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At least 811 records · Page 45Linked to original sources
PubMed2005

Multidimensional protein identification technology (MudPIT): technical overview of a profiling method optimized for the comprehensive proteomic investigation of normal and diseased heart tissue.

An optimized analytical expression profiling strategy based on gel-free multidimensional protein identification technology (MudPIT) is reported for the systematic investigation of biochemical (mal)-adaptations associated with healthy and diseased heart tissue. Enhanced shotgun proteomic detection coverage and improved biological inference is achieved by pre-fractionation of excised mouse cardiac muscle into subcellular components, with each organellar fraction investigated exhaustively using multiple repeat MudPIT analyses. Functional-enrichment, high-confidence identification, and relative quantification of hundreds of organelle- and tissue-specific proteins are achieved readily, including detection of low abundance transcriptional regulators, signaling factors, and proteins linked to cardiac disease. Important technical issues relating to data validation, including minimization of artifacts stemming from biased under-sampling and spurious false discovery, together with suggestions for further fine-tuning of sample preparation, are discussed. A framework for follow-up bioinformatic examination, pattern recognition, and data mining is also presented in the context of a stringent application of MudPIT for probing fundamental aspects of heart muscle physiology as well as the discovery of perturbations associated with heart failure.

Animals↗
PubMed2005

Two-dimensional electrophoretic profiling of normal human kidney glomerulus proteome and construction of an extensible markup language (XML)-based database.

To contribute to physiology and pathophysiology of the glomerulus of human kidney, we have launched a proteomic study of human glomerulus, and compiled a profile of proteins expressed in the glomerulus of normal human kidney by two-dimensional gel electrophoresis (2-DE) and identification with matrix-assisted laser desorption/ionization-time of flight mass spectrometry (MALDI-TOF MS) and/or liquid chromatography-tandem mass spectrometry (LC-MS/MS). Kidney cortices with normal appearance were obtained from patients under surgical nephrectomy due to renal tumor, and glomeruli were highly purified by a standard sieving method followed by picking-up under a phase-contrast microscope. The glomerular proteins were separated by 2-DE with 24 cm immobilized pH gradient strips in the 3-10 range in the first dimension and 26 x 20 cm sodium dodecyl sulfate polyacrylamide electrophoresis gels of 12.5% in the second dimension. Gels were silver-stained, and valid spots were processed for identification through an integrated robotic system that consisted of a spot picker, an in-gel digester, and a MALDI-TOF MS and / or a LC-MS/MS. From 2-DE gel images of glomeruli of four subjects with no apparent pathologic manifestations, a synthetic gel image of normal glomerular proteins was created. The synthetic gel image contained 1713 valid spots, of which 1559 spots were commonly observed in the respective 2-DE gels. Among the 1559 spots, 347 protein spots, representing 212 proteins, have so far been identified, and used for the construction of an extensible markup language (XML)-based database. The database is deposited on a web site (http://www.sw.nec.co.jp/bio/rd/hgldb/index.html) in a form accessible to researchers to contribute to proteomic studies of human glomerulus in health and disease.

Chromatography, Liquid↗
PubMed2005

Genetics and proteomics in scleroderma.

The pathogenesis of systemic sclerosis (SSc; scleroderma) is still enigmatic. Genetic, familial, and twin studies suggest that SSc occurs in genetically susceptible individuals. Recent high-throughput technologies, including gene expression profiling and proteomics, have accelerated the rate of information acquired on possible mechanisms involved in SSc pathogenesis. The potential of the data obtained lies in their use for identifying new disease markers, monitoring disease activity, and developing tailored therapies. The result is an era of unprecedented advance that will benefit SSc and many other diseases.

Alleles↗
PubMed2005

Prognostic factors in acute myeloid leukemia.

PURPOSE OF REVIEW: Cytogenetics offers the most important prognostic information at both presentation and relapse. However, this classification appears to be insufficient, especially for patients presenting with standard-risk cytogenetics, whose relapse risk is variable. Other prognostic factors, stratifying this heterogeneous group of patients into more clearly defined risk groups, are warranted. RECENT FINDINGS: Several molecular markers have been described that predict for long-term outcome in this heterogeneous group of patients; however, there is as yet no consensus as to the prognostic significance of each. Time to morphologic and molecular remission may also be important; however, further studies are warranted to establish their prognostic role in acute myeloid leukemia. SUMMARY: Much has been learnt over the past decade and a better understanding of disease biology, determined by gene expression profiling and proteomic analyses, may help to target therapy and improve the outcome.

