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PubMed2002

Analysis of signaling pathways using functional proteomics.

Advances in analytical methods for protein analysis by mass spectrometry provide new tools for global analysis of the expressed protein profile of cells (referred to as proteomics). Currently, available methodology samples only part of the proteome. This is sufficient for analysis of signal transduction, because signaling pathways contain enzymes, which modify high-abundance proteins other than those of the pathway. Thus, modulation of the signaling through a pathway will produce a "footprint" in the proteome that is characteristic of a specific cell phenotype. Comparison of different samples to identify these differences in posttranslational modification or protein expression is referred to as functional proteomics. This review surveys the methods in widest use in functional proteomics, as well as a few promising new ones. Although proteomic analyses were first conducted 26 years ago, a renewed interest is fueled by several recent advances. Most important are the availability of public genome and protein databases and the development of high-sensitivity, easy-to-use mass spectrometers and database search engines capable of exploiting these databases. Other important advances include improved two-dimensional polyacrylamide gel electrophoresis (2D-PAGE), computer programs for analysis of the 2D-PAGE gel images, protocols for proteolytic digestion of proteins in excised gel pieces, and low-flow chromatography methods. Despite the limitations of these methods, they can distinguish subtle changes in the phenotype of cells, providing the basis for future studies in regulation of the phenotype.

Animals↗
PubMed2004

Candidate-based proteomics in the search for biomarkers of cardiovascular disease.

The key concept of proteomics (looking at many proteins at once) opens new avenues in the search for clinically useful biomarkers of disease, treatment response and ageing. As the number of proteins that can be detected in plasma or serum (the primary clinical diagnostic samples) increases towards 1000, a paradoxical decline has occurred in the number of new protein markers approved for diagnostic use in clinical laboratories. This review explores the limitations of current proteomics protein discovery platforms, and proposes an alternative approach, applicable to a range of biological/physiological problems, in which quantitative mass spectrometric methods developed for analytical chemistry are employed to measure limited sets of candidate markers in large sets of clinical samples. A set of 177 candidate biomarker proteins with reported associations to cardiovascular disease and stroke are presented as a starting point for such a 'directed proteomics' approach.

Animals↗
PubMed2005

How molecular profiling could revolutionize drug discovery.

Information from genomic, proteomic and metabolomic measurements has already benefited target discovery and validation, assessment of efficacy and toxicity of compounds, identification of disease subgroups and the prediction of responses of individual patients. Greater benefits can be expected from the application of these technologies on a significantly larger scale; by simultaneously collecting diverse measurements from the same subjects or cell cultures; by exploiting the steadily improving quantitative accuracy of the technologies; and by interpreting the emerging data in the context of underlying biological models of increasing sophistication. The benefits of applying molecular profiling to drug discovery and development will include much lower failure rates at all stages of the drug development pipeline, faster progression from discovery through to clinical trials and more successful therapies for patient subgroups. Upheavals in existing organizational structures in the current 'conveyor belt' models of drug discovery might be required to take full advantage of these methods.

Animals↗
PubMed2026

Murine metabolic HFpEF is associated with altered mitochondrial substrate handling and S-nitrosylation remodeling.

