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PubMed2006

ProteomeCommons.org JAF: reference information and tools for proteomics.

SUMMARY: Analysis of proteomics data, specifically mass spectrometry data, commonly relies on libraries of known information such as atomic masses, known stable isotopes, atomic compositions of amino acids, observed modifications of known amino acids and ion masses that directly correspond to known amino acid sequences. The Java Analysis Framework (JAF) for proteomics provides a freely usable, open-source library of Java code that abstracts all of the aforementioned data, enabling more rapid development of proteomics tools. The JAF also includes several user tools that can be run directly from a web browser. AVAILABILITY: The current version and an archive of all older versions of the Java Analysis Framework for Proteomics is freely available, including complete source-code, at http://www.proteomecommons.org/current/511/.

Database Management Systems↗

PubMed2006

Convergence of the proteomic pattern in cancer.

MOTIVATION: On the histological level the differentiation of normal epithelial tissues is well known. The phenomenon of dedifferentiation, which occurs as cells develop towards malignancy is also well described. To identify an epithelial tumor-specific proteomic profile as well as to measure the proximities between we used data from tumor tissue and adjacent normal tissue microdissected from head and neck and colon cancer samples which were analyzed using ProteinChip technology and performed a bioinformatic meta-analysis on the resulting four complex datasets. RESULTS: All four groups could be identified based on their proteomic signatures and the tumor tissues were found to be more similar to one another than to the normal epithelial tissue from which they progressed. This study shows at the proteomic level that changes in the histological features of tumors as compared to the tissues from which they arise are reflected in the convergence of proteomic pattern during the development to cancer.

Cell Differentiation↗

PubMed2006

Integrated analysis of transcriptomic and proteomic data of Desulfovibrio vulgaris: zero-inflated Poisson regression models to predict abundance of undetected proteins.

MOTIVATION: Integrated analysis of global scale transcriptomic and proteomic data can provide important insights into the metabolic mechanisms underlying complex biological systems. However, because the relationship between protein abundance and mRNA expression level is complicated by many cellular and physical processes, sophisticated statistical models need to be developed to capture their relationship. RESULTS: In this study, we describe a novel data-driven statistical model to integrate whole-genome microarray and proteomic data collected from Desulfovibrio vulgaris grown under three different conditions. Based on the Poisson distribution pattern of proteomic data and the fact that a large number of proteins were undetected (excess zeros), zero-inflated Poisson (ZIP)-based models were proposed to define the correlation pattern between mRNA and protein abundance. In addition, by assuming that there is a probability mass at zero representing unexpressed genes and expressed proteins that were undetected owing to technical limitations, a Potential ZIP model was established. Two significant improvements introduced by this approach are (1) the predicted protein abundance level values for experimentally detected proteins are corrected by considering their mRNA levels and (2) protein abundance values can be predicted for undetected proteins (in the case of this study, approximately 83% of the proteins in the D.vulgaris genome) for better biological interpretation. We demonstrated the use of these statistical models by comparatively analyzing proteomic and microarray results from D.vulgaris grown on lactate-based versus formate-based media. These models correctly predicted increased expression of Ech hydrogenase and decreased expression of Coo hydrogenase for D.vulgaris grown on formate.

Adenosine Triphosphate↗

PubMed2006

Automatic clustering of orthologs and inparalogs shared by multiple proteomes.

MOTIVATION: The complete sequencing of many genomes has made it possible to identify orthologous genes descending from a common ancestor. However, reconstruction of evolutionary history over long time periods faces many challenges due to gene duplications and losses. Identification of orthologous groups shared by multiple proteomes therefore becomes a clustering problem in which an optimal compromise between conflicting evidences needs to be found. RESULTS: Here we present a new proteome-scale analysis program called MultiParanoid that can automatically find orthology relationships between proteins in multiple proteomes. The software is an extension of the InParanoid program that identifies orthologs and inparalogs in pairwise proteome comparisons. MultiParanoid applies a clustering algorithm to merge multiple pairwise ortholog groups from InParanoid into multi-species ortholog groups. To avoid outparalogs in the same cluster, MultiParanoid only combines species that share the same last ancestor. To validate the clustering technique, we compared the results to a reference set obtained by manual phylogenetic analysis. We further compared the results to ortholog groups in KOGs and OrthoMCL, which revealed that MultiParanoid produces substantially fewer outparalogs than these resources. AVAILABILITY: MultiParanoid is a freely available standalone program that enables efficient orthology analysis much needed in the post-genomic era. A web-based service providing access to the original datasets, the resulting groups of orthologs, and the source code of the program can be found at http://multiparanoid.cgb.ki.se.

