Browsing by Author "Huang, Xiaoqing"

Now showing 1 - 5 of 5
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    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 Medicine
    Reactive 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.
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    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 Medicine
    Tau 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.
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    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 Medicine
    We 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.
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    Identify Signature Genes/Pathways to Characterize Alzheimer's Disease Subtypes Based on Uncoupled Tauopathies and Cognitive Decline
    (2024-06) Huang, Xiaoqing; Huang, Kun; Zhang, Jie; Johnson, Travis; Zhang, Jianjun
    Alzheimer's disease (AD) is a slow-progressing dementia usually found in elderlies, with heterogeneous clinical phenotypes and possible underlying mechanisms. Widely spread tauopathy is one of the pathological change hallmarks in AD brains, in which microtube protein tau forms scar-like neurofibrillary tangles that kill neurons. However, subgroups of patients present unmatched tauopathy progression with their cognitive decline. A detailed study on these so-called atypical AD patients allows for a deeper understanding of possible various disease mechanisms and the factors contributing to disease vulnerability or resilience, which can help guide the drug development and treatment strategy tailored to different subgroups, as well as establish foundations for disease prevention. By identifying specific molecular biomarkers associated with each subtype, I hope to help clinicians diagnose various AD subtypes at an earlier stage. In this work, I have performed transcriptomic and proteomic characterization of two atypical AD subtypes on two large AD/normal brain cohorts to further understand the role of tauopathy in the AD etiology, identified several pathways that are associated with the two phenotypes’ AD-resilient and AD-vulnerable characteristics, and tried to identify the potential drug targets for the precision treatment of AD using extensive bioinformatic approaches. In the meanwhile, two methodologies were developed and applied. One is a new type of interpretable deep learning model (ParsVNN) coupled with the neural network architecture with the hierarchical structure of the gene/protein pathways is introduced and leveraged to address the complexity and improve the interpretability by making its biological hierarchy simple and specific to the predicted subgroup. The other is a label transferring approach using optimal transport from brain samples to blood samples in the hope of finding serum biomarkers for atypical AD groups in live patients and predicting their disease progression in a non-invasive fashion. Conclusively, the study improves our understanding of AD etiology and leads to more personalized care and disease prevention. It acknowledges the complexity of the disease and aims to uncover mechanistic distinctions within the broad Alzheimer’s disease spectrum.
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    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 Medicine
    Prediction 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.