Research Interests

Functional Genomics and Systems Biology

Based on the functional genomics technology available at the University of Kansas Genomics Facility and interdisciplinary collaborations with scientists in the Computer Science, Statistics and Bioinformatics fields, my main research interests on functional genomics and systems biology include:

- Biological knowledge extraction from high-through genomics data. Modern genomics technologies have revolutionized life science research; however, the current genomics data mining capabilities are still far from optimal. How to extract as much information as possible from these data for hypothesis formulation still poses a challenge. Currently we are employing a multi-faceted approach to achieve this goal;

- Gene network construction and visualization. Instead of working independently, genes (or the proteins they enccode) exert their functions through a network of interactions. Building and visualizing such a complicated network is an ultimate goal for systems biology. Now we are working to apply the expertise accumulated in the study of non-biological systems to the biological network study;

- Integrated analysis of heterogeneous high-throughput biological data. Presently along with the large amount of microarray data generated in the past decade, there is increasing amount of high-throughput data pumped from other -omics technologies (such as proteomics). How to integrate these heterogeneous data will become more and more important with the accumulation of these data.

Neurogenomics

Powerful as they are, the functional genomics and systems biology approaches must be applied to real-world biological problems. My strong interest, in this regard, is in the exciting area of neurobiology. One of the main neurobiological problems I have been focusing on is a phenomenon termed selective neuronal vulnerability (SNV). In many neurological disorders and during the aging process, only certain neuronal populations are vulnerable and die. For example, in epilepsy, the CA1 region of the hippocampus suffers severe sclerosis as a result of recurrent seizures, while the adjacent CA3 region is relatively spared. Although common, little is known on the cause(s) of SNV. Our functional genomics analyses have suggested that oxidative stress, signal transduction, and inflammatory response, are related to this phenomenon.

Currently we are also looking at the relationship between alternative pre-mRNA splicing and the development of epilepsy. Alternative splicing of a pre-mRNA can lead to generation of different mature mRNAs and subsequently proteins from a single gene. Alternative pre-mRNA splicing is an important subject of modern genomics research and aberrant alternative splicing has been linked to an increasing list of neurological disorders. The genetic link between alternative splicing and epilepsy is hardly known, but there are hints that dysfunctional alternative splicing of some genes may underlie epileptogenesis.

Recent Publications

Wang X, Zaidi A, Pal R, Garrett AS, Braceras R, Chen X-W, Michaelis ML, Michaelis EK (2009) Genomic and biochemical approaches in the discovery of mechanisms for selective neuronal vulnerability to oxidative stress. BMC Neuroscience, 10: 12. doi:10.1186/1471-2202-10-12

Oien DB, Wang X, Moskovitz J (2008) Genomic and proteomic analyses of the methionine sulfoxide reductase a knockout mouse. Current Proteomics, 5(2): 96-103. doi: 10.2174/157016408784911891

Wang X, Pal R, Chen X-W, Kumar KN, Kim O-J, Michaelis EK (2007) Genome-wide transcriptome profiling of region-specific vulnerability to oxidative stress in the hippocampus. Genomics, 90(2): 201-212. doi: 10.1016/j.ygeno.2007.03.007

Chen X-W, Anantha G, Wang X (2006) An effective structure learning method for constructing gene networks. Bioinformatics, 22(11): 1367-1374. doi: 10.1093/bioinformatics/btl090

Wang X, Pal R, Chen X-W, Limpeanchob N, Kumar KN, Michaelis EK (2005) High intrinsic oxidative stress may underlie selective vulnerability of the hippocampal CA1 region. Brain Res Mol Brain Res, 140(1-2): 120-126. doi: 10.1016/j.molbrainres.2005.07.018