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Browsing by Author "Huang, Xiaoqing"
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Item Activated endothelial cells induce a distinct type of astrocytic reactivity(Springer Nature, 2022-03-29) Taylor, Xavier; Cisternas, Pablo; Jury, Nur; Martinez, Pablo; Huang, Xiaoqing; You, Yanwen; Redding-Ochoa, Javier; Vidal, Ruben; Zhang, Jie; Troncoso, Juan; Lasagna-Reeves, Cristian A.; Anatomy, Cell Biology and Physiology, School of MedicineReactive astrogliosis is a universal response of astrocytes to abnormal events and injuries. Studies have shown that proinflammatory microglia can polarize astrocytes (designated A1 astrocytes) toward a neurotoxic phenotype characterized by increased Complement Component 3 (C3) expression. It is still unclear if inflammatory stimuli from other cell types may also be capable of inducing a subset of C3+ neurotoxic astrocytes. Here, we show that a subtype of C3+ neurotoxic astrocytes is induced by activated endothelial cells that is distinct from astrocytes activated by microglia. Furthermore, we show that endothelial-induced astrocytes have upregulated expression of A1 astrocytic genes and exhibit a distinctive extracellular matrix remodeling profile. Finally, we demonstrate that endothelial-induced astrocytes are Decorin-positive and are associated with vascular amyloid deposits but not parenchymal amyloid plaques in mouse models and AD/CAA patients. These findings demonstrate the existence of potentially extensive and subtle functional diversity of C3+-reactive astrocytes.Item Bassoon contributes to tau-seed propagation and neurotoxicity(Springer Nature, 2022) Martinez, Pablo; Patel, Henika; You, Yanwen; Jury, Nur; Perkins, Abigail; Lee-Gosselin, Audrey; Taylor, Xavier; You, Yingjian; Di Prisco, Gonzalo Viana; Huang, Xiaoqing; Dutta, Sayan; Wijeratne, Aruna B.; Redding-Ochoa, Javier; Shahid, Syed Salman; Codocedo, Juan F.; Min, Sehong; Landreth, Gary E.; Mosley, Amber L.; Wu, Yu-Chien; McKinzie, David L.; Rochet, Jean-Christophe; Zhang, Jie; Atwood, Brady K.; Troncoso, Juan; Lasagna-Reeves, Cristian A.; Anatomy, Cell Biology and Physiology, School of MedicineTau aggregation is a defining histopathological feature of Alzheimer’s disease and other tauopathies. However, the cellular mechanisms involved in tau propagation remain unclear. Here, we performed an unbiased quantitative proteomic study to identify proteins that specifically interact with this tau seed. We identified Bassoon (BSN), a presynaptic scaffolding protein, as an interactor of the tau seed isolated from a mouse model of tauopathy, and from Alzheimer’s disease and progressive supranuclear palsy postmortem samples. We show that BSN exacerbates tau seeding and toxicity in both mouse and Drosophila models for tauopathy, and that BSN downregulation decreases tau spreading and overall disease pathology, rescuing synaptic and behavioral impairments and reducing brain atrophy. Our findings improve the understanding of how tau seeds can be stabilized by interactors such as BSN. Inhibiting tau-seed interactions is a potential new therapeutic approach for neurodegenerative tauopathies.Item Diagnostic Evidence GAuge of Single cells (DEGAS): a flexible deep transfer learning framework for prioritizing cells in relation to disease(BMC, 2022-02-01) Johnson, Travis S.; Yu, Christina Y.; Huang, Zhi; Xu, Siwen; Wang, Tongxin; Dong, Chuanpeng; Shao, Wei; Zaid, Mohammad Abu; Huang, Xiaoqing; Wang, Yijie; Bartlett, Christopher; Zhang, Yan; Walker, Brian A.; Liu, Yunlong; Huang, Kun; Zhang, Jie; Medicine, School of MedicineWe propose DEGAS (Diagnostic Evidence GAuge of Single cells), a novel deep transfer learning framework, to transfer disease information from patients to cells. We call such transferrable information "impressions," which allow individual cells to be associated with disease attributes like diagnosis, prognosis, and response to therapy. Using simulated data and ten diverse single-cell and patient bulk tissue transcriptomic datasets from glioblastoma multiforme (GBM), Alzheimer's disease (AD), and multiple myeloma (MM), we demonstrate the feasibility, flexibility, and broad applications of the DEGAS framework. DEGAS analysis on myeloma single-cell transcriptomics identified PHF19high myeloma cells associated with progression.Item ParsVNN: parsimony visible neural networks for uncovering cancer-specific and drug-sensitive genes and pathways(Oxford University Press, 2021-10-27) Huang, Xiaoqing; Huang, Kun; Johnson, Travis; Radovich, Milan; Zhang, Jie; Ma, Jianzhu; Wang, Yijie; Biostatistics and Health Data Science, School of MedicinePrediction of cancer-specific drug responses as well as identification of the corresponding drug-sensitive genes and pathways remains a major biological and clinical challenge. Deep learning models hold immense promise for better drug response predictions, but most of them cannot provide biological and clinical interpretability. Visible neural network (VNN) models have emerged to solve the problem by giving neurons biological meanings and directly casting biological networks into the models. However, the biological networks used in VNNs are often redundant and contain components that are irrelevant to the downstream predictions. Therefore, the VNNs using these redundant biological networks are overparameterized, which significantly limits VNNs' predictive and explanatory power. To overcome the problem, we treat the edges and nodes in biological networks used in VNNs as features and develop a sparse learning framework ParsVNN to learn parsimony VNNs with only edges and nodes that contribute the most to the prediction task. We applied ParsVNN to build cancer-specific VNN models to predict drug response for five different cancer types. We demonstrated that the parsimony VNNs built by ParsVNN are superior to other state-of-the-art methods in terms of prediction performance and identification of cancer driver genes. Furthermore, we found that the pathways selected by ParsVNN have great potential to predict clinical outcomes as well as recommend synergistic drug combinations.