Biomedical subjects

Sean McWilliam

Publications and source records attributed to Sean McWilliam.

4 recordsLinked to original sources

PubMed2006

Identification of a gene network contributing to hypertrophy in callipyge skeletal muscle.

The callipyge mutation in sheep results in postnatal skeletal muscle hypertrophy in the pelvic limbs and loins with little or no effect on anterior skeletal muscles. Associated with the phenotype are changes in the expression of a number of imprinted genes flanking the site of the mutation, which lies in an intergenic region at the telomeric end of ovine chromosome 18. The manner in which these local changes in gene expression are translated into muscle hypertrophy is not known. Microarray-based transcriptional profiling was used to identify differentially expressed genes in longissimus dorsi skeletal muscle samples taken at birth and 12 wk of age from callipyge and wild-type sheep. The phenotype was only expressed at the latter developmental time and associated with decreased type 1 fibers (slow oxidative) and a shift toward type IIx and IIb fibers (fast-twitch glycolytic). We have identified 131 genes in the samples taken at 12 wk of age that were differentially expressed as a function of genotype but not due to the fiber type changes. The gene expression changes occurring as a function of genotype in the samples taken at birth indicated that the transcriptional framework underpinning the phenotype was emerging prior to expression of the phenotype. Eight genes were differentially expressed as a function of genotype at both developmental times. A model is proposed describing a core network of genes and histone epigenetic modifications that is likely to underpin the fiber type changes and muscle hypertrophy characteristic of callipyge sheep.

Animals↗

PubMed2005

Construction and validation of a Bovine Innate Immune Microarray.

BACKGROUND: Microarray transcript profiling has the potential to illuminate the molecular processes that are involved in the responses of cattle to disease challenges. This knowledge may allow the development of strategies that exploit these genes to enhance resistance to disease in an individual or animal population. RESULTS: The Bovine Innate Immune Microarray developed in this study consists of 1480 characterised genes identified by literature searches, 31 positive and negative control elements and 5376 cDNAs derived from subtracted and normalised libraries. The cDNA libraries were produced from 'challenged' bovine epithelial and leukocyte cells. The microarray was found to have a limit of detection of 1 pg/microg of total RNA and a mean slide-to-slide correlation co-efficient of 0.88. The profiles of differentially expressed genes from Concanavalin A (ConA) stimulated bovine peripheral blood lymphocytes were determined. Three distinct profiles highlighted 19 genes that were rapidly up-regulated within 30 minutes and returned to basal levels by 24 h; 76 genes that were up-regulated between 2-8 hours and sustained high levels of expression until 24 h and 10 genes that were down-regulated. Quantitative real-time RT-PCR on selected genes was used to confirm the results from the microarray analysis. The results indicate that there is a dynamic process involving gene activation and regulatory mechanisms re-establishing homeostasis in the ConA activated lymphocytes. The Bovine Innate Immune Microarray was also used to determine the cross-species hybridisation capabilities of an ovine PBL sample. CONCLUSION: The Bovine Innate Immune Microarray has been developed which contains a set of well-characterised genes and anonymous cDNAs from a number of different bovine cell types. The microarray can be used to determine the gene expression profiles underlying innate immune responses in cattle and sheep.

Animals↗

PubMed2005

Characterisation and application of a bovine U6 promoter for expression of short hairpin RNAs.

BACKGROUND: The use of small interfering RNA (siRNA) molecules in animals to achieve double-stranded RNA-mediated interference (RNAi) has recently emerged as a powerful method of sequence-specific gene knockdown. As DNA-based expression of short hairpin RNA (shRNA) for RNAi may offer some advantages over chemical and in vitro synthesised siRNA, a number of vectors for expression of shRNA have been developed. These often feature polymerase III (pol. III) promoters of either mouse or human origin. RESULTS: To develop a shRNA expression vector specifically for bovine RNAi applications, we identified and characterised a novel bovine U6 small nuclear RNA (snRNA) promoter from bovine sequence data. This promoter is the putative bovine homologue of the human U6-8 snRNA promoter, and features a number of functional sequence elements that are characteristic of these types of pol. III promoters. A PCR based cloning strategy was used to incorporate this promoter sequence into plasmid vectors along with shRNA sequences for RNAi. The promoter was then used to express shRNAs, which resulted in the efficient knockdown of an exogenous reporter gene and an endogenous bovine gene. CONCLUSION: We have mined data from the bovine genome sequencing project to identify a functional bovine U6 promoter and used the promoter sequence to construct a shRNA expression vector. The use of this native bovine promoter in shRNA expression is an important component of our future development of RNAi therapeutic and transgenic applications in bovine species.

Animals↗

PubMed2004

Validation of alternative methods of data normalization in gene co-expression studies.

MOTIVATION: Clusters of genes encoding proteins with related functions, or in the same regulatory network, often exhibit expression patterns that are correlated over a large number of conditions. Protein associations and gene regulatory networks can be modelled from expression data. We address the question of which of several normalization methods is optimal prior to computing the correlation of the expression profiles between every pair of genes. RESULTS: We use gene expression data from five experiments with a total of 78 hybridizations and 23 diverse conditions. Nine methods of data normalization are explored based on all possible combinations of normalization techniques according to between and within gene and experiment variation. We compare the resulting empirical distribution of gene x gene correlations with the expectations and apply cross-validation to test the performance of each method in predicting accurate functional annotation. We conclude that normalization methods based on mixed-model equations are optimal.

Algorithms↗