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PubMed2005

HUPO Brain Proteome Project Pilot Studies: bioinformatics at work.

The data acquisition phase of initial pilot studies (human and mouse brain samples) of the Human Proteome Organisation (HUPO) Brain Proteome Project (BPP) is now complete and the data generated by the participating laboratories has been submitted to the central Data Collection Center. The BPP Bioinformatics Group met on 8th April 2005 at the European Bioinformatics Institute (Hinxton, UK) to discuss strategies for the reanalysis of the pooled data from all the participating laboratories. A summary of the results of the data reprocessing will be presented at the 4th HUPO World Congress that will be held in August/September 2005.

Animals↗
PubMed2006

Flagellar motility is required for the viability of the bloodstream trypanosome.

The 9 + 2 microtubule axoneme of flagella and cilia represents one of the most iconic structures built by eukaryotic cells and organisms. Both unity and diversity are present among cilia and flagella on the evolutionary as well as the developmental scale. Some cilia are motile, whereas others function as sensory organelles and can variously possess 9 + 2 and 9 + 0 axonemes and other associated structures. How such unity and diversity are reflected in molecular repertoires is unclear. The flagellated protozoan parasite Trypanosoma brucei is endemic in sub-Saharan Africa, causing devastating disease in humans and other animals. There is little hope of a vaccine for African sleeping sickness and a desperate need for modern drug therapies. Here we present a detailed proteomic analysis of the trypanosome flagellum. RNA interference (RNAi)-based interrogation of this proteome provides functional insights into human ciliary diseases and establishes that flagellar function is essential to the bloodstream-form trypanosome. We show that RNAi-mediated ablation of various proteins identified in the trypanosome flagellar proteome leads to a rapid and marked failure of cytokinesis in bloodstream-form (but not procyclic insect-form) trypanosomes, suggesting that impairment of flagellar function may provide a method of disease control. A postgenomic meta-analysis, comparing the evolutionarily ancient trypanosome with other eukaryotes including humans, identifies numerous trypanosome-specific flagellar proteins, suggesting new avenues for selective intervention.

Animals↗
PubMed2005

SpectralNET--an application for spectral graph analysis and visualization.

BACKGROUND: Graph theory provides a computational framework for modeling a variety of datasets including those emerging from genomics, proteomics, and chemical genetics. Networks of genes, proteins, small molecules, or other objects of study can be represented as graphs of nodes (vertices) and interactions (edges) that can carry different weights. SpectralNET is a flexible application for analyzing and visualizing these biological and chemical networks. RESULTS: Available both as a standalone .NET executable and as an ASP.NET web application, SpectralNET was designed specifically with the analysis of graph-theoretic metrics in mind, a computational task not easily accessible using currently available applications. Users can choose either to upload a network for analysis using a variety of input formats, or to have SpectralNET generate an idealized random network for comparison to a real-world dataset. Whichever graph-generation method is used, SpectralNET displays detailed information about each connected component of the graph, including graphs of degree distribution, clustering coefficient by degree, and average distance by degree. In addition, extensive information about the selected vertex is shown, including degree, clustering coefficient, various distance metrics, and the corresponding components of the adjacency, Laplacian, and normalized Laplacian eigenvectors. SpectralNET also displays several graph visualizations, including a linear dimensionality reduction for uploaded datasets (Principal Components Analysis) and a non-linear dimensionality reduction that provides an elegant view of global graph structure (Laplacian eigenvectors). CONCLUSION: SpectralNET provides an easily accessible means of analyzing graph-theoretic metrics for data modeling and dimensionality reduction. SpectralNET is publicly available as both a .NET application and an ASP.NET web application from http://chembank.broad.harvard.edu/resources/. Source code is available upon request.

Algorithms↗
PubMed2006

Sampling and analytical strategies for biomarker discovery using mass spectrometry.

