定量蛋白质组同位素标记技术及应用

定量蛋白质组学是对蛋白质组进行精确的定量和鉴定的学科,突破了传统蛋白质组研究集中于对蛋白质的分离和鉴定,着重于定性定量解析细胞蛋白质的动态变化信息,更真实地反映了细胞功能、过程机制等综合信息。以同位素为内标的质谱分析新技术的提出,显示出可同时自动鉴定和精确定量的能力,代表了目前定量蛋白质组研究的主要发展方向。对近年来定量蛋白质组学同位素标记技术和应用研究所取得的重要进展以及最新的发展动态进行了综述。     

Development and application of proteomics technologies in Saccharomyces cerevisiae

Proteomics research focuses on the identification and quantification of "all" proteins present in cells, organisms or tissue. Proteomics is technically complicated because it encompasses the characterization and functional analysis of all proteins that are expressed by a genome. Moreover, because the expression levels of proteins strongly depend on complex regulatory systems, the proteome is highly dynamic. This review focuses on the two major proteomics methodologies, one based on 2D gel electrophoresis and the other based on liquid chromatography coupled to mass spectrometry. The recent developments of these methodologies and their application to quantitative proteomics are described. The model system Saccharomyces cerevisiae is considered to be the optimal vehicle for proteomics and we review studies investigating yeast adaptation to changes in (nutritional) environment.     

Proteomics in the post-genome age.

The genome sequencing effort has helped spawn the burgeoning field of proteomics. This review article examines state-of-the-art proteomics methods that are helping change the discovery paradigm in a variety of biological disciplines and, in particular, protein biochemistry. The review discusses both classical and novel methods to perform high-throughput qualitative and quantitative "global" as well as targeted proteome analysis of complex biological systems. From a drug discovery standpoint, the synergy between genomics and proteomics will help elucidate disease mechanisms, identify novel drug targets, and identify surrogate biomarkers that could be used to conduct clinical trials.     

Building integrated approaches for the proteomics of complex, dynamic systems: NIH programs in technology and infrastructure development

Proteomics technology and methods remain inadequate. Technological constraints contribute to an artificially static view of complex biological systems and a barrier between quantitative and interaction studies. Several NIH programs combine proteomics technology development with research on challenging biological problems to drive progress. A new initiative of the NIH Roadmap focuses on characterization of dynamic systems. The success of these programs will be judged by their impact on relevant biological problems.     

Comparison of full versus partial metabolic labeling for quantitative proteomics analysis in Arabidopsis thaliana

In recent years a variety of quantitative proteomics techniques have been developed, allowing characterization of changes in protein abundance in a variety of organisms under various biological conditions. Because it allows excellent control for error at all steps in sample preparation and analysis, full metabolic labeling using (15)N has emerged as an important strategy for quantitative proteomics, having been applied in a variety of organisms from yeast to Arabidopsis and even rats. However, challenges associated with complete replacement of (14)N with (15)N can make its application in many complex eukaryotic systems impractical on a routine basis. Extending a concept proposed by Whitelegge et al. (Whitelegge, J. P., Katz, J. E., Pihakari, K. A., Hale, R., Aguilera, R., Gomez, S. M., Faull, K. F., Vavilin, D., and Vermaas, W. (2004) Subtle modification of isotope ratio proteomics; an integrated strategy for expression proteomics. Phytochemistry 65, 1507-1515), we investigate an alternative strategy for quantitative proteomics that relies upon the subtle changes in isotopic envelope shape that result from partial metabolic labeling to compare relative abundances of labeled and unlabeled peptides in complex mixtures. We present a novel algorithm for the automated quantitative analysis of partial incorporation samples via LC-MS. We then compare the performance of partial metabolic labeling with traditional full metabolic labeling for quantification of controlled mixtures of labeled and unlabeled Arabidopsis peptides. Finally we evaluate the performance of each technique for comparison of light- versus dark-grown Arabidopsis with respect to reproducibility and numbers of peptide and protein identifications under more realistic experimental conditions. Overall full metabolic labeling and partial metabolic labeling prove to be comparable with respect to dynamic range, accuracy, and reproducibility, although partial metabolic labeling consistently allows quantification of a higher percentage of peptide observations across the dynamic range. This difference is especially pronounced at extreme ratios. Ultimately both full metabolic labeling and partial metabolic labeling prove to be well suited for quantitative proteomics characterization.     

Proteomics: the industrialization of protein chemistry

Establishing a proteomics platform in the industrial setting initially required implementation of a series of robotic systems to allow a high-throughput approach to analysis and identification of differences observed on 2-D electrophoresis gels. Now, a simpler alternative approach employing chromatography-based systems is emerging for identification of many components of complex mixtures, which can also provide quantitative comparisons through the use of a new labeling methodology.     

