Survey of activated FLT3 signaling in leukemia

Activating mutations of FMS-like tyrosine kinase-3 (FLT3) are found in approximately 30% of patients with acute myeloid leukemia (AML). FLT3 is therefore an attractive drug target. However, the molecular mechanisms by which FLT3 mutations lead to cell transformation in AML remain unclear. To develop a better understanding of FLT3 signaling as well as its downstream effectors, we performed detailed phosphoproteomic analysis of FLT3 signaling in human leukemia cells. We identified over 1000 tyrosine phosphorylation sites from about 750 proteins in both AML (wild type and mutant FLT3) and B cell acute lymphoblastic leukemia (normal and amplification of FLT3) cell lines. Furthermore, using stable isotope labeling by amino acids in cell culture (SILAC), we were able to quantified over 400 phosphorylation sites (pTyr, pSer, and pThr) that were responsive to FLT3 inhibition in FLT3 driven human leukemia cell lines. We also extended this phosphoproteomic analysis on bone marrow from primary AML patient samples, and identify over 200 tyrosine and 800 serine/threonine phosphorylation sites in vivo. This study showed that oncogenic FLT3 regulates proteins involving diverse cellular processes and affects multiple signaling pathways in human leukemia that we previously appreciated, such as Fc epsilon RI-mediated signaling, BCR, and CD40 signaling pathways. It provides a valuable resource for investigation of oncogenic FLT3 signaling in human leukemia.     

Non Linear Programming (NLP) Formulation for Quantitative Modeling of Protein Signal Transduction Pathways

Modeling of signal transduction pathways plays a major role in understanding cells'' function and predicting cellular response. Mathematical formalisms based on a logic formalism are relatively simple but can describe how signals propagate from one protein to the next and have led to the construction of models that simulate the cells response to environmental or other perturbations. Constrained fuzzy logic was recently introduced to train models to cell specific data to result in quantitative pathway models of the specific cellular behavior. There are two major issues in this pathway optimization: i) excessive CPU time requirements and ii) loosely constrained optimization problem due to lack of data with respect to large signaling pathways. Herein, we address both issues: the former by reformulating the pathway optimization as a regular nonlinear optimization problem; and the latter by enhanced algorithms to pre/post-process the signaling network to remove parts that cannot be identified given the experimental conditions. As a case study, we tackle the construction of cell type specific pathways in normal and transformed hepatocytes using medium and large-scale functional phosphoproteomic datasets. The proposed Non Linear Programming (NLP) formulation allows for fast optimization of signaling topologies by combining the versatile nature of logic modeling with state of the art optimization algorithms.     

Phosphoproteomics: unraveling the signaling web

In recent years, phosphoproteomic technologies have increased our understanding of cellular signaling networks. Here, we frame recent phosphoproteomics-based advances in the context of the DNA damage response and ErbB receptor family signaling and offer a perspective on how the molecular insights arising from the integration of such proteomic approaches might be used for clinical applications.     

Quantitative phosphoproteomics strategies for understanding protein kinase-mediated signal transduction pathways

Protein phosphorylation is a central regulatory mechanism of cell signaling pathways. This highly controlled biochemical process is involved in most cellular functions, and defects in protein kinases and phosphatases have been implicated in many diseases, highlighting the importance of understanding phosphorylation-mediated signaling networks. However, phosphorylation is a transient modification, and phosphorylated proteins are often less abundant. Therefore, the large-scale identification and quantification of phosphoproteins and their phosphorylation sites under different conditions are one of the most interesting and challenging tasks in the field of proteomics. Both 2D gel electrophoresis and liquid chromatography-tandem mass spectrometry serve as key phosphoproteomic technologies in combination with prefractionation, such as enrichment of phosphorylated proteins/peptides. Recently, new possibilities for quantitative phosphoproteomic analysis have been offered by technical advances in sample preparation, enrichment, separation, instrumentation, quantification and informatics. In this article, we present an overview of several strategies for quantitative phosphoproteomics and discuss how phosphoproteomic analysis can help to elucidate signaling pathways that regulate various cellular processes.     

