Protein expression profiling in human lung, breast, bladder, renal, colorectal and ovarian cancers.
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Gene Expression Profiling↗
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Results for “proteomic profiling”
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[Research progress in SELDI-TOF MS and its clinical applications].
Proteinchip profiling is a powerful and innovative proteomic technology for biomarker discovery and diagnostic/prognostic assay development. Based on surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF MS), Ciphergen's proteinchip system offers a single, unified, high-throughput platform for a multitude of proteomic research applications. Proteins are the major functional components of the cell, the study of proteomics provides mankind with a better understanding of disease and life. The remarkable findings in disease biomarkers have shed light to the early diagnosis, monitoring and predicting prognosis of various diseases, especially for cancer. In this article, the development and technology of SELDI-TOF MS are introduced. Some research progress and encouraging research results in oncoproteomics, infectious diseases, neurological diseases and diabetes mellitus using SELDI-TOF MS are also reviewed. The paper is closed by the appraisals on its pros and cons, as well as the future prospective is also expounded.
Animals↗
Capillary isoelectric focusing-based multidimensional concentration/separation platform for proteome analysis.
An integrated proteome concentration/separation approach involving on-line combination of capillary isoelectric focusing (CIEF) with capillary reversed-phase liquid chromatography (CRPLC) is developed for providing significant analyte concentration and extremely high resolving power toward protein and peptide mixtures. Upon completion of analyte focusing, the self-sharpening effect greatly restricts analyte diffusion and contributes to analyte stacking in narrowly focused bands with a concentration factor of approximately 240. In addition to analyte focusing, CIEF as the first separation dimension resolves proteins/peptides on the basis of their differences in pI and offers greater resolving power than that achieved in strong cation exchange chromatography. The grouping of two highly resolving and completely orthogonal separation techniques of CIEF and CRPLC, together with analyte focusing and concentration, significantly enhances the dynamic range and sensitivity of conventional mass spectrometry toward the identification of low-abundance proteins. The CIEF-based multidimensional separation/concentration platform enables the identification of a greater number of yeast soluble proteins than methods presented in the literature, yet requires a protein loading of only 9.6 microg. This protein loading is 2-3 orders of magnitude lower than those employed by the reported non-gel-based proteome techniques. The distribution of a codon adaptation index value for identified yeast proteins approximates to that predicted for the entire yeast proteome and supports the capability of CIEF-based proteome separation technology for achieving comprehensive proteome analysis. By reducing the inner diameter of chromatography columns from 180 microm to 100 microm, the required protein loading is further decreased from 9.6 microg to 960 ng, illustrating the potential usage of this proteome technology for the analysis of protein profiles within small cell populations or limited tissue samples.
Chromatography, Liquid↗
Assignment of endogenous substrates to enzymes by global metabolite profiling.
Enzymes regulate biological processes through the conversion of specific substrates to products. Therefore, of fundamental interest for every enzyme is the elucidation of its natural substrates. Here, we describe a general strategy for identifying endogenous substrates of enzymes by untargeted liquid chromatography-mass spectrometry (LC-MS) analysis of tissue metabolomes from wild-type and enzyme-inactivated organisms. We use this method to discover several brain lipids regulated by the mammalian enzyme fatty acid amide hydrolase (FAAH) in vivo, including known signaling molecules (e.g., the endogenous cannabinoid anandamide) and a novel family of nervous system-enriched natural products, the taurine-conjugated fatty acids. Remarkably, the relative hydrolytic activity that FAAH exhibited for lipid metabolites in vitro was not predictive of the identity of specific FAAH substrates in vivo. Thus, global metabolite profiling establishes unanticipated connections between the proteome and metabolome that enable assignment of an enzyme's unique biochemical functions in vivo.
Amidohydrolases↗
Transcriptome PETs: a genome's best friends.
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5' Flanking Region↗
Acbd7 is essential for preserving hair cell-mediated auditory and vestibular function.
Understanding the molecular basis of hair cell function is essential for elucidating inner ear physiology and developing therapies for auditory-vestibular disorders. Here, we identify acyl-CoA binding domain-containing 7 (Acbd7) as a hair cell-specific gene critical for sensory maintenance. Single-cell transcriptomics of mouse cochlear organoids revealed Acbd7 as a top hair cell-enriched transcript, with its spatiotemporal expression confirmed from embryonic development through adulthood in both auditory and vestibular hair cells. Acbd7‑deficient mice exhibited pronounced hair cell degeneration, characterized by synaptic defects and diminished calcium currents in inner hair cells and loss of outer hair cells. Transcriptomic and proteomic analyses linked Acbd7 to the regulation of Ca2+ signaling and fatty acid metabolism pathways. Our findings establish Acbd7 as a critical regulator of Ca2+ homeostasis and functional integrity in hair cells, thereby elucidating a key mechanism by which a fatty acid metabolism factor sustains hair cell function and providing potential therapeutic targets for inner ear disorders.
