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At least 163 records · Page 9Linked to original sources
PubMed2006

Selective enrichment and ultrasensitive identification of trace peptides in proteome analysis using transient capillary isotachophoresis/zone electrophoresis coupled with nano-ESI-MS.

Besides the complexity in protein samples of biological origin, probably the greatest challenge presently facing comprehensive proteome analysis is related to the large variation of protein relative abundances (>6 orders of magnitude), having potential biological significance in mammalian systems. As demonstrated in this work, transient capillary ITP/zone electrophoresis (CITP/CZE) provides selective analyte enrichment through electrokinetic stacking and extremely high resolving power toward protein and peptide mixtures. The result of the CITP process is that major components may be diluted, but trace compounds are concentrated. The on-column transition of CITP to CZE minimizes additional band broadening while providing superior analyte resolution. Online coupling of transient CITP/CZE with nano-ESI-MS allows ultrasensitive detection of trace peptides at levels of subnanomolar concentration or subfemtomole mass in complex peptide mixtures. More importantly, selective enrichment of trace peptides enables the identification and sequence analysis of low-abundance peptides co-migrated with highly abundant species at a concentration ratio of 1:500,000. The combined CITP/CZE-nano-ESI-MS system is demonstrated to be at least one to two orders of magnitude more sensitive than that attained in conventional electrophoretic and chromatographic-based proteome technologies over a wide dynamic concentration range, potentially allowing comprehensive analysis of protein profiles within a small cell population and limited tissue samples using conventional mass spectrometers. Furthermore, the speed of CITP/CZE separation and the lack of column equilibration in CITP/CZE not only improve the throughput of proteome analysis, but also facilitate its seamless integration with other separation technologies in a multidimensional protein identification platform.

Cytochromes c↗
PubMed2006

Contribution of genomics and proteomics in understanding the role of modifying factors in Parkinson's disease.

Parkinson's disease (PD) is a complex neurological disorder, characterized by selective degeneration of nigrostriatal dopaminergic neurons. It is a multi-factorial disease, contributed by a combination of age, genetic and environmental factors. Etiology of sporadic PD and mechanism underlying selective loss of dopaminergic neurons has not yet been clearly understood. Recent developments in genomics and proteomics have revolutionized the research on PD at genetic level. Differential gene expression patterns (DNA biochip technology), age-dependent complex genetic patterns (SNP genotyping), and protein expression profiles (proteomics) of PD patients have started providing the specific and rigorous molecular explanation and role of modifying factors in PD. Genomics and proteomics are further expected to help in developing biomarkers for diagnosis of early onset PD and also to develop valuable and potential therapeutic strategies for its treatment. In this review, we have discussed the progress made by genomics and proteomics, in understanding the role of modifying factors in PD.

Animals↗
PubMed2005

Biomarker discovery using protein microarray technology platforms: antibody-antigen complex profiling.

Protein microarrays represent an important new tool in proteomic systems biology. This review focuses on the contributions of protein microarrays to the discovery of novel disease biomarkers through antibody-based assays. Of particular interest is the use of protein microarrays for immune response profiling, through which a disease-specific antibody repertoire may be defined. The antigens and antibodies revealed by these studies are useful for clinical assay development, with enormous potential to aid in diagnosis, prognosis, disease staging and treatment selection. The discovery and characterization of novel biomarkers specifically tailored to disease type and stage are expected to enable personalized medicine by facilitating preventative medicine, predictive diagnostics and individualized curative therapies.

Allergens↗
PubMed2005

Unraveling the pathogenesis of type 1 diabetes with proteomics: present and future directions.