Quantitative phosphoproteomics strategies for understanding protein kinase-mediated signal transduction pathways

Protein phosphorylation is a central regulatory mechanism of cell signaling pathways. This highly controlled biochemical process is involved in most cellular functions, and defects in protein kinases and phosphatases have been implicated in many diseases, highlighting the importance of understanding phosphorylation-mediated signaling networks. However, phosphorylation is a transient modification, and phosphorylated proteins are often less abundant. Therefore, the large-scale identification and quantification of phosphoproteins and their phosphorylation sites under different conditions are one of the most interesting and challenging tasks in the field of proteomics. Both 2D gel electrophoresis and liquid chromatography-tandem mass spectrometry serve as key phosphoproteomic technologies in combination with prefractionation, such as enrichment of phosphorylated proteins/peptides. Recently, new possibilities for quantitative phosphoproteomic analysis have been offered by technical advances in sample preparation, enrichment, separation, instrumentation, quantification and informatics. In this article, we present an overview of several strategies for quantitative phosphoproteomics and discuss how phosphoproteomic analysis can help to elucidate signaling pathways that regulate various cellular processes.     

Network Reconstruction Based on Proteomic Data and Prior Knowledge of Protein Connectivity Using Graph Theory

Modeling of signal transduction pathways is instrumental for understanding cells’ function. People have been tackling modeling of signaling pathways in order to accurately represent the signaling events inside cells’ biochemical microenvironment in a way meaningful for scientists in a biological field. In this article, we propose a method to interrogate such pathways in order to produce cell-specific signaling models. We integrate available prior knowledge of protein connectivity, in a form of a Prior Knowledge Network (PKN) with phosphoproteomic data to construct predictive models of the protein connectivity of the interrogated cell type. Several computational methodologies focusing on pathways’ logic modeling using optimization formulations or machine learning algorithms have been published on this front over the past few years. Here, we introduce a light and fast approach that uses a breadth-first traversal of the graph to identify the shortest pathways and score proteins in the PKN, fitting the dependencies extracted from the experimental design. The pathways are then combined through a heuristic formulation to produce a final topology handling inconsistencies between the PKN and the experimental scenarios. Our results show that the algorithm we developed is efficient and accurate for the construction of medium and large scale signaling networks. We demonstrate the applicability of the proposed approach by interrogating a manually curated interaction graph model of EGF/TNFA stimulation against made up experimental data. To avoid the possibility of erroneous predictions, we performed a cross-validation analysis. Finally, we validate that the introduced approach generates predictive topologies, comparable to the ILP formulation. Overall, an efficient approach based on graph theory is presented herein to interrogate protein–protein interaction networks and to provide meaningful biological insights.     

Comparison of three quantitative phosphoproteomic strategies to study receptor tyrosine kinase signaling

There are three quantitative phosphoproteomic strategies most commonly used to study receptor tyrosine kinase (RTK) signaling. These strategies quantify changes in: (1) all three forms of phosphosites (phosphoserine, phosphothreonine and phosphotyrosine) following enrichment of phosphopeptides by titanium dioxide or immobilized metal affinity chromatography; (2) phosphotyrosine sites following anti- phosphotyrosine antibody enrichment of phosphotyrosine peptides; or (3) phosphotyrosine proteins and their binding partners following anti-phosphotyrosine protein immunoprecipitation. However, it is not clear from literature which strategy is more effective. In this study, we assessed the utility of these three phosphoproteomic strategies in RTK signaling studies by using EphB receptor signaling as an example. We used all three strategies with stable isotope labeling with amino acids in cell culture (SILAC) to compare changes in phosphoproteomes upon EphB receptor activation. We used bioinformatic analysis to compare results from the three analyses. Our results show that the three strategies provide complementary information about RTK pathways.     

Quantitative phosphoproteomics

Cells are highly responsive to their environment. One of the main strategies used by cells in signal transduction is protein phosphorylation, a reversible modification that regulates numerous biological processes. Misregulation of phosphorylation-mediated processes is often implicated in many human diseases and cancers. A global and quantitative analysis of protein phosphorylation provides a powerful new approach and has the potential to reveal new insights in signaling pathways. Recent technological advances in high resolution mass spectrometers and multidimensional liquid chromatography, combined with the use of stable isotope labeling of proteins, have led to the application of quantitative phosphoproteomics to study in vivo signal transduction events on a proteome-wide scale. Here we review recent advancements in quantitative phosphoproteomic technologies, discuss their potentials, and identify areas for future development. A key objective of proteomic technology is its application to addressing biological questions. We will therefore describe how current quantitative phosphoproteomic technology can be used to study the molecular basis of phosphorylation events in the DNA damage response.     