Heart failure with preserved ejection fraction (HFpEF) is a heterogeneous condition with incompletely defined myocardial mechanisms. Here, using a two-hit murine model of cardiometabolic HFpEF induced by high-fat diet and endothelial nitric oxide synthase inhibition, we define a mitochondrial metabolic phenotype characterized by altered substrate handling, redox stress, and S-nitrosylation remodeling. While global proteomic changes were modest, metabolomic profiling revealed selective remodeling of tricarboxylic acid cycle intermediates, increased dicarboxylic acids, and altered redox-associated metabolites, consistent with mitochondrial metabolic and redox imbalance in this experimental setting. S-nitrosylation proteomics demonstrated a highly organized and bidirectional remodeling pattern affecting proteins involved in fatty acid/lipid metabolism, carbohydrate metabolism, mitochondrial energy metabolism, amino acid and organic acid metabolism, nucleotide/co-factor metabolism, and redox defense. Stable isotope tracing showed reduced glucose-derived and increased palmitate-derived acetyl-CoA in HFpEF, whereas Na-βHB reduced palmitate contribution and increased βHB-derived acetyl-CoA without restoring glucose contribution, indicating substrate redistribution and preserved ketone oxidation. Na-βHB supplementation increased oligomycin-sensitive respiration in freshly prepared left ventricular tissue, partially normalized selected TCA-cycle intermediates, reduced mitochondrial ROS and the NADH/NAD+ ratio, restored the GSH/GSSG ratio, and improved diastolic function without altering ejection fraction. Together, these findings define a redox-sensitive mitochondrial metabolic state in the HFD/l-NAME model and identify ketone supplementation as a partial metabolic rescue strategy in this context. At the same time, these findings highlight an important limitation of the murine HFD/l-NAME model, which should be interpreted as an experimental system for studying high-fat-induced cardiometabolic stress rather than as a metabolic equivalent of human HFpEF.

Animals↗
PubMed2005

Application of proteomics for comparison of proteome of Neospora caninum and Toxoplasma gondii tachyzoites.

Protein profiles of two isolates of Neospora caninum (KBA-2 and JPA1) and Toxoplasma gondii RH strain were investigated by proteomic approach. Approximately, 78% of protein spots on two-dimensional gel electrophoresis (2-DE) profiles and 80% of antigen spots on 2-DE immunoblotting profiles were exhibited to share the same pI and M(r) between KBA-2 and JPA1 of N. caninum. On the other hand, a total of 30 antigen spots of T. gondii were recognized on 2-DE immunoblotting profile using rabbit antiserum against N. caninum KBA-2. A number of homologue proteins, such as heat shock protein 70, tubulin alpha- and beta-chain, putative protein disulfide isomerase, actin, enolase and 14-3-3 protein homologue are believed as the conserved proteins in both N. caninum and T. gondii. On the contrary, NcSUB1, NcGRA2 and NCDG1 (NcGRA7) might be the species-specific proteins for N. caninum tachyzoites. The present study showed that the high degree of similarity between N. caninum isolates (KBA-2 and JPA1), whereas large differences between N. caninum and T. gondii were noticed by proteome comparisons.

Animals↗
PubMed2002

Integrated genomic and proteomic analysis of signaling pathways in dendritic cell differentiation and maturation.

Dendritic cells (DCs) are antigen-presenting cells that play a major role in initiating primary immune responses. Their phenotypic and functional characteristics are intimately linked to their stage of maturation. The specific biochemical pathways and genes whose expression mediates differentiation of progenitors to DCs and their maturation are largely undefined. We recently utilized two approaches, DNA microarrays and proteomics, to analyze the expression profile of human CD14(+) blood monocytes and their derived DCs. Approximately 4% of the genes or proteins expressed were found to be regulated during DC differentiation. Most of these genes were not previously associated with DCs and included genes highly relevant to DC functions (genes involved in antigen presentation, cell adhesion and motility, lipid metabolism). Genes involved in specific signaling pathways, including IkappaBalpha, PPAR-gamma and C/EBPalpha as well as two members of the family of transcription factors, interferon regulatory factors (IRFs), were also modified. Modulation of IRF gene expression is of particular interest because of their functional roles in innate and adaptive immune responses. IRF-family members control the expression of proteins that include type-1 interferons, interleukin-12, interleukin-15, MHC molecules and adhesion molecules. They have also been found to play an important role in lymphocyte development. In contrast to DC differentiation, very few genes were modified at the transcript level during DC maturation as determined by microarray experiments. Further analysis suggested that DC maturation is largely controlled by posttranscriptional and posttranslational modifications. The use of proteomics is therefore necessary for a full comprehension of DC maturation process.

Animals↗
PubMed2006

Comprehensive quantitative proteome analysis of 20S proteasome subtypes from rat liver by isotope coded affinity tag and 2-D gel-based approaches.

