Proteomics makes progress in cartilage and arthritis research

Understanding biology at the systems level is a powerful method for discovery of previously unrecognized molecular pathways and mechanisms in human disease. The application of proteomics to arthritis research has lagged behind many other clinical targets, partly due to the unique biochemical properties of cartilage and associated biological fluids such as synovial fluid. In recent years, however, proteomic-based studies in cartilage and arthritis research have risen sharply and have started to make a significant impact on our understanding of joint disease, including the discovery of new and promising biomarkers of cartilage degeneration, a hallmark of arthritis. In this review we will make the case for the ongoing proteomic analysis of cartilage and other tissues affected by joint disease, overview some of the core proteomic techniques and discuss how the challenge of cartilage proteomics has been met through technical innovation. The major outcomes and information obtained from recent proteomic analysis of synovial fluid, cartilage and chondrocytes will also be described. In addition, we present some novel insights into post-translational regulation of cartilage proteins, through proteomic identification of proteolytic fragments in mouse cartilage extracts and explant culture media. We conclude with our prediction of how emerging proteomic technologies that have yet to be applied in arthritis research are likely to contribute further important information.     

Practical 4'-phosphopantetheine active site discovery from proteomic samples

Polyketide and nonribosomal peptides constitute important classes of small molecule natural products. Due to the proven biological activities of these compounds, novel methods for discovery and study of the polyketide synthase (PKS) and nonribosomal peptide synthetase (NRPS) enzymes responsible for their production remains an area of intense interest, and proteomic approaches represent a relatively unexplored avenue. While these enzymes may be distinguished from the proteomic milieu by their use of the 4'-phosphopantetheine (PPant) post-translational modification, proteomic detection of PPant peptides is hindered by their low abundance and labile nature which leaves them unassigned using traditional database searching. Here we address key experimental and computational challenges to facilitate practical discovery of this important post-translational modification during shotgun proteomics analysis using low-resolution ion-trap mass spectrometers. Activity-based enrichment maximizes MS input of PKS/NRPS peptides, while targeted fragmentation detects putative PPant active sites. An improved data analysis pipeline allows experimental identification and validation of these PPant peptides directly from MS2 data. Finally, a machine learning approach is developed to directly detect PPant peptides from only MS2 fragmentation data. By providing new methods for analysis of an often cryptic post-translational modification, these methods represent a first step toward the study of natural product biosynthesis in proteomic settings.     

Alternative profiling platform based on MELDI and its applicability in clinical proteomics

The presence of numerous proteomics data and their results in literature reveal the importance and influence of proteins and peptides on human cell cycle. For instance, the proteomic profiling of biological samples, such as serum, plasma or cells, and their organelles, carried out by surface-enhanced laser desorption/ionization mass spectrometry, has led to the discovery of numerous key proteins involved in many biological disease processes. However, questions still remain regarding the reproducibility, bioinformatic artifacts and cross-validations of such experimental set-ups. The authors have developed a material-based approach, termed material-enhanced laser desorption/ionization mass spectrometry (MELDI-MS), to facilitate and improve the robustness of large-scale proteomic experiments. MELDI-MS includes a fully automated protein-profiling platform, from sample preparation and analysis to data processing involving state-of-the-art methods, which can be further improved. Multiplexed protein pattern analysis, based on material morphology, physical characteristics and chemical functionalities provides a multitude of protein patterns and allows prostate cancer samples to be distinguished from non-prostate cancer samples. Furthermore, MELDI-MS enables not only the analysis of protein signatures, but also the identification of potential discriminating peaks via capillary liquid chromatography mass spectrometry. The optimized MELDI approach offers a complete proteomics platform with improved sensitivity, selectivity and short sample preparation times.     

Alternative profiling platform based on MELDI and its applicability in clinical proteomics

The presence of numerous proteomics data and their results in literature reveal the importance and influence of proteins and peptides on human cell cycle. For instance, the proteomic profiling of biological samples, such as serum, plasma or cells, and their organelles, carried out by surface-enhanced laser desorption/ionization mass spectrometry, has led to the discovery of numerous key proteins involved in many biological disease processes. However, questions still remain regarding the reproducibility, bioinformatic artifacts and cross-validations of such experimental set-ups. The authors have developed a material-based approach, termed material-enhanced laser desorption/ionization mass spectrometry (MELDI-MS), to facilitate and improve the robustness of large-scale proteomic experiments. MELDI-MS includes a fully automated protein-profiling platform, from sample preparation and analysis to data processing involving state-of-the-art methods, which can be further improved. Multiplexed protein pattern analysis, based on material morphology, physical characteristics and chemical functionalities provides a multitude of protein patterns and allows prostate cancer samples to be distinguished from non-prostate cancer samples. Furthermore, MELDI-MS enables not only the analysis of protein signatures, but also the identification of potential discriminating peaks via capillary liquid chromatography mass spectrometry. The optimized MELDI approach offers a complete proteomics platform with improved sensitivity, selectivity and short sample preparation times.     

