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Understanding the complex interactions between transcriptome and proteome is essential in uncovering cellular mechanisms both in health and disease contexts. The limited correlations between corresponding transcript and protein abundance suggest that regulatory processes tightly govern information flow surrounding transcription and translation, and beyond. In this study we adopt an approach which expands the feature scope that models the human proteome: we develop machine learning models that incorporate sequence-derived features (SDFs), sometimes in conjunction with corresponding mRNA levels. We develop a large resource of sequence-derived features which cover a significant proportion of the H. sapiens proteome, demonstrate which of these features are significant in prediction on multiple cell lines, and suggest insights into which biological processes can be explained using these features. We reveal that (a) SDFs are significantly better at protein abundance prediction across multiple cell lines both in steady-state and dynamic contexts, (b) that SDFs can cover the domain of translation with relative efficiency but struggle with cell-line specific pathways and (c) provide a resource which can be plugged into many subsequent protein-centric analyses.

Despite the extensive study of MYCN-amplified neuroblastomas, there is a significant unmet clinical need in MYCN non-amplified neuroblastomas. In particular, the extent of heterogeneity within the MYCN non-amplified population is unknown. Here, we investigate whether transcriptional subtyping of MYCN non-amplified neuroblastomas can identify molecular subtypes with discrete prognosis and therapeutic vulnerabilities. Using tumour expression data and ConsensusClusterPlus, we demonstrate that MYCN non-amplified neuroblastomas are heterogeneous and can be classified into 3 subgroups based on their transcriptional signatures. Within these groups, subgroup 2 has the worst prognosis and this group shows a "MYCN" signature that is potentially induced by the overexpression of Aurora Kinase A (AURKA); whilst subgroup 3 is characterised by an "inflamed" gene signature. The clinical implications of this subtype classification are significant, as each subtype demonstrates unique prognosis and vulnerability to investigational therapies. We propose that matching baseline tumour subtype to therapy may enhance precision prognosis and therapy stratification for patients with MYCN non-amplified neuroblastomas.

Triple-negative breast cancer (TNBC) is the most aggressive type of breast cancer that lacks the oestrogen receptor, progesterone receptor and human epidermal growth factor receptor 2, making it difficult to target therapeutically. Targeting synthetic lethality is an alternative approach for cancer treatment. TNBC shows frequent loss of phosphatase and tensin homologue (PTEN) expression, which is associated with poor prognosis and treatment response. To identify PTEN synthetic lethal interactions, TCGA analysis coupled with a whole-genome siRNA screen in isogenic PTEN-negative and -positive cells were performed. Among the candidate genes essential for the survival of PTEN-inactive TNBC cells, WDHD1 (WD repeat and high-mobility group box DNA-binding protein 1) expression was increased in the low vs. high PTEN TNBC samples. It was also the top hit in the siRNA screen and its knockdown significantly inhibited cell viability in PTEN-negative cells, which was further validated in 2D and 3D cultures. Mechanistically, WDHD1 is important to mediate a high demand of protein translation in PTEN-inactive TNBC. Finally, the importance of WDHD1 in TNBC was confirmed in patient samples obtained from the TCGA and tissue microarrays with clinic-pathological information. Taken together, as an essential gene for the survival of PTEN-inactive TNBC cells, WDHD1 could be a potential biomarker or a therapeutic target for TNBC.

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 GSK3β, we found that β-Catenin levels and canonical Wnt activity are unaffected by knockout of GSK3β and therefore use 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 characterize the phenotype and altered proteomic profiles associated with this. We also characterize the perturbation of metabolic pathways resulting from GSK3β knockout and identify defects in glycogen metabolism. In summary, using a precision colorectal cancer cell-line knockout model with constitutively activated β-Catenin we are able to identify several of the diverse pathways and processes associated with GSK3β function. p { margin-bottom: 0.25cm; direction: ltr; color: #00000a; line-height: 120%; text-align: left; orphans: 2; widows: 2 }p.western { font-family: "Times New Roman", serif }p.cjk { font-family: "PMingLiU", "????"; so-language: zh-CN }p.ctl { font-family: "Times New Roman"; font-size: 10pt }a:link { color: #0000ff }

