprim), and single-stranded DNA binding complex, (RPA). Activities and interactions of these complexes are known to be modulated by post-translational modifications; however, high-sensitivity proteomic analyses of the PTMs and proteins associated have been lacking. High-resolution liquid chromatography tandem mass spectrometry (LC–MS/MS) of the immunoprecipitated factors (IPMS) identified 479 novel phosphorylated amino acid residues (PAARs) on the three factors; the function of one has been validated. IPMS revealed 374, 453, and 183 novel proteins associated with the three, respectively. A significant transcription-related process network identified by Gene Ontology (GO) enrichment analysis was unique to LT. Although unidentified by IPMS, the ETS protooncogene 1, transcription factor (ETS1) was significantly overconnected to our dataset indicating its involvement in PyV processes. This result was validated by demonstrating that ETS1 coimmunoprecipitates with LT. Identification of a novel PAAR that regulates PyV replication and LT’s association with the protooncogenic Ets1 transcription factor demonstrates the value of these results for studies in PyV biology.

Background: The identification of cancer driver genes and key molecular pathways has been the focus of large-scale cancer genome studies. Network-based methods detect significantly perturbed subnetworks as putative cancer pathways by incorporating genomics data with the topological information of PPI networks. However, commonly used PPI networks have distinct topological structures, making the results of the same method vary widely when applied to different networks. Furthermore, emerging context-specific PPI networks often have incomplete topological structures, which pose serious challenges for existing subnetwork detection algorithms. Methods: In this paper, we propose a novel method, referred to as MultiFDRnet, to address the above issues. The basic idea is to model a set of PPI networks as a multiplex network to preserve the topological structure of individual networks, while introducing dependencies among them, and, then, to detect significantly perturbed subnetworks on the modeled multiplex network using all the structural information simultaneously. Results: To illustrate the effectiveness of the proposed approach, an extensive benchmark analysis was conducted on both simulated and real cancer data. The experimental results showed that the proposed method is able to detect significantly perturbed subnetworks jointly supported by multiple PPI networks and to identify novel modular structures in context-specific PPI networks.