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At least 793 records · Page 44Linked to original sources
PubMed2003

Profiling of the cell surface proteome.

The in depth-mining of the proteome necessitates the comprehensive analysis of proteins in individual subcellular compartments to uncover interesting patterns of protein expression that include assessment of protein location, trafficking and of post-translational modifications that are location specific. One of the compartments of substantial interest from a diagnostic and therapeutic point of view is the plasma membrane which contains intrinsic membrane proteins and other proteins expressed on the cell surface. Technologies are currently available for the comprehensive profiling of the cell surface proteome that rely on protein tagging of intact cells. Studies are emerging that point to unexpected patterns of expression of specific proteins on the cell surface, with a common occurrence of proteins previously considered to occur predominantly in other compartments, notably the endoplasmic reticulum. The profiling of the cell surface and plasma membrane proteomes will likely provide novel insights and uncover disease related alterations.

ADP-ribosyl Cyclase↗
PubMed2026

Impact of Genomic Mutations on the Transcriptional Pathways and Tumor Microenvironment Landscape of Localized Early Prostate Cancer.

BACKGROUND: The management of intermediate-risk early prostate cancer (PCa) is challenging due to the difficulty in distinguishing indolent from aggressive tumors. This study explores the association between genomic alterations and the tumor and its microenvironment (TME) and implications for disease progression. METHODS: We performed multi-omic profiling in a cohort of 53 localized PCa using targeted sequencing, transcriptional, and proteomic spatial profiling. RESULTS: Somatic mutations and copy number alterations in RB1 (21%), PTEN (18%), and TP53 (9%) were identified. Kaplan-Meier analysis revealed that alterations in the RB and Cell Cycle pathways, particularly aberrations in PTEN, TP53, or RB1, were associated with shorter biochemical recurrence-free survival (p&#x2009;<&#x2009;0.001). Spatial proteomic analysis demonstrated a complex immune landscape in patients with mutations. The tumor compartment demonstrated higher expression of immune checkpoint markers, T-cell activation proteins, and proliferation markers; and a TME that is enriched with CD8&#x2009;+&#x2009;T cells and antigen-presenting cells, but also with immunosuppressive M2 macrophages, suggesting adaptive immune resistance. CONCLUSIONS: Our analysis demonstrates that genomic alterations in PTEN, TP53, or RB1 are not only prognostic for poor outcomes but are also associated with a unique, immunologically complex TME in this Brazilian cohort.

Humans↗
PubMed2004

Abscisic acid promoted changes in the protein profiles of rice seedling by proteome analysis.

This study investigates the influence of exogenously applied abscisic acid (ABA) on the leaves and leaf sheaths of two-week-old rice seedling at the level of the proteome. Significant differences were observed in the two-dimensional polyacrylamide gel electrophoresis protein profiles between control and ABA treated samples. Amino-acid sequence analysis of affected proteins revealed that ABA caused drastic changes in the major photosynthetic protein, ribulose 1,5-bisphosphate carboxylase/oxygenase and accumulation of certain defense/stress-related proteins. Moreover, cutting or treating leaf sheaths with jasmonic acid (JA) rapidly increased the endogenous level of ABA, suggesting a role for ABA during the defense/stress-response. Comparative study indicated a potential overlap between ABA and JA as detected at the level of the proteome. Furthermore, in vitro protein phosphorylation experiments and in-gel kinase assays also revealed considerable changes in the phosphorylation status of some proteins, and differential effects on myelin basic protein and calcium-dependent protein kinase activities by ABA treatment, which suggests involvement of kinase in the downstream signaling cascade. These results provide evidence at proteome level for the involvement of ABA in stress-response in rice seedling.

Abscisic Acid↗
PubMed2005

The proteomics of sickle cell disease: profiling of erythrocyte membrane proteins by 2D-DIGE and tandem mass spectrometry.