Type 1 diabetes (T1D) is the result of selective destruction of the insulin-producing beta-cells in the pancreatic islets of Langerhans. T1D is due to a complex interplay between the beta-cell, the immune system, and the environment in genetically susceptible individuals. The initiating mechanism(s) behind the development of T1D are largely unknown, and no genes or proteins are specific for most T1D cases. Different pro-apoptotic cytokines, IL-1 beta in particular, are present in the islets during beta-cell destruction and are able to modulate beta-cell function and induce beta-cell death. In beta-cells exposed to IL-1 beta, a race between destructive and protective events are initiated and in susceptible individuals the deleterious events prevail. Proteins are involved in most cellular processes, and it is thus expected that their cumulative expression profile reflects the specific activity of cells. Proteomics may be useful in describing the protein expression profile and thus the diabetic phenotype. Relatively few studies using proteomics technologies to investigate the T1D pathogenesis have been published to date despite the defined target organ, the beta-cell. Proteomics has been applied in studies of differentiating beta-cells, cytokine exposed islets, dietary manipulated islets, and in transplanted islets. Although that the studies have revealed a complex and detailed picture of the protein expression profiles many functional implications remain to be answered. In conclusion, a rather detailed picture of protein expression in beta-cell lines, islets, and transplanted islets both in vitro and in vivo have been described. The data indicate that the beta-cell is an active participant in its own destruction during diabetes development. No single protein alone seems to be responsible for the development of diabetes. Rather the cumulative pattern of changes seems to be what favors a transition from dynamic stability in the unperturbed beta-cell to dynamic instability and eventually to beta-cell destruction.

Animals↗
PubMed2005

The agony and ecstasy of "OMIC" technologies in drug development.

Over the last decade we have witnessed a fundamental change in how biomedical research is carried out and we can now assess the impact of the Human Genome Project on drug discovery and development. Advances in "omics" technologies (genomics, transcriptomics, proteomics and metabonomics) were touted as having the potential to revolutionize our approach to disease diagnosis, prognostication and development of novel therapeutics. However, the promise of rapid advances in medicine "from the lab bench to the bedside" has not manifested as of yet. Indeed it appears that the translational applications of genomic-based research have preceded the development of both (i) a conceptual framework for disease understanding and (ii) effective tools that can exploit the vast amounts of data derived from these efforts. In reality great progress has been made, however understanding processes such as disease progression (or drug response) requires systematic insight into dynamic (and temporal) differences in gene regulation, interaction and function. This review will discuss "omic" technologies with the emphasis upon advances in our understanding of the human genome derived transcriptome (RNA), and its proteome (proteins), while focusing upon the translation of this information into the drug development paradigm.

Computational Biology↗
PubMedDate not supplied

A role for oligonucleotide-based RNA-knock down technologies in functional genomics.

Functional genomics is inundating the pharmaceutical industry with large numbers of potential gene targets from several sources such as gene expression profiling experiments (DNA microchips, proteomics) or database mining. Oligonucleotide-based RNA-knock down technologies such as antisense or RNA interference can aid in the filtering and prioritization of target candidates in the drug discovery process.

Drug Industry↗
PubMed2001

Proteomic profiling from human samples: the body fluid alternative.

Proteomics is one of the technologies rapidly changing our approach to drug development. The applications of proteomics, particularly with reference to analysis of body fluid samples, will be described. Proteomic analysis involves the systematic separation, identification and characterisation of proteins present in a biological sample. By comparing the proteins present in diseased samples with those present in normal samples, it is possible to identify changes in expression of proteins that potentially may be related to organ toxicity. Proteomics is regarded as a sister technology to genomics. Although the pattern of gene activity will be abnormal in a tissue with a pathological lesion, there can be a poor correlation between the level of activity of different genes and the relative abundance within the tissue of the corresponding proteins. This is especially true where the mode of action of the test material interferes with protein synthesis and/or post translational modification. Consequently, the information about a pathological process that can be derived at the level of gene activity is incomplete. Proteomics has now made it possible to analyse proteins using high throughput, automated techniques. Although both mRNA and proteomic profiling can be applied to tissue samples, analysis of body fluids (e.g. serum, urine, CSF, synovial fluid) is restricted to proteomics. In these cases the protein composition is derived from many tissues and processes. Proteomic analysis can yield information on disease processes and potential response to treatment. Examples will be presented of the identification of surrogate markers for hepatocellular carcinoma, breast cancer, from cerebrospinal fluid in humans and gentamicin toxicity in the rat.

