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PubMed2003

Analysis of the Arabidopsis nuclear proteome and its response to cold stress.

The nucleus is the subcellular organelle that contains nearly all the genetic information required for the regulated expression of cellular proteins. In this study, we comprehensively characterized the Arabidopsis nuclear proteome. Nuclear proteins were isolated and analyzed using two-dimensional (2D) gel electrophoresis and matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS). Approximately 500-700 spots were detected in reference 2D gels of nuclear proteins. Proteomic analyses led to the identification of 184 spots corresponding to 158 different proteins implicated in a variety of cellular functions. We additionally analyzed the changes in the nuclear proteome in response to cold stress. Of the 184 identified proteins, 54 were up- or downregulated with a greater than twofold change in response to cold treatment. Among these, six proteins were selected for further characterization. Northern analysis data revealed that gene expression of these proteins was also altered by cold stress. Following transient expression in BY-2 protoplasts, two proteins were detected in both the cytoplasm and the nucleus and four others were detected exclusively in the nucleus, which correlates well with the nuclear localization patterns of the proteomic data. Our study provides an initial insight into the Arabidopsis nuclear proteome and its response to cold stress.

Arabidopsis↗
PubMed2003

Proteomic analyses in Waldenstrom's macroglobulinemia and other plasma cell dyscrasias.

The proteomic analysis of tumor cells emerges as a key complement to gene expression profiling, primarily because regulation of protein expression (at the translational and post-translational levels) can buffer the magnitude of changes occurring at the gene transcription level, in order to fine tune cellular functions. Herein we describe the concept of proteomic analysis of the signaling state of tumor cells, as well as its application in the study of signaling pathways in plasma cell dyscrasias, such as Waldenstrom's macroglobulinemia (WM) and multiple myeloma (MM). Comparative studies of WM versus MM cells at baseline and in the setting of drug treatment reveals proteomic profiles of the signaling state with significant overlap (that could reflect a putative B-cell lineage-related proteomic signature), but also distinct differences, possibly associated with differential features in the biologic behavior and drug sensitivity of these diseases. These proteomic studies pave the way for a more comprehensive insight into the molecular basis of WM versus other B-cell malignancies.

Electrophoresis, Gel, Two-Dimensional↗
PubMed2003

The yeast mitochondrial proteome, a study of fermentative and respiratory growth.

Saccharomyces cerevisiae is able to switch from fermentation to respiration (diauxic shift) with major changes in metabolic activity. This phenomenon has been previously studied on the transcriptional level. Here we present a parallel analysis of the yeast mitochondrial proteome and the corresponding transcriptional activity in cells grown on glucose (fermentation) and glycerol (respiration). A two-dimensional reference gel for this organelle proteome was established (available at www.biochem.oulu.fi/proteomics/), which contains about 800 intense spots. From 459 spots 253 individual proteins were identified, among them low abundant and hydrophobic proteins, and 37 proteins previously deemed hypothetical, with partially unknown cellular localization. After the diauxic shift, mitochondrial levels of only 18 proteins were changed (17 increased, with 1 decreased), among them proteins involved in the tricarboxylic acid cycle (Sdh1p, Sdh2p, and Sdh4p) and the respiratory chain (Cox4p, Cyb2p, and Qcr7p), proteins contributing to other respiratory pathways (Ach1p, Adh2p, Ald4p, Cat2p, Icl2p, and Pdh1p), and two proteins with unknown function (Om45p and Ybr230p). Apart from an overall increase in mitochondrial protein mass, the mitochondrial proteome remains remarkably constant, even in a major metabolic adaptation. This seemingly disagrees with results of the DNA microarray analyses, where a rather heterogenous up- or down-regulation of genes encoding mitochondrial proteins implies large changes in the proteome. We propose that the discrepancy between proteome and transcriptional regulation, apart from different translation efficiency, indicates a changed turnover rate of proteins in different physiological conditions.

Citric Acid Cycle↗
PubMed2003

Proteome mapping of the protozoan parasite Leishmania and application to the study of drug targets and resistance mechanisms.