Animals↗
Active-site peptide "fingerprinting" of glycosidases in complex mixtures by mass spectrometry. Discovery of a novel retaining beta-1,4-glycanase in Cellulomonas fimi.
New proteomics methods are required for targeting and identification of subsets of a proteome in an activity-based fashion. Here, we report the first gel-free, mass spectrometry-based strategy for mechanism-based profiling of retaining beta-endoglycosidases in complex proteomes. Using a biotinylated, cleavable 2-deoxy-2-fluoroxylobioside inactivator, we have isolated and identified the active-site peptides of target retaining beta-1,4-glycanases in systems of increasing complexity: pure enzymes, artificial proteomes, and the secreted proteome of the aerobic mesophilic soil bacterium Cellulomonas fimi. The active-site peptide of a new C. fimi beta-1,4-glycanase was identified in this manner, and the peptide sequence, which includes the catalytic nucleophile, is highly conserved among glycosidase family 10 members. The glycanase gene (GenBank accession number DQ146941) was cloned using inverse PCR techniques, and the protein was found to comprise a catalytic domain that shares approximately 70% sequence identity with those of xylanases from Streptomyces sp. and a family 2b carbohydrate-binding module. The new glycanase hydrolyzes natural and artificial xylo-configured substrates more efficiently than their cello-configured counterparts. It has a pH dependence very similar to that of known C. fimi retaining glycanases.
Amino Acid Sequence↗
Identification of candidate regulators of embryonic stem cell differentiation by comparative phosphoprotein affinity profiling.
Embryonic stem cells are a unique cell population capable both of self-renewal and of differentiation into all tissues in the adult organism. Despite the central importance of these cells, little information is available regarding the intracellular signaling pathways that govern self-renewal or early steps in the differentiation program. Embryonic stem cell growth and differentiation correlates with kinase activities, but with the exception of the JAK/STAT3 pathway, the relevant substrates are unknown. To identify candidate phosphoproteins with potential relevance to embryonic stem cell differentiation, a systems biology approach was used. Proteins were purified using phosphoprotein affinity columns, then separated by two-dimensional gel electrophoresis, and detected by silver stain before being identified by tandem mass spectrometry. By comparing preparations from undifferentiated and differentiating mouse embryonic stem cells, a set of proteins was identified that exhibited altered post-translational modifications that correlated with differentiation state. Evidence for altered post-translational modification included altered gel mobility, altered recovery after affinity purification, and direct mass spectra evidence. Affymetrix microarray analysis indicated that gene expression levels of these same proteins had minimal variability over the same differentiation period. Bioinformatic annotations indicated that this set of proteins is enriched with chromatin remodeling, catabolic, and chaperone functions. This set of candidate phosphoprotein regulators of stem cell differentiation includes products of genes previously noted to be enriched in embryonic stem cells at the mRNA expression level as well as proteins not associated previously with stem cell differentiation status.
Animals↗
Biologic, cytogenetic, and molecular factors in mesothelial proliferations.
Although mesothelioma cases may have peaked in the 1990s in developed countries, it is expected that there will be over 70,000 cases diagnosed in the United States over the next 5 decades. With the industrial expansion in Southeast Asia and China and the continued use of asbestos, an epidemic of mesothelioma cases is anticipated over the next several decades. A considerable amount has been learned about the cytogenetic and molecular genetics of mesotheliomas. However, in-depth studies are needed to further define specific factors that may provide for early diagnosis, surgical treatment, oncologic management, and gene therapy. Serologic markers for surveillance of those with asbestos exposure and at risk for mesothelioma are needed. Targeted therapy using molecular markers and gene therapy may provide a means to reverse mesothelial proliferations or stabilize tumor growth and allow for surgical resection. The future holds great promise in identifying mesothelioma gene expression profiles (genomics, gene microarrays) and proteins (proteomics) that may produce the key to dealing with this dismal and devastating neoplasm.
Asbestos↗
SpatioMark: quantifying the impact of spatial proximity on cell phenotype.
MOTIVATION: As research advances in spatially resolving the biological archetype of various diseases, technologies that capture the spatial relationships between cells are demonstrating increasing value. Whilst there are an increasing number of analytical methods being developed to identify the complex web of interactions between cells, the downstream impacts of these cell-cell relationships are under explored. RESULTS: We present SpatioMark, a statistical framework that simplifies the assessment of gene or protein expression changes within a cell type that are associated with the spatial proximity to other cell types. We demonstrate its performance across spatial proteomics and transcriptomics datasets. We link identified relationships with differences in patient survival. We highlight key challenges in identifying changes in molecular markers associated with the localization of cells. We propose correction strategies that reduce artefact-induced relationships. AVAILABILITY AND IMPLEMENTATION: SpatioMark is implemented in the Statial R package on Bioconductor: https://bioconductor.org/packages/release/bioc/html/Statial.html.