A high-quality catalog of the Drosophila melanogaster proteome

Understanding how proteins and their complex interaction networks convert the genomic information into a dynamic living organism is a fundamental challenge in biological sciences. As an important step towards understanding the systems biology of a complex eukaryote, we cataloged 63% of the predicted Drosophila melanogaster proteome by detecting 9,124 proteins from 498,000 redundant and 72,281 distinct peptide identifications. This unprecedented high proteome coverage for a complex eukaryote was achieved by combining sample diversity, multidimensional biochemical fractionation and analysis-driven experimentation feedback loops, whereby data collection is guided by statistical analysis of prior data. We show that high-quality proteomics data provide crucial information to amend genome annotation and to confirm many predicted gene models. We also present experimentally identified proteotypic peptides matching approximately 50% of D. melanogaster gene models. This library of proteotypic peptides should enable fast, targeted and quantitative proteomic studies to elucidate the systems biology of this model organism.     

Driving biochemical discovery with quantitative proteomics

Proteomic analysis of biological samples plays an increasing role in modern research. Although the application of proteomics technologies varies across many disciplines, proteomics largely is a tool for discovery that then leads to novel hypotheses. In recent years, new methods and technologies have been developed and applied in many areas of proteomics, and there is a strong push towards using proteomics in a quantitative manner. Indeed, mass spectrometry-based, quantitative proteomics approaches have been applied to great success in a variety of biochemical studies. In particular, the use of quantitative proteomics provides new insights into protein complexes and post-translational modifications and leads to the generation of novel insights into these important biochemical systems.     

2D electrophoresis-based expression proteomics: a microbiologist's perspective

Quantitative proteomics based on 2D electrophoresis (2-DE) coupled with peptide mass fingerprinting is still one of the most widely used quantitative proteomics approaches in microbiology research. Our view on the exploitation of this global expression analysis technique and its contribution and potential to push forward the field of molecular microbial physiology towards a molecular systems microbiology perspective is discussed in this article. The advances registered in 2-DE-based quantitative proteomic analysis leading to increased protein resolution, sensitivity and accuracy, and the promising use of 2-DE to gain insights into post-translational modifications at a proteome-wide level (considering all the proteins/protein forms expressed by the genome) are focused on. Given the progress made in this field, it is foreseen that the 2-DE-based approach to quantitative proteomics will continue to be a fundamental tool for microbiologists working at a genome-wide scale. Guidelines are also provided for the exploitation of expression proteomics data, based on useful computational tools, and for the integration of these data with other genome-wide expression information. The advantages and limitations of a complete 2-DE-based expression proteomics analysis, envisaging the quantification of the global changes occurring in the proteome of a given cell depending on environmental or genetic manipulations, are discussed from the microbiologist's perspective. Particular focus is given to the emerging field of toxicoproteomics, a new systems toxicity approach that offers a powerful tool to directly monitor the earliest stages of the toxicological response by identifying critical proteins and pathways that are affected by, and respond to, a chemical stress. The experimental design and the bioinformatics analysis of data used in our laboratory to gain mechanistic insights through expression proteomics into the responses of the eukaryotic model Saccharomyces cerevisiae or of Pseudomonas strains to environmental toxicants are presented as case studies.     

2D electrophoresis-based expression proteomics: a microbiologist’s perspective

Quantitative proteomics based on 2D electrophoresis (2-DE) coupled with peptide mass fingerprinting is still one of the most widely used quantitative proteomics approaches in microbiology research. Our view on the exploitation of this global expression analysis technique and its contribution and potential to push forward the field of molecular microbial physiology towards a molecular systems microbiology perspective is discussed in this article. The advances registered in 2-DE-based quantitative proteomic analysis leading to increased protein resolution, sensitivity and accuracy, and the promising use of 2-DE to gain insights into post-translational modifications at a proteome-wide level (considering all the proteins/protein forms expressed by the genome) are focused on. Given the progress made in this field, it is foreseen that the 2-DE-based approach to quantitative proteomics will continue to be a fundamental tool for microbiologists working at a genome-wide scale. Guidelines are also provided for the exploitation of expression proteomics data, based on useful computational tools, and for the integration of these data with other genome-wide expression information. The advantages and limitations of a complete 2-DE-based expression proteomics analysis, envisaging the quantification of the global changes occurring in the proteome of a given cell depending on environmental or genetic manipulations, are discussed from the microbiologist’s perspective. Particular focus is given to the emerging field of toxicoproteomics, a new systems toxicity approach that offers a powerful tool to directly monitor the earliest stages of the toxicological response by identifying critical proteins and pathways that are affected by, and respond to, a chemical stress. The experimental design and the bioinformatics analysis of data used in our laboratory to gain mechanistic insights through expression proteomics into the responses of the eukaryotic model Saccharomyces cerevisiae or of Pseudomonas strains to environmental toxicants are presented as case studies.     