Leishmania is a protozoan parasite responsible for significant morbidity and mortality worldwide. Few parasites have been subjected to proteomic analysis to date, but a genome sequencing project for Leishmania major is currently underway, making these studies possible. Here we present a high resolution proteome for L. major comprising almost 3700 spots, making it the most complete two-dimensional gel representation of a parasite proteome generated to date. We have identified a number of landmark proteins by mass spectrometry and show that several of these are valid for the related species Leishmania donovani infantum. We have also observed several forms and fragments of alpha- and beta-tubulins and show that the number and amount of these fragments increase with the age of the parasite culture. Trypanothione reductase (TRYR), which replaces glutathione reductase in trypanosomatid parasites, is an essential protein specific to these parasites and as such is under considerable scrutiny as a drug target. Two-dimensional gel analysis of a L. major strain overexpressing TRYR revealed increased amounts of five spots, all at the predicted molecular weight for TRYR and differing by 0.08 pH units in pI. Mass spectrometry identified four of these as TRYR, leading to the novel suggestion that it could be post-translationally modified. Finally quantitative comparative analysis of a methotrexate-resistant mutant of L. major generated in vitro found that a known primary resistance mediator, the pteridine reductase PTR1, was overexpressed. This constitutes the first proteomic analysis of drug resistance in a parasite and also the clearest identification of a primary drug resistance mechanism using this approach. Together these results provide a framework for further proteomic studies of Leishmania species and demonstrate that these tools are valuable for the essential study of potential drug targets and drug resistance mechanisms.

Animals↗
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↗
PubMed2003

A comparative analysis of an orthologous proteomic environment in the yeasts Saccharomyces cerevisiae and Schizosaccharomyces pombe.

The sequential application of protein tagging, affinity purification, and mass spectrometry enables highly accurate charting of proteomic environments by the characterization of stable protein assemblies and the identification of subunits that are shared between two or more protein complexes, termed here "proteomic hyperlinks." We have charted the proteomic environments surrounding the histone methyltransferase, Set1, in both yeasts Saccharomyces cerevisiae and Schizosaccharomyces pombe. Although the composition of these nonessential Set1 complexes is remarkably conserved, they differ with respect to their hyperlinks to their proteomic environments. We speculate that conservation of the core components of protein assemblies and variability of hyperlinks represents a general principle in the molecular organization of eukaryotic proteomes.

DNA-Binding Proteins↗
PubMed2003

Profiling core proteomes of human cell lines by one-dimensional PAGE and liquid chromatography-tandem mass spectrometry.

Protein expression profiles vary considerably between human cell lines and tissues, which is in part a reflection of their specialized roles within an organism. It is of considerable practical use to establish which proteins constitute the primary components of the respective proteomes. When compiled into databases, such information can facilitate the assessment of selectivity and specificity of a wide range of proteomic experiments. Here we describe the major constituents of proteomes of six human immortalized cell lines. By employing a combination of one-dimensional SDS-PAGE and nanocapillary liquid chromatography-tandem mass spectrometry (LC-MS/MS), we identified up to 1785 non-redundant cytoplasmic and nuclear proteins from a single cell line using 50 and 30 microg of total protein from the corresponding fractions. Up to 38 proteins could be identified from a single band in one liquid chromatography-MS/MS experiment. When combined with systematic gridding of gel lanes into 48 slices, a dynamic range for protein identification of approximately 1:2000 can be envisaged for this approach. Identified proteins range from 4-553 kDa in size, cover the pI range between 3.4 and 12.8, and include 255 proteins with predicted transmembrane domains. Repeated analysis of peptides derived from the same gel band showed that the reproducibility of nanocapillary liquid chromatography-MS/MS of such complex mixtures is about 60-70% suggesting that a particular analytical experiment would need to be repeated about three times to arrive at a representative estimate of the set of highly abundant proteins in a given proteome. Given its technical simplicity, sensitivity, and wealth of generated information, we have adopted this experimental approach to characterize every cell line and tissue that is the subject of experimentation in our laboratory. The combined dataset for the six cell lines consists of 2341 non-redundant human proteins and thus constitutes one of the largest collections of human proteomic data published to date.

Cell Line↗
PubMed2004

The human plasma proteome: a nonredundant list developed by combination of four separate sources.

