Proteomic analysis of purified coronavirus infectious bronchitis virus particles

Background  

Infectious bronchitis virus (IBV) is the coronavirus of domestic chickens causing major economic losses to the poultry industry. Because of the complexity of the IBV life cycle and the small number of viral structural proteins, important virus-host relationships likely remain to be discovered. Toward this goal, we performed two-dimensional gel electrophoresis fractionation coupled to mass spectrometry identification approaches to perform a comprehensive proteomic analysis of purified IBV particles.     

Labeling, detection and identification of newly synthesized proteomes with bioorthogonal non-canonical amino-acid tagging

A major aim of proteomics is the identification of proteins in a given proteome at a given metabolic state. This protocol describes the step-by-step labeling, purification and detection of newly synthesized proteins in mammalian cells using the non-canonical amino acid azidohomoalanine (AHA). In this method, metabolic labeling of newly synthesized proteins with AHA endows them with the unique chemical functionality of the azide group. In the subsequent click chemistry tagging reaction, azide-labeled proteins are covalently coupled to an alkyne-bearing affinity tag. After avidin-based affinity purification and on-resin trypsinization, the resulting peptide mixture is subjected to tandem mass spectrometry for identification. In combination with deuterated leucine-based metabolic colabeling, candidate proteins can be immediately validated. Bioorthogonal non-canonical amino-acid tagging can be combined with any subcellular fractionation, immunopurification or other proteomic method to identify specific subproteomes, thereby reducing sample complexity and enabling the identification of subtle changes in a proteome. This protocol can be completed in 5 days.     

Proteomics of breast cancer: From differential to functional analysis

From differential analysis to identify biomarkers, to functional analysis for finding new therapeutic targets, proteomics bring new comprehensive information for a better understanding of the molecular basis of oncology and new perspectives for the clinic. However the major limitation of proteomic investigations, more generally of post-genomic approaches, remains the molecular and cellular complexity of the mammary gland that is still a major challenge.     

Why complexity and entropy matter: information, posttranslational modifications, and assay fidelity

Computationally aided protein identification of mass spectrometry (MS) data has been a challenge for the proteomic community since the field's inception. As the community attempts to address challenges such as computational site assignment of posttranslational modifications (PTMs), selective reaction monitoring data analysis, and the transition of proteomic assays to the clinic a robust framework with defined metrics is essential. The frameworks used for protein identification are currently score based, not hypothesis driven nor deterministic, resulting in a gradation from poor to good but without the ability to recognize when a given tandem MS spectrum has insufficient information in the context of the proteome to be identified. Means of computing deterministic and pseudodeterministic assays have been proposed by both us and others [Sherman, J., McKay, M.J., Ashman, K., Molloy, M.P., Unique ion signature mass spectrometry, a deterministic method to assign peptide identity. Mol. Cell. Proteomics 2009, 8, 2051-2062; Kiyonami, R., Schoen, A., Prakash, A., Peterman, S., et al., Increased selectivity, analytical precision, and through-put in targeted proteomics. Mol. Cell. Proteomics 2011, 10, M110.002931]. We believe it is crucial to incorporate the complexity of the proteome into the design of the experiment/data analysis upstream and that by doing so proteomic data analysis will become more robust. The seminal paper by Claude Shannon published in 1948 provides the robust mathematical framework now called Information Theory, to achieve this goal. Information Theory defines appropriate metrics for information and complexity as well as means to account for interference, all of which is directly applicable to peptide identification. We attempt to encourage the adoption of Information Theory as a means to ensure that appropriate metrics are used to measure the uncertainty in a peptide identification with or without PTM site assignment.     

Progress and prospects for the discovery of biomarkers for gastric cancer: a focus on proteomics

Introduction: Patient outcomes from gastric cancer vary due to the complexity of stomach carcinogenesis. Recent research using proteomic technologies has targeted components of all of these systems in order to develop biomarkers to aid the early diagnosis of gastric cancer and to assist in prognostic stratification.

Areas covered: This review is comprised of evidence obtained from literature searches from PubMed. It covers the evidence of diagnostic, prognostic, and predictive biomarkers for gastric cancer using proteomic technologies, and provides up-to-date references.

