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PubMed2006

Protein and proteome phosphorylation stoichiometry analysis by element mass spectrometry.

Protein phosphorylation stoichiometry was assessed by two analytical strategies. Both are based on element mass spectrometry (ICPMS, inductively coupled plasma mass spectrometry) and simultaneous monitoring of (31)P and (34)S. One strategy employs a combination of 1D gel electrophoresis, in-gel digestion, and final microLC-ICPMS analysis (microLC = capillary liquid chromatography). The other strategy uses the combination of 1D gel electrophoresis, protein blotting, and imLA-ICPMS (imLA = imaging laser ablation). The two methods were evaluated with standard phosphoproteins and were applied to the analysis of the cytoplasmatic proteome of bacterial cells (Corynebacterium glutamicum) and eukaryotic cells (Mus musculus). The eukaryotic proteome was found to exhibit a significantly higher phosphorylation degree (approximately 0.8 mol of P/mol of protein) compared to the bacterial proteome (approximately 0.01 mol of P/mol of protein). Both analytical strategies revealed consistent quantitative results, with the microLC-ICPMS approach providing the higher sensitivity. In summary, two ICPMS-based methods for quantitative estimation of the phosphorylation degree of a cellular proteome are presented which access the native proteome state and do not require any type of label introduction or derivatization.

Phosphorylation↗

PubMed2006

Antigen-epitope retrieval to facilitate proteomic analysis of formalin-fixed archival brain tissue.

Formalin is a routine fixative facilitating tissue preservation and histopathology. Proteomic techniques require freshly frozen specimens, which are often difficult to procure, and methods facilitating proteomic analysis of archival formalin-fixed brain tissue are lacking. We employed antigen-epitope-retrieval principles to facilitate proteomic analysis of brain tissue that had been fixed and stored in formalin for 3-7 years. Twenty-micrometer-thick cryopreserved OCT-embedded sections from inferior temporal cortex of human (7 years in formalin) or mouse brain specimens (3 years in formalin) were hematoxylin-/eosin-stained. Approximately 16-64-mm2 areas of the tissue sections were manually scraped off slides, or approximately 2 mm2 of human brain cortex was captured off membrane-coated slides using laser microdissection. Tissue was treated using various pH and temperature conditions prior to trypsin digestion and nano-LC-MS/MS. The largest number of proteins were retrieved by solubilization at pH 9 at 95 degrees C for 1 h; treatments at pH 4 or 6 at 25 or 65 degrees C were generally ineffective. Three-year formalin-fixed murine tissue did not yield more proteins compared to human tissue. Use of formalin-fixed tissue for proteomics is an invaluable tool for medical research. The combination of proteomics and microdissection enables selective enrichment and identification of novel, unique, or abundant proteins that may be important in pathogenesis.

Animals↗

PubMed2005

A tandem orthogonal proteolysis strategy for high-content chemical proteomics.

The field of proteomics aims to assign functions to the numerous protein products encoded by eukaryotic and prokaryotic genomes. Toward this end, chemical strategies have emerged as a powerful means to enrich specific classes of proteins based on shared functional properties, such as catalytic activity [activity-based protein profiling (ABPP)], and post-translational modification state. The theoretical information content in chemical proteomic experiments greatly exceeds the actual data procured, due in large part to limitations in existing analytical technologies. Here, we present a tandem orthogonal proteolysis (TOP) strategy for high-content chemical proteomics that enables the parallel characterization of probe-labeled proteins and sites of probe modification. The TOP approach exploits "click chemistry" to introduce a multifunctional tag onto probe-labeled proteins that contains both a biotin group for protein enrichment and a tobacco etch virus (TEV) protease cleavage site for selective release of probe-modified peptides. Following capture on streptavidin beads, protein targets of probes and their sites of labeling are sequentially identified by a two-step proteolysis strategy (trypsin and TEV, respectively). We apply the TOP method to characterize targets of sulfonate ester ABPP probes in tissue proteomes, resulting in the discovery of numerous active site-labeled enzymes. Enzymes modified on regulatory sites and proteins of unknown function were also identified. These findings indicate that a wide range of functional residues are targeted by sulfonate ester probes and highlight the value of TOP-based chemical proteomics for the characterization of proteins and the residues that regulate their activity.