Quantitative protein profiling is an essential part of proteomics and requires technologies that accurately, reproducibly, and comprehensively identify and quantify proteins. Over the past years, many quantitative proteomic methods have been developed. Here, 20S proteasome subtypes isolated from rat were compared by four approaches based on the combination of isotope-coded affinity tag (ICAT), 2-DE, LC and ESI and MALDI MS: (i) 2-DE, (ii) ICAT/2-DE MALDI-MS, (iii) ICAT/LC-ESI-MS, (iv) ICAT/LC-MALDI-MS. A definite qualitative advantage of 2-DE gels was the separation of all known protein species, the identification of cysteine sulfoxide of alpha-4 (RC6-IS) and N-terminal acetylation of several subunits. Furthermore, quantitative differences between the standard subunits beta-2, and beta-5 and their immunosubunits were only detected by 2-DE image analysis revealing a higher replacement of standard- by immuno-beta-subunits in subtype IV. It was obvious that for relative quantification only protein spot and mass peaks with a certain level of intensity displayed acceptable values of SD. However, ICAT in conjunction with LC/MALDI-MS was the most accurate method for quantification. The experimental data of this investigation are accessible via http://www.mpiib-berlin.mpg.de/2D-PAGE/.

Acetylation↗
PubMedDate not supplied

Clinical cancer proteomics: promises and pitfalls.

Proteome analysis promises to be valuable for the identification of tissue and serum biomarkers associated with human malignancies. In addition, proteome technologies offer the opportunity to analyze protein expression profiles and to analyze the activity of signaling pathways. Many published proteomic studies of human tumor tissue are associated with weaknesses in tumor representativity, sample contamination by nontumor cells and serum proteins. Studies often include a moderate number of tumors which may not be representative of clinical materials. It is therefore very important that biomarkers identified by proteomics are validated in representative tumor materials by other techniques, such as immunohistochemistry. Proteome technologies can be used to identify disease markers in human serum. Tumor derived proteins are present at nanomolar to picomolar concentrations in cancer patient sera, 10(6)-10(9)-fold lower than albumin, and will give rise to correspondingly smaller spots/peaks in protein separations. This leads to the need to prefractionate serum samples before analysis. Despite various pitfalls, proteomic analysis is a promising approach to the identification of biomarkers, and for generation of protein expression profiles that can be analyzed by artificial learning methods for improved diagnosis of human malignancy. Recent advances in the field of proteomic analysis of human tumors are summarized in the present review.

Biomarkers, Tumor↗
PubMed2005

Complementary gene and protein expression studies and integrative approaches in toxicogenomics.

Parallel transcript and protein profiling is a strategy to gain further insight into the mechanisms of toxicity and disease. The technologies used to measure expression at the transcript and protein levels each convey different information and have different technical capabilities that can complement each other when combined. In this review, over twenty studies are considered for the use of -Omics platform, the chemical or disease being profiled, tissues, the number of genes and proteins found by each platform and common expression products. A strategy is suggested for toxicant expression profiling that combines the transcriptomics and proteomics of both the target tissue and blood/serum that could provide a more complete characterization of toxicity as well as synergize expression technologies toward biomarker discovery.

Animals↗
PubMed2004

Profiling treatment-specific post-translational modifications in a complex proteome with subtractive substrate phage display.

Proteolytic activation of zymogens or controlled degradation of inhibitory factors is part of a major regulatory system on the post-translational level to regulate treatment induced cellular stress responses. The identification of differential activity based substrates is thus of high interest to prioritize and validate candidate targets for drug discovery. Here we present a novel subtractive substrate phage display screening method for the selection of treatment induced post-translational peptide modifications in complex proteomes. We investigated this approach with tumor cells in response to a protease activating anticancer treatment modality using subtractive and iterative screening of cellular extracts derived from control and treated cells. Specific phage were identified that served as substrates for proteolytic activities in response to treatment related activity changes and could be distinguished from substrates for unspecific proteolytic background activities. Novel, selected peptide substrates were investigated in vitro and in vivo and showed high substrate specificity and functional biological significance.

Amino Acid Sequence↗
PubMed2004

Activity profile of dust mite allergen extract using substrate libraries and functional proteomic microarrays.

