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PubMed2003

Interrogating mouse mammary cancer models: insights from gene expression profiling.

Numerous mouse models for mammary cancer have been developed and characterized based upon their biological, molecular, and histopathological features. In an effort to dissect the molecular anatomy of such models and compare their gene expression profiles to those of human breast cancer, six models representing various oncogenic pathways have been investigated using cDNA microarray technology. Results of these analyses are presented and discussed in the context of technological challenges presented by analyzing data on such a large scale. Further expression profiling coupled with emerging proteomic technologies will more completely define and distinguish mouse models of mammary cancer from each other and provide a comprehensive basis for comparing such models with the human disease they are intended to represent.

Animals↗
PubMed2005

Methods of comparative proteomic profiling for disease diagnostics.

The recent development of numerous technologies for proteome analysis holds the promise of new and more precise methods for disease diagnosis. In this review, we provide an overview of some of these technologies including two-dimensional gel electrophoresis (2DE), historically the workhorse of proteomic analysis, as well as some newer approaches such as liquid phase separations combined with mass spectrometry, and protein microarrays. It is evident that each method has its own strengths and weaknesses and no single method will be optimal in all applications. However, the continuing development of innovative strategies for protein separation and analysis is providing a wealth of new tools for multi-dimensional protein profiling that will advance our capabilities in disease diagnostics and our understanding of disease pathology.

Chromatography, Liquid↗
PubMed2007

A proteomic analysis of adult rat bone reveals the presence of cartilage/chondrocyte markers.

The non-mineral component of bone matrix consists of 90% collagenous, 10% non-collagenous proteins. These proteins regulate mineralization, growth, cell signaling and differentiation, and provide bone with its tensile strength. Expression of bone matrix proteins have historically been studied individually or in small numbers owing to limitations in analytical technologies. Current mass-spectrometric and separations technologies allow a global view of protein expression patterns in complex samples. To our knowledge, no proteome profile of bone matrix has yet been reported. Therefore, we have used mass spectrometry as a tool to generate a profile of proteins present in the extracellular matrix of adult rat bone. Overall, 108 and 25 proteins were identified with high confidence in the metaphysis and diaphysis, respectively, using a bottom up proteomic technique. Twenty-one of these proteins were present in both the metaphysis and diaphysis including the bone specific proteins, osteocalcin, type I collagen, osteopontin, osteoregulin, and bone sialoprotein. Interestingly, type II collagen, a protein thought to be exclusively expressed in cartilage, was identified in both the metaphysis and diaphysis. This observation was validated by Western blot. Additionally, the presence of aggrecan, another protein expressed in cartilage was identified in the bone matrix extracts by Western blot. The proteome profile generated using this technology represents an initial survey of the acid soluble proteins of bone matrix which provides a reference for the analysis of deviations from the normal composition due to perturbations or disease states.

Aggrecans↗
PubMed2005

Multidimensional protein identification technology (MudPIT): technical overview of a profiling method optimized for the comprehensive proteomic investigation of normal and diseased heart tissue.

An optimized analytical expression profiling strategy based on gel-free multidimensional protein identification technology (MudPIT) is reported for the systematic investigation of biochemical (mal)-adaptations associated with healthy and diseased heart tissue. Enhanced shotgun proteomic detection coverage and improved biological inference is achieved by pre-fractionation of excised mouse cardiac muscle into subcellular components, with each organellar fraction investigated exhaustively using multiple repeat MudPIT analyses. Functional-enrichment, high-confidence identification, and relative quantification of hundreds of organelle- and tissue-specific proteins are achieved readily, including detection of low abundance transcriptional regulators, signaling factors, and proteins linked to cardiac disease. Important technical issues relating to data validation, including minimization of artifacts stemming from biased under-sampling and spurious false discovery, together with suggestions for further fine-tuning of sample preparation, are discussed. A framework for follow-up bioinformatic examination, pattern recognition, and data mining is also presented in the context of a stringent application of MudPIT for probing fundamental aspects of heart muscle physiology as well as the discovery of perturbations associated with heart failure.

Animals↗
PubMed2005

Mapping molecular networks using proteomics: a vision for patient-tailored combination therapy.