Body Fluids↗
PubMed2006

Hepatic gene expression profiling using Genechips in zebrafish exposed to 17alpha-ethynylestradiol.

Genomic, proteomic, and metabolomic technologies continue to receive increasing interest from environmental toxicologists. This interest is due to the great potential of these technologies to identify detailed modes of action and to provide assistance in the evaluation of a contaminant's risk to aquatic organisms. Our experimental model is the zebrafish (Danio rerio) exposed to reference endocrine disrupting compounds in order to investigate compound-induced changes in gene transcript profiles. Adult, female zebrafish were exposed to 0, 15, 40, and 100ng/L of 17alpha-ethynylestradiol (EE2) and concentration and time-dependent changes in hepatic gene expression were examined using Affymetrix GeneChip Zebrafish Genome Microarrays. At 24, 48, and 168h, fish were sacrificed and liver mRNA was extracted for gene expression analysis (24 and 168h only). In an effort to link gene expression changes to effects on higher levels of biological organization, body and ovary weights were measured and blood was collected for measurement of plasma steroid hormones (17beta-estradiol (E2), testosterone (T)) and vitellogenin (VTG) using ELISA. EE2 exposure significantly affected gene expression, GSI, E2, T, and VTG. We observed 1622 genes that were significantly affected (p< or =0.001) in a concentration-dependent manner by EE2 exposure at either 24 or 168h. Gene ontology (GO) analysis revealed that EE2 exposure affected genes involved in hormone metabolism, vitamin A metabolism, steroid binding, sterol metabolism, and cell growth. Plasma VTG was significantly increased at 24, 48, and 168h (p< or =0.05) at 40 and 100ng/L and at 15ng/L at 168h. E2 and T were significantly reduced following EE2 exposure at 48 and 168h. GSI was decreased in a concentration-dependent manner at 168h. In this study, we identified genes involved in a variety of biological processes that have the potential to be used as markers of exposure to estrogenic substances. Future work will evaluate the use of these genes in zebrafish exposed to weak estrogens to determine if these genes are indicative of exposure to estrogens with varying potencies.

Animals↗
PubMed2004

Towards a comprehensive understanding of Bacillus subtilis cell physiology by physiological proteomics.

Using Bacillus subtilis as a model system for functional genomics, this review will provide insights how proteomics can be used to bring the virtual life of genes to the real life of proteins. Physiological proteomics will generate a new and broad understanding of cellular physiology because the majority of proteins synthesized in the cell can be visualized. From a physiological point of view two major proteome fractions can be distinguished: proteomes of growing cells and proteomes of nongrowing cells. In the main analytical window almost 50% of the vegetative proteome expressed in growing cells of B. subtilis were identified. This proteomic view of growing cells can be employed for analyzing the regulation of entire metabolic pathways and thus opens the chance for a comprehensive understanding of metabolism and growth processes of bacteria. Proteomics, on the other hand, is also a useful tool for analyzing the adaptational network of nongrowing cells that consists of several partially overlapping regulation groups induced by stress/starvation stimuli. Furthermore, proteomic signatures for environmental stimuli can not only be applied to predict the physiological state of cells, but also offer various industrial applications from fermentation monitoring up to the analysis of the mode of action of drugs. Even if DNA array technologies currently provide a better overview of the gene expression profile than proteome approaches, the latter address biological problems in which they can not be replaced by mRNA profiling procedures. This proteomics of the second generation is a powerful tool for analyzing global control of protein stability, the protein interaction network, protein secretion or post-translational modifications of proteins on the way towards the elucidation of the mystery of life.

Bacillus subtilis↗
PubMed2002

Design of proteome-based studies in combination with serology for the identification of biomarkers and novel targets.

