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Ubiquitin-specific protease (USP7), also known as Herpesvirus-associated ubiquitin-specific protease (HAUSP), is a deubiquitinase. There has been significant recent attention on USP7 following the discovery that USP7 is a key regulator of the p53-MDM2 pathway. The USP7 protein is 130 kDa in size and has multiple domains which bind to a diverse set of proteins. These interactions mediate key developmental and homeostatic processes including the cell cycle, immune response, and modulation of transcription factor and epigenetic regulator activity and localization. USP7 also promotes carcinogenesis through aberrant activation of the Wnt signalling pathway and stabilization of HIF-1α. These findings have shown that USP7 may induce tumour progression and be a therapeutic target. Together with interest in developing USP7 as a target, several studies have defined new protein interactions and the regulatory networks within which USP7 functions. In this review, we focus on the protein interactions of USP7 that are most important for its cancer-associated roles.

DNA methylation is a critical epigenetic modification that is established and maintained across the genome by DNA methyltransferase enzymes (Dnmts). Altered patterns of DNA methylation are a frequent occurrence in many tumor genomes, and inhibitors of Dnmts have become important epigenetic drugs. Azacitidine is a cytidine analog that is incorporated into DNA and induces the specific inhibition and proteasomal-mediated degradation of Dnmts. The downstream effects of azacitidine on CpG methylation and on gene transcription have been widely studied in many systems, but how azacitidine impacts the proteome is not well-understood. In addition, with its specific ability to induce the rapid degradation of Dnmts (in particular, the primary maintenance DNA methyltransferase, Dnmt1), it may be employed as a specific chemical knockdown for investigating the Dnmt1-associated functional or physical interactome. In this study, we use quantitative proteomics to analyze the degradation profile of proteins in the nuclear proteome of cells treated with azacitidine. We identify specific proteins as well as multiple pathways and processes that are impacted by azacitidine. The Dnmt1 interaction partner, Uhrf1, exhibits significant azacitidine-induced degradation, and this azacitidine-induced degradation is independent of the levels of Dnmt1 protein. We identify multiple other chromatin- and epigenetic-associated factors, including the bromodomain-containing transcriptional regulator, Brd2. We show that azacitidine induces highly specific perturbations of the Dnmt1-associated proteome, and while interaction partners such as Uhrf1 are sensitive to azacitidine, others such as the Dnmt1 interaction partner and stability regulator, Usp7, are not. In summary, we have conducted the first comprehensive proteomic analysis of the azacitidine-sensitive nuclear proteome, and we show how 5-azacitidine can be used as a specific probe to explore Dnmt- and chromatin-related protein networks.

Glycogen-specific kinase (GSK3β) is an integral regulator of the Wnt signalling pathway as well as many other diverse signalling pathways and processes. Dys-regulation of GSK3β is implicated in many different pathologies, including neurodegenerative disorders as well as many different tumour types. In the context of tumour development, GSK3β has been shown to play both oncogenic and tumour suppressor roles, depending upon tissue, signalling environment or disease progression. Although multiple substrates of the GSK3β kinase have been identified, the wider protein networks within which GSK3β participates are not well known, and the consequences of these interactions not well understood. In this study, LC-MS/MS expression analysis was performed using knockout GSK3β colorectal cancer cells and isogenic controls in colorectal cancer cell lines carrying dominant stabilizing mutations of β-catenin. Consistent with the role of GSK3β, we found that β-catenin levels and canonical Wnt activity are unaffected by knockout of GSK3β and therefore used this knockout cell model to identify other processes in which GSK3β is implicated. Quantitative proteomic analysis revealed perturbation of proteins involved in cell-cell adhesion, and we characterized the phenotype and altered proteomic profiles associated with this. We also characterized the perturbation of metabolic pathways resulting from GSK3β knockout and identified defects in glycogen metabolism. In summary, using a precision colorectal cancer cell-line knockout model with constitutively activated β-catenin we identified several of the diverse pathways and processes associated with GSK3β function.

