Mycobacterium tuberculosis functional network analysis by global subcellular protein profiling

Trends in increased tuberculosis infection and a fatality rate of approximately 23% have necessitated the search for alternative biomarkers using newly developed postgenomic approaches. Here we provide a systematic analysis of Mycobacterium tuberculosis (Mtb) by directly profiling its gene products. This analysis combines high-throughput proteomics and computational approaches to elucidate the globally expressed complements of the three subcellular compartments (the cell wall, membrane, and cytosol) of Mtb. We report the identifications of 1044 proteins and their corresponding localizations in these compartments. Genome-based computational and metabolic pathways analyses were performed and integrated with proteomics data to reconstruct response networks. From the reconstructed response networks for fatty acid degradation and lipid biosynthesis pathways in Mtb, we identified proteins whose involvements in these pathways were not previously suspected. Furthermore, the subcellular localizations of these expressed proteins provide interesting insights into the compartmentalization of these pathways, which appear to traverse from cell wall to cytoplasm. Results of this large-scale subcellular proteome profile of Mtb have confirmed and validated the computational network hypothesis that functionally related proteins work together in larger organizational structures.     

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.     

Orbitrap Exploris 480质谱在定量蛋白质组学应用中的优化和评测

基于数据依赖的扫描模式(data-dependent acquisition, DDA)和数据非依赖的扫描模式(data-independent acquisition,DIA)的非标记定量(label-free quantitative,LFQ)和同位素标记TMT (tandem mass tag)定量是蛋白质组学定量中较常见的技术.本文利用最新的Orbitrap Exploris 480质谱,优化了DDA、FAIMS DDA、FAIMS DIA的非标记定量方法以及TMT定量策略的关键质谱参数,并将其应用在人细胞蛋白质组、单细胞蛋白质组、血浆蛋白质组和酵母蛋白质组分析.结果表明,在DDA实验中,设置碰撞能量为27、二级谱图的分辨率为15 K、最大离子注入时间为22 ms是最佳的参数组合.针对极微量样品200 pg～5 ng,可以根据样品量相应设置最佳的质谱参数.使用200 pg和500 pg的HeLa细胞样品,分别鉴定到1 259和1 725个蛋白质,从而实现了单细胞蛋白质组学的深度覆盖.在FAIMS DDA实验中,60 min或90 min梯度时选择CV-45V的补偿电压,120 ...     

A proteome resource of ovarian cancer ascites: integrated proteomic and bioinformatic analyses to identify putative biomarkers

Epithelial ovarian cancer is the most lethal gynecological malignancy, and disease-specific biomarkers are urgently needed to improve diagnosis, prognosis, and to predict and monitor treatment efficiency. We present an in-depth proteomic analysis of selected biochemical fractions of human ovarian cancer ascites, resulting in the stringent and confident identification of over 2500 proteins. Rigorous filter schemes were applied to objectively minimize the number of false-positive identifications, and we only report proteins with substantial peptide evidence. Integrated computational analysis of the ascites proteome combined with several recently published proteomic data sets of human plasma, urine, 59 ovarian cancer related microarray data sets, and protein-protein interactions from the Interologous Interaction Database I (2)D ( http://ophid.utoronto.ca/i2d) resulted in a short-list of 80 putative biomarkers. The presented proteomics analysis provides a significant resource for ovarian cancer research, and a framework for biomarker discovery.     

Yeast proteome map (update 2006)

To improve the potential of two-dimensional gel electrophoresis for proteomic investigations in yeast we have undertaken the systematic identification of Saccharomyces cerevisiae proteins separated on 2-D gels. We report here the identification of 187 novel protein spots. They were identified by two methods, mass spectrometry and gene inactivation. These identifications extend the number of protein spots identified on our yeast 2-D proteome map to 602, i.e. nearly half the detectable spots of the proteome map. These spots correspond to 417 different proteins. The reference map and the list of identified proteins can be accessed on the Yeast Protein Map server (www.ibgc.u-bordeaux2.fr/YPM).     

Concepts and strategies of soybean seed proteomics using the shotgun proteomics approach

ABSTRACT

Introduction: The last decade has yielded significant developments in the field of proteomics, especially in mass spectrometry (MS) and data analysis tools. In particular, a shift from gel-based to MS-based proteomics has been observed, thereby providing a platform with which to construct proteome atlases for all life forms. Nevertheless, the analysis of plant proteomes, especially those of samples that contain high-abundance proteins (HAPs), such as soybean seeds, remains challenging.