Expert commentary: The proteomic technologies have not only enabled the screening of a large number of samples, but also enabled the identification of diagnostic, prognostic and predictive biomarkers for gastric cancer. While major challenges still remain, to date, proteomic studies in gastric cancer have provided a wealth of information in revealing proteome alterations associated with the disease.     

Disentanglement of protease substrate repertoires

Identification of protease substrates and detailed characterization of processed sites are essential for understanding the biological function of proteases. Because of inherent complexity reasons, this however remains a formidable analytical challenge, illustrated by the fact that the majority of the more than 500 human proteases are uncharacterized to date. Recently, in addition to conventional genetic and biochemical approaches, diverse quantitative peptide-centric proteomics approaches, some of which selectively recover N-terminal peptides, have emerged. These latter proteomic technologies in particular allow the identification of natural protease substrates and delineation of cleavage sites in a complex, natural background of thousands of different proteins. We here review current biochemical, genetic and proteomic methods for global analysis of substrates of proteases and discuss selected applications.     

Weighing in on ubiquitin: the expanding role of mass-spectrometry-based proteomics

Mass-spectrometry-based proteomics has become an essential tool for the qualitative and quantitative analysis of cellular systems. The biochemical complexity and functional diversity of the ubiquitin system are well suited to proteomic studies. This review summarizes advances involving the identification of ubiquitinated proteins, the elucidation of ubiquitin-modification sites and the determination of polyubiquitin chain linkages, as well as offering a perspective on the application of emerging technologies for mechanistic and functional studies of protein ubiquitination.     

Two‐step OFFGEL approach for effective peptide separation compatible with iTRAQ labeling

Shotgun proteomic analyses are increasingly becoming methods of choice for complex samples. The development of effective methods for fractionating peptides to reduce the complexity of the sample before mass analysis is a key point in this strategy. The OFFGEL technology has recently become a tool of choice in proteomic analysis at peptide level. This OFFGEL electrophoresis (OGE) approach allows the in‐solution separation of peptides from various biological sources by isoelectric focusing in highly resolved 24 fractions. It was also demonstrated that OGE technology is a filtering tool for pI‐based validation of peptide identification. As peptide OGE is compatible with iTRAQ labeling, OGE is finding valuable applications in quantitative proteomics as well. The aim of this study is to explain a new 2D‐OGE approach that improves the proteomic coverage of complex mixtures such as colorectal cell line lysates, and which is compatible with iTRAQ labeling.     

Challenges and solutions in proteomics

The accelerated growth of proteomics data presents both opportunities and challenges. Large-scale proteomic profiling of biological samples such as cells, organelles or biological fluids has led to discovery of numerous key and novel proteins involved in many biological/disease processes including cancers, as well as to the identification of novel disease biomarkers and potential therapeutic targets. While proteomic data analysis has been greatly assisted by the many bioinformatics tools developed in recent years, a careful analysis of the major steps and flow of data in a typical highthroughput analysis reveals a few gaps that still need to be filled to fully realize the value of the data. To facilitate functional and pathway discovery for large-scale proteomic data, we have developed an integrated proteomic expression analysis system, iProXpress, which facilitates protein identification using a comprehensive sequence library and functional interpretation using integrated data. With its modular design, iProXpress complements and can be integrated with other software in a proteomic data analysis pipeline. This novel approach to complex biological questions involves the interrogation of multiple data sources, thereby facilitating hypothesis generation and knowledge discovery from the genomic-scale studies and fostering disease diagnosis and drug development.     

Proteomic approaches to study plant-pathogen interactions

The analysis of plant proteomes has drastically expanded in the last few years. Mass spectrometry technology, stains, software and progress in bioinformatics have made identification of proteins relatively easy. The assignment of proteins to particular organelles and the development of better algorithms to predict sub-cellular localization are examples of how proteomic studies are contributing to plant biology. Protein phosphorylation and degradation are also known to occur during plant defense signaling cascades. Despite the great potential to give contributions to the study of plant-pathogen interactions, only recently has the proteomic approach begun to be applied to this field. Biological variation and complexity in a situation involving two organisms in intimate contact are intrinsic challenges in this area, however, for proteomics studies yet, there is no substitute for in planta studies with pathogens, and ways to address these problems are discussed. Protein identification depends not only on mass spectrometry, but also on the existence of complete genome sequence databases for comparison. Although the number of completely sequenced genomes is constantly growing, only four plants have their genomes completely sequenced. Additionally, there are already a number of pathosystems where both partners in the interaction have genomes fully sequenced and where functional genomics tools are available. It is thus to be expected that great progress in understanding the biology of these pathosystems will be made over the next few years. Cheaper sequencing technologies should make protein identification in non-model species easier and the bottleneck in proteomic research should shift from unambiguous protein identification to determination of protein function.     