Triple-negative breast cancer (TNBC) is the most aggressive type of breast cancer that lacks the oestrogen receptor, progesterone receptor and human epidermal growth factor receptor 2, making it difficult to target therapeutically. Targeting synthetic lethality is an alternative approach for cancer treatment. TNBC shows frequent loss of phosphatase and tensin homologue (PTEN) expression, which is associated with poor prognosis and treatment response. To identify PTEN synthetic lethal interactions, TCGA analysis coupled with a whole-genome siRNA screen in isogenic PTEN-negative and -positive cells were performed. Among the candidate genes essential for the survival of PTEN-inactive TNBC cells, WDHD1 (WD repeat and high-mobility group box DNA-binding protein 1) expression was increased in the low vs. high PTEN TNBC samples. It was also the top hit in the siRNA screen and its knockdown significantly inhibited cell viability in PTEN-negative cells, which was further validated in 2D and 3D cultures. Mechanistically, WDHD1 is important to mediate a high demand of protein translation in PTEN-inactive TNBC. Finally, the importance of WDHD1 in TNBC was confirmed in patient samples obtained from the TCGA and tissue microarrays with clinic-pathological information. Taken together, as an essential gene for the survival of PTEN-inactive TNBC cells, WDHD1 could be a potential biomarker or a therapeutic target for TNBC.

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.

BACKGROUND: Epithelial-to-mesenchymal transition (EMT) is a key step in the transformation of epithelial cells into migratory and invasive tumour cells. Intricate positive and negative regulatory processes regulate EMT. Many oncogenic signalling pathways can induce EMT, but the specific mechanisms of how this occurs, and how this process is controlled are not fully understood.METHODS: RNA-Seq analysis, computational analysis of protein networks and large-scale cancer genomics datasets were used to identify ELF3 as a negative regulator of the expression of EMT markers. Western blotting coupled to siRNA as well as analysis of tumour/normal colorectal cancer panels was used to investigate the expression and function of ELF3.RESULTS: RNA-Seq analysis of colorectal cancer cells expressing mutant and wild-type β-catenin and analysis of colorectal cancer cells expressing inducible mutant RAS showed that ELF3 expression is reduced in response to oncogenic signalling and antagonizes Wnt and RAS oncogenic signalling pathways. Analysis of gene-expression patterns across The Cancer Genome Atlas (TCGA) and protein localization in colorectal cancer tumour panels showed that ELF3 expression is anti-correlated with β-catenin and markers of EMT and correlates with better clinical prognosis.CONCLUSIONS: ELF3 is a negative regulator of the EMT transcription factor (EMT-TF) ZEB1 through its function as an antagonist of oncogenic signalling.

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.

Signaling networks mediate environmental information to the cell nucleus. To perform this task effectively they must be able to integrate multiple stimuli and distinguish persistent signals from transient environmental fluctuations. However, the ways in which signaling networks process environmental noise are not well understood. Here we outline a mathematical framework that relates a network’s structure to its capacity to process noise, and use this framework to dissect the noise-processing ability of signaling networks. We find that complex networks that are dense in directed paths are poor noise processors, while those that are sparse and strongly directional process noise well. These results suggest that while cross-talk between signaling pathways may increase the ability of signaling networks to integrate multiple stimuli, too much cross-talk may compromise the ability of the network to distinguish signal from noise. To illustrate these general results we consider the structure of the signaling network that maintains pluripotency in mouse embryonic stem cells, and find an incoherent feedforward loop structure involving Stat3, Tfcp2l1, Esrrb, Klf2 and Klf4 is particularly important for noise-processing. Taken together these results suggest that noise-processing is an important function of signaling networks and they may be structured in part to optimize this task.

BackgroundDNA methyltransferase I is the primary eukaryotic DNA methyltransferase engaged in maintenance of CpG DNA methylation patterns across the genome. Alteration of CpG methylation patterns and levels is a frequent and significant occurrence across many cancers, and targeted inhibition of Dnmt1 has become an approach of choice for select malignancies. There has been significant interest both in the methyltransferase activity as well as methylation-independent functions of Dnmt1. A previously generated hypomorphic allele of Dnmt1 in HCT116 colorectal cancer cells has become an important tool for understanding Dnmt1 function and how CpG methylation patterns are modulated across the genome. Colorectal cancer cells with the Dnmt1 hypomorphic allele carry a homozygous deletion of exons 3 to 5 of Dnmt1, resulting in greatly reduced Dnmt1 protein expression whilst still exhibiting a limited functional activity and methyltransferase ability. Although this cell model of reduced Dnmt1 levels and function have been used to study the downstream effects on the epigenome and transcriptome, the broader effects of the Dnmt1 hypomorph on the proteome and wider cell signalling are largely unknown.ResultsIn this study, we used quantitative proteomic analysis of nuclear-enriched samples of HCT116 Dnmt1 hypomorph cells to identify signalling pathways and processes dysregulated in the hypomorph cells as compared to wild-type HCT116 cells. Unexpectedly, we observed a clear signature of increased expression of Epithelial-to-Mesenchymal (EMT) in Dnmt1 hypomorph cells. We also observed reduced expression and sub-cellular re-localization of Beta-Catenin in Dnmt1 hypomorph cells. Expression of wild-type Dnmt1 in hypomorph cells or knock-down of wild-type Dnmt1 did not recapitulate or rescue the observed protein profiles in Dnmt1 hypomorph cells suggesting that hypomorphic Dnmt1 causes changes not solely attributableConclusionsIn summary we present the first comprehensive proteomic analysis of the widely studied Dnmt1 hypomorph colorectal cancer cells and identify redistribution of Dnmt1 and its interaction partner Beta-Catenin as well as the dysregulation of EMT related processes and signalling pathways related to the development of a cancer stem cell phenotype.

