Browsing by Author "Li, Hua"

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    Detecting epistatic interactions contributing to human gene expression using the CEPH family data
    Li, Hua; Gao, Guimin; Li, Jian; Page, Grier P.; Zhang, Kui (2007-12-18)
    It is believed that epistatic interactions among loci contribute to variations in quantitative traits. Several methods are available to detect epistasis using population-based data. However, methods to characterize epistasis for quantitative traits in family-based association analysis are not well developed, especially for studying thousands of gene expression traits. Here, we proposed a linear mixed-model approach to detect epistasis for quantitative traits using family data. The proposed method was implemented in a widely used software program SOLAR. We evaluated the power of the method by simulation studies and applied this method to the analysis of the Centre d'Etude du Polymorphisme Humain family gene expression data provided by Genetics Analysis Workshop 15 (GAW15).
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    Quantitative Proteomics Reveals the Beneficial Effects of Low Glucose on Neuronal Cell Survival in an in vitro Ischemic Penumbral Model
    Li, Hua; Kittur, Farooqahmed S.; Hung, Chiu-Yueh; Li, P. Andy; Ge, Xinghong; Sane, David C.; Xie, Jiahua (2020-09-01)
    Understanding proteomic changes in the ischemic penumbra are crucial to rescue those salvageable cells and reduce the damage of an ischemic stroke. Since the penumbra region is dynamic with heterogeneous cells/tissues, tissue sampling from animal models of stroke for the molecular study is a challenge. In this study, cultured hippocampal HT22 cells under hypoxia treatment for 17.5 h with 0.69 mM low glucose (H+LG) could mimic ischemic penumbral cells since they had much higher cell viability and viable cell number compared to hypoxia without glucose (H-G) treatment. To validate established cell-based ischemic penumbral model and understand the beneficial effects of low glucose (LG), quantitative proteomics analysis was performed on H+LG, H-G, and normoxia with normal 22 mM glucose (N+G) treated cells. We identified 427 differentially abundant proteins (DAPs) between H-G and N+G and further identified 105 DAPs between H+LG and H-G. Analysis of 105 DAPs revealed that LG promotes cell survival by activating HIF1 alpha to enhance glycolysis; preventing the dysregulations of extracellular matrix remodeling, cell cycle and division, and antioxidant and detoxification; as well as attenuating inflammatory reaction response, protein synthesis and neurotransmission activity. Our results demonstrated that this established cell-based system could mimic penumbral conditions and can be used for molecular studies.