TSLP signaling network revealed by SILAC-based phosphoproteomics

Thymic stromal lymphopoietin (TSLP) is a cytokine that plays diverse roles in the regulation of immune responses. TSLP requires a heterodimeric receptor complex consisting of IL-7 receptor α subunit and its unique TSLP receptor (gene symbol CRLF2) to transmit signals in cells. Abnormal TSLP signaling (e.g. overexpression of TSLP or its unique receptor TSLPR) contributes to the development of a number of diseases including asthma and leukemia. However, a detailed understanding of the signaling pathways activated by TSLP remains elusive. In this study, we performed a global quantitative phosphoproteomic analysis of the TSLP signaling network using stable isotope labeling by amino acids in cell culture. By employing titanium dioxide in addition to antiphosphotyrosine antibodies as enrichment methods, we identified 4164 phosphopeptides on 1670 phosphoproteins. Using stable isotope labeling by amino acids in cell culture-based quantitation, we determined that the phosphorylation status of 226 proteins was modulated by TSLP stimulation. Our analysis identified activation of several members of the Src and Tec families of kinases including Btk, Lyn, and Tec by TSLP for the first time. In addition, we report TSLP-induced phosphorylation of protein phosphatases such as Ptpn6 (SHP-1) and Ptpn11 (Shp2), which has also not been reported previously. Co-immunoprecipitation assays showed that Shp2 binds to the adapter protein Gab2 in a TSLP-dependent manner. This is the first demonstration of an inducible protein complex in TSLP signaling. A kinase inhibitor screen revealed that pharmacological inhibition of PI-3 kinase, Jak family kinases, Src family kinases or Btk suppressed TSLP-dependent cellular proliferation making them candidate therapeutic targets in diseases resulting from aberrant TSLP signaling. Our study is the first phosphoproteomic analysis of the TSLP signaling pathway that greatly expands our understanding of TSLP signaling and provides novel therapeutic targets for TSLP/TSLPR-associated diseases in humans.     

Phosphoproteomics by mass spectrometry: insights,implications, applications and limitations

Phosphorylation of proteins is a predominant, reversible post-translational modification. It is central to a wide variety of physiological responses and signaling mechanisms. Recent advances have allowed the global scope of phosphorylation to be addressed by mass spectrometry using phosphoproteomic approaches. In this perspective, we discuss four aspects of phosphoproteomics: the insights and implications from recently published phosphoproteomic studies and the applications and limitations of current phosphoproteomic strategies. Since approximately 50,000 known phosphorylation sites do not yet have any ascribed function, we present our perspectives on a major function of protein phosphorylation that may be of predictive value in hypothesis-based investigations. Finally, we discuss strategies to measure the stoichiometry of phosphorylation in a proteome-wide manner that is not provided by current phosphoproteomic approaches.     

Integrating proteomic and phosphoproteomic data for pathway analysis in breast cancer

Background

As protein is the basic unit of cell function and biological pathway, shotgun proteomics, the large-scale analysis of proteins, is contributing greatly to our understanding of disease mechanisms. Proteomics study could detect the changes of both protein expression and modification. With the releases of large-scale cancer proteome studies, how to integrate acquired proteomic and phosphoproteomic data in more comprehensive pathway analysis becomes implemented, but remains challenging. Integrative pathway analysis at proteome level provides a systematic insight into the signaling network adaptations in the development of cancer.

Results

Here we integrated proteomic and phosphoproteomic data to perform pathway prioritization in breast cancer. We manually collected and curated breast cancer well-known related pathways from the literature as target pathways (TPs) or positive control in method evaluation. Three different strategies including Hypergeometric test based over-representation analysis, Kolmogorov-Smirnov (K-S) test based gene set analysis and topology-based pathway analysis, were applied and evaluated in integrating protein expression and phosphorylation. In comparison, we also assessed the ranking performance of the strategy using information of protein expression or protein phosphorylation individually. Target pathways were ranked more top with the data integration than using the information from proteomic or phosphoproteomic data individually. In the comparisons of pathway analysis strategies, topology-based method outperformed than the others. The subtypes of breast cancer, which consist of Luminal A, Luminal B, Basal and HER2-enriched, vary greatly in prognosis and require distinct treatment. Therefore we applied topology-based pathway analysis with integrating protein expression and phosphorylation profiles on four subtypes of breast cancer. The results showed that TPs were enriched in all subtypes but their ranks were significantly different among the subtypes. For instance, p53 pathway ranked top in the Basal-like breast cancer subtype, but not in HER2-enriched type. The rank of Focal adhesion pathway was more top in HER2- subtypes than in HER2+ subtypes. The results were consistent with some previous researches.

Conclusions

The results demonstrate that the network topology-based method is more powerful by integrating proteomic and phosphoproteomic in pathway analysis of proteomics study. This integrative strategy can also be used to rank the specific pathways for the disease subtypes.