Mapping tumor cell protein networks in vivo will be critical for realizing the promise of patient-tailored molecular therapy. Cancer can be defined as a dysregulation or hyperactivity in the network of intracellular and extracellular signaling cascades. These protein signaling circuits are the ultimate targets of molecular therapy. Each patient's tumor may be driven by a distinct series of molecular pathogenic defects. Thus, for any single molecular targeted therapy, only a subset of cancer patients may respond. Individualization of therapy, which tailors a therapeutic regimen to a tumor molecular portrait, may be the solution to this dilemma. Until recently, the field lacked the technology for molecular profiling at the genomic and proteomic level. Emerging proteomic technology, used concomitantly with genomic analysis, promises to meet this need and bring to reality the clinical adoption of molecular stratification. The activation state of kinase-driven signal networks contains important information relative to cancer pathogenesis and therapeutic target selection. Proteomic technology offers a means to quantify the state of kinase pathways, and provides post-translational phosphorylation data not obtainable by gene arrays. Case studies using clinical research specimens are provided to show the feasibility of generating the critical information needed to individualize therapy. Such technology can reveal potential new pathway interconnections, including differences between primary and metastatic lesions. We provide a vision for individualized combinatorial therapy based on proteomic mapping of phosphorylation end points in clinical tissue material.

Antineoplastic Combined Chemotherapy Protocols↗
PubMed2006

Activity-based proteomics: enzymatic activity profiling in complex proteomes.

In the postgenomic era new technologies are emerging for global analysis of protein function. The introduction of active site-directed chemical probes for enzymatic activity profiling in complex mixtures, known as activity-based proteomics has greatly accelerated functional annotation of proteins. Here we review probe design for different enzyme classes including serine hydrolases, cysteine proteases, tyrosine phosphatases, glycosidases, and others. These probes are usually detected by their fluorescent, radioactive or affinity tags and their protein targets are analyzed using established proteomics techniques. Recent developments, such as the design of probes for in vivo analysis of proteomes, as well as microarray technologies for higher throughput screenings of protein specificity and the application of activity-based probes for drug screening are highlighted. We focus on biological applications of activity-based probes for target and inhibitor discovery and discuss challenges for future development of this field.

Animals↗
PubMed2000

Mining genomes and mapping proteomes: identification and characterization of protein subunit vaccines.

Currently, there is an extensive and unprecedented effort to obtain the complete nucleotide sequence of the complex genomes of many micro-organisms. In this post-genomic era, based on the availability of the entire genome sequence of an organism, three new disciplines of molecular biology have emerged: genomics, transcriptional profiling and proteomics. All these technologies have the potential to accelerate the process of identifying protective protein antigens as subunit vaccine targets as well as validating and extending the range of available candidate antigens. The progress of these technologies has led to the origination of the science of bioinformatics for management and critical evaluation of the large amount of information generated. Although genomics, transcriptional profiling and proteomics are each based on different principles, there is considerable synergy between them. Appropriate application of any one, or a combination of two or more of these approaches, coupled with bioinformatics, would allow identification of a short-list of vaccine candidates from the entire list of several hundreds to thousands of proteins encoded by the genome. These candidates would then require usual channelling through the subsequent process involving recombinant expression, purification and testing for immunogenicity and protective efficacy.

Animals↗
PubMed2005

Use of 'Omic' technologies to study humans exposed to benzene.

'Omic' technologies include genomics, transcriptomics (gene expression profiling), proteomics and metabolomics. We are utilizing these new technologies in an effort to develop novel biomarkers of exposure, susceptibility and response to benzene. Advances in genomics allow one to study hundreds to thousands of single nucleotide polymorphisms simultaneously on small quantities of DNA using array-based technologies. We are currently utilizing these technologies to examine genetic variation in pathways relating to biotransformation, DNA repair, folate metabolism and immune response with the goal of finding biomarkers of susceptibility to benzene hematotoxicity. Transcriptomics is used to measure the full complement of activated genes, mRNAs or transcripts in a particular tissue at a particular time typically using microarray technology. We have applied microarrays to the study of global gene expression in the peripheral blood cells of benzene-exposed workers. More than 100 genes were identified as being potentially differentially expressed, with genes related to apoptosis and immune function being the most significantly affected. Initial studies employing proteomics have also shown that several proteins are altered in the serum of exposed compared to control subjects and these proteins are potential biomarkers of benzene exposure. Omic technologies therefore have significant potential in generating novel biomarkers of exposure, susceptibility and response to benzene.