Humans↗
Artificial gene networks for objective comparison of analysis algorithms.
MOTIVATION: Large-scale gene expression profiling generates data sets that are rich in observed features but poor in numbers of observations. The analysis of such data sets is a challenge that has been object of vigorous research. The algorithms in use for this purpose have been poorly documented and rarely compared objectively, posing a problem of uncertainty about the outcomes of the analyses. One way to objectively test such analysis algorithms is to apply them on computational gene network models for which the mechanisms are completely know. RESULTS: We present a system that generates random artificial gene networks according to well-defined topological and kinetic properties. These are used to run in silico experiments simulating real laboratory microarray experiments. Noise with controlled properties is added to the simulation results several times emulating measurement replicates, before expression ratios are calculated. AVAILABILITY: The data sets and kinetic models described here are available from http://www.vbi.vt.edu/~mendes/AGN/as biochemical dynamic models in SBML and Gepasi formats.
Algorithms↗
A knowledge based approach for representing and reasoning about signaling networks.
MOTIVATION: In this paper we propose to use recent developments in knowledge representation languages and reasoning methodologies for representing and reasoning about signaling networks. Our approach is different from most other qualitative systems biology approaches in that it is based on reasoning (or inferencing) rather than simulation. Some of the advantages of our approach are, we can use recent advances in reasoning with incomplete and partial information to deal with gaps in signal network knowledge; and can perform various kinds of reasoning such as planning, hypothetical reasoning and explaining observations. RESULTS: Using our approach we have developed the system BioSigNet-RR for representation and reasoning about signaling networks. We use a NFkappaB related signaling pathway to illustrate the kinds of reasoning and representation that our system can currently do. AVAILABILITY: The system is available on the Web at http://www.public.asu.edu/~cbaral/biosignet
Algorithms↗
Parameter estimation using Simulated Annealing for S-system models of biochemical networks.
MOTIVATION: High-throughput technologies now allow the acquisition of biological data, such as comprehensive biochemical time-courses at unprecedented rates. These temporal profiles carry topological and kinetic information regarding the biochemical network from which they were drawn. Retrieving this information will require systematic application of both experimental and computational methods. RESULTS: S-systems are non-linear mathematical approximative models based on the power-law formalism. They provide a general framework for the simulation of integrated biological systems exhibiting complex dynamics, such as genetic circuits, signal transduction and metabolic networks. We describe how the heuristic optimization technique simulated annealing (SA) can be effectively used for estimating the parameters of S-systems from time-course biochemical data. We demonstrate our methods using three artificial networks designed to simulate different network topologies and behavior. We then end with an application to a real biochemical network by creating a working model for the cadBA system in Escherichia coli. AVAILABILITY: The source code written in C++ is available at http://www.engg.upd.edu.ph/~naval/bioinformcode.html. All the necessary programs including the required compiler are described in a document archived with the source code. SUPPLEMENTARY INFORMATION: Supplementary material is available at Bioinformatics online.
Biochemistry↗
MetaQuant: a tool for the automatic quantification of GC/MS-based metabolome data.
UNLABELLED: MetaQuant is a Java-based program for the automatic and accurate quantification of GC/MS-based metabolome data. In contrast to other programs MetaQuant is able to quantify hundreds of substances simultaneously with minimal manual intervention. The integration of a self-acting calibration function allows the parallel and fast calibration for several metabolites simultaneously. Finally, MetaQuant is able to import GC/MS data in the common NetCDF format and to export the results of the quantification into Systems Biology Markup Language (SBML), Comma Separated Values (CSV) or Microsoft Excel (XLS) format. AVAILABILITY: MetaQuant is written in Java and is available under an open source license. Precompiled packages for the installation on Windows or Linux operating systems are freely available for download. The source code as well as the installation packages are available at http://bioinformatics.org/metaquant
Algorithms↗
Cell encapsulation technology as a therapeutic strategy for CNS malignancies.
