Contact Information

Bio-Technology Research Park, Building One
800 E. Leigh Street, Suite 100
Box 980126
Richmond, VA 23298-0126

Assistant Professor
3-390B
(804) 828-3594

PubMed

Vladimir Vladimirov, M.D., Ph.D.

Education

Charles University – Prague, 2001, PhD
Medical University – Sofia, 1996, MD

Research Interests

Research Description

The major interest of my lab is to study microRNA (miRNA) expression differences between schizophrenic (SZ) and bipolar (BP) patients and healthy controls. MiRNAs are small RNA species which primarily function to negatively regulate gene expression. Using Taqman low density arrays (TLDA), we have identified significantly differentially expressed miRNAs in both SZ and BP subjects. Furthermore, using a RNP immunoprecipitation-microarray (RIP-ChIP) approach, we have identified multiple subsets of mRNA targets for these disease related miRNAs. Some of these mRNA targets are co-regulated on transcriptional as well as translational levels and might have related functions in disease development. The significance of these findings is based on the fact that the miRNAs themselves, not their target genes, should be considered as liability factors for disease development.

In addition to miRNAs, my lab also focuses on the genetic control of global gene expression. Recent studies have used genome-wide association studies (GWAS) to discover genes that are associated with complex psychiatric illnesses such as alcoholism, schizophrenia or bipolar disorders. Although such studies are important in narrowing the potentially causative genes, they tell us little about the molecular mechanisms by which these genes cause or modulate disease development. Thus, using post-mortem brain samples, the research in my lab is focused on studying the genetic and molecular mechanisms of gene expression in the development of schizophrenia, bipolar disorders and alcoholism. More specifically, gene expression changes in brain are assessed through medium to high-throughput technologies such as Taqman Low density arrays (TLDA), expression based microarrays and massive parallel signature sequencing (MPSS), all of which allow for an unbiased, global quantitation of all transcripts within the neuronal cells. Next, using genome-wide genotypic data, we can identify a potential disease- relevant expression quantitative trait loci (eQTL) which would provide a direct link between disease-associated genetic variants (e.g. SNPs) or epi-genetic (e.g. methylation) modifications and gene expression changes.

Awards

Selected Publications

None available