There is an often unspoken truth behind the course of scientific investigation that involves not what is necessarily academically worthy of study, but rather what is scientifically worthy in the eyes of funding agencies. The perception of worthy research is, as cost is driven in the simplest sense in economics, often driven by demand. Presently, the demand for novel diagnostic and therapeutic protein biomarkers that possess high sensitivity and specificity is placing major impact on the field of proteomics. The focal discovery technology that is being relied on is mass spectrometry (MS), whereas the challenge of biomarker discovery often lies not in the application of MS but in the underlying proteome sampling and bioinformatic processing strategies. Although biomarker discovery research has been historically technology-driven, it is clear from the meager success in generating validated biomarkers that increasing attention must be placed at the pre-analytic stage, such as sample retrieval and preparation. As diseases vary, so do the combinations of sampling and sample analyses necessary to discover novel biomarkers. In this review, we highlight different strategies used toward biomarker discovery and discuss them in terms of their reliance on technology and methodology.

Biomarkers↗
PubMed2004

Two-dimensional gel electrophoresis database of murine R1 embryonic stem cells.

Embryonic stem (ES) cell lines represent a population of undifferentiated pluripotent cells capable of multilineage differentiation in vitro. Although very useful for studying developmental processes, human ES cell lines have also been suggested as a potential and unlimited source for cellular transplantation and the treatment of human disease. The proteomic basis of embryonic stemness (pluripotentiality and multilineage differentiation) and the transitions that lead to specific cell lineages however, remain to be defined. As an important first step in defining these processes, we have performed a proteomic analysis of undifferentiated mouse R1 ES cell lines using pH 3-10, 4-7 and 6-11 two-dimensional electrophoresis gels, matrix-assisted laser desorption/ionization and tandem mass spectrometry. Of the 700 gel spots analyzed, 241 distinct protein species were identified corresponding to 218 unique proteins, with a significant proportion functionally related to protein expression.

Animals↗
PubMed2004

Towards two-dimensional electrophoresis mapping of the cerebrospinal fluid proteome from a single individual.

We test the ability of state-of-the-art two-dimensional electrophoresis (2-DE) technology to enable the proteome mapping of ante mortem cerebrospinal fluid (CSF) from a single individual. Using the sensitive technologies of a fluorescent protein stain and fluorescence laser scanning of 2-DE gels, combined with matrix-assisted laser desorption/ionization-time of flight/time of flight-mass spectrometry (MALDI-TOF/TOF-MS) for protein identification, a highly detailed 2-DE map of the CSF proteome was created. The 2-DE map contains 600 identified spots representing 82 different proteins. Of the 82 proteins identified, 25 have not appeared in any previously published 2-DE map of CSF, and 11 have not been previously reported to exist in CSF. Most of the identifications originate from an ante mortem CSF sample collected from a single hydrocephalus patient. A supplemental map created from neurologically normal patients is also presented. A webpage with protein identification and scoring information from both maps is available at http://www.leelab.org/csfmap.

Cerebrospinal Fluid↗
PubMed2005

Proteome imaging: a closer look at life's organization.

Imaging the proteome is a term that is used in many different contexts. The term implies that the entire cohort of proteins and their modifications are visualized. This unfortunately is not the case. In this mini-review, a concise overview is provided on different imaging technologies that are currently used to investigate the structure, function and dynamics of proteins and their organization. These techniques have been selected for review based on the unique insights they provide in subsets of the proteome. These techniques have been illustrated with practical examples of their merits. Mass spectrometry-based imaging technologies are playing a key role in proteome research and have been reviewed in more detail. They hold the promise of detailed molecular insight in the spatial organization of living system.

Electrophoresis, Gel, Two-Dimensional↗
PubMed2002

Clinical potential of proteomics in the diagnosis of ovarian cancer.

The need for specific and sensitive markers of ovarian cancer is critical. Finding a sensitive and specific test for its detection has an important public health impact. Currently, there are no effective screening options available for patients with ovarian cancer. CA-125, the most widely used biomarker for ovarian cancer, does not have a high positive predictive value and it is only effective when used in combination with other diagnostic tests. However, pathologic changes taking place within the ovary may be reflected in biomarker patterns in the serum. Combination of mass spectra generated by new proteomic technologies, such as surface-enhanced laser desorption ionization time-of-flight (SELDI-TOF) and artificial-intelligence-based informatic algorithms, have been used to discover a small set of key protein values and discriminate normal from ovarian cancer patients. Serum proteomic pattern analysis might be applied ultimately in medical screening clinics, as a supplement to the diagnostic work-up and evaluation.

Biomarkers, Tumor↗
PubMed2005

Using ontologies in PROTEUS for modeling proteomics data mining applications.

