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PubMed2003

Integration of proteomics and genomics in platelets: a profile of platelet proteins and platelet-specific genes.

Platelets, while anucleate, contain RNA, some of which is translated into protein upon activation. Hypothesising that the platelet proteome is reflected in the transcriptome, we identified 82 proteins secreted from activated platelets and compared these, as well as published proteomic data, to the transcriptional profile. We also compared the transcriptome of platelets to other tissues to identify platelet-specific genes and used ontology to determine gene categories over-represented in platelets. RNA was isolated from highly pure platelet preparations for hybridization to Affymetrix oligonucleotide arrays. We identified 2,928 distinct messages as being present in platelets. The platelet transcriptome was compared with the proteome by relating both to UniGene clusters. Platelet proteomic data correlated well with the transcriptome, with 69% of secreted proteins detectable at the mRNA level, and similar concordance was obtained using two published datasets. While many of the most abundant mRNAs are for known platelet proteins, messages were detected for proteins not previously reported in platelets. Some of these may represent residual megakaryocyte messages; however, proteomic analysis confirmed the expression of many previously unreported genes in platelets. Transcripts for well-described platelet proteins are among the most platelet-specific messages. Ontological categories related to signal transduction, receptors, ion channels, and membranes are over-represented in platelets, while categories involved in protein synthesis are depleted. Despite the absence of gene transcription, the platelet proteome is mirrored in the transcriptome. Conversely, transcriptional analysis predicts the presence of novel proteins in the platelet. Transcriptional analysis is relevant to platelet biology, providing insights into platelet function and the mechanisms of platelet disorders.

Blood Platelets↗
PubMed2006

Quantitative proteomic comparison of rat mitochondria from muscle, heart, and liver.

Mitochondria, through oxidative phosphorylation, are the primary source of energy production in all tissues under aerobic conditions. Although critical to life, energy production is not the only function of mitochondria, and the composition of this organelle is tailored to meet the specific needs of each cell type. As an organelle, the mitochondrion has been a popular subject for proteomic analysis, but quantitative proteomic methods have yet to be applied to tease apart subtle differences among mitochondria from different tissues or muscle types. Here we used mass spectrometry-based proteomics to analyze mitochondrial proteins extracted from rat skeletal muscle, heart, and liver tissues. Based on 689 proteins identified with high confidence, mitochondria from the different tissues are qualitatively quite similar. However, striking differences emerged from the quantitative comparison of protein abundance between the tissues. Furthermore we applied similar methods to analyze mitochondrial matrix and intermembrane space proteins extracted from the same mitochondrial source, providing evidence for the submitochondrial localization of a number of proteins in skeletal muscle and liver. Several proteins not previously thought to reside in mitochondria were identified, and their presence in this organelle was confirmed by protein correlation profiling. Hierarchical clustering of microarray expression data provided further evidence that some of the novel mitochondrial candidates identified in the proteomic survey might be associated with mitochondria. These data reveal several important distinctions between mitochondrial and submitochondrial proteomes from skeletal muscle, heart, and liver tissue sources. Indeed approximately one-third of the proteins identified in the soluble fractions are associated predominantly to one of the three tissues, indicating a tissue-dependent regulation of mitochondrial proteins. Furthermore a small percentage of the mitochondrial proteome is unique to each tissue.

Amino Acid Sequence↗
PubMed2002

The human plasma proteome: history, character, and diagnostic prospects.

The human plasma proteome holds the promise of a revolution in disease diagnosis and therapeutic monitoring provided that major challenges in proteomics and related disciplines can be addressed. Plasma is not only the primary clinical specimen but also represents the largest and deepest version of the human proteome present in any sample: in addition to the classical "plasma proteins," it contains all tissue proteins (as leakage markers) plus very numerous distinct immunoglobulin sequences, and it has an extraordinary dynamic range in that more than 10 orders of magnitude in concentration separate albumin and the rarest proteins now measured clinically. Although the restricted dynamic range of conventional proteomic technology (two-dimensional gels and mass spectrometry) has limited its contribution to the list of 289 proteins (tabulated here) that have been reported in plasma to date, very recent advances in multidimensional survey techniques promise at least double this number in the near future. Abundant scientific evidence, from proteomics and other disciplines, suggests that among these are proteins whose abundances and structures change in ways indicative of many, if not most, human diseases. Nevertheless, only a handful of proteins are currently used in routine clinical diagnosis, and the rate of introduction of new protein tests approved by the United States Food and Drug Administration (FDA) has paradoxically declined over the last decade to less than one new protein diagnostic marker per year. We speculate on the reasons behind this large discrepancy between the expectations arising from proteomics and the realities of clinical diagnostics and suggest approaches by which protein-disease associations may be more effectively translated into diagnostic tools in the future.