Areas covered: Here, we review recent progress in soybean seed proteomics and highlight advances in HAPs depletion methods and peptide pre-fractionation, identification, and quantification methods. We also suggest a pipeline for future proteomic analysis, in order to increase the dynamic coverage of the soybean seed proteome.

Expert opinion: Because HAPs limit the dynamic resolution of the soybean seed proteome, the depletion of HAPs is a prerequisite of high-throughput proteome analysis, and owing to the use of two-dimensional gel electrophoresis-based proteomic approaches, few soybean seed proteins have been identified or characterized. Recent advances in proteomic technologies, which have significantly increased the proteome coverage of other plants, could be used to overcome the current complexity and limitation of soybean seed proteomics.     

Integrative analysis of the mitochondrial proteome in yeast

In this study yeast mitochondria were used as a model system to apply, evaluate, and integrate different genomic approaches to define the proteins of an organelle. Liquid chromatography mass spectrometry applied to purified mitochondria identified 546 proteins. By expression analysis and comparison to other proteome studies, we demonstrate that the proteomic approach identifies primarily highly abundant proteins. By expanding our evaluation to other types of genomic approaches, including systematic deletion phenotype screening, expression profiling, subcellular localization studies, protein interaction analyses, and computational predictions, we show that an integration of approaches moves beyond the limitations of any single approach. We report the success of each approach by benchmarking it against a reference set of known mitochondrial proteins, and predict approximately 700 proteins associated with the mitochondrial organelle from the integration of 22 datasets. We show that a combination of complementary approaches like deletion phenotype screening and mass spectrometry can identify over 75% of the known mitochondrial proteome. These findings have implications for choosing optimal genome-wide approaches for the study of other cellular systems, including organelles and pathways in various species. Furthermore, our systematic identification of genes involved in mitochondrial function and biogenesis in yeast expands the candidate genes available for mapping Mendelian and complex mitochondrial disorders in humans.     

Parallel proteomics to improve coverage and confidence in the partially annotated Oryctolagus cuniculus mitochondrial proteome

The ability to decipher the dynamic protein component of any system is determined by the inherent limitations of the technologies used, the complexity of the sample, and the existence of an annotated genome. In the absence of an annotated genome, large-scale proteomic investigations can be technically difficult. Yet the functional and biological species differences across animal models can lead to selection of partially or nonannotated organisms over those with an annotated genome. The outweighing of biology over technology leads us to investigate the degree to which a parallel approach can facilitate proteome coverage in the absence of complete genome annotation. When studying species without complete genome annotation, a particular challenge is how to ensure high proteome coverage while meeting the bioinformatic stringencies of high-throughput proteomics. A protein inventory of Oryctolagus cuniculus mitochondria was created by overlapping "protein-centric" and "peptide-centric" one-dimensional and two-dimensional liquid chromatography strategies; with additional partitioning into membrane-enriched and soluble fractions. With the use of these five parallel approaches, 2934 unique peptides were identified, corresponding to 558 nonredundant protein groups. 230 of these proteins (41%) were identified by only a single technical approach, confirming the need for parallel techniques to improve annotation. To determine the extent of coverage, a side-by-side comparison with human and mouse cardiomyocyte mitochondrial studies was performed. A nonredundant list of 995 discrete proteins was compiled, of which 244 (25%) were common across species. The current investigation identified 142 unique protein groups, the majority of which were detected here by only one technical approach, in particular peptide- and protein-centric two-dimensional liquid chromatography. Although no single approach achieved more than 40% coverage, the combination of three approaches (protein- and peptide-centric two-dimensional liquid chromatography and subfractionation) contributed 96% of all identifications. Parallel techniques ensured minimal false discovery, and reduced single peptide-based identifications while maximizing sequence coverage in the absence of the annotated rabbit proteome.     

快速发展的亚细胞蛋白质组学

亚细胞蛋白质组是蛋白质组学领域中的一支新生力量 ,已成为蛋白质组学新的主流方向 ,通过多种策略和技术方法 ,一些重要的亚细胞结构的蛋白质组不断的得到分析 ,到目前为止 ,几乎所有亚细胞结构的蛋白质组学研究都有报道 ,而且已经深入到亚细胞器和复合体水平 ;另外 ,不仅局限于对亚细胞结构的蛋白组成进行简单分析 ,而且更注重功能性分析 ,将定量技术和差异分析引入亚细胞蛋白质组学 ,来观察此亚细胞结构的蛋白质组在某些生理或病理条件下的变化 ,这已经成为亚细胞蛋白质组学新的发展方向 .亚细胞蛋白质组学最大的困难在于怎样确认鉴定出来蛋白质的定位 ,是在提取过程中的污染还是真正在此亚细胞结构中有定位 ?这将是亚细胞蛋白质组学需要努力解决的挑战 .文章全面介绍了亚细胞蛋白质组学的最新研究进展 ,阐述了亚细胞蛋白质组学面临的挑战 ,并对亚细胞蛋白质组学的发展方向作了展望 .     