Biomarkers, Tumor↗
PubMed2003

Pharmacogenetics of anticancer drug sensitivity in non-small cell lung cancer.

In mammalian cells, the process of malignant transformation is characterized by the loss or down-regulation of tumor-suppressor genes and/or the mutation or overexpression of proto-oncogenes, whose products promote dysregulated proliferation of cells and extend their life span. Deregulation in intracellular transduction pathways generates mitogenic signals that promote abnormal cell growth and the acquisition of an undifferentiated phenotype. Genetic abnormalities in cancer have been widely studied to identify those factors predictive of tumor progression, survival, and response to chemotherapeutic agents. Pharmacogenetics has been founded as a science to examine the genetic basis of interindividual variation in drug metabolism, drug targets, and transporters, which result in differences in the efficacy and safety of many therapeutic agents. The traditional pharmacogenetic approach relies on studying sequence variations in candidate genes suspected of affecting drug response. However, these studies have yielded contradictory results because of the small number of molecular determinants of drug response examined, and in several cases this approach was revealed to be reductionistic. This limitation is now being overcome by the use of novel techniques, i.e., high-density DNA and protein arrays, which allow genome- and proteome-wide tumor profiling. Pharmacogenomics represents the natural evolution of pharmacogenetics since it addresses, on a genome-wide basis, the effect of the sum of genetic variants on drug responses of individuals. Development of pharmacogenomics as a new field has accelerated the progress in drug discovery by the identification of novel therapeutic targets by expression profiling at the genomic or proteomic levels. In addition to this, pharmacogenetics and pharmacogenomics provide an important opportunity to select patients who may benefit from the administration of specific agents that best match the genetic profile of the disease, thus allowing maximum activity.

Antineoplastic Agents↗
PubMed2004

Serum proteomics in cancer diagnosis and management.

Mass spectrometry-based diagnostics has the potential to revolutionize molecular medicine. Using modern mass-spectrometer technologies, clinical tests can be developed that are practical, robust, accurate, and inexpensive. Serum proteomic pattern profiling couples mass spectrometry with adaptive artificial-intelligence-based bioinformatics, which can now be employed to detect pathological states reflected in the serum proteome. With this approach, rapid and cost-effective tests with exquisite clinical sensitivity and specificity are emerging. These tools may dramatically change how disease is detected, monitored, and managed.

Blood Proteins↗
PubMed2006

The heterogeneity of human mesenchymal stem cell preparations--evidence from simultaneous analysis of proteomes and transcriptomes.

OBJECTIVE: Mesenchymal stem cells (MSC) raise high hopes in clinical applications. However, the lack of common standards and a precise definition of MSC preparations remains a major obstacle in research and application of MSC. Whereas surface antigen markers have failed to precisely define this population, a combination of proteomic data and microarray data provides a new dimension for the definition of MSC preparations. METHODS: In our continuing effort to characterize MSC, we have analyzed the differential transcriptome and proteome expression profiles of MSC preparations isolated from human bone marrow under two different expansion media (BM-MSC-M1 and BM-MSC-M2). RESULTS: In proteomics, 136 protein spots were unambiguously identified by MALDI-TOF-MS and corresponding cDNA spots were selected on our "Human Transcriptome cDNA Microarray." Combination of datasets revealed a correlation in differential gene expression and protein expression of BM-MSC-M1 vs BM-MSC-M2. Genes involved in metabolism were more highly expressed in BM-MSC-M1, whereas genes involved in development, morphogenesis, extracellular matrix, and differentiation were more highly expressed in BM-MSC-M2. Interchanging culture conditions for 8 days revealed that differential expression was retained in several genes whereas it was altered in others. CONCLUSION: Our results have provided evidence that homogeneous BM-MSC preparations can reproducibly be isolated under standardized conditions, whereas culture conditions exert a prominent impact on transcriptome, proteome, and cellular organization of BM-MSC.

Cell Culture Techniques↗
PubMedDate not supplied

Nanotechnologies and microchips in genetic diseases.

Microarrays or microchips represent a new area of high technology, which will completely change the methodological approach to basic research and clinical diagnostics. This technology can be used for genotyping, expression profiling and proteome analysis. Genetics and molecular medicine have an expanding need for rapid genotyping, mutational analysis and DNA re-sequencing technologies, i.e. microarrays that have a clear potential for miniaturization, parallelization, and automation and enable high-throughput screening. Expression profiling technology is a new tool for investigating expression patterns, identifying new disease genes either for monogenic disorders or for complex traits, identifying new functional and cellular relationships and identifying new pathways and possible related drugs. This technology has been successfully applied to the study of complex traits, i.e. cardiovascular diseases, cancer and type II diabetes, providing new insights into possible pathogenetic mechanisms and new therapeutical approaches. Finally, microarray can further improve proteome analysis. This review discusses these points.