Quantitative changes in the red blood cell membrane proteome in sickle cell disease were analyzed using the two-dimensional fluorescence difference gel electrophoresis 2D-DIGE technique. From over 500 analyzed two-dimensional gel spots, we found 49 protein gel spots whose content in sickle cell membranes were changed by at least 2.5-fold as compared to control cells. In 38 cases we observed an increase and in 11 cases a decrease in content in the sickle cell membranes. The proteins of interest were identified by in-gel tryptic digestion followed by liquid chromatography in line with tandem mass spectrometry. From 38 analyzed gel spots, we identified 44 protein forms representing different modifications of 22 original protein sequences. The majority of the identified proteins fall into small groups of related proteins of the following five categories: actin accessory proteins--four proteins, components of lipid rafts--two proteins, scavengers of oxygen radicals--two proteins, protein repair participants--six proteins, and protein turnover components--three proteins. The number of proteins whose content in sickle RBC membrane is decreased is noticeably smaller, and most are either components of lipid rafts or actin accessory proteins. Elevated content of protein repair participants as well as oxygen radical scavengers may reflect the increased oxidative stress observed in sickle cells.

Anemia, Sickle Cell↗
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↗
PubMed2006

DNA microarray and proteomic analyses of the RpoS regulon in Geobacter sulfurreducens.

The regulon of the sigma factor RpoS was defined in Geobacter sulfurreducens by using a combination of DNA microarray expression profiles and proteomics. An rpoS mutant was examined under steady-state conditions with acetate as an electron donor and fumarate as an electron acceptor and with additional transcriptional profiling using Fe(III) as an electron acceptor. Expression analysis revealed that RpoS acts as both a positive and negative regulator. Many of the RpoS-dependent genes determined play roles in energy metabolism, including the tricarboxylic acid cycle, signal transduction, transport, protein synthesis and degradation, and amino acid metabolism and transport. As expected, RpoS activated genes involved in oxidative stress resistance and adaptation to nutrient limitation. Transcription of the cytochrome c oxidase operon, necessary for G. sulfurreducens growth using oxygen as an electron acceptor, and expression of at least 13 c-type cytochromes, including one previously shown to participate in Fe(III) reduction (MacA), were RpoS dependent. Analysis of a subset of the rpoS mutant proteome indicated that 15 major protein species showed reproducible differences in abundance relative to those of the wild-type strain. Protein identification using mass spectrometry indicated that the expression of seven of these proteins correlated with the microarray data. Collectively, these results indicate that RpoS exerts global effects on G. sulfurreducens physiology and that RpoS is vital to G. sulfurreducens survival under conditions typically encountered in its native subsurface environments.

Adaptation, Physiological↗
PubMed2025

Venomics of the Arabian saw-scaled viper (Echis coloratus) through transcriptome-guided proteomics and in vitro functional profiling.

The Arabian saw-scaled viper (Echis coloratus) is among the snakes of highest medical relevance in the Middle East and North Africa. However, to date, its venom has been investigated in a very limited number of studies, and much remains unknown regarding its compositional and functional properties. By integrating proteotranscriptomics with bioactivity profiling, we present a comprehensive transcriptome-level catalogue of E. coloratus venom components and their associated biological activities. Our analysis identified 183 venom components belonging to 17 distinct protein families. Relative toxin abundances revealed that 92% of the venom proteome is composed of C-type lectin and C-type lectin-related protein (CTL), L-amino acid oxidase (LAAO), phospholipase A2 (PLA2), snake venom serine protease (SVSP), and snake venom metalloproteinase (SVMP), with CTL and PLA2 alone accounting for 73% of the total composition. Bioassays targeting key aspects of viperid envenomation demonstrated potent protease and PLA2 activity in a concentration-dependent manner. In contrast, Factor Xa-like, plasmin-like, and haemolytic activities were negligible. Marked cytotoxicity was observed at the highest concentration tested (i.e., 25 &#x3bc;g/ml) in the mammalian cell lines MDCK II and Calu-3, whereas cytotoxic effects were minimal at lower concentrations. These findings highlight the complexity and potency of E. coloratus venom, and provide a valuable foundation for improving our understanding of envenomation caused by this species.

