Browsing by Author "Xu, Dong"

Now showing 1 - 10 of 12
  • Thumbnail Image
    Item
    2K09 and thereafter : the coming era of integrative bioinformatics, systems biology and intelligent computing for functional genomics and personalized medicine research
    (BMC, 2010-11-29) Yang, Jack Y.; Niemierko, Andrzej; Bajcsy, Ruzena; Xu, Dong; Athey, Brian D.; Zhang, Aidong; Ersoy, Okan K.; Li, Guo-zheng; Borodovsky, Mark; Zhang, Joe C.; Arabnia, Hamid R.; Deng, Youping; Dunker, A. Keith; Liu, Yunlong; Ghafoor, Arif; Medicine, School of Medicine
    Significant interest exists in establishing synergistic research in bioinformatics, systems biology and intelligent computing. Supported by the United States National Science Foundation (NSF), International Society of Intelligent Biological Medicine (http://www.ISIBM.org), International Journal of Computational Biology and Drug Design (IJCBDD) and International Journal of Functional Informatics and Personalized Medicine, the ISIBM International Joint Conferences on Bioinformatics, Systems Biology and Intelligent Computing (ISIBM IJCBS 2009) attracted more than 300 papers and 400 researchers and medical doctors world-wide. It was the only inter/multidisciplinary conference aimed to promote synergistic research and education in bioinformatics, systems biology and intelligent computing. The conference committee was very grateful for the valuable advice and suggestions from honorary chairs, steering committee members and scientific leaders including Dr. Michael S. Waterman (USC, Member of United States National Academy of Sciences), Dr. Chih-Ming Ho (UCLA, Member of United States National Academy of Engineering and Academician of Academia Sinica), Dr. Wing H. Wong (Stanford, Member of United States National Academy of Sciences), Dr. Ruzena Bajcsy (UC Berkeley, Member of United States National Academy of Engineering and Member of United States Institute of Medicine of the National Academies), Dr. Mary Qu Yang (United States National Institutes of Health and Oak Ridge, DOE), Dr. Andrzej Niemierko (Harvard), Dr. A. Keith Dunker (Indiana), Dr. Brian D. Athey (Michigan), Dr. Weida Tong (FDA, United States Department of Health and Human Services), Dr. Cathy H. Wu (Georgetown), Dr. Dong Xu (Missouri), Drs. Arif Ghafoor and Okan K Ersoy (Purdue), Dr. Mark Borodovsky (Georgia Tech, President of ISIBM), Dr. Hamid R. Arabnia (UGA, Vice-President of ISIBM), and other scientific leaders. The committee presented the 2009 ISIBM Outstanding Achievement Awards to Dr. Joydeep Ghosh (UT Austin), Dr. Aidong Zhang (Buffalo) and Dr. Zhi-Hua Zhou (Nanjing) for their significant contributions to the field of intelligent biological medicine.
  • Thumbnail Image
    Item
    Advances in translational bioinformatics facilitate revealing the landscape of complex disease mechanisms
    (Springer (Biomed Central Ltd.), 2014) Yang, Jack Y.; Dunker, A. Keith; Liu, Jun S.; Qin, Xiang; Arabnia, Hamid R.; Yang, William; Niemierko, Andrzej; Chen, Zhongxue; Luo, Zuojie; Wang, Liangjiang; Liu, Yunlong; Xu, Dong; Deng, Youping; Tong, Weida; Yang, Mary Qu; Department of Biochemistry and Molecular Biology, IU School of Medicine
    Advances of high-throughput technologies have rapidly produced more and more data from DNAs and RNAs to proteins, especially large volumes of genome-scale data. However, connection of the genomic information to cellular functions and biological behaviours relies on the development of effective approaches at higher systems level. In particular, advances in RNA-Seq technology has helped the studies of transcriptome, RNA expressed from the genome, while systems biology on the other hand provides more comprehensive pictures, from which genes and proteins actively interact to lead to cellular behaviours and physiological phenotypes. As biological interactions mediate many biological processes that are essential for cellular function or disease development, it is important to systematically identify genomic information including genetic mutations from GWAS (genome-wide association study), differentially expressed genes, bidirectional promoters, intrinsic disordered proteins (IDP) and protein interactions to gain deep insights into the underlying mechanisms of gene regulations and networks. Furthermore, bidirectional promoters can co-regulate many biological pathways, where the roles of bidirectional promoters can be studied systematically for identifying co-regulating genes at interactive network level. Combining information from different but related studies can ultimately help revealing the landscape of molecular mechanisms underlying complex diseases such as cancer.
