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PubMedDate not supplied

Bioinformatics and genomic medicine.

Bioinformatics is a rapidly emerging field of biomedical research. A flood of large-scale genomic and postgenomic data means that many of the challenges in biomedical research are now challenges in computational science. Clinical informatics has long developed methodologies to improve biomedical research and clinical care by integrating experimental and clinical information systems. The informatics revolution in both bioinformatics and clinical informatics will eventually change the current practice of medicine, including diagnostics, therapeutics, and prognostics. Postgenome informatics, powered by high-throughput technologies and genomic-scale databases, is likely to transform our biomedical understanding forever, in much the same way that biochemistry did a generation ago. This paper describes how these technologies will impact biomedical research and clinical care, emphasizing recent advances in biochip-based functional genomics and proteomics. Basic data preprocessing with normalization and filtering, primary pattern analysis, and machine-learning algorithms are discussed. Use of integrative biochip informatics technologies, including multivariate data projection, gene-metabolic pathway mapping, automated biomolecular annotation, text mining of factual and literature databases, and the integrated management of biomolecular databases, are also discussed.

Computational Biology↗
PubMed2003

Protein microarrays: promising tools for proteomic research.

Miniaturized and parallelized ligand binding assays are of great interest in postgenomic research because microarray technology allows the simultaneous determination of a large number of parameters from a minute amount of sample within a single experiment. Assay systems based on this technology are used for the identification and quantification of proteins as well as for the study of protein interactions. Protein affinity assays have been implemented that allow the analysis of interactions between proteins with other proteins, peptides, low molecular weight compounds, oligosaccharides or DNA. Microarray technology is an emerging technology used in global analytical approaches and has a considerable impact on proteomic research.

Animals↗
PubMed2005

Mortalin is over-expressed by colorectal adenocarcinomas and correlates with poor survival.

Using comparative proteomic analysis we have identified over-expression of mortalin in colorectal adenocarcinomas. Mortalin, also known as mitochondrial heat-shock protein 70 (mhsp 70), is involved in cell cycle regulation with important roles in cellular senescence and immortalization pathways. It is known to bind to and inactivate wild-type tumour suppressor protein p53 and influences the Ras-Raf-MAPK pathway. By immunostaining a colorectal cancer tissue microarray linked to a patient database, we further found that mortalin over-expression correlates with poor patient survival and, in multivariate analysis, is independent of standard prognostic variables (p = 0.04). Our findings demonstrate that mortalin over-expression may predict outcome in colorectal cancer and suggest that this protein is involved in colorectal neoplasia. Our experimental approach emphasises the analytical power of combining proteomics with tissue microarray analysis in the context of a well-defined tumour database.

Adenocarcinoma↗
PubMed2003

Serum protein fingerprinting.

The core technologies in the rapidly expanding field of proteomics have matured to the point where quantitative measurements of thousands of proteins can be conducted, enabling truly global measurements of protein expression. This advent has brought with it the hope of discovering novel biomarkers that promise a renaissance in clinical medicine. To meet this need, many proteomic studies have focused on the identification and subsequent comparative analysis of the thousands of proteins that populate complex biological systems such as serum and tissues. A novel application of mass spectrometry has been in proteomic pattern analysis, which has emerged as an effective method for the early diagnosis of diseases. In stark contrast to 'classical' proteomics, proteomic pattern analysis relies on the pattern of proteins observed, rather than on the discrete identification of a protein. Proteomic pattern technology allows hundreds of clinical samples to be analyzed per day and promises to be a novel, highly sensitive predictive clinical tool to improve diagnostic and prognostic medicine.

Blood Proteins↗
PubMed2005

Polydiacetylene (PDA)-based colorimetric detection of biotin-streptavidin interactions.

The natural binding of streptavidin (STA) for a small molecule, biotin, has made it a useful tool in specific targeting application, due to their most specific noncovalent biological interactions in nature. Here, we describe a polydiacetylene (PDA)-based colorimetric biosensor to detect the STA-biotin interactions. We synthesized two kinds of biotin-labeled 10,12-pentacosadiynoic acid (PCDA) monomers having 2(ethylene oxide) or 3(ethylene oxide) spacer and prepared biotin-modified PDA liposomes using them. Upon the addition of STA, the modified PDA liposomes showed a color change from blue to red in about 1h followed by their aggregation and precipitation. The aggregation was found to be due to the cross-linking between the PDA liposomes caused by the STA having four biotin binding sites, which was confirmed by their TEM analysis. Since the STA-biotin interactions are widely adopted for various biological detection systems; the biotin-modified PDA sensor developed in this study has enormous potentials for the development of various colorimetric biosensors.