Large scale protein profiling by combination of protein fractionation and multidimensional protein identification technology (MudPIT)

In the past decade, shotgun proteomic analysis has been utilized extensively to answer complex biological questions. New challenges arise in large scale proteomic profiling when dealing with complex biological mixtures such as the mammalian cell lysate. In this study, we explored the approach of protein separation prior to the shotgun multidimensional protein identification technology (MudPIT) analysis. We fractionated the mammalian cancer cell lysate using the PF 2D ProteomeLab system and analyzed the distribution of molecular weight, isoelectric point, and cellular localization of the eluted proteins. As a result, we were able to reduce sample complexity by protein fractionation and increase the possibility of detecting proteins with lower abundance in the complex protein mixture.     

Proteomic code

On the basis of recent fundamentally novel developments in the protein structure a proteomic code is suggested, that would potentially allow to describe sequence, structure, and function of proteins by a spectrum of elementary loop-n-lock units. All major characteristics of the nearly standard units are described, and first five "codons" of the proteomic code are presented with their respective unique sequences, structures, and functions. More such codons are to be discovered, and the general procedure for their identification is described.     

Mass spectrometric identification of proteins in complex post-genomic projects. Soluble proteins of the metabolically versatile, denitrifying 'Aromatoleum' sp. strain EbN1

The rapidly developing proteomics technologies help to advance the global understanding of physiological and cellular processes. The lifestyle of a study organism determines the type and complexity of a given proteomic project. The complexity of this study is characterized by a broad collection of pathway-specific subproteomes, reflecting the metabolic versatility as well as the regulatory potential of the aromatic-degrading, denitrifying bacterium 'Aromatoleum' sp. strain EbN1. Differences in protein profiles were determined using a gel-based approach. Protein identification was based on a progressive application of MALDI-TOF-MS, MALDI-TOF-MS/MS and LC-ESI-MS/MS. This progression was result-driven and automated by software control. The identification rate was increased by the assembly of a project-specific list of background signals that was used for internal calibration of the MS spectra, and by the combination of two search engines using a dedicated MetaScoring algorithm. In total, intelligent bioinformatics could increase the identification yield from 53 to 70% of the analyzed 5,050 gel spots; a total of 556 different proteins were identified. MS identification was highly reproducible: most proteins were identified more than twice from parallel 2DE gels with an average sequence coverage of >50% and rather restrictive score thresholds (Mascot >or=95, ProFound >or=2.2, MetaScore >or=97). The MS technologies and bioinformatics tools that were implemented and integrated to handle this complex proteomic project are presented. In addition, we describe the basic principles and current developments of the applied technologies and provide an overview over the current state of microbial proteome research.     

Microdissection—An Essential Prerequisite for Spatial Cancer Omics

The problem with cancer tissue is that its intratumoral heterogeneity and its complexity is extremely high as cells possess, depending on their location and function, different mutations, different mRNA expression and the highest intricacy in the protein pattern. Prior to genomic and proteomic analyses, it is therefore indispensable to identify the exact part of the tissue or even the exact cell. Laser‐based microdissection is a tried and tested technique able to produce pure and well‐defined cell material for further analysis with proteomic and genomic techniques. It sheds light on the heterogeneity of cancer or other complex diseases and enables the identification of biomarkers. This review aims to raise awareness for the reconsideration of laser‐based microdissection and seeks to present current state‐of‐the‐art combinations with omic techniques.     