Pluripotent stem cells (PSCs) are a popular model system for investigating development, tissue regeneration, and repair. Although much is known about the molecular mechanisms that regulate the balance between self-renewal and lineage commitment in PSCs, the spatiotemporal integration of responsive signaling pathways with core transcriptional regulatory networks are complex and only partially understood. Moreover, measurements made on populations of cells reveal only average properties of the underlying regulatory networks, obscuring their fine detail. Here, we discuss the reconstruction of regulatory networks in individual cells using novel single-cell transcriptomics and proteomics, in order to expand our understanding of the molecular basis of pluripotency, including the role of cell–cell variability within PSC populations, and ways in which networks may be controlled in order to reliably manipulate cell behaviorior.

Computational tools for multiomics data integration have usually been designed for unsupervised detection of multiomics features explaining large phenotypic variations. To achieve this, some approaches extract latent signals in heterogeneous data sets from a joint statistical error model, while others use biological networks to propagate differential expression signals and find consensus signatures. However, few approaches directly consider molecular interaction as a data feature, the essential linker between different omics data sets. The increasing availability of genome-scale interactome data connecting different molecular levels motivates a new class of methods to extract interactive signals from multiomics data. Here we developed iOmicsPASS, a tool to search for predictive subnetworks consisting of molecular interactions within and between related omics data types in a supervised analysis setting. Based on user-provided network data and relevant omics data sets, iOmicsPASS computes a score for each molecular interaction, and applies a modified nearest shrunken centroid algorithm to the scores to select densely connected subnetworks that can accurately predict each phenotypic group. iOmicsPASS detects a sparse set of predictive molecular interactions without loss of prediction accuracy compared to alternative methods, and the selected network signature immediately provides mechanistic interpretation of the multiomics profile representing each sample group. Extensive simulation studies demonstrate clear benefit of interaction-level modeling. iOmicsPASS analysis of TCGA/CPTAC breast cancer data also highlights new transcriptional regulatory network underlying the basal-like subtype as positive protein markers, a result not seen through analysis of individual omics data.

The acquisition of mutations that activate oncogenes or inactivate tumor suppressors is a primary feature of most cancers. Mutations that directly alter protein sequence and structure drive the development of tumors through aberrant expression and modification of proteins, in many cases directly impacting components of signal transduction pathways and cellular architecture. Cancer-associated mutations may have direct or indirect effects on proteins and their interactions and while the effects of mutations on signaling pathways have been widely studied, how mutations alter underlying protein–protein interaction networks is much less well understood. Systematic mapping of oncoprotein protein interactions using proteomics techniques as well as computational network analyses is revealing how oncoprotein mutations perturb protein–protein interaction networks and drive the cancer phenotype.

The increase in quantity of spatial proteomics data requires a range of analytical techniques to effectively analyse the data. We provide a method of integrating spatial proteomics data together with protein-protein interaction (PPI) networks to enable the extraction of more information. A strong relationship between spatial proteomics and PPI network data was demonstrated. Then a method of converting the PPI network into vectors using spatial proteomics data was explained which allows the integration of the two datasets. The resulting vectors were tested using machine learning techniques and reasonable predictive accuracy was found.

Wnt signalling is a fundamentally important signalling pathway that regulates many aspects of metazoan development and is frequently dysregulated in cancer. Although many of the core components of the Wnt signalling pathway, such as β-catenin, have been extensively studied, the broad systems level responses of the mammalian cell to Wnt signalling are less well understood. In addition, the cell- or tissue-specific protein networks that modulate Wnt signalling in the diverse tissues or developmental stages in which it functions remain to be defined. To address these questions, we undertook a broad survey of the Wnt response in different human cell lines using both interaction and expression proteomics approaches. Our data reveal both similar and divergent responses of pathways and processes in the three cell-lines analyzed as well as a marked attenuation of the response to exogenous Wnt treatment in cells harbouring a stabilizing (activating) mutation of β-catenin. We also identify cell-type specific components of the Wnt signalling network and find that by integrating expression and interaction proteomics data a more complete description of the Wnt interaction network can be achieved. Finally, our results attest to the power of LC-MS/MS to reveal novel cellular responses in even relatively well studied biological pathways such as Wnt signalling.

