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At least 883 records · Page 49Linked to original sources
PubMed2005

[Study on differential proteomic expression in human liver cells stimulated by trichloroethylene with proteomics].

OBJECTIVE: To explore the differential proteomic expression in human liver cells L-02 induced by different dosages of trichloroethylene (TCE). METHODS: Human liver cells L-02 were treated with different concentrations of TCE and the solvent control (dimethylsulfoxide). The total cellular proteins were separated using 2DE and visualized with silver staining after TCE treatment. The images were analyzed with Image Master 2D Platinum 5.0 analysis software. The differentially expressed protein spots were identified by matrix assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF-TOF-MS). RESULTS: Fifteen protein spots with significant difference were found, and went upward or downward or disappeared after the stimulation of TCE with different dosages, which indicated that TCE induced the change of the proteomic expression in the liver cells. The mass spectrum identification and the IPI human database retrieval were used for identifying 9 proteins related to the L-02 Liver cells induced by TCE. CONCLUSION: The result provides an insight to TCE-related molecular mechanism and which might be useful for further study of the TCE-associated proteins and molecular markers.

Cell Line↗
PubMed2006

Integrating forward and reverse proteomics to unravel protein function.

To date, proteomics approaches have aimed to either identify novel proteins or change in protein expression/modification in various organisms under normal or disease conditions. One major aspect of functional proteomics is to identify protein biological properties in a given context, however, forward proteomics approaches alone cannot complete this goal. Indeed, with the increasing successes of such proteomics-based research strategies and the subsequent increasing amounts of proteins identified with unknown molecular functions, approaches allowing for systematic analyses of protein functions are desired. In this review, we propose to depict the complementarities of forward and reverse proteomics approaches in the definite understanding of protein functions. This dual strategy requires a data integration loop which allows for systematic characterization of protein function(s). The details of the integrative process combining both in silico and experimental resources and tools are presented. Altogether, we believe that the integration of forward and reverse proteomics approaches supported by bioinformatics will provide an efficient path towards systems biology.

Animals↗
PubMed2006

On the way to understand biological complexity in plants: S-nutrition as a case study for systems biology.

The establishment of technologies for high-throughput DNA sequencing (genomics), gene expression (transcriptomics), metabolite and ion analysis (metabolomics/ionomics) and protein analysis (proteomics) carries with it the challenge of processing and interpreting the accumulating data sets. Publicly accessible databases and newly development and adapted bioinformatic tools are employed to mine this data in order to filter relevant correlations and create models describing physiological states. These data allow the reconstruction of networks of interactions of the various cellular components as enzyme activities and complexes, gene expression, metabolite pools or pathway flux modes. Especially when merging information from transcriptomics, metabolomics and proteomics into consistent models, it will be possible to describe and predict the behaviour of biological systems, for example with respect to endogenous or environmental changes. However, to capture the interactions of network elements requires measurements under a variety of conditions to generate or refine existing models. The ultimate goal of systems biology is to understand the molecular principles governing plant responses and consistently explain plant physiology.

Arabidopsis↗
PubMed2004

Current status of proteomic standards development.

The generation of proteomic data is becoming ever more high throughput. Both the technologies and experimental designs used to generate and analyze data are becoming increasingly complex. The need for methods by which such data can be accurately described, stored and exchanged between experimenters and data repositories has been recognized. Work by the Proteome Standards Initiative of the Human Proteome Organization has laid the foundation for the development of standards by which experimental design can be described and data exchange facilitated. The Minimum Information About a Proteomic Experiment data model describes both the scope and purpose of a proteomics experiment and encompasses the development of more specific interchange formats such as the mzData model of mass spectrometry. The eXtensible Mark-up Language-MI data interchange format, which allows exchange of molecular interaction data, has already been published and major databases within this field are supplying data downloads in this format.

Databases, Protein↗
PubMed2006

Automated reprocessing pipeline for searching heterogeneous mass spectrometric data of the HUPO Brain Proteome Project pilot phase.

The newly available techniques for sensitive proteome analysis and the resulting amount of data require a new bioinformatics focus on automatic methods for spectrum reprocessing and peptide/protein validation. Manual validation of results in such studies is not feasible and objective enough for quality relevant interpretation. The necessity for tools enabling an automatic quality control is, therefore, important to produce reliable and comparable data in such big consortia as the Human Proteome Organization Brain Proteome Project. Standards and well-defined processing pipelines are important for these consortia. We show a way for choosing the right database model, through collecting data, processing these with a decoy database and end up with a quality controlled protein list merged from several search engines, including a known false-positive rate.

