Glucotoxicity and pancreatic proteomics

Chronic hyperglycaemia is one of the main characteristics of a diabetic state. This is also the first cause of diabetic complications. However, it is now generally accepted that glucotoxicity also participates in the worsening of type 2 diabetes, by affecting the secretion of β-cells. So far, different mechanisms have been proposed to explain the adverse effects of chronic hyperglycaemia. One of them suggests that the modulation of expression of several key proteins during a hyperglycaemia state, may explain the toxic effect of glucotoxicity. Therefore, proteomic analysis of biological samples represents an interesting method to study the effect of chronic hyperglycaemia on protein expression. The discovery of new proteins for which the expression could be modulated by chronic hyperglycaemia may probably help to better understand the mechanisms underlying glucotoxicity. In this review, we will first present an introduction of the different mechanisms known to be involved in the control of glucose homeostasis and in the development of glucotoxicity. In a second part, some proteomic data linked with the effect of glucotoxicity in pancreas, pancreatic islets and β-cells will be presented and discussed.     

Proteomics and ovarian cancer: Integrating proteomics information into clinical care

The power of proteomics allows unparalleled opportunity to query the molecular mechanisms of a malignant cell and the tumor microenvironment in patients with ovarian cancer and other solid tumors. This information has given us insight into the perturbations of signaling pathways within tumor cells and has aided the discovery of new drug targets for the tumor and possible prognostic indicators of outcome and disease response to therapy. Proteomics analysis of serum and ascites has also given us sources with which to discover possible early markers for the presence of new disease and for the progression of established cancer throughout the course of treatment. Unfortunately, this wealth of information has yielded little to date in changing the clinical care of these patients from a diagnostic, prognostic, or treatment perspective. The rational examination and translation of proteomics data in the context of past clinical trials and the design of future clinical trials must occur before we can march forward into the future of personalized medicine.     

A platform to standardize,store, and visualize proteomics experimental data

With the development of functional genomics research, large-scale proteomics studies are now widespread, presenting significant challenges for data storage, exchange, and analysis. Here we present the Integrated Proteomics Exploring Database （IPED） as a platform for managing proteomics experimental data （both process and result data）. IPED is based on the schema of the Proteome Experimental Data Repository （PEDRo）, and complies with the General Proteomics Standard （GPS） drafted by the Proteomics Standards Committee of the Human Proteome Organization. In our work, we developed three components for the IPED platform： the IPED client editor, IPED server software, and IPED web interface. The client editor collects experimental data and generates an extensible markup language （XML） data file compliant with PEDRo and GPS; the server software parses the XML data file and loads information into a core database; and the web interface displays experimental results, to provide a convenient graphic representation of data. Given software convenience and data abundance, IPED is a powerful platform for data exchange and presents an important resource for the proteomics community. In its current release, IPED is available at http：//www. biosino.org/iped2.     

Intracellular metabolism analysis of Clostridium cellulovorans via modeling integrating proteomics,metabolomics and fermentation

A consolidated bioprocess for cellulosic n-butanol production has been developed by engineering Clostridium cellulovorans to overexpress a bifunctional aldehyde/alcohol dehydrogenase. Rational metabolic engineering is important to further improve butanol production. This study aimed to investigate intracellular metabolism and identify the key regulators of cellulosic butanol formation in C. cellulovorans via integrated Omics and fermentation kinetics data analysis. First, comparative proteomics and metabolomics analyses of wild type and n-butanol producing mutant strain were conducted, which quantified 624 host cell proteins and 474 primary and secondary metabolites. Compared to wild type, most cellulases in cellulolysis were up-regulated, but three glycolysis enzymes and three enzymes in central pathway were down-regulated in the n-butanol producing strain. Second, a dynamic model integrating Omics and fermentation data was developed to identify key regulators in butanol biosynthesis, which were ranked by further metabolic control analysis. Finally, rational metabolic engineering was performed in C. cellulovorans by overexpressing two genes (thl and hbd) identified as important factors limiting butanol biosynthesis, which improved butanol yield and C4/C2 ratio. This study demonstrated a research approach to integrate multi-Omics and fermentation data of C. cellulovorans and guide its rational metabolic engineering, which can also be applied to other microorganisms.     