Affinity-purification mass spectrometry (AP-MS) is the preeminent technique for identification of eukaryotic protein complexes in vivo. AP-MS workflows typically express epitope-tagged bait proteins, immunopurify, and then identify associated protein complexes using mass spectrometry. However, challenges of existing strategies include the construction of expression vectors for large open reading frames and the possibility that overexpression of bait proteins may result in expression of nonphysiological levels of the bait protein with concomitant perturbation of endogenous protein complexes. To address these issues, we use human cell lines with epitope-tagged endogenous genes as AP-MS substrates to develop a platform that we call "knock-in AP-MS", thereby avoiding the challenges of expression vector construction and ensuring that expression of tagged proteins is driven by endogenous regulatory mechanisms. Using three different bait genes (MRE11A, DNMT1 and APC), we show that cell lines expressing epitope-tagged endogenous genes make good substrates for sensitive and reproducible identification of protein interactions using AP-MS. In particular, we identify novel interactors of the important oncoprotein Adenomatous Polyposis Coli (APC), including an interaction with Flightless-1 homologue (FLII) that is enriched in nuclear fractions.

Liquid chromatography-mass spectrometry (LC-MS)-based proteomics is becoming an increasingly important tool in characterizing the abundance of proteins in biological samples of various types and across conditions. Effects of disease or drug treatments on protein abundance are of particular interest for the characterization of biological processes and the identification of biomarkers. Although state-of-the-art instrumentation is available to make high-quality measurements and commercially available software is available to process the data, the complexity of the technology and data presents challenges for bioinformaticians and statisticians. Here, we describe a pipeline for the analysis of quantitative LC-MS data. Key components of this pipeline include experimental design (sample pooling, blocking, and randomization) as well as deconvolution and alignment of mass chromatograms to generate a matrix of molecular abundance profiles. An important challenge in LC-MS-based quantitation is to be able to accurately identify and assign abundance measurements to members of protein families. To address this issue, we implement a novel statistical method for inferring the relative abundance of related members of protein families from tryptic peptide intensities. This pipeline has been used to analyze quantitative LC-MS data from multiple biomarker discovery projects. We illustrate our pipeline here with examples from two of these studies, and show that the pipeline constitutes a complete workable framework for LC-MS-based differential quantitation. Supplementary material is available at http://iec01.mie.utoronto.ca/~thodoros/Bukhman/.

Aberrant activation of the Wnt signaling pathway is an important step in the initiation and progression of tumor development in diverse cancers. The central effector of canonical Wnt signaling, β-catenin (CTNNB1), is a multifunctional protein, and has been extensively studied with respect to its roles in cell–cell adhesion and in regulation of Wnt-driven transcription. Here, a novel mass spectrometry–based proteomics technique in colorectal cancer cells expressing stabilized β-catenin, was used to identify a protein–protein interaction between β-catenin and DNA methyltransferase I (Dnmt1) protein, the primary regulator of DNA methylation patterns in mammalian cells. Dnmt1 and β-catenin strongly colocalized in the nuclei of colorectal cancer cells, and the interaction is mediated by the central domain of the Dnmt1 protein. Dnmt1 protein abundance is dependent upon the levels of β-catenin, and is increased in cells expressing stabilized mutant β-catenin. Conversely, the Dnmt1 regulates the levels of nuclear β-catenin and β-catenin/TCF–driven transcription. In addition, lysine-specific demethylase 1 (LSD1/KDM1A), a regulator of DNMT1 stability, was identified as a component of the Dnmt1–β-catenin protein complex and perturbation of the Dnmt1–β-catenin interaction altered DNA methylation. In summary, a functional protein–protein interaction was identified between two critically important oncoproteins, in turn revealing a link between Wnt signaling and downstream nuclear functions mediated by Dnmt1. Implications: Two critical oncoproteins, Dnmt1 and β-catenin, mutually regulate one each other's levels and activities in colorectal cancer cells. Mol Cancer Res; 13(6); 969–81. ©2015 AACR.