Algorithms↗
PubMed2004

Sequit: software for de novo peptide sequencing by matrix-assisted laser desorption/ionization post-source decay mass spectrometry.

Peptide sequencing by mass spectrometry is gaining increasing importance for peptide chemistry and proteomics. However, available tools for interpreting matrix-assisted laser desorption/ionization post-source decay (MALDI-PSD) mass spectra depend on databases, and identify peptides by matching experimental data with spectra calculated from database sequences. This severely obstructs the identification of proteins and peptides not listed in databases or of variations, e.g. mutated proteins. The development of a new computer program for database-independent peptide sequencing by MALDI-PSD mass spectrometry is reported here. This computer program was validated by the determination of the correct sequences for various peptides including sequences listed in the sequence databases, but also for peptides that deviate from database sequences or are completely artificial. This strategy should substantially facilitate the identification of novel or variant peptides and proteins, and increase the power of MALDI-PSD analyses in proteomics.

Amino Acid Sequence↗
PubMed2006

Complete and integrated pyrene degradation pathway in Mycobacterium vanbaalenii PYR-1 based on systems biology.

Mycobacterium vanbaalenii PYR-1 was the first bacterium isolated by virtue of its ability to metabolize the high-molecular-weight polycyclic aromatic hydrocarbon (PAH) pyrene. We used metabolic, genomic, and proteomic approaches in this investigation to construct a complete and integrated pyrene degradation pathway for M. vanbaalenii PYR-1. Genome sequence analyses identified genes involved in the pyrene degradation pathway that we have proposed for this bacterium. To identify proteins involved in the degradation, we conducted a proteome analysis of cells exposed to pyrene using one-dimensional gel electrophoresis in combination with liquid chromatography-tandem mass spectrometry. Database searching performed with the M. vanbaalenii PYR-1 genome resulted in identification of 1,028 proteins with a protein false discovery rate of <1%. Based on both genomic and proteomic data, we identified 27 enzymes necessary for constructing a complete pathway for pyrene degradation. Our analyses indicate that this bacterium degrades pyrene to central intermediates through o-phthalate and the beta-ketoadipate pathway. Proteomic analysis also revealed that 18 enzymes in the pathway were upregulated more than twofold, as indicated by peptide counting when the organism was grown with pyrene; three copies of the terminal subunits of ring-hydroxylating oxygenase (NidAB2, MvanDraft_0817/0818, and PhtAaAb), dihydrodiol dehydrogenase (MvanDraft_0815), and ring cleavage dioxygenase (MvanDraft_3242) were detected only in pyrene-grown cells. The results presented here provide a comprehensive picture of pyrene metabolism in M. vanbaalenii PYR-1 and a useful framework for understanding cellular processes involved in PAH degradation.

Bacterial Proteins↗
PubMed2003

['Proteomics': the mapping of all human proteins].

The genomes of many organisms, including humans, are now largely known. In the wake of this there is a need to identify and measure all proteins that are encoded by the genome (proteomics). This need leads to turbulent developments in the area of analytical techniques, such as two-dimensional electrophoresis, mass spectrometry, and protein chips. The rapidity of advancements justifies the expectation that in the next 5-10 years it will indeed become possible to determine the proteome of an organism or its components such as plasma, serum, or tissues. In combination with information on initiation and progress of disease, proteomics will contribute to improving health and to better primary and secondary prevention.

Databases, Protein↗
PubMed2005

High throughput proteome screening for biomarker detection.

Mass spectrometry-based quantitative proteomics has become an important component of biological and clinical research. Current methods, while highly developed and powerful, are falling short of their goal of routinely analyzing whole proteomes mainly because the wealth of proteomic information accumulated from prior studies is not used for the planning or interpretation of present experiments. The consequence of this situation is that in every proteomic experiment the proteome is rediscovered. In this report we describe an approach for quantitative proteomics that builds on the extensive prior knowledge of proteomes and a platform for the implementation of the method. The method is based on the selection and chemical synthesis of isotopically labeled reference peptides that uniquely identify a particular protein and the addition of a panel of such peptides to the sample mixture consisting of tryptic peptides from the proteome in question. The platform consists of a peptide separation module for the generation of ordered peptide arrays from the combined peptide sample on the sample plate of a MALDI mass spectrometer, a high throughput MALDI-TOF/TOF mass spectrometer, and a suite of software tools for the selective analysis of the targeted peptides and the interpretation of the results. Applying the method to the analysis of the human blood serum proteome we demonstrate the feasibility of using mass spectrometry-based proteomics as a high throughput screening technology for the detection and quantification of targeted proteins in a complex system.