Animals↗
PubMed2006

Cardiovascular proteomics: tools to develop novel biomarkers and potential applications.

Proteomics is the new systems biological approach to the study of proteins and protein variation on a large scale as a result of biological processes and perturbations. The field is undergoing a dramatic transformation, owing to the completion and annotation of the human genome as well as technological advances to study proteins on a large scale. The new science of proteomics can potentially yield novel biomarkers reflecting cardiovascular disease, establish earlier detection strategies, and monitor responses to therapy. Technological advances permit the unprecedented large-scale identification of peptide sequences in a biological sample with mass spectrometry, whereas gel-based techniques provide further refinement on the status of post-translational modification. The application of high throughput protein evaluation with a subset of predefined targets, identified through proteomics, microarray profiling, and pathway analysis in animal models and human tissues, is gaining momentum in research and clinical applications. Proteomic analysis has provided important insights into ischemic heart disease, heart failure, and cardiovascular pathophysiology. The combination of proteomic biomarkers with clinical phenotypes and genetic haplotype information can lead to a more precise diagnosis and therapy on an individual basis--the fundamental premise of "personalized medicine."

Biomarkers↗
PubMed2004

Proteomic analysis of lung biopsies: Differential protein expression profile between peritumoral and tumoral tissue.

The cellular proteome shows a dynamic profile and is subjected to changes in response to various stimuli and disease progression. Lung cancer remains the leading cause of cancer death in industrialized countries. In an attempt to find new disease markers, patients suffering from lung carcinoma have been selected to achieve differential protein expression patterns between normal and neoplasic tissue. After two-dimensional electrophoresis, the spots of interest were digested and identified by matrix-assisted laser desorption/ionization (MALDI) peptide mass fingerprinting. This information will provide a more comprehensive understanding of the disease progression and might constitute a method to complement histopathological diagnosis.

Databases, Protein↗
PubMed2026

An Integrated Proteomics and Genomics Approach to Identify Essential Protein Kinases During Human Trophoblast Development.

In the developing human placenta, three subtypes of trophoblast cells, cytotrophoblasts (CTBs), extravillous trophoblasts (EVTs), and syncytiotrophoblasts (STBs), mediate critical functions essential for a successful pregnancy. CTBs constitute the stem/progenitor compartment and differentiate into STBs and EVTs within the floating and anchoring villi, respectively. STBs establish the maternal-fetal exchange interface and secrete human chorionic gonadotropin (hCG), a hormone vital for the maintenance of early pregnancy. EVTs anchor the maternal endometrium and invade the uterine tissue to remodel maternal cells, supporting implantation and progression of pregnancy. In this study, we used human trophoblast stem cells (hTSCs) as a model system and performed quantitative, label-free liquid chromatography-tandem mass spectrometry (LC-MS/MS) to profile the proteome and phosphoproteome in TSC stem state (analogous to undifferentiated CTBs) and following their differentiation to STBs and EVTs. Through a multiomics approach, we integrated our proteomics data with global gene expression profiles to correlate cell-type specific gene and protein expression during human trophoblast development. We also identified global phosphoproteome and analyzed kinases that are specifically active in hTSC stem state, as well as in differentiated STBs and EVTs. We experimentally validated specific kinases, such as BUB1B, PAK6, PKYMT1, and TNIK, that are essential for maintaining the hTSC stem-state. Additionally, atypical protein kinase C isoforms PKC&#x3b6; are essential for STB development, whereas PTK2B, SRC, TRIO, and LYN are important for EVT development. Our findings highlight key kinases uniquely required for specific stages of trophoblast development during human placentation and suggest that pharmacological inhibition of these kinases could negatively impact the placentation process during pregnancy.