Gene Expression Profiling↗
PubMed2026

Ribo-ITP enables identification of translons from limited input samples.

In the last decade, an unexpectedly large number of translated regions (translons) have been discovered using ribosome profiling and proteomics. Translons can act as regulatory elements or encode functional micropeptides. However, identification of translons has been limited to cell lines or large organs due to high input requirements for conventional ribosome profiling and mass spectrometry. Here, we address this input limitation using Ribo-ITP on difficult-to-collect samples such as microdissected hippocampal tissues and single preimplantation embryos to identify thousands of translons. To test the translational capacity of the identified translons, we engineer a translon-dependent GFP reporter system and detect expression of translons initiating at ATG and near-cognate start codons in mouse embryonic stem cells (mESCs). We identify distinct expression patterns of translons using a comparative analysis of more than a thousand ribosome profiling datasets across a wide range of cell types. Further, using a machine learning model, we predict that specific upstream translons in synaptically enriched mRNAs regulate translation efficiency of the annotated coding region. Taken together, we present a proof-of-concept study to identify non-canonical translation events from low input samples which can be applied to cell and tissue types inaccessible to conventional methods.

Animals↗
PubMedDate not supplied

Proteomic analysis of human ventricular cerebrospinal fluid from neurologically normal, elderly subjects using two-dimensional LC-MS/MS.

A two-dimensional liquid chromatography separation scheme coupled to tandem mass spectrometry (2-D LC-MS/MS) was utilized to profile the proteome of human CSF. Ventricular CSF samples acquired post-mortem from 10 cognitively normal elderly subjects (mean +/- SEM Braak stage = 1.7 +/- 0.2) were analyzed to determine their protein composition. Raw CSF samples were subjected to an immunobased processing method to remove highly abundant albumin and immunoglobulin (Ig), allowing better detection of lower-abundance proteins. Samples were subjected to trypsin proteolysis followed by C18 solid-phase extraction. Tryptic CSF peptides were separated using a 2-D LC column, in which both strong cation exchange (SCX) and C18 phases were packed into a single capillary. MS/MS spectra of CSF peptides were searched against a human sub-database of the NBCI nonredundant database using the SEQUEST algorithm. Search results were further filtered using DTAselect, and individual samples were compared to one another using Contrast. Using this method, we were able to unambiguously identify 249 CSF proteins from 10 subjects. Of these proteins, 38% were unique to individual subjects, whereas only 6% were common to all 10 subjects. These results suggest considerable subject-to-subject variability in the CSF proteome.

Aged↗
PubMed2004

Principles and applications of multidimensional protein identification technology.

Multidimensional chromatography coupled to tandem mass spectrometry is an emerging technique for the analysis of proteomes and is rapidly being implemented by many researchers for proteomic analysis. In this technology profile, a particular proteomic approach known as multidimensional protein identification technology (MudPIT) is discussed. In MudPIT, a biphasic microcapillary column is packed with high-performance liquid chromatography grade reversed phase and strong cation exchange packing materials, loaded with a complex peptide mixture and placed in line with quaternary high-performance liquid chromatography and a tandem mass spectrometer. MudPIT has the capability to analyze highly complex proteomic mixtures such as whole proteomes, organelles and protein complexes.

Animals↗
PubMed2005

Evaluating preparative isoelectric focusing of complex peptide mixtures for tandem mass spectrometry-based proteomics: a case study in profiling chromatin-enriched subcellular fractions in Saccharomyces cerevisiae.

We have evaluated the use of free-flow electrophoresis, an emerging separation method for preparative isoelectric focusing of complex peptide mixtures, as a tool for high-throughput tandem mass spectrometry-based proteomic analysis. In this study, we investigated the ability of free-flow electrophoresis to resolve and fractionate complex peptide mixtures and also the effectiveness of using peptide isoelectric point in conjunction with peptide match probability scoring in sequence database searching. As a model system for this study, we analyzed a chromatin-enriched fraction from the yeast Saccharomyces cerevisiae. This mixture was fractionated using preparative isoelectric focusing by free-flow electrophoresis, followed by online capillary liquid chromatography electrospray tandem mass spectrometry and sequence database searching. Our results demonstrate that (1) FFE effectively resolves and fractionates complex peptide mixtures on the basis of peptide isoelectric point and (2) the introduction of peptide pI is effective in minimizing both false positive and false negative sequence matches in sequence database searching of tandem mass spectrometry data.

