PTMScout, a Web resource for analysis of high throughput post-translational proteomics studies

A global map of associations between types of protein posttranslational modifications and human genetic diseases

There are >200 types of protein posttranslational modifications (PTMs) described in eukaryotes, each with unique proteome coverage and functions. We hypothesized that some genetic diseases may be caused by the removal of a specific type of PTMs by genomic variants and the consequent deregulation of particular functions. We collected >320,000 human PTMs representing 59 types and crossed them with >4M nonsynonymous DNA variants annotated with predicted pathogenicity and disease associations. We report >1.74M PTM-variant co-occurrences that an enrichment analysis distributed into 215 pairwise associations between 18 PTM types and 148 genetic diseases. Of them, 42% were not previously described. Removal of lysine acetylation exerts the most pronounced effect, and less studied PTM types such as S-glutathionylation or S-nitrosylation show relevance. Using pathogenicity predictions, we identified PTM sites that may produce particular diseases if prevented. Our results provide evidence of a substantial impact of PTM-specific removal on the pathogenesis of genetic diseases and phenotypes.

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There is an enrichment of disease-associated nsSNVs preventing certain types of PTMs

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We report 215 pairwise associations between 18 PTM types and 148 genetic diseases

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The removal of lysine acetylation exerts the most pronounced effect

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We report a set of PTM sites that may produce particular diseases if prevented

Evaluation and validation of commercial antibodies for the detection of Shb

Antibodies are among the most important tools for protein detection but, prior to their usage, proper validation of their appropriateness for given applications is required. The utility of an antibody depends on its sensitivity and specificity. We studied these two aspects in a panel of commercial antibodies against Shb, a platform protein involved in receptor tyrosine kinase signalling, but the function of which is still incompletely understood. Several of the antibodies showed shortcomings or were not acceptable for detection of the endogenous protein. The few that could detect Shb were doing so in either western blotting or immunoprecipitation experiments but a given antibody could not work in both applications. This article provides a resource for the available molecular tools that can be used in future research on Shb.

Future of the Genetic Code

The methods for establishing synthetic lifeforms with rewritten genetic codes comprising non-canonical amino acids (NCAA) in addition to canonical amino acids (CAA) include proteome-wide replacement of CAA, insertion through suppression of nonsense codon, and insertion via the pyrrolysine and selenocysteine pathways. Proteome-wide reassignments of nonsense codons and sense codons are also under development. These methods enable the application of NCAAs to enrich both fundamental and applied aspects of protein chemistry and biology. Sense codon reassignment to NCAA could incur problems arising from the usage of anticodons as identity elements on tRNA, and possible misreading of NNY codons by UNN anticodons. Evidence suggests that the problem of anticodons as identity elements can be diminished or resolved through removal from the tRNA of all identity elements besides the anticodon, and the problem of misreading of NNY codons by UNN anticodon can be resolved by the retirement of both the UNN anticodon and its complementary NNA codon from the proteome in the event that a restrictive post-transcriptional modification of the UNN anticodon by host enzymes to prevent the misreading cannot be obtained.

Bioinformatics Analysis of Functional Associations of PTMs

Post-translational modifications (PTMs) are an important source of protein regulation; they fine-tune the function, localization, and interaction with other molecules of the majority of proteins and are partially responsible for their multifunctionality. Usually, proteins have several potential modification sites, and their patterns of occupancy are associated with certain functional states. These patterns imply cross talk among PTMs within and between proteins, the majority of which are still to be discovered. Several methods detect associations between PTMs; these have recently combined into a global resource, the PTMcode database, which contains already known and predicted functional associations between pairs of PTMs from more than 45,000 proteins in 19 eukaryotic species.

Mapping Biological Networks from Quantitative Data-Independent Acquisition Mass Spectrometry: Data to Knowledge Pipelines

Data-independent acquisition mass spectrometry (DIA-MS) strategies and applications provide unique advantages for qualitative and quantitative proteome probing of a biological sample allowing constant sensitivity and reproducibility across large sample sets. These advantages in LC-MS/MS are being realized in fundamental research laboratories and for clinical research applications. However, the ability to translate high-throughput raw LC-MS/MS proteomic data into biological knowledge is a complex and difficult task requiring the use of many algorithms and tools for which there is no widely accepted standard and best practices are slowly being implemented. Today a single tool or approach inherently fails to capture the full interpretation that proteomics uniquely supplies, including the dynamics of quickly reversible chemically modified states of proteins, irreversible amino acid modifications, signaling truncation events, and, finally, determining the presence of protein from allele-specific transcripts. This chapter highlights key steps and publicly available algorithms required to translate DIA-MS data into knowledge.

