Deficiency of alpha2-laminin (merosin) underlies classical congenital muscular dystrophy in humans and dy/dy muscular dystrophy in mice and causes severe muscle dysfunction in both species. To gain greater insight into the biochemical and molecular events that link alpha2-laminin deficiency with muscle fiber necrosis, and the associated compensatory responses, gene expression profiles were characterized in diaphragm muscle from 8-wk-old dy/dy mice using oligonucleotide microarrays. Compared with age-matched normal muscle, dystrophic diaphragm was characterized by predominantly augmented gene expression, irrespective of the fold-change threshold. Among the 69 genes with at least plus or minus twofold significantly altered expression, 30 belonged to statistically overrepresented Gene Ontology (GO) biological process groups. These covered four specific themes: development including muscle development, cell motility with an emphasis on muscle contraction, defense/immune response, and cell adhesion. An additional 11 gene transcripts were assigned to more general overrepresented GO biological process groups (e.g., cellular process, organismal physiological process); the remaining 28 did not belong to any overrepresented groups. GO cellular constituent assignment resulted in the highest degree of overrepresentation in extracellular and muscle fiber locations, whereas GO molecular function assignment was most notable for various types of binding. RT-PCR was performed on 38 of 41 genes with at least plus or minus twofold significantly altered expression that were assigned to overrepresented GO biological process groups, with expression changes verified for 36 of 38 genes. These results indicate that several specific groups of genes have altered expression in response to genetic alpha2-laminin deficiency, with both similarities and differences compared with data reported for dystrophin-deficient muscular dystrophies.
[Show abstract][Hide abstract] ABSTRACT: Diabetes profoundly affects gene expression in organs such as heart, skeletal muscle, kidney and liver, with areas of perturbation including carbohydrate and lipid metabolism, oxidative stress, and protein ubiquitination. Type 1 diabetes impairs lung function, but whether gene expression alterations in the lung parallel those of other tissue types is largely unexplored.
Lung from a rat model of diabetes mellitus induced by streptozotocin was subjected to gene expression microarray analysis.
Glucose levels were 67 and 260 mg/dl (p < 0.001) in control and diabetic rats, respectively. There were 46 genes with at least +/- 1.5-fold significantly altered expression (19 increases, 27 decreases). Gene ontology groups with significant over-representation among genes with altered expression included apoptosis, response to stress (p = 0.03), regulation of protein kinase activity (p = 0.04), ion transporter activity (p = 0.01) and collagen (p = 0.01). All genes assigned to the apoptosis and response to stress groups had increased expression whereas all genes assigned to the collagen group had decreased expression. In contrast, the protein kinase activity and ion transporter activity groups had genes with both increased and decreased expression.
Gene expression in the lung is affected by type 1 diabetes in several specific areas, including apoptosis. However, the lung is resistant to changes in gene expression related to lipid and carbohydrate metabolism and oxidative stress that occur in other tissue types such as heart, skeletal muscle and kidney.
"Gene expression array studies were performed in a manner similar to that described previously , , . Total RNA was extracted using Trizol (GibcoBRL, Rockville, MD), and the RNA pellets were resuspended at 1 µg RNA/µl DEPC-treated water. "
[Show abstract][Hide abstract] ABSTRACT: Respiratory muscle contractile performance is impaired by diabetes, mechanisms of which included altered carbohydrate and lipid metabolism, oxidative stress and changes in membrane electrophysiology. The present study examined to what extent these cellular perturbations involve changes in gene expression.
Diaphragm muscle from streptozotocin-diabetic rats was analyzed with Affymetrix gene expression arrays. Diaphragm from diabetic rats had 105 genes with at least +/-2-fold significantly changed expression (55 increased, 50 decreased), and these were assigned to gene ontology groups based on over-representation analysis using DAVID software. There was increased expression of genes involved in palmitoyl-CoA hydrolase activity (a component of lipid metabolism) (P = 0.037, n = 2 genes, fold change 4.2 to 27.5) and reduced expression of genes related to carbohydrate metabolism (P = 0.000061, n = 8 genes, fold change -2.0 to -8.5). Other gene ontology groups among upregulated genes were protein ubiquitination (P = 0.0053, n = 4, fold change 2.2 to 3.4), oxidoreductase activity (P = 0.024, n = 8, fold change 2.1 to 6.0), and morphogenesis (P = 0.012, n = 10, fold change 2.1 to 4.3). Other downregulated gene groups were extracellular region (including extracellular matrix and collagen) (P = 0.00032, n = 13, fold change -2.2 to -3.7) and organogenesis (P = 0.032, n = 7, fold change -2.1 to -3.7). Real-time PCR confirmed the directionality of changes in gene expression for 30 of 31 genes tested.
These data indicate that in diaphragm muscle type 1 diabetes increases expression of genes involved in lipid energetics, oxidative stress and protein ubiquitination, decreases expression of genes involved in carbohydrate metabolism, and has little effect on expression of ion channel genes. Reciprocal changes in expression of genes involved in carbohydrate and lipid metabolism may change the availability of energetic substrates and thereby directly modulate fatigue resistance, an important issue for a muscle like the diaphragm which needs to contract without rest for the entire lifetime of the organism.
PLoS ONE 11/2009; 4(11):e7832. DOI:10.1371/journal.pone.0007832 · 3.23 Impact Factor
"This type of experiment often leads to the identification of hundreds of differences in the expression of genes. Statistical analysis tools using GO annotations in the context of the GO hierarchy are used to cluster those genes into functional categories (Jensen et al., 2004; Xu et al., 2007; Hecht et al., 2007; Zhu et al., 2007; Matsuki et al., 2005; van Lunteren et al., 2006; Clemente et al., 2006; Agbemafle et al., 2005; Ivins et al., 2005; Choi et al., 2007; Baguma-Nibasheka et al., 2007; Vaes et al., 2006; Zhang et al., 2004; Li et al., 2006). An example of this type of experiment is the one carried out by James et al to study gene expression during chondrocyte differentiation (James et al., 2005). "
[Show abstract][Hide abstract] ABSTRACT: Developmental biology, like many other areas of biology, has undergone a dramatic shift in the perspective from which developmental processes are viewed. Instead of focusing on the actions of a handful of genes or functional RNAs, we now consider the interactions of large functional gene networks and study how these complex systems orchestrate the unfolding of an organism, from gametes to adult. Developmental biologists are beginning to realize that understanding ontogeny on this scale requires the utilization of computational methods to capture, store and represent the knowledge we have about the underlying processes. Here we review the use of the Gene Ontology (GO) to study developmental biology. We describe the organization and structure of the GO and illustrate some of the ways we use it to capture the current understanding of many common developmental processes. We also discuss ways in which gene product annotations using the GO have been used to ask and answer developmental questions in a variety of model developmental systems. We provide suggestions as to how the GO might be used in more powerful ways to address questions about development. Our goal is to provide developmental biologists with enough background about the GO that they can begin to think about how they might use the ontology efficiently and in the most powerful ways possible.
Molecular Reproduction and Development 01/2009; 77(4):314-29. DOI:10.1002/mrd.21130 · 2.53 Impact Factor
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