Proteome of monoclonal antibody‐purified haustoria from Puccinia triticina Race‐1

Puccinia triticina causes leaf rust, a disease that causes annual yield losses in wheat. It is an obligate parasite that invades the host leaf and forms intracellular structures called haustoria, which obtain nutrients and suppress host immunity using secreted proteins called effectors. Since effector proteins act at the frontier between plant and pathogen and help determine the outcome of the interaction, it is critical to understand their functions. Here, we used a direct proteomics approach to identify effector candidates from P. triticina Race 1 haustoria isolated with a specific monoclonal antibody. Haustoria were >95% pure and free of host contaminants. Using high resolution MS we have identified 1192 haustoria proteins. These were quantified using normalized spectral counts and spanned a dynamic range of three orders of magnitude, with unknown proteins and metabolic enzymes as the most highly represented. The dataset contained 140 candidate effector proteins, based on the presence of a signal peptide and the absence of a known function for the protein. Some of these candidates were significantly enriched with cysteine, with up to 13 residues per protein and up to 6.8% cysteine in composition.     

Recent progress in liquid chromatography-based separation and label-free quantitative plant proteomics

Recent innovations in liquid chromatography-mass spectrometry (LC-MS)-based methods have facilitated quantitative and functional proteomic analyses of large numbers of proteins derived from complex samples without any need for protein or peptide labelling. Regardless of its great potential, the application of these proteomics techniques to plant science started only recently. Here we present an overview of label-free quantitative proteomics features and their employment for analysing plants. Recent methods used for quantitative protein analyses by MS techniques are summarized and major challenges associated with label-free LC-MS-based approaches, including sample preparation, peptide separation, quantification and kinetic studies, are discussed. Database search algorithms and specific aspects regarding protein identification of non-sequenced organisms are also addressed. So far, label-free LC-MS in plant science has been used to establish cellular or subcellular proteome maps, characterize plant-pathogen interactions or stress defence reactions, and for profiling protein patterns during developmental processes. Improvements in both, analytical platforms (separation technology and bioinformatics/statistical analysis) and high throughput nucleotide sequencing technologies will enhance the power of this method.     

Recent advances and novel strategies in vaccine development

This review is intended to cover some recent advances in identification of vaccine candidates and in methods of delivery of vaccine antigens. Sequencing of bacterial genomes has led to rapid utilization of the predicted open reading frames to identify potential candidates for evaluation and, with improvements in proteomics combined with microanalytical sequencing techniques, to identify expressed proteins. Expression of vaccine antigens in human food sources has been greatly improved, opening the possibility of orally delivered subunit vaccines, as has the ability to modify the immune response with cytokines and chemokines. These techniques are slowly making their way to human studies and show great promise for future human use.     

Ubiquitin ligase substrate identification through quantitative proteomics at both the protein and peptide levels

Protein ubiquitination is a key regulatory process essential to life at a cellular level; significant efforts have been made to identify ubiquitinated proteins through proteomics studies, but the level of success has not reached that of heavily studied post-translational modifications, such as phosphorylation. HRD1, an E3 ubiquitin ligase, has been implicated in rheumatoid arthritis, but no disease-relevant substrates have been identified. To identify these substrates, we have taken both peptide and protein level approaches to enrich for ubiquitinated proteins in the presence and absence of HRD1. At the protein level, a two-step strategy was taken using cells expressing His(6)-tagged ubiquitin, enriching proteins first based on their ubiquitination and second based on the His tag with protein identification by LC-MS/MS. Application of this method resulted in identification and quantification of more than 400 ubiquitinated proteins, a fraction of which were found to be sensitive to HRD1 and were therefore deemed candidate substrates. In a second approach, ubiquitinated peptides were enriched after tryptic digestion by peptide immunoprecipitation using an antibody specific for the diglycine-labeled internal lysine residue indicative of protein ubiquitination, with peptides and ubiquitination sites identified by LC-MS/MS. Peptide immunoprecipitation resulted in identification of over 1800 ubiquitinated peptides on over 900 proteins in each study, with several proteins emerging as sensitive to HRD1 levels. Notably, significant overlap exists between the HRD1 substrates identified by the protein-based and the peptide-based strategies, with clear cross-validation apparent both qualitatively and quantitatively, demonstrating the effectiveness of both strategies and furthering our understanding of HRD1 biology.     