Bioinformatics applications are often characterized by a combination of (pre) processing of raw data representing biological elements, (e.g. sequence alignment, structure prediction), and an high level data mining analysis. Developing such applications needs knowledge of both data mining and bioinformatics domains, that can be effectively achieved by combining ontology about the application domain and ontology about the approaches and processes to solve the given problem. In this paper we talk about using ontologies to model proteomics in silico experiments. In particular data mining of mass spectrometry proteomics data is considered.

Computational Biology↗
PubMed2004

Protein-protein interaction networks: from interactions to networks.

The goal of interaction proteomics that studies the protein-protein interactions of all expressed proteins is to understand biological processes that are strictly regulated by these interactions. The availability of entire genome sequences of many organisms and high-throughput analysis tools has led scientists to study the entire proteome (Pandey and Mann, 2000). There are various high-throughput methods for detecting protein interactions such as yeast two-hybrid approach and mass spectrometry to produce vast amounts of data that can be utilized to decipher protein functions in complicated biological networks. In this review, we discuss recent developments in analytical methods for large-scale protein interactions and the future direction of interaction proteomics.

Animals↗
PubMed2025

An integrative network approach for longitudinal stratification in Parkinson's disease.

Parkinson's disease (PD) is a neurodegenerative disorder characterized by motor symptoms resulting from the loss of dopamine-producing neurons in the brain. Currently, there is no cure for the disease which is in part due to the heterogeneity in patient symptoms, trajectories and manifestations. There is a known genetic component of PD and genomic datasets have helped to uncover some aspects of the disease. Understanding the longitudinal variability of PD is essential as it has been theorised that there are different triggers and underlying disease mechanisms at different points during disease progression. In this paper, we perform longitudinal and cross-sectional experiments to identify which data modalities or combinations of modalities are informative at different time points. We use clinical, genomic, and proteomic data from the Parkinson's Progression Markers Initiative. We validate the importance of flexible data integration by highlighting the varying combinations of data modalities for optimal stratification at different disease stages in idiopathic PD. We show there is a shared signal in the DNAm signatures of participants with a mutation in a causal gene of PD and participants with idiopathic PD. We also show that integration of SNPs and DNAm data modalities has potential for use as an early diagnostic tool for individuals with a genetic cause of PD.

Parkinson Disease↗
PubMed2004

Two-dimensional electrophoresis database of Listeria monocytogenes EGDe proteome and proteomic analysis of mid-log and stationary growth phase cells.

Listeria monocytogenes is the causative agent of listeriosis, one of the most significant foodborne diseases in industrialized countries. The complete genome of the L. monocytogenes EGDe strain, belonging to the serogroup 1/2a, has been sequenced and is comprised of 2853 open reading frames. The objective of the current study was to construct a two-dimensional (2-D) database of the proteome of this strain. The soluble protein fractions of the microorganism were recovered either in the mid-log or in the stationary phase of growth at 37 degrees C. These fractions were analyzed by 2-D electrophoresis (2-DE), using immobilized pH gradient strips of various pH values (3-10, 3-6, and 5-8) for the first-dimensional separations and 12.5% acrylamide gels for sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE). 201 protein spots corresponding to 126 different proteins were identified by matrix assisted laser desorption/ionization-time of flight-mass spectrometry (MALDI-TOF-MS). The 2-DE maps presented here provide a first basis for further investigations of protein expression in L. monocytogenes. In this way, the comparison of proteome between cells in the exponential or stationary phase of growth at 37 degrees C allowed us to characterize 161 variations in protein spot intensity, of which 38 were identified. Among the differentially expressed proteins were ribosomal proteins (RpsF, RplJ, and RpmE), proteins involved in cellular metabolism (GlpD, PdhD, Pgm, Lmo1372, Lmo2696, and Lmo2743) or in stress adaptation (GroES and ferritin), a fructose-specific phosphotransferase enzyme IIB (Lmo0399) and different post-translational modified forms of listeriolysin (LLO).

Bacterial Proteins↗
PubMed2006

Present status of clinical proteomic analysis for the early detection and determination of therapeutic strategy in lung cancer.

