Microarray Analysis of Gene Expression by Skeletal Muscle of Three Mouse Models of Kennedy Disease/Spinal Bulbar Muscular Atrophy

Emerging evidence implicates altered gene expression within skeletal muscle in the pathogenesis of Kennedy disease/spinal bulbar muscular atrophy (KD/SBMA). We therefore broadly characterized gene expression in skeletal muscle of three independently generated mouse models of this disease. The mouse models included a polyglutamine expanded (polyQ) AR knock-in model (AR113Q), a polyQ AR transgenic model (AR97Q), and a transgenic mouse that overexpresses wild type AR solely in skeletal muscle (HSA-AR). HSA-AR mice were included because they substantially reproduce the KD/SBMA phenotype despite the absence of polyQ AR.We performed microarray analysis of lower hindlimb muscles taken from these three models relative to wild type controls using high density oligonucleotide arrays. All microarray comparisons were made with at least 3 animals in each condition, and only those genes having at least 2-fold difference and whose coefficient of variance was less than 100% were considered to be differentially expressed. When considered globally, there was a similar overlap in gene changes between the 3 models: 19% between HSA-AR and AR97Q, 21% between AR97Q and AR113Q, and 17% between HSA-AR and AR113Q, with 8% shared by all models. Several patterns of gene expression relevant to the disease process were observed. Notably, patterns of gene expression typical of loss of AR function were observed in all three models, as were alterations in genes involved in cell adhesion, energy balance, muscle atrophy and myogenesis. We additionally measured changes similar to those observed in skeletal muscle of a mouse model of Huntington's Disease, and to those common to muscle atrophy from diverse causes.By comparing patterns of gene expression in three independent models of KD/SBMA, we have been able to identify candidate genes that might mediate the core myogenic features of KD/SBMA

Whole-Genome Analysis Reveals That Active Heat Shock Factor Binding Sites Are Mostly Associated with Non-Heat Shock Genes in Drosophila melanogaster

During heat shock (HS) and other stresses, HS gene transcription in eukaryotes is up-regulated by the transcription factor heat shock factor (HSF). While the identities of the major HS genes have been known for more than 30 years, it has been suspected that HSF binds to numerous other genes and potentially regulates their transcription. In this study, we have used a chromatin immunoprecipitation and microarray (ChIP-chip) approach to identify 434 regions in the Drosophila genome that are bound by HSF. We have also performed a transcript analysis of heat shocked Kc167 cells and third instar larvae and compared them to HSF binding sites. The heat-induced transcription profiles were quite different between cells and larvae and surprisingly only about 10% of the genes associated with HSF binding sites show changed transcription. There were also genes that showed changes in transcript levels that did not appear to correlate with HSF binding sites. Analysis of the locations of the HSF binding sites revealed that 57% were contained within genes with approximately 2/3rds of these sites being in introns. We also found that the insulator protein, BEAF, has enriched binding prior to HS to promoters of genes that are bound by HSF upon HS but that are not transcriptionally induced during HS. When the genes associated with HSF binding sites in promoters were analyzed for gene ontology terms, categories such as stress response and transferase activity were enriched whereas analysis of genes having HSF binding sites in introns identified those categories plus ones related to developmental processes and reproduction. These results suggest that Drosophila HSF may be regulating many genes besides the known HS genes and that some of these genes may be regulated during non-stress conditions

Prediction of Drosophila melanogaster gene function using Support Vector Machines

Abstract Background While the genomes of hundreds of organisms have been sequenced and good approaches exist for finding protein encoding genes, an important remaining challenge is predicting the functions of the large fraction of genes for which there is no annotation. Large gene expression datasets from microarray experiments already exist and many of these can be used to help assign potential functions to these genes. We have applied Support Vector Machines (SVM), a sigmoid fitting function and a stratified cross‐validation approach to analyze a large microarray experiment dataset from Drosophila melanogaster in order to predict possible functions for previously un‐annotated genes. A total of approximately 5043 different genes, or about one‐third of the predicted genes in the D. melanogaster genome, are represented in the dataset and 1854 (or 37%) of these genes are un‐annotated. Results 39 Gene Ontology Biological Process (GO‐BP) categories were found with precision value equal or larger than 0.75, when recall was fixed at the 0.4 level. For two of those categories, we have provided additional support for assigning given genes to the category by showing that the majority of transcripts for the genes belonging in a given category have a similar localization pattern during embryogenesis. Additionally, by assessing the predictions using a confidence score, we have been able to provide a putative GO‐BP term for 1422 previously un‐annotated genes or about 77% of the un‐annotated genes represented on the microarray and about 19% of all of the un‐annotated genes in the D. melanogaster genome. Conclusions Our study successfully employs a number of SVM classifiers, accompanied by detailed calibration and validation techniques, to generate a number of predictions for new annotations for D. melanogaster genes. The applied probabilistic analysis to SVM output improves the interpretability of the prediction results and the objectivity of the validation procedure