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.

Stratified medicine requires discretisation of disease populations for targeted treatments. We have developed and applied a discrete Morse theory clustering algorithm to a Topological Data Analysis (TDA) network model of 498 gene expression profiles of peripheral blood from asthma and healthy participants. The Morse clustering algorithm defined nine clusters, BC1-9, representing molecular phenotypes with discrete phenotypes including Type-1, 2 & 17 cytokine inflammatory pathways. The TDA network model and clusters were also characterised by activity of glucocorticoid receptor signalling associated with different expression profiles of glucocorticoid receptor (GR), according to microarray probesets targeted to the start or end of the GR mRNA’s 3’ UTR; suggesting differential GR mRNA processing as a possible driver of asthma phenotypes including steroid insensitivity.

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.

Proteins have many different functions in biological systems, and the molecular functions of proteins are dependent on their three-dimensional structures. Mapping protein structures is therefore an important strategy in understanding gene and protein function. Structural proteomics or structural genomics refers to systematic efforts to functionally annotate protein molecular structures of whole or selected parts of genomes and/or proteomes. Structural proteomics studies have significantly added to our knowledge of protein structures over the past few years and a large fraction of available protein structures in public databases result from high-throughput structural proteomics studies. Although structural proteomics techniques are continually being improved, significant challenges remain in protein expression and crystallisation and in particular for solving protein structures for challenging classes of protein such as membrane proteins. Key Concepts Protein function is directly linked to the three-dimensional structure of the protein. Structural proteomics refers to large-scale mapping of protein structures. Techniques such as protein crystallisation and X-ray bombardment allow the three-dimensional arrangement of atoms in protein molecules to be determined. Technical and biological challenges remain in structural proteomics – in particular for certain classes of proteins such as proteins that span membranes. Large-scale structural proteomics is an enabling technology for systems biology. Keywords: structural genomics; high throughput; protein; X-ray; nuclear magnetic resonance methods; crystallography; systems biology

Protein tyrosine nitration occurs under both physiological and pathological conditions1. However, enzymes that remove this protein modification have not yet been identified. Here we report that the pseudo-phosphatase domain of protein tyrosine receptor T (PTPRT) is a denitrase that removes nitro-groups from tyrosine residues in paxillin. PTPRT normally functions as a tumor suppressor and is frequently mutated in a variety of human cancers including colorectal cancer2,3. We demonstrate that some of the tumor-derived mutations located in the pseudophosphatase domain impair the denitrase activity. Moreover, PTPRT mutant mice that inactivate the denitrase activity are susceptible to carcinogen-induced colon tumor formation. This study uncovers a novel enzymatic activity that is involved in tumor suppression.

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.

Embryonic stem (ES) cells represent 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 ES cells, the spatiotemporal integration of responsive signalling 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 ES cell populations, and ways in which networks may be controlled in order to reliably manipulate cell behaviour.

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.

Pluripotent stem cells are capable of differentiating into all cell types of the body and therefore hold tremendous promise for regenerative medicine. Despite their widespread use in laboratories across the world, a detailed understanding of the molecular mechanisms that regulate the pluripotent state is currently lacking. Mouse embryonic (mESC) and epiblast (mEpiSC) stem cells are two closely related classes of pluripotent stem cells, derived from distinct embryonic tissues. Although both mESC and mEpiSC are pluripotent, these cell types show important differences in their properties suggesting distinct pluripotent ground states. To understand the molecular basis of pluripotency, we analyzed the nuclear proteomes of mESCs and mEpiSCs to identify protein networks that regulate their respective pluripotent states. Our study used label-free LC-MS/MS to identify and quantify 1597 proteins in embryonic and epiblast stem cell nuclei. Immunoblotting of a selected protein subset was used to confirm that key components of chromatin regulatory networks are differentially expressed in mESCs and mEpiSCs. Specifically, we identify differential expression of DNA methylation, ATP-dependent chromatin remodeling and nucleosome remodeling networks in mESC and mEpiSC nuclei. This study is the first comparative study of protein networks in cells representing the two distinct, pluripotent states, and points to the importance of DNA and chromatin modification processes in regulating pluripotency. In addition, by integrating our data with existing pluripotency networks, we provide detailed maps of protein networks that regulate pluripotency that will further both the fundamental understanding of pluripotency as well as efforts to reliably control the differentiation of these cells into functional cell fates.