The ability to quantitatively compare protein levels across different regions of the brain to identify disease mechanisms remains a fundamental research challenge. It requires both a robust method to efficiently isolate proteins from small amounts of tissue and a differential technique that provides a sensitive and comprehensive analysis of these proteins. Here, we describe a proteomic approach for the quantitative mapping of membrane proteins between mouse fore- and hindbrain regions. The approach focuses primarily on a recently developed method for the fractionation of membranes and on-membrane protein digestion, but incorporates off-line SCX-fractionation of the peptide mixture and nano-LC-MS/MS analysis using an LTQ-FT-ICR instrument as part of the analytical method. Comparison of mass spectral peak intensities between samples, mapping of peaks to peptides and protein sequences, and statistical analysis were performed using in-house differential analysis software (DAS). In total, 1213 proteins were identified and 967 were quantified; 81% of the identified proteins were known membrane proteins and 38% of the protein sequences were predicted to contain transmembrane helices. Although this paper focuses primarily on characterizing the efficiency of this purification method from a typical sample set, for many of the quantified proteins such as glutamate receptors, GABA receptors, calcium channel subunits, and ATPases, the observed ratios of protein abundance were in good agreement with the known mRNA expression levels and/or intensities of immunostaining in rostral and caudal regions of murine brain. This suggests that the approach would be well-suited for incorporation in more rigorous, larger scale quantitative analysis designed to achieve biological significance.

An in-depth study of the reproducibility of data acquired for comparative proteomics analysis using a prototype two-stage heated laminar flow chamber fitted to a commercial high-performance liquid chromatography/tandem mass spectrometry (HPLC/MS/MS) instrument was undertaken. The study is based on 24 replicate samples from four independent membrane preparations derived from two matched breast cancer cell lines. Variation and reproducibility in the data were evaluated at several levels highlighting the relative efficiency and variability of the acquisition routines used. Specifically, variation in the number and relative intensities of chromatographic peaks eluted from the LC column, precursor ion selection and sequence identification were evaluated. On average, approximately 6500 chromatographic peaks were generated for each acquisition with a corresponding coefficient of variance (CV) of less than 20%. Precursor ion selection and sequence identification averaged 1380 and 780 events per acquisition sample, respectively, with corresponding CVs of less than 10% for each. The reproducibility in the precursor ion selection was typically better than 60% between similar replicates. Using protein and peptide internal standards, it was found that the CV in retention time across the gradient between two acquisition pairs was typically less than 5%, whereas the average intensity ratio was 1.0 (expected) with a CV approaching 20%. An evaluation of the intensity ratios calculated from endogenous peptide sequences, identified across the acquisition set, indicated a CV of ∼30%. Similarly, the CV associated with the top 1000 peptides indicated a mean and median of 28.4 and 26.95%. For a given acquisition pair it was also found that ∼11% of the chromatographic peaks eluting from the column were linked to a sequence or identified. For these experiments, less than 10% of the peak pairs had absolute ratios greater than 2.0 and of those only ∼10% had sequences linked to them. For each matched acquisition set on average 406 proteins were identified with a CV of less than 10%. Of the proteins that were identified approximately 30% had at least one predicted trans-membrane domain, indicating a four-fold increase over a crude homogenate sample with only minor enrichment. During these experiments it was found that the interface did not significantly alter the relative charge state distribution of ions, nor did it introduce significant interference from background ions. The interface was capable of 24-hour acquisition cycles. Copyright © 2004 John Wiley & Sons, Ltd.

Mycobacterium tuberculosis (Mtb) cell wall glycolipid mannose‐capped lipoarabinomannan (ManLAM) inhibits CD4+ T‐cell activation by inhibiting proximal T‐cell receptor (TCR) signaling when activated by anti‐CD3. To understand the impact of ManLAM on CD4+ T‐cell function when both the TCR–CD3 complex and major costimulator CD28 are engaged, we performed label‐free quantitative MS and network analysis. Mixed‐effect model analysis of peptide intensity identified 149 unique peptides representing 131 proteins that were differentially regulated by ManLAM in anti‐CD3‐ and anti‐CD28‐activated CD4+ T cells. Crosstalker, a novel network analysis tool identified dysregulated translation, TCA cycle, and RNA metabolism network modules. PCNA, Akt, mTOR, and UBC were found to be bridge node proteins connecting these modules of dysregulated proteins. Altered PCNA expression and cell cycle analysis showed arrest at the G2M phase. Western blot confirmed that ManLAM inhibited Akt and mTOR phosphorylation, and decreased expression of deubiquitinating enzymes Usp9x and Otub1. Decreased NF‐κB phosphorylation suggested interference with CD28 signaling through inhibition of the Usp9x‐Akt‐mTOR pathway. Thus, ManLAM induced global changes in the CD4+ T‐cell proteome by affecting Akt‐mTOR signaling, resulting in broad functional impairment of CD4+ T‐cell activation beyond inhibition of proximal TCR–CD3 signaling.

