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Expression Level Changes (expression + level_change)

Distribution by Scientific Domains

Life Sciences	50%

Selected Abstracts

Expression and chromosomal organization of mouse meiotic genes

MOLECULAR REPRODUCTION & DEVELOPMENT, Issue 3 2010
Hiba Waldman Ben-Asher
Microarray technology which enables large scale analysis of gene expression and thus comparison between transcriptomes of different cell types, cells undergoing different treatments or cells at different developmental stages has also been used to study the transcriptome involved with spermatogenesis. Many new germ cell-specific genes were determined, and the resulting genes were classified according to different criteria. However, the biological significance of these classifications and their clustering according to developmental transcriptional patterns during spermatogenesis have not yet been addressed. In this study we utilized mouse testicular transcriptome analysis at five distinct post-natal ages (Days 7, 10, 12, 14, and 17), representing distinct meiotic stages, in an attempt to better understand the biological significance of genes clustered into similar expression patterns during this process. Among 790 sequences that showed an expression level change of twofold or more in any of the five key stages that were monitored, relative to the geometric average of all stages, about 40% peaked and about 30% were specifically suppressed at post-natal day 14 (representing the early pachytene stage of spermatocytes), reflecting tight transcriptional regulation at this stage. We also found that each of the six main transcription clusters that were determined was characterized by statistically significant representation of genes related to specific biological processes. Finally, our results indicated that genes important for meiosis are not randomly distributed along the mouse genome but rather preferentially located on specific chromosomes, suggesting for the first time that chromosomal location might be a regulating factor of meiotic gene expression. Mol. Reprod. Dev. 77: 241,248, 2010. © 2009 Wiley-Liss, Inc. [source]

Candidate glioblastoma development gene identification using concordance between copy number abnormalities and gene expression level changes

GENES, CHROMOSOMES AND CANCER, Issue 10 2007
Ken C. Lo
Copy number abnormalities (CNAs) in tumor cells are presumed to affect expression levels of genes located in region of abnormality. To investigate this relationship we have surveyed the losses, gains and amplifications in 30 glioblastomas using array comparative genome hybridization and compared these data with gene expression changes in the same tumors using the Affymetrix U133Plus2.0 oligonucleotide arrays. The two datasets were overlaid using our in-house overlay tool which highlights concordance between CNAs and expression level changes for the same tumors. In this survey we have highlighted genes frequently overexpressed in amplified regions on chromosomes 1, 4, 11, and 12 and have identified novel amplicons on these chromosomes. Deletions of specific regions on chromosomes 9, 10, 11, 14, and 15 have also been correlated with reduced gene expression in the regions of minimal overlap. In addition we describe a novel approach for comparing gene expression levels between tumors based on the presence or absence of chromosome CNAs. This genome wide screen provides an efficient and comprehensive survey of genes which potentially serve as the drivers for the CNAs in GBM. © 2007 Wiley-Liss, Inc. [source]

Proteomics cataloging analysis of human expressed prostatic secretions reveals rich source of biomarker candidates

PROTEOMICS - CLINICAL APPLICATIONS, Issue 4 2008
Runsheng Li
Abstract Expressed prostatic secretions (EPS) contain proteins of prostate origin that may reflect the health status of the prostate and be used as diagnostic markers for prostate diseases including prostatitis, benign prostatic hyperplasia, and prostate cancer. Despite their importance and potential applications, a complete catalog of EPS proteins is not yet available. We, therefore, undertook a comprehensive analysis of the EPS proteome using 2-D micro-LC combined with MS/MS. Using stringent filtering criteria, we identified a list of 114 proteins with at least two unique-peptide hits and an additional 75 proteins with only a single unique-peptide hit. The proteins identified include kallikrein 2 (KLK2), KLK3 (prostate-specific antigen), KLK11, and nine cluster of differentiation (CD) molecules including CD10, CD13, CD14, CD26, CD66a, CD66c, CD 143, CD177, and CD224. To our knowledge, this list represents the first comprehensive characterization of the EPS proteome, and it provides a candidate biomarker list for targeted quantitative proteomics analysis using a multiple reaction monitoring (MRM) approach. To help prioritize candidate biomarkers, we constructed a protein,protein interaction network of the EPS proteins using Cytoscape (www.cytoscape.org), and overlaid the expression level changes from the Oncomine database onto the network. [source]