Algorithms↗

PubMed2006

JVirGel 2.0: computational prediction of proteomes separated via two-dimensional gel electrophoresis under consideration of membrane and secreted proteins.

MOTIVATION: After the publication of JVirGel 1.0 in 2003 we got many requests and suggestions from the proteomics community to further improve the performance of the software and to add additional useful new features. RESULTS: The integration of the PrediSi algorithm for the prediction of signal peptides for the Sec-dependent protein export into JVirGel 2.0 allows the exclusion of most exported preproteins from calculated proteomic maps and provides the basis for the calculation of Sec-based secretomes. A tool for the identification of transmembrane helices carrying proteins (JCaMelix) and the prediction of the corresponding membrane proteome was added. Finally, in order to directly compare experimental and calculated proteome data, a function to overlay and evaluate predicted and experimental two-dimensional gels was included. AVAILABILITY: JVirGel 2.0 is freely available as precompiled package for the installation on Windows or Linux operating systems. Furthermore, there is a completely platform-independent Java version available for download. Additionally, we provide a Java Server Pages based version of JVirGel 2.0 which can be operated in nearly all web browsers. All versions are accessible at http://www.jvirgel.de

Algorithms↗

PubMed2002

Serum proteomic patterns for detection of prostate cancer.

Pathologic states within the prostate may be reflected by changes in serum proteomic patterns. To test this hypothesis, we analyzed serum proteomic mass spectra with a bioinformatics tool to reveal the most fit pattern that discriminated the training set of sera of men with a histopathologic diagnosis of prostate cancer (serum prostate-specific antigen [PSA] > or =4 ng/mL) from those men without prostate cancer (serum PSA level <1 ng/mL). Mass spectra of blinded sera (N = 266) from a test set derived from men with prostate cancer or men without prostate cancer were matched against the discriminating pattern revealed by the training set. A predicted diagnosis of benign disease or cancer was rendered based on similarity to the discriminating pattern discovered from the training set. The proteomic pattern correctly predicted 36 (95%, 95% confidence interval [CI] = 82% to 99%) of 38 patients with prostate cancer, while 177 (78%, 95% CI = 72% to 83%) of 228 patients were correctly classified as having benign conditions. For men with marginally elevated PSA levels (4-10 ng/mL; n = 137), the specificity was 71%. If validated in future series, serum proteomic pattern diagnostics may be of value in deciding whether to perform a biopsy on a man with an elevated PSA level.

Biomarkers, Tumor↗

PubMed2006

Methods of quantitative proteomics and their application to plant organelle characterization.

Many cell biologists wish to know the subcellular localization of proteins of interest. Proteomics methods have the potential to describe the entire protein content of organelles. However, practical limitations in organelle isolation and analysis of low abundance proteins have meant that organelle proteomics has had, until recently, only limited success. Some examples of quantitative proteomic methods and their use in the study of plant organelle proteomes are discussed here. It is concluded that 2D-difference gel electrophoresis (2D-DIGE) as well as differential isotope tagging strategies coupled to non-gel-based LC-MS are proving useful in this area of research.

Arabidopsis↗

PubMed2006

A versatile method for deciphering plant membrane proteomes.