  • Thumbnail Image
    Item
    Coxiella burnetii Virulent Phase I and Avirulent Phase II Variants Differentially Manipulate Autophagy Pathway in Neutrophils
    (American Society for Microbiology, 2022) Kumaresan, Venkatesh; Wang, Juexin; Zhang, Wendy; Zhang, Yan; Xu, Dong; Zhang, Guoquan; Medical and Molecular Genetics, School of Medicine
    Coxiella burnetii is an obligate intracellular Gram-negative bacterium that causes Q fever in humans. The virulent C. burnetii Nine Mile phase I (NMI) strain causes disease in animal models, while the avirulent NM phase II (NMII) strain does not. In this study, we found that NMI infection induces severe splenomegaly and bacterial burden in the spleen in BALB/c mice, while NMII infection does not. A significantly higher number of CD11b+ Ly6G+ neutrophils accumulated in the liver, lung, and spleen of NMI-infected mice than in NMII-infected mice. Thus, neutrophil accumulation correlates with NMI and NMII infection-induced inflammatory responses. In vitro studies also demonstrated that although NMII exhibited a higher infection rate than NMI in mouse bone marrow neutrophils (BMNs), NMI-infected BMNs survived longer than NMII-infected BMNs. These results suggest that the differential interactions of NMI and NMII with neutrophils may be related to their ability to cause disease in animals. To understand the molecular mechanism underlying the differential interactions of NMI and NMII with neutrophils, global transcriptomic gene expressions were compared between NMI- and NMII-infected BMNs by RNA sequencing (RNA-seq) analysis. Interestingly, several genes involved in autophagy-related pathways, particularly membrane trafficking and lipid metabolism, are upregulated in NMII-infected BMNs but downregulated in NMI-infected BMNs. Immunofluorescence and immunoblot analyses indicate that compared to NMI-infected BMNs, vacuoles in NMII-infected-BMNs exhibit increased autophagic flux along with phosphatidylserine translocation in the cell membrane. Similar to neutrophils, NMII activated LC3-mediated autophagy in human macrophages. These findings suggest that the differential manipulation of autophagy of NMI and NMII may relate to their pathogenesis.
  • Thumbnail Image
    Item
    CrossMP: Enabling Cross-Modality Translation between Single-Cell RNA-Seq and Single-Cell ATAC-Seq through Web-Based Portal
    (MDPI, 2024-07-05) Lyu, Zhen; Dahal, Sabin; Zeng, Shuai; Wang, Juexin; Xu, Dong; Joshi, Trupti; Biomedical Engineering and Informatics, Luddy School of Informatics, Computing, and Engineering
    In recent years, there has been a growing interest in profiling multiomic modalities within individual cells simultaneously. One such example is integrating combined single-cell RNA sequencing (scRNA-seq) data and single-cell transposase-accessible chromatin sequencing (scATAC-seq) data. Integrated analysis of diverse modalities has helped researchers make more accurate predictions and gain a more comprehensive understanding than with single-modality analysis. However, generating such multimodal data is technically challenging and expensive, leading to limited availability of single-cell co-assay data. Here, we propose a model for cross-modal prediction between the transcriptome and chromatin profiles in single cells. Our model is based on a deep neural network architecture that learns the latent representations from the source modality and then predicts the target modality. It demonstrates reliable performance in accurately translating between these modalities across multiple paired human scATAC-seq and scRNA-seq datasets. Additionally, we developed CrossMP, a web-based portal allowing researchers to upload their single-cell modality data through an interactive web interface and predict the other type of modality data, using high-performance computing resources plugged at the backend.