EBP, a program for protein identification using multiple tandem mass spectrometry datasets

MS/MS combined with database search methods can identify the proteins present in complex mixtures. High throughput methods that infer probable peptide sequences from enzymatically digested protein samples create a challenge in how best to aggregate the evidence for candidate proteins. Typically the results of multiple technical and/or biological replicate experiments must be combined to maximize sensitivity. We present a statistical method for estimating probabilities of protein expression that integrates peptide sequence identifications from multiple search algorithms and replicate experimental runs. The method was applied to create a repository of 797 non-homologous zebrafish (Danio rerio) proteins, at an empirically validated false identification rate under 1%, as a resource for the development of targeted quantitative proteomics assays. We have implemented this statistical method as an analytic module that can be integrated with an existing suite of open-source proteomics software.     

The development of selected reaction monitoring methods for targeted proteomics via empirical refinement

Software advancements in the last several years have had a significant impact on proteomics from method development to data analysis. Herein, we detail a method, which uses our in-house developed software tool termed Skyline, for empirical refinement of candidate peptides from targeted proteins. The method consists of four main steps from generation of a testable hypothesis, method development, peptide refinement, to peptide validation. The ultimate goal is to identify the best performing peptide in terms of ionization efficiency, reproducibility, specificity, and chromatographic characteristics to monitor as a proxy for protein abundance. It is important to emphasize that this method allows the user to perform this refinement procedure in the sample matrix and organism of interest with the instrumentation available. Finally, the method is demonstrated in a case study to determine the best peptide to monitor the abundance of surfactant protein B in lung aspirates.     

Multiplex targeted proteomic assay for biomarker detection in plasma: a pancreatic cancer biomarker case study

Biomarkers are most frequently proteins that are measured in the blood. Their development largely relies on antibody creation to test the protein candidate performance in blood samples of diseased versus nondiseased patients. The creation of such antibody assays has been a bottleneck in biomarker progress due to the cost, extensive time, and effort required to complete the task. Targeted proteomics is an emerging technology that is playing an increasingly important role to facilitate disease biomarker development. In this study, we applied a SRM-based targeted proteomics platform to directly detect candidate biomarker proteins in plasma to evaluate their clinical utility for pancreatic cancer detection. The characterization of these protein candidates used a clinically well-characterized cohort that included plasma samples from patients with pancreatic cancer, chronic pancreatitis, and healthy age-matched controls. Three of the five candidate proteins, including gelsolin, lumican, and tissue inhibitor of metalloproteinase 1, demonstrated an AUC value greater than 0.75 in distinguishing pancreatic cancer from the controls. In addition, we provide an analysis of the reproducibility, accuracy, and robustness of the SRM-based proteomics platform. This information addresses important technical issues that could aid in the adoption of the targeted proteomics platform for practical clinical utility.     

Identification and dynamics of proteins adhering to the surface of medical silicones in vivo and in vitro

Silicone has been used in medical practice as a paradigmatic implant material for decades despite significant detrimental side effects. Our targeted proteomics approach was aimed at identification of the proteins adsorbed to the surface of silicone because they have been characterized as key components in the onset and perpetuation of local immune reactions to silicone. The composition of the proteinacious film, the dynamics of protein deposition, and protein modifications after adsorption were analyzed both in vivo and in vitro. Differential analysis of protein deposition was performed, followed by protein identification with mass spectrometry, database matching, and Western blots. Thus far, we have identified the 30 most abundant proteins deposited on the surface of silicone, the largest known inventory of such proteins so far. Structural and extracellular matrix proteins predominated, followed by mediators of host defense, metabolism, transport, and stress related proteins. In addition, several biochemical modifications of fibronectin, vitronectin, and heat shock protein 60 were detected. Our analyses also revealed previously undetected proteins deposited on the surface of silicone. As tentative initiators and/or modulators of the response to silicone, they are therefore valuable candidates for prognosis and therapy.     

