IU Indianapolis ScholarWorks :: Browsing by Author "Huang, Xiaoqing"

Browsing by Author "Huang, Xiaoqing"

Now showing 1 - 9 of 9

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.
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.
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.
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.
Interactome Analysis of Tau‐seed Isolated from AD Brains Suggests New Mechanism for Tau Aggregation and Spreading
(Wiley, 2025-01-03) Martinez, Pablo; You, Yanwen; Patel, Henika; Jury, Nur; Min, Yuhao; Redding, Javier; Huang, Xiaoqing; Dutta, Sayan; Mosley, Amber L.; Rochet, Jean-Christophe; Zhang, Jie; Ertekin-Taner, Nilüfer; Troncoso, Juan C.; Lasagna Reeves, Cristian A.; Anatomy, Cell Biology and Physiology, School of Medicine
Background: Tau aggregates, a hallmark of Alzheimer’s disease (AD) and other tauopathies, spread throughout the brain, contributing to neurodegeneration. How this propagation occurs remains elusive. Previous research suggests that tau‐seed interactors play a crucial role. Based on this, the study aimed to identify novel tau‐seed interactors in AD brains and validate their impact in vivo. Method: AD and control brain extracts were separated in fractions by Size Exclusion Chromatography. Fractions with the highest tau seeding activity, measured using a tai‐biosensor cell line, were analyzed by mass spectrometry to identify interacting proteins. Bioinformatic tools dissected enriched pathways, identifying interactors that were validated in a Drosophila tauopathy model by genetically interfering with their homologs and assessing tau accumulation and eye degeneration. Results: Tau seeding activity was concentrated in high molecular weight fractions containing only a small portion of total tau in the AD brains. Compared to controls, AD brains revealed a distinct interactome for tau‐seeds, enriched in proteins associated with synaptic and mitochondrial pathways. Notably, Drosophila screening confirmed that several novel interactors significantly reduced tau accumulation and eye degeneration, suggesting their potential therapeutic relevance. Conclusion: This study sheds light on tau propagation mechanisms in AD by identifying novel tau‐seed interactors. These interactors, particularly those involved in synaptic and mitochondrial pathways, offer promising targets for therapeutic interventions aimed at decreasing tau spread and potentially preventing neurodegeneration in tauopathies. The findings add to the growing evidence that targeting tau‐seed interactors, like previously identified BSN, could represent a novel strategy for treating these debilitating conditions.
Multiple Myeloma Insights from Single-Cell Analysis: Clonal Evolution, the Microenvironment, Therapy Evasion, and Clinical Implications
(MDPI, 2025-02-14) Li, Sihong; Liu, Jiahui; Peyton, Madeline; Lazaro, Olivia; McCabe, Sean D.; Huang, Xiaoqing; Liu, Yunlong; Shi, Zanyu; Zhang, Zhiqi; Walker, Brian A.; Johnson, Travis S.; Biostatistics and Health Data Science, Richard M. Fairbanks School of Public Health
Multiple myeloma (MM) is a complex and heterogeneous hematologic malignancy characterized by clonal evolution, genetic instability, and interactions with a supportive tumor microenvironment. These factors contribute to treatment resistance, disease progression, and significant variability in clinical outcomes among patients. This review explores the mechanisms underlying MM progression, including the genetic and epigenetic changes that drive clonal evolution, the role of the bone marrow microenvironment in supporting tumor growth and immune evasion, and the impact of genomic instability. We highlight the critical insights gained from single-cell technologies, such as single-cell transcriptomics, genomics, and multiomics, which have enabled a detailed understanding of MM heterogeneity at the cellular level, facilitating the identification of rare cell populations and mechanisms of drug resistance. Despite the promise of advanced technologies, MM remains an incurable disease and challenges remain in their clinical application, including high costs, data complexity, and the need for standardized bioinformatics and ethical considerations. This review emphasizes the importance of continued research and collaboration to address these challenges, ultimately aiming to enhance personalized treatment strategies and improve patient outcomes in MM.
Optimal Transport‐Based Transcriptomic Mapping Revealed Atypical Disease Progression Subtypes in Living Alzheimer’s Disease Patients
(Wiley, 2025-01-09) Huang, Xiaoqing; Zhang, Jie; Huang, Kun; Lasagna Reeves, Cristian A.; Jury, Nur; Medical and Molecular Genetics, School of Medicine