Air Pollutants, Occupational↗
PubMed2005

From genomics via proteomics to cellular physiology of the Gram-positive model organism Bacillus subtilis.

Complementing proteomic technologies enable an unbiased view of cellular adaptation and thus may provide a new understanding of cellular physiology, particularly for microorganisms because a major fraction of their proteome is accessible to currently available technology. In combination with transcriptional profiling expression proteomics provides access to interesting candidate genes and proteins that will then need to be validated and supplemented by traditional physiological, biochemical and genetic approaches. After a description of the current status of the technology, we display the potential of microbial proteomics using the model organism Bacillus subtilis as example. Starting from a proteome map a proteomic view of the metabolism will be provided. Furthermore, we demonstrate that proteomics complemented by transcriptomics is also useful for the study of stress and starvation responses and that integration of these data will lead to a comprehensive understanding of the adaptational network of bacterial cells. Thus, B. subtilis constitutes a highly versatile and tractable model organism for the study of generic stress responses and the expertise that has been gained can easily be transferred to the study of the cellular physiology of related Gram-positive pathogens and their pathophysiology.

Bacillus subtilis↗
PubMed2005

Proteomic profiling of mature CD10+ B-cell lymphomas.

Proteomic profiling with protein-chip technology has been used successfully to discover biomarkers with potential clinical usefulness in several cancer types. Little proteomic study has been done in B-cell lymphomas. We determined whether the expression of a set of proteins by protein-chip technology coupled with new informatics tools could be used to build a model to molecularly classify B-cell lymphoma subgroups. We used surface-enhanced laser desorption/ionization time-of-flight mass spectrometry to analyze 18 CD10+ B-cell lymphomas, including 6 grade 1 (G1) follicular lymphomas (FLs), 7 grade 3 (G3) FLs, and 5 Burkitt lymphomas. We used 7 reactive follicular hyperplasia cases as a control group. By using SAX2 ProteinChip arrays (Ciphergen Biosystems, Fremont, CA), we found a unique protein expression profile for each type of lesion. Two-way hierarchical clustering analysis of these protein expression profiles differentiated reactive follicular hyperplasia, FL, and Burkitt lymphoma, with 5 major clusters of differentially expressed protein peaks. In addition, we identified histone H4 as a potential differentially expressed protein marker that seems to distinguish G1 from G3 FL. To our knowledge, this is the first proteomic study using protein-chip technology for molecular classification of B-cell lymphoma subtypes with clinical samples.

Biomarkers, Tumor↗
PubMed2006

Overcoming the dynamic range problem in mass spectrometry-based shotgun proteomics.

Protein profiling using mass spectrometry technology has emerged as a powerful method for analyzing large-scale protein-expression patterns in cells and tissues. However, a number of challenges are present in proteomics research, one of the greatest being the high degree of protein complexity and huge dynamic range of proteins expressed in the complex biological mixtures, which exceeds six orders of magnitude in cells and ten orders of magnitude in body fluids. Since many important signaling proteins have low expression levels, methods to detect the low-abundance proteins in a complex sample are required. This review will focus on the fundamental fractionation and mass spectrometry techniques currently used for large-scale shotgun proteomics research.

Animals↗
PubMed2006

A perspective on the use of iTRAQ reagent technology for protein complex and profiling studies.

Proteomic research includes the characterization of protein mixtures in order to understand complex biological systems and determine relationships between proteins, their function, and protein-protein interactions. Often the goal of such research is to monitor changes of proteins in perturbed systems, a type of study referred to as differential expression analysis. To perform these studies requires the ability to execute some type of differential comparison of a given protein state in reference to some type of a control. The iTRAQ reagents are a set of isobaric reagents which are amine specific and allow for the identification and quantitation of up to four different samples simultaneously. The amine specificity of these reagents makes most peptides in a sample amenable to this labeling strategy with no loss of information from samples involving post-translational modifications, such as the scrutiny of signal transduction pathways that often involve phosphorylation phenomena. In addition, the multiplexing capacity of these reagents allows for information replication within certain LC-MS/MS experimental regimes, providing additional statistical validation within any given experiment. The results presented herein demonstrate a few examples of the wide variety of quantitative information that can be realized when undertaking such experimental approaches. These include temporal analysis of drug-induced-protein expression, discovery and elucidation of disease markers, and protein-protein interactions in multi-protein complexes.