Background One of the primary challenges in translational research data management is breaking down the barriers between the multiple data silos and the integration of 'omics data with clinical information to complete the cycle from the bench to the bedside. The role of contextual metadata, also called provenance information, is a key factor ineffective data integration, reproducibility of results, correct attribution of original source, and answering research queries involving "W hat", "W here", "W hen", "W hich", "W ho", "How", and "W hy" (also known as the W7 model). But, at present there is limited or no effective approach to managing and leveraging provenance information for integrating data across studies or projects. Hence, there is an urgent need for a paradigm shift in creating a "provenance-aware" informatics platform to address this challenge. We introduce an ontology-driven, intuitive Sem antic P ro teomics D ashboard (SemPoD) that uses provenance together with domain information (semantic provenance) to enable researchers to query, compare, and correlate different types of data across multiple projects, and allow integration with legacy data to support their ongoing research. Results The SemPoD platform, currently in use at the Case Center for Proteomics and Bioinformatics (CPB), consists of three components: (a) Ontology-driven Visual Query Composer, (b) Result Explorer, and (c) Query Manager. Currently, SemPoD allows provenance-aware querying of 1153 mass-spectrometry experiments from 20 different projects. SemPod uses the systems molecular biology provenance ontology (SysPro) to support a dynamic query composition interface, which automatically updates the components of the query interface based on previous user selections and efficientlyprunes the result set usinga "smart filtering" approach. The SysPro ontology re-uses terms from the PROV-ontology (PROV-O) being developed by the World Wide Web Consortium (W3C) provenance working group, the minimum information required for reporting a molecular interaction experiment (MIMIx), and the minimum information about a proteomics experiment (MIAPE) guidelines. The SemPoD was evaluated both in terms of user feedback and as scalability of the system. Conclusions SemPoD is an intuitive and powerful provenance ontology-driven data access and query platform that uses the MIAPE and MIMIx metadata guideline to create an integrated view over large-scale systems molecular biology datasets. SemPoD leverages the SysPro ontology to create an intuitive dashboard for biologists to compose queries, explore the results, and use a query manager for storing queries for later use. SemPoD can be deployed over many existing database applications storing 'omics data, including, as illustrated here, the LabKey data-management system. The initial user feedback evaluating the usability and functionality of SemPoD has been very positive and it is being considered for wider deployment beyond the proteomics domain, and in other 'omics' centers.

Background Affinity-Purification Mass-Spectrometry (AP-MS) provides a powerful means of identifying protein complexes and interactions. Several important challenges exist in interpreting the results of AP-MS experiments. First, the reproducibility of AP-MS experimental replicates can be low, due both to technical variability and the dynamic nature of protein interactions in the cell. Second, the identification of true protein-protein interactions in AP-MS experiments is subject to inaccuracy due to high false negative and false positive rates. Several experimental approaches can be used to mitigate these drawbacks, including the use of replicated and control experiments and relative quantification to sensitively distinguish true interacting proteins from false ones. Methods To address the issues of reproducibility and accuracy of protein-protein interactions, we introduce a two-step method, called ROCS, which makes use of Indicator Prey Proteins to select reproducible AP-MS experiments, and of Confidence Scores to select specific protein-protein interactions. The Indicator Prey Proteins account for measures of protein identifiability as well as protein reproducibility, effectively allowing removal of outlier experiments that contribute noise and affect downstream inferences. The filtered set of experiments is then used in the Protein-Protein Interaction (PPI) scoring step. Prey protein scoring is done by computing a Confidence Score, which accounts for the probability of occurrence of prey proteins in the bait experiments relative to the control experiment, where the significance cutoff parameter is estimated by simultaneously controlling false positives and false negatives against metrics of false discovery rate and biological coherence respectively. In summary, the ROCS method relies on automatic objective criterions for parameter estimation and error-controlled procedures. Results We illustrate the performance of our method by applying it to five previously published AP-MS experiments, each containing well characterized protein interactions, allowing for systematic benchmarking of ROCS. We show that our method may be used on its own to make accurate identification of specific, biologically relevant protein-protein interactions, or in combination with other AP-MS scoring methods to significantly improve inferences. Conclusions Our method addresses important issues encountered in AP-MS datasets, making ROCS a very promising tool for this purpose, either on its own or in conjunction with other methods. We anticipate that our methodology may be used more generally in proteomics studies and databases, where experimental reproducibility issues arise. The method is implemented in the R language, and is available as an R package called “ROCS”, freely available from the CRAN repository http://cran.r-project.org/.

