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PubMed2005

Clinical applications for targeted therapy in bladder cancer.

The tremendous amount of data accumulated through genomics, proteomics, and metabolomic technologies has not led to a definitive understanding of the mechanisms underlying cancer. The challenge remains as to how to integrate all of the relevant knowledge and data in a systematic manner so that researchers can gain the knowledge needed to devise the best therapeutic and diagnostic strategies. Human transitional cell carcinoma of the bladder is genetically heterogeneous, and it is surrounded by a complex tissue microenvironment involving vasculature, stromal cells, and connective tissue. One of the most challenging problems facing cancer researchers is the lack of correlation between in vitro cell lines and animal tumor models and human in vivo tumors. A few promising approaches are being devised that will help address this issue in the coming years. One such approach is the measurements of molecular levels of receptors, ligands, pathways components, and so on, directly in human tumors through in vivo imaging, or through proteomic profiling, as it has been proposed as standard protocol for cancer diagnostics and therapeutics.

Antineoplastic Agents↗

PubMed2006

PROMPT: a protein mapping and comparison tool.

BACKGROUND: Comparison of large protein datasets has become a standard task in bioinformatics. Typically researchers wish to know whether one group of proteins is significantly enriched in certain annotation attributes or sequence properties compared to another group, and whether this enrichment is statistically significant. In order to conduct such comparisons it is often required to integrate molecular sequence data and experimental information from disparate incompatible sources. While many specialized programs exist for comparisons of this kind in individual problem domains, such as expression data analysis, no generic software solution capable of addressing a wide spectrum of routine tasks in comparative proteomics is currently available. RESULTS: PROMPT is a comprehensive bioinformatics software environment which enables the user to compare arbitrary protein sequence sets, revealing statistically significant differences in their annotation features. It allows automatic retrieval and integration of data from a multitude of molecular biological databases as well as from a custom XML format. Similarity-based mapping of sequence IDs makes it possible to link experimental information obtained from different sources despite discrepancies in gene identifiers and minor sequence variation. PROMPT provides a full set of statistical procedures to address the following four use cases: i) comparison of the frequencies of categorical annotations between two sets, ii) enrichment of nominal features in one set with respect to another one, iii) comparison of numeric distributions, and iv) correlation of numeric variables. Analysis results can be visualized in the form of plots and spreadsheets and exported in various formats, including Microsoft Excel. CONCLUSION: PROMPT is a versatile, platform-independent, easily expandable, stand-alone application designed to be a practical workhorse in analysing and mining protein sequences and associated annotation. The availability of the Java Application Programming Interface and scripting capabilities on one hand, and the intuitive Graphical User Interface with context-sensitive help system on the other, make it equally accessible to professional bioinformaticians and biologically-oriented users. PROMPT is freely available for academic users from http://webclu.bio.wzw.tum.de/prompt/.

Computational Biology↗

PubMed2006

The identification of human tumour antigens: Current status and future developments.

The biggest challenge facing us today in cancer control and prevention is the identification of novel biomarkers for detection and improved therapeutic interventions to reduce mortality and morbidity rates. Biomarkers are important indicators to inform us of the physiological state of the cell at a specific time. It is now clear that malignant transformation occurs by changes in cellular DNA and protein expression with subsequent clonal proliferation of the altered cells. The affected genes and their expressed protein products or biomarkers are those involved in the normal growth and maintenance of the cancerous cells. These biomarkers could prove pivotal for the identification of early cancer and people at risk of developing cancer. Altered proteins or changes in gene expression in malignant cells may lead to the expression of tumour antigens recognised by host immune system. In this review we discuss current research into the molecular technologies making possible the global genomic-wide analysis of changes in DNA (genotyping), RNA expression (transcriptomics) and protein expression (proteomics) that have accelerated the rate of new biomarker/tumour antigen discovery. To gain a comprehensive understanding of the physiology and pathophysiology of cancer an approach that harmoniously integrates the various 'omic' platforms are key to unraveling the complexity 'needle-in-a-haystack' quality of biomarker/tumour antigen discovery.