Biological Assay↗
PubMedDate not supplied

Toxicoproteomics: serum proteomic pattern diagnostics for early detection of drug induced cardiac toxicities and cardioprotection.

Proteomics is more than just generating lists of proteins that increase or decrease in expression as a cause or consequence of pathology. The goal should be to characterize the information flow through the intercellular protein circuitry which communicates with the extracellular microenvironment and then ultimately to the serum/plasma macroenvironment. The nature of this information can be a cause, or a consequence, of disease and toxicity based processes as cascades of reinforcing information percolate through the system and become reflected in changing proteomic information content of the circulation. Serum Proteomic Pattern Diagnostics is a new type of proteomic platform in which patterns of proteomic signatures from high dimensional mass spectrometry data are used as a diagnostic classifier. While this approach has shown tremendous promise in early detection of cancers, detection of drug-induced toxicity may also be possible with this same technology. Analysis of serum from rat models of anthracycline and anthracenedione induced cardiotoxicity indicate the potential clinical utility of diagnostic proteomic patterns where low molecular weight peptides and protein fragments may have higher accuracy than traditional biomarkers of cardiotoxicity such as troponins. These fragments may one day be harvested by circulating nanoparticles designed to absorb, enrich and amplify the diagnostic biomarker repertoire generated even at the critical initial stages of toxicity.

Animals↗
PubMed2002

Proteomics and its impact upon biomedical science.

Proteomics is the protein equivalent of genomics and is the study of gene expression at a functional level. The proteome of an organism is the protein complement of its genome. However, unlike the genome, the proteome is dynamic: it varies according to the cell type and the functional state of the cell. In addition, the proteome shows characteristic perturbations in response to disease and external stimuli. Proteomics combines state of the art analytical methods with bioinformatics. Here, we review the concept and technology of proteomics with specific reference to applications in medical microbiology, cellular pathology, clinical chemistry, haematology/immunology, pharmacology and toxicology.

Computational Biology↗
PubMed2006

Chimeric plastid proteome in the Florida "red tide" dinoflagellate Karenia brevis.

Current understanding of the plastid proteome comes almost exclusively from studies of plants and red algae. The proteome in these taxa has a relatively simple origin via integration of proteins from a single cyanobacterial primary endosymbiont and the host. However, the most successful algae in marine environments are the chlorophyll c-containing chromalveolates such as diatoms and dinoflagellates that contain a plastid of red algal origin derived via secondary or tertiary endosymbiosis. Virtually nothing is known about the plastid proteome in these taxa. We analyzed expressed sequence tag data from the toxic "Florida red tide" dinoflagellate Karenia brevis that has undergone a tertiary plastid endosymbiosis. Comparative analyses identified 30 nuclear-encoded plastid-targeted proteins in this chromalveolate that originated via endosymbiotic or horizontal gene transfer (HGT) from multiple different sources. We identify a fundamental divide between plant/red algal and chromalveolate plastid proteomes that reflects a history of mixotrophy in the latter group resulting in a highly chimeric proteome. Loss of phagocytosis in the "red" and "green" clades effectively froze their proteomes, whereas chromalveolate lineages retain the ability to engulf prey allowing them to continually recruit new, potentially adaptive genes through subsequent endosymbioses and HGT. One of these genes is an electron transfer protein (plastocyanin) of green algal origin in K. brevis that likely allows this species to thrive under conditions of iron depletion.

Algal Proteins↗
PubMed2005

Rheumatology: a close encounter with proteomics.