Protein interaction network maps have been generated for multiple species, making use of large-scale methods such as yeast two-hybrid (Y2H) and affinity purification mass spectrometry (AP-MS). These methods take fundamentally different approaches toward characterizing protein networks, and the resulting data sets provide complementary views of the protein interactome. The specific determinants of the outcome of Y2H and AP-MS experiments, in terms of detection of interacting proteins are, however, poorly understood. Here we show that a statistical model built using sequence- and annotation-based features of bait proteins is able to identify bait features that are significant determinants of the outcome of interaction proteomics experiments. We show that bait features are able to explain in part the disparities observed between Y2H and AP-MS constructed networks and can be used to derive the "bait compatibility index", a numeric score that assesses the compatibility of bait proteins with each technology. Aside from understanding the bias and limitations of interaction proteomics, our approach provides a rational, data-driven method for prioritization of baits for interaction proteomics experiments, an essential requirement for future proteome-wide applications of these technologies.

Mass–spectrometry-based quantification methods have been increasingly applied to measure proteomic changes in biological systems between different physiological states. In this report, we compared two popular mass-spectrometry-based quantification strategies, stable isotope labeling and label free approaches. We spiked known amounts of standard peptides into a complex biological sample and analyzed this mixture with both stable isotope 18O labeling and label free mass spectrometry methods. We optimized data pre-processing and normalization algorithms for each method, and compared their sensitivities and accuracies. We found that both methods gave relatively accurate results, and the label free methods provided higher proteome coverage.

We used a systems biology approach to identify and score protein interaction subnetworks whose activity patterns are discriminative of late stage human colorectal cancer (CRC) versus control in colonic tissue. We conducted two gel-based proteomics experiments to identify significantly changing proteins between normal and late stage tumor tissues obtained from an adequately sized cohort of human patients. A total of 67 proteins identified by these experiments was used to seed a search for protein-protein interaction subnetworks. A scoring scheme based on mutual information, calculated using gene expression data as a proxy for subnetwork activity, was developed to score the targets in the subnetworks. Based on this scoring, the subnetwork was pruned to identify the specific protein combinations that were significantly discriminative of late stage cancer versus control. These combinations could not be discovered using only proteomics data or by merely clustering the gene expression data. We then analyzed the resultant pruned subnetwork for biological relevance to human CRC. A number of the proteins in these smaller subnetworks have been associated with the progression (CSNK2A2, PLK1, and IGFBP3) or metastatic potential (PDGFRB) of CRC. Others have been recently identified as potential markers of CRC (IFITM1), and the role of others is largely unknown in this disease (CCT3, CCT5, CCT7, and GNA12). The functional interactions represented by these signatures provide new experimental hypotheses that merit follow-on validation for biological significance in this disease. Overall the method outlines a quantitative approach for integrating proteomics data, gene expression data, and the wealth of accumulated legacy experimental data to discover significant protein subnetworks specific to disease.

Proteomic technologies, such as yeast two-hybrid, mass spectrometry (MS), protein/peptide arrays and fluorescence microscopy, yield multi-dimensional data sets, which are often quite large and either not published or published as supplementary information that is not easily searchable. Without a system in place for standardizing and sharing data, it is not fruitful for the biomedical community to contribute these types of data to centralized repositories.