The main goal of systems medicine is to provide predictive models of the patho-physiology of complex diseases as well as define healthy states. The reason is clear--we hope accurate models will ultimately lead to more specific and sensitive markers of disease that will help clinicians better stratify their patient populations and optimize treatment plans. In addition, we expect that these models will define novel targets for combating disease. However, for many complex diseases, particularly at the clinical level, it is becoming increasingly clear that one or a few genomic variations alone (e.g., simple models) cannot adequately explain the multiple phenotypes related to disease states, or the variable risks that attend disease progression. We suggest that models that account for the activities of many interacting proteins will explain a wider range of variability inherent in these phenotypes. These models, which encompass protein interaction networks dysregulated for specific diseases and specific patient sub-populations, will be constructed by integrating protein interaction data with multiple types of other relevant cellular information. Protein interaction databases are thus playing an increasingly important role in systems biology approaches to the study of disease. They present us with a static, but highly functional view of the cellular state, and thus give us a better understanding of not only the normal phenotype, but also the overall disease phenotype at the level of the whole organism when certain interactions become dysregulated.

Signaling by growth factor receptor tyrosine kinases is manifest through networks of proteins that are substrates and/or bind to the activated receptors. FGF receptor-3 (FGFR3) is a drug target in a subset of human multiple myelomas (MM) and is mutationally activated in some cervical and colon and many bladder cancers and in certain skeletal dysplasias. To define the FGFR3 network in multiple myeloma, mass spectrometry was used to identify and quantify phosphotyrosine (pY) sites modulated by FGFR3 activation and inhibition in myeloma-derived KMS11 cells. Label-free quantification of peptide ion currents indicated the activation of FGFR3 by phosphorylation of tandem tyrosines in the kinase domain activation loop when cellular pY phosphatases were inhibited by pervanadate. Among the 175 proteins that accumulated pY in response to pervanadate was a subset of 52 including FGFR3 that contained a total of 61 pY sites that were sensitive to inhibition by the FGFR3 inhibitor PD173074. The FGFR3 isoform containing the tandem pY motif in its activation loop was targeted by PD173074. Forty of the drug-sensitive pY sites, including two located within the 35-residue cytoplasmic domain of the transmembrane growth factor binding proteoglycan (and multiple myeloma biomarker) Syndecan-1/CD138, were also stimulated in cells treated with the ligand FGF1, providing additional validation of their link to FGFR3. The identification of these overlapping sets of co-modulated tyrosine phosphorylations presents an outline of an FGFR3 network in the MM model and demonstrates the potential for pharmacodynamic monitoring by label-free quantitative phospho-proteomics.

Diabetes mellitus is estimated to affect approximately 24 million people in the United States and more than 150 million people worldwide. There are numerous end organ complications of diabetes, the onset of which can be delayed by early diagnosis and treatment. Although assays for diabetes are well founded, tests for its complications lack sufficient specificity and sensitivity to adequately guide these treatment options. In our study, we employed a streptozotocin-induced rat model of diabetes to determine changes in urinary protein profiles that occur during the initial response to the attendant hyperglycemia (e.g. the first two months) with the goal of developing a reliable and reproducible method of analyzing multiple urine samples as well as providing clues to early markers of disease progression. After filtration and buffer exchange, urinary proteins were digested with a specific protease, and the relative amounts of several thousand peptides were compared across rat urine samples representing various times after administration of drug or sham control. Extensive data analysis, including imputation of missing values and normalization of all data was followed by ANOVA analysis to discover peptides that were significantly changing as a function of time, treatment and interaction of the two variables. The data demonstrated significant differences in protein abundance in urine before observable pathophysiological changes occur in this animal model and as function of the measured variables. These included decreases in relative abundance of major urinary protein precursor and increases in pro-alpha collagen, the expression of which is known to be regulated by circulating levels of insulin and/or glucose. Peptides from these proteins represent potential biomarkers, which can be used to stage urogenital complications from diabetes. The expression changes of a pro-alpha 1 collagen peptide was also confirmed via selected reaction monitoring.

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/.