Antigens, Neoplasm↗

PubMed2004

Analysis of curated and predicted plastid subproteomes of Arabidopsis. Subcellular compartmentalization leads to distinctive proteome properties.

Carefully curated proteomes of the inner envelope membrane, the thylakoid membrane, and the thylakoid lumen of chloroplasts from Arabidopsis were assembled based on published, well-documented localizations. These curated proteomes were evaluated for distribution of physical-chemical parameters, with the goal of extracting parameters for improved subcellular prediction and subsequent identification of additional (low abundant) components of each membrane system. The assembly of rigorously curated subcellular proteomes is in itself also important as a parts list for plant and systems biology. Transmembrane and subcellular prediction strategies were evaluated using the curated data sets. The three curated proteomes differ strongly in average isoelectric point and protein size, as well as transmembrane distribution. Removal of the cleavable, N-terminal transit peptide sequences greatly affected isoelectric point and size distribution. Unexpectedly, the Cys content was much lower for the thylakoid proteomes than for the inner envelope. This likely relates to the role of the thylakoid membrane in light-driven electron transport and helps to avoid unwanted oxidation-reduction reactions. A rule of thumb for discriminating between the predicted integral inner envelope membrane and integral thylakoid membrane proteins is suggested. Using a combination of predictors and experimentally derived parameters, four plastid subproteomes were predicted from the fully annotated Arabidopsis genome. These predicted subproteomes were analyzed for their properties and compared to the curated proteomes. The sensitivity and accuracy of the prediction strategies are discussed. Data can be extracted from the new plastid proteome database (http://ppdb.tc.cornell.edu).

Arabidopsis↗

PubMed2005

A subcellular prefractionation protocol for minute amounts of mammalian cell cultures and tissue.

Subcellular localization represents an essential, albeit often neglected, aspect of proteome analysis. Generally, the subcellular location of proteins determines the function of cells and tissues. Here we present a robust and versatile prefractionation protocol for mammalian cells and tissues which is appropriate for minute sample amounts. The protocol yields three fractions: a nuclear, a cytoplasmic, and a combined membrane and organelle fraction. The subcellular specificity and the composition of the fractions were demonstrated by immunoblot analysis of five marker proteins and analysis of 43 proteins by two-dimensional gel electrophoresis and mass spectrometry. To cover all protein species, both conventional two-dimensional and benzyldimethyl-n-hexadecyl ammonium chloride-sodium dodecyl sulfate (16-BAC-SDS) gel electrophoresis were performed. Integral membrane proteins and strongly basic nuclear histones were detected only in the 16-BAC-SDS gel electrophoresis system, confirming its usefulness for proteome analysis. All but one protein complied to the respective subcellular composition of the analyzed fractions. Taken together, the data make our subcellular prefractionation protocol an attractive alternative to other prefractionation methods which are based on less physiological protein properties.

Animals↗

PubMed2005

Universal biases in protein composition of model prokaryotes.

The levels of cellular organization in living organisms are the results of a variety of selection pressures. We have investigated here the final outcome of this integrated selective process in proteins of the best known microbial models Escherichia coli, Bacillus subtilis, and Methanococcus jannaschii, supposed to have undergone separate evolution for more than 1 billion years. Using multivariate analysis methods, including correspondence analysis, we studied the overall amino acid composition of all proteins making a proteome. Starting from and further developing previous results that had pointed out some general forces driving the amino acid composition of the proteomes of these model bacteria, we explored the correlations existing between the structure and functions of the proteins forming a proteome and their amino acid composition. The electric charge of amino acids measured against hydrophobicity creates a highly homogeneous cluster, made exclusively of proteins that are core components of the cytoplasmic membrane of the cell (integral inner membrane proteins). A second bias is imposed by the G+C content of the genome, indicating that protein functions are so robust with respect to amino acid changes that they can accommodate a large shift in the nucleotide content of the genome. A remarkable role of aromatic amino acids was uncovered. Expressed orphan proteins are enriched in these residues, suggesting that they might participate in a process of gain of function during evolution.