     

Knowledge based identification of essential signaling from genome-scale siRNA experiments

Background  

A systems biology interpretation of genome-scale RNA interference (RNAi) experiments is complicated by scope, experimental variability and network signaling robustness. Over representation approaches (ORA), such as the Hypergeometric or z-score, are an established statistical framework used to associate RNA interference effectors to biologically annotated gene sets or pathways. These methods, however, do not directly take advantage of our growing understanding of the interactome. Furthermore, these methods can miss partial pathway activation and may be biased by protein complexes. Here we present a novel ORA, protein interaction permutation analysis (PIPA), that takes advantage of canonical pathways and established protein interactions to identify pathways enriched for protein interactions connecting RNAi hits.     

Proteomic analysis of phosphorylation in cancer

Constitutive activity of kinases is known to be crucial for a tumor to maintain its malignant phenotype, a phenomenon which is often referred to as oncogene addiction. The in-depth analysis of aberrant signaling pathways by the analysis of protein phosphorylation has become feasible through recent advances in proteomics technology. In this article we will review developments in the field of phosphoproteomics and its application in cancer research. The most widely used technologies for the generic enrichment of phosphopeptides are discussed as well as targeted approaches for the analysis of a specific subset of phosphopeptides. Validation experiments of phosphorylation sites using targeted mass spectrometry are also explained. Finally, we will highlight applications of phosphoproteomic technology in cancer research using cell lines and tissue.     

Phosphoproteomic characterization of PYK2 signaling pathways involved in osteogenesis

The PYK2 tyrosine kinase is a negative regulator of bone formation, but aside from the requirement for PYK2 kinase activity there has been little progress toward understanding of the molecular mechanism involved in this function. To gain insight into the signaling pathways modulated by PYK2 we sought to identify PYK2 substrates. Challenges inherent to a quantitative phosphoproteomic analysis for non-receptor tyrosine kinases were overcome by employing an inducible PYK2 overexpression system in NIH3T3 cells in combination with a selective PYK2 inhibitor. The identification of a number of known PYK2 substrates and interacting partners validated the methodology. Results of the inducible cell system were extended to a cell model of osteogenesis, examining the effect of the PYK2 inhibitor on the phosphorylation state of targets identified in the phosphoproteomic study. Consistent with phosphoproteomic analysis, increased osteogenesis associated with a selective PYK2 inhibitor was accompanied by reduced phosphorylation of paxillin, Gab1 and p130^Cas, along with reduction of phosphorylation levels of the Met activation loop. These results further confirmed the utility of the methodology and point to a previously unknown bi-directional activation pathway between PYK2 and Met.     

Immunoaffinity profiling of tyrosine phosphorylation in cancer cells

Tyrosine kinases play a prominent role in human cancer, yet the oncogenic signaling pathways driving cell proliferation and survival have been difficult to identify, in part because of the complexity of the pathways and in part because of low cellular levels of tyrosine phosphorylation. In general, global phosphoproteomic approaches reveal small numbers of peptides containing phosphotyrosine. We have developed a strategy that emphasizes the phosphotyrosine component of the phosphoproteome and identifies large numbers of tyrosine phosphorylation sites. Peptides containing phosphotyrosine are isolated directly from protease-digested cellular protein extracts with a phosphotyrosine-specific antibody and are identified by tandem mass spectrometry. Applying this approach to several cell systems, including cancer cell lines, shows it can be used to identify activated protein kinases and their phosphorylated substrates without prior knowledge of the signaling networks that are activated, a first step in profiling normal and oncogenic signaling networks.     

A highly multiplexed quantitative phosphosite assay for biology and preclinical studies

Reliable methods to quantify dynamic signaling changes across diverse pathways are needed to better understand the effects of disease and drug treatment in cells and tissues but are presently lacking. Here, we present SigPath, a targeted mass spectrometry (MS) assay that measures 284 phosphosites in 200 phosphoproteins of biological interest. SigPath probes a broad swath of signaling biology with high throughput and quantitative precision. We applied the assay to investigate changes in phospho‐signaling in drug‐treated cancer cell lines, breast cancer preclinical models, and human medulloblastoma tumors. In addition to validating previous findings, SigPath detected and quantified a large number of differentially regulated phosphosites newly associated with disease models and human tumors at baseline or with drug perturbation. Our results highlight the potential of SigPath to monitor phosphoproteomic signaling events and to nominate mechanistic hypotheses regarding oncogenesis, response, and resistance to therapy.