We have merged four different views of the human plasma proteome, based on different methodologies, into a single nonredundant list of 1175 distinct gene products. The methodologies used were 1) literature search for proteins reported to occur in plasma or serum; 2) multidimensional chromatography of proteins followed by two-dimensional electrophoresis and mass spectroscopy (MS) identification of resolved proteins; 3) tryptic digestion and multidimensional chromatography of peptides followed by MS identification; and 4) tryptic digestion and multidimensional chromatography of peptides from low-molecular-mass plasma components followed by MS identification. Of 1,175 nonredundant gene products, 195 were included in more than one of the four input datasets. Only 46 appeared in all four. Predictions of signal sequence and transmembrane domain occurrence, as well as Genome Ontology annotation assignments, allowed characterization of the nonredundant list and comparison of the data sources. The "nonproteomic" literature (468 input proteins) is strongly biased toward signal sequence-containing extracellular proteins, while the three proteomics methods showed a much higher representation of cellular proteins, including nuclear, cytoplasmic, and kinesin complex proteins. Cytokines and protein hormones were almost completely absent from the proteomics data (presumably due to low abundance), while categories like DNA-binding proteins were almost entirely absent from the literature data (perhaps unexpected and therefore not sought). Most major categories of proteins in the human proteome are represented in plasma, with the distribution at successively deeper layers shifting from mostly extracellular to a distribution more like the whole (primarily cellular) proteome. The resulting nonredundant list confirms the presence of a number of interesting candidate marker proteins in plasma and serum.

Biomarkers, Tumor↗
PubMed2004

Identification of new intrinsic proteins in Arabidopsis plasma membrane proteome.

Identification and characterization of anion channel genes in plants represent a goal for a better understanding of their central role in cell signaling, osmoregulation, nutrition, and metabolism. Though channel activities have been well characterized in plasma membrane by electrophysiology, the corresponding molecular entities are little documented. Indeed, the hydrophobic protein equipment of plant plasma membrane still remains largely unknown, though several proteomic approaches have been reported. To identify new putative transport systems, we developed a new proteomic strategy based on mass spectrometry analyses of a plasma membrane fraction enriched in hydrophobic proteins. We produced from Arabidopsis cell suspensions a highly purified plasma membrane fraction and characterized it in detail by immunological and enzymatic tests. Using complementary methods for the extraction of hydrophobic proteins and mass spectrometry analyses on mono-dimensional gels, about 100 proteins have been identified, 95% of which had never been found in previous proteomic studies. The inventory of the plasma membrane proteome generated by this approach contains numerous plasma membrane integral proteins, one-third displaying at least four transmembrane segments. The plasma membrane localization was confirmed for several proteins, therefore validating such proteomic strategy. An in silico analysis shows a correlation between the putative functions of the identified proteins and the expected roles for plasma membrane in transport, signaling, cellular traffic, and metabolism. This analysis also reveals 10 proteins that display structural properties compatible with transport functions and will constitute interesting targets for further functional studies.

Arabidopsis↗
PubMed2005

A combined proteome and microarray investigation of inorganic phosphate-induced pre-osteoblast cells.

Inorganic phosphate, which is generated during osteoblast differentiation and mineralization, has recently been identified as an important signaling molecule capable of altering signal transduction pathways and gene expression. A large scale quantitative proteomic investigation of pre-osteoblasts stimulated with inorganic phosphate for 24 h resulted in the identification of 2501 proteins, of which 410 (16%) had an altered abundance ratio of greater than or equal to 1.75-fold, either up or down, revealing both novel and previously defined osteoblast-regulated proteins. A pathway/function analysis of these proteins revealed an increase in cell cycle and proliferation that was subsequently verified by conventional biochemical means. To further analyze the mechanisms by which inorganic phosphate regulates cellular protein levels, we undertook a mRNA microarray analysis of pre-osteoblast cells at 18, 21, and 24 h after inorganic phosphate exposure. Comparison of the mRNA microarray data with the 24-hour quantitative proteomic data resulted in a generally weak overall correlation; the 21-hour RNA sample showed the highest correlation to the proteomic data. However, an analysis of osteoblast relevant proteins revealed a much higher correlation at all time points. A comparison of the microarray and proteomic datasets allowed for the identification of a number of candidate proteins that are post-transcriptionally regulated by elevated inorganic phosphate, including Fra-1, a member of the activator protein-1 family of transcription factors. The analysis of the data presented here not only sheds new light on the important roles of inorganic phosphate in osteoblast function but also begins to address the contribution of post-transcriptional and post-translational regulation to a cell's expressed proteome. The ability to accurately measure changes in both protein abundance and mRNA levels on a system-wide scale represents a novel means to extract data from previously one-dimensional datasets.

3T3 Cells↗
PubMed2005

Proteomic studies of the Singapore grouper iridovirus.