Recently proteome analysis has rapidly developed in the post-genome era and is now widely accepted as a complementary technology to genetic profiling. The improvement in the technology of both two-dimensional electrophoresis (2-DE) analysis as well as protein identification has made proteomics a valuable and powerful tool to study human diseases. A combination of conventional proteome analysis with serology has been developed as a promising experimental approach for the discovery of serological markers in different malignancies. However, the design of proteome-based studies has to be carefully performed since there are a number of critical needs for systematic and reproducible proteome analysis. In particular, the selection of tissue and its preparation represent an important step in proteome analysis. Besides the preparation of protein samples, the 2-DE and protein identification is a further critical issue. So far proteome-based technologies have been successfully used in tumor immunnology for the identification of tumor-specific autoantigens. Similarly, this technology has been employed for the detection of virulence factors, antigens and vaccine candidates in infectious diseases, as well as for the identification of diagnostic and prognostic markers, suggesting that proteome-based analysis is a promising tool for the identification of prognostic, diagnostic markers as well as for novel therapeutic targets which could be used for treatment of diseases. The integration of proteome-based approaches with data from genomic or genetic profiling will lead to a better understanding of different diseases, which will then contribute to the direct translation of the research findings into clinical practice.

Antigens, Neoplasm↗
PubMed2025

Proteomic signatures and predictive modeling of cadmium-associated anxiety in middle-aged and elderly populations: an environmental exposure association study.

BACKGROUND: Emerging evidence implicates environmental contaminants such as cadmium (Cd) as modifiable risk factors for anxiety. Despite growing recognition of heavy metal toxicity in neuropsychiatric disorders, the molecular mechanisms linking environmental exposure to anxiety pathogenesis remain poorly understood. METHODS: Based on the established cohort of individuals with cognitive impairment in cadmium-contaminated areas, this cross-sectional association study enrolled 50 middle-aged and elderly hospitalized patients from these regions, adhering to the STROBE guidelines. Blood concentrations of cadmium (Cd), lead (Pb), and mercury (Hg) were analyzed in relation to anxiety severity assessed via the Hamilton Anxiety Rating Scale (HAMA). Plasma proteomic profiling was performed using data-independent acquisition (DIA) quantitative technology with an LC-MS/MS platform (timsTOF Pro, Bruker Daltonics), systematically characterizing 2,531 proteins across all samples. Machine learning techniques, specifically XGBoost and LASSO, were employed to identify biomarkers that were subsequently validated through mediation analysis and animal experiments, allowing for the screening of key protein signatures. Finally, clinical variables were integrated to construct a comprehensive model, which was then thoroughly evaluated. RESULTS: Anxious individuals exhibited significantly higher blood Cd levels than controls (&#x3b2;&#x2009;=&#x2009;0.50, 95% CI: 0.07-0.93, p&#x2009;<&#x2009;0.01), with anxiety positively correlating with depression (r&#x2009;=&#x2009;0.62, p&#x2009;=&#x2009;0.003) and inversely with ApoE3 genotype prevalence. Proteomics identified 120 differentially expressed proteins in anxious patients, enriched in oxidative phosphorylation and neurodegenerative pathways. CCDC126 emerged as a cadmium-associated biomarker, validated in rat models exposed to Cd. Combining CCDC126, blood Cd, Pb, and hypertension, a clinical prediction model achieved robust discrimination (AUC&#x2009;=&#x2009;0.80, validation cohort). CONCLUSIONS: This first integrative environmental-proteomic study highlights cadmium's synergistic role in anxiety pathophysiology and psychiatric comorbidity. The predictive model offers translatable potential for early risk stratification, while CCDC126 provides mechanistic insights for targeted interventions in populations exposed to environmental pollutants.

Cadmium↗
PubMed2026

Spatial multiomics in biomedical research: advances beyond transcriptomics.

Coordinated changes in gene expression, epigenetic regulation, protein and metabolic activities together drive disease progression and determine clinical outcomes. While spatially resolved transcriptomics has been widely adopted across biomedical fields, it offers an incomplete picture limited to transcriptomic levels. Here, we survey the latest developments in spatial multiomics technologies, with particular emphasis on platforms that extend beyond conventional transcriptomics and profile genomics, epigenomics, proteomics, or metabolomics within intact tissues. These approaches are rapidly becoming commercialized, and here we highlight major technical breakthroughs, enhanced sample compatibility, emerging applications, and computational tools for data analysis. This Review aims to equip researchers with a clear understanding of the current technological landscape and to accelerate the adoption of spatial multiomics methods in biomedical research.