Proteomics is a fast-growing discipline in biomedicine that can be defined as the large-scale characterization of the entire protein complement of a cell, tissue or organism. Because protein levels and function may be critically dependent upon post-transcriptional mechanisms (e.g. post-translational modifications), there has been significant interest in directly examining protein structure and function. It is now clear that proteomics studies may unmask previously unknown functions of proteins or protein interactions. However, proteomics in the field of rheumatology is still in its infancy. This review guides the reader through the consecutive steps of a proteomics study and provides an outline of the applications in the field of rheumatology, which may range from proteome analyses of biological fluids of rheumatic diseases to identify possible new diagnostic tools, towards more pathophysiological studies on target tissues, such as synovial tissue or articular cartilage. Proteomics has great potential in the field of rheumatology and will no doubt have a great impact on our molecular understanding of these complex diseases.

Amino Acid Sequence↗
PubMed2004

Serum proteomic profiling can discriminate prostate cancer from benign prostates in men with total prostate specific antigen levels between 2.5 and 15.0 ng/ml.

PURPOSE: Artificial intelligence based pattern recognition algorithms have been developed and successfully used to analyze complex serum proteomic data streams generated by surface enhanced, laser desorption ionization time-of-flight mass spectroscopy. In the current study we used a high performance, hybrid quadrupole time-of-flight mass spectrometer to generate discriminatory serum proteomic profiles to determine if this technology could be used to determine the need for prostate biopsy in men with elevated prostate specific antigen (PSA). MATERIALS AND METHODS: Serum samples were collected from 154 men with serum PSA 2.5 to 15.0 ng/ml and/or abnormal digital rectal examination prior to transrectal ultrasound guided biopsy. Serum samples were applied to WCX2 (weak cation exchange protein chip) Protein Arrays (Ciphergen Biosystems, Fremont, California) by a Biomek 2000 robotic liquid handler (Beckman-Coulter, Chaska, Minnesota) and low molecular weight (less than 20 kDa) proteomic patterns were generated with an API QSTAR Pulsar i LC/MS/MS System (Applied Biosystems, Framingham, Massachusetts). High resolution mass spectra were analyzed with a pattern recognition bioinformatics tool, that is Proteome Quest beta version 1.0 (Correlogic Systems, Inc., Bethesda, Maryland), in an attempt to identify and discover key discriminating ion signatures. Serum samples from 63 men (2 or more negative prostate biopsies in 23, 1 negative biopsy in 10 and biopsy detected prostate cancer [CaP] in 30) were used to train the diagnostic algorithm. The remaining 91 samples, including 28 of prostate cancer and 63 of 1 or more negative biopsies, were analyzed in blinded fashion. RESULTS: The most discriminatory model was found using the WCX2 chip. Testing the remaining 91 men with this model yielded 100% sensitivity and 67% specificity. In other words, if the proteomic pattern had been used to determine the need for prostate biopsy in this cohort of men with PSA between 2.5 and 15.0 ng/ml, 67% (42 of 63) with negative biopsies would have avoided unnecessary biopsy, while no cancers would have been missed. CONCLUSIONS: Our data demonstrate that high resolution mass spectroscopy can generate serum proteomic patterns that discriminate men with elevated PSA due to benign processes from men with CaP even when PSA is within the diagnostic gray zone. We are currently expanding the testing set to determine the reliability of this new technology to decrease unnecessary prostate biopsies without compromising the detection of curable CaP.

Algorithms↗
PubMed2004

A proteomic analysis of maize chloroplast biogenesis.

Proteomics studies to explore global patterns of protein expression in plant and green algal systems have proliferated within the past few years. Although most of these studies have involved mapping of the proteomes of various organs, tissues, cells, or organelles, comparative proteomics experiments have also led to the identification of proteins that change in abundance in various developmental or physiological contexts. Despite the growing use of proteomics in plant studies, questions of reproducibility have not generally been addressed, nor have quantitative methods been widely used, for example, to identify protein expression classes. In this report, we use the de-etiolation ("greening") of maize (Zea mays) chloroplasts as a model system to explore these questions, and we outline a reproducible protocol to identify changes in the plastid proteome that occur during the greening process using techniques of two-dimensional gel electrophoresis and mass spectrometry. We also evaluate hierarchical and nonhierarchical statistical methods to analyze the patterns of expression of 526 "high-quality," unique spots on the two-dimensional gels. We conclude that Adaptive Resonance Theory 2-a nonhierarchical, neural clustering technique that has not been previously applied to gene expression data-is a powerful technique for discriminating protein expression classes during greening. Our experiments provide a foundation for the use of proteomics in the design of experiments to address fundamental questions in plant physiology and molecular biology.