Amino Acid Sequence↗
PubMed2003

Development of a Listeria monocytogenes EGDe partial proteome reference map and comparison with the protein profiles of food isolates.

A partially annotated proteome reference map of the food pathogen Listeria monocytogenes was developed for exponentially growing cells under standardized, optimal conditions by using the sequenced strain EGDe (serotype 1/2a) as a model organism. The map was developed by using a reproducible total protein extraction and two-dimensional (2-D) polyacrylamide gel electrophoresis analysis procedure, and it contained 33 identified proteins representing the four main protein functional classes. In order to facilitate analysis of membrane proteins, a protein compartmentalization procedure was assessed. The method used provided partial fractionation of membrane and cytosolic proteins. The total protein 2-D profiles of three serotype 1/2a strains and one serotype 1/2b strain isolated from food were compared to the L. monocytogenes EGDe proteome. An average of 13% of the major protein spots in the food strain proteomes were not matched in the strain EGDe proteome. The variation was greater for the less intense spots, and on average 28% of these spots were not matched. Two of the proteins identified in L. monocytogenes EGDe were missing in one or more of the food isolates. These two proteins were proteins involved in the main glycolytic pathway and in metabolism of coenzymes and prosthetic groups. The two corresponding genes were found by PCR amplification to be present in the four food isolates. Our results show that the L. monocytogenes EGDe reference map is a valuable starting point for analyses of strains having various origins and could be useful for analyzing the proteomes of different isolates of this pathogen.

Animals↗
PubMed2003

Integration of proteomics and genomics in platelets: a profile of platelet proteins and platelet-specific genes.

Platelets, while anucleate, contain RNA, some of which is translated into protein upon activation. Hypothesising that the platelet proteome is reflected in the transcriptome, we identified 82 proteins secreted from activated platelets and compared these, as well as published proteomic data, to the transcriptional profile. We also compared the transcriptome of platelets to other tissues to identify platelet-specific genes and used ontology to determine gene categories over-represented in platelets. RNA was isolated from highly pure platelet preparations for hybridization to Affymetrix oligonucleotide arrays. We identified 2,928 distinct messages as being present in platelets. The platelet transcriptome was compared with the proteome by relating both to UniGene clusters. Platelet proteomic data correlated well with the transcriptome, with 69% of secreted proteins detectable at the mRNA level, and similar concordance was obtained using two published datasets. While many of the most abundant mRNAs are for known platelet proteins, messages were detected for proteins not previously reported in platelets. Some of these may represent residual megakaryocyte messages; however, proteomic analysis confirmed the expression of many previously unreported genes in platelets. Transcripts for well-described platelet proteins are among the most platelet-specific messages. Ontological categories related to signal transduction, receptors, ion channels, and membranes are over-represented in platelets, while categories involved in protein synthesis are depleted. Despite the absence of gene transcription, the platelet proteome is mirrored in the transcriptome. Conversely, transcriptional analysis predicts the presence of novel proteins in the platelet. Transcriptional analysis is relevant to platelet biology, providing insights into platelet function and the mechanisms of platelet disorders.

Blood Platelets↗
PubMed2026

Integrated metabolomic, transcriptomic, and proteomic analyses reveal changes in the non-volatile metabolite profile of LED light-withered oolong tea.

LED light withering is a crucial method for overcoming weather limitations and enhancing the quality of oolong tea. To elucidate the underlying molecular mechanisms, this study simulated solar spectra using multiwavelength LED light and compared the resulting metabolic, transcriptomic, and proteomic profiles during the enzymatic-catalysis process (ECP) in oolong tea processing. Results indicated that LED light withering altered gene expression and protein regulation of secondary metabolism, particularly in the flavonoid biosynthesis pathway. These shifts encompassed key quality-related compounds, including flavonoids (quercetin-3-O-rhamnoside, dihydroquercetin), amino acids (L-asparagine, L-histidine), guanosine 5'-monophosphate (GMP), and carbohydrates. Furthermore, LED light withering accelerated tea leaf water loss, influenced gene expression involved in photosynthetic cellular components (chloroplasts, thylakoids), increased ascorbate peroxidase regulation under stress, and subsequently modulated energy metabolism and signal transduction in tea leaves. This study offers molecular theoretical framework for the controlled light-withering of oolong tea under bad weather and the associated improvements in its quality.

Camellia sinensis↗
PubMed2005

Towards the application of proteomics in renal disease diagnosis.