Humans↗
PubMed2005

Intact-protein-based high-resolution three-dimensional quantitative analysis system for proteome profiling of biological fluids.

The substantial complexity and vast dynamic range of protein abundance in biological fluids, notably serum and plasma, present a formidable challenge for comprehensive protein analysis. Integration of multiple technologies is required to achieve high-resolution and high-sensitivity proteomics analysis of biological fluids. We have implemented an orthogonal three-dimensional intact-protein analysis system (IPAS), coupled with protein tagging and immunodepletion of abundant proteins, to quantitatively profile the human plasma proteome. Following immunodepletion, plasma proteins in each of paired samples are concentrated and labeled with a different Cy dye, before mixing. Proteins are subsequently separated in three dimensions according to their charge, hydrophobicity, and molecular mass. Differences in the abundance of resolved proteins are determined based on Cy dye ratios. We have applied this strategy to profile the plasma proteome for changes that occur with acute graft-versus-host disease (GVHD), following allogeneic bone marrow transplantation (BMT). Using capillary HPLC ESI Q-TOF MS, we identified 75 proteins in the micromolar to femtomolar range that exhibited quantitative differences between the pre- and post-GVHD samples. These proteins included serum amyloid A, apolipoproteins A-I/A-IV, and complement C3 that are well-known acute-phase reactants likely reflecting the post-BMT inflammatory state. In addition, we identified some potentially interesting immunologically relevant molecules including vitamin D-binding protein, fetuin, vitronectin, proline-rich protein 3 and 4, integrin-alpha, and leukocyte antigen CD97. IPAS provides a combination of comprehensive profiling and quantitative analysis, with a substantial dynamic range, for disease-related applications.

Adult↗
PubMed2005

Metabolome analysis: the potential of in vivo labeling with stable isotopes for metabolite profiling.

Metabolome analysis technologies are still in early development because, unlike genome, transcriptome and proteome analyses, metabolome analysis has to deal with a highly diverse range of biomolecules. Combinations of different analytical platforms are therefore required for comprehensive metabolomic studies. Each of these platforms covers only part of the metabolome. To establish multiparallel technologies, thorough standardization of each measured metabolite is required. Standardization is best achieved by addition of a specific stable isotope-labeled compound, a mass isotopomer, for each metabolite. This suggestion, at first glance, seems unrealistic because of cost and time constraints. A possible solution to this problem is discussed in this article. Saturation in vivo labeling with stable isotopes enables the biosynthesis of differentially mass-labeled metabolite mixtures, which can be exploited for highly standardized metabolite profiling by mass isotopomer ratios.

Biochemistry↗
PubMed2005

Analysis of protein interaction and function with a 3-dimensional MALDI-MS protein array.

Protein profiling and characterization of protein interactions in biological samples ultimately require indicator-free methods of signal detection, which likewise offer an opportunity to distinguish specific interactions from nonspecific protein binding. Here we describe a new 3-dimensional protein microchip for detecting biomolecular interactions with matrix-assisted laser desorption-ionization mass spectrometry (MALDI-MS); the microchip comprises a high-density array of methacrylate polymer elements containing immobilized proteins as capture molecules and directly interfaces with a commercially available mass spectrometer. We demonstrated the performance of the chip in three types of experiments by detecting antibody-antigen interactions, enzymatic activity, and enzyme-inhibitor interactions. MALDI-MS biochip-based tumor necrosisfactor alpha (TNF-alpha) immunoassays demonstrated the feasibility of detecting antigens in complex biological samples by identifying molecular masses of bound proteins even at high nonspecific protein binding. By detecting model interactions of trypsin with trypsin inhibitors, we showed that the protein binding capacity of methacrylate polymer elements and the sensitivity of MALDI-MS detection of proteins bound to these elements surpassed that of other 2- and 3-dimensional substrates tested Immobilized trypsin retained functional (enzymatic) activity within the protein microchip and the specificity of macromolecular interactions even in complex biological samples. We believe that the underlying technology should therefore be extensible to whole-proteome protein expression profiling and interaction mapping.