Global proteome discovery using an online three-dimensional LC-MS/MS

We have developed a proteomics technology featuring on-line three-dimensional liquid chromatography coupled to tandem mass spectrometry (3D LC-MS/MS). Using 3D LC-MS/MS, the yeast-soluble, urea-solubilized peripheral membrane and SDS-solubilized membrane protein samples collectively yielded 3019 unique yeast protein identifications with an average of 5.5 peptides per protein from the 6300-gene Saccharomyces Genome Database searched with SEQUEST. A single run of the urea-solubilized sample yielded 2255 unique protein identifications, suggesting high peak capacity and resolving power of 3D LC-MS/MS. After precipitation of SDS from the digested membrane protein sample, 3D LC-MS/MS allowed the analysis of membrane proteins. Among 1221 proteins containing two or more predicted transmembrane domains, 495 such proteins were identified. The improved yeast proteome data allowed the mapping of many metabolic pathways and functional categories. The 3D LC-MS/MS technology provides a suitable tool for global proteome discovery.     

Profiling serine hydrolase activities in complex proteomes

Serine hydrolases represent one of the largest and most diverse families of enzymes in higher eukaryotes, comprising numerous proteases, lipases, esterases, and amidases. The activities of many serine hydrolases are tightly regulated by posttranslational mechanisms, limiting the suitability of standard genomics and proteomics methods for the functional characterization of these enzymes. To facilitate the global analysis of serine hydrolase activities in complex proteomes, a biotinylated fluorophosphonate (FP-biotin) was recently synthesized and shown to serve as an activity-based probe for several members of this enzyme family. However, the extent to which FP-biotin reacts with the complete repertoire of active serine hydrolases present in a given proteome remains largely unexplored. Herein, we describe the synthesis and utility of a variant of FP-biotin in which the agent's hydrophobic alkyl chain linker was replaced by a more hydrophilic poly(ethylene glycol) moiety (FP-peg-biotin). When incubated with both soluble and membrane proteomes for extended reaction times, FP-biotin and FP-peg-biotin generated similar "maximal coverage" serine hydrolase activity profiles. However, kinetic analyses revealed that several serine hydrolases reacted at different rates with each FP agent. These rate differences were exploited in studies that used the biotinylated FPs to examine the target selectivity of reversible serine hydrolase inhibitors directly in complex proteomes. Finally, a general method for the avidin-based affinity isolation of FP-biotinylated proteins was developed, permitting the rapid and simultaneous identification of multiple serine peptidases, lipases, and esterases. Collectively, these studies demonstrate that chemical probes such as the biotinylated FPs can greatly accelerate both the functional characterization and molecular identification of active enzymes in complex proteomes.     

Proteomic analysis of ligamentum flavum from patients with lumbar spinal stenosis

Lumbar spinal stenosis (LSS) is a syndromic degenerative spinal disease and is characterized by spinal canal narrowing with subsequent neural compression causing gait disturbances. Although LSS is a major age‐related musculoskeletal disease that causes large decreases in the daily living activities of the elderly, its molecular pathology has not been investigated using proteomics. Thus, we used several proteomic technologies to analyze the ligamentum flavum (LF) of individuals with LSS. Using comprehensive proteomics with strong cation exchange fractionation, we detected 1288 proteins in these LF samples. A GO analysis of the comprehensive proteome revealed that more than 30% of the identified proteins were extracellular. Next, we used 2D image converted analysis of LC/MS to compare LF obtained from individuals with LSS to that obtained from individuals with disc herniation (nondegenerative control). We detected 64 781 MS peaks and identified 1675 differentially expressed peptides derived from 286 proteins. We verified four differentially expressed proteins (fibronectin, serine protease HTRA1, tenascin, and asporin) by quantitative proteomics using SRM/MRM. The present proteomic study is the first to identify proteins from degenerated and hypertrophied LF in LSS, which will help in studying LSS.     