Background: Alzheimer’s disease (AD) exhibits substantial heterogeneity in its disease trajectory. A subset of AD patients with unmatched cognitive decline/tauopathy severity has not been well studied. We identified such atypical subgroups in post‐mortem AD brain studies. However, such atypical subtypes may not be easily identified in living patients, as obtaining brain samples are unfeasible, and NFT measurement is not accurate. In this study, we utilize the matched transcriptomic data from both brain and blood of ROSMAP cohort to identify such atypical AD groups in the blood transcriptomic data of live patients in other cohorts using transfer learning‐based approach, to uncover distinct molecular signatures and biomarkers for earlier and more accurate disease subtyping and prognosis in living AD patients. Method: Three subgroups were defined from ROSMAP cohort with the blood and brain RNA‐seq data based on the clinical information of their tauopathies and disease progression, namely, Asymptomatic AD, Low‐NFT AD, Typical AD, plus normal Control, which serves as our training dataset for a supervised transfer learning. Then, the labels were transferred to the blood RNA‐seq samples from two new cohorts, ADNI and ANMerge using optimal transport. Next, we identify the genes consistently expressed in three independent cohorts for that specific AD subtype. Lastly, the diffusion pseudo‐time analysis infers the temporal order of the gene expression patterns within each subgroups. Dominant genes with a consistent expression pattern across cohorts are considered as the signature for each subgroup, and their relevance to AD pathology is analyzed. Result: We identified distinctive genes with consistent expression patterns across cohorts for each AD subgroup. Remarkably, our analysis also reveals the temporal gene expression dynamics differs for sex, age (late/early onset), and onset pattern (sudden/gradual) across the cohorts. Conclusion: Through a deep transfer learning‐based approach on the blood and brain transcriptomic data, we successfully identified the atypical disease progression subgroups among live AD patient cohorts in ADNI and ANMerge with promising biomarkers/gene signatures. The molecular signatures identified in this study not only enhance our comprehension of the underlying pathophysiological mechanisms but also hold promise for developing early prognosis and effective personalized treatments for AD and related tauopathies.
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.
Predicting Alzheimer's disease subtypes and understanding their molecular characteristics in living patients with transcriptomic trajectory profiling
(Wiley, 2025) Huang, Xiaoqing; Jannu, Asha Jacob; Song, Ziyan; Jury-Garfe, Nur; Lasagna-Reeves, Cristian A.; Alzheimer’s Disease Neuroimaging Initiative; Johnson, Travis S.; Huang, Kun; Zhang, Jie; Biostatistics and Health Data Science, Richard M. Fairbanks School of Public Health
Introduction: Deciphering the diverse molecular mechanisms in living Alzheimer's disease (AD) patients is a big challenge but is pivotal for disease prognosis and precision medicine development. Methods: Utilizing an optimal transport approach, we conducted graph-based mapping of transcriptomic profiles to transfer AD subtype labels from ROSMAP monocyte samples to ADNI and ANMerge peripheral blood mononuclear cells. Subsequently, differential expression followed by comparative pathway and diffusion pseudotime analysis were applied to each cohort to infer the progression trajectories. Survival analysis with real follow-up time was used to obtain potential biomarkers for AD prognosis. Results: AD subtype labels were accurately transferred onto the blood samples of ADNI and ANMerge living patients. Pathways and associated genes in neutrophil degranulation-like immune process, immune acute phase response, and IL-6 signaling were significantly associated with AD progression. Discussion: The work enhanced our understanding of AD progression in different subtypes, offering insights into potential biomarkers and personalized interventions for improved patient care. Highlights: We applied an innovative optimal transport-based approach to map transcriptomic data from different Alzheimer's disease (AD) cohort studies and transfer known AD subtype labels from ROSMAP monocyte samples to peripheral blood mononuclear cell (PBMC) samples within ADNI and ANMerge cohorts. Through comprehensive trajectory and comparative analysis, we investigated the molecular mechanisms underlying different disease progression trajectories in AD. We validated the accuracy of our AD subtype label transfer and identified prognostic genetic markers associated with disease progression, facilitating personalized treatment strategies. By identifying and predicting distinctive AD subtypes and their associated pathways, our study contributes to a deeper understanding of AD heterogeneity.

Browsing by Author "Huang, Xiaoqing"

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