  • Thumbnail Image
    Item
    Deep learning analysis of single‐cell data in empowering clinical implementation
    (Wiley, 2022) Ma, Anjun; Wang, Juexin; Xu, Dong; Ma, Qin; Medical and Molecular Genetics, School of Medicine
  • Thumbnail Image
    Item
    DeePaN: deep patient graph convolutional network integrating clinico-genomic evidence to stratify lung cancers for immunotherapy
    (Nature, 2021) Fang, Chao; Xu, Dong; Su, Jing; Dry, Jonathan R.; Linghu, Bolan; Biostatistics, School of Public Health
    Immuno-oncology (IO) therapies have transformed the therapeutic landscape of non-small cell lung cancer (NSCLC). However, patient responses to IO are variable and influenced by a heterogeneous combination of health, immune, and tumor factors. There is a pressing need to discover the distinct NSCLC subgroups that influence response. We have developed a deep patient graph convolutional network, we call “DeePaN”, to discover NSCLC complexity across data modalities impacting IO benefit. DeePaN employs high-dimensional data derived from both real-world evidence (RWE)-based electronic health records (EHRs) and genomics across 1937 IO-treated NSCLC patients. DeePaN demonstrated effectiveness to stratify patients into subgroups with significantly different (P-value of 2.2 × 10−11) overall median survival of 20.35 months and 9.42 months post-IO therapy. Significant differences in IO outcome were not seen from multiple non-graph-based unsupervised methods. Furthermore, we demonstrate that patient stratification from DeePaN has the potential to augment the emerging IO biomarker of tumor mutation burden (TMB). Characterization of the subgroups discovered by DeePaN indicates potential to inform IO therapeutic insight, including the enrichment of mutated KRAS and high blood monocyte count in the IO beneficial and IO non-beneficial subgroups, respectively. Our work has proven the concept that graph-based AI is feasible and can effectively integrate high-dimensional genomic and EHR data to meaningfully stratify cancer patients on distinct clinical outcomes, with potential to inform precision oncology.
  • Thumbnail Image
    Item
    Define and visualize pathological architectures of human tissues from spatially resolved transcriptomics using deep learning
    (Elsevier, 2022-08-24) Chang, Yuzhou; He, Fei; Wang, Juexin; Chen, Shuo; Li, Jingyi; Liu, Jixin; Yu, Yang; Su, Li; Ma, Anjun; Allen, Carter; Lin, Yu; Sun, Shaoli; Liu, Bingqiang; Otero, José Javier; Chung, Dongjun; Fu, Hongjun; Li, Zihai; Xu, Dong; Ma, Qin; Medical and Molecular Genetics, School of Medicine
    Spatially resolved transcriptomics provides a new way to define spatial contexts and understand the pathogenesis of complex human diseases. Although some computational frameworks can characterize spatial context via various clustering methods, the detailed spatial architectures and functional zonation often cannot be revealed and localized due to the limited capacities of associating spatial information. We present RESEPT, a deep-learning framework for characterizing and visualizing tissue architecture from spatially resolved transcriptomics. Given inputs such as gene expression or RNA velocity, RESEPT learns a three-dimensional embedding with a spatial retained graph neural network from spatial transcriptomics. The embedding is then visualized by mapping into color channels in an RGB image and segmented with a supervised convolutional neural network model. Based on a benchmark of 10x Genomics Visium spatial transcriptomics datasets on the human and mouse cortex, RESEPT infers and visualizes the tissue architecture accurately. It is noteworthy that, for the in-house AD samples, RESEPT can localize cortex layers and cell types based on pre-defined region- or cell-type-enriched genes and furthermore provide critical insights into the identification of amyloid-beta plaques in Alzheimer's disease. Interestingly, in a glioblastoma sample analysis, RESEPT distinguishes tumor-enriched, non-tumor, and regions of neuropil with infiltrating tumor cells in support of clinical and prognostic cancer applications.