Chloroplasts↗
PubMed2005

Proteomics meets microbiology: technical advances in the global mapping of protein expression and function.

The availability of complete genome sequences for a large number of pathogenic organisms has opened the door for large-scale proteomic studies to dissect both protein expression/regulation and function. This review highlights key proteomic methods including two-dimensional gel electrophoresis, reference mapping, protein expression profiling and recent advances in gel-free separation techniques that have made a significant impact on the resolution of complex proteomes. In addition, we highlight recent developments in the field of chemical proteomics, a branch of proteomics aimed at functionally profiling a proteome. These techniques include the development of activity-based probes and activity-based protein profiling methods as well as the use of synthetic small molecule libraries to screen for pharmacological tools to perturb basic biological processes. This review will focus on the applications of these technologies to the field of microbiology.

Bacterial Proteins↗
PubMed2026

Canine Tear Proteomics: A New Frontier in Veterinary Ophthalmology.

Canine tear proteomics is an emerging field with significant potential for advancing both veterinary ophthalmology and comparative biomedical research. Tears are a readily accessible, non-invasive biofluid that contain a mixture of proteins involved in ocular surface protection, immune defense, and intercellular signaling. In dogs, tear proteomics studies have revealed biomarkers associated with various ocular and non-ocular diseases including keratoconjunctivitis sicca (KCS), glaucoma, neoplasia, and diabetes mellitus. This review compiles all previous studies conducted on the comprehensive canine tear proteome and highlights some of the key tear proteomic studies in human biomedical research. Tear film composition, study design, technological advancements, and select tear proteins are discussed along with key protein alterations and their use as potential biomarkers of disease. Fundamental challenges, clinical implications, and future directions of this rapidly growing field are discussed in detail. As proteomic technology and analytical techniques continue to evolve, canine tear proteomics will become a valuable tool for the veterinary ophthalmologist, enabling the early identification and diagnosis of ocular disease as well as providing a means for monitoring treatment outcomes, disease progression, and overall prognosis for the canine patient.

Animals↗
PubMed2002

Cutting-edge technology. II. Proteomics: core technologies and applications in physiology.

Technologies for proteomics, e.g., studies examining the protein complement of the genome, have been in development for over 20 years. More recently, proteomics has become formalized by combining techniques for large-scale protein separation with very precise, high-fidelity approaches that analyze, identify, and characterize the separated proteins. These methods bring to reality the powerful scope of proteomics, enabling researchers to investigate cellular function at the protein level and thus representing one of proteomics' most fitting applications. In this review, we take a brief and concise look at some of the current, physiologically relevant technologies that comprise proteomics and report specific applications in which proteomics has provided valuable biological insight.

Animals↗
PubMed2006

The application of DIGE-based proteomics to renal physiology.

Proteomics is seeing increasing use as a means of identifying new mechanistic hypotheses in physiology. Proteomics based on two-dimensional electrophoresis (2-DE) has recently been optimized with the development of Difference Gel Electrophoresis (DIGE). In DIGE-based proteomics, the experimental and control samples are derivatized with different fluorophores and are run in the same gel, thereby minimizing technical variation. DIGE is currently one of the few techniques to perform quantitative proteomics, generating a statistical output to differences in protein abundances. In this review, we discuss the principles of DIGE-based proteomics, including sample preparation, 2-DE, statistical analysis of 2D-gels, and mass spectrometry. Strengths and weaknesses of DIGE are discussed, including possible solutions to overcome certain limitations, such as the identification of low abundance and integral membrane proteins. In addition, we provide a brief synopsis of our recent experiments in which DIGE-based proteomics was applied to study vasopressin signaling in the renal collecting duct. Finally, we illustrate how quantification based on the DIGE approach combined with bioinformatics may facilitate the study of systems biology of the kidney.

