Research Interest

The complex balance in various regulated activities are achieved in a living organism by a network of multiple genes in pathways. Knowledge based only on single gene investigation cannot draft out the underlying physiological and biochemical pathways. I am interested in interpreting the function of network components by using modern high throughput technology and statistical genetic tools. By exploring changes in the network under different conditions, we can locate the regulatory variants for further concentration in a systematic view and figure out how the cell responds to environment change via relevant pathways.

Mentored by Dr. McIntyre my research topic in the next step focuses on the FoxO transcription factors. In a previous study (Gershman B et al 2007) the dynamics of transcriptional response to nutrition in Drosophila, a set of genes downstream of FoxO has been detected. To continue this work on a higher level, we will subdivide the network and will group these respondent genes in clusters. Due to the high conservation of the nutrient sensing system between drosophila and human, the identification of key functional factors in the drosophila model would be highlighted by their application on human disease.

I worked on the collaborated project of designing a custom SNP chip for Drosophila simulans. This chip is aimed to establish an allele-specific array platform that will facilitate additional studies of allele-specific transcription. Two alleles from different parents have different levels of expression, annotated as Y1 and Y2 respectively. Conventional arrays measure the gene expression level by taking the sum of Y1 and Y2 as a total. The actual Y1 and Y2 are not always equal. We have detected polymorphisms in the transcriptome by using this custom design SNP chip and separate Y1 and Y2.

I have previous experience working on plant and microbe during my undergraduate study. I have a background mainly regarding gene manipulation and molecular cloning. Two large research projects I have participated in were: functional validation of a few secondary metabolic genes in Streptomyces avermitilis, and Identification of the mutant gene in the super dwarf rice. My first year rotation acquainted me with the evolutionary development and molecular evolution.

Currently I am working on the analysis of tissue-specific microRNA regulation.

This project is co-supervised by Dr. Rolf Renne who is a microRNA expert and virologist. microRNAs are 19-24 nucleotide small RNAs, transcribed by RNA polymerase II. The primary transcript contains a stem-loop structure, which is the defining characteristic required by later processing. The primary transcript is processed first in the nuclear by Drosha, then exported to the cytoplasm and processed by Dicer. The mature micrRNA has to be incorporated into the RNA-induced silencing complex, the RISC, which facilitates miR’s binding to their targets. micrRs recognize the 3’ untranslated regions(3’UTRs) of messenger RNAs, and induce degradation or silencing.

microRNAs have recently been discovered playing a crucial role in many biological processes. Their regulations are strictly time- and space-restricted. For a better understanding of that, we want to explore their expression and regulation pattern in different tissues.

miR-155 is an oncogenic human miR. It was first known as the B-cell integration cluster gene, leading to B-cell lymphoma induction, a serious gene, but lacking sequence conservation except for a 100 short fragment. Later they discovered that this conserved region is not responsible for any protein product, but encodes a miR. Deletion of miR-155 caused a complex alteration of immune response. On the other hand, miR-K12-11, encoded by KSHV, is demonstrated as an orthologue of miR-155.

This project will have two lines of evidence individually, to test the hypothesis of tissue-specific miRNA: one is experimental microarry profiling, the other is bioinformatics analysis of known data. For the experimental profiling, we compare an edodthelia cell line with a lymphocyte line. Both types of cell will be transfected with miRNA gene, and search targets of the miRNAs by comparing the difference between the miRNA-infected and the control in gene profiling result. Once we get a list of target genes in endothelial cells, and we get a list of target genes in B cells, we can compare these two groups and the difference would be tissue-specific. The bioinfomatic approach follows the similar logic. We will take advantage of the profiling data of cells infected by KSHV – endothelial origin and lymphoid origin, respectively. Search miRNA targets, flagging them as endothelial or B cell specific, and identify groups that are tissue-specific or common targets.