Amino Acid Sequence↗

PubMed2006

Application of two-dimensional gel electrophoresis to interrogate alterations in the proteome of gentically modified crops. 3. Assessing unintended effects.

The current procedures to assess the safety of food and feed derived from modern biotechnology include the investigation of possible unintended effects. To improve the probability of detecting unintended effects, profiling techniques such as proteomics are currently tested as complementary analytical tools to the existing safety assessment. An optimized two-dimensional gel electrophoresis (2DE) method was used as a proteomics approach to investigate insertional and pleiotropic effects on the proteome due to genetic engineering. Twelve transgenic Arabidopsis thaliana lines were analyzed by 2DE, and their seed proteomes were compared to that of their parental line as well as to 12 Arabidopsis ecotype lines. The genetic modification of the Arabidopsis lines, using three different genes and three different promoters, did not cause unintended changes to the analyzed seed proteome. Differences in spot quantity between transgenic and nontransgenic lines fell in the range of values found in the 12 Arabidopsis ecotype lines or were related to the introduced gene.

Arabidopsis↗

PubMedDate not supplied

Analysis of shotgun proteomics and RNA profiling data from Arabidopsis thaliana chloroplasts.

The integration of data from transcriptional profiling and shotgun proteomics experiments provides additional information about the identified proteins that goes beyond their plain detection. We have analyzed results from MS/MS shotgun detection of 426 Arabidopsis chloroplast proteins and genome-wide RNA profiling to identify correlations between gene expression, protein abundance and protein characteristics that influence their detection in high-throughput proteome analyses. The integrated data analysis revealed a significant molecular mass bias for the detection of proteins that were expressed at low transcript levels. Overall, the sequence coverage of most of the identified proteins increases with transcript levels indicating a positive correlation between transcript and relative protein abundance. This does not apply to a subset of the identified proteins suggesting specific properties that alter their detection in shotgun proteomics. This integrative comparison is a suitable strategy to validate large scale proteomics data and offers an assessment of the depth of the proteome analysis and the confidence in protein identification.

Arabidopsis↗

PubMedDate not supplied

Quest for novel cardiovascular biomarkers by proteomic analysis.

Atherosclerosis, and the resulting coronary heart disease and stroke, is the most common cause of death in developed countries. Atherosclerosis is an inflammatory process that results in the development of complex lesions or plaques that protrude into the arterial lumen. Plaque rupture and thrombosis result in the acute clinical complications of myocardial infarction (MI) and stroke. Although certain risk factors (dyslipidemias, diabetes, hypertension) and humoral markers of plaque vulnerability (C-reactive protein, interleukin-6, 10 and 18, CD40L) have been identified, a highly sensitive and specific biomarker or protein profile, which could provide information on the stability/vulnerability of atherosclerotic lesions, remains to be identified. In this review, we report several proteomic approaches which have been applied to circulating or resident cells, atherosclerotic plaques or plasma, in the search for new proteins that could be used as cardiovascular biomarkers. First, an example using a differential proteomic approach (2-DE and MS) comparing the secretome from control mammary arteries and atherosclerotic plaques is displayed. Among the different proteins identified, we showed that low levels of HSP-27 could be a potential marker of atherosclerosis. Second, we have revised several studies performed in cells involved in the pathogenesis of atherosclerosis (foam cells and smooth muscle cells). Another approach consists of performing proteomic analysis on circulating cells or plasma, which will provide a global view of the whole body response to atherosclerotic aggression. Circulating cells can bear information reflecting directly an inflammatory or pro-coagulant state related to the pathology. As an illustration, we report that circulating monocytes and plasma in patients with acute coronary syndromes has disclosed that mature Cathepsin D is increased both in the plasma and monocytes of these patients. Finally, the problems of applying proteomic approach directly to plasma will be discussed. The purpose of this review is to provide the reader with an overview of different proteomic approaches that can be used to identify new biomarkers in vascular diseases.

