Analysis of mass spectrometry profiles of the serum proteome.
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Humans↗
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Analysis of mass spectrometry profiles of the serum proteome.
Explore the source record for details and available documents.
Algorithms↗
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↗
Proteomic evaluation of core biopsy specimens from breast lesions.
Analysis of tumour samples by a proteomic technology, which combines two-dimensional gel electrophoresis and mass spectrometry analysis, is a promising approach for molecular characterization of cancer. Proteomic analysis of neoplasms is usually performed on surgical material. The possibility to perform proteomic analysis on pre-operative samples might be useful for diagnostic purposes or for determination of tumour sensitivity to therapy. In this study, we report how tissues from core biopsy of breast lesions can be routinely used to obtain accurate protein expression profiles by proteomic analysis. Protein profiles from fibroadenomas were compared to those from ductal infiltrating carcinomas. By using mass spectrometry, identification of proteins overexpressed in carcinomas with respect to fibroadenomas was obtained. Thus, our study provides a methodology to perform proteomic analysis on pre-operative samples of breast lesions.
Biomarkers, Tumor↗
Vascular biology: cellular and molecular profiling.
Our understanding of the mechanisms underlying cerebrovascular atherosclerosis has improved in recent years, but significant gaps remain. New insights into the vascular biological processes that result in ischemic stroke may come from cellular and molecular profiling studies of the peripheral blood. In recent cellular profiling studies, increased levels of a proinflammatory T-cell subset (CD4 (+)CD28 (-)) have been associated with stroke recurrence and death. Expansion of this T-cell subset may occur after ischemic stroke and be a pathogenic mechanism leading to recurrent stroke and death. Increases in certain phenotypes of endothelial cell microparticles have been found in stroke patients relative to controls, possibly indicating a state of increased vascular risk. Molecular profiling approaches include gene expression profiling and proteomic methods that permit large-scale analyses of the transcriptome and the proteome, respectively. Ultimately panels of genes and proteins may be identified that are predictive of stroke risk. Cellular and molecular profiling studies of the peripheral blood and of atherosclerotic plaques may also pave the way for the development of therapeutic agents for primary and secondary stroke prevention.
Atherosclerosis↗
Human liver proteome project: plan, progress, and perspectives.
The Human Liver Proteome Project is the first initiative of the human proteome project for human organs/tissues and aims at writing a modern Prometheus myth. Its global scientific objectives are to reveal the "solar system" of the human liver proteome, expression profiles, modification profiles, a protein linkage (protein-protein interaction) map, and a proteome localization map, and to define an ORFeome, physiome, and pathome. Since it was first proposed in April 2002, the Human Liver Proteome Project has attracted more than 100 laboratories from all over the world. In the ensuing 3 years, we set up a management infrastructure, identified reference laboratories, confirmed standard operating procedures, initiated international research collaborations, and finally achieved the first set of expression profile data.
Humans↗
3D Proteomics: Structural, Functional, Chemical and Biomarker Discovery Proteomics With LiP-MS.
Protein structural dynamics drive changes in protein function, making the capture of such dynamics essential for interrogating biological systems. Here we review limited proteolysis coupled to mass spectrometry (LiP-MS), a structural and chemical proteomics method that uses changes in susceptibility to protease cleavage to profile proteome-wide protein structural changes within complex biological samples. In the decade since its development, LiP-MS has become a broadly used structural proteomics method, with peptide-level resolution. It has identified drug targets, delineated altered cellular pathways in response to complex perturbations, revealed structural information on otherwise challenging protein targets, and demonstrated the new concept of structural biomarkers of disease. Because LiP-MS simultaneously probes numerous types of molecular events, such as molecular binding, changes in enzyme activity, chemical modifications, allosteric conformational changes, aggregation, and unfolding, it supports a new proteomics workflow which we term 3D proteomics. This workflow enables the detection of specific functional sites within proteins that are altered upon perturbation, thereby guiding the generation of molecular hypotheses. Further, by globally profiling structural in addition to protein abundance changes, LiP-MS has proven able to greatly increase the information content of functional proteomics screens. In sum, LiP-MS has supported the development of a novel conceptual framework for generating, visualizing, and interpreting structural proteomics data with peptide level resolution, thereby comprehensively probing biological systems. Here we survey the applications of LiP-MS, discuss methodological variants developed by us and others, and describe the use of this new type of omics readout for structural, functional, chemical, and biomarker discovery proteomics.