Little is known about proteomic differences between pluripotent human peripheral blood monocytes (MN) and their terminally-differentiated pulmonary counterparts, alveolar macrophages (AM). To better characterize these cell populations, we performed a label-free shotgun proteomics assessment of matched AM and MN preparations from eight healthy volunteers. With an FDR of less than 0.45%, we identified 1754 proteins within AM and 1445 from MN. Comparison of the two proteomes revealed that 1239 of the proteins found in AM were shared with MN, whereas 206 proteins were uniquely identified in MN and 515 were unique to AM. Molecular and cellular functions, protein classes, development associations and membership in physiological systems and canonical pathways were identified among the detected proteins. Analysis of biologic processes represented by these proteomes indicated that MN were most prominently enriched for proteins involved in cellular movement and immune cell trafficking. In contrast, AM were enriched for proteins involved in protein trafficking, molecular transport, and cellular assembly and organization.. These findings provide a baseline proteomic resource for further studies aimed at better understanding of the functional differences between MN and AM in both health and disease. This article is protected by copyright. All rights reserved.

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.

Two interrelated problems in biology are understanding the regulatory logic and predictability of morphological evolution. Here, we studied these problems by comparing Arabidopsis thaliana, which has simple leaves, and its relative, Cardamine hirsuta, which has dissected leaves comprising leaflets. By transferring genes between the two species, we provide evidence for an inverse relationship between the pleiotropy of SHOOTMERISTEMLESS (STM) and BREVIPEDICELLUS (BP) homeobox genes and their ability to modify leaf form. We further show that cis-regulatory divergence of BP results in two alternative configurations of the genetic networks controlling leaf development. In C. hirsuta, ChBP is repressed by the microRNA164A (MIR164A)/ChCUP-SHAPED COTYLEDON (ChCUC) module and ChASYMMETRIC LEAVES1 (ChAS1), thus creating cross-talk between MIR164A/CUC and AS1 that does not occur in A. thaliana. These different genetic architectures lead to divergent interactions of network components and growth regulation in each species. We suggest that certain regulatory genes with low pleiotropy are predisposed to readily integrate into or disengage from conserved genetic networks influencing organ geometry, thus rapidly altering their properties and contributing to morphological divergence.

We used DNA microarray technology to identify genes involved in the low-oxygen response of Arabidopsis root cultures. A microarray containing 3500 cDNA clones was screened with cDNA samples taken at various times (0.5, 2, 4, and 20 h) after transfer to low-oxygen conditions. A package of statistical tools identified 210 differentially expressed genes over the four time points. Principal component analysis showed the 0.5-h response to contain a substantially different set of genes from those regulated differentially at the other three time points. The differentially expressed genes included the known anaerobic proteins as well as transcription factors, signal transduction components, and genes that encode enzymes of pathways not known previously to be involved in low-oxygen metabolism. We found that the regulatory regions of genes with a similar expression profile contained similar sequence motifs, suggesting the coordinated transcriptional control of groups of genes by common sets of regulatory factors.

Summary Chitin oligomers, released from fungal cell walls by endochitinase, induce defence and related cellular responses in many plants. However, little is known about chitin responses in the model plant Arabidopsis. We describe here a large-scale characterization of gene expression patterns in Arabidopsis in response to chitin treatment using an Arabidopsis microarray consisting of 2375 EST clones representing putative defence-related and regulatory genes. Transcript levels for 71 ESTs, representing 61 genes, were altered three-fold or more in chitin-treated seedlings relative to control seedlings. A number of transcripts exhibited altered accumulation as early as 10 min after exposure to chitin, representing some of the earliest changes in gene expression observed in chitin-treated plants. Included among the 61 genes were those that have been reported to be elicited by various pathogen-related stimuli in other plants. Additional genes, including genes of unknown function, were also identified, broadening our understanding of chitin-elicited responses. Among transcripts with enhanced accumulation, one cluster was enriched in genes with both the W-box promoter element and a novel regulatory element. In addition, a number of transcripts had decreased abundance, encoding several proteins involved in cell wall strengthening and wall deposition. The chalcone synthase promoter element was identified in the upstream regions of these genes, suggesting that pathogen signals may suppress the expression of some genes. These data indicate that Arabidopsis should be an excellent model to elucidate the mechanisms of chitin elicitation in plant defence.