Rosette Assay: Highly Customizable Dot-Blot for SH2 Domain Screening

With a growing number of high-throughput studies, structural analyses, and availability of protein-protein interaction databases, it is now possible to apply web-based prediction tools to SH2 domain-interactions. However, in silico prediction is not always reliable and requires experimental validation. Rosette assay is a dot blot-based reverse-phase assay developed for the assessment of binding between SH2 domains and their ligands. It is conveniently customizable, allowing for low- to high-throughput analysis of interactions between various numbers of SH2 domains and their ligands, e.g., short peptides, purified proteins, and cell lysates. The binding assay is performed in a 96-well plate (MBA or MWA apparatus) in which a sample spotted membrane is incubated with up to 96 labeled SH2 domains. Bound domains are detected and quantified using a chemiluminescence or near-infrared fluorescence (IR) imaging system. In this chapter, we describe a practical protocol for rosette assay to assess interactions between synthesized tyrosine phosphorylated peptides and a library of GST-tagged SH2 domains. Since the methodology is not confined to assessment of SH2-pTyr interactions, rosette assay can be broadly utilized for ligand and drug screening using different protein interaction domains or antibodies.

Mining Large Scale Tandem Mass Spectrometry Data for Protein Modifications Using Spectral Libraries

Experimental improvements in post-translational modification (PTM) detection by tandem mass spectrometry (MS/MS) has allowed the identification of vast numbers of PTMs. Open modification searches (OMSs) of MS/MS data, which do not require prior knowledge of the modifications present in the sample, further increased the diversity of detected PTMs. Despite much effort, there is still a lack of functional annotation of PTMs. One possibility to narrow the annotation gap is to mine MS/MS data deposited in public repositories and to correlate the PTM presence with biological meta-information attached to the data. Since the data volume can be quite substantial and contain tens of millions of MS/MS spectra, the data mining tools must be able to cope with big data. Here, we present two tools, Liberator and MzMod, which are built using the MzJava class library and the Apache Spark large scale computing framework. Liberator builds large MS/MS spectrum libraries, and MzMod searches them in an OMS mode. We applied these tools to a recently published set of 25 million spectra from 30 human tissues and present tissue specific PTMs. We also compared the results to the ones obtained with the OMS tool MODa and the search engine X!Tandem.

In silico prediction and characterization of protein post-translational modifications

This review outlines the computational approaches and procedures for predicting post translational modification (PTM)-induced changes in protein conformation and their influence on protein function(s), the latter being assessed as differential affinity in interaction with either low (ligands for receptors or transporters, substrates for enzymes) or high molecular mass molecules (proteins or nucleic acids in supramolecular assemblies). The scope for an in silico approach is discussed against a summary of the in vitro evidence on the structural and functional outcome of protein PTM.

Large-scale models of signal propagation in human cells derived from discovery phosphoproteomic data

Mass spectrometry is widely used to probe the proteome and its modifications in an untargeted manner, with unrivalled coverage. Applied to phosphoproteomics, it has tremendous potential to interrogate phospho-signalling and its therapeutic implications. However, this task is complicated by issues of undersampling of the phosphoproteome and challenges stemming from its high-content but low-sample-throughput nature. Hence, methods using such data to reconstruct signalling networks have been limited to restricted data sets and insights (for example, groups of kinases likely to be active in a sample). We propose a new method to handle high-content discovery phosphoproteomics data on perturbation by putting it in the context of kinase/phosphatase-substrate knowledge, from which we derive and train logic models. We show, on a data set obtained through perturbations of cancer cells with small-molecule inhibitors, that this method can study the targets and effects of kinase inhibitors, and reconcile insights obtained from multiple data sets, a common issue with these data.

Phosphoproteomics can offer significant insight into cell signalling and how signalling is modified in response to perturbations. Here the authors develop a new tool for the analysis of high-content phosphoproteomics in the context of kinase/phosphatase-substrate knowledge, which is used to train logic models.

Quantitative Profiling of Lysine Acetylation Reveals Dynamic Crosstalk between Receptor Tyrosine Kinases and Lysine Acetylation

Lysine acetylation has been primarily investigated in the context of transcriptional regulation, but a role for acetylation in mediating other cellular responses has emerged. Multiple studies have described global lysine acetylation profiles for particular biological states, but none to date have investigated the temporal dynamics regulating cellular response to perturbation. Reasoning that lysine acetylation may be altered in response to growth factors, we implemented quantitative mass spectrometry-based proteomics to investigate the temporal dynamics of lysine acetylation in response to growth factor stimulation in cultured carcinoma cell lines. We found that lysine acetylation changed rapidly in response to activation of several different receptor tyrosine kinases by their respective ligands. To uncover the effects of lysine acetylation dynamics on tyrosine phosphorylation signaling networks, cells were treated with an HDAC inhibitor. This short-term pharmacological inhibition of histone deacetylase activity modulated signaling networks involving phosphorylated tyrosine and thereby altered the response to receptor tyrosine kinase activation. This result highlights the interconnectivity of lysine acetylation and tyrosine phosphorylation signaling networks and suggests that HDAC inhibition may influence cellular responses by affecting both types of post-translational modifications.