  • Thumbnail Image
    Item
    Dimension-agnostic and granularity-based spatially variable gene identification
    (Research Square, 2023-03-22) Wang, Juexin; Li, Jinpu; Kramer, Skyler; Su, Li; Chang, Yuzhou; Xu, Chunhui; Ma, Qin; Xu, Dong; BioHealth Informatics, School of Informatics and Computing
    Identifying spatially variable genes (SVGs) is critical in linking molecular cell functions with tissue phenotypes. Spatially resolved transcriptomics captures cellular-level gene expression with corresponding spatial coordinates in two or three dimensions and can be used to infer SVGs effectively. However, current computational methods may not achieve reliable results and often cannot handle three-dimensional spatial transcriptomic data. Here we introduce BSP (big-small patch), a spatial granularity-guided and non-parametric model to identify SVGs from two or three-dimensional spatial transcriptomics data in a fast and robust manner. This new method has been extensively tested in simulations, demonstrating superior accuracy, robustness, and high efficiency. BSP is further validated by substantiated biological discoveries in cancer, neural science, rheumatoid arthritis, and kidney studies with various types of spatial transcriptomics technologies.
  • Thumbnail Image
    Item
    Dimension-agnostic and granularity-based spatially variable gene identification using BSP
    (Springer Nature, 2023-11-14) Wang, Juexin; Li, Jinpu; Kramer, Skyler T.; Su, Li; Chang, Yuzhou; Xu, Chunhui; Eadon, Michael T.; Kiryluk, Krzysztof; Ma, Qin; Xu, Dong; Biomedical Engineering and Informatics, Luddy School of Informatics, Computing, and Engineering
    Identifying spatially variable genes (SVGs) is critical in linking molecular cell functions with tissue phenotypes. Spatially resolved transcriptomics captures cellular-level gene expression with corresponding spatial coordinates in two or three dimensions and can be used to infer SVGs effectively. However, current computational methods may not achieve reliable results and often cannot handle three-dimensional spatial transcriptomic data. Here we introduce BSP (big-small patch), a non-parametric model by comparing gene expression pattens at two spatial granularities to identify SVGs from two or three-dimensional spatial transcriptomics data in a fast and robust manner. This method has been extensively tested in simulations, demonstrating superior accuracy, robustness, and high efficiency. BSP is further validated by substantiated biological discoveries in cancer, neural science, rheumatoid arthritis, and kidney studies with various types of spatial transcriptomics technologies.
  • Thumbnail Image
    Item
    Genetic mosaicism, intrafamilial phenotypic heterogeneity, and molecular defects of a novel missense SLC6A1 mutation associated with epilepsy and ADHD
    (Elsevier, 2021) Poliquin, Sarah; Hughes, Inna; Shen, Wangzhen; Mermer, Felicia; Wang, Juexin; Mack, Taralynn; Xu, Dong; Kang, Jing-Qiong; Medical and Molecular Genetics, School of Medicine
    Background: Mutations in SLC6A1, encoding γ-aminobutyric acid (GABA) transporter 1 (GAT-1), have been recently associated with a spectrum of neurodevelopmental disorders ranging from variable epilepsy syndromes, intellectual disability (ID), autism and others. To date, most identified mutations are de novo. We here report a pedigree of two siblings associated with myoclonic astatic epilepsy, attention deficit hyperactivity disorder (ADHD), and ID. Methods: Next-generation sequencing identified a missense mutation in the SLC6A1 gene (c.373G > A(p.Val125Met)) in the sisters but not in their shared mother who is also asymptomatic, suggesting gonadal mosaicism. We have thoroughly characterized the clinical phenotypes: EEG recordings identified features for absence seizures and prominent bursts of occipital intermittent rhythmic delta activity (OIRDA). The molecular pathophysiology underlying the clinical phenotypes was assessed using a multidisciplinary approach including machine learning, confocal microscopy, and high-throughput 3H radio-labeled GABA uptake assays in mouse astrocytes and neurons. Results: The GAT-1(Val125Met) mutation destabilizes the global protein conformation and reduces transporter protein expression at total and cell surface. The mutant transporter protein was localized intracellularly inside the endoplasmic reticulum (ER) in both HEK293T cells and astrocytes which may directly contribute to seizures in patients. Radioactive 3H-labeled GABA uptake assay indicated the mutation reduced the function of the mutant GAT-1(Val125Met) to ~30% of the wildtype. Conclusions: The seizure phenotypes, ADHD, and impaired cognition are likely caused by a partial loss-of-function of GAT-1 due to protein destabilization resulting from the mutation. Reduced GAT-1 function in astrocytes and neurons may consequently alter brain network activities such as increased seizures and reduced attention.