mRNA degradation provides a powerful means for controlling gene expression during growth, development, and many physiological transitions in plants and other systems. Rates of decay help define the steady state levels to which transcripts accumulate in the cytoplasm and determine the speed with which these levels change in response to the appropriate signals. When fast responses are to be achieved, rapid decay of mRNAs is necessary. Accordingly, genes with unstable transcripts often encode proteins that play important regulatory roles. Although detailed studies have been carried out on individual genes with unstable transcripts, there is limited knowledge regarding their nature and associations from a genomic perspective, or the physiological significance of rapid mRNA turnover in intact organisms. To address these problems, we have applied cDNA microarray analysis to identify and characterize genes with unstable transcripts in Arabidopsis thaliana ( AtGUTs ). Our studies showed that at least 1% of the 11,521 clones represented on Arabidopsis Functional Genomics Consortium microarrays correspond to transcripts that are rapidly degraded, with estimated half-lives of less than 60 min. AtGUTs encode proteins that are predicted to participate in a broad range of cellular processes, with transcriptional functions being over-represented relative to the whole Arabidopsis genome annotation. Analysis of public microarray expression data for these genes argues that mRNA instability is of high significance during plant responses to mechanical stimulation and is associated with specific genes controlled by the circadian clock.

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.

Genome-wide expression profiling with DNA microarrays has and will provide a great deal of data to the plant scientific community. However, reliability concerns have required the development data quality tests for common systematic biases. Fortunately, most large-scale systematic biases are detectable and some are correctable by normalization. Technical replication experiments and statistical surveys indicate that these biases vary widely in severity and appearance. As a result, no single normalization or correction method currently available is able to address all the issues. However, careful sequence selection, array design, experimental design and experimental annotation can substantially improve the quality and biological of microarray data. In this review, we discuss these issues with reference to examples from the Arabidopsis Functional Genomics Consortium (AFGC) microarray project.

DNA methylation is an important epigenetic mechanism that ensures correct gene expression and maintains genetic stability. DNA methyltransferase 1 (DNMT1) is the primary enzyme that maintains DNA methylation during replication. Dysregulation of DNMT1 is implicated in a variety of diseases. DNMT1 protein stability is regulated via various post-translational modifications, such as acetylation and ubiquitination, but also through protein-protein interactions. These mechanisms ensure DNMT1 is properly activated during the correct time of the cell cycle and at correct genomic loci, as well as in response to appropriate extracellular cues. Further understanding of these regulatory mechanisms may help to design novel therapeutic approaches for human diseases.

Large, publicly available collections of expressed sequence tags (ESTs) have been generated from Arabidopsis thaliana and rice (Oryza sativa). A potential, but relatively unexplored application of this data is in the study of plant gene expression. Other EST data, mainly from human and mouse, have been successfully used to point out genes exhibiting tissue- or disease-specific expression, as well as for identification of alternative transcripts. In this report, we go a step further in showing that computer analyses of plant EST data can be used to generate evidence of correlated expression patterns of genes across various tissues. Furthermore, tissue types and organs can be classified with respect to one another on the basis of their global gene expression patterns. As in previous studies, expression profiles are first estimated from EST counts. By clustering gene expression profiles or whole cDNA library profiles, we show that genes with similar functions, or cDNA libraries expected to share patterns of gene expression, are grouped together. Promising uses of this technique include functional genomics, in which evidence of correlated expression might complement (or substitute for) those of sequence similarity in the annotation of anonymous genes and identification of surrogate markers. The analysis presented here combines the application of a correlation-based clustering method with a graphical color map allowing intuitive visualization of patterns within a large table of expression measurements.

RbcS genes exist as multigene families in most plant species examined. In this paper, we report an investigation into the expression patterns of two maize RbcS genes, designated in this report as RbcS1 and RbcS2. We present the sequence of RbcS2 and show that the structure of the gene has several features in common with other monocot RbcS genes. To determine whether RbcS1 and RbcS2 fulfil different functional roles with respect to the C3 and C4 carbon fixation pathways, we have investigated the expression patterns of the two genes in different maize tissue types. Transcripts of both genes are found at high levels specifically in bundle-sheath cells of maize seedling leaves, indicating that both genes are expressed in the C4-type pattern. However, we show that RbcS1 transcripts are relatively more abundant than RbcS2 transcripts in C3 tissues such as husk leaves. These results are discussed with respect to the evolution of C4 carbon fixation and the mechanisms required for the cell-specific expression of RbcS genes.