Proteomics is a very powerful approach to link the information contained in sequenced genomes, like that of Arabidopsis, to the functional knowledge provided by studies of plant cell compartments. This article summarizes the different steps of a versatile strategy that has been developed to decipher plant membrane proteomes. Initiated with envelope membranes from spinach chloroplasts, this strategy has been adapted to thylakoids, and further extended to a series of membranes from the model plant Arabidopsis: chloroplast envelope membranes, plasma membrane, and mitochondrial membranes. The first step is the preparation of highly purified membrane fractions from plant tissues. The second step in the strategy is the fractionation of membrane proteins on the basis of their physico-chemical properties. Chloroform/methanol extraction and washing of membranes with NaOH, NaCl or any other agent led to the simplification of the protein content of the fraction to be analysed. The next step is the genuine proteomic step, i.e. the separation of proteins by 1D-gel electrophoresis followed by in-gel proteolytic digestion of the polypeptides, analysis of the proteolytic peptides using mass spectrometry, and protein identification by searching through databases. The last step is the validation of the procedure by checking the subcellular location. The results obtained by using this strategy demonstrate that a combination of different proteomics approaches, together with bioinformatics, indeed provide a better understanding of the biochemical machinery of the different plant membranes at the molecular level.

Alkalies↗

PubMed2006

The proteomics of plant cell membranes.

Membrane proteins are involved in many different functions depending on their location in the cell. Characterization of the membrane proteome can bring new insights to the function of different plant membrane systems and the subcellular compartments where the proteins are found. Plant membrane proteomics can also provide valuable information about plant-specific biological processes. Despite recent advances in the separation and techniques for the analysis of plant membrane proteins, characterization of these proteins, especially the hydrophobic ones, is still challenging. In this review, plant membrane proteomics data, compiled from the literature on Arabidopsis thaliana, are described. In addition, initial attempts towards determining the physiological significance of some proteins identified from membrane proteomics in rice are also described.

Arabidopsis↗

PubMed2006

Expanded protein information at SGD: new pages and proteome browser.

The recent explosion in protein data generated from both directed small-scale studies and large-scale proteomics efforts has greatly expanded the quantity of available protein information and has prompted the Saccharomyces Genome Database (SGD; http://www.yeastgenome.org/) to enhance the depth and accessibility of protein annotations. In particular, we have expanded ongoing efforts to improve the integration of experimental information and sequence-based predictions and have redesigned the protein information web pages. A key feature of this redesign is the development of a GBrowse-derived interactive Proteome Browser customized to improve the visualization of sequence-based protein information. This Proteome Browser has enabled SGD to unify the display of hidden Markov model (HMM) domains, protein family HMMs, motifs, transmembrane regions, signal peptides, hydropathy plots and profile hits using several popular prediction algorithms. In addition, a physico-chemical properties page has been introduced to provide easy access to basic protein information. Improvements to the layout of the Protein Information page and integration of the Proteome Browser will facilitate the ongoing expansion of sequence-specific experimental information captured in SGD, including post-translational modifications and other user-defined annotations. Finally, SGD continues to improve upon the availability of genetic and physical interaction data in an ongoing collaboration with BioGRID by providing direct access to more than 82,000 manually-curated interactions.

Computer Graphics↗

PubMed2006

plprot: a comprehensive proteome database for different plastid types.

Different plant plastid types contain a distinct protein complement for specialized functions and metabolic activities. plprot was established as a plastid proteome database to provide information about the proteomes of chloroplasts, etioplasts and undifferentiated plastids. The current version of plprot features 2,043 protein entries and consists of two modules. Module one contains a BLAST search option and provides comparative information on the proteomes of different plastid types. The second module contains four searchable databases, three for each individual plastid type and one comprehensive composite database that provides the results of plastid proteome analyses from different laboratories. plprot is accessible at http://www.plprot.ethz.ch.

Databases, Protein↗

PubMed2003

Proteomics.

PURPOSE OF REVIEW: Proteomics encompasses a group of technologies that attempt to separate, identify and characterize a global set of proteins. This review will highlight the technologies available, outline the capabilities, advantages and disadvantages of each and briefly describe applications in nephrology. RECENT FINDINGS: Proteomics provides information about abundance, location, chemical modification and protein-protein interactions that is not available from genomic technologies. Several proteomic approaches are now widely available. Liquid chromatography/mass spectrometry, two-dimensional gel electrophoresis, antibody arrays and protein chips (surface enhanced laser desorption ionization) provide opportunities to identify and compare an abundance of proteins as well as to determine posttranslational modifications, subcellular location and molecular interactions. Recent advances such as multidimensional chromatographic analysis and isotope coded affinity tags have expanded the usefulness of these approaches. SUMMARY: Proteomic technologies are improving and developing rapidly. These techniques will be valuable tools to develop markers for disease, identify and evaluate proteins as drug targets and understand renal physiology at the protein level.