PEIMAN 1.0: Post-translational modification Enrichment, Integration and Matching ANalysis

Conventional proteomics has discovered a wide gap between protein sequences and biological functions. The third generation of proteomics was provoked to bridge this gap. Targeted and untargeted post-translational modification (PTM) studies are the most important parts of today’s proteomics. Considering the expensive and time-consuming nature of experimental methods, computational methods are developed to study, analyze, predict, count and compute the PTM annotations on proteins. The enrichment analysis softwares are among the common computational biology and bioinformatic software packages. The focus of such softwares is to find the probability of occurrence of the desired biological features in any arbitrary list of genes/proteins. We introduce Post-translational modification Enrichment Integration and Matching Analysis (PEIMAN) software to explore more probable and enriched PTMs on proteins. Here, we also represent the statistics of detected PTM terms used in enrichment analysis in PEIMAN software based on the latest released version of UniProtKB/Swiss-Prot. These results, in addition to giving insight to any given list of proteins, could be useful to design targeted PTM studies for identification and characterization of special chemical groups.

Database URL:http://bs.ipm.ir/softwares/PEIMAN/

ProteomeScout: a repository and analysis resource for post-translational modifications and proteins

ProteomeScout (https://proteomescout.wustl.edu) is a resource for the study of proteins and their post-translational modifications (PTMs) consisting of a database of PTMs, a repository for experimental data, an analysis suite for PTM experiments, and a tool for visualizing the relationships between complex protein annotations. The PTM database is a compendium of public PTM data, coupled with user-uploaded experimental data. ProteomeScout provides analysis tools for experimental datasets, including summary views and subset selection, which can identify relationships within subsets of data by testing for statistically significant enrichment of protein annotations. Protein annotations are incorporated in the ProteomeScout database from external resources and include terms such as Gene Ontology annotations, domains, secondary structure and non-synonymous polymorphisms. These annotations are available in the database download, in the analysis tools and in the protein viewer. The protein viewer allows for the simultaneous visualization of annotations in an interactive web graphic, which can be exported in Scalable Vector Graphics (SVG) format. Finally, quantitative data measurements associated with public experiments are also easily viewable within protein records, allowing researchers to see how PTMs change across different contexts. ProteomeScout should prove useful for protein researchers and should benefit the proteomics community by providing a stable repository for PTM experiments.

Decoding neuroproteomics: integrating the genome, translatome and functional anatomy

The immense intercellular and intracellular heterogeneity of the CNS presents major challenges for high-throughput omic analyses. Transcriptional, translational and post-translational regulatory events are localized to specific neuronal cell types or subcellular compartments, resulting in discrete patterns of protein expression and activity. A spatial and quantitative knowledge of the neuroproteome is therefore critical to understanding both normal and pathological aspects of the functional genomics and anatomy of the CNS. Improvements in mass spectrometry allow the profiling of proteins at a sufficient depth to complement results from high-throughput genomic and transcriptomic assays. However, there are challenges in integrating proteomic data with other data modalities and even greater challenges in obtaining comprehensive neuroproteomic data with cell-type specificity. Here we discuss how proteomics should be exploited to enhance high-throughput functional genomic analysis by tighter integration of data analyses. We also discuss experimental strategies to achieve finer cellular and subcellular resolution in transcriptomic and proteomic studies of neural tissues.