Automation↗
PubMed2003

An identification method for altered proteins in tissues utilizing fluorescence derivatization, liquid chromatography, tandem mass spectrometry, and a database-searching algorithm.

Two-dimensional polyacrylamide gel electrophoresis (2D-PAGE) is now widely used as a tool for proteomic studies. For the sensitive determination of proteins in 2D-PAGE, fluorescence derivatization of primary amino moieties of proteins with cyanine dyes was recently developed. However, precipitation of the proteins could occur if completely derivatized because of the lower solubility of the resultant derivatives owing to the hydrophobicity of the reagents and the loss of the hydrophilic primary amino moieties. Thus, in this paper, a water-soluble and thiol-specific fluorogenic reagent, ammonium 7-fluoro-2,1,3-benzoxadiazole-4-sulfonate, was adopted for the derivatization of proteins in tissues either with and without stimulation. Then, the method follows a separation of the derivatives by liquid chromatography with fluorescence detection, an isolation of only the altered proteins, an enzymatic digestion of the isolated proteins, and an identification of the proteins by liquid chromatography/MS/MS with the database-searching algorithm. By using this method, we identified the altered expressions of five increased proteins (e.g., pancreatic polypeptide) as well as three decreased proteins (e.g., insulin 2) in the islets of Langerhans in Wistar rats 2 days after they were subcutaneously administered with dexamethasone.

Algorithms↗
PubMed2005

Chemical effects in biological systems--data dictionary (CEBS-DD): a compendium of terms for the capture and integration of biological study design description, conventional phenotypes, and 'omics data.

A critical component in the design of the Chemical Effects in Biological Systems (CEBS) Knowledgebase is a strategy to capture toxicogenomics study protocols and the toxicity endpoint data (clinical pathology and histopathology). A Study is generally an experiment carried out during a period of time for the purpose of obtaining data, and the Study Design Description captures the methods, timing, and organization of the Study. The CEBS Data Dictionary (CEBS-DD) has been designed to define and organize terms in an attempt to standardize nomenclature needed to describe a toxicogenomics Study in a structured yet intuitive format and provide a flexible means to describe a Study as conceptualized by the investigator. The CEBS-DD will organize and annotate information from a variety of sources, thereby facilitating the capture and display of toxicogenomics data in biological context in CEBS, i.e., associating molecular events detected in highly-parallel data with the toxicology/pathology phenotype as observed in the individual Study Subjects and linked to the experimental treatments. The CEBS-DD has been developed with a focus on acute toxicity studies, but with a design that will permit it to be extended to other areas of toxicology and biology with the addition of domain-specific terms. To illustrate the utility of the CEBS-DD, we present an example of integrating data from two proteomics and transcriptomics studies of the response to acute acetaminophen toxicity (A. N. Heinloth et al., 2004, Toxicol. Sci. 80, 193-202).

Acetaminophen↗
PubMed2002

Plant functional genomics.

Functional genome analysis of plants has entered the high-throughput stage. The complete genome information from key species such as Arabidopsis thaliana and rice is now available and will further boost the application of a range of new technologies to functional plant gene analysis. To broadly assign functions to unknown genes, different fast and multiparallel approaches are currently used and developed. These new technologies are based on known methods but are adapted and improved to accommodate for comprehensive, large-scale gene analysis, i.e. such techniques are novel in the sense that their design allows researchers to analyse many genes at the same time and at an unprecedented pace. Such methods allow analysis of the different constituents of the cell that help to deduce gene function, namely the transcripts, proteins and metabolites. Similarly the phenotypic variations of entire mutant collections can now be analysed in a much faster and more efficient way than before. The different methodologies have developed to form their own fields within the functional genomics technological platform and are termed transcriptomics, proteomics, metabolomics and phenomics. Gene function, however, cannot solely be inferred by using only one such approach. Rather, it is only by bringing together all the information collected by different functional genomic tools that one will be able to unequivocally assign functions to unknown plant genes. This review focuses on current technical developments and their impact on the field of plant functional genomics. The lower plant Physcomitrella is introduced as a new model system for gene function analysis, owing to its high rate of homologous recombination.

Databases, Factual↗
PubMed2003

Functional genomics and proteomics as a foundation for systems biology.