The success of mass spectrometry-based proteomics in emerging applications such as biomarker discovery and clinical diagnostics, is predicated substantially on its ability to achieve growing demands for throughput. Support for high throughput implies sophisticated tracking of experiments and the experimental steps, larger amounts of data to be organized and summarized, more complex algorithms for inferring and tracking protein expression across multiple experiments, statistical methods to access data quality, and a streamlined proteomics-centric bioinformatics environment to establish the biological context and relevance of the experimental measurements. This paper presents a bioinformatics platform that was built for an industrial mass spectrometry-based proteomics laboratory focusing on biomarker discovery. The basis of the platform is a robust and scalable information management environment supported by database and workflow management technology that is employed for the integration of heterogeneous data, applications and processes across the entire laboratory workflow. This paper focuses on selected features of the platform which include: (a) a method for improving the accuracy of protein assignment, (b) novel software tools for protein expression analysis that combine differential MS quantitation with tandem MS for peptide identification, and (c) integration of methods to aid the biological relevance and statistical significance of differentially expressed proteins.

Protein phosphorylation is a key mechanism of cell regulation in normal and cancer cells. Various new cancer drugs and drug candidates are aimed at protein kinase targets. However, selecting patients likely to respond to these treatments, even among individuals with tumors expressing validated kinase targets remains a major challenge. There exists a need for biomarkers to facilitate the monitoring of modulation of drug-targeted kinase pathways. Phospho-proteomics involves the enrichment of phosphorylated proteins from tissue, and the application of technologies such as mass spectrometry (MS) for the identification and quantification of protein phosphorylation sites. It has potential to provide pharmacodynamic readouts of disease states and cellular drug responses in tumor samples, but technical hurdles and bioinformatics challenges will need to be addressed.

Affinity purification coupled with mass spectrometry (AP-MS) is a widely used approach for the identification of protein-protein interactions. However, for any given protein of interest, determining which of the identified polypeptides represent bona fide interactors versus those that are background contaminants (for example, proteins that interact with the solid-phase support, affinity reagent or epitope tag) is a challenging task. The standard approach is to identify nonspecific interactions using one or more negative-control purifications, but many small-scale AP-MS studies do not capture a complete, accurate background protein set when available controls are limited. Fortunately, negative controls are largely bait independent. Hence, aggregating negative controls from multiple AP-MS studies can increase coverage and improve the characterization of background associated with a given experimental protocol. Here we present the contaminant repository for affinity purification (the CRAPome) and describe its use for scoring protein-protein interactions. The repository (currently available for Homo sapiens and Saccharomyces cerevisiae) and computational tools are freely accessible at http://www.crapome.org/. © 2013 Nature America, Inc. All rights reserved.

Allogeneic hematopoietic stem cell transplantation (SCT) is the only curative therapy for many malignant and nonmalignant conditions. Idiopathic pneumonia syndrome (IPS) is a frequently fatal complication that limits successful outcomes. Preclinical models suggest that IPS represents an immune mediated attack on the lung involving elements of both the adaptive and the innate immune system. However, the etiology of IPS in humans is less well understood. To explore the disease pathway and uncover potential biomarkers of disease, we performed two separate label-free, proteomics experiments defining the plasma protein profiles of allogeneic SCT patients with IPS. Samples obtained from SCT recipients without complications served as controls. The initial discovery study, intended to explore the disease pathway in humans, identified a set of 81 IPS-associated proteins. These data revealed similarities between the known IPS pathways in mice and the condition in humans, in particular in the acute phase response. In addition, pattern recognition pathways were judged to be significant as a function of development of IPS, and from this pathway we chose the lipopolysaccaharide-binding protein (LBP) protein as a candidate molecular diagnostic for IPS, and verified its increase as a function of disease using an ELISA assay. In a separately designed study, we identified protein-based classifiers that could predict, at day 0 of SCT, patients who: 1) progress to IPS and 2) respond to cytokine neutralization therapy. Using cross-validation strategies, we built highly predictive classifier models of both disease progression and therapeutic response. In sum, data generated in this report confirm previous clinical and experimental findings, provide new insights into the pathophysiology of IPS, identify potential molecular classifiers of the condition, and uncover a set of markers potentially of interest for patient stratification as a basis for individualized therapy.