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.     

Studying the effects of correlation on protein selection in proteomics data

Recently, Efron (2007) provided methods for assessing the effect of correlation on false discovery rate (FDR) in large‐scale testing problems in the context of microarray data. Although FDR procedure does not require independence of the tests, existence of correlation grossly under‐ or overestimates the number of critical genes. Here, we briefly review Efron's method and apply it to a relatively smaller spectrometry proteomics data. We show that even here the correlation can affect the FDR values and the number of proteins declared as critical.     

Instrumentation for advanced microseparations in pharmaceutical analysis and proteomics

Small-volume chromatographic columns are only able to generate narrow peaks when flow rates, injection volume and instrument components, such as detector, connecting tubing and fittings, are matched to the peak dispersion from the column. Criteria for the proper design of chromatographic instrumentation are therefore derived from a general model on total dispersion. The performance of such a system is then experimentally evaluated from applications run on narrow-bore, small-volume columns. In order to achieve flow rates that match the dimensions of such columns, a new concept for electronic flow control (EFC) is introduced. A theoretical optimization of column efficiency and throughput is discussed and the results verified with practical examples on short, narrow-bore columns packed with small, porous and superficially porous particles. For complex sample mixtures, the concept of peak capacity is introduced and applied to orthogonal separation principles in multiple chromatographic dimensions through column switching techniques.     

The role of proteomics in toxicology: identification of biomarkers of toxicity by protein expression analysis

Proteomics, i.e. the high throughput separation, display and identification of proteins, has the potential to be a powerful tool in drug development. It could increase the predictability of early drug development and identify non-invasive biomarkers of toxicity or efficacy. This review provides an introduction to modern proteomics, with particular reference to applications in toxicology. A literature search was carried out to identify studies in two broad classes: screening/predictive toxicology, and mechanistic toxicology. The strengths and limitations of current methods and the likely impact of techniques in drug development are also considered. Proteomics can increase the speed and sensitivity of toxicological screening by identifying protein markers of toxicity. Proteomics studies have already provided insights into the mechanisms of action of a wide range of substances, from metals to peroxisome proliferators. Current limitations involving speed of throughput are being overcome by increasing automation and the development of new techniques. The isotope-coded affinity tag (ICAT) method appears particularly promising. The application of proteomics to drug development has given rise to the new field of pharmacoproteomics. New associations between proteins and toxicopathological effects are constantly being identified, and major progress is on the horizon as we move into the post-genomic era.     

The role of proteomics in toxicology: identification of biomarkers of toxicity by protein expression analysis

Proteomics, i.e. the high throughput separation, display and identification of proteins, has the potential to be a powerful tool in drug development. It could increase the predictability of early drug development and identify non-invasive biomarkers of toxicity or efficacy. This review provides an introduction to modern proteomics, with particular reference to applications in toxicology. A literature search was carried out to identify studies in two broad classes: screening/predictive toxicology, and mechanistic toxicology. The strengths and limitations of current methods and the likely impact of techniques in drug development are also considered. Proteomics can increase the speed and sensitivity of toxicological screening by identifying protein markers of toxicity. Proteomics studies have already provided insights into the mechanisms of action of a wide range of substances, from metals to peroxisome proliferators. Current limitations involving speed of throughput are being overcome by increasing automation and the development of new techniques. The isotope-coded affinity tag (ICAT) method appears particularly promising. The application of proteomics to drug development has given rise to the new field of pharmacoproteomics. New associations between proteins and toxicopathological effects are constantly being identified, and major progress is on the horizon as we move into the post-genomic era.     

