Proteomics of breast carcinoma

Beast cancer is the most diagnosed cancer in women, accounting for approximately 40,000 deaths annually in the USA. Significant advances have been made in the areas of detection and treatment, but a significant number of breast cancers are detected late. The advent of proteomics provides the hope of discovering novel biological markers that can be used for early detection, disease diagnosis, prognostication and prediction of response to therapy. Several proteomics technologies including 2D-PAGE, 2D-DIGE, ICAT, SELDI-TOF, MudPIT and protein arrays have been used to uncover molecular mechanisms associated with breast carcinoma at the global level, and a number of these technologies, particularly the SELDI-TOF hold promise as a proteomic approach that can be applied at the bedside for discovering protein patterns that distinguish disease and disease-free states with high sensitivity and specificity. Laser microdissection, a method for selection of homogenous cell populations, coupled to 2D-DIGE or MudPIT constitute a new proteomics-based paradigm for detecting disease in pathology specimens and monitoring disease response to therapy. This review describes proteomics technologies, and their application in the proteomic analysis of breast carcinoma.     

蛋白质组学-引领后基因组时代

蛋白质组学是建立在高通量筛选技术的基础上发展的方法学,用于研究细胞功能网络模块中蛋白相互作用及在疾病或病变中蛋白和蛋白相互作用所发生的系统动态的差异变化;其研究技术奠基于双向凝胶电泳。及至世纪之交,随着质谱及蛋白质芯片的引进,蛋白质组学已广泛应用在生命科学上。其在医学上的应用,主要旨在发现疾病的特异性蛋白质分子或其蛋白质纹印,以揭示疾病的发生机制,也作为早期诊断、分子分型、疗效及预后判断的依据,并找出可能成为新药物设计的分子靶点,为疾病提供新的治疗方案。随着人类基因序列的完成,蛋白质组学热浪掀起了后基因组年代的序幕,人类将更深入地了解疾病和生命的本源。现就蛋白质组学10年来的发展历程、研究技术、在人类疾病中的应用及未来展望等作出精简的评述。     

Proteomics, networks and connectivity indices

Describing the connectivity of chemical and/or biological systems using networks is a straight gate for the introduction of mathematical tools in proteomics. Networks, in some cases even very large ones, are simple objects that are composed at least by nodes and edges. The nodes represent the parts of the system and the edges geometric and/or functional relationships between parts. In proteomics, amino acids, proteins, electrophoresis spots, polypeptidic fragments, or more complex objects can play the role of nodes. All of these networks can be numerically described using the so-called Connectivity Indices (CIs). The transformation of graphs (a picture) into CIs (numbers) facilitates the manipulation of information and the search for structure-function relationships in Proteomics. In this work, we review and comment on the challenges and new trends in the definition and applications of CIs in Proteomics. Emphasis is placed on 1-D-CIs for DNA and protein sequences, 2-D-CIs for RNA secondary structures, 3-D-topographic indices (TPGIs) for protein function annotation without alignment, 2-D-CIs and 3-D-TPGIs for the study of drug-protein or drug-RNA quantitative structure-binding relationships, and pseudo 3-D-CIs for protein surface molecular recognition. We also focus on CIs to describe Protein Interaction Networks or RNA co-expression networks. 2-D-CIs for patient blood proteome 2-DE maps or mass spectra are also covered.     

Unraveling the dynamics of protein interactions with quantitative mass spectrometry

Knowledge of structure and dynamics of proteins and protein complexes is important to unveil the molecular basis and mechanisms involved in most biological processes. Protein complex dynamics can be defined as the changes in the composition of a protein complex during a cellular process. Protein dynamics can be defined as conformational changes in a protein during enzyme activation, for example, when a protein binds to a ligand or when a protein binds to another protein. Mass spectrometry (MS) combined with affinity purification has become the analytical tool of choice for mapping protein-protein interaction networks and the recent developments in the quantitative proteomics field has made it possible to identify dynamically interacting proteins. Furthermore, hydrogen/deuterium exchange MS is emerging as a powerful technique to study structure and conformational dynamics of proteins or protein assemblies in solution. Methods have been developed and applied for the identification of transient and/or weak dynamic interaction partners and for the analysis of conformational dynamics of proteins or protein complexes. This review is an overview of existing and recent developments in studying the overall dynamics of in vivo protein interaction networks and protein complexes using MS-based methods.     