Bioinformatics analysis of mass spectrometry-based proteomics data sets

Proteomics has made tremendous progress, attaining throughput and comprehensiveness so far only seen in genomics technologies. The consequent avalanche of proteome level data poses great analytical challenges for downstream interpretation. We review bioinformatic analysis of qualitative and quantitative proteomic data, focusing on current and emerging paradigms employed for functional analysis, data mining and knowledge discovery from high resolution quantitative mass spectrometric data. Many bioinformatics tools developed for microarrays can be reused in proteomics, however, the uniquely quantitative nature of proteomics data also offers entirely novel analysis possibilities, which directly suggest and illuminate biological mechanisms.     

基于约束的基因组规模代谢网络模型构建方法研究进展

基因组规模代谢网络模型(Genome-scale metabolic network model,GSMM)正成为细胞代谢特性研究的重要工具,经过多年发展相关理论方法取得了诸多进展。近年来,在基础GSMM模型基础上,通过整合基因组、转录组、蛋白组和热力学数据,实现基于各种约束的GSMM构建,在基因靶点识别、系统代谢工程、药物发现、人类疾病机理研究等多个方面取得了进一步的发展和理论突破。文中重点综述包括转录组约束、蛋白组约束、以及热力学约束条件在GSMM中的实施方法、相应方法的不足及应用限制等。最后介绍了如何综合运用转录、蛋白及热力学约束,实现GSMM的全整合模型及其细化,并对基于约束的GSMM构建及应用前景进行了展望。     

A pipeline that integrates the discovery and verification of plasma protein biomarkers reveals candidate markers for cardiovascular disease

We developed a pipeline to integrate the proteomic technologies used from the discovery to the verification stages of plasma biomarker identification and applied it to identify early biomarkers of cardiac injury from the blood of patients undergoing a therapeutic, planned myocardial infarction (PMI) for treatment of hypertrophic cardiomyopathy. Sampling of blood directly from patient hearts before, during and after controlled myocardial injury ensured enrichment for candidate biomarkers and allowed patients to serve as their own biological controls. LC-MS/MS analyses detected 121 highly differentially expressed proteins, including previously credentialed markers of cardiovascular disease and >100 novel candidate biomarkers for myocardial infarction (MI). Accurate inclusion mass screening (AIMS) qualified a subset of the candidates based on highly specific, targeted detection in peripheral plasma, including some markers unlikely to have been identified without this step. Analyses of peripheral plasma from controls and patients with PMI or spontaneous MI by quantitative multiple reaction monitoring mass spectrometry or immunoassays suggest that the candidate biomarkers may be specific to MI. This study demonstrates that modern proteomic technologies, when coherently integrated, can yield novel cardiovascular biomarkers meriting further evaluation in large, heterogeneous cohorts.     

羧肽酶Y标记蛋白质羧基端方法的优化及应用

蛋白质水解是一种重要的翻译后修饰,它在许多生化过程 (如细胞凋亡和肿瘤细胞转移等) 中起着极其重要的作用。鉴定蛋白质水解位点可以进一步加深我们对这些生化过程的认识。尽管蛋白质氨基端标记方法和蛋白质组学在复杂生物体系中鉴定获得了许多蛋白质的水解位点,但这种方法存在固有的缺陷。羧基端标记方法是另一种可行的鉴定蛋白质水解位点的方法。本文优化了蛋白质羧基端生物酶标记方法,提高了亲和标记效率,从而可以更好地利用正向分离方法对蛋白质羧基端多肽进行分离并用质谱鉴定。我们用优化后的羧基端标记方法来标记大肠杆菌Escherichia coli复杂蛋白样品后鉴定到了120多个蛋白质羧基端多肽和内切多肽。在其所鉴定的蛋白质水解位点中,我们发现了许多已知和未知的位点,这些新的水解位点有可能在正常生化过程的调控发挥着重要的作用。该研究提供了一个可以与蛋白质氨基端组学互为补充、可在复杂体系中鉴定蛋白质水解的方法。     

