Multi-level Polyomic Strategies in Translational Experimental and Human Studies

Dr Martin McBride's principle research interests include identification of genetic determinants underlying complex, polygenic disease, including hypertension, cardiac hypertrophy and stroke, in experimental and human studies. 

Working closely with clinical colleagues, in vivo researchers and computational scientists one of McBride's principle research goals is to characterise our excellent experimental models of human cardiovascular disease at the molecular level.  We have congenic and transgenic strains with intermediate phenotypes for a number of blood pressure phenotypes, cardiac hypertrophy and fibrosis.  We have generated ‘omic data, genome DNA sequence, transcriptome (mRNA and miRNA), proteome and metabolome from various tissues including kidney, heart, brain, plasma and urine. In collaboration with Professor Mark Girolami, we have been applying Bayesian statistics to develop novel analysis methods. We have developed a novel method of microarray data analysis that couples model based clustering and binary classification to form clusters of 'response-relevant' genes; that is, genes that are informative when discriminating between the different values of the response (i.e. disease vs control). This novel method of analysis can be applied to any high-dimensional data including microarray gene expression and proteomic datasets (McBride et al. Nucleic Acids Res 2010; Figure 1). It is clear that the integration of these datasets will allow findings to be interpreted in the context of disease pathways instead of the more traditional focus on single gene/ genetic variants and provide a powerful platform for translational studies in humans.

Dr McBride plays a prominent role in the EURATRANS consortium, an FP7 European grant focused on large-scale functional genomics translational research in animal models and reside on the training panel that is responsible for funds to allow an exchange of students and post-doctoral scientists between member laboratories to learn and share knowledge and scientific techniques.

Recently, in collaboration with clinical and research scientists, we have been applying these exciting new post-genomic experimental strategies to different settings of human disease including pre-eclampsia, stroke and nerve injury and regeneration.

Figure 1. Meta-covariate analysis is a novel method that identifies clusters of genes that are functionally related to the phenotype under study.

Publications

1. Nash B, Thomson C, Linington C, Arthur AT, McClure JD, McBride MW and Barnett SC. The Modulating Affect of Astrocyte Polarisation on Myelination is Negatively Mediated By Cxcl10. J Neurosci. 2011;31:13028-38.

2. Denby L, Ramdas V, McBride MW, Wang J, Robinson H, McClure J, Crawford W, Lu R, Hillyard DZ, Khanin R, Agami R, Dominiczak AF, Sharpe CC, Baker AH. miR-21 and miR-214 are consistently modulated during renal injury in rodent models. Am J Pathol. 2011;179:661-72.

3. Yogi A; Callera GE; Aranha AB; Graham, D, McBride MW, Dominiczak A.F, Touyz RM. Sphingosine-1-phosphate-induced inflammation involves receptor tyrosine kinase transactivation in vascular cells: upregulation in hypertension. Hypertension. 2011;57:809-18.

4. McBride MW, Hopcroft LE, Harris KJ, Sampson AK, McClure JD, Graham D, Young G, Holyoake TL, Girolami MA, Dominiczak AF. Predictive response-relevant clustering of expression data provides insights into disease processes. Nucleic Acids Res. 2010;38:6831-40.

5. Padmanabhan S, Melander O, Johnson T, Di Blasio AM, Lee WK, Gentilini D, Hastie CE, Menni C, Monti MC, Delles C, Laing S, Corso B, Navis G, Kwakernaak AJ, van der Harst P, Bochud M, Maillard M, Burnier M, Hedner T, Kjeldsen S, Wahlstrand B, Sjögren M, Fava C, Montagnana M, Danese E, Torffvit O, Hedblad B, Snieder H, Connell JM, Brown M, Samani NJ, Farrall M, Cesana G, Mancia G, Signorini S, Grassi G, Eyheramendy S, Wichmann HE, Laan M, Strachan DP, Sever P, Shields DC, Stanton A, Vollenweider P, Teumer A, Völzke H, Rettig R, Newton-Cheh C, Arora P, Zhang F, Soranzo N, Spector TD, Lucas G, Kathiresan S, Siscovick DS, Luan J, Loos RJ, Wareham NJ, Penninx BW, Nolte IM, McBride MW, Miller WH, Nicklin SA, Baker AH, Graham D, McDonald RA, Pell JP, Sattar N, Welsh P; Global BPgen Consortium, Munroe P, Caulfield MJ, Zanchetti A, Dominiczak AF. Genome-wide association study of blood pressure extremes identifies variant near UMOD associated with hypertension. PLoS Genet. 2010;6:e1001177

6. Taurino C, Miller WH, McBride MW, McClure JD, Khanin R, Moreno MU, Dymott JA, Delles C, Dominiczak AF. Gene expression profiling in whole blood of patients with coronary artery disease. Clin Sci (Lond). 2010;119:335-43.