The cell surface proteome of Staphylococcus aureus

The Gram-positive bacterium Staphylococcus aureus is a wide spread opportunistic pathogen that can cause a range of life-threatening diseases. To obtain a better understanding of the global mechanisms for pathogenesis and to identify novel targets for therapeutic interventions, the S. aureus proteome has been recently 'dissected' in several studies. Proteins that are exposed on the cell surface - collectively referred to as the 'surfacome' - have received particular attention, because they can directly interact with extracellular molecules, including drugs and antibodies. Accordingly, these proteins represent interesting candidate targets for active or passive immunization against S. aureus. Here, we review the proteomics strategies used, and we compare the results that were so far obtained. Since the surfacome is part of the cell wall proteome, we first present an overview of general properties of the S. aureus cell envelope, cell wall-associated proteins and mechanisms for protein attachment to the cell wall. Then we zoom in on the surfacome, and discuss the pro's and con's of the specific strategies that have been applied for surfacome profiling. The insights thus obtained may serve as leads for future studies on the S. aureus surfacome and possible applications.     

Multi high-throughput approach for highly selective identification of vaccine candidates: the Group A Streptococcus case

We propose an experimental strategy for highly accurate selection of candidates for bacterial vaccines without using in vitro and/or in vivo protection assays. Starting from the observation that efficacious vaccines are constituted by conserved, surface-associated and/or secreted components, the strategy contemplates the parallel application of three high throughput technologies, i.e. mass spectrometry-based proteomics, protein array, and flow-cytometry analysis, to identify this category of proteins, and is based on the assumption that the antigens identified by all three technologies are the protective ones. When we tested this strategy for Group A Streptococcus, we selected a total of 40 proteins, of which only six identified by all three approaches. When the 40 proteins were tested in a mouse model, only six were found to be protective and five of these belonged to the group of antigens in common to the three technologies. Finally, a combination of three protective antigens conferred broad protection against a panel of four different Group A Streptococcus strains. This approach may find general application as an accelerated and highly accurate path to bacterial vaccine discovery.     

Comparative profiling of serum glycoproteome by sequential purification of glycoproteins and 2-nitrobenzensulfenyl (NBS) stable isotope labeling: a new approach for the novel biomarker discovery for cancer

The recent progress in various proteomic technologies allows us to screen serum biomarker including carbohydrate antigens. However, only a limited number of proteins could be detected by current conventional methods such as shotgun proteomics, primarily because of the enormous concentration distribution of serum proteins and peptides. To circumvent this difficulty and isolate potential cancer-specific biomarkers for diagnosis and treatment, we established a new screening system consisting of the sequential steps of (1) immunodepletion of 6 high-abundance proteins, (2) targeted enrichment of glycoproteins by lectin column chromatography, and (3) the quantitative proteome analysis using 12C6- or 13C6-NBS (2-nitrobenzenesulfenyl) stable isotope labeling followed by MALDI-QIT-TOF mass spectrometric analysis. Through this systematic analysis for five serum samples derived from patients with lung adenocarcinoma, we identified as candidate biomarkers 34 serum glycoproteins that revealed significant difference in alpha1,6-fucosylation level between lung cancer and healthy control, clearly demonstrating that the carbohydrate-focused proteomics could allow for the detection of serum components with cancer-specific features. In addition, we developed a more simplified and practical technique, mass spectrometry-based glycan structure analysis and lectin blotting, in order to validate glycan structure of candidate biomarkers that could be applicable in clinical use. Our new glycoproteomic strategy will provide highly sensitive and quantitative profiling of specific glycan structures on multiple proteins, which should be useful for serum biomarker discovery.     

Proteomic approaches unravel the intricacy of secreted proteins of Leishmania: An updated review

Leishmaniasis, a parasitic protozoan disease, is still a worldwide concern due to persistent issues with chemotherapy, rapid emerging drug resistance; and non- availability of approved vaccine for the control of disease. Therefore, the search of parasite specific proteins to identify new anti-leishmanial drug targets and vaccine candidates is an urgent priority. In this context, proteins that are secreted, in vitro during parasite growth under defined conditions, can be explored as potential tool for studying their roles in parasite survival inside host and disease pathogenesis. From the last few years, various approaches have been exploited to identify the proteins secreted out by the parasites under defined conditions at particular stage or time. Due to availability of genomic information on various Leishmania species, proteomics have been emerged as most promising approach for analyzing the complexity of exoproteome of different Leishmania species. Herein, we have summarized various secretion mechanisms used by Leishmania parasites to export the proteins into the extracellular space; followed by the role of proteomics in exoproteome analysis along with special emphasis on various applications to study the exoproteome, which might provide potential targets for drug design or novel antigens for vaccine development.     