Caloric restriction (CR) markedly extends life span and improves the health of a broad number of species. Energy metabolism fundamentally contributes to the beneficial effects of CR, but the underlying mechanisms that are responsible for this effect remain enigmatic. A multidisciplinary approach that involves quantitative proteomics, immunochemistry, metabolic quantification, and life span analysis was used to determine how CR, which occurs in the Caenorhabditis elegans eat-2 mutants, modifies energy metabolism of the worm, and whether the observed modifications contribute to the CR-mediated physiological responses. A switch to fatty acid metabolism as an energy source and an enhanced rate of energy metabolism by eat-2 mutant nematodes were detected. Life span analyses validated the important role of these previously unknown alterations of energy metabolism in the CR-mediated longevity of nematodes. As observed in mice, the overexpression of the gene for the nematode analog of the cytosolic form of phosphoenolpyruvate carboxykinase caused a marked extension of the life span in C. elegans, presumably by enhancing energy metabolism via an altered rate of cataplerosis of tricarboxylic acid cycle anions. We conclude that an increase, not a decrease in fuel consumption, via an accelerated oxidation of fuels in the TCA cycle is involved in life span regulation; this mechanism may be conserved across phylogeny.

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/.

Wnt signaling is a critically important signaling pathway in development, differentiation and embryogenesis and is broadly conserved amongst multicellular animals. Dis-regulation of Wnt signaling is associated with many human cancers, in particular colorectal cancer. Multiple branches of the Wnt signaling pathway (‘canonical’ and ‘non-canonical’) have been defined, although increasingly these are viewed as a single interconnected signaling network. A principal challenge in Wnt signaling research is to understand the connectivity between different branches, and to understand how different branches of Wnt signaling function in different biological states. To better understand this complex network, we analyze Wnt signaling in the context of diverse cell-lines, tissues and adenomas. By integrating gene-expression datasets representing these different biological states with interaction networks, we identify Wnt subnetworks activated in different biological states. We identified 10 active subnetworks in the network of 2072 protein-protein interactions from a Wnt-focused interaction network. These subnetworks were used for classification of samples and we show that classification of these samples using subnetworks rather than gene-expression profiles alone identifies potentially significant commonalities between samples. Specifically, we identify a subnetwork common to lung and colon adenoma samples, reflecting the common origin of these tumors. The approach described here will be used in future for integration of diverse Wnt-focused ‘omics’ datasets, for both defining components of the signaling network itself, as well as to reveal commonalities and distinctions between Wnt signaling under different biological states.

DNA methyltransferase 1 (DNMT1) is the primary enzyme that maintains DNA methylation. We describe a previously unknown mode of regulation of DNMT1 protein stability through the coordinated action of an array of DNMT1-associated proteins. DNMT1 was destabilized by acetylation by the acetyltransferase Tip60, which triggered ubiquitination by the E3 ligase UHRF1, thereby targeting DNMT1 for proteasomal degradation. In contrast, DNMT1 was stabilized by histone deacetylase 1 (HDAC1) and the deubiquitinase HAUSP (herpes virus-associated ubiquitin-specific protease). Analysis of the abundance of DNMT1 and Tip60, as well as the association between HAUSP and DNMT1, suggested that during the cell cycle the initiation of DNMT1 degradation was coordinated with the end of DNA replication and the need for DNMT activity. In human colon cancers, the abundance of DNMT1 correlated with that of HAUSP. HAUSP knockdown rendered colon cancer cells more sensitive to killing by HDAC inhibitors both in tissue culture and in tumor xenograft models. Thus, these studies provide a mechanism-based rationale for the development of HDAC and HAUSP inhibitors for combined use in cancer therapy.

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.

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.

Mapping protein-protein interactions is an invaluable tool for understanding protein function. Here, we report the first large-scale study of protein-protein interactions in human cells using a mass spectrometry-based approach. The study maps protein interactions for 338 bait proteins that were selected based on known or suspected disease and functional associations. Large-scale immunoprecipitation of Flag-tagged versions of these proteins followed by LC-ESI-MS/MS analysis resulted in the identification of 24,540 potential protein interactions. False positives and redundant hits were filtered out using empirical criteria and a calculated interaction confidence score, producing a data set of 6463 interactions between 2235 distinct proteins. This data set was further cross-validated using previously published and predicted human protein interactions. In-depth mining of the data set shows that it represents a valuable source of novel protein-protein interactions with relevance to human diseases. In addition, via our preliminary analysis, we report many novel protein interactions and pathway associations.

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.

Large-scale expression data, such as that generated by hybridization to microarrays, is potentially a rich source of information on gene function and regulation. By clustering genes according to their expression profiles, groups of genes involved in the same pathways or sharing common regulatory mechanisms may be identified. Publicly-available EST collections are a largely unexplored source of expression data. We previously used a sample of rice ESTs to generate 'digital expression profiles' by counting the frequency of tags for different genes sequenced from different cDNA libraries. A simple statistical test was used to associate genes or cDNA libraries having similar expression profiles. Here we further validate this approach using larger samples of ESTs from the UniGene projects (clustered human, mouse and rat ESTs). Our results show that genes clustered on the basis of expression profile may represent genes implicated in similar pathways or coding for different subunits of multi-component enzyme complexes. In addition we suggest that comparison of clusters from different species, may be useful for confirmation or prediction of orthologs.