Toponomics and neurotoponomics: a new way to medical systems biology

The fluorescence robot imaging technology multi-epitope-ligand-cartography/toponome imaging system has revolutionized the field of proteomics/functional genomics, because it enables the investigator to locate and decipher functional protein networks, the toponome, consisting of hundreds of different proteins in a single cell or tissue section. The technology has been proven to solve key problems in biology and therapy research. It has uncovered a new cellular transdifferentiation mechanism of vascular cells giving rise to myogenic cells in situ and in vivo; a finding that has led to efficient cell therapy models of muscle disorders, and discovered a new target protein in sporadic amyotrophic lateral sclerosis by hierarchical protein network analysis, a finding that has been confirmed by a mouse knockout model. A lead target protein in tumor cells that controls cell polarization as a mechanism that is fundamental for migration and metastasis formation has also been uncovered, and new functional territories in the CNS defined by high-dimensional synaptic protein clusters have been unveiled. The technology can be effectively interlocked with genomics and proteomics to optimize time-to-market and the overall attrition rate of new drugs. This review outlines major proofs of principle with an emphasis on neurotoponomics.     

Unraveling the dynamics of protein interactions with quantitative mass spectrometry

Knowledge of structure and dynamics of proteins and protein complexes is important to unveil the molecular basis and mechanisms involved in most biological processes. Protein complex dynamics can be defined as the changes in the composition of a protein complex during a cellular process. Protein dynamics can be defined as conformational changes in a protein during enzyme activation, for example, when a protein binds to a ligand or when a protein binds to another protein. Mass spectrometry (MS) combined with affinity purification has become the analytical tool of choice for mapping protein–protein interaction networks and the recent developments in the quantitative proteomics field has made it possible to identify dynamically interacting proteins. Furthermore, hydrogen/deuterium exchange MS is emerging as a powerful technique to study structure and conformational dynamics of proteins or protein assemblies in solution. Methods have been developed and applied for the identification of transient and/or weak dynamic interaction partners and for the analysis of conformational dynamics of proteins or protein complexes. This review is an overview of existing and recent developments in studying the overall dynamics of in vivo protein interaction networks and protein complexes using MS-based methods.     

Differential proteomics of plant development

In this mini-review, recent advances in plant developmental proteomics are summarized. The growing interest in plant proteomics continually produces large numbers of developmental studies on plant cell division, elongation, differentiation, and formation of various organs. The brief overview of changes in proteome profiles emphasizes the participation of stress-related proteins in all developmental processes, which substantially changes the view on functional classification of these proteins. Next, it is noteworthy that proteomics helped to recognize some metabolic and housekeeping proteins as important signaling inducers of developmental pathways. Further, cell division and elongation are dependent on proteins involved in membrane trafficking and cytoskeleton dynamics. These protein groups are less prevalently represented in studies concerning cell differentiation and organ formation, which do not target primarily cell division. The synthesis of new proteins, generally observed during developmental processes, is followed by active protein folding. In this respect, disulfide isomerase was found to be commonly up-regulated during several developmental processes. The future progress in plant proteomics requires new and/or complementary approaches including cell fractionation, specific chemical treatments, molecular cloning and subcellular localization of proteins combined with more sensitive methods for protein detection and identification.     