Use of combinatorial peptide libraries for T-cell epitope mapping

T lymphocytes play important roles not only in infectious diseases and autoimmunity, but also in immune responses against tumors. For many of these disorders, the relevant target antigens are not known. Designing effective methods that allow the search for T-cell epitopes is therefore an important goal in the areas of infectious diseases, oncology, vaccine development, and numerous other biomedical specialties. So far, the strategies used to examine T-cell recognition have been based largely on mapping T-cell epitopes with overlapping peptides from known proteins or with entire proteins, e.g., from a specific virus, bacterium, or human tissue. These approaches are tedious and have a number of limitations. It is, for example, almost impossible to isolate T cells that infiltrate an organ or infectious site and identify their specificity unless one already has a concept as to which antigens may be relevant. During recent years, a number of laboratories have developed less biased approaches that employ either the selection of putative T-cell epitopes based on the prediction of binding to certain major histocompatibilty complex (MHC) molecules and peptide or protein libraries that have been generated in expression systems, e.g. phage, or rely on combinatorial peptide chemistry. The latter technique has been refined by a number of laboratories including ours. Bead-bound or, preferably, positional scanning synthetic and soluble combinatorial peptide libraries allow the identification of T-cell epitopes within complex mixtures of proteins even for T cells that have been expanded from an organ infiltrate with a polyclonal stimulus. The practical steps that are involved in the latter method are described in this article.     

Identification of potential tumor differentiation factor (TDF) receptor from steroid-responsive and steroid-resistant breast cancer cells

Tumor differentiation factor (TDF) is a recently discovered protein, produced by the pituitary gland and secreted into the bloodstream. TDF and TDF-P1, a 20-amino acid peptide selected from the open reading frame of TDF, induce differentiation in human breast and prostate cancer cells but not in other cells. TDF protein has no identified site of action or receptor, and its mechanism of action is unknown. Here, we used TDF-P1 to purify and identify potential TDF receptor (TDF-R) candidates from MCF7 steroid-responsive breast cancer cells and non-breast HeLa cancerous cells using affinity purification chromatography (AP), and mass spectrometry (MS). We identified four candidate proteins from the 70-kDa heat shock protein (HSP70) family in MCF7 cells. Experiments in non-breast HeLa cancerous cells did not identify any TDF-R candidates. AP and MS experiments were validated by AP and Western blotting (WB). We additionally looked for TDF-R in steroid-resistant BT-549 cells and human dermal fibroblasts (HDF-a) using AP and WB. TDF-P1 interacts with potential TDF-R candidates from MCF7 and BT-549 breast cells but not from HeLa or HDF-a cells. Immunofluorescence (IF) experiments identified GRP78, a TDF-R candidate, at the cell surface of MCF7, BT-549 breast cells, and HeLa cells but not HDF-a cells. IF of other HSP70 proteins demonstrated labeling on all four cell types. These results point toward GRP78 and HSP70 proteins as strong TDF-R candidates and suggest that TDF interacts with its receptor, exclusively on breast cells, through a steroid-independent pathway.     

Cutting-edge technology. II. Proteomics: core technologies and applications in physiology

Technologies for proteomics, e.g., studies examining the protein complement of the genome, have been in development for over 20 years. More recently, proteomics has become formalized by combining techniques for large-scale protein separation with very precise, high-fidelity approaches that analyze, identify, and characterize the separated proteins. These methods bring to reality the powerful scope of proteomics, enabling researchers to investigate cellular function at the protein level and thus representing one of proteomics' most fitting applications. In this review, we take a brief and concise look at some of the current, physiologically relevant technologies that comprise proteomics and report specific applications in which proteomics has provided valuable biological insight.     

What is targeted proteomics? A concise revision of targeted acquisition and targeted data analysis in mass spectrometry

Targeted proteomics has gained significant popularity in mass spectrometry‐based protein quantification as a method to detect proteins of interest with high sensitivity, quantitative accuracy and reproducibility. However, with the emergence of a wide variety of targeted proteomics methods, some of them with high‐throughput capabilities, it is easy to overlook the essence of each method and to determine what makes each of them a targeted proteomics method. In this viewpoint, we revisit the main targeted proteomics methods and classify them in four categories differentiating those methods that perform targeted data acquisition from targeted data analysis, and those methods that are based on peptide ion data (MS1 targeted methods) from those that rely on the peptide fragments (MS2 targeted methods).     