Animals↗
PubMed2005

Proteomic patterns of preinvasive bronchial lesions.

PURPOSE: A proteomics approach is warranted to further elucidate the molecular steps involved in lung tumor development. We asked whether we could classify preinvasive lesions of airway epithelium according to their proteomic profile. EXPERIMENTAL DESIGN: We obtained matrix-assisted laser desorption/ionization time-of-flight mass spectrometry profiles from 10-microm sections of fresh-frozen tissue samples: 25 normal lung, 29 normal bronchial epithelium, and 20 preinvasive and 36 invasive lung tumor tissue samples from 53 patients. Proteomic profiles were calibrated, binned, and normalized before analysis. We performed class comparison, class prediction, and supervised hierarchic cluster analysis. We tested a set of discriminatory features obtained in a previously published dataset to classify this independent set of normal, preinvasive, and invasive lung tissues. RESULTS: We found a specific proteomic profile that allows an overall predictive accuracy of over 90% of normal, preinvasive, and invasive lung tissues. The proteomic profiles of these tissues were distinct from each other within a disease continuum. We trained our prediction model in a previously published dataset and tested it in a new blinded test set to reach an overall 74% accuracy in classifying tumors from normal tissues. CONCLUSIONS: We found specific patterns of protein expression of the airway epithelium that accurately classify bronchial and alveolar tissue with normal histology from preinvasive bronchial lesions and from invasive lung cancer. Although further study is needed to validate this approach and to identify biomarkers of tumor development, this is a first step toward a new proteomic characterization of the human model of lung cancer tumorigenesis.

Aged↗
PubMed2006

High throughput profile-profile based fold recognition for the entire human proteome.

BACKGROUND: In order to maintain the most comprehensive structural annotation databases we must carry out regular updates for each proteome using the latest profile-profile fold recognition methods. The ability to carry out these updates on demand is necessary to keep pace with the regular updates of sequence and structure databases. Providing the highest quality structural models requires the most intensive profile-profile fold recognition methods running with the very latest available sequence databases and fold libraries. However, running these methods on such a regular basis for every sequenced proteome requires large amounts of processing power. In this paper we describe and benchmark the JYDE (Job Yield Distribution Environment) system, which is a meta-scheduler designed to work above cluster schedulers, such as Sun Grid Engine (SGE) or Condor. We demonstrate the ability of JYDE to distribute the load of genomic-scale fold recognition across multiple independent Grid domains. We use the most recent profile-profile version of our mGenTHREADER software in order to annotate the latest version of the Human proteome against the latest sequence and structure databases in as short a time as possible. RESULTS: We show that our JYDE system is able to scale to large numbers of intensive fold recognition jobs running across several independent computer clusters. Using our JYDE system we have been able to annotate 99.9% of the protein sequences within the Human proteome in less than 24 hours, by harnessing over 500 CPUs from 3 independent Grid domains. CONCLUSION: This study clearly demonstrates the feasibility of carrying out on demand high quality structural annotations for the proteomes of major eukaryotic organisms. Specifically, we have shown that it is now possible to provide complete regular updates of profile-profile based fold recognition models for entire eukaryotic proteomes, through the use of Grid middleware such as JYDE.

Algorithms↗
PubMed2006

Exploring glycopeptide-resistance in Staphylococcus aureus: a combined proteomics and transcriptomics approach for the identification of resistance-related markers.