Statistics for proteomics: a review of tools for analyzing experimental data

Most proteomics experiments make use of 'high throughput' technologies such as 2-DE, MS or protein arrays to measure simultaneously the expression levels of thousands of proteins. Such experiments yield large, high-dimensional data sets which usually reflect not only the biological but also technical and experimental factors. Statistical tools are essential for evaluating these data and preventing false conclusions. Here, an overview is given of some typical statistical tools for proteomics experiments. In particular, we present methods for data preprocessing (e.g. calibration, missing values estimation and outlier detection), comparison of protein expression in different groups (e.g. detection of differentially expressed proteins or classification of new observations) as well as the detection of dependencies between proteins (e.g. protein clusters or networks). We also discuss questions of sample size planning for some of these methods.     

Proteomics of Staphylococcus aureus--current state and future challenges

This paper presents a short review of the proteome of Staphylococcus aureus, a gram-positive human pathogen of increasing importance for human health as a result of the increasing antibiotic resistance. A proteome reference map is shown which can be used for future studies and is followed by a demonstration of how proteomics could be applied to obtain new information on S. aureus physiology. The proteomic approach can provide new data on the regulation of metabolism as well as of the stress or starvation responses. Proteomic signatures encompassing specific stress or starvation proteins are excellent tools to predict the physiological state of a cell population. Furthermore proteomics is very useful for analysing the size and function of known and unknown regulons and will open a new dimension in the comprehensive understanding of regulatory networks in pathogenicity. Finally, some fields of application of S. aureus proteomics are discussed, including proteomics and strain evaluation, the role of proteomics for analysis of antibiotic resistance or for discovering new targets and diagnostics tools. The review also shows that the post-genome era of S. aureus which began in 2001 with the publication of the genome sequence is still in a preliminary stage, however, the consequent application of proteomics in combination with DNA array techniques and supported by bioinformatics will provide a comprehensive picture on cell physiology and pathogenicity in the near future.     

Toponomics and neurotoponomics: a new way to medical systems biology

The fluorescence robot imaging technology multi-epitope-ligand-cartography/toponome imaging system has revolutionized the field of proteomics/functional genomics, because it enables the investigator to locate and decipher functional protein networks, the toponome, consisting of hundreds of different proteins in a single cell or tissue section. The technology has been proven to solve key problems in biology and therapy research. It has uncovered a new cellular transdifferentiation mechanism of vascular cells giving rise to myogenic cells in situ and in vivo; a finding that has led to efficient cell therapy models of muscle disorders, and discovered a new target protein in sporadic amyotrophic lateral sclerosis by hierarchical protein network analysis, a finding that has been confirmed by a mouse knockout model. A lead target protein in tumor cells that controls cell polarization as a mechanism that is fundamental for migration and metastasis formation has also been uncovered, and new functional territories in the CNS defined by high-dimensional synaptic protein clusters have been unveiled. The technology can be effectively interlocked with genomics and proteomics to optimize time-to-market and the overall attrition rate of new drugs. This review outlines major proofs of principle with an emphasis on neurotoponomics.     

Predicting protein--protein interactions from primary structure

MOTIVATION: An ambitious goal of proteomics is to elucidate the structure, interactions and functions of all proteins within cells and organisms. The expectation is that this will provide a fuller appreciation of cellular processes and networks at the protein level, ultimately leading to a better understanding of disease mechanisms and suggesting new means for intervention. This paper addresses the question: can protein-protein interactions be predicted directly from primary structure and associated data? Using a diverse database of known protein interactions, a Support Vector Machine (SVM) learning system was trained to recognize and predict interactions based solely on primary structure and associated physicochemical properties. RESULTS: Inductive accuracy of the trained system, defined here as the percentage of correct protein interaction predictions for previously unseen test sets, averaged 80% for the ensemble of statistical experiments. Future proteomics studies may benefit from this research by proceeding directly from the automated identification of a cell's gene products to prediction of protein interaction pairs.     