Amino Acids↗

PubMed2005

Addressing the intrinsic disorder bottleneck in structural proteomics.

The Center for Eukaryotic Structural Genomics (CESG), as part of the Protein Structure Initiative (PSI), has established a high-throughput structure determination pipeline focused on eukaryotic proteins. NMR spectroscopy is an integral part of this pipeline, both as a method for structure determinations and as a means for screening proteins for stable structure. Because computational approaches have estimated that many eukaryotic proteins are highly disordered, about 1 year into the project, CESG began to use an algorithm (the Predictor of Naturally Disordered Regions, PONDR to avoid proteins that were likely to be disordered. We report a retrospective analysis of the effect of this filtering on the yield of viable structure determination candidates. In addition, we have used our current database of results on 70 protein targets from Arabidopsis thaliana and 1 from Caenorhabditis elegans, which were labeled uniformly with nitrogen-15 and screened for disorder by NMR spectroscopy, to compare the original algorithm with 13 other approaches for predicting disorder from sequence. Our study indicates that the efficiency of structural proteomics of eukaryotes can be improved significantly by removing targets predicted to be disordered by an algorithm chosen to provide optimal performance.

Algorithms↗

PubMed2006

Proteomic analysis of phosphorylated proteins.

Reversible protein phosphorylation is of crucial importance in regulatory mechanisms and signaling pathways. Novel and efficient tools and strategies are being actively developed to allow, beyond the primary identification of phosphorylated proteins, the identification of phosphorylation sites and ultimately their quantification. These approaches are being used at various scales, from studies that have dedicated and functional goals to work with more exploratory and cataloguing objectives. The information thus generated now makes it possible to use bioinformatics to revisit previous knowledge about protein phosphorylation in plants and the pertinence of available prediction models. Although the analysis of phosphorylated proteins remains a challenging task, recent success and current developments are likely to mark the transition towards the introduction of phosphoproteomics as one of the main integration levels in post-genome plant biology.

Computational Biology↗

PubMed2001

An automated multidimensional protein identification technology for shotgun proteomics.

We describe an automated method for shotgun proteomics named multidimensional protein identification technology (MudPIT), which combines multidimensional liquid chromatography with electrospray ionization tandem mass spectrometry. The multidimensional liquid chromatography method integrates a strong cation-exchange (SCX) resin and reversed-phase resin in a biphasic column. We detail the improvements over a system described by Link et al. (Link, A. J.; Eng, J.; Schieltz, D. M.; Carmack, E.; Mize, G. J.; Morris, D. R.; Garvik, B. M.; Yates, J. R., III. Nat. Biotechnol. 1999, 17, 676-682) that separates and acquires tandem mass spectra for thousands of peptides. Peptides elute off the SCX phase by increasing pI, and elution off the SCX material is evenly distributed across an analysis. In addition, we describe the chromatographic benchmarks of MudPIT. MudPIT was reproducible within 0.5% between two analyses. Furthermore, a dynamic range of 10000 to 1 between the most abundant and least abundant proteins/peptides in a complex peptide mixture has been demonstrated. By improving sample preparation along with separations, the method improves the overall analysis of proteomes by identifying proteins of all functional and physical classes.

Autoanalysis↗

PubMed2006

Inner ear proteomics: a fad or hear to stay.

Proteomics, the large-scale analysis of the structure and function of proteins, as well as of protein-protein interactions, has evolved into a major component of 'systems analysis'. This requires the integration of information from different sources and at multiple levels, and involves two distinct parameters, (1) high-throughput protein separation, identification, and characterization, and (2) the extension of the obtained analytical data for the determination of the physiological function. The inner ear poses exceptional challenges to the study of proteomics because of its minute size, poor accessibility, association with complex fluid spaces, and diversity of cell types. Various approaches to the study of proteomics of the inner ear are presented, and success stories, noteworthy failures and what lies ahead, will be discussed.

Animals↗

PubMed2006

Proteomics as a tool for assessment of therapeutics in transfusion medicine: evaluation of prothrombin complex concentrates.