Mapping in vivo signal transduction defects by phosphoproteomics

Abnormal protein phosphorylation is implicated in a variety of diseases, but until recently the complexity of tissue material, technical limitations, and the substantial volume of required data processing did not allow large-scale phosphoproteomic analysis of patient material, despite tremendous progress in developing mass spectrometry technologies. Phosphoproteomic approaches were primarily developed using model systems such as transformed cell lines, but technological advances in proteomics now make it feasible to analyze thousands of phosphorylation sites in a quantitative manner in patient materials or complex animal and cellular model systems to identify signaling abnormalities. This review summarizes very recent phosphoproteomic studies on complex tissue material, including tissue samples in biobanks, to complement recent reviews that focus primarily on technical advances in instrumentation and methods. Several successful examples reviewed here suggest it is now possible to apply phosphoproteomic techniques to address more challenging medical questions such as mapping within patient samples signal transduction defects that are relevant for diagnosis and individualized treatment development.     

Quantitative plant proteomics

Quantitation is an inherent requirement in comparative proteomics and there is no exception to this for plant proteomics. Quantitative proteomics has high demands on the experimental workflow, requiring a thorough design and often a complex multi-step structure. It has to include sufficient numbers of biological and technical replicates and methods that are able to facilitate a quantitative signal read-out. Quantitative plant proteomics in particular poses many additional challenges but because of the nature of plants it also offers some potential advantages. In general, analysis of plants has been less prominent in proteomics. Low protein concentration, difficulties in protein extraction, genome multiploidy, high Rubisco abundance in green tissue, and an absence of well-annotated and completed genome sequences are some of the main challenges in plant proteomics. However, the latter is now changing with several genomes emerging for model plants and crops such as potato, tomato, soybean, rice, maize and barley. This review discusses the current status in quantitative plant proteomics (MS-based and non-MS-based) and its challenges and potentials. Both relative and absolute quantitation methods in plant proteomics from DIGE to MS-based analysis after isotope labeling and label-free quantitation are described and illustrated by published studies. In particular, we describe plant-specific quantitative methods such as metabolic labeling methods that can take full advantage of plant metabolism and culture practices, and discuss other potential advantages and challenges that may arise from the unique properties of plants.     

Evaluation of empirical rule of linearly correlated peptide selection (ERLPS) for proteotypic peptide‐based quantitative proteomics

Precise protein quantification is essential in comparative proteomics. Currently, quantification bias is inevitable when using proteotypic peptide‐based quantitative proteomics strategy for the differences in peptides measurability. To improve quantification accuracy, we proposed an “empirical rule for linearly correlated peptide selection (ERLPS)” in quantitative proteomics in our previous work. However, a systematic evaluation on general application of ERLPS in quantitative proteomics under diverse experimental conditions needs to be conducted. In this study, the practice workflow of ERLPS was explicitly illustrated; different experimental variables, such as, different MS systems, sample complexities, sample preparations, elution gradients, matrix effects, loading amounts, and other factors were comprehensively investigated to evaluate the applicability, reproducibility, and transferability of ERPLS. The results demonstrated that ERLPS was highly reproducible and transferable within appropriate loading amounts and linearly correlated response peptides should be selected for each specific experiment. ERLPS was used to proteome samples from yeast to mouse and human, and in quantitative methods from label‐free to O18/O16‐labeled and SILAC analysis, and enabled accurate measurements for all proteotypic peptide‐based quantitative proteomics over a large dynamic range.     

Enzyme Kinetics for Complex System Enables Accurate Determination of Specificity Constants of Numerous Substrates in a Mixture by Proteomics Platform

Many important experiments in proteomics including protein digestion, enzyme substrate screening, enzymatic labeling, etc., involve the enzymatic reactions in a complex system where numerous substrates coexists with an enzyme. However, the enzyme kinetics in such a system remains unexplored and poorly understood. Herein, we derived and validated the kinetics equations for the enzymatic reactions in complex system. We developed an iteration approach to depict the enzymatic reactions in complex system. It was validated by 630 time-course points from 24 enzymatic reaction experiments and was demonstrated to be a powerful tool to simulate the reactions in the complex system. By applying this approach, we found that the ratio of substrate depletion is independent of other coexisted substrates under specific condition. This observation was then validated by experiments. Based on this striking observation, a simplified model was developed to determine the catalytic efficiencies of numerous competing substrates presented in the complex enzyme reaction system. When coupled with high-throughput quantitative proteomics technique, this simplified model enabled the accurate determination of catalytic efficiencies for 2369 peptide substrates of a protease by using only one enzymatic reaction experiment. Thus, this study provided, in the first time, a validated model for the large scale determination of specificity constants which could enable the enzyme substrate screening approach turned from a qualitative method of identifying substrates to a quantitative method of identifying and prioritizing substrates. Data are available via ProteomeXchange with identifier PXD004665.     