Humans↗
PubMed2004

Neuroproteomics: expression profiling of the brain's proteomes in health and disease.

The term "proteome" describes the protein complement of a genome. Proteomes of cells are dynamic and are directly affected by environmental factors, such as stress and/or drug treatment, or as a result of aging and disease. One of the distinct advantages of proteomic analysis, not attainable with RNA expression data, is the ability to fractionate the cell's proteins into various subpopulations. In neuroscience, "neuromics" (proteomics in the central nervous system) is in its infancy, with a paucity of studies in the context of the brain. One of the objectives of this review is to illustrate the potential of neuromics to profile differences in the distribution of thousands of proteins as a function of disease markers. We have previously used this approach to determine the effects of varied postmortem interval in examining human brain tissue and to identify biomarkers. Here we review proteomic studies of schizophrenia, Alzheimer's disease, and Parkinson's disease. Experimental results regarding Parkinson's disease are presented to illustrate the potential of neuromics to identify pathways of pathogenesis and novel therapeutic targets.

Alzheimer Disease↗
PubMed2006

Urinary biomarker profiling in transitional cell carcinoma.

Urinary biomarkers or profiles that allow noninvasive detection of recurrent transitional cell carcinoma (TCC) of the bladder are urgently needed. We obtained duplicate proteomic (SELDI) profiles from 227 subjects (118 TCC, 77 healthy controls and 32 controls with benign urological conditions) and used linear mixed effects models to identify peaks that are differentially expressed between TCC and controls and within TCC subgroups. A Random Forest classifier was trained on 130 profiles to develop an algorithm to predict the presence of TCC in a randomly selected initial test set (n = 54) and an independent validation set (n = 43) several months later. Twenty two peaks were differentially expressed between all TCC and controls (p < 10(-7)). However potential confounding effects of age, sex and analytical run were identified. In an age-matched sub-set, 23 peaks were differentially expressed between TCC and combined benign and healthy controls at the 0.005 significance level. Using the Random Forest classifier, TCC was predicted with 71.7% sensitivity and 62.5% specificity in the initial set and with 78.3% sensitivity and 65.0% specificity in the validation set after 6 months, compared with controls. Several peaks of importance were also identified in the linear mixed effects model. We conclude that SELDI profiling of urine samples can identify patients with TCC with comparable sensitivities and specificities to current tumor marker tests. This is the first time that reproducibility has been demonstrated on an independent test set analyzed several months later. Identification of the relevant peaks may facilitate multiplex marker assay development for detection of recurrent disease.

Adult↗
PubMed2025

Mistranslating tRNA variants impact the proteome and phosphoproteome of Saccharomyces cerevisiae.

Transfer RNAs (tRNAs) ensure accurate decoding of the genetic code. However, mutations in tRNAs can lead to mis-incorporation of an amino acid that differs from the genetic message in a process known as mistranslation. As mistranslating tRNAs modify how the genetic message is decoded, they have potential as therapeutic tools for diseases caused by nonsense and missense mutations. Despite this, they also produce proteome-wide mis-made proteins which can disrupt proteostasis. To better understand the impact of mistranslating tRNA variants, we profile the proteome and phosphoproteome of yeast expressing three different mistranslating tRNAs. While the overall impacts were similar, the extent of growth defects and proteome changes varied with the substitution type. Although the global impacts were modest, mistranslation influenced key cellular processes, including proteostasis, cell cycle and translation. These findings highlight the need to consider cellular consequences when developing mistranslating tRNAs for therapeutic applications.

mass spectrometry↗
PubMed2006

High throughput profile-profile based fold recognition for the entire human proteome.