Wallerian degeneration is the degeneration of the distal stump of an injured axon. It normally occurs over a time course of around 24 hr but it is delayed in the slow Wallerian degeneration mutant mouse (C57BL/ Wld s ) for up to 3 weeks. The gene, which protects from rapid Wallerian degeneration, Wld , previously has been mapped to distal chromosome 4. This paper reports the fine genetic mapping of the Wld locus, the generation of a 1.4-Mb bacterial artificial chromosome and P1 artificial chromosome contig, and the identification of an 85-kb tandem triplication mapping within the candidate region. The mutation is unique to C57BL/ Wld s among 36 strains tested and therefore is a strong candidate for the mutation that leads to delayed Wallerian degeneration. There are very few reports of tandem triplications in a vertebrate and no evidence for a mutation mechanism so this unusual mutation was characterized in more detail. Sequence analysis of the boundaries of the repeat unit revealed a minisatellite array at the distal boundary and a matching 8-bp sequence at the proximal boundary. This finding suggests that recombination between short homologous sequences (“illegitimate” or “nonhomologous” recombination) was involved in the rearrangement. In addition, a duplication allele was identified in two Wld s mice, indicating some instability in the repeat copy number and suggesting that the triplication arose from a duplication by unequal crossing over.

PIK3CA, which encodes the catalytic subunit of phosphoinositide 3-kinase PI3Kα (p110α), is frequently mutated in human cancers. PI3Kα consists of a catalytic subunit p110α and one of several regulatory subunits (a major one being p85α). The p110α subunit contains an N-terminal adaptor-binding domain (ABD), a Ras-binding domain (RBD), a C2 domain, a helical domain and a catalytic domain. In the basal state, the regulatory p85 subunit stabilizes the catalytic p110α subunit and inhibits its enzymatic activity. Upon growth factor stimulation, the SH2 domains of p85 bind to the phospho-tyrosine residues on the receptor protein kinases or adaptor proteins such as insulin receptor substrate 1 (IRS1), thereby activating the lipid kinase activity of PI3Kα. Activated PI3Kα converts phosphatidylinositol-4,5-bisphosphate (PIP2) to phosphatidylinositol-3,4,5-triphosphate (PIP3). The second messenger PIP3 then activates downstream AKT signaling. The majority of these mutations occur at two hot-spots located in the helical (E545K) and the kinase (H1047R) domains. Here, we report that p110α E545K mutant proteins, but not the wild-type and H1047R mutant proteins, directly associate with IRS1 and that this interaction does not require the p85 regulatory subunit of PI3Kα. Disruption of IRS1-p110α E545K specific interaction results in reduction of AKT phosphorylation and slower xenograft tumor growth of a colon cancer cell line harboring this mutation. Moreover, IRS1- p110α E545K interaction stabilizes p110α proteins and brings it cytoplasmic membrane. Our study establishes a new paradigm whereby a mutant protein exerts its oncogenic functions through rewiring an oncogenic signaling pathway. Moreover, we designed an 18 amino acid hydrocarbon-stapled peptide encompassing the p110α E545K mutation that disrupts IRS1-p110α E545K interaction. The stapled mutant peptide reduces AKT phosphorylation in cancer cell lines with a p110α E545K mutation, whereas it has no effect on those cancer cell lines harboring a p110α H1047R mutation. When injected into xenograft tumors, the stapled peptide specifically inhibits growth of tumors with a p110α E545K mutation. Therefore, our data suggest that interference of IRS1-p110α E545K interaction may be exploited for targeted therapy of cancer patients harboring this mutation. The discovery of frequent mutations of PIK3CA in human cancer provides a strong rationale for inhibition of mutated p110α activities for targeted cancer therapy. However, it has remained a challenge to develop p110α isoform-specific inhibitors. Our data suggest that disruption of the interactions between helical domain mutations of p110α and IRS1 may be exploited as a more accessible targeted therapy approach. Citation Format: Yujun Hao, Chao Wang, Weiping Zheng, David Sedwick, Sanford Markowitz, Rob Ewing, Lili Liu, Zhenghe Wang. Gain of direct interaction with IRS1 by the helical domain mutations of p110 alpha is crucial for their oncogenic functions. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 5155. doi:10.1158/1538-7445.AM2013-5155

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.