Proteogenomic convergence for understanding cancer pathways and networks

During the past several decades, the understanding of cancer at the molecular level has been primarily focused on mechanisms on how signaling molecules transform homeostatically balanced cells into malignant ones within an individual pathway. However, it is becoming more apparent that pathways are dynamic and crosstalk at different control points of the signaling cascades, making the traditional linear signaling models inadequate to interpret complex biological systems. Recent technological advances in high throughput, deep sequencing for the human genomes and proteomic technologies to comprehensively characterize the human proteomes in conjunction with multiplexed targeted proteomic assays to measure panels of proteins involved in biologically relevant pathways have made significant progress in understanding cancer at the molecular level. It is undeniable that proteomic profiling of differentially expressed proteins under many perturbation conditions, or between normal and "diseased" states is important to capture a first glance at the overall proteomic landscape, which has been a main focus of proteomics research during the past 15-20 years. However, the research community is gradually shifting its heavy focus from that initial discovery step to protein target verification using multiplexed quantitative proteomic assays, capable of measuring changes in proteins and their interacting partners, isoforms, and post-translational modifications (PTMs) in response to stimuli in the context of signaling pathways and protein networks. With a critical link to genotypes (i.e., high throughput genomics and transcriptomics data), new and complementary information can be gleaned from multi-dimensional omics data to (1) assess the effect of genomic and transcriptomic aberrations on such complex molecular machinery in the context of cell signaling architectures associated with pathological diseases such as cancer (i.e., from genotype to proteotype to phenotype); and (2) target pathway- and network-driven changes and map the fluctuations of these functional units (proteins) responsible for cellular activities in response to perturbation in a spatiotemporal fashion to better understand cancer biology as a whole system.

SysPTM 2.0: an updated systematic resource for post-translational modification

Post-translational modifications (PTMs) of proteins play essential roles in almost all cellular processes, and are closely related to physiological activity and disease development of living organisms. The development of tandem mass spectrometry (MS/MS) has resulted in a rapid increase of PTMs identified on proteins from different species. The collection and systematic ordering of PTM data should provide invaluable information for understanding cellular processes and signaling pathways regulated by PTMs. For this original purpose we developed SysPTM, a systematic resource installed with comprehensive PTM data and a suite of web tools for annotation of PTMs in 2009. Four years later, there has been a significant advance with the generation of PTM data and, consequently, more sophisticated analysis requirements have to be met. Here we submit an updated version of SysPTM 2.0 (http://lifecenter.sgst.cn/SysPTM/), with almost doubled data content, enhanced web-based analysis tools of PTMBlast, PTMPathway, PTMPhylog, PTMCluster. Moreover, a new session SysPTM-H is constructed to graphically represent the combinatorial histone PTMs and dynamic regulation of histone modifying enzymes, and a new tool PTMGO is added for functional annotation and enrichment analysis. SysPTM 2.0 not only facilitates resourceful annotation of PTM sites but allows systematic investigation of PTM functions by the user. Database URL: http://lifecenter.sgst.cn/SysPTM/.

Injury-induced HDAC5 nuclear export is essential for axon regeneration

Reactivation of a silent transcriptional program is a critical step in successful axon regeneration following injury. Yet how such a program is unlocked after injury remains largely unexplored. We found that axon injury in peripheral sensory neurons elicits a back-propagating calcium wave that invades the soma and causes nuclear export of HDAC5 in a PKCμ-dependent manner. Injury-induced HDAC5 nuclear export enhances histone acetylation to activate a proregenerative gene-expression program. HDAC5 nuclear export is required for axon regeneration, as expression of a nuclear-trapped HDAC5 mutant prevents axon regeneration, whereas enhancing HDAC5 nuclear export promotes axon regeneration in vitro and in vivo. Components of this HDAC5 pathway failed to be activated in a model of central nervous system injury. These studies reveal a signaling mechanism from the axon injury site to the soma that controls neuronal growth competence and suggest a role for HDAC5 as a transcriptional switch controlling axon regeneration.

Phosphoproteomics of collagen receptor networks reveals SHP-2 phosphorylation downstream of wild-type DDR2 and its lung cancer mutants

Collagen is an important extracellular matrix component that directs many fundamental cellular processes including differentiation, proliferation and motility. The signalling networks driving these processes are propagated by collagen receptors such as the β1 integrins and the DDRs (discoidin domain receptors). To gain an insight into the molecular mechanisms of collagen receptor signalling, we have performed a quantitative analysis of the phosphorylation networks downstream of collagen activation of integrins and DDR2. Temporal analysis over seven time points identified 424 phosphorylated proteins. Distinct DDR2 tyrosine phosphorylation sites displayed unique temporal activation profiles in agreement with in vitro kinase data. Multiple clustering analysis of the phosphoproteomic data revealed several DDR2 candidate downstream signalling nodes, including SHP-2 (Src homology 2 domain-containing protein tyrosine phosphatase 2), NCK1 (non-catalytic region of tyrosine kinase adaptor protein 1), LYN, SHIP-2 [SH2 (Src homology 2)-domain-containing inositol phosphatase 2], PIK3C2A (phosphatidylinositol-4-phosphate 3-kinase, catalytic subunit type 2α) and PLCL2 (phospholipase C-like 2). Biochemical validation showed that SHP-2 tyrosine phosphorylation is dependent on DDR2 kinase activity. Targeted proteomic profiling of a panel of lung SCC (squamous cell carcinoma) DDR2 mutants demonstrated that SHP-2 is tyrosine-phosphorylated by the L63V and G505S mutants. In contrast, the I638F kinase domain mutant exhibited diminished DDR2 and SHP-2 tyrosine phosphorylation levels which have an inverse relationship with clonogenic potential. Taken together, the results of the present study indicate that SHP-2 is a key signalling node downstream of the DDR2 receptor which may have therapeutic implications in a subset of DDR2 mutations recently uncovered in genome-wide lung SCC sequencing screens.