Arteriosclerosis↗

PubMed2006

Quantitative proteomic and genomic profiling reveals metastasis-related protein expression patterns in gastric cancer cells.

Gastric cancer is a leading cause of death worldwide, and patients have an overall 5-year survival rate of less than 10%. Using quantitative proteomic techniques together with microarray chips, we have established comprehensive proteome and transcriptome profiles of the metastatic gastric cancer TMC-1 cells and the noninvasive gastric cancer SC-M1 cell. Our qualitative protein profiling strategy offers the first comprehensive analysis of the gastric cancer cell proteome, identifying 926 and 909 proteins from SC-M1 and TMC-1 cells, respectively. Cleavable isotope-coded affinity tagging analysis allows quantitation of a total of 559 proteins (with a protein false-positive rate of <0.005), and 240 proteins were differentially expressed (>1.3-fold) between the SC-M1 and TMC-1 cells. We identified numerous proteins not previously associated with gastric cancer. Notably, a large subset of differentially expressed proteins was associated with tumor metastasis, including proteins functioning in cell-cell and cell-extracellular matrix (cell-ECM) adhesion, cell motility, proliferation, and tumor immunity. Gene expression profiling by DNA microarray revealed differential expression (of >2-fold) of about 1000 genes. The weak correlation observed between protein and mRNA profiles highlights the important complementarities of DNA microarray and proteomics approaches. These comparative data enabled us to map the disease-perturbed cell-cell and cell-ECM adhesion and Rho GTPase-mediated cytoskeletal pathways. Further validation of a subset of genes suggests the potential use of vimentin and galectin 1 as markers for metastasis. We demonstrate that combining proteomic and genomic approaches not only provides a rapid, robust, and sensitive platform to elucidate the molecular mechanisms underlying gastric cancer metastasis but also may identify candidate diagnostic markers and therapeutic targets.

Biomarkers, Tumor↗

PubMed2002

Proteomic technologies in modern biomedical science.

This review highlights modern technologies employed in proteomics. Methods of sample preparations are discussed with special emphasis on the requirements for preparation of biological material, which may seriously influence the results of proteomic studies. Methods of solubilization, electrophoresis, chromatographic protein separation, and visualization of protein spots in gels are described. Modern methods of mass spectrometry used in proteomic studies include combination of protein chips with mass spectrometry. The review also describes approaches of functional proteomics, i.e., interactomics, and also bioinformatic resources used in proteomics for image analysis of 2D-gel-electrophoresis and for identification of protein sequences by mass spectra.

Biomedical Research↗

PubMed2004

Neuroproteomics: expression profiling of the brain's proteomes in health and disease.

The term "proteome" describes the protein complement of a genome. Proteomes of cells are dynamic and are directly affected by environmental factors, such as stress and/or drug treatment, or as a result of aging and disease. One of the distinct advantages of proteomic analysis, not attainable with RNA expression data, is the ability to fractionate the cell's proteins into various subpopulations. In neuroscience, "neuromics" (proteomics in the central nervous system) is in its infancy, with a paucity of studies in the context of the brain. One of the objectives of this review is to illustrate the potential of neuromics to profile differences in the distribution of thousands of proteins as a function of disease markers. We have previously used this approach to determine the effects of varied postmortem interval in examining human brain tissue and to identify biomarkers. Here we review proteomic studies of schizophrenia, Alzheimer's disease, and Parkinson's disease. Experimental results regarding Parkinson's disease are presented to illustrate the potential of neuromics to identify pathways of pathogenesis and novel therapeutic targets.

Alzheimer Disease↗

PubMed2000

Proteomics to study genes and genomes.