Human saliva proteome analysis and disease biomarker discovery

Human saliva is an attractive body fluid for disease diagnosis and prognosis because saliva testing is simple, safe, low-cost and noninvasive. Comprehensive analysis and identification of the proteomic content in human whole and ductal saliva will not only contribute to the understanding of oral health and disease pathogenesis, but also form a foundation for the discovery of saliva protein biomarkers for human disease detection. In this article, we have summarized the proteomic technologies for comprehensive identification of proteins in human whole and ductal saliva. We have also discussed potential quantitative proteomic approaches to the discovery of saliva protein biomarkers for human oral and systemic diseases. With the fast development of mass spectrometry and proteomic technologies, we are enthusiastic that saliva protein biomarkers will be developed for clinical diagnosis and prognosis of human diseases in the future.     

Challenges in translating plasma proteomics from bench to bedside: update from the NHLBI Clinical Proteomics Programs

The emerging scientific field of proteomics encompasses the identification, characterization, and quantification of the protein content or proteome of whole cells, tissues, or body fluids. The potential for proteomic technologies to identify and quantify novel proteins in the plasma that can function as biomarkers of the presence or severity of clinical disease states holds great promise for clinical use. However, there are many challenges in translating plasma proteomics from bench to bedside, and relatively few plasma biomarkers have successfully transitioned from proteomic discovery to routine clinical use. Key barriers to this translation include the need for "orthogonal" biomarkers (i.e., uncorrelated with existing markers), the complexity of the proteome in biological samples, the presence of high abundance proteins such as albumin in biological samples that hinder detection of low abundance proteins, false positive associations that occur with analysis of high dimensional datasets, and the limited understanding of the effects of growth, development, and age on the normal plasma proteome. Strategies to overcome these challenges are discussed.     

Functional proteomics to identify critical proteins in signal transduction pathways

Reversible protein phosphorylation plays a crucial role in the regulation of signaling pathways that control various biological responses, such as cell growth, differentiation, invasion, metastasis and apoptosis. Proteomics is a powerful research approach for fully monitoring global molecular responses to the activation of signal transduction pathways. Identification of different phosphoproteins and their phosphorylation sites by functional proteomics provides informational insights into signaling pathways triggered by all kinds of factors. This review summarizes how functional proteomics can be used to answer specific questions related to signal transduction systems of interest. By examining our own example on identifying the novel phosphoproteins in signaling pathways activated by EB virus-encoded latent membrane protein 1 (LMP1), we demonstrated a functional proteomic strategy to elucidate the molecular activity of phosphorylated annexin A2 in LMP1 signaling pathway. Functional profiling of signaling pathways is promising for the identification of novel targets for drug discovery and for the understanding of disease pathogenesis.     

Human saliva proteome analysis and disease biomarker discovery

Human saliva is an attractive body fluid for disease diagnosis and prognosis because saliva testing is simple, safe, low-cost and noninvasive. Comprehensive analysis and identification of the proteomic content in human whole and ductal saliva will not only contribute to the understanding of oral health and disease pathogenesis, but also form a foundation for the discovery of saliva protein biomarkers for human disease detection. In this article, we have summarized the proteomic technologies for comprehensive identification of proteins in human whole and ductal saliva. We have also discussed potential quantitative proteomic approaches to the discovery of saliva protein biomarkers for human oral and systemic diseases. With the fast development of mass spectrometry and proteomic technologies, we are enthusiastic that saliva protein biomarkers will be developed for clinical diagnosis and prognosis of human diseases in the future.     

Integrated mass spectrometry strategy for functional protein complex discovery and structural characterization

The discovery of functional protein complex and the interrogation of the complex structure-function relationship (SFR) play crucial roles in the understanding and intervention of biological processes. Affinity purification-mass spectrometry (AP-MS) has been proved as a powerful tool in the discovery of protein complexes. However, validation of these novel protein complexes as well as elucidation of their molecular interaction mechanisms are still challenging. Recently, native top-down MS (nTDMS) is rapidly developed for the structural analysis of protein complexes. In this review, we discuss the integration of AP-MS and nTDMS in the discovery and structural characterization of functional protein complexes. Further, we think the emerging artificial intelligence (AI)-based protein structure prediction is highly complementary to nTDMS and can promote each other. We expect the hybridization of integrated structural MS with AI prediction to be a powerful workflow in the discovery and SFR investigation of functional protein complexes.     