BACKGROUND: Proteomic technologies are evolving tools to analyze complex protein patterns that to date have been rarely applied to transfusion medicine. The analysis of prothrombin complex concentrates (PCCs) was used as a model to evaluate to what extent these technologies can detect differences in blood-derived therapeutics beyond that of standard quality control. STUDY DESIGN AND METHODS: Three PCCs (two batches each) were individually analyzed for differences in protein content by functional assays, two-dimensional gel electrophoresis, and mass spectrometry. The results were compared to a pool of 72 normal plasma samples. RESULTS: A highly complex protein pattern was found that varied considerably among the three PCCs: 192 spots comprising 40 different proteins were identified. Factor (F) IX activities of the three PCCs were comparable, but their F IX protein contents differed considerably. Many proteins were present in multiple spots (e.g. FII, FX, protein C, vitronectin), indicating a high degree of posttranslational modifications. In comparison with untreated pooled plasma, PCCs displayed several low-molecular-weight variants of proteins that likely constitute potential degradation products. CONCLUSION: Proteomic technologies allow the identification of potentially modified proteins in clotting factor concentrates, indicating that they could become a useful tool for transfusion medicine to assess the impact of processing on the integrity of blood-derived therapeutics.

Blood Chemical Analysis↗

PubMed2004

Functional genomics and proteomics: application in neurosciences.

The sequencing of the complete genome for many organisms, including man, has opened the door to the systematic understanding of how complex structures such as the brain integrate and function, not only in health but also in disease. This blueprint, however, means that the piecemeal analysis regimes of the past are being rapidly superseded by new methods that analyse not just tens of genes or proteins at any one time, but thousands, if not the entire repertoire of a cell population or tissue under investigation. Using the most appropriate method of analysis to maximise the available data therefore becomes vital if a complete picture is to be obtained of how a system or individual cell is affected by a treatment or disease. This review examines what methods are currently available for the large scale analysis of gene and protein expression, and what are their limitations.

Brain↗

PubMedDate not supplied

Genomes and proteomes: towards a multidimensional view of biology.

The third Siena proteomics conference held August 31-September 4, 1998, heralded a change in emphasis from technology development to using proteomics to assist in resolving biological questions. In this review, proteomics is placed in context with other major influences in the way discovery research is conducted in biology. The current status of genomics is examined in its broadest sense, including how such studies may influence the development of proteomics. It is suggested that we are entering a new phase in biology where information is no longer limiting and integration of different technologies is required to attack the big problems of biology. While much of the focus of funding bodies, both in the public and private sector, is on practical outcomes (new drugs, etc.), the new technologies are equally amenable to attacking long-standing fundamental challenges, such as cell division, cell patterning and morphogenesis.

Animals↗

PubMedDate not supplied

The Kazusa cDNA project for identification of unknown human transcripts.

The Kazusa cDNA project is unique by its focus on sequencing large human cDNAs (>4 kb). We describe an overview of the human cDNA sequence data accumulated during the first phase of the project on over 2000 cDNAs and its integration with the genome sequence. In the second phase of the project, which aims at bridging the human genome and proteome using the output of the first phase, we are very carefully evaluating our cDNA clones and, when necessary, experimentally revising them.

DNA, Complementary↗

PubMed2003

Proteomic identification of a novel protein regulated in CA1 and CA3 hippocampal regions during intermittent hypoxia.

The CA1 and CA3 regions of the hippocampus markedly differ in their susceptibility to hypoxia in general, and more particularly to the intermittent hypoxia (IH) that characterizes sleep apnea. We used proteomic analysis to build a database of proteins expressed in normoxic CA1 and CA3. The current hippocampus protein database identifies 106 proteins. A hypothetical protein with accession number AK006737 (gimid R:12839969) was strongly upregulated in the CA1, but not CA3 hippocampal region. Bioinformatic analysis revealed that the unknown protein contained a high stringency protein kinase e binding site. Domain analysis demonstrated the presence of a conserved sequence indicative of macrophage scavenger receptors. Using proteomic analysis we have previously demonstrated that acute (6 h) IH-mediated CA1 injury results from complex interactions between pathways involving increased metabolism, induction of stress-induced proteins and apoptosis, and ultimately disruption of structural proteins and cell integrity. The current findings identify a hypothetical protein that may play a key role in the response of CA1 to IH. These findings provide initial insights into mechanisms underlying differences in susceptibility to hypoxia in neural tissue and demonstrate how proteomic analysis can be used to generate new hypotheses, which define neuronal adaptation to IH.