Animals↗
PubMed2005

Multidimensional protein identification technology: current status and future prospects.

Protein profiling using high-throughput tandem mass spectrometry has become a powerful method for analyzing changes in global protein expression patterns in cells and tissues as a function of developmental, physiologic and disease processes. This review summarizes the utility and practical application of multidimensional protein identification technology as a platform for comprehensive proteomic profiling of complex biologic samples. The strengths and potential problems and limitations associated with this powerful technology are discussed, with an emphasis placed on one of the biggest challenges currently facing large-scale expression profiling projects -- namely, data analysis. Complementary bioinformatic computational data mining strategies, such as clustering, functional annotation and statistical inference, are also discussed as these are increasingly necessary for interpreting the results of global proteomic profiling studies.

Animals↗
PubMed2006

Clinical proteomics: present and future prospects.

Advances in proteomics technology offer great promise in the understanding and treatment of the molecular basis of disease. The past decade of proteomics research, the study of dynamic protein expression, post-translational modifications, cellular and sub-cellular protein distribution, and protein-protein interactions, has culminated in the identification of many disease-related biomarkers and potential new drug targets. While proteomics remains the tool of choice for discovery research, new innovations in proteomic technology now offer the potential for proteomic profiling to become standard practice in the clinical laboratory. Indeed, protein profiles can serve as powerful diagnostic markers, and can predict treatment outcome in many diseases, in particular cancer. A number of technical obstacles remain before routine proteomic analysis can be achieved in the clinic; however the standardisation of methodologies and dissemination of proteomic data into publicly available databases is starting to overcome these hurdles. At present the most promising application for proteomics is in the screening of specific subsets of protein biomarkers for certain diseases, rather than large scale full protein profiling. Armed with these technologies the impending era of individualised patient-tailored therapy is imminent. This review summarises the advances in proteomics that has propelled us to this exciting age of clinical proteomics, and highlights the future work that is required for this to become a reality.

Journal Article↗
PubMed2004

Surface-enhanced laser desorption/ionization-time of flight-mass spectrometry approach to biomarker discovery in blue mussels (Mytilus edulis) exposed to polyaromatic hydrocarbons and heavy metals under field conditions.

Proteomics provide potential in the discovery of new sensitive biomarkers for environmental pollution. To evaluate this potential, we have utilized ProteinChip technology to analyze the proteomic profile of blue mussels (Mytilus edulis) from polluted marine habitats surrounding the island of Karmøy, Norway. Two different types of contamination, heavy metals and polyaromatic hydrocarbons (PAHs), were compared to a clean reference site. Differentially expressed proteins/peptides were found, which showed a specific induction or a general suppression associated with the field site of origin. By combining sets of protein markers in a tree-building algorithm, we were able to correctly classify samples from these sites with an accuracy of 90%.

Algorithms↗
PubMed2004

Serum tumor markers in the management of ovarian, endometrial and cervical cancer.