A proteomics approach to the study of absorption, distribution, metabolism, excretion, and toxicity.

A proteomics approach was used to identify liver proteins that displayed altered levels in mice following treatment with a candidate drug. Samples from livers of mice treated with candidate drug or untreated were prepared, quantified, labeled with CyDye DIGE Fluors, and subjected to two-dimensional electrophoresis. Following scanning and imaging of gels from three different isoelectric focusing intervals (3-10, 7-11, 6.2-7.5), automated spot handling was performed on a large number of gel spots including those found to differ more than 20% between the treated and untreated condition. Subsequently, differentially regulated proteins were subjected to a three-step approach of mass spectrometry using (a) matrix-assisted laser desorption/ionization time-of-flight mass spectrometry peptide mass fingerprinting, (b) post-source decay utilizing chemically assisted fragmentation, and (c) liquid chromatography-tandem mass spectrometry. Using this approach we have so far resolved 121 differentially regulated proteins following treatment of mice with the candidate drug and identified 110 of these using mass spectrometry. Such data can potentially give improved molecular insight into the metabolism of drugs as well as the proteins involved in potential toxicity following the treatment. The differentially regulated proteins could be used as targets for metabolic studies or as markers for toxicity.     

Triplex protein quantification based on stable isotope labeling by peptide dimethylation applied to cell and tissue lysates

Stable isotope labeling is at present one of the most powerful methods in quantitative proteomics. Stable isotope labeling has been performed at both the protein as well as the peptide level using either metabolic or chemical labeling. Here, we present a straightforward and cost-effective triplex quantification method that is based on stable isotope dimethyl labeling at the peptide level. Herein, all proteolytic peptides are chemically labeled at their alpha- and epsilon-amino groups. We use three different isotopomers of formaldehyde to enable the parallel analysis of three different samples. These labels provide a minimum of 4 Da mass difference between peaks in the generated peptide triplets. The method was evaluated based on the quantitative analysis of a cell lysate, using a typical "shotgun" proteomics experiment. While peptide complexity was increased by introducing three labels, still more than 1300 proteins could be identified using 60 microg of starting material, whereby more than 600 proteins could be quantified using at least four peptides per protein. The triplex labeling was further utilized to distinguish specific from aspecific cAMP binding proteins in a chemical proteomics experiment using immobilized cAMP. Thereby, differences in abundance ratio of more than two orders of magnitude could be quantified.     

A bioinformatics approach for biomarker identification in radiation-induced lung inflammation from limited proteomics data

Many efforts have been made to discover novel bio-markers for early disease detection in oncology. However, the lack of efficient computational strategies impedes the discovery of disease-specific biomarkers for better understanding and management of treatment outcomes. In this study, we propose a novel graph-based scoring function to rank and identify the most robust biomarkers from limited proteomics data. The proposed method measures the proximity between candidate proteins identified by mass spectrometry (MS) analysis utilizing prior reported knowledge in the literature. Recent advances in mass spectrometry provide new opportunities to identify unique biomarkers from peripheral blood samples in complex treatment modalities such as radiation therapy (radiotherapy), which enables early disease detection, disease progression monitoring, and targeted intervention. Specifically, the dose-limiting role of radiation-induced lung injury known as radiation pneumonitis (RP) in lung cancer patients receiving radiotherapy motivates the search for robust predictive biomarkers. In this case study, plasma from 26 locally advanced non-small cell lung cancer (NSCLC) patients treated with radiotherapy in a longitudinal 3 × 3 matched-control cohort was fractionated using in-line, sequential multiaffinity chromatography. The complex peptide mixtures from endoprotease digestions were analyzed using comparative, high-resolution liquid chromatography (LC)-MS to identify and quantify differential peptide signals. Through analysis of survey mass spectra and annotations of peptides from the tandem spectra, we found candidate proteins that appear to be associated with RP. On the basis of the proposed methodology, α-2-macroglobulin (α2M) was unambiguously ranked as the top candidate protein. As independent validation of this candidate protein, enzyme-linked immunosorbent assay (ELISA) experiments were performed on independent cohort of 20 patients' samples resulting in early significant discrimination between RP and non-RP patients (p = 0.002). These results suggest that the proposed methodology based on longitudinal proteomics analysis and a novel bioinformatics ranking algorithm is a potentially promising approach for the challenging problem of identifying relevant biomarkers in sample-limited clinical applications.