BACKGROUND: To unravel molecular targets involved in glycopeptide resistance, three isogenic strains of Staphylococcus aureus with different susceptibility levels to vancomycin or teicoplanin were subjected to whole-genome microarray-based transcription and quantitative proteomic profiling. Quantitative proteomics performed on membrane extracts showed exquisite inter-experimental reproducibility permitting the identification and relative quantification of >30% of the predicted S. aureus proteome. RESULTS: In the absence of antibiotic selection pressure, comparison of stable resistant and susceptible strains revealed 94 differentially expressed genes and 178 proteins. As expected, only partial correlation was obtained between transcriptomic and proteomic results during stationary-phase. Application of massively parallel methods identified one third of the complete proteome, a majority of which was only predicted based on genome sequencing, but never identified to date. Several over-expressed genes represent previously reported targets, while series of genes and proteins possibly involved in the glycopeptide resistance mechanism were discovered here, including regulators, global regulator attenuator, hyper-mutability factor or hypothetical proteins. Gene expression of these markers was confirmed in a collection of genetically unrelated strains showing altered susceptibility to glycopeptides. CONCLUSION: Our proteome and transcriptome analyses have been performed during stationary-phase of growth on isogenic strains showing susceptibility or intermediate level of resistance against glycopeptides. Altered susceptibility had emerged spontaneously after infection with a sensitive parental strain, thus not selected in vitro. This combined analysis allows the identification of hundreds of proteins considered, so far as hypothetical protein. In addition, this study provides not only a global picture of transcription and expression adaptations during a complex antibiotic resistance mechanism but also unravels potential drug targets or markers that are constitutively expressed by resistant strains regardless of their genetic background, amenable to be used as diagnostic targets.

Anti-Bacterial Agents↗
PubMed2006

Identification of regulators of germ layer morphogenesis using proteomics in zebrafish.

During vertebrate gastrulation, a well-orchestrated series of morphogenetic changes leads to the formation of the three germ layers: the ectoderm, mesoderm and endoderm. The analysis of gene expression patterns during gastrulation has been central to the identification of genes involved in germ layer formation. However, many proteins are regulated on a translational or post-translational level and are thus undetectable by gene expression analysis. Therefore, we developed a 2D-gel-based comparative proteomic approach to target proteins involved in germ layer morphogenesis during zebrafish gastrulation. Proteomes of ectodermal and mesendodermal progenitor cells were compared and 35 significantly regulated proteins were identified by mass spectrometry, including several proteins with predicted functions in cytoskeletal organization. A comparison of our proteomic results with data obtained in an accompanying microarray-based gene expression analysis revealed no significant overlap, confirming the complementary nature of proteomics and transcriptomics. The regulation of ezrin2, which was identified based on a reduction in spot intensity in mesendodermal cells, was independently validated. Furthermore, we show that ezrin2 is activated by phosphorylation in mesendodermal cells and is required for proper germ layer morphogenesis. We demonstrate the feasibility of proteomics in zebrafish, concluding that proteomics is a valuable tool for analysis of early development.

Animals↗
PubMed2004

Prediction of liver fibrosis and cirrhosis in chronic hepatitis B infection by serum proteomic fingerprinting: a pilot study.

BACKGROUND: Most noninvasive predictive models of liver fibrosis are complicated and have suboptimal sensitivity. This study was designed to identify serum proteomic signatures associated with liver fibrosis and to develop a proteome-based fingerprinting model for prediction of liver fibrosis. METHODS: Serum proteins from 46 patients with chronic hepatitis B (CHB) were profiled quantitatively on surface-enhanced laser desorption/ionization (SELDI) ProteinChip arrays. The identified liver fibrosis-associated proteomic fingerprint was used to construct an artificial neural network (ANN) model that produced a fibrosis index with a range of 0-6. The clinical value of this index was evaluated by leave-one-out cross-validation. RESULTS: Thirty SELDI proteomic features were significantly associated with the degree of fibrosis. Cross-validation showed that the ANN fibrosis indices derived from the proteomic fingerprint strongly correlated with Ishak scores (r = 0.831) and were significantly different among stages of fibrosis. ROC curve areas in predicting significant fibrosis (Ishak score >or=3) and cirrhosis (Ishak score >or=5) were 0.906 and 0.921, respectively. At 89% specificity, the sensitivity of the ANN fibrosis index in predicting fibrosis was 89%. The sensitivity for prediction increased with degree of fibrosis, achieving 100% for patients with Ishak scores >4. The accuracy for prediction of cirrhosis was also 89%. Inclusion of International Normalized Ratio, total protein, bilirubin, alanine transaminase, and hemoglobin in the ANN model improved the predictive power, giving accuracies >90% for the prediction of fibrosis and cirrhosis. CONCLUSIONS: A unique serum proteomic fingerprint is present in the sera of patients with fibrosis. An ANN fibrosis index derived from this fingerprint could differentiate between different stages of fibrosis and predict fibrosis and cirrhosis in CHB infection.

Blood Proteins↗