Circular RNAs
Circular RNAs (circRNAs) are a novel class of highly enriched non-coding RNAs that regulate gene expression in normal and diseased brains. However, despite the demonstrated circRNA function in human neurons, circRNA landscapes in other brain cells remain elusive, partly due to the lack of an effective means for circRNA identification. We have developed CARP (CircRNA identification using A-tailing RNase R approach and Pseudo-reference alignment), a comprehensive computational framework that allows for sensitive, accurate, and quantitative circRNA identification. Using CARP, we identified developmentally programmed circRNA landscapes and novel circRNA-miRNA-mRNA pathways in human oligodendroglia, the myelinating cells that govern neuronal conductance. Our studies offered a methodological advancement in circRNA discovery and revealed novel insights that govern early differentiation of human oligodendroglia, a key step affected in numerous myelin-related human brain diseases. Taking advantage of these established experimental and computational platform, we are currently exploring the key circRNA dysregulation and their potential contribution to Alzheimer’s Diseases.
Bing Yao, PhDAssistant Professor
Neonatal GALT
In this manuscript, we present the results of a time course study using a self-complimentary AAV9 vector to restore GALT activity shortly after birth in a GALT-null rat model of classic galactosemia. Our results document not only restoration of GALT activity in both liver and brain, but also clear metabolic rescue in these and other tissues. Finally, we demonstrate significant correction of 2 adverse outcomes associated with classic galactosemia: cataracts and pre-pubertal growth delay. Combined, these pre-clinical data demonstrate the promise of gene therapy as a possible intervention for improved patient outcomes in classic galactosemia.
Judith Fridovich-Keil, PhDProfessor
TIGAR-V2
We developed an efficient tool TIGAR-V2 for enabling Transcriptome-wide Association Studies (TWAS), which can fit both Elastic-Net penalized regression model and nonparametric Bayesian Dirichlet Process regression model for training gene expression imputation models.TIGAR-V2 conducts both Burden and Variance component TWAS testing using both individual-level and summary-level GWAS data. We also provided trained gene expression imputation models by nonparametric Bayesian method for 49 tissue types from GTEx V8.
Jingjing Yang, PhDAssistant Professor