Discovery of biomarkers for gastric cancer: a proteomics approach

Gastric cancer is the second leading cause of cancer-related deaths worldwide. Although many treatment options exist for patients with gastric tumors, the incidence and mortality rate of gastric cancer are on the rise. The early stages of gastric cancer are non-symptomatic, and the treatment response is unpredictable. This situation is further aggravated by a lack of diagnostic biomarkers that can aid in the early detection and prognosis of gastric cancer and in the prediction of chemoresistance. Moreover, clinical surgical specimens are rarely obtained, and traditional biomarkers of gastric cancer are not very effective. Many studies in the field of proteomics have contributed to the discovery and establishment of powerful diagnostic tools (e.g., ProteinChip array) in the management of cancer. The evolution in proteomic technologies has not only enabled the screening of a large number of samples but also enabled the identification of pathologically significant proteins, such as phosphoproteins, and the quantitation of difference in protein expression under different conditions. Multiplexed assays are used widely to accurately fractionate various complex samples such as blood, tissue, cells, and Helicobacter pylori-infected specimens to identify differentially expressed proteins. Biomarker detection studies have substantially contributed to the areas of secretome, metabolome, and phosphoproteome. Here, we review the development of potential biomarkers in the natural history of gastric cancer, with specific emphasis on the characteristics of target protein convergence.     

Shotgun proteomics of bacterial pathogens: advances,challenges and clinical implications

Mass spectrometry-based proteomics is increasingly used in analysis of bacterial pathogens. Simple experimental set-ups based on high accuracy mass spectrometry and powerful biochemical and bioinformatics tools are capable of reliably quantifying levels of several thousand bacterial proteins in a single experiment, reaching the analytical capacity to completely map whole proteomes. Here the authors present the state-of-the-art in bacterial pathogen proteomics and discuss challenges that the field is facing, especially in analysis of low abundant, modified proteins from organisms that are difficult to culture. Constant improvements in speed and sensitivity of mass spectrometers, as well as in bioinformatic and biochemical workflows will soon allow for comprehensive analysis of regulatory mechanisms of pathogenicity and enable routine application of proteomics in the clinical setting.     

PROTEOMER: A workflow‐optimized laboratory information management system for 2‐D electrophoresis‐centered proteomics

In recent years proteomics became increasingly important to functional genomics. Although a large amount of data is generated by high throughput large‐scale techniques, a connection of these mostly heterogeneous data from different analytical platforms and of different experiments is limited. Data mining procedures and algorithms are often insufficient to extract meaningful results from large datasets and therefore limit the exploitation of the generated biological information. In our proteomic core facility, which almost exclusively focuses on 2‐DE/MS‐based proteomics, we developed a proteomic database custom tailored to our needs aiming at connecting MS protein identification information to 2‐DE derived protein expression profiles. The tools developed should not only enable an automatic evaluation of single experiments, but also link multiple 2‐DE experiments with MS‐data on different levels and thereby helping to create a comprehensive network of our proteomics data. Therefore the key feature of our “PROTEOMER” database is its high cross‐referencing capacity, enabling integration of a wide range of experimental data. To illustrate the workflow and utility of the system, two practical examples are provided to demonstrate that proper data cross‐referencing can transform information into biological knowledge.     

基于串联质谱标签法和平行反应监测技术的氟喹诺酮耐药沙门菌蛋白质组学分析

【背景】随着沙门菌对氟喹诺酮类药物的耐药性不断增强,对其耐药机理的研究显得尤为迫切和重要,蛋白质组学分析将为沙门菌的耐药机理研究提供新的靶点和方向。【目的】对鼠伤寒沙门菌诱导获得耐药性前后进行蛋白质组学分析,为深入研究沙门菌耐药机理奠定基础。【方法】用环丙沙星对鼠伤寒沙门菌ATCC13311进行耐药性诱导,利用串联质谱标签法(Tandem mass tag,TMT)对其耐药性进行差异蛋白的筛选和生物信息学分析,并选取15个差异蛋白进行平行反应监测(Parallel reaction monitoring,PRM)靶向蛋白验证。【结果】筛选出318个差异表达蛋白,其中上调159个,下调159个,涉及的KEGG通路主要包括细菌趋药性、ABC转运蛋白、双组分系统等;PRM定量到13个验证蛋白且变化趋势与TMT一致。【结论】通过TMT定量结合PRM靶向验证对鼠伤寒沙门菌耐药前后进行蛋白质组学分析,筛选出多个差异蛋白和代谢通路,包括外排泵相关蛋白、外膜蛋白、双组分相关蛋白及通路、细菌趋化性相关蛋白及通路等,为沙门菌氟喹诺酮类耐药机理的深入研究奠定了基础。     