Vascular endothelial growth factor (VEGF) and platelet (PF-4) factor 4 inputs modulate human microvascular endothelial signaling in a three-dimensional matrix migration context

The process of angiogenesis is under complex regulation in adult organisms, particularly as it often occurs in an inflammatory post-wound environment. As such, there are many impacting factors that will regulate the generation of new blood vessels which include not only pro-angiogenic growth factors such as vascular endothelial growth factor, but also angiostatic factors. During initial postwound hemostasis, a large initial bolus of platelet factor 4 is released into localized areas of damage before progression of wound healing toward tissue homeostasis. Because of its early presence and high concentration, the angiostatic chemokine platelet factor 4, which can induce endothelial anoikis, can strongly affect angiogenesis. In our work, we explored signaling crosstalk interactions between vascular endothelial growth factor and platelet factor 4 using phosphotyrosine-enriched mass spectrometry methods on human dermal microvascular endothelial cells cultured under conditions facilitating migratory sprouting into collagen gel matrices. We developed new methods to enable mass spectrometry-based phosphorylation analysis of primary cells cultured on collagen gels, and quantified signaling pathways over the first 48 h of treatment with vascular endothelial growth factor in the presence or absence of platelet factor 4. By observing early and late signaling dynamics in tandem with correlation network modeling, we found that platelet factor 4 has significant crosstalk with vascular endothelial growth factor by modulating cell migration and polarization pathways, centered around P38α MAPK, Src family kinases Fyn and Lyn, along with FAK. Interestingly, we found EphA2 correlational topology to strongly involve key migration-related signaling nodes after introduction of platelet factor 4, indicating an influence of the angiostatic factor on this ambiguous but generally angiogenic signal in this complex environment.

Mechanosensitive microRNAs-role in endothelial responses to shear stress and redox state

Endothelial functions are highly regulated by imposed shear stress in vivo. The characteristics of shear stress determine mechanotransduction events that regulate phenotypic outcomes including redox and inflammatory states. Recent data indicate that microRNAs (miRs) in vascular endothelial cells play an essential role in shear stress-regulated endothelial responses. More specifically, atheroprotective pulsatile flow (PS) induces miRs that inhibit mediators of oxidative stress and inflammation while promoting those involved in maintaining vascular homeostasis. Conversely, oscillatory flow (OS) elicits the opposing networks. This is exemplified by the PS-responsive transcription factor Krüppel-like factor 2 (KLF2), which regulates miR expression but is also regulated by OS-sensitive miRs to ultimately regulate the oxidative and inflammatory state of the endothelium. In this review, we outline important findings demonstrating the multifaceted roles of shear stress-regulated miRs in endothelial redox and inflammatory balance. Furthermore, we discuss the use of algorithms in deciphering signaling networks differentially regulated by PS and OS.

Molecular network analysis of phosphotyrosine and lipid metabolism in hepatic PTP1b deletion mice

Metabolic syndrome describes a set of obesity-related disorders that increase diabetes, cardiovascular, and mortality risk. Studies of liver-specific protein-tyrosine phosphatase 1b (PTP1b) deletion mice (L-PTP1b(-/-)) suggest that hepatic PTP1b inhibition would mitigate metabolic-syndrome through amelioration of hepatic insulin resistance, endoplasmic-reticulum stress, and whole-body lipid metabolism. However, the altered molecular-network states underlying these phenotypes are poorly understood. We used mass spectrometry to quantify protein-phosphotyrosine network changes in L-PTP1b(-/-) mouse livers relative to control mice on normal and high-fat diets. We applied a phosphosite-set-enrichment analysis to identify known and novel pathways exhibiting PTP1b- and diet-dependent phosphotyrosine regulation. Detection of a PTP1b-dependent, but functionally uncharacterized, set of phosphosites on lipid-metabolic proteins motivated global lipidomic analyses that revealed altered polyunsaturated-fatty-acid (PUFA) and triglyceride metabolism in L-PTP1b(-/-) mice. To connect phosphosites and lipid measurements in a unified model, we developed a multivariate-regression framework, which accounts for measurement noise and systematically missing proteomics data. This analysis resulted in quantitative models that predict roles for phosphoproteins involved in oxidation-reduction in altered PUFA and triglyceride metabolism.