Discovery of regulatory molecular events and biomarkers using 2D capillary chromatography and mass spectrometry

An important component of proteomic research is the high-throughput discovery of novel proteins and protein-protein interactions that control molecular events that contribute to critical cellular functions and human disease. The interactions of proteins are essential for cellular functions. Identifying perturbation of normal cellular protein interactions is vital for understanding the disease process and intervening to control the disease. A second area of proteomics research is the discovery of proteins that will serve as biomarkers for the early detection, diagnosis and drug treatment response for specific diseases. These studies have been referred to as clinical proteomics. To discover biomarkers, proteomics research employs the quantitative comparison of peptide and protein expression in body fluids and tissues from diseased individuals (case) versus normal individuals (control). Methods that couple 2D capillary liquid chromatography (LC) and tandem mass spectrometry (MS/MS) analysis have greatly facilitated this discovery science. Coupling 2D-LC/MS/MS analysis with automated genome-assisted spectra interpretation allows a direct, high-throughput and high-sensitivity identification of thousands of individual proteins from complex biological samples. The systematic comparison of experimental conditions and controls allows protein function or disease states to be modeled. This review discusses the different purification and quantification strategies that have been developed and used in combination with 2D-LC/MS/MS and computational analysis to examine regulatory protein networks and clinical samples.     

Discovery of regulatory molecular events and biomarkers using 2D capillary chromatography and mass spectrometry

An important component of proteomic research is the high-throughput discovery of novel proteins and protein–protein interactions that control molecular events that contribute to critical cellular functions and human disease. The interactions of proteins are essential for cellular functions. Identifying perturbation of normal cellular protein interactions is vital for understanding the disease process and intervening to control the disease. A second area of proteomics research is the discovery of proteins that will serve as biomarkers for the early detection, diagnosis and drug treatment response for specific diseases. These studies have been referred to as clinical proteomics. To discover biomarkers, proteomics research employs the quantitative comparison of peptide and protein expression in body fluids and tissues from diseased individuals (case) versus normal individuals (control). Methods that couple 2D capillary liquid chromatography (LC) and tandem mass spectrometry (MS/MS) analysis have greatly facilitated this discovery science. Coupling 2D-LC/MS/MS analysis with automated genome-assisted spectra interpretation allows a direct, high-throughput and high-sensitivity identification of thousands of individual proteins from complex biological samples. The systematic comparison of experimental conditions and controls allows protein function or disease states to be modeled. This review discusses the different purification and quantification strategies that have been developed and used in combination with 2D-LC/MS/MS and computational analysis to examine regulatory protein networks and clinical samples.     

High-throughput proteomics using antibody microarrays

Antibody-based microarrays are a novel technology that hold great promise in proteomics. Microarrays can be printed with thousands of recombinant antibodies carrying the desired specificities, the biologic sample (e.g., an entire proteome) and any specifically bound analytes detected. The microarray patterns that are generated can then be converted into proteomic maps, or molecular fingerprints, revealing the composition of the proteome. Using this tool, global proteome analysis and protein expression profiling will thus provide new opportunities for biomarker discovery, drug target identification and disease diagnostics, as well as providing insights into disease biology. Intense work is currently underway to develop this novel technology platform into the high-throughput proteomic tool required by the research community.     

High-throughput siRNA-based functional target validation

The drug discovery process pursued by major pharmaceutical companies for many years starts with target identification followed by high-throughput screening (HTS) with the goal of identifying lead compounds. To accomplish this goal, significant resources are invested into automation of the screening process or HTS. Robotic systems capable of handling thousands of data points per day are implemented across the pharmaceutical sector. Many of these systems are amenable to handling cell-based screening protocols as well. On the other hand, as companies strive to develop innovative products based on novel mechanisms of action(s), one of the current bottlenecks of the industry is the target validation process. Traditionally, bioinformatics and HTS groups operate separately at different stages of the drug discovery process. The authors describe the convergence and integration of HTS and bioinformatics to perform high-throughput target functional identification and validation. As an example of this approach, they initiated a project with a functional cell-based screen for a biological process of interest using libraries of small interfering RNA (siRNA) molecules. In this protocol, siRNAs function as potent gene-specific inhibitors. siRNA-mediated knockdown of the target genes is confirmed by TaqMan analysis, and genes with impacts on biological functions of interest are selected for further analysis. Once the genes are confirmed and further validated, they may be used for HTS to yield lead compounds.