Immunoisolation and subfractionation of synaptic vesicle proteins

Within recent years, the advances in proteomics techniques have resulted in considerable novel insights into the protein expression patterns of specific tissues, cells, and organelles. The information acquired from large-scale proteomics approaches indicated, however, that the proteomic analysis of whole cells or tissues is often not suited to fully unravel the proteomes of individual organellar constituents or to identify proteins that are present at low copy numbers. In addition, the identification of hydrophobic proteins is still a challenge. Therefore, the development of techniques applicable for the enrichment of low-abundance membrane proteins is essential for a comprehensive proteomic analysis. In addition to the enrichment of particular subcellular structures by subcellular fractionation, the spectrum of techniques applicable for proteomics research can be extended toward the separation of integral and peripheral membrane proteins using organic solvents, detergents, and detergent-based aqueous two-phase systems with water-soluble polymers. Here, we discuss the efficacy of a number of experimental protocols. We demonstrate that the appropriate selection of physicochemical conditions results in the isolation of synaptic vesicles of high purity whose proteome can be subfractionated into integral membrane proteins and soluble proteins by several phase separation techniques.     

Analysis of the extracellular matrix vesicle proteome in mineralizing osteoblasts

Many key processes central to bone formation and homeostasis require the involvement of osteoblasts, cells responsible for accumulation and mineralization of the extracellular matrix (ECM). During this complex and only partially understood process, osteoblasts generate and secrete matrix vesicles (MVs) into the ECM to initiate mineralization. Although they are considered an important component of mineralization process, MVs still remain a mystery. To better understand their function and biogenesis, a proteomic analysis of MVs has been conducted. MVs were harvested by two sample preparation approaches and mass spectrometry was utilized for protein identification. A total of 133 proteins were identified in common from the two MV preparations, among which were previously known proteins, such as annexins and peptidases, along with many novel proteins including a variety of enzymes, osteoblast-specific factors, ion channels, and signal transduction molecules, such as 14-3-3 family members and Rab-related proteins. To compare the proteome of MV with that of the ECM we conducted a large-scale proteomic analysis of collagenase digested mineralizing osteoblast matrix. This analysis resulted in the identification of 1,327 unique proteins. A comparison of the proteins identified from the two MV preparations with the ECM analysis revealed 83 unique, non-redundant proteins identified in all three samples. This investigation represents the first systematic proteomic analysis of MVs and provides insights into both the function and origin of these important mineralization-regulating vesicles.     

Protein-protein interactions in plants

The study of protein-protein interactions (PPIs) is essential to uncover unknown functions of proteins at the molecular level and to gain insight into complex cellular networks. Affinity purification and mass spectrometry (AP-MS), yeast two-hybrid, imaging approaches and numerous diverse databases have been developed as strategies to analyze PPIs. The past decade has seen an increase in the number of identified proteins with the development of MS and large-scale proteome analyses. Consequently, the false-positive protein identification rate has also increased. Therefore, the general consensus is to confirm PPI data using one or more independent approaches for an accurate evaluation. Furthermore, identifying minor PPIs is fundamental for understanding the functions of transient interactions and low-abundance proteins. Besides establishing PPI methodologies, we are now seeing the development of new methods and/or improvements in existing methods, which involve identifying minor proteins by MS, multidimensional protein identification technology or OFFGEL electrophoresis analyses, one-shot analysis with a long column or filter-aided sample preparation methods. These advanced techniques should allow thousands of proteins to be identified, whereas in-depth proteomic methods should permit the identification of transient binding or PPIs with weak affinity. Here, the current status of PPI analysis is reviewed and some advanced techniques are discussed briefly along with future challenges for plant proteomics.     

Proteomic Analysis of the Aqueous Humor in Age-related Macular Degeneration (AMD) Patients

Age-related macular degeneration (AMD) can lead to irreversible central vision loss in the elderly. Although large number of growth factor pathways, including the vascular endothelial growth factor (VEGF), has been implicated in the pathogenesis of AMD, no study has directly assessed the whole proteomic composition in the aqueous humor (AH) among AMD patients. The AH contains proteins secreted from the anterior segment tissue, and these proteins may play an important role in the pathogenesis of AMD. Thus, comparisons between the AH proteomic profiles of AMD patients and non-AMD controls may lead to the verification of novel pathogenic proteins useful as potential clinical biomarkers. In this study, we used discovery-based proteomics and Multiple Reaction Monitoring Mass Spectrometry (MRM-MS) to analyze AH from AMD patients and AH from controls who underwent cataract surgery. A total of 154 proteins with at least two unique peptides were identified in the AH. Of these 154 proteins identified by discovery-based proteomics, 10 AH proteins were novel identifications. The protein composition in the AH was different between AMD patients and non-AMD controls. Subsequently, a systematic MRM-MS assay was performed in seven highly abundant differentially expressed proteins from these groups. Differential expression of three proteins was observed in the AH of AMD patients compared with that of cataract controls (p < 0.0312). Elucidation of the aqueous proteome will establish a foundation for protein function analysis and identify differentially expressed markers associated with AMD. This study demonstrates that integrated proteomic technologies can yield novel biomarkers to detect exudative AMD.     