Antibodies↗

PubMed2004

The future of proteomics in the study of alcoholism.

This article represents the proceedings of a workshop at the 2003 annual meeting of the Research Society on Alcoholism in Fort Lauderdale, FL. The workshop organizers/chairpersons were Chinnaswamy Kasinathan and Paul Manowitz. The presentations were (1) Introduction to the field of proteomics, by Kent Vrana; (2) Use of proteomics in the identification of urinary biomarkers for alcohol intake, by Chinnaswamy Kasinathan, Paul Thomas, and Paul Manowitz; (3) Proteomics screening illuminates ethanol-mediated induction of HDL proteins in macaques, by Kent Vrana, Randy Gooch, Travis Worst, Stephen Walker, Aaron Xu, Peter Pierre, Heather Green, and Kathleen Grant; and (4) Proteomics applied to the study of the liver, by Laura Beretta.

Alcoholism↗

PubMed2004

Bioinformatic analysis of the urine proteome of acute allograft rejection.

The urinary proteome in health and disease attracts increasing attention because of the potential diagnostic and pathophysiologic biomarker information carried by specific excreted proteins or their constellations. This cross-sectional study aimed to analyze the urinary proteome in patients with biopsy-proven acute rejection (n = 23) compared with transplant recipients with stable graft function (n = 22) and healthy volunteers (n = 20) and to correlate this with clinical, morphologic, and laboratory data. Urine samples were preadsorbed on four different protein chip surfaces, and the protein composition was analyzed using a surface-enhanced laser desorption/ionization time-of-flight mass spectrometer platform. The data were analyzed using two independent approaches to sample classification. Patients who experienced acute rejection could be distinguished from stable patients with a sensitivity of 90.5 to 91.3% and a specificity of 77.2 to 83.3%, depending on the classifier used. Protein masses that were important in constructing the classification algorithms included those of mass 2003.0, 2802.6, 4756.3, 5872.4, 6990.6, 19,018.8, and 25,665.7 Da. Normal urine was distinguished from transplant urine using a protein marker of mass 78,531.2 Da with both a sensitivity and a specificity of 100%. In conclusion, (1) urine proteome in transplant recipients with stable graft function was significantly different from healthy control subjects, and (2) acute rejections were characterized by a constellation of excreted proteins. Analysis of the urinary proteome may expedite the noninvasive prediction of acute graft rejection, thus importantly assisting in establishing the diagnosis.

Acute Disease↗

PubMedDate not supplied

Advancing cancer biotherapy with proteomics.

Proteomics is becoming increasingly important for cancer biotherapy. The development of high-throughput platforms now allows the analysis of multiple proteins from small quantities of material. While these techniques are being used to discover new biomarkers, they are particularly important for assessing complex biologic processes such as immunotherapy for cancer. Recent advances in this field are reviewed, as well as the use of proteomics to assess the effectiveness and toxicities of high-dose IL-2 cancer therapy. Proteomics is becoming useful in assessing cancer biotherapies and in unraveling their mechanisms of action. High-throughput proteomic technologies have now advanced to a stage where they have the potential to become effective discovery tools for biomarkers/predictors of disease, disease recurrence, and response to therapy.

Humans↗

PubMed2006

Proteomic analysis of inclusion body myositis.