Gene therapy using viral vectors has to date failed to reveal its definitive clinical usefulness. Cell encapsulation technology represents an alternative, nonviral approach for the delivery of biologically active compounds to tumors. This strategy involves the use of genetically engineered producer cells that secrete a protein with therapeutic potential. The cells are encapsulated in an immunoisolating material that makes them suitable for transplantation. The capsules, or bioreactors, permit the release of recombinant proteins that may assert their effects in the tumor microenvironment. During the last decades, there has been significant progress in the development of encapsulation technologies that comprise devices for both macro- and microencapsulation. The polysaccharide alginate is the most commonly used material for cell encapsulation and is well tolerated by various tissues. A wide spectrum of cells and tissues has been encapsulated and implanted, both in animals and humans, indicating the general applicability of this approach for both research and medical purposes, including CNS malignancies. Gliomas most frequently recur at the resection site. To provide local and sustained drug delivery, the bioreactors can be implanted in the brain parenchyma or in the ventricular system. The development of comprehensive analyses of geno- and phenotypic profiles of a tumor (genomics and proteomics) may provide new and important guidelines for choosing the optimal combination of bioreactors and recombinant proteins for therapeutic use.
Alginates↗
Role of biologic markers in patient selection and application to disease prevention.
Aromatase inhibitors (AIs) are now under investigation for the treatment of early stage breast cancer and for disease prevention as alternatives to standard treatment with tamoxifen. Currently identified genetic risk factors of breast cancer include BRCA-1/BRCA-2 mutations, ATM mutations, and history of high estrogen levels, as evidenced by plasma analyses and/or dense bones. To date, estrogen receptor (ER) and progesterone receptor (PgR) status has predictive value for determining response to therapy in patients with hormone receptor-positive breast cancer (ER+ and/or PgR+ tumors). Recent studies have shown AIs to be safer and more effective than tamoxifen in postmenopausal women with advanced disease. Some data suggest that letrozole may be a more effective treatment than tamoxifen for patients with ER+ and/or PgR+ early breast cancers expressing ErbB-1 and/or ErbB-2. Changes in cell proliferation markers (e.g., S-phase fraction and Ki67 antigen), plasma lipid levels, and the bone resorption marker C-terminal peptide are biomarkers that have been evaluated for preventive and prognostic value in breast cancer patients and normal volunteers. Results from biomarker screens can be used to define inclusion criteria for clinical trials and eventually to individualize treatment. Gene expression profiling (microarray analysis), i.e., genomic and proteomic studies, will probably advance the discovery of new biomarkers for breast cancer prevention and treatment.
Antineoplastic Agents↗
A computational approach for peptidomic analysis in taxonomic study of amphibian species.
Peptides in the skin secretion of frogs have been studied for some time now because they frequently possess important biological activity such as antibiotic, antimicrobial, or anticancer properties. In this paper, we present a computational approach for measuring the degree of similarity between the entire peptide complement of the skin secretion of specimens from the same or different species. The first step in the analysis is the generation of a mass spectral profile from an experimental high-performance liquid chromatography/electrosparay ionization analysis of the sample. An "overlap" between the mass spectral profiles of different specimens is then proposed as a measure of their similarity. Analysis of specimens from three species of the genus Litoria, viz., L. Aurea, L. Caerulea, and L. Infrafrenata, and Rana Capito of genus Rana shows that the degree of similarity is highest between specimens from the same species, lower for specimens from different species of the same genus, and lowest between specimens from different genera. This indicates that comparison of skin peptide profiles (i.e., mass spectral profiles of skin secretion) is potentially a useful aid in the taxonomic study of amphibian species.
Algorithms↗
Multi-Omics Landscape of Paraspinal Muscles in Spinal Muscular Atrophy With Scoliosis.
Most spinal muscular atrophy (SMA) patients develop severe scoliosis by late adolescence. Given that the paraspinal muscles-particularly the multifidus-are indispensable for maintaining spinal stability, their site-specific multi-omics characteristics in SMA remain insufficiently defined. Herein, integrated multi-omics sequencing was performed on bilateral multifidus samples from SMA patients and surgical controls. We identified 5219 differentially expressed genes, 1063 differentially expressed proteins and 370 differential metabolites between the control and SMA, showing significant enrichment in glucose and amino acid metabolism pathways, specifically key steps of glycolysis/gluconeogenesis. Key enzymes in the glycolytic process such as PFKM, ENO3 and PKM1 were markedly downregulated. Notably, a comparative analysis of the bilateral paraspinal muscles in SMA revealed asymmetrical metabolic signatures in carbohydrate and amino acid processing between the concave and convex sides. Key regulatory enzymes exhibited significant differential expression: PYGL, a central driver of starch and sucrose metabolism; creatine kinase, involved in arginine and proline metabolism; and PGAM2, a key mediator of glycine, serine, and threonine metabolism. These metabolic signatures indicate a complex metabolic reprogramming in the multifidus, where asymmetric disparities point to the influence of mechanical loading, while systemic dysregulation aligns with the effects of SMN depletion.
Humans↗