Developments in high-throughput measurement technologies for biological molecules have created a paradigm shift in modern life science research. The field of systems biology attempts to provide a systems-level understanding by systematically organising the genomic, functional genomic and proteomic data obtained from genetic and environmental perturbations of interest and using the data to build a descriptive and mechanistic model of the biological phenomena. The goal is to build a mathematical framework with some predictive abilities. This review highlights the need for system-level understanding, lists some of the high-throughput measurement tools of importance in systems biology, reviews various types of experimental and computational approaches being used in systems biology research and attempts to address some of the challenges facing this research community.

Animals↗
PubMed2006

High dynamic range characterization of the trauma patient plasma proteome.

Although human plasma represents an attractive sample for disease biomarker discovery, the extreme complexity and large dynamic range in protein concentrations present significant challenges for characterization, candidate biomarker discovery, and validation. Herein we describe a strategy that combines immunoaffinity subtraction and subsequent chemical fractionation based on cysteinyl peptide and N-glycopeptide captures with two-dimensional LC-MS/MS to increase the dynamic range of analysis for plasma. Application of this "divide-and-conquer" strategy to trauma patient plasma significantly improved the overall dynamic range of detection and resulted in confident identification of 22,267 unique peptides from four different peptide populations (cysteinyl peptides, non-cysteinyl peptides, N-glycopeptides, and non-glycopeptides) that covered 3,654 different proteins with 1,494 proteins identified by multiple peptides. Numerous low abundance proteins were identified, exemplified by 78 "classic" cytokines and cytokine receptors and by 136 human cell differentiation molecules. Additionally a total of 2,910 different N-glycopeptides that correspond to 662 N-glycoproteins and 1,553 N-glycosylation sites were identified. A panel of the proteins identified in this study is known to be involved in inflammation and immune responses. This study established an extensive reference protein database for trauma patients that provides a foundation for future high throughput quantitative plasma proteomic studies designed to elucidate the mechanisms that underlie systemic inflammatory responses.

Blood Proteins↗
PubMed2005

A proteomic tool for protein identification from tandem mass spectral data.

Instead of using the probability mean, a simple and yet effective heuristic approach was employed to treat experimentally obtained tandem mass spectrometry (MS/MS) data for protein identification. The proposed approach is based on the total number (T) of identified experimental MS/MS data. To warrant the subsequent ranking, the total number of identified b- and y-type ions (Tb+y) must be greater than 50% of T. Peptides having the same T and Tb+y are either ranked by the contiguity of identified ions or discarded during identification. When compared to other protein identification tools, good agreement with the searched results was seen.

Algorithms↗
PubMed2001

Nanoflow gradient generator coupled with mu-LC-ESI-MS/MS for protein identification.

The large-scale identification of proteins from proteomes of complex organisms, and the availability of various types of protein and DNA databases, increasingly require the additional information provided by tandem mass spectrometry. HPLC and microLC coupled to ESI-MS/MS presently dominate the field of protein identification by tandem mass spectrometry and database searching. The analysis of protein digests is typically performed using HPLC or LC columns with 50-100-microm diameters, requiring the delivery of solvent gradients at low to mid nanoliter per minute flow rates. This has been typically achieved using expensive generic HPLC pumping systems for the delivery of microliter per minute gradients that were either flow-split or sampled. Here we present an alternative system for the delivery of nanoliter per minute gradients. The inexpensive nanoflow gradient generator (etagrad) described here can be modulated to reproducibly deliver selected gradients. The performance of the etagrad on-line with a microLC-ESI-MS/MS system has been demonstrated for the identification of standard protein digests. Moreover, the performance of the etagrad-microLC-ESI-MS/MS system, with protein prefractionation by IPG isoelectric focusing, was also evaluated for rapid study of yeast and human proteomes.

Chromatography, Liquid↗
PubMed2004

Genomic and proteomic profiling for biomarkers and signature profiles of toxicity.

Toxicity profiling measures and compares all gene expression changes among biological samples after toxicant exposure. Toxicity profiling with DNA microarrays to measure all mRNA transcripts (transcriptomics), or by global separation and identification of proteins (proteomics), has led to the discovery of better descriptors of toxicity, toxicant classification and exposure monitoring than current indicators. A shared goal in transcript and proteomic profiling is the development of biomarkers and signatures of chemical toxicity. In this review, biomarkers and signature profiles are described for specific chemical toxicants that affect target organs such as liver, kidney, neural tissues, gastrointestinal tract and skeletal muscle, for specific disease models such as cancer and inflammation, and for unique chemical-protein adducts underlying cell injury. The recent introduction of toxicogenomics databases support researchers in sharing, analyzing, visualizing and mining expression data, assist the integration of transcriptomics, proteomics and toxicology datasets, and eventually will permit in silico biomarker and signature pattern discovery.

Animals↗