Enzymatic activity in the fecal droppings from the house dust mite has been postulated to contribute to the elicited allergic response. Screening dust mite extracts through 137,180 tetrapeptide fluorogenic substrates allowed for the characterization of proteolytic substrate specificity from the potential cysteine and serine proteases in the extract. The extract was further screened against a 4000 member peptide nucleic acid (PNA) encoded inhibitor library designed to target cysteine proteases using microarray detection. Affinity chromatography coupled with mass spectrometry identified Der p 1 as one of the proteases targeted by the PNA inhibitors in the dust mite lysate. A phenotypic readout of Der p 1 function in allergy progression was demonstrated by the inhibition of CD25 cleavage from T cells by dust mite extract that had been treated with the Der p 1 inhibitor identified from the PNA-encoded inhibitor library.

Amino Acid Sequence↗
PubMed2004

High throughput approaches in neuroscience.

Traditional approaches to understanding biological problems are now being advanced with the use of high throughput technologies, which analyse multiple samples simultaneously, or thousands of analytes in a single sample. The application of these technologies in neurochemistry and neuroscience is beginning to be explored and is assisting in the development of new models of drug action, neuroanatomical investigations, and in identifying molecular pathways involved in neurological and psychiatric disease. Tools such as microarray-based gene expression profiling and 2D and multidimensional proteomic methods are uncovering functional components to a wide variety of neuroscience paradigms and the application of these technologies is set to become standard in analysis.

Animals↗
PubMed2004

High-throughput proteomics using antibody microarrays.

Antibody-based microarrays are a novel technology that hold great promise in proteomics. Microarrays can be printed with thousands of recombinant antibodies carrying the desired specificities, the biologic sample (e.g., an entire proteome) and any specifically bound analytes detected. The microarray patterns that are generated can then be converted into proteomic maps, or molecular fingerprints, revealing the composition of the proteome. Using this tool, global proteome analysis and protein expression profiling will thus provide new opportunities for biomarker discovery, drug target identification and disease diagnostics, as well as providing insights into disease biology. Intense work is currently underway to develop this novel technology platform into the high-throughput proteomic tool required by the research community.

Antibodies↗
PubMed2005

Analysis of prostate cancer by proteomics using tissue specimens.

PURPOSE: Prostate cancer cells differ from their normal counterpart in gene expression. Therefore, cancer and normal tissue/cells would show differences in their proteomes. From a diagnostic standpoint differences in the composition of secreted protein species are especially relevant. We used ProteinChip Array (Ciphergen Biosystems, Fremont, California) surface enhanced laser desorption/ionization (SELDI) time of flight mass spectrometry to profile prostate tissue samples to generate phenomic fingerprints. We used quantitative proteomics based on glycopeptide capture followed by tandem mass spectrometry to identify differentially expressed proteins. MATERIALS AND METHODS: Patient matched cancer and noncancer specimens were digested by collagenase to single cells. After digestion the cells were pelleted and the cell-free supernatant was used for analysis. A reversed phase hydrophobic ProteinChip Array was used to generate SELDI patterns from 43 primary prostate tumors, including 26 with matched noncancer specimens. Quantitative proteomics was applied to 1 specimen and the expression pattern was verified by Western blotting and immunohistochemistry. RESULTS: SELDI profiles showed that cancers of similar TNM stages were more likely to have similar profiles. On quantitative proteomics tissue metalloproteinase inhibitor-1 was identified to be down-regulated in cancer. Tissue metalloproteinase inhibitor-1 expression was localized to secretory cells. CONCLUSIONS: Protein profiling by SELDI is relatively easy to perform and it has great potential in prostate cancer diagnosis through pattern recognition. Quantitative proteomics can potentially determine the identity of many biomarkers specific for prostate cancer.

Biomarkers, Tumor↗
PubMed2005

Mechanism-based proteomics tools based on ubiquitin and ubiquitin-like proteins: crystallography, activity profiling, and protease identification.