Helicobacter pylori proteomics by 2‐DE/MS, 1‐DE‐LC/MS and functional data mining

With its predicted proteome of 1550 proteins (data set Etalon) Helicobacter pylori 26695 represents a perfect model system of medium complexity for investigating basic questions in proteomics. We analyzed urea‐solubilized proteins by 2‐DE/MS (data set 2‐DE) and by 1‐DE‐LC/MS (Supprot); proteins insoluble in 9 M urea but solubilized by SDS (Pellet); proteins precipitating in the Sephadex layer at the application side of IEF (Sephadex) by 1‐DE‐LC/MS; and proteins precipitating close to the application side within the IEF gel by LC/MS (Startline). The experimental proteomics data of H. pylori comprising 567 proteins (protein coverage: 36.6%) were stored in the Proteome Database System for Microbial Research ( http://www.mpiib‐berlin.mpg.de/2D‐PAGE/ ), which gives access to raw mass spectra (MALDI‐TOF/TOF) in T2D format, as well as to text files of peak lists. For data mining the protein mapping and comparison tool PROMPT ( http://webclu.bio.wzw.tum.de/prompt/ ) was used. The percentage of proteins with transmembrane regions, relative to all proteins detected, was 0, 0.2, 0, 0.5, 3.8 and 6.3% for 2‐DE, Supprot, Startline, Sephadex, Pellet, and Etalon, respectively. 2‐DE does not separate membrane proteins because they are insoluble in 9 M urea/70 mM DTT and 2% CHAPS. SDS solubilizes a considerable portion of the urea‐insoluble proteins and makes them accessible for separation by SDS‐PAGE and LC. The 2‐DE/MS analysis with urea‐solubilized proteins and the 1‐DE‐LC/MS analysis with the urea‐insoluble protein fraction (Pellet) are complementary procedures in the pursuit of a complete proteome analysis. Access to the PROMPT‐generated diagrams in the Proteome Database allows the mining of experimental data with respect to other functional aspects.     

The hard cell: from proteomics to a whole cell model

Proteomics has provided a wealth of data related to the nature of the proteome, including subcellular location, copy number, interaction partners and protein complexes. This raises the question of whether it is feasible to combine these data, together with other data related to overall cellular structure, to construct a static picture of the cell. In this minireview, we discuss these data, and the issues of turning them into whole cell models.     

Triglyceride reference database: large scale storage of 3D triglyceride conformers and web-based analysis tools

The Triglyceride Reference Database (TRDB) contains conformer libraries of two triglycerides (tributyrin and triacetin) optimized at various levels of theory. The purpose of the TRDB project and its accompanying interface is to generate, store, process and retrieve triglyceride conformers; hence, it addresses problems and questions concerning geometrical properties, or relative conformer energies, that are of importance (among other uses) to force field evaluations.     

Serum proteomics analysis reveals the thermal fitness of crossbred dairy buffalo to chronic heat stress

Chronic heat stress (CHS) reduces the production efficiency of the buffalo dairy industry. Relatively low-abundance proteins with particular functions in biological processes are changed by CHS. The present study aimed to quantify the differences in low-abundance proteins of crossbred dairy buffaloes under CHS and thermal-neutral (TN) conditions. With label-free quantification, 344 low-abundance proteins were identified in serum. Of these, 17 differentially expressed low-abundance proteins with known functions were detected, and six of the differentially expressed proteins related to heat stress were validated with parallel reaction monitoring. Lipase (LPL), glutathione peroxidase 3 (GPX3), cathelicidin-2 (CATHL2), ceruloplasmin (CP), and hemoglobin subunit alpha 1 (HBA1) cooperatively played roles in the thermal fitness of dairy buffalo by decreasing heat production and increasing blood oxygen delivery. Also, dairy buffaloes may adapt to CHS and hypoxia with high levels of RBCs, HBA1 and CP to increase blood oxygen delivery capacity.