Proteomics is widely envisioned as playing a significant role in the translation of genomics to clinically useful applications, especially in the areas of diagnostics and prognostics. In the diagnosis and treatment of kidney disease, a major priority is the identification of disease-associated biomarkers. Proteomics, with its high-throughput and unbiased approach to the analysis of variations in protein expression patterns (actual phenotypic expression of genetic variation), promises to be the most suitable platform for biomarker discovery. Combining such classic analytical techniques as two-dimensional gel electrophoresis with more sophisticated techniques, such as MS, has enabled considerable progress to be made in cataloguing and quantifying proteins present in urine and various kidney tissue compartments in both normal and diseased physiological states. Despite these accomplishments, there remain a number of important challenges that will need to be addressed in order to pave the way for the universal acceptance of proteomics as a clinically relevant diagnostic tool. We discuss issues related to three such critical developmental tasks as follows: (i) completely defining the proteome in the various biological compartments (e.g. tissues, serum and urine) in both health and disease, which presents a major challenge given the dynamic range and complexity of such proteomes; (ii) achieving the routine ability to accurately and reproducibly quantify proteomic expression profiles; and (iii) developing diagnostic platforms that are readily applicable and technically feasible for use in the clinical setting that depend on the fruits of the preceding two tasks to profile multiple disease biomarkers.

Animals↗
PubMed2005

Serum proteomic fingerprinting discriminates between clinical stages and predicts disease progression in melanoma patients.

PURPOSE: Currently known serum biomarkers do not predict clinical outcome in melanoma. S100-beta is widely established as a reliable prognostic indicator in patients with advanced metastatic disease but is of limited predictive value in tumor-free patients. This study was aimed to determine whether molecular profiling of the serum proteome could discriminate between early- and late-stage melanoma and predict disease progression. PATIENTS AND METHODS: Two hundred five serum samples from 101 early-stage (American Joint Committee on Cancer [AJCC] stage I) and 104 advanced stage (AJCC stage IV) melanoma patients were analyzed by matrix-assisted laser desorption/ionisation (MALDI) time-of-flight (ToF; MALDI-ToF) mass spectrometry utilizing protein chip technology and artificial neural networks (ANN). Serum samples from 55 additional patients after complete dissection of regional lymph node metastases (AJCC stage III), with 28 of 55 patients relapsing within the first year of follow-up, were analyzed in an attempt to predict disease recurrence. Serum S100-beta was measured using a sandwich immunoluminometric assay. RESULTS: Analysis of 205 stage I/IV serum samples, utilizing a training set of 94 of 205 and a test set of 15 of 205 samples for 32 different ANN models, revealed correct stage assignment in 84 (88%) of 96 of a blind set of 96 of 205 serum samples. Forty-four (80%) of 55 stage III serum samples could be correctly assigned as progressors or nonprogressors using random sample cross-validation statistical methodologies. Twenty-three (82%) of 28 stage III progressors were correctly identified by MALDI-ToF combined with ANN, whereas only six (21%) of 28 could be detected by S100-beta. CONCLUSION: Validation of these findings may enable proteomic profiling to become a valuable tool for identifying high-risk melanoma patients eligible for adjuvant therapeutic interventions.

Disease Progression↗
PubMed2007

Ovarian cancer biomarkers: a focus on genomic and proteomic findings.

Among the gynaecological malignancies, ovarian cancer is one of the neoplastic forms with the poorest prognosis and with the bad overall and disease-free survival rates than other gynaecological cancers; several studies, analyzing clinical data and pathological features on ovarian cancers, have focused on the identification of both diagnostic and prognostic markers for applications in clinical practice. High-throughput technologies have accelerated the process of biomarker discovery, but their validity should be still demonstrated by extensive researches on sensibility and sensitivity of ovarian cancer novel biomarkers, determining whether gene profiling and proteomics could help differentiate between patients with metastatic ovarian cancer and primary ovarian carcinomas, and their potential impact on management.Therefore, considerable interest lies in identifying molecular prognostic biomarkers and protein indicators to guide treatment decisions and clinical follow up; the current state of knowledge about the potential clinical value of gene expression profiling in ovarian cancer is discussed, focusing on three main areas: distinguishing normal ovarian tissue from ovarian tumors, identifying different subtypes of ovarian cancer and identifying cancer likely to be responsive to therapy.In this elaborate we discuss the use of novel molecules, discovered by proteomics and genomics approaches, as potential protein biomarkers in the management of ovarian cancer, to improve the anticancer therapy for malignant ovarian tumors and to monitor the clinical follow up.

Ovarian cancer↗