Proteomics and metabolomics of Arabidopsis responses to perturbation of glucosinolate biosynthesis

To understand plant molecular networks of glucosinolate metabolism, perturbation of aliphatic glucosinolate biosynthesis was established using inducible RNA interference (RNAi) in Arabidopsis. Two RNAi lines were chosen for examining global protein and metabolite changes using complementary proteomics and metabolomics approaches. Proteins involved in metabolism including photosynthesis and hormone metabolism, protein binding, energy, stress, and defense showed marked responses to glucosinolate perturbation. In parallel, metabolomics revealed major changes in the levels of amino acids, carbohydrates, peptides, and hormones. The metabolomics data were correlated with the proteomics results and revealed intimate molecular connections between cellular pathways/processes and glucosinolate metabolism. This study has provided an unprecedented view of the molecular networks of glucosinolate metabolism and laid a foundation towards rationale glucosinolate engineering for enhanced defense and quality.     

Quantitative protein profiling using two-dimensional gel electrophoresis,isotope-coded affinity tag labeling,and mass spectrometry

Quantitative protein profiling is an essential part of proteomics and requires new technologies that accurately, reproducibly, and comprehensively identify and quantify the proteins contained in biological samples. We describe a new strategy for quantitative protein profiling that is based on the separation of proteins labeled with isotope-coded affinity tag reagents by two-dimensional gel electrophoresis and their identification and quantification by mass spectrometry. The method is based on the observation that proteins labeled with isotopically different isotope-coded affinity tag reagents precisely co-migrate during two-dimensional gel electrophoresis and that therefore two or more isotopically encoded samples can be separated concurrently in the same gel. By analyzing changes in the proteome of yeast (Saccharomyces cerevisiae) induced by a metabolic shift we show that this simple method accurately quantifies changes in protein abundance even in cases in which multiple proteins migrate to the same gel coordinates. The method is particularly useful for the quantitative analysis and structural characterization of differentially processed or post-translationally modified forms of a protein and is therefore expected to find wide application in proteomics research.     

Chemical proteomics from a nuclear magnetic resonance spectroscopy perspective

Proteomics is the study of the protein complement of a genome and employs a number of newly emerging tools. One such tool is chemical proteomics, which is a branch of proteomics devoted to the exploration of protein function using both in vitro and in vivo chemical probes. Chemical proteomics aims to define protein function and mechanism at the level of directly observed protein–ligand interactions, whereas chemical genomics aims to define the biological role of a protein using chemical knockouts and observing phenotypic changes. Chemical proteomics is therefore traditional mechanistic biochemistry performed in a systems-based manner, using either activity- or affinity-based probes that target proteins related by chemical reactivities or by binding site shape/properties, respectively. Systems are groups of proteins related by metabolic pathway, regulatory pathway or binding to the same ligand. Studies can be based on two main types of proteome samples: pooled proteins (1 mixture of N proteins) or isolated proteins in a given system and studied in parallel (N single protein samples). Although the field of chemical proteomics originated with the use of covalent labeling strategies such as isotope-coded affinity tagging, it is expanding to include chemical probes that bind proteins noncovalently, and to include more methods for observing protein–ligand interactions. This review presents an emerging role for nuclear magnetic resonance spectroscopy in chemical proteomics, both in vitro and in vivo. Applications include: functional proteomics using cofactor fingerprinting to assign proteins to gene families; gene family-based structural characterizations of protein–ligand complexes; gene family-focused design of drug leads; and chemical proteomic probes using nuclear magnetic resonance SOLVE and studies of protein–ligand interactions in vivo.     

Mass spectrometry-based quantitative proteomics

A major aim of present-day proteomics is to study changes in protein expression levels at a global level, ideally monitoring all proteins present in cells or tissue. Mass spectrometry is a well-respected technology in proteomics that is widely used for the identification of proteins. More recently, methodologies have been introduced showing that mass spectrometry can also be used for protein quantification. This article reviews various mass spectrometry-based technologies in quantitative proteomics, highlighting several interesting applications in areas ranging from cell biology to clinical applications.