CA 125 is the most reliable serum marker for ovarian carcinoma. Whereas its role in the screening of the malignancy is controversial, serum CA 125 assay is very useful for both the differential diagnosis of ovarian masses, particularly in postmenopause, and the monitoring of the response to chemotherapy and follow-up of patients with histologically proven ovarian carcinoma. Tumor-associated antigens other than CA 125, such as CA 19.9, CA 15.3 and TAG.72, firstly identified in gastro-intestinal or breast malignancies, have been detected also in tissue and serum samples from patients with ovarian carcinoma. In particular CA19.9 offers the advantage of high sensitivity for mucinous histotype, which often fails to express CA 125. Serum CA 125 correlates with the clinical course of disease better than the other antigens, and in patients with positive CA 125 assay at diagnosis the concomitant evaluation of CA 19.9 or CA 72.4 or CA 15.3 does not offer any additional benefit for monitoring ovarian carcinoma. Conversely, the serial measurements of these other antigens may represent an interesting biochemical tool for the management of patients with negative CA 125 assay. Serum alphaFP and betaHCG are very useful in the preoperative evaluation and management of nondysgerminomatous ovarian germ cell tumors, whereas elevated serum inhibin levels can be detected in patients with granulosa cell tumors of the ovary. As for endometrial carcinoma, preoperative serum CA 125 levels correlate with stage, depth of myometrial invasion, histologic grade, cervical invasion, peritoneal cytology, lymph node status and clinical outcome. Moreover, serial CA 125 assay is a good indicator of disease activity and a useful biochemical tool for post-treatment surveillance of patients with endometrial carcinoma. SCC is the most reliable serum marker for squamous cell cervical carcinoma, and in patients with this malignancy pretreatment SCC levels are related to tumor stage, tumor size, depth of cervical invasion, lymph-vascular space involvement, lymph node status and clinical outcome. Serial SCC measurements parallel the response to radiotherapy and chemotherapy as well as the clinical course of disease after the completion of treatment. Serum CYFRA 21.1 seems to be less sensitive than serum SCC for squamous cell cervical carcinoma. Elevated CA 125 levels can be often detected in patients with cervical adenocarcinoma. The future for tumor marker research is represented by the emerging technologies of transcriptional profiling and proteomics.

Animals↗
PubMed2001

Gaucher disease: understanding the molecular pathogenesis of sphingolipidoses.

Gaucher disease is a typical lysosomal storage disease, resulting from an inborn deficiency of glucocerebrosidase. This leads to the accumulation of glycolipids in macrophages, particularly those in the liver, bone marrow, spleen and lung. In addition, disease of the nervous system can arise as a result of the accumulation of endogenous glycosphingolipid metabolites in brain tissue. About 150 mutations of the glucocerebrosidase gene have been identified in patients with Gaucher disease, some of which are predictive of phenotype. However, even patients and siblings with the same mutation, including monozygotic twins, may exhibit marked variability in disease expression and severity, illustrating our lack of understanding of the phenotype-genotype relationship in the sphingolipidoses. Massive organomegaly, particularly of the spleen, is a frequent feature of the disease. Although the liver and spleen may increase greatly in size, the amount of pathological lipid stored in the affected macrophages (Gaucher cells) accounts for less than 2% of the additional tissue mass. It is therefore clear that an inflammatory response occurs in affected individuals and that the clinical phenotype is due to an effect of macrophage storage beyond the physical presence of the Gaucher cells. Factors released by Gaucher cells, including pro-inflammatory cytokines and perhaps cathepsins, provide a mechanistic link between lysosomal storage and the diverse clinical manifestations of Gaucher disease. Emerging proteomic technology and gene expression profiling should not only improve our understanding of pathogenesis but also offer the prospect of identifying novel biomarkers that can be used as surrogate measures of disease activity and responses to treatment.

Gaucher Disease↗
PubMed2004

Inferring pathways from gene lists using a literature-derived network of biological relationships.

MOTIVATION: A number of omic technologies such as transcriptional profiling, proteomics, literature searches, genetic association, etc. help in the identification of sets of important genes. A subset of these genes may act in a coordinated manner, possibly because they are part of the same biological pathway. Interpreting such gene lists and relating them to pathways is a challenging task. Databases of biological relationships between thousands of mammalian genes can help in deciphering omics data. The relationships between genes can be assembled into a biological network with each protein as a node and each relationship as an edge between two proteins (or nodes). This network may then be searched for subnetworks consisting largely of interesting genes from the omics experiment. The subset of genes in the subnetwork along with the web of relationships between them helps to decipher the underlying pathways. Finding such subnetworks that maximally include all proteins from the query set but few others is the focus for this paper. RESULTS: We present a heuristic algorithm and a scoring function that work well both on simulated data and on data from known pathways. The scoring function is an extension of a previous study for a single biological experiment. We use a simple set of heuristics that provide a more efficient solution than the simulated annealing method. We find that our method works on reasonably complex curated networks containing approximately 9000 biological entities (genes and metabolites), and approximately 30,000 biological relationships. We also show that our method can pick up a pathway signal from a query list including a moderate number of genes unrelated to the pathway. In addition, we quantify the sensitivity and specificity of the technique.

Algorithms↗