Although human DNA sequences have already been successfully decoded, there is still much we do not understand about the pathogenesis of human diseases. Proteomic analysis is comprehensive analysis of proteins, and there is a high probability that recent advances in this field will enable its application to individualized therapy based on thorough understanding of the pathogenesis of human diseases. Two-dimensional polyacrylamide gel electrophoresis (2-DE) and mass spectrometry (MS) are indispensable tools for proteomic analysis, and these technologies enable discovery of tiny changes of protein expression associated with pathogenesis. In this review we introduce the present status of proteomic analysis for cancer-related proteins, and especially lung cancer-related proteins in the attempt to discover new biomarkers for early detection and determine novel target-molecules for treatment. We believe that proteomic analysis will provide crucial information for diagnosis and treatment.

Adenocarcinoma↗
PubMed2004

Informatics platform for global proteomic profiling and biomarker discovery using liquid chromatography-tandem mass spectrometry.

We have developed an integrated suite of algorithms, statistical methods, and computer applications to support large-scale LC-MS-based gel-free shotgun profiling of complex protein mixtures using basic experimental procedures. The programs automatically detect and quantify large numbers of peptide peaks in feature-rich ion mass chromatograms, compensate for spurious fluctuations in peptide signal intensities and retention times, and reliably match related peaks across many different datasets. Application of this toolkit markedly facilitates pattern recognition and biomarker discovery in global comparative proteomic studies, simplifying mechanistic investigation of physiological responses and the detection of proteomic signatures of disease.

Algorithms↗
PubMed2003

PROTEOME-3D: an interactive bioinformatics tool for large-scale data exploration and knowledge discovery.

Comprehensive understanding of biological systems requires efficient and systematic assimilation of high-throughput datasets in the context of the existing knowledge base. A major limitation in the field of proteomics is the lack of an appropriate software platform that can synthesize a large number of experimental datasets in the context of the existing knowledge base. Here, we describe a software platform, termed PROTEOME-3D, that utilizes three essential features for systematic analysis of proteomics data: creation of a scalable, queryable, customized database for identified proteins from published literature; graphical tools for displaying proteome landscapes and trends from multiple large-scale experiments; and interactive data analysis that facilitates identification of crucial networks and pathways. Thus, PROTEOME-3D offers a standardized platform to analyze high-throughput experimental datasets for the identification of crucial players in co-regulated pathways and cellular processes.

Computational Biology↗
PubMed2007

Generation of plant protein microarrays and investigation of antigen-antibody interactions.

The application of proteomics methods, such as the protein microarray technology, in plant science has been strongly supported by the completion of genome sequencing projects of Arabidopsis thaliana and rice. In this chapter we describe a method to generate plant protein microarrays and to use them for characterizing monoclonal antibodies or polyclonal sera with regard to their specificity and cross-reactivity. The method starts with characterized E. coli cDNA expression clones encoding His-tagged plant proteins. After expression and purification of these recombinant proteins in high throughput, protein microarrays are generated utilizing a contact printer. For the detection of the recombinant proteins on the microarrays, an anti-RGS-His6 antibody is used. To characterize specific antibodies, the microarrays are incubated with the respective antibody solutions followed by fluorescently labeled secondary antibodies. Signal detection is performed by means of an arrayscanner system. Protein microarrays containing the whole proteome of a plant will represent the ideal format to test antibody specificity and cross-reactivity in the future.

Antibody Specificity↗
PubMed2003

Computing motif correlations in proteins.

Protein motifs, which are specific regions and conserved regions, are found by comparing multiple protein sequences. These conserved regions in general play an important role in protein functions and protein folds, for example, for their binding properties or enzymatic activities. The aim here is to find the existence correlations of protein motifs. The knowledge of protein motif/domain sharing should be important in shedding new light on the biologic functions of proteins and offering a basis in analyzing the evolution in the human genome or other genomes. The protein sequences used here are obtained from the PIR-NREF database and the protein motifs are retrieved from the PROSITE database. We apply data mining approach to discover the occurrence correlations of motif in protein sequences. The correlation of motifs mined can be used in evolution analyses and protein structure prediction. We discuss the latter, i.e., protein structure prediction in this study. The correlations mined are stored and maintained in a database system. The database is now available at http://bioinfo.csie.ncu.edu.tw/ProMotif/.

Algorithms↗