Proteomics↗
Detection of coregulation in differential gene expression profiles.
Genomics and proteomics approaches generate distinct gene expression and protein profiles, listing individual genes embedded in broad functional terms as gene ontologies. However, interpretation of gene profiles in a regulatory and functional context remains a major issue. Elucidation of regulatory mechanisms at the gene expression level via analysis of promoter regions is a prominent procedure to decipher such gene regulatory networks. We propose a novel genetic algorithm (GA) to extract joint promoter modules in a set of coexpressed genes as resulting from differential gene expression experiments. Algorithm design has focused on the following constraints: (I) identification of the major promoter modules, which are (II) characterized by a maximum number of joint motifs and (III) are found in a maximum number of coexpressed genes. The capability of the GA in detecting multiple modules was evaluated on various test data sets, analyzing the impact of the number of motifs per promoter module, the number of genes associated with a module, as well as the total number of distinct promoter modules encoded in a sequence set. In addition to the test data sets, the GA was evaluated on two biological examples, namely a muscle-specific data set and the upstream sequences of the beta-actin gene (ACTB) derived from different species, complemented by a comparison to alternative promoter module identification routines.
Actins↗
Decoupling dynamical systems for pathway identification from metabolic profiles.
RATIONALE: Modern molecular biology is generating data of unprecedented quantity and quality. Particularly exciting for biochemical pathway modeling and proteomics are comprehensive, time-dense profiles of metabolites or proteins that are measurable, for instance, with mass spectrometry, nuclear magnetic resonance or protein kinase phosphorylation. These profiles contain a wealth of information about the structure and dynamics of the pathway or network from which the data were obtained. The retrieval of this information requires a combination of computational methods and mathematical models, which are typically represented as systems of ordinary differential equations. RESULTS: We show that, for the purpose of structure identification, the substitution of differentials with estimated slopes in non-linear network models reduces the coupled system of differential equations to several sets of decoupled algebraic equations, which can be processed efficiently in parallel or sequentially. The estimation of slopes for each time series of the metabolic or proteomic profile is accomplished with a 'universal function' that is computed directly from the data by cross-validated training of an artificial neural network (ANN). CONCLUSIONS: Without preprocessing, the inverse problem of determining structure from metabolic or proteomic profile data is challenging and computationally expensive. The combination of system decoupling and data fitting with universal functions simplifies this inverse problem very significantly. Examples show successful estimations and current limitations of the method. AVAILABILITY: A preliminary Web-based application for ANN smoothing is accessible at http://bioinformatics.musc.edu/webmetabol/. S-systems can be interactively analyzed with the user-friendly freeware PLAS (http://correio.cc.fc.ul.pt/~aenf/plas.html) or with the MATLAB module BSTLab (http://bioinformatics.musc.edu/bstlab/), which is currently being beta-tested.
Algorithms↗
Perineuronal net degradation in aggressive glioblastomas with KANK1::NTRK2 fusions.
BACKGROUND: Approximately 10% of glioblastomas harbor targetable genomic fusions. NTRK2 participates in a variety of fusion events that drive tumorigenesis. Two previous reports have described KANK1::NTRK2 fusions in adult glioblastoma patients with poor survival. METHODS: We performed a retrospective analysis of glioblastoma patients treated at Dartmouth-Hitchcock Medical Center (DHMC) from 2020 to 2025 to identify cases harboring KANK1::NTRK2 fusions. Clinical presentation, treatment, histopathologic features, and outcomes were reviewed. In addition, we conducted GeoMx whole-transcriptome and high-plex proteomic digital spatial profiling of a KANK1::NTRK2-positive glioblastoma and a comparator tumor from a long-term survivor. Candidate biomarkers were orthogonally validated using immunohistochemistry and/or immunofluorescence. RESULTS: Two patients with KANK1::NTRK2 fusion glioblastoma were identified, both demonstrating rapid progression, therapeutic resistance, and survival of less than 7 months. Proteomic profiling showed increased expression and activation of canonical NTRK2 downstream signaling pathways, particularly MEK1/2 and ERK1/2. This was accompanied by upregulation of extracellular matrix remodeling enzymes, including MMP3, MMP14, and ADAM15, along with reduced expression of extracellular matrix-associated transcripts and perineuronal net components in particular compared to a non-fusion glioblastoma. CONCLUSIONS: These limited, hypothesis-generating findings suggest constitutive NTRK2 signaling may promote coordinated extracellular matrix degradation and remodeling, potentially facilitating rapid and aggressive tumor growth and invasion in a subset of glioblastomas.