Acetylene↗
PubMed2006

Development of peptide microarrays for epitope mapping of antibodies against the human TSH receptor.

Accurate characterization of the antigen binding region of antibodies is of great value in many fields of research, assay development and clinical diagnostics. Up to now, there is an unmet clinical need to use antibodies as diagnostic markers for the prediction of both prognosis and therapeutic response. To this end, comprehensive but differentiated immunoassays need to be generated. We have developed a peptide microarray for the diagnosis and epitope mapping of anti-thyrotropin receptor antibodies. The primary sequence of the human thyrotropin receptor (hTSHR) was represented by a library of 251 synthetic peptides. The peptides were site-specifically immobilized in a two-step procedure first by coupling of biotinylated peptides to hydrazide-modified streptavidin and then utilizing a subsequent chemoselective reaction between the hydrazide linkers of the streptavidin and an aldehyde coated glass surface. The technology was used to map the epitopes of seven commercially available murine monoclonal antibodies specific for the human TSH receptor (mTSHRAb). A previously unknown epitope recognized by mTSHRAb 4C1 was identified at amino acids (AA) 379 through 384 and the epitope recognized by mTSHRAb A9 was also localized (AA 214-222). Previously identified epitopes recognized by mTSHRAbs 2C11 (AA 349-360), 28 (AA 34-39), 49 (AA 289-297), A7 (AA 406-411) and A10 (AA 34-39) were confirmed. The peptide microarray exhibited excellent performance in single and multiplex antibody analysis and high specificity. This technology may have potential as a multi-determinate in vitro diagnostic assay for the differential analysis of a heterogeneity of antibodies involved in the pathogenesis of autoimmune diseases.

Aldehydes↗
PubMed2005

Discovery and identification of alpha-defensins as low abundant, tumor-derived serum markers in colorectal cancer.

BACKGROUND & AIMS: Although colorectal cancer is one of the best characterized tumors with regard to the multistep genetic progression, it remains one of the most frequent and deadly neoplasms in Western countries. This is mainly due to the fact that, up to now, no clinically relevant serum markers could be established in an early routine diagnostic procedure. METHODS: We comparatively analyzed microdissected normal and tumorous colonic epithelium by ProteinChip technology to detect proteins specific for the tumor directly in the tissue. Immunohistochemistry (IHC) was used for the in situ localization of the discovered proteins, and an ELISA was performed to quantify these proteins in serum. RESULTS: By this approach, we found and identified alpha-defensins 1-3 (HNP1-3) to be more highly expressed in the tumor than in normal epithelium. These findings could be confirmed by IHC. Detection of these peptides in the corresponding serum samples was subsequently performed with ELISA, resulting in an average sensitivity of 69% and specificity of 100% for the recognition of colorectal cancer when using the HNP1-3 level in the serum of the patients. CONCLUSIONS: The direct analysis of microdissected tissue for the discovery of tumor-specific markers followed by the specific detection of these markers in serum by antibody-based methods proved to be a successful strategy in this study. Therefore, we can conclude that these promising markers would not have been found in serum without the information gained through the analysis of microdissected tissue by ProteinChip technology.

Adenoma↗
PubMed2006

Antibody microarrays: current status and key technological advances.

Antibody-based microarrays are among the novel classes of rapidly evolving proteomic technologies that holds great promise in biomedicine. Miniaturized microarrays (< 1 cm2) can be printed with thousands of individual antibodies carrying the desired specificities, and with biological sample (e.g., an entire proteome) added, virtually any specifically bound analytes can be detected. While consuming only minute amounts (< microL scale) of reagents, ultra- sensitive assays (zeptomol range) can readily be performed in a highly multiplexed manner. The microarray patterns generated can then be transformed into proteomic maps, or detailed molecular fingerprints, revealing the composition of the proteome. Thus, protein expression profiling and global proteome analysis using this tool will offer new opportunities for drug target and biomarker discovery, disease diagnostics, and insights into disease biology. Adopting the antibody microarray technology platform, several biomedical applications, ranging from focused assays to proteome-scale analysis will be rapidly emerging in the coming years. This review will discuss the current status of the antibody microarray technology focusing on recent technological advances and key issues in the process of evolving the methodology into a high-performing proteomic research tool.

Animals↗
PubMed2003

Clinical applications of proteomics.

Proteomics, the systematic evaluation of changes in the protein constituency of a cell, is more than just the generation of lists of proteins that increase or decrease in expression as a cause or consequence of disease. The ultimate goal is to characterize the information flow through protein pathways that interconnect the extracellular microenvironment with the control of gene transcription. The nature of this information can be a cause or a consequence of disease processes. Clinical applications of proteomics involve the use of proteomic technologies at the bedside. The analysis of human cancer as a model for how proteomics can have an impact at the bedside is now employing several new proteomic technologies that are being developed for early detection, therapeutic targeting and finally, patient-tailored therapy.