Isopeptidases that specifically remove ubiquitin or ubiquitin-like molecules from polypeptide adducts are emerging as key regulatory enzymes in a multitude of biochemical pathways. We have developed a set of tools that covalently target the active site of ubiquitin or ubiquitin-like deconjugating enzymes. We have used epitope-tagged ubiquitin and ubiquitin-like derivatives in immunoprecipitation assays to identify active proteases by mass spectrometry (MS/MS). The epitope tag confers the ability to conduct an immunoblot-based profiling assay for active isopeptidases in cell extracts. We have applied a ubiquitin-based probe in the structural analysis of the ubiquitin hydrolase UCH-L3 in its ligand-bound state. We describe the use of these electrophilic derivatives of ubiquitin and ubiquitin-like molecules in the identification, activity profiling, and structural analysis of these proteases. These tools can be used to rapidly profile activity of multiple Ub/UBL-specific proteases in parallel in cell extracts. We also show that in vitro these probes can be conjugated onto parts of the Ub/UBL conjugating machinery.

Crystallography↗
PubMed2003

Interplay of transcriptomics and proteomics.

Despite the obvious attractions of parallel profiling of transcripts and proteins on a global 'omic' scale, there are practical and biological differences involved in their application. Transcriptomics is now a robust, high-throughput, cost-effective technology capable of simultaneously quantifying tens of thousands of defined mRNA species in a miniaturized, automated format. Conversely, proteomic analysis is currently much more limited in breadth and depth of coverage owing to variations in protein abundance, hydrophobicity, stability, size and charge. Nevertheless, transcriptomic and proteomic data can be compared and contrasted provided the studies are carefully designed and interpreted. Differential splicing, post-translational modifications and data integration are among some of the future challenges to tackle.

Biotechnology↗
PubMed2025

Leveraging structure-informed machine learning for fast steric zipper propensity prediction across whole proteomes.

Predicting the amyloid fold and the propensity of peptide segments to adopt amyloid-like structures remain a challenge. However, recent progress has facilitated structure-based prediction of steric zipper propensity and the use of machine learning to accelerate the calculation of predictive models across many scientific areas. Leveraging these advances, we have developed a new approach for rapid proteome-wide assessment of zipper profiles that is informed by four million steric zipper predictions collected over ten years. This collection is used to build a machine learning model capable of rapidly predicting steric zipper propensity, and allowing for the assessment of zippers at both the protein and proteome level. Our predictions show enrichment for zipper forming segments in proteins involved in cell wall reorganization in yeast, highlighting a potential category of interest for experimental characterization. Overall, our predictive model allows for the exploration of amyloid formation across the tree of life and provides a tool for assessment of both novel and designed sequences for zipper density.

Machine Learning↗
PubMed2002

Identification of novel membrane proteins by searching for patterns in hydropathy profiles.

A technique has been developed to search a proteome database for new members of a functional class of membrane protein. It takes advantage of the highly conserved secondary structure of functionally related membrane proteins. Such proteins typically have the same number of transmembrane domains located at similar relative positions in their polypeptide sequence. This gives rise to a characteristic pattern of peaks in their hydropathy profiles. To conduct a search, each member of a polypeptide database is converted to a hydropathy profile, peaks are automatically detected, and the pattern of peaks is compared with a template. A template was designed for the acetylcholine (ACh) and glycine receptors of the cys-loop receptor superfamily. The key feature was a closely spaced triplet of hydropathy peaks bracketed by deep valleys. When applied to the human proteome the search procedure retrieved 153 profiles with a receptor-like triplet of peaks. The approach was highly selective with 70% of the retrieved profiles annotated as known or putative receptors. These included ACh, glycine, gamma-amino butyric acid and serotonin receptors, which are all related by sequence. However, ionotropic glutamate receptors, which have almost no sequence homology with ACh receptors, were also retrieved. Thus, the strategy can find members of a functional class that cannot be identified by sequence alignment. To demonstrate that the strategy can easily be extended to other membrane protein families, a template was developed for the neurotransmitter/Na+ symporter family, and similar results were obtained. This approach should prove a useful adjunct to sequence-based retrieval tools when searching for novel membrane proteins.

Databases, Protein↗