Robust co-regulation of tyrosine phosphorylation sites on proteins reveals novel protein interactions

Cell signaling networks propagate information from extracellular cues via dynamic modulation of protein-protein interactions in a context-dependent manner. Networks based on receptor tyrosine kinases (RTKs), for example, phosphorylate intracellular proteins in response to extracellular ligands, resulting in dynamic protein-protein interactions that drive phenotypic changes. Most commonly used methods for discovering these protein-protein interactions, however, are optimized for detecting stable, longer-lived complexes, rather than the type of transient interactions that are essential components of dynamic signaling networks such as those mediated by RTKs. Substrate phosphorylation downstream of RTK activation modifies substrate activity and induces phospho-specific binding interactions, resulting in the formation of large transient macromolecular signaling complexes. Since protein complex formation should follow the trajectory of events that drive it, we reasoned that mining phosphoproteomic datasets for highly similar dynamic behavior of measured phosphorylation sites on different proteins could be used to predict novel, transient protein-protein interactions that had not been previously identified. We applied this method to explore signaling events downstream of EGFR stimulation. Our computational analysis of robustly co-regulated phosphorylation sites, based on multiple clustering analysis of quantitative time-resolved mass-spectrometry phosphoproteomic data, not only identified known sitewise-specific recruitment of proteins to EGFR, but also predicted novel, a priori interactions. A particularly intriguing prediction of EGFR interaction with the cytoskeleton-associated protein PDLIM1 was verified within cells using co-immunoprecipitation and in situ proximity ligation assays. Our approach thus offers a new way to discover protein-protein interactions in a dynamic context- and phosphorylation site-specific manner.

Molecular characterization of EGFR and EGFRvIII signaling networks in human glioblastoma tumor xenografts

Glioblastoma multiforme (GBM) is a malignant primary brain tumor with a mean survival of 15 months with the current standard of care. Genetic profiling efforts have identified the amplification, overexpression, and mutation of the wild-type (wt) epidermal growth factor receptor tyrosine kinase (EGFR) in ≈ 50% of GBM patients. The genetic aberration of wtEGFR is frequently accompanied by the overexpression of a mutant EGFR known as EGFR variant III (EGFRvIII, de2-7EGFR, ΔEGFR), which is expressed in 30% of GBM tumors. The molecular mechanisms of tumorigenesis driven by EGFRvIII overexpression in human tumors have not been fully elucidated. To identify specific therapeutic targets for EGFRvIII driven tumors, it is important to gather a broad understanding of EGFRvIII specific signaling. Here, we have characterized signaling through the quantitative analysis of protein expression and tyrosine phosphorylation across a panel of glioblastoma tumor xenografts established from patient surgical specimens expressing wtEGFR or overexpressing wtEGFR (wtEGFR+) or EGFRvIII (EGFRvIII+). S100A10 (p11), major vault protein, guanylate-binding protein 1(GBP1), and carbonic anhydrase III (CAIII) were identified to have significantly increased expression in EGFRvIII expressing xenograft tumors relative to wtEGFR xenograft tumors. Increased expression of these four individual proteins was found to be correlated with poor survival in patients with GBM; the combination of these four proteins represents a prognostic signature for poor survival in gliomas. Integration of protein expression and phosphorylation data has uncovered significant heterogeneity among the various tumors and has highlighted several novel pathways, related to EGFR trafficking, activated in glioblastoma. The pathways and proteins identified in these tumor xenografts represent potential therapeutic targets for this disease.

Specification, annotation, visualization and simulation of a large rule-based model for ERBB receptor signaling

BACKGROUND

Mathematical/computational models are needed to understand cell signaling networks, which are complex. Signaling proteins contain multiple functional components and multiple sites of post-translational modification. The multiplicity of components and sites of modification ensures that interactions among signaling proteins have the potential to generate myriad protein complexes and post-translational modification states. As a result, the number of chemical species that can be populated in a cell signaling network, and hence the number of equations in an ordinary differential equation model required to capture the dynamics of these species, is prohibitively large. To overcome this problem, the rule-based modeling approach has been developed for representing interactions within signaling networks efficiently and compactly through coarse-graining of the chemical kinetics of molecular interactions.

RESULTS

Here, we provide a demonstration that the rule-based modeling approach can be used to specify and simulate a large model for ERBB receptor signaling that accounts for site-specific details of protein-protein interactions. The model is considered large because it corresponds to a reaction network containing more reactions than can be practically enumerated. The model encompasses activation of ERK and Akt, and it can be simulated using a network-free simulator, such as NFsim, to generate time courses of phosphorylation for 55 individual serine, threonine, and tyrosine residues. The model is annotated and visualized in the form of an extended contact map.