Gene Expression Profiling↗
PubMed2006

Protein microarray platform for the multiplex analysis of biomarkers in human sera.

Many biomarkers are currently used to monitor patients in clinical studies. Technologies which evaluate, validate and monitor biomarkers in a cost effective and efficient manner are a necessity. Here we describe the development, validation and implementation of a protein microarray platform for the quantitative and simultaneous analysis of six proteins: IL-1beta, IL-1ra, IL-6, IL-8, MCP-1 and TNFalpha. The platform utilizes a 96-well plate as a solid support on which antibodies are immobilized using non-contact piezoelectric printing. The reaction is based on a sandwich ELISA and the signal is quantified by chemiluminescence with a CCD camera. The robustness and reproducibility of the methodology was investigated using the Food and Drug Administration (FDA) regulatory guidelines for pharmacokinetic assay validation, in which a spike-recovery validation test was elaborated and run over 3 days. The method was shown to be both quantitative and reproducible, with assay accuracy between 70% and 130%, and an assay precision of less than 30%. In addition, protein microarray performance was compared with the classical ELISA approach. Sera collected from a total of 78 individuals were assayed using both approaches. Correlation coefficients (R2) between the two technologies were calculated for each of the analytes: 0.90 for IL-1beta, 0.60 for IL-1ra, 0.93 for IL-6, 0.96 for IL-8, 0.94 for MCP-1 and 0.95 for TNFalpha. The results obtained demonstrate the applicability of this protein microarray for quantitative and simultaneous analysis of IL-1beta, IL-1ra, IL-6, IL-8, MCP-1 and TNFalpha in clinical samples.

Biomarkers↗
PubMed2004

Artificial neural networks analysis of surface-enhanced laser desorption/ionization mass spectra of serum protein pattern distinguishes colorectal cancer from healthy population.

PURPOSE: The low specificity and sensitivity of the carcinoembryonic antigen test makes it not an ideal biomarker for the detection of colorectal cancer. We developed and evaluated a proteomic approach for the simultaneous detection and analysis of multiple proteins for distinguishing individuals with colorectal cancer from healthy individuals. EXPERIMENTAL DESIGN: We subjected serum samples (including 55 colorectal cancer patients and 92 age- and sex-matched healthy individuals) from 147 individuals, for analysis by surface-enhanced laser desorption/ionization (SELDI) mass spectrometry. Peaks were detected with Ciphergen SELDI software version 3.0. Using a multilayer artificial neural network with a back propagation algorithm, we developed a classifier for separating the colorectal cancer groups from the healthy groups. RESULTS: The artificial neural network classifier separated the colorectal cancer from the healthy samples, with a sensitivity of 91% and specificity of 93%. Four top-scored peaks, at m/z of 5,911, 8,930, 8,817, and 4,476, were finally selected as the potential "fingerprints" for detection of colorectal cancer. CONCLUSIONS: The combination of SELDI-TOF mass spectrometry with the artificial neural networks in the analysis of serum protein yields significantly higher sensitivity and specificity values for the detection and diagnosis of colorectal cancer.

Adult↗
PubMed2005

Overexpression of ADC2 in Arabidopsis induces dwarfism and late-flowering through GA deficiency.

We have obtained Arabidopsis thaliana transgenic plants constitutively overexpressing ADC2, one of the two genes encoding arginine decarboxylase (ADC) in Arabidopsis. These plants contained very high levels of putrescine (Put) but no changes were observed in spermidine and spermine contents. The results obtained from quantification of free and conjugated polyamines suggest that conjugation may be a limiting step for control of Put homeostasis within a non-toxic range for plant survival. Transgenic plants with increased levels of ADC2 transcript and elevated Put content showed dwarfism and late-flowering, and the phenotype was rescued by gibberellin A3 (GA3) application. The contents of bioactive GA4 and GA1, and of GA9 (a precursor of GA4), as well as the levels of AtGA20ox1, AtGA3ox1 and AtGA3ox3 transcripts (quantified by real-time PCR) were lower in the ADC2 overexpressor plants than in the wild type. No change in the expression of genes encoding earlier enzymes in the GA biosynthesis pathway was detected by microarray analysis. These results suggest that Put accumulation affects GA metabolism through the repression of biosynthetic steps catalyzed by GA 20-oxidase and GA 3-oxidase.