Equipment Design↗
PubMed2026

Proteomics-based approaches to neutrophil biology.

INTRODUCTION: Neutrophils are central effectors of innate immunity and key contributors to inflammation, host defense, and tissue injury across a wide range of physiological and pathological contexts. Due to their short lifespan, rapid activation, and extensive post-translational regulation, comprehensive molecular characterization of neutrophil function requires approaches that go beyond transcriptomics or marker-based analyses. AREAS COVERED: This review summarizes how proteomic technologies have advanced the understanding of neutrophil biology by enabling unbiased, system-wide profiling of protein abundance, subcellular organization, post-translational modifications, and functional heterogeneity. We discuss global and subcellular proteomics, PTM-centric analyses, and emerging low-input and single-cell proteomic strategies, highlighting recent studies of infection, cancer, metabolic disorders, aging, autoimmune disease, and inflammation. The literature covered includes current large-scale quantitative proteomics, targeted PTMs, and integrative multi-omics studies in both human samples and relevant experimental models. EXPERT OPINION: Proteomics has established neutrophils as highly plastic and context-dependent cells whose functions are governed by coordinated remodeling of signaling, metabolism, and effector pathways. Future progress will depend on expanding neutrophil-specific PTM maps, improving low-input workflows, and integrating single-cell and spatial proteomics. Together, these advances are expected to redefine neutrophil functional states and accelerate translation toward clinically meaningful biomarkers and therapeutic strategies.

Humans↗
PubMed2003

A data-analytic strategy for protein biomarker discovery: profiling of high-dimensional proteomic data for cancer detection.

With recent advances in mass spectrometry techniques, it is now possible to investigate proteins over a wide range of molecular weights in small biological specimens. This advance has generated data-analytic challenges in proteomics, similar to those created by microarray technologies in genetics, namely, discovery of 'signature' protein profiles specific to each pathologic state (e.g. normal vs. cancer) or differential profiles between experimental conditions (e.g. treated by a drug of interest vs. untreated) from high-dimensional data. We propose a data-analytic strategy for discovering protein biomarkers based on such high-dimensional mass spectrometry data. A real biomarker-discovery project on prostate cancer is taken as a concrete example throughout the paper: the project aims to identify proteins in serum that distinguish cancer, benign hyperplasia, and normal states of prostate using the Surface Enhanced Laser Desorption/Ionization (SELDI) technology, a recently developed mass spectrometry technique. Our data-analytic strategy takes properties of the SELDI mass spectrometer into account: the SELDI output of a specimen contains about 48,000 (x, y) points where x is the protein mass divided by the number of charges introduced by ionization and y is the protein intensity of the corresponding mass per charge value, x, in that specimen. Given high coefficients of variation and other characteristics of protein intensity measures (y values), we reduce the measures of protein intensities to a set of binary variables that indicate peaks in the y-axis direction in the nearest neighborhoods of each mass per charge point in the x-axis direction. We then account for a shifting (measurement error) problem of the x-axis in SELDI output. After this pre-analysis processing of data, we combine the binary predictors to generate classification rules for cancer, benign hyperplasia, and normal states of prostate. Our approach is to apply the boosting algorithm to select binary predictors and construct a summary classifier. We empirically evaluate sensitivity and specificity of the resulting summary classifiers with a test dataset that is independent from the training dataset used to construct the summary classifiers. The proposed method performed nearly perfectly in distinguishing cancer and benign hyperplasia from normal. In the classification of cancer vs. benign hyperplasia, however, an appreciable proportion of the benign specimens were classified incorrectly as cancer. We discuss practical issues associated with our proposed approach to the analysis of SELDI output and its application in cancer biomarker discovery.

Algorithms↗