Comprehensive Network Analysis of Cancer Stem Cell Signalling through Systematic Integration of Post-Translational Modification Dynamics

Post‐translational modifications, such as phosphorylation, acetylation and ubiquitination, are widely known to play various important roles in cellular signalling. Recent significant advances in mass spectrometry‐based proteomics technology enable us not only to comprehensively identify expressed proteins but also to unveil their post‐translational modifications with high sensitivity. In our advanced proteome bioinformatics frameworks, statistical network analyses of large‐scale information on various post‐translational modification dynamics were conducted to define the key machinery for cancer stem cell properties. The bioinformatical approaches using IPA (ingenuity pathway analysis), NetworKIN and a newly developed platform named PTMapper (post‐translational modification mapper) allowed us to perform network‐wide prediction of upstream interactors/kinases with the related information on the diseases and functions, leading to systematic finding of novel drug candidates to regulate aberrant signalling in cancer stem cells. In this chapter, we apply patient‐derived glioblastoma stem cells as a representative model of cancer stem cells to introduce some useful platforms for statistical and mathematical network analyses based on the large‐scale phosphoproteome data

Phosphoproteomics-Based Systems Analysis of Signal Transduction Networks

Signal transduction systems coordinate complex cellular information to regulate biological events such as cell proliferation and differentiation. Although the accumulating evidence on widespread association of signaling molecules has revealed essential contribution of phosphorylation-dependent interaction networks to cellular regulation, their dynamic behavior is mostly yet to be analyzed. Recent technological advances regarding mass spectrometry-based quantitative proteomics have enabled us to describe the comprehensive status of phosphorylated molecules in a time-resolved manner. Computational analyses based on the phosphoproteome dynamics accelerate generation of novel methodologies for mathematical analysis of cellular signaling. Phosphoproteomics-based numerical modeling can be used to evaluate regulatory network elements from a statistical point of view. Integration with transcriptome dynamics also uncovers regulatory hubs at the transcriptional level. These omics-based computational methodologies, which have firstly been applied to representative signaling systems such as the epidermal growth factor receptor pathway, have now opened up a gate for systems analysis of signaling networks involved in immune response and cancer

Proteomic Dissection of Plasmodium falciparum Maurer\u27s cleft Compartments Using SBP1

Oral Session

Determination of a phosphorylation site in Nipah virus nucleoprotein and its involvement in virus transcription

Many viruses use their host’s cellular machinery to regulate the functions of viral proteins. The phosphorylation of viral proteins is known to play a role in genome transcription and replication in paramyxoviruses. The paramyxovirus nucleoprotein (N), the most abundant protein in infected cells, is a component of the N–RNA complex and supports the transcription and replication of virus mRNA and genomic RNA. Recently, we reported that the phosphorylation of measles virus N is involved in the regulation of viral RNA synthesis. In this study, we report a rapid turnover of phosphorylation in the Nipah virus N (NiV-N). The phosphorylated NiV-N was hardly detectable in steady-state cells, but was detected after inhibition of cellular protein phosphatases. We identified a phosphorylated serine residue at Ser451 of NiV-N by peptide mass fingerprinting by electrospray ionization–quadrupole time-of-flight mass spectrometry. In the NiV minigenome assay, using luciferase as a reporter gene, the substitution of Ser451 for alanine in NiV-N resulted in a reduction in luciferase activity of approximately 45 % compared with the wild-type protein. Furthermore, the substitution of Ser451 for glutamic acid, which mimics a phosphoserine, led to a more significant decrease in luciferase activity – approximately 81 %. Northern blot analysis showed that both virus transcription and replication were reduced by these mutations. These results suggest that a rapid turnover of the phosphorylation of NiV-N plays an important role in virus transcription and replication

AYUMS: an algorithm for completely automatic quantitation based on LC-MS/MS proteome data and its application to the analysis of signal transduction

BACKGROUND: Comprehensive description of the behavior of cellular components in a quantitative manner is essential for systematic understanding of biological events. Recent LC-MS/MS (tandem mass spectrometry coupled with liquid chromatography) technology, in combination with the SILAC (Stable Isotope Labeling by Amino acids in Cell culture) method, has enabled us to make relative quantitation at the proteome level. The recent report by Blagoev et al. (Nat. Biotechnol., 22, 1139–1145, 2004) indicated that this method was also applicable for the time-course analysis of cellular signaling events. Relative quatitation can easily be performed by calculating the ratio of peak intensities corresponding to differentially labeled peptides in the MS spectrum. As currently available software requires some GUI applications and is time-consuming, it is not suitable for processing large-scale proteome data. RESULTS: To resolve this difficulty, we developed an algorithm that automatically detects the peaks in each spectrum. Using this algorithm, we developed a software tool named AYUMS that automatically identifies the peaks corresponding to differentially labeled peptides, compares these peaks, calculates each of the peak ratios in mixed samples, and integrates them into one data sheet. This software has enabled us to dramatically save time for generation of the final report. CONCLUSION: AYUMS is a useful software tool for comprehensive quantitation of the proteome data generated by LC-MS/MS analysis. This software was developed using Java and runs on Linux, Windows, and Mac OS X. Please contact [email protected] if you are interested in the application. The project web page is

Phosphoproteomics-Based Modeling Defines the Regulatory Mechanism Underlying Aberrant EGFR Signaling

BACKGROUND: Mutation of the epidermal growth factor receptor (EGFR) results in a discordant cell signaling, leading to the development of various diseases. However, the mechanism underlying the alteration of downstream signaling due to such mutation has not yet been completely understood at the system level. Here, we report a phosphoproteomics-based methodology for characterizing the regulatory mechanism underlying aberrant EGFR signaling using computational network modeling. METHODOLOGY/PRINCIPAL FINDINGS: Our phosphoproteomic analysis of the mutation at tyrosine 992 (Y992), one of the multifunctional docking sites of EGFR, revealed network-wide effects of the mutation on EGF signaling in a time-resolved manner. Computational modeling based on the temporal activation profiles enabled us to not only rediscover already-known protein interactions with Y992 and internalization property of mutated EGFR but also further gain model-driven insights into the effect of cellular content and the regulation of EGFR degradation. Our kinetic model also suggested critical reactions facilitating the reconstruction of the diverse effects of the mutation on phosphoproteome dynamics. CONCLUSIONS/SIGNIFICANCE: Our integrative approach provided a mechanistic description of the disorders of mutated EGFR signaling networks, which could facilitate the development of a systematic strategy toward controlling disease-related cell signaling