NTRK gene fusion↗
Datamining methodology for LC-MALDI-MS based peptide profiling.
This report will provide a brief overview of the application of data mining in proteomic peptide profiling used for medical biomarker research. Mass spectrometry based profiling of peptides and proteins is frequently used to distinguish disease from non-disease groups and to monitor and predict drug effects. It has the promising potential to enter clinical laboratories as a general purpose diagnostic tool. Data mining methodologies support biomedical science to manage the vast data sets obtained from these instrumentations. Here we will review the typical workflow of peptide profiling, together with typical data mining methodology. Mass spectrometric experiments in peptidomics raise numerous questions in the fields of signal processing, statistics, experimental design and discriminant analysis.
Animals↗
Protein microarrays: molecular profiling technologies for clinical specimens.
Proteomics, the study of protein function within biologic systems, will further our understanding of cancer pathogenesis. Coupled with transcript profiling, proteomics can herald the advent of molecular therapy tailored to the individual patient's neoplasm. Protein microarrays, one emerging class of proteomic technologies, have broad applications for discovery and quantitative analysis. This technology is uniquely suited to gather information about the post-translational modifications of proteins reflecting the activity state of signal pathways and networks. Protein microarrays now make it feasible to conduct signal network profiling within cellular samples. Nevertheless, to be successful, design and use of protein microarrays must take into consideration enormous analytical challenges. A subclass of protein microarrays, Reverse Phase Arrays, created to meet these challenges, has been optimized for use with tissue specimens, and is now in use for the analysis of biopsy samples for clinical trial research.
Antibodies↗
MACSPI enables tissue-selective proteomic and interactomic analyses in multicellular organisms.
Multicellular organisms are composed of many tissue types that have distinct morphologies and functions, which are largely driven by specialized proteomes and interactomes. To define the proteome and interactome of a specific type of tissue in an intact animal, we developed a localized proteomics approach called Methionine Analog-based Cell-Specific Proteomics and Interactomics (MACSPI). This method uses the tissue-specific expression of an engineered methionyl-tRNA synthetase to label proteins with a bifunctional amino acid 2-amino-5-diazirinylnonynoic acid in selected cells. We applied MACSPI in Caenorhabditis elegans, a model multicellular organism, to selectively label, capture, and profile the proteomes of the body wall muscle and the nervous system, which led to the identification of tissue-specific proteins. Using the photo-cross-linker, we successfully profiled HSP90 interactors in muscles and neurons and identified tissue-specific interactors and stress-related interactors. Our study demonstrates that MACSPI can be used to profile tissue-specific proteomes and interactomes in intact multicellular organisms.
Animals↗
Discriminatory proteomic biomarker analysis identifies free hemoglobin in the cerebrospinal fluid of women with severe preeclampsia.
OBJECTIVE: Preeclampsia is an idiopathic multisystem disorder specific to human pregnancy. This study used proteomic analysis of cerebrospinal fluid (CSF) to identify protein biomarkers characteristic of preeclampsia and related to its severity. STUDY DESIGN: CSF was collected from women diagnosed clinically with severe preeclampsia (sPE: n = 7), mild preeclampsia (mPE: n = 8), and normotensive controls (CRL: n = 8). Samples were subjected to proteomic analysis using surface-enhanced laser desorption/ionization time-of-flight (SELDI-TOF) mass spectroscopy. A discriminative proteomic biomarker profile was extracted by applying Mass Restricted analysis, and a Preeclampsia Proteomic Biomarker (PPB) score developed based on the presence or absence of four discriminatory protein peaks in individual CSF SELDI tracings. In-gel tryptic digests, Western blot analysis, on-chip immunoassays, ELISA, and spectral analysis were used to identify the biomarkers composing the PPB score. RESULTS: PPB score distinguished patients with a clinical diagnosis of sPE from mPE and CRLs. (PPB median [range]: sPE: 4 [0-4] vs mPE: 1 [0-1] vs CRL: 0 [0-0]; P < 0.001). PPB scores were unaffected by parity, magnesium seizure prophylaxis, CSF leukocyte counts, and total protein content. Proteomic identification techniques matched the discriminatory protein peaks to the alpha- and beta-hemoglobin chains. ELISA confirmed that women diagnosed clinically with sPE had significantly higher CSF hemoglobin concentrations than women with mPE or CRL (median [range]: sPE: 6.6 [0.0-10.3] microg/mL vs mPE: 0 [0-1.3] microg/mL vs CRL: 0 [0-0] microg/mL; P < 0.001). CONCLUSION: Proteomic analysis of CSF can accurately distinguish sPE from both mPE and CRL. Patients with sPE have nanomolar amounts of free hemoglobin in their CSF. Further studies are needed to confirm these observations and determine their physiologic implications.