Sporadic inclusion body myositis (IBM) is the most frequently acquired inflammatory myopathy of late adult life, yet its diagnostic criteria and pathogenesis remain poorly defined. Because effective treatment is lacking, research efforts have intensified to identify specific markers for this debilitating disorder. In this study, proteomic analysis of 4 cases of sporadic IBM was compared with 5 cases of inflammatory myopathy without clinicopathologic features of IBM to distinguish the IBM-specific proteome. Proteins were separated by 2-dimensional polyacrylamide gel electrophoresis and profiled by mass spectrometric sequencing. Expression of most proteins remained unchanged; however, 16 proteins were upregulated and 6 proteins were downregulated in IBM compared with cases of non-IBM inflammatory myopathy. These IBM-specific proteins included apolipoprotein A-I, amyloid beta precursor protein, and transthyretin, which have been associated with amyloidosis; superoxide dismutase, enolase, and various molecular chaperones indicate perturbations in detoxification, energy metabolism, and protein folding, respectively. The IBM-downregulated proteins mainly serve as carriers for muscle contraction and other normal muscle functions. We further applied Western blot and immunohistochemistry to verify the proteomic findings. This study validates proteomics as a powerful tool in the study of muscle disease and indicates a unique pattern of protein expression in IBM.

Aged↗

PubMed2005

Genomics, proteomics, metabolomics: what is in a word for multiple sclerosis?

PURPOSE OF REVIEW: Multiple sclerosis (MS) is the most common chronic inflammatory neurological disease. Despite major advances the aetiology of this disease it is still not completely understood. In the post-genome era, advances in global screening technologies offer an opportunity to accelerate the search of new pathological pathways and to identify new therapeutic targets. Some recent publications using novel global screening methods at the genome, transcriptome, proteome and metabolome levels are discussed. RECENT FINDINGS: The genetic association of susceptibility to MS with loci outside the MHC has been reconfirmed. Evidence of parent-of-origin and seasonal effects on disease susceptibility add further complexity to the genetics of MS. The search for MS susceptibility genes continues using the candidate-gene approach as well as large-scale single-nucleotide-polymorphism association studies and novel cross-species synteny analysis. Genome-wide expression profiling using microarrays produced numerous therapeutic targets and is progressing towards profiling of rare cells. Advances in classical proteomics methods paved the way to new initiatives aiming at determining the proteome of the nervous system in normal and diseased states. Although progress is still slow, array-based methods are making an impact on the MS field. SUMMARY: The complexity of MS is clearly reflected in the latest findings using global profiling methods. Nevertheless, these new technologies are confirming some of the basic aspects of the disease pathophysiology, i.e. its polygenicity, the central role of neuroinflammation and the emerging neurodegenerative processes. These data are primarily the results of genomic approaches, yet promising attempts are also made using proteomics and metabolomics.

Animals↗

PubMed2005

Behavioural manipulation in a grasshopper harbouring hairworm: a proteomics approach.

The parasitic Nematomorph hairworm, Spinochordodes tellinii (Camerano) develops inside the terrestrial grasshopper, Meconema thalassinum (De Geer) (Orthoptera: Tettigoniidae), changing the insect's responses to water. The resulting aberrant behaviour makes infected insects more likely to jump into an aquatic environment where the adult parasite reproduces. We used proteomics tools (i.e. two-dimensional gel electrophoresis (2-DE), computer assisted comparative analysis of host and parasite protein spots and MALDI-TOF mass spectrometry) to identify these proteins and to explore the mechanisms underlying this subtle behavioural modification. We characterized simultaneously the host (brain) and the parasite proteomes at three stages of the manipulative process, i.e. before, during and after manipulation. For the host, there was a differential proteomic expression in relation to different effects such as the circadian cycle, the parasitic status, the manipulative period itself, and worm emergence. For the parasite, a differential proteomics expression allowed characterization of the parasitic and the free-living stages, the manipulative period and the emergence of the worm from the host. The findings suggest that the adult worm alters the normal functions of the grasshopper's central nervous system (CNS) by producing certain 'effective' molecules. In addition, in the brain of manipulated insects, there was found to be a differential expression of proteins specifically linked to neurotransmitter activities. The evidence obtained also suggested that the parasite produces molecules from the family Wnt acting directly on the development of the CNS. These proteins show important similarities with those known in other insects, suggesting a case of molecular mimicry. Finally, we found many proteins in the host's CNS as well as in the parasite for which the function(s) are still unknown in the published literature (www) protein databases. These results support the hypothesis that host behavioural changes are mediated by a mix of direct and indirect chemical manipulation.

Animals↗