BACKGROUND: In order to maintain the most comprehensive structural annotation databases we must carry out regular updates for each proteome using the latest profile-profile fold recognition methods. The ability to carry out these updates on demand is necessary to keep pace with the regular updates of sequence and structure databases. Providing the highest quality structural models requires the most intensive profile-profile fold recognition methods running with the very latest available sequence databases and fold libraries. However, running these methods on such a regular basis for every sequenced proteome requires large amounts of processing power. In this paper we describe and benchmark the JYDE (Job Yield Distribution Environment) system, which is a meta-scheduler designed to work above cluster schedulers, such as Sun Grid Engine (SGE) or Condor. We demonstrate the ability of JYDE to distribute the load of genomic-scale fold recognition across multiple independent Grid domains. We use the most recent profile-profile version of our mGenTHREADER software in order to annotate the latest version of the Human proteome against the latest sequence and structure databases in as short a time as possible. RESULTS: We show that our JYDE system is able to scale to large numbers of intensive fold recognition jobs running across several independent computer clusters. Using our JYDE system we have been able to annotate 99.9% of the protein sequences within the Human proteome in less than 24 hours, by harnessing over 500 CPUs from 3 independent Grid domains. CONCLUSION: This study clearly demonstrates the feasibility of carrying out on demand high quality structural annotations for the proteomes of major eukaryotic organisms. Specifically, we have shown that it is now possible to provide complete regular updates of profile-profile based fold recognition models for entire eukaryotic proteomes, through the use of Grid middleware such as JYDE.

Algorithms↗
PubMed2006

Expression profiling of glycosyltransferases and related enzymes using gene microarrays.

Gene expression analysis has become a standard tool for studies of biological models of disease processes, growth, and development and has been used increasingly in the field of clinical research. The completion of the human genome sequence is now providing the glycomics research community with the tools to develop a synergism with related fields such as MS glycan profiling and proteomics. The use of microarray technology to measure changes in gene expression is a useful approach for the study of the expression profiles of glycosyltransferases, carbohydrate binding proteins, and related enzymes. The availability of standardized microarray platforms, high-quality reagents for sample preparation and hybridization, and image and statistical analysis tools has contributed to recent advances in the application of this technology to glycobiology. Most microarray experiments today are done within specialized core facilities equipped with instrumentation, software, and personnel with expertise in their application. The focus of this chapter is on aspects of microarray analysis common to all platforms and within the realm of the glycobiologist: consideration of experimental design issues and methods for the preparation of RNA samples from a variety of tissue sources suitable for microarray analysis.

Animals↗
PubMed2006

Comparative and functional genomics of Listeria spp.

The genus Listeria comprises a group of non-sporulating, Gram-positive, soil bacteria belonging to the low G+C group of microorganisms. The genus consists of only six species, L. monocytogenes, L. ivanovii, L. seeligeri, L. innocua, L. welshimeri, and L. grayi.L. monocytogenes and L. ivanovii are the only known pathogens of this group. Comparative whole-genome sequencing of representative strains comprising the entire genus is currently being performed and nearing completion. In the genus Listeria, genome reduction has led to the generation of non-pathogenic species from pathogenic progenitor strains. Indeed, many of the regions absent in the non-pathogenic species represent commonly deleted genes. Speciation and diversity of strains has been achieved by horizontal gene transfer of DNA encoding novel genes probably required for niche specific survival. The sequencing of several listerial genomes has also been accompanied by studies using global strategies involving whole-genome transcriptional profiling and proteomics to examine the adaptative changes of L. monocytogenes to growth in different environments and to catalogue the genes mediating these responses. We review this data and present information on the expression profile of L. monocytogenes EGD-e inside the vacuolar and the cytosolic environments of the host cell using whole-genome microarray analysis. Of the 484 genes regulated during intracellular growth 41 genes are species-specific, being absent from the genome of the non-pathogenic L. innocua CLIP 11262 strain. There were 25 genes that are strain-specific i.e. absent from the genome of the L. monocytogenes F2365 serotype 4b strain suggesting heterogeneity in the gene pool required for intracellular survival of L. monocytogenes in host cells.

Chromosomes, Bacterial↗