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Protein Expression Data (protein + expression_data)

Distribution by Scientific Domains

Medical Sciences	60%

Selected Abstracts

EndothelinA (ETA) and ETB receptor-mediated regulation of nitric oxide synthase 1 (NOS1) and NOS3 isoforms in the renal inner medulla

ACTA PHYSIOLOGICA, Issue 4 2007
J. C. Sullivan
Abstract Aim:, Our laboratory and others have shown that endothelin (ET)-1 directly stimulates nitric oxide (NO) production in inner medullary collecting duct (IMCD) cells. The goal of this study was to determine which NO synthase (NOS) isoforms in IMCD are sensitive to ET-1, and the role of ETA and ETB receptor activation in vivo and in vitro. Methods:, NOS enzymatic activity and NOS isoform protein expression were examined in cultured IMCD-3 cells and isolated renal inner medulla. ETB receptor-deficient homozygous rats (sl/sl) have elevated levels of circulating ET-1 and lack a functional ETB signalling pathway in kidneys, and furthermore provides a unique model to study ETA receptor signalling in the renal inner medulla in vivo. Results:, Incubation of IMCD-3 cells with exogenous ET-1 (50 nm) resulted in ETA -dependent increased NOS1 protein expression in IMCD-3 cells with no effect on NOS2 or NOS3 expression. ETB receptor antagonism has no effect on NOS expression in IMCD-3 cells. Consistent with in vitro results, cytosolic NOS1 protein expression was significantly greater in the renal inner medulla of sl/sl rats compared with heterozygous (sl/+) controls, with no alteration in NOS3 expression. In contrast to protein expression data, NOS1- and NOS3-specific enzymatic activities decreased in the cytosolic fraction from the renal inner medulla of sl/sl compared with sl/+. Conclusion:, These results provide evidence that both ETA and ETB receptors regulate NOS isoform activity in the renal inner medulla and specifically support the hypothesis that ETA receptor activation increases NOS1 expression. [source]

Proteomic profiling reveals a catalogue of new candidate proteins for human skin aging

EXPERIMENTAL DERMATOLOGY, Issue 10 2010
Martin Laimer
Abstract:, Studies of skin aging are usually performed at the genomic level by investigating differentially regulated genes identified through subtractive hybridization or microarray analyses. In contrast, relatively few studies have investigated changes in protein expression of aged skin using proteomic profiling by two-dimensional (2-D) gel electrophoresis and mass spectrometry, although this approach at the protein level is suggested to reflect more accurately the aging phenotype. We undertook such a proteomic analysis of intrinsic human skin aging by quantifying proteins extracted and fluorescently labeled from sun-protected human foreskin samples pooled from ,young' and ,old' men. In addition, we analyzed these candidate gene products by 1-D and 2-D western blotting to obtain corroborative protein expression data, and by both real-time PCR (RT-PCR) and microarray analyses to confirm expression at the mRNA level. We discovered 30 putative proteins for skin aging, including previously unrecognized, post-translationally regulated candidates such as phosphatidyl-ethanolamine binding protein (PEBP) and carbonic anhydrase 1 (CA1). [source]

Molecular classification of borderline ovarian tumors using hierarchical cluster analysis of protein expression profiles

INTERNATIONAL JOURNAL OF CANCER, Issue 6 2002
Ayodele A. Alaiya
Abstract Ovarian tumors range from benign to aggressive malignant tumors, including an intermediate class referred to as borderline carcinoma. The prognosis of the disease is strongly dependent on tumor classification, where patients with borderline tumors have much better prognosis than patients with carcinomas. We here describe the use of hierarchical clustering analysis of quantitative protein expression data for classification of this type of tumor. An accurate classification was not achieved using an unselected set of 1,584 protein spots for clustering analysis. Different approaches were used to select spots that were differentially expressed between tumors of different malignant potential and to use these sets of spots for classification. When sets of proteins were selected that differentiated benign and malignant tumors, borderline tumors clustered in the benign group. This is consistent with the biologic properties of these tumors. Our results indicate that hierarchical clustering analysis is a useful approach for analysis of protein profiles and show that this approach can be used for differential diagnosis of ovarian carcinomas and borderline tumors. © 2002 Wiley-Liss, Inc. [source]

SiPep: a system for the prediction of tissue-specific minor histocompatibility antigens

INTERNATIONAL JOURNAL OF IMMUNOGENETICS, Issue 4 2006
M. Halling-Brown
Summary Approximately 50 years ago it was found that inbred strains of mice were able to reject tumours and skin grafts from major histocompatibility complex (MHC) identical donors. They proposed that additional transplantation antigens must exist outside the MHC. These were described as minor histocompatibility antigens (mHAgs). Since then, related studies in humans have identified 16 human mHAgs. The aim of this work is to increase the number of known mHAgs by prediction of candidate minor histocompatibility loci by identifying coding single nucleotide polymorphisms (SNPs) where the amino acid variation lies within an MHC-binding peptide and alters the ability of that peptide to bind. We have developed an algorithm called SiPep which uses peptide sequences derived from the flanking regions of known non-synonymous SNPs, various MHC-binding and proteolytic cleavage evaluation methods and protein expression data to predict mHAgs. We have processed 45094 SNPs using the SiPep algorithm and have stored the results in a database called SNPBinder. The facilities to process submitted proteins through the SiPep algorithm as well as the SNPBinder database are available to the public. A set of peptides that are predicted as possible mHAgs by the SiPep algorithm have been tested using refolding assays and gel filtration and the results are presented in this paper. The SiPep tools and SNPBinder database are available free of charge via the internet. An HTML interface providing search facilities can be found at the following address: http://www.sipep.org/. [source]

Multiple approaches to data-mining of proteomic data based on statistical and pattern classification methods

PROTEINS: STRUCTURE, FUNCTION AND BIOINFORMATICS, Issue 9 2003
Jacob W. Tatay
Abstract The data-mining challenge presented is composed of two fundamental problems. Problem one is the separation of forty-one subjects into two classifications based on the data produced by the mass spectrometry of protein samples from each subject. Problem two is to find the specific differences between protein expression data of two sets of subjects. In each problem, one group of subjects has a disease, while the other group is nondiseased. Each problem was approached with the intent to introduce a new and potentially useful tool to analyze protein expression from mass spectrometry data. A variety of methodologies, both conventional and nonconventional were used in the analysis of these problems. The results presented show both overlap and discrepancies. What is important is the breadth of the techniques and the future direction this analysis will create. [source]