UCLA

Genome-wide approaches have been successfully applied to obtain a precise and comprehensive picture of the biological and pathological processes underlying neural development and neurological diseases. Currently two main approaches are used to generate large-scale data in a rapid and inexpensive manner, namely the microarrays and the next-generation sequencing (NGS) technologies. The work presented here focuses on elucidating the complex transcriptional network and epigenetic regulation during neural development and neurological disease using whole-genome approaches.

In Chapter 2 of this dissertation, I showed a link between the regulation of DNA methylation and astrocyte differentiation in embryonic neural progenitor cells (NPCs). DNA methylation is one of the essential epigenetic mechanisms involved in regulating gene expression and it is highly dynamic during the development as well as across different cell types. The precise regulation of DNA methylation is crucial for normal development of central nerve system (CNS). Our lab has previously demonstrated that the de novo methyltransferases Dnmt3a is required for neurogenesis in postnatal neural stem cells. Here I showed that the expression of methylcytosine dioxygenase Tet2 is essential for astrocyte differentiation in the NPCs. By analyzing and comparing the gene expression profiles and genome-wide DNA methylation/hydroxymethylation pattern during the differentiation of NPCs, I found that Tet2 preferentially targets the proximal promoter of astrocytic genes. Tet2 mediated DNA demethylation at the promoter sites is essential for the suppression of the astrocytic genes. I also showed that the basic-Helix-Loop-Helix (bHLH) transcription factor Olig2 directly binds to the promoter of Tet2, and Olig2 represses the differentiation towards astrocyte lineage through transcriptional repression of Tet2.

In Chapter 3 of this dissertation, I described the application of Beadarray technology and bioinformatics analysis in characterizing the temporal changes in global gene expression in a spinal cord injury (SCI) mouse model. Using data-driven network based transcription analysis (Weighted Gene Co-expression Network Analysis, WGCNA) coupled with knowledge-driven Gene Ontology (GO) analysis, we can accurately and comprehensively capture the molecular events occur at different stages after SCI. In this study, I showed an example of how global gene profiling can be translated to identify clusters of genes as indicators of functional recovery and genes of interest as potential therapeutic targets