Project description:Aging animals display a decline in a multitude of physical and physiological functions, including muscle function and strength. Muscle gene expression in dogs has been evaluated for a few select genes under pathogenic or varying dietary conditions, but global gene expression profiles of aged animals has not been performed. Because the mechanisms contributing to age-related decline in muscle function are poorly defined, we used canine microarrays to compare gene expression profiles of muscle tissue from geriatric and young adult dogs. Skeletal muscle (biceps femoris) samples were collected from 6 geriatric (12 yr-old) and 6 young adult (1 yr-old) female beagles after being fed one of two diets (animal protein-based versus plant-protein based) for 12 months. RNA samples were hybridized to Affymetrix GeneChip Canine Genome Arrays. Statistical analyses indicated that age had the greatest impact on gene expression, with 262 genes differentially expressed in geriatric dogs. Although not as robust as age, diet affected mRNA abundance of 22 genes. The effect of age was most notable in genes related to metabolism, cell cycle and cell development, and transcription function, with all of these functional groups being predominantly down-regulated in older animals. The effect of diet on gene expression was mostly limited to the geriatric animals, but interactions between age and diet do not allow for a clear-cut pattern of gene expression to be observed. Keywords: age; diet
Project description:Comparative oncology is a developing research discipline that is being used to assist our understanding of human neoplastic diseases. Companion canines are a preferred animal oncology model due to spontaneous tumor development and similarity to human disease at the pathophysiological level. We use a paired RNA sequencing (RNA-Seq)/microarray analysis of a set of four normal canine lymph nodes and ten canine lymphoma fine needle aspirates to identify technical biases and variation between the technologies and convergence on biological disease pathways. Surrogate Variable Analysis (SVA) provides a formal multivariate analysis of the combined RNA-Seq/microarray data set. Applying SVA to the data allows us to decompose variation into contributions associated with transcript abundance, differences between the technology, and latent variation within each technology. A substantial and highly statistically significant component of the variation reflects transcript abundance, and RNA-Seq proved more sensitive for detection of transcripts expressed at low levels. Latent random variation among RNA-Seq samples is also distinct in character from that impacting microarray samples. In particular, we observed variation between RNA-Seq samples that reflects transcript GC content. Platform-independent variable decomposition without a priori knowledge of the sources of variation using SVA represents a generalizable method for accomplishing cross-platform data analysis. We identified genes differentially expressed between normal lymph nodes of disease free dogs and a subset of the diseased dogs diagnosed with B-cell lymphoma using each technology. There is statistically significant overlap between the RNA-Seq and microarray sets of differentially expressed genes. Analysis of overlapping genes in the context of biological systems suggests elevated expression and activity of PI3K signaling in B-cell lymphoma biopsies compared with normal biopsies, consistent with literature describing successful use of drugs targeting this pathway in lymphomas.
Project description:Comparative oncology is a developing research discipline that is being used to assist our understanding of human neoplastic diseases. Companion canines are a preferred animal oncology model due to spontaneous tumor development and similarity to human disease at the pathophysiological level. We use a paired RNA sequencing (RNA-Seq)/microarray analysis of a set of four normal canine lymph nodes and ten canine lymphoma fine needle aspirates to identify technical biases and variation between the technologies and convergence on biological disease pathways. Surrogate Variable Analysis (SVA) provides a formal multivariate analysis of the combined RNA-Seq/microarray data set. Applying SVA to the data allows us to decompose variation into contributions associated with transcript abundance, differences between the technology, and latent variation within each technology. A substantial and highly statistically significant component of the variation reflects transcript abundance, and RNA-Seq proved more sensitive for detection of transcripts expressed at low levels. Latent random variation among RNA-Seq samples is also distinct in character from that impacting microarray samples. In particular, we observed variation between RNA-Seq samples that reflects transcript GC content. Platform-independent variable decomposition without a priori knowledge of the sources of variation using SVA represents a generalizable method for accomplishing cross-platform data analysis. We identified genes differentially expressed between normal lymph nodes of disease free dogs and a subset of the diseased dogs diagnosed with B-cell lymphoma using each technology. There is statistically significant overlap between the RNA-Seq and microarray sets of differentially expressed genes. Analysis of overlapping genes in the context of biological systems suggests elevated expression and activity of PI3K signaling in B-cell lymphoma biopsies compared with normal biopsies, consistent with literature describing successful use of drugs targeting this pathway in lymphomas. RNA was extracted from 10 lymphoma fine needle aspirates attained from companion canines. 4 normal lymph node samples were obtained from a Beagle breeding colony at Pfizer, including two samples that were taken from the same dog but different lymph nodes. This Series represents the Affymetrix gene expression only, not RNA-Seq referenced above. RNA-Seq data have been submitted to SRA as SRA059558.