CONCLUSIONS

With the development of software that implements novel computational methods for calculating the dynamics of large-scale rule-based representations of cellular signaling networks, it is now possible to build and analyze models that include a significant fraction of the protein interactions that comprise a signaling network, with incorporation of the site-specific details of the interactions. Modeling at this level of detail is important for understanding cellular signaling.

Toward quantitative phosphotyrosine profiling in vivo

Tyrosine phosphorylation is a dynamic reversible post-translational modification that regulates many aspects of cell biology. To understand how this modification controls biological function, it is necessary to not only identify the specific sites of phosphorylation, but also to quantify how phosphorylation levels on these sites may be altered under specific physiological conditions. Due to its sensitivity and accuracy, mass spectrometry (MS) has widely been applied to the identification and characterization of phosphotyrosine signaling across biological systems. In this review we highlight the advances in both MS and phosphotyrosine enrichment methods that have been developed to enable the identification of low level tyrosine phosphorylation events. Computational and manual approaches to ensure confident identification of phosphopeptide sequence and determination of phosphorylation site localization are discussed along with methods that have been applied to the relative quantification of large numbers of phosphorylation sites. Finally, we provide an overview of the challenges ahead as we extend these technologies to the characterization of tyrosine phosphorylation signaling in vivo. With these latest developments in analytical and computational techniques, it is now possible to derive biological insight from quantitative MS-based analysis of signaling networks in vitro and in vivo. Application of these approaches to a wide variety of biological systems will define how signal transduction regulates cellular physiology in health and disease.

Computational approaches for analyzing information flow in biological networks

The advancements in "omics" (proteomics, genomics, lipidomics, and metabolomics) technologies have yielded large inventories of genes, transcripts, proteins, and metabolites. The challenge is to find out how these entities work together to regulate the processes by which cells respond to external and internal signals. Mathematical and computational modeling of signaling networks has a key role in this task, and network analysis provides insights into biological systems and has applications for medicine. Here, we review experimental and theoretical progress and future challenges toward this goal. We focus on how networks are reconstructed from data, how these networks are structured to control the flow of biological information, and how the design features of the networks specify biological decisions.

Swimming upstream: identifying proteomic signals that drive transcriptional changes using the interactome and multiple "-omics" datasets

Signaling and transcription are tightly integrated processes that underlie many cellular responses to the environment. A network of signaling events, often mediated by post-translational modification on proteins, can lead to long-term changes in cellular behavior by altering the activity of specific transcriptional regulators and consequently the expression level of their downstream targets. As many high-throughput, "-omics" methods are now available that can simultaneously measure changes in hundreds of proteins and thousands of transcripts, it should be possible to systematically reconstruct cellular responses to perturbations in order to discover previously unrecognized signaling pathways. This chapter describes a computational method for discovering such pathways that aims to compensate for the varying levels of noise present in these diverse data sources. Based on the concept of constraint optimization on networks, the method seeks to achieve two conflicting aims: (1) to link together many of the signaling proteins and differentially expressed transcripts identified in the experiments "constraints" using previously reported protein-protein and protein-DNA interactions, while (2) keeping the resulting network small and ensuring it is composed of the highest confidence interactions "optimization". A further distinctive feature of this approach is the use of transcriptional data as evidence of upstream signaling events that drive changes in gene expression, rather than as proxies for downstream changes in the levels of the encoded proteins. We recently demonstrated that by applying this method to phosphoproteomic and transcriptional data from the pheromone response in yeast, we were able to recover functionally coherent pathways and to reveal many components of the cellular response that are not readily apparent in the original data. Here, we provide a more detailed description of the method, explore the robustness of the solution to the noise level of input data and discuss the effect of parameter values.

Proteome-wide post-translational modification statistics: frequency analysis and curation of the swiss-prot database

Post-translational modifications (PTMs) broadly contribute to the recent explosion of proteomic data and possess a complexity surpassing that of protein design. PTMs are the chemical modification of a protein after its translation, and have wide effects broadening its range of functionality. Based on previous estimates, it is widely believed that more than half of proteins are glycoproteins. Whereas mutations can only occur once per position, different forms of post-translational modifications may occur in tandem. With the number and abundances of modifications constantly being discovered, there is no method to readily assess their relative levels. Here we report the relative abundances of each PTM found experimentally and putatively, from high-quality, manually curated, proteome-wide data, and show that at best, less than one-fifth of proteins are glycosylated. We make available to the academic community a continuously updated resource (http://selene.princeton.edu/PTMCuration) containing the statistics so scientists can assess “how many” of each PTM exists.