Proteomic analysis of non-tumoral breast tissue

Breast cancer is a complex and heterogeneous disease. In spite of the advances made in recent decades, a better understanding of the intrinsic mechanisms of this disease is crucial. The development of new biomarkers is absolutely necessary to improve diagnosis and prognosis. Research using the proteomic approach has generated interesting results; however, the complexity of the mammary gland and of breast tumors remains a major limitation to the development of new markers. An initial step is to characterize non-tumoral human breast tissue. We present data from classical proteomic analysis based on 2-D electrophoresis and peptide mass fingerprinting identification, which were performed on six non-tumoral samples from patients with invasive ductal breast carcinomas. Forty-four different proteins from 70 spots were identified and classified according to their biological function. Cytoskeleton and associated proteins represent the largest class (30%) followed by the proteins with binding function (27%). Several of the proteins have been described in breast tumors, such as vimentin, endoplasmin, small heat shock beta-6, disulfide isomerase and some cell growth, and proliferation regulators, suggesting the importance of including data on the characterization of non-tumoral breast and to studies on differential expression in cancer tissue.     

Redefining Tissue Crosstalk via Shotgun Proteomic Analyses of Plasma Extracellular Vesicles

Protein signaling between tissues, or tissue cross‐talk is becoming recognized as a fundamental biological process that is incompletely understood. Shotgun proteomic analyses of tissues and plasma to explore this concept are regularly challenged by high dynamic range of protein abundance, which limits the identification of lower abundance proteins. In this viewpoint article, it is highlighted how a focus on proteins contained within extracellular vesicles (EVs) not only partially addresses this issue, but can also reveal an underappreciated complexity of the circulating proteome in various physiological and pathological contexts. Furthermore, how quantitative proteomics can inform EV mediated crosstalk is highlighted and the importance of high coverage, sensitive proteomic analyses of EVs to identify both the optimal methods to isolate EV subtypes of interest and proteins that characterize them is stressed.     

High throughput peptide mass fingerprinting and protein macroarray analysis using chemical printing strategies

We describe a chemical printer that uses piezoelectric pulsing for rapid, accurate, and non-contact microdispensing of fluid for proteomic analysis of immobilized protein macroarrays. We demonstrate protein digestion and peptide mass fingerprinting analysis of human plasma and platelet proteins direct from a membrane surface subsequent to defined microdispensing of trypsin and matrix solutions, hence bypassing multiple liquid-handling steps. Detection of low abundance, alkaline proteins from whole human platelet extracts has been highlighted. Membrane immobilization of protein permits archiving of samples pre-/post-analysis and provides a means for subanalysis using multiple chemistries. This study highlights the ability to increase sequence coverage for protein identification using multiple enzymes and to characterize N-glycosylation modifications using a combination of PNGase F and trypsin. We also demonstrate microdispensing of multiple serum samples in a quantitative microenzyme-linked immunosorbent assay format to rapidly screen protein macroarrays for pathogen-derived antigens. We anticipate the chemical printer will be a major component of proteomic platforms for high throughput protein identification and characterization with widespread applications in biomedical and diagnostic discovery.     

Affinity chromatography: a useful tool in proteomics studies

Separation or fractionation of a biological sample in order to reduce its complexity is often a prerequisite to qualitative or quantitative proteomic approaches. Affinity chromatography is an efficient protein separation method based on the interaction between target proteins and specific immobilized ligands. The large range of available ligands allows to separate a complex biological extract in different protein classes or to isolate the low abundance species such as post-translationally modified proteins. This method plays an essential role in the isolation of protein complexes and in the identification of protein-protein interaction networks. Affinity chromatography is also required for quantification of protein expression by using isotope-coded affinity tags.     

Identification of keratinocyte-specific markers using phage display and mass spectrometry

Specific molecular markers for various normal and pathogenic cell states and cell types provide knowledge of basic biological systems and have a direct application in targeted therapy. We describe a proteomic method based on the combination of new and improved phage display antibody technologies and mass spectrometry that allows identification of cell type-specific protein markers. The most important features of the method are (i) reduction of experimental noise originating from background binding of phage particles and (ii) isolation of affinity binders after a single round of selection, which assures a high diversity of binders. The method demonstrates, for the first time, the ability to detect, identify, and analyze both secreted and membrane-associated extracellular proteins as well as a variety of different cellular structures including proteins and carbohydrates. The optimized phage display method was applied to analysis of human skin keratinocytes resulting in the isolation of a panel of antibodies. Fourteen of these antibodies were further characterized, half of which predominantly recognized keratinocytes in a screen of a range of different cell types. Three cognate keratinocyte antigens were subsequently identified by mass spectrometry as laminin-5, plectin, and fibronectin. The combination of phage display technology with mass spectrometry methods for protein identification is a general and promising approach for proteomic analysis of cell surface complexity.