Animals↗

PubMed2003

Sensitive quantitative predictions of peptide-MHC binding by a 'Query by Committee' artificial neural network approach.

We have generated Artificial Neural Networks (ANN) capable of performing sensitive, quantitative predictions of peptide binding to the MHC class I molecule, HLA-A*0204. We have shown that such quantitative ANN are superior to conventional classification ANN, that have been trained to predict binding vs non-binding peptides. Furthermore, quantitative ANN allowed a straightforward application of a 'Query by Committee' (QBC) principle whereby particularly information-rich peptides could be identified and subsequently tested experimentally. Iterative training based on QBC-selected peptides considerably increased the sensitivity without compromising the efficiency of the prediction. This suggests a general, rational and unbiased approach to the development of high quality predictions of epitopes restricted to this and other HLA molecules. Due to their quantitative nature, such predictions will cover a wide range of MHC-binding affinities of immunological interest, and they can be readily integrated with predictions of other events involved in generating immunogenic epitopes. These predictions have the capacity to perform rapid proteome-wide searches for epitopes. Finally, it is an example of an iterative feedback loop whereby advanced, computational bioinformatics optimize experimental strategy, and vice versa.

HLA-A Antigens↗

PubMed2006

Differential proteomic alterations between localised and metastatic prostate cancer.

Molecular alterations in the prostate cancer proteome mediate the functional and phenotypic transformation from clinically localised to metastatic cancer, a transition that drives patient's mortality and challenges therapeutic intervention. A first approximation of differential proteomic alterations stratified by disease stage has yielded repertoires of potential diagnostic and prognostic markers, multiplex signatures of predictive value, and yield fundamental insight into molecular commonalities in cancer progression. Deciphering these causative proteomic alterations from the molecular noise will continue to mature our understanding of tumour biology and drive new computational and integrative approaches to model a system's view that accommodates the heterogeneity of prostate cancer progression.

Biomarkers, Tumor↗

PubMed2004

Quantitative genomics: exploring the genetic architecture of complex trait predisposition.

Most phenotypes with agricultural or biomedical relevance are multifactorial traits controlled by complex contributions of genetics and environment. Genetic predisposition results from combinations of relatively small effects due to variations within a large number of genes, known as QTL. Well over 200 QTL have been reported for growth and body composition traits in the mouse, which likely represent at least 50 to 100 distinct genes. Molecular biology has yielded significant advances in understanding these traits at the metabolic and physiological levels; however, little has been learned regarding the identity and nature of the underlying polygenes. In addition to the significantly poor precision inherent to QTL localization, it is very difficult to differentiate between co-localization and coincidence when comparing QTL with other QTL and with potential candidate genes. The wide gap between our knowledge of physiological mechanisms underlying complex traits and the nature of genetic predisposition significantly impairs discovery of genes underlying QTL. Identification and genetic mapping of key transcriptional, proteomic, metabolomic, and endocrine events will uncover large lists of significant positional candidate genes for growth and body composition. However, integration of experimental approaches to jointly evaluate predisposition and physiology will increase success of QTL identification by merging the power of recombination with functional analysis. Measuring physiologically relevant subphenotypes within a structured QTL mapping population will not only facilitate pathway-specific prioritization among candidate genes, but may also directly identify genes underlying QTL. This would advance our understanding of the genetic architecture of complex traits by testing the central hypothesis that genes controlling predisposition to a quantitative trait are primarily involved in trans-regulation of the primary physiological pathways that regulate the trait.

Animals↗