Proteomics, the large-scale analysis of proteins, will contribute greatly to our understanding of gene function in the post-genomic era. Proteomics can be divided into three main areas: (1) protein micro-characterization for large-scale identification of proteins and their post-translational modifications; (2) 'differential display' proteomics for comparison of protein levels with potential application in a wide range of diseases; and (3) studies of protein-protein interactions using techniques such as mass spectrometry or the yeast two-hybrid system. Because it is often difficult to predict the function of a protein based on homology to other proteins or even their three-dimensional structure, determination of components of a protein complex or of a cellular structure is central in functional analysis. This aspect of proteomic studies is perhaps the area of greatest promise. After the revolution in molecular biology exemplified by the ease of cloning by DNA methods, proteomics will add to our understanding of the biochemistry of proteins, processes and pathways for years to come.

Animals↗

PubMed2001

Large-scale analysis of the yeast proteome by multidimensional protein identification technology.

We describe a largely unbiased method for rapid and large-scale proteome analysis by multidimensional liquid chromatography, tandem mass spectrometry, and database searching by the SEQUEST algorithm, named multidimensional protein identification technology (MudPIT). MudPIT was applied to the proteome of the Saccharomyces cerevisiae strain BJ5460 grown to mid-log phase and yielded the largest proteome analysis to date. A total of 1,484 proteins were detected and identified. Categorization of these hits demonstrated the ability of this technology to detect and identify proteins rarely seen in proteome analysis, including low-abundance proteins like transcription factors and protein kinases. Furthermore, we identified 131 proteins with three or more predicted transmembrane domains, which allowed us to map the soluble domains of many of the integral membrane proteins. MudPIT is useful for proteome analysis and may be specifically applied to integral membrane proteins to obtain detailed biochemical information on this unwieldy class of proteins.

Algorithms↗

PubMed2005

Proteomic applications of surface plasmon resonance biosensors: analysis of protein arrays.

Proteomics is one of the most important issues in the post-genomic area, because it can greatly contribute to identifying protein biomarkers for disease diagnosis and drug screening. Protein array is a key technology for proteome researches and has been analyzed by various methods including fluorescence, mass spectrometry, atomic force microscopy and surface plasmon resonance (SPR). SPR biosensor is a promising technology in proteomics, since it has various advantages including real-time measurement of biomolecular interactions without labeling and the simple optical system for the device. SPR biosensors have a strong potential for analyzing proteomes by SPR imaging and SPR spectroscopic imaging, even though the challenge is to produce proteins on a proteomic scale.

Animals↗

PubMed2002

Proteomic profiling of mechanistically distinct enzyme classes using a common chemotype.

Proteomics research requires methods to characterize the expression and function of proteins in complex mixtures. Toward this end, chemical probes that incorporate known affinity labeling agents have facilitated the activity-based profiling of certain enzyme families. To accelerate the discovery of proteomics probes for enzyme classes lacking cognate affinity labels, we describe here a combinatorial strategy. Members of a probe library bearing a sulfonate ester chemotype were screened against complex proteomes for activity-dependent protein reactivity, resulting in the labeling of at least six mechanistically distinct enzyme classes. Surprisingly, none of these enzymes represented targets of previously described proteomics probes. The sulfonate library was used to identify an omega-class glutathione S-transferase whose activity was upregulated in invasive human breast cancer lines. These results indicate that activity-based probes compatible with whole-proteome analysis can be developed for numerous enzyme classes and applied to identify enzymes associated with discrete pathological states.

Affinity Labels↗

PubMed2005

Mass spectrometry-based proteomics turns quantitative.

The field of proteomics is built on technologies to analyze large numbers of proteins--ideally the entire proteome--in the same experiment. Mass spectrometry (MS) has been successfully used to characterize proteins in complex mixtures, but results so far have largely been qualitative. Two recently developed methodologies offer the opportunity to obtain quantitative proteomic information. Comparing the signals from the same peptide under different conditions yields a rough estimate of relative protein abundance between two proteomes. Alternatively, and more accurately, peptides are labeled with stable isotopes, introducing a predictable mass difference between peptides from two experimental conditions. Stable isotope labels can be incorporated 'post-harvest', by chemical approaches or in live cells through metabolic incorporation. This isotopic handle facilitates direct quantification from the mass spectra. Using these quantitative approaches, precise functional information as well as temporal changes in the proteome can be captured by MS.