GProX, a user-friendly platform for bioinformatics analysis and visualization of quantitative proteomics data

Recent technological advances have made it possible to identify and quantify thousands of proteins in a single proteomics experiment. As a result of these developments, the analysis of data has become the bottleneck of proteomics experiment. To provide the proteomics community with a user-friendly platform for comprehensive analysis, inspection and visualization of quantitative proteomics data we developed the Graphical Proteomics Data Explorer (GProX)(1). The program requires no special bioinformatics training, as all functions of GProX are accessible within its graphical user-friendly interface which will be intuitive to most users. Basic features facilitate the uncomplicated management and organization of large data sets and complex experimental setups as well as the inspection and graphical plotting of quantitative data. These are complemented by readily available high-level analysis options such as database querying, clustering based on abundance ratios, feature enrichment tests for e.g. GO terms and pathway analysis tools. A number of plotting options for visualization of quantitative proteomics data is available and most analysis functions in GProX create customizable high quality graphical displays in both vector and bitmap formats. The generic import requirements allow data originating from essentially all mass spectrometry platforms, quantitation strategies and software to be analyzed in the program. GProX represents a powerful approach to proteomics data analysis providing proteomics experimenters with a toolbox for bioinformatics analysis of quantitative proteomics data. The program is released as open-source and can be freely downloaded from the project webpage at http://gprox.sourceforge.net.     

Multistep mass tagging coupled with 2D LC-MS--an approach to increasing the number of identified proteins.

Identification of large numbers of proteins from complex biological samples is a continuing challenge in the area of quantitative proteomics. We introduce here a simple and reliable multistep mass tagging technique using our recently developed solid phase mass tagging reagents. When coupled with two-dimensional liquid chromatography/nano-electrospray ionization ion trap mass spectrometry (2D-LC/nano-ESI-MS), this method allows enhanced protein identification when tested on samples from prokaryotic and eukaryotic sources. The proteome of Escherichia coli D21 grown to either mid-exponential or stationary phase, and the membrane proteome from established breast cancer cell lines BT474 and MCF7 were used as model systems in these experiments. In both experiments, the numbers of total identified proteins are at least twice the numbers identified from a single tagging cycle. The sample complexity can be effectively reduced with corresponding increases in protein identification using the multistep method. The strategy described here represents a potentially powerful technique for large-scale qualitative and quantitative proteome research.     

Farm animal proteomics--a review

In agricultural sciences as in all other areas of life science, the implementation of proteomics and other post-genomic tools is an important step towards more detailed understanding of the complex biological systems that control physiology and pathology of living beings. Farm animals are raised in large-scale operations, with the aim to obtain animal products for human consumption. Hence, understanding the biological traits that impact yield and quality of these products is the specific aim of much biological experimentation. However, most of the data gathered from experiments on e.g. swine and cattle are relevant not only for farm animal sciences, but also for adding to our understanding of complex biological mechanisms of health and disease in humans. The aim of this review is to present an overview of the specific topics of interest within farm animal proteomics, and to highlight some of the areas where synergy between classic model organism proteomics and farm animal proteomics is rapidly emerging. Focus will be on introducing the special biological traits that play an important role in food production, and on how proteomics may help optimize farm animal production.     

Eicosanomics: targeted lipidomics of eicosanoids in biological systems

Recent advancements in mass spectrometry, especially the development of electrospray tandem mass spectrometry (ESI/LC/MS2) and matrix-assisted laser desorption time-of-flight mass spectrometry (MALDI/TOF), have greatly facilitated analysis of complex biomolecules. It has now become possible to profile, in relatively short periods of time, large multicomponent groups of compounds biosynthesized by biological systems. The efficiency and accuracy of analysis have led to the development of new concepts of mass spectrometric profiling, mapping, and imaging. Profiling of proteins in biological material (proteomics) has become a widely accepted strategy for identification of mechanisms involved in the biochemistry of disease processes, and has become a novel tool for unraveling new drug targets. Evolution of proteomics has relied on ESI/LC/MS2 and MALDI/TOF, techniques that are also useful in the novel area of quantitative proteomics.