The Singapore grouper iridovirus (SGIV) genome consists of a double-stranded circular DNA of 140,131 base pairs with 162 predicted open reading frames. Our earlier study using peptide mass fingerprints generated from MALDI-TOF MS led to the identification of 26 viral proteins. The present investigation aimed to achieve a more comprehensive and precise identification of the SGIV viral proteome by two workflows: one-dimensional gel electrophoresis (1-DE) separation followed by protein identification by MALDI-TOF/TOF MS/MS (1-DE-MALDI workflow) and shotgun proteomics in which the whole virus was digested by trypsin and the resulting peptides were separated by nano-LC and analyzed by MALDI-TOF/TOF MS/MS (LC-MALDI workflow). In total, 44 viral proteins were identified, 25 of which were reported for the first time. Fourteen proteins were uniquely identified by the 1-DE-MALDI workflow, whereas another 10 proteins were only identified by the LC-MALDI workflow with 20 proteins found by both approaches. Moreover 13 proteins were found to have acetylated N termini. Twenty-three proteins identified contain predicted transmembrane domains, accounting for 52.3% of the total proteins identified. RT-PCR confirmed the transcription products of all the identified viral proteins. A large number of proteins identified by both the 1-DE-MALDI and the LC-MALDI workflows from this study have significantly enhanced the coverage of the SGIV proteome. The SGIV proteome is at present the only iridoviral proteome that has been extensively characterized. Our results should provide further insights into the biology of SGIV and other iridoviruses.

Amino Acid Sequence↗
PubMed2005

Interpretation of shotgun proteomic data: the protein inference problem.

The shotgun proteomic strategy based on digesting proteins into peptides and sequencing them using tandem mass spectrometry and automated database searching has become the method of choice for identifying proteins in most large scale studies. However, the peptide-centric nature of shotgun proteomics complicates the analysis and biological interpretation of the data especially in the case of higher eukaryote organisms. The same peptide sequence can be present in multiple different proteins or protein isoforms. Such shared peptides therefore can lead to ambiguities in determining the identities of sample proteins. In this article we illustrate the difficulties of interpreting shotgun proteomic data and discuss the need for common nomenclature and transparent informatic approaches. We also discuss related issues such as the state of protein sequence databases and their role in shotgun proteomic analysis, interpretation of relative peptide quantification data in the presence of multiple protein isoforms, the integration of proteomic and transcriptional data, and the development of a computational infrastructure for the integration of multiple diverse datasets.

Amino Acid Sequence↗
PubMed2005

Human liver proteome project: plan, progress, and perspectives.

The Human Liver Proteome Project is the first initiative of the human proteome project for human organs/tissues and aims at writing a modern Prometheus myth. Its global scientific objectives are to reveal the "solar system" of the human liver proteome, expression profiles, modification profiles, a protein linkage (protein-protein interaction) map, and a proteome localization map, and to define an ORFeome, physiome, and pathome. Since it was first proposed in April 2002, the Human Liver Proteome Project has attracted more than 100 laboratories from all over the world. In the ensuing 3 years, we set up a management infrastructure, identified reference laboratories, confirmed standard operating procedures, initiated international research collaborations, and finally achieved the first set of expression profile data.

Humans↗
PubMedDate not supplied

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

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

Animals↗
PubMedDate not supplied

Application of proteomic strategies to the identification of urinary biomarkers for prostate cancer: a review.

In the post-genomic era, genes and proteins are now studied on a more comprehensive scale. Studying disease processes at only the genetic or transcriptomic level will give an incomplete amount of information. A proteomic approach potentially allows for a more global overview of how disease processes affect the proteins present in cells, tissues and organisms. The challenge arises in determining which proteins are affected in specific diseases and establishing which of these changes are unique to a particular disease. Existing and emerging proteomic technologies allow for high throughput analysis of proteins in a variety of sample types. Prostate cancer is a significant male health problem in the Western world. It is widely accepted that more specific prognostic and diagnostic markers of prostate cancer are urgently required. The present paper suggests that urine may be an attractive biofluid in which to pursue the identification of novel biomarkers of prostate cancer. This review introduces some proteomic techniques including mass spectrometry and the newer, quantitative proteomic strategies. It focuses on the potential application of these platforms to novel urinary biomarker identification in prostate malignancy. It also includes a synopsis of the current literature on urinary proteomics.

Biomarkers, Tumor↗
PubMed2006

Toward the application of proteomics to human thyroid tissue.