Arabidopsis↗
PubMed2006

Plant phosphoproteomics: a long road ahead.

Phosphoproteomics can be defined as the comprehensive study of protein phosphorylation by identification of the phosphoproteins, exact mapping of the phosphorylation sites, quantification of phosphorylation, and eventually, revealing their biological function. Its place in today's research is vitally important to address the most fundamental question - how the phosphorylation events control most, if not all, of the cellular processes in a given organism? Despite the immense importance of phosphorylation, the analysis of phosphoproteins on a proteome-wide scale remains a formidable challenge. Nevertheless, several technologies have been developed, mostly in yeast and mammals, to conduct a large-scale phosphoproteomic study. Some of these technologies have been successfully applied to plants with a few modifications, resulting in documentation of phosphoproteins, phosphorylation site mapping, identification of protein kinase substrates, etc. at the global level. In this review, we summarize in vitro and in vivo approaches for detection and analysis of phosphoproteins including protein kinases and we discuss the importance of phosphoproteomics in understanding plant biology. These approaches along with bioinformatics will help plant researchers to design and apply suitable phosphoproteomic strategies in helping to find answers to their biological questions.

Chromatography↗
PubMed2006

Advanced techniques in placental biology -- workshop report.

Major advances in placental biology have been realized as new technologies have been developed and existing methods have been refined in many areas of biological research. Classical anatomy and whole-organ physiology tools once used to analyze placental structure and function have been supplanted by more sophisticated techniques adapted from molecular biology, proteomics, and computational biology and bioinformatics. In addition, significant refinements in morphological study of the placenta and its constituent cell types have improved our ability to assess form and function in highly integrated manner. To offer an overview of modern technologies used by investigators to study the placenta, this workshop: Advanced techniques in placental biology, assembled experts who discussed fundamental principles and real time examples of four separate methodologies. Y. Sadovsky presented the principles of microRNA function as an endogenous mechanism of gene regulation. J. Robinson demonstrated the utility of correlative microscopy in which light-level and transmission electron microscopy are combined to provide cellular and subcellular views of placental cells. A. Croy provided a lecture on the use of microdissection techniques which are invaluable for isolating very small subsets of cell types for molecular analysis. Finally, G. Rice presented an overview methods on profiling of complex protein mixtures within tissue and/or fluid samples that, when refined, will offer databases that will underpin a systems approach to modern trophoblast biology.

Animals↗
PubMed2004

A proteomic microarray approach for exploring ligand-initiated nuclear hormone receptor pharmacology, receptor selectivity, and heterodimer functionality.

Nuclear hormone receptors (NHRs) are major regulators of development and homeostasis in multiple organ systems. These proteins are ligand-modulated transcription factors that regulate gene expression in response to changes in circulating levels of their cognate hormones or hormone analogs. When NHRs bind ligands, they adopt distinct conformations that enable or disable the binding of coregulator proteins in a manner that reflects the agonist versus antagonist character of the ligand. Using the estrogen receptor ligand binding domain as a representative member of the NHR family, we show the development of functional protein microarrays and use them to explore coactivator recruitment and NHR homo- and heterodimer functionality. These NHR protein microarrays can be fabricated in either a forward mode (coactivator recruited to printed NHR) or a reversed mode (NHR recruited to printed coactivator). From these microarrays, we can predict the potency and pharmacological character of various NHR ligands through the nature of their coactivator recruitment. Additionally different coactivator proteins can be functionally classified and their affinity for NHRs can be quantified. NHR-selective antagonist ligands and small molecule coactivator mimics disrupt the coactivator-NHR complex. This novel proteomic approach was also used to assess coactivator recruitment to explore heterodimer functionality. Heterodimers of the estrogen receptor were found only to recruit coactivators when both monomers are bound with agonist ligands, an observation that provides an insight into the complex biology of hormones that act on tissues containing both NHR subtypes. We can extend this NHR proteomic approach to the analysis of multidomain full-length NHR constructs and can concurrently monitor the activation state of different classes of NHRs with a mixture of endogenous or synthetic ligands of varying NHR selectivity and pharmacology.

Binding Sites↗
PubMed2006

ProCAT: a data analysis approach for protein microarrays.

Protein microarrays provide a versatile method for the analysis of many protein biochemical activities. Existing DNA microarray analytical methods do not translate to protein microarrays due to differences between the technologies. Here we report a new approach, ProCAT, which corrects for background bias and spatial artifacts, identifies significant signals, filters nonspecific spots, and normalizes the resulting signal to protein abundance. ProCAT provides a powerful and flexible new approach for analyzing many types of protein microarrays.

Cyclic AMP-Dependent Protein Kinases↗
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↗