Biomarkers↗
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↗
Global profiling of surface plasma membrane proteome of oviductal epithelial cells.
In mammalian reproduction, many important events occur within the female reproductive tract, especially within the oviduct. These include transport and final maturation of the female and male gametes, fertilization, embryonic development, and transport of the embryo to the uterus. The plasma membrane molecules of oviductal epithelia that are in direct contact with gametes and embryo(s) and potentially mediate these processes are poorly characterized, and their function is poorly understood. Defining the oviductal cell surface proteome could provide a better understanding of the basis of reproductive processes taking place within the oviduct. We aimed to provide a detailed profile of the surface plasma membrane proteome of the oviductal epithelium by biotinylation of proteins at the cell surface, followed by highly specific purification of these proteins using avidin. This approach for enrichment of oviductal cell surface proteome was validated by immunohistochemistry, gel electrophoresis, and western blot analysis experiments. The enriched molecules were identified using two different technologies: (i) the combination of 2D gel electrophoresis with mass spectrometry and (ii) 1D gel electrophoresis with mass spectrometry (a modified multidimensional protein identification technology (MudPIT) technique). The number of proteins identified using the MudPIT approach was approximately 7 times the number of proteins identified by 2D gel electrophoresis using the same samples (40 versus 276, respectively). Some of the proteins found at the surface of oviductal cells had previously been reported as present in the oviduct and to have known functions in relation to reproductive processes. The other category of proteins that were highly represented in the oviductal surface proteome were various members of the family of heat-shock proteins. To the best of our knowledge, this is the first comprehensive study to identify and characterize proteins at the surface of the epithelium of the mammalian oviduct.
Animals↗
Identification of diagnostic markers for tuberculosis by proteomic fingerprinting of serum.
BACKGROUND: We investigated the potential of proteomic fingerprinting with mass spectrometric serum profiling, coupled with pattern recognition methods, to identify biomarkers that could improve diagnosis of tuberculosis. METHODS: We obtained serum proteomic profiles from patients with active tuberculosis and controls by surface-enhanced laser desorption ionisation time of flight mass spectrometry. A supervised machine-learning approach based on the support vector machine (SVM) was used to obtain a classifier that distinguished between the groups in two independent test sets. We used k-fold cross validation and random sampling of the SVM classifier to assess the classifier further. Relevant mass peaks were selected by correlational analysis and assessed with SVM. We tested the diagnostic potential of candidate biomarkers, identified by peptide mass fingerprinting, by conventional immunoassays and SVM classifiers trained on these data. FINDINGS: Our SVM classifier discriminated the proteomic profile of patients with active tuberculosis from that of controls with overlapping clinical features. Diagnostic accuracy was 94% (sensitivity 93.5%, specificity 94.9%) for patients with tuberculosis and was unaffected by HIV status. A classifier trained on the 20 most informative peaks achieved diagnostic accuracy of 90%. From these peaks, two peptides (serum amyloid A protein and transthyretin) were identified and quantitated by immunoassay. Because these peptides reflect inflammatory states, we also quantitated neopterin and C reactive protein. Application of an SVM classifier using combinations of these values gave diagnostic accuracies of up to 84% for tuberculosis. Validation on a second, prospectively collected testing set gave similar accuracies using the whole proteomic signature and the 20 selected peaks. Using combinations of the four biomarkers, we achieved diagnostic accuracies of up to 78%. INTERPRETATION: The potential biomarkers for tuberculosis that we identified through proteomic fingerprinting and pattern recognition have a plausible biological connection with the disease and could be used to develop new diagnostic tests.
Adolescent↗
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↗