MCAM: multiple clustering analysis methodology for deriving hypotheses and insights from high-throughput proteomic datasets

Advances in proteomic technologies continue to substantially accelerate capability for generating experimental data on protein levels, states, and activities in biological samples. For example, studies on receptor tyrosine kinase signaling networks can now capture the phosphorylation state of hundreds to thousands of proteins across multiple conditions. However, little is known about the function of many of these protein modifications, or the enzymes responsible for modifying them. To address this challenge, we have developed an approach that enhances the power of clustering techniques to infer functional and regulatory meaning of protein states in cell signaling networks. We have created a new computational framework for applying clustering to biological data in order to overcome the typical dependence on specific a priori assumptions and expert knowledge concerning the technical aspects of clustering. Multiple clustering analysis methodology (‘MCAM’) employs an array of diverse data transformations, distance metrics, set sizes, and clustering algorithms, in a combinatorial fashion, to create a suite of clustering sets. These sets are then evaluated based on their ability to produce biological insights through statistical enrichment of metadata relating to knowledge concerning protein functions, kinase substrates, and sequence motifs. We applied MCAM to a set of dynamic phosphorylation measurements of the ERRB network to explore the relationships between algorithmic parameters and the biological meaning that could be inferred and report on interesting biological predictions. Further, we applied MCAM to multiple phosphoproteomic datasets for the ERBB network, which allowed us to compare independent and incomplete overlapping measurements of phosphorylation sites in the network. We report specific and global differences of the ERBB network stimulated with different ligands and with changes in HER2 expression. Overall, we offer MCAM as a broadly-applicable approach for analysis of proteomic data which may help increase the current understanding of molecular networks in a variety of biological problems.

Proteomic measurements, especially modification measurements, are greatly expanding the current knowledge of the state of proteins under various conditions. Harnessing these measurements to understand how these modifications are enzymatically regulated and their subsequent function in cellular signaling and physiology is a challenging new problem. Clustering has been very useful in reducing the dimensionality of many types of high-throughput biological data, as well inferring function of poorly understood molecular species. However, its implementation requires a great deal of technical expertise since there are a large number of parameters one must decide on in clustering, including data transforms, distance metrics, and algorithms. Previous knowledge of useful parameters does not exist for measurements of a new type. In this work we address two issues. First, we develop a framework that incorporates any number of possible parameters of clustering to produce a suite of clustering solutions. These solutions are then judged on their ability to infer biological information through statistical enrichment of existing biological annotations. Second, we apply this framework to dynamic phosphorylation measurements of the ERBB network, constructing the first extensive analysis of clustering of phosphoproteomic data and generating insight into novel components and novel functions of known components of the ERBB network.

Phosphoproteomics of vasopressin signaling in the kidney

Protein phosphorylation plays a critical role in the signaling pathways regulating water and solute transport in the distal renal tubule (i.e., renal collecting duct). A central mediator in this process is the antidiuretic peptide hormone arginine vasopressin, which regulates a number of transport proteins including water channel aquaporin-2 and urea transporters (UT-A1 and UT-A3). Within the past few years, tandem mass spectrometry-based proteomics has played a pivotal role in revealing global changes in the phosphoproteome in response to vasopressin signaling in the renal collecting duct. This type of large-scale 'shotgun' approach has resulted in an exponential increase in the number of phosphoproteins known to be regulated by vasopressin and has expanded on the established signaling mechanisms and kinase pathways regulating collecting duct physiology. This article will provide a brief background on vasopressin action, will highlight a number of recent quantitative phosphoproteomic studies in both native rat kidney and cultured collecting duct cells, and will conclude with a perspective focused on emerging trends in the field of phosphoproteomics.

The potential cost of high-throughput proteomics

Improvements in speed and mass accuracy of mass spectrometers revolutionized proteomics, with high-throughput proteomics enabling the profiling of complete proteomes and thousands of posttranslational modification sites. The limits of high-throughput proteomics are constantly pushed to new frontiers, and mass spectrometry-based proteomics may eventually permit the analysis of protein expression profiles in less than a day. Increased data acquisition speed has led to a dramatic increase in the total number of tandem mass spectrometry (MS/MS) spectra, such that millions of MS/MS spectra are now acquired in a given set of analyses. Many of these spectra are insufficiently validated; instead, statistical tools are commonly used to estimate false-positive or false-discovery rates for these data sets. Many laboratories may not realize the costs associated with using these widely available, but minimally validated, data sets. The costs associated with use of these data can include missed opportunities for biological insight, the pollution of databases with increasing numbers of false-positive identifications, and time spent by biologists investigating false leads, resulting in a lack of faith in proteomics data. Improved strategies for data validation need to be implemented, along with a change in the culture of high-throughput proteomics, linking proteomics closer to biology.