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Browsing by Author "Thapa, Kriti"
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Item 38766 Massively Parallel Reporter Assay Reveals Functional Impact of 3™-UTR SNPs Associated with Neurological and Psychiatric Disorders(Cambridge University Press, 2021) Chen, Andy B.; Thapa, Kriti; Gao, Hongyu; Reiter, Jill L.; Zhang, Junjie; Xuei, Xiaoling; Gu, Hongmei; Wang, Yue; Edenberg, Howard J.; Liu, Yunlong; Medical and Molecular Genetics, School of MedicineABSTRACT IMPACT: Screening the effect of thousands of non-coding genetic variants will help identify variants important in the etiology of diseases OBJECTIVES/GOALS: Massively parallel reporter assays (MPRAs) can experimentally evaluate the impact of genetic variants on gene expression. In this study, our objective was to systematically evaluate the functional activity of 3’-UTR SNPs associated with neurological disorders and use those results to help understand their contributions to disease etiology. METHODS/STUDY POPULATION: To choose variants to evaluate with the MPRA, we first gathered SNPs from the GWAS Catalog that were associated with any neurological disorder trait with p-value < 10-5. For each SNP, we identified the region that was in linkage disequilibrium (r2 > 0.8) and retrieved all the common 3’-UTR SNPs (allele-frequency > 0.05) within that region. We used an MPRA to measure the impact of these 3’-UTR variants in SH-SY5Y neuroblastoma cells and a microglial cell line. These results were then used to train a deep-learning model to predict the impact of variants and identify features that contribute to the predictions. RESULTS/ANTICIPATED RESULTS: Of the 13,515 3’-UTR SNPs tested, 400 and 657 significantly impacted gene expression in SH-SY5Y and microglia, respectively. Of the 84 SNPs significantly impacted in both cells, the direction of impact was the same in 81. The direction of eQTL in GTEx tissues agreed with the assay SNP effect in SH-SY5Y cells but not microglial cells. The deep-learning model predicted sequence activity level correlated with the experimental activity level (Spearman’s corr = 0.45). The deep-learning model identified several predictive motifs similar to motifs of RNA-binding proteins. DISCUSSION/SIGNIFICANCE OF FINDINGS: This study demonstrates that MPRAs can be used to evaluate the effect of non-coding variants, and the results can be used to train a machine learning model and interpret its predictions. Together, these can help identify causal variants and further understand the etiology of diseases.Item Effects of chronic intermittent ethanol exposure and withdrawal on neuroblastoma cell transcriptome(Elsevier, 2020-06) McClintick, Jeanette N.; Thapa, Kriti; Liu, Yunlong; Xuei, Xiaoling; Edenberg, Howard J.; Biochemistry and Molecular Biology, School of MedicineCycles of heavy drinking and abstinence can lead to ethanol abuse disorder. We studied the effects of chronic intermittent ethanol exposure (CIE) over three weeks on neuroblastoma cells, using an ethanol concentration frequently attained in binge drinking (40 mM, 184 mg/dl). There were many changes in gene expression but most were small. CIE affected pathways instrumental in the development or plasticity of neurons, including axonal guidance, reelin signaling and synaptogenesis. Genes involved in dopamine and serotonin signaling were also affected. Changes in transporters and receptors could dampen both NMDA and norepinephrine transmissions. Decreased expression of the GABA transporter SLC6A11 could increase GABA transmission and has been associated with a switch from sweet drinking to ethanol consumption in rats. Ethanol increased stress responses such as unfolded protein response. TGF-β and NFκB signaling were increased. Most of the genes involved in cholesterol biosynthesis were decreased in expression. Withdrawal for 24 h after CIE caused most of the CIE-induced expression changes to move back toward unexposed levels.