Base Sequence↗

PubMed2002

Clinical proteomics: translating benchside promise into bedside reality.

The ultimate goal of proteomics is to characterize the information flow through protein networks. This information can be a cause, or a consequence, of disease processes. Clinical proteomics is an exciting new subdiscipline of proteomics that involves the application of proteomic technologies at the bedside, and cancer, in particular, is a model disease for studying such applications. Here, we describe proteomic technologies that are being developed to detect cancer earlier, to discover the next generation of targets and imaging biomarkers, and finally to tailor the therapy to the patient.

Female↗

PubMed2026

AI proteomics: from protein identification to virtual cells.

Artificial intelligence (AI) is transforming scientific research, including proteomics. In this Perspective, we highlight key mass spectrometry (MS)-based proteomics areas where AI is driving innovation, ranging from protein identification to building AI virtual cells. These include improving peptide and protein identification and quantification; characterizing protein-protein interactions and protein complexes; advancing spatial and perturbation proteomics; integrating multi-omics data; and, ultimately, enabling AI virtual cells. Finally, we call for global collaboration among data producers, data consumers and other stakeholders to establish an AI-friendly ecosystem for MS-based proteomics, laying the foundation for transformative advancements in proteomics driven by AI.

Proteomics↗

PubMed2026

Comprehensive proximity proteomics expand the known interactome of the oncoprotein β-catenin.

The oncoprotein β-catenin has critical roles in cell adhesion and cell signalling. β-catenin affects human physiology and pathology through numerous interaction partners, of which many have been discovered by standard affinity purification-based proteomics. However, the interaction landscape of β-catenin remains incompletely understood, highlighting a need for new experimental approaches for the exploration of β-catenin biology. Proximity proteomics, which facilitate the discovery of molecules vicinal to proteins-of-interest by mass spectrometry, have recently emerged as a powerful complementary tool for the study of protein-protein interactions, but have not been applied to β-catenin so far. We investigated the interactome of β-catenin in model cell lines by proximity proteomics using expression constructs with the biotin ligases BioID and TurboID. Mass spectrometry analyses following biotin labelling identified numerous candidate interactors of β-catenin, including many that had not been observed in earlier studies using standard proteomics. Enrichment analyses suggested that proximity proteomics capture proteins associated with the known biological functions of β-catenin, including cell adhesion, Wnt/β-catenin signalling, and transcription regulation. The molecular tools and data generated in this study provide new insights into β-catenin biology and highlight potential new regulators of β-catenin function that warrant further exploration.

beta Catenin↗

PubMed2005

The molecular make-up of a tumour: proteomics in cancer research.

The enormous progress in proteomics, enabled by recent advances in MS (mass spectrometry), has brought protein analysis back into the limelight of cancer research, reviving old areas as well as opening new fields of study. In this review, we discuss the basic features of proteomic technologies, including the basics of MS, and we consider the main current applications and challenges of proteomics in cancer research, including (i) protein expression profiling of tumours, tumour fluids and tumour cells; (ii) protein microarrays; (iii) mapping of cancer signalling pathways; (iv) pharmacoproteomics; (v) biomarkers for diagnosis, staging and monitoring of the disease and therapeutic response; and (vi) the immune response to cancer. All these applications continue to benefit from further technological advances, such as the development of quantitative proteomics methods, high-resolution, high-speed and high-sensitivity MS, functional protein assays, and advanced bioinformatics for data handling and interpretation. A major challenge will be the integration of proteomics with genomics and metabolomics data and their functional interpretation in conjunction with clinical results and epidemiology.

Biomarkers, Tumor↗