Current trends in computational inference from mass spectrometry-based proteomics

Mass spectrometry offers a high-throughput approach to quantifying the proteome associated with a biological sample and hence has become the primary approach of proteomic analyses. Computation is tightly coupled to this advanced technological platform as a required component of not only peptide and protein identification, but quantification and functional inference, such as protein modifications and interactions. Proteomics faces several key computational challenges such as identification of proteins and peptides from tandem mass spectra as well as their quantitation. In addition, the application of proteomics to systems biology requires understanding the functional proteome, including how the dynamics of the cell change in response to protein modifications and complex interactions between biomolecules. This review presents an overview of recently developed methods and their impact on these core computational challenges currently facing proteomics.     

Antigen retrieval for proteomic characterization of formalin-fixed and paraffin-embedded tissues

Formalin-fixed and paraffin-embedded tissues represent the vast majority of archived tissue. Access to such tissue specimens via shotgun-based proteomic analyses may open new avenues for both prospective and retrospective translational research. In this study, we evaluate the effects of fixation time on antigen retrieval for the purposes of shotgun proteomics. For the first time, we demonstrate the capability of a capillary isotachophoresis (CITP)-based proteomic platform for the shotgun proteomic analysis of proteins recovered from FFPE tissues. In comparison to our previous studies utilizing capillary isoelectric focusing, the CITP-based analysis is more robust and increases proteome coverage. In this case, results from three FFPE liver tissues yield a total of 4098 distinct Swiss-Prot identifications at a 1% false-discovery rate. To judge the accuracy of these assignments, immunohistochemistry is performed on a panel of 17 commonly assayed proteins. These proteins span a wide range of protein abundances as inferred from relative quantitation via spectral counting. Among the panel were 4 proteins identified by a single peptide hit, including three clusters of differentiation (CD) markers: CD74, CD117, and CD45. Because single peptide hits are often regarded with skepticism, it is notable that all proteins tested by IHC stained positive.     

Bioinformatics and data mining in proteomics

Proteomic studies involve the identification as well as qualitative and quantitative comparison of proteins expressed under different conditions, and elucidation of their properties and functions, usually in a large-scale, high-throughput format. The high dimensionality of data generated from these studies will require the development of improved bioinformatics tools and data-mining approaches for efficient and accurate data analysis of biological specimens from healthy and diseased individuals. Mining large proteomics data sets provides a better understanding of the complexities between the normal and abnormal cell proteome of various biological systems, including environmental hazards, infectious agents (bioterrorism) and cancers. This review will shed light on recent developments in bioinformatics and data-mining approaches, and their limitations when applied to proteomics data sets, in order to strengthen the interdependence between proteomic technologies and bioinformatics tools.     

Advances and challenges in liquid chromatography-mass spectrometry-based proteomics profiling for clinical applications

Recent advances in proteomics technologies provide tremendous opportunities for biomarker-related clinical applications; however, the distinctive characteristics of human biofluids such as the high dynamic range in protein abundances and extreme complexity of the proteomes present tremendous challenges. In this review we summarize recent advances in LC-MS-based proteomics profiling and its applications in clinical proteomics as well as discuss the major challenges associated with implementing these technologies for more effective candidate biomarker discovery. Developments in immunoaffinity depletion and various fractionation approaches in combination with substantial improvements in LC-MS platforms have enabled the plasma proteome to be profiled with considerably greater dynamic range of coverage, allowing many proteins at low ng/ml levels to be confidently identified. Despite these significant advances and efforts, major challenges associated with the dynamic range of measurements and extent of proteome coverage, confidence of peptide/protein identifications, quantitation accuracy, analysis throughput, and the robustness of present instrumentation must be addressed before a proteomics profiling platform suitable for efficient clinical applications can be routinely implemented.