CONTEXT: In this paper we describe for the first time a systematic approach to proteome analysis of human thyroid tissue. OBJECTIVE AND DESIGN: We report different methods to decrease the complexity of the human thyroid tissue proteome by applying different solubilization strategies and correcting for thyroglobulin protein abundance; to increase the protein resolution by prefractionation and by the use of narrow-range pH gradients; to detect proteins using sensitive and quantitative stains; and to identify soluble and membrane-bound thyroid tissue proteins by mass spectrometry analysis. MAIN OUTCOME/RESULTS: We found that buffers containing high contents of urea and detergents allow the best solubilization of human thyroid tissue proteins; highly variable abundance of thyroglobulin is a major pitfall of human thyroid proteome analysis, which in contrast to centrifugal ultrafiltration, size-exclusion chromatography and microdissection, can be countered best by adapting the protein amount to the thyroglobulin content per sample; prefractionation leads to a significant enrichment of proteins and allows subcellar localization of thyroid proteins; application of narrow-range immobilized pH gradient (IPG) strips allows further improvement of spot detection and separation; and protein detection with the fluorescent stain ruthenium II Tris bathophenanthroline disulfonate (RuBPs) is a highly sensitive and reliable tool for quantitative proteome analysis. Finally, in a pilot study of four patients with benign nodular thyroid disease we found that the described procedures allow a highly reproducible detection and identification of alterations in protein expression between nodular and corresponding normal thyroid tissues. CONCLUSIONS: Application of the described methods provides the basis for a highly sensitive and reproducible proteome analysis of the human thyroid, providing an additional novel tool to elucidate complex proteins changes in human thyroid biology as well as pathophysiology of human thyroid disease.

Cell Fractionation↗
PubMed2002

Proteomics: technologies and applications.

Enabling technologies for proteomics (studies examining the protein complement of the genome) have been in development for over 20 years. However, more recently, the field has become formalized by combining the techniques for large-scale protein separation (two-dimensional electrophoresis) with very precise, high fidelity approaches to the analysis and characterisation of the separated proteins (mass spectrometry). These approaches bring to reality the powerful scope of proteomics, enabling the interpretation of cell physiology through profiling the molecular biodescriptors of gene expression, the proteins. Applications of proteomics are wide ranging and can be found within many disciplines, although one of the more developed fields that is examined here covers the arena of toxicological and pharmacological profiling. In this paper, the current technologies that make proteomics possible will be looked at. Some areas where proteomics has delivered valuable biological insight will also be considered, and there will be a look towards future areas of development.

Electrophoresis, Gel, Two-Dimensional↗
PubMed2005

A novel approach for clustering proteomics data using Bayesian fast Fourier transform.

MOTIVATION: Bioinformatics clustering tools are useful at all levels of proteomic data analysis. Proteomics studies can provide a wealth of information and rapidly generate large quantities of data from the analysis of biological specimens. The high dimensionality of data generated from these studies requires the development of improved bioinformatics tools for efficient and accurate data analyses. For proteome profiling of a particular system or organism, a number of specialized software tools are needed. Indeed, significant advances in the informatics and software tools necessary to support the analysis and management of these massive amounts of data are needed. Clustering algorithms based on probabilistic and Bayesian models provide an alternative to heuristic algorithms. The number of clusters (diseased and non-diseased groups) is reduced to the choice of the number of components of a mixture of underlying probability. The Bayesian approach is a tool for including information from the data to the analysis. It offers an estimation of the uncertainties of the data and the parameters involved. RESULTS: We present novel algorithms that can organize, cluster and derive meaningful patterns of expression from large-scaled proteomics experiments. We processed raw data using a graphical-based algorithm by transforming it from a real space data-expression to a complex space data-expression using discrete Fourier transformation; then we used a thresholding approach to denoise and reduce the length of each spectrum. Bayesian clustering was applied to the reconstructed data. In comparison with several other algorithms used in this study including K-means, (Kohonen self-organizing map (SOM), and linear discriminant analysis, the Bayesian-Fourier model-based approach displayed superior performances consistently, in selecting the correct model and the number of clusters, thus providing a novel approach for accurate diagnosis of the disease. Using this approach, we were able to successfully denoise proteomic spectra and reach up to a 99% total reduction of the number of peaks compared to the original data. In addition, the Bayesian-based approach generated a better classification rate in comparison with other classification algorithms. This new finding will allow us to apply the Fourier transformation for the selection of the protein profile for each sample, and to develop a novel bioinformatic strategy based on Bayesian clustering for biomarker discovery and optimal diagnosis.

Algorithms↗