Browsing by Author "Wu, Xiaogang"

Now showing 1 - 7 of 7
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    Circulating microRNAs and life expectancy among identical twins
    (Wiley, 2016-09) Wu, Shenghui; Kim, Taek-Kyun; Wu, Xiaogang; Scherler, Kelsey; Baxter, David; Wang, Kai; Krasnow, Ruth E.; Reed, Terry; Dai, Jun; Department of Medical & Molecular Genetics, IU School of Medicine
    Human life expectancy is influenced not only by longevity assurance mechanisms and disease susceptibility loci but also by the environment, gene–environment interactions, and chance. MicroRNAs (miRNAs) are a class of small noncoding RNAs closely related to genes. Circulating miRNAs have been shown as promising noninvasive biomarkers in the development of many pathophysiological conditions. However, the concentration of miRNA in the circulation may also be affected by environmental factors. We used a next-generation sequencing platform to assess the association of circulating miRNA with life expectancy, for which deaths are due to all causes independent of genes. In addition, we showed that miRNAs are present in 41-year archived plasma samples, which may be useful for both life expectancy and all-cause mortality risk assessment. Plasma miRNAs from nine identical male twins were profiled using next-generation sequencing. The average absolute difference in the minimum life expectancy was 9.68 years. Intraclass correlation coefficients were above 0.4 for 50% of miRNAs. Comparing deceased twins with their alive co-twin brothers, the concentrations were increased for 34 but decreased for 30 miRNAs. Identical twins discordant in life expectancy were dissimilar in the majority of miRNAs, suggesting that environmental factors are pivotal in miRNAs related to life expectancy.
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    Computational Biomarker Discovery: From Systems Biology to Predictive and Personalized Medicine Applications
    (Office of the Vice Chancellor for Research, 2010-04-09) Chen, Jake Yue; Wu, Xiaogang; Zhang, Fan; Pandey, Ragini; Huang, Hui; Huan, Tianxiao
    With the advent of Genome-based Medicine, there is an escalating need for discovering how the modifications of biological molecules, either individually or as an ensemble, can be uniquely associated with human physiological states. This knowledge could lead to breakthroughs in the development of clinical tests known as "biomarker tests" to assess disease risks, early onset, prognosis, and treatment outcome predictions. Therefore, development of molecular biomarkers is a key agenda in the next 5-10 years to take full advantage of the human genome to improve human well-beings. However, the complexity of human biological systems and imperfect instrumentations of high-throughput biological instruments/results have created significant hurdles in biomarker development. Only recently did computational methods become an important player of the research topic, which has seen conventional molecular biomarkers development both extremely long and cost-ineffective. At Indiana Center for Systems Biology and Personalized Medicine, we are developing several computational systems biology strategies to address these challenges. We will show examples of how we approach the problem using a variety of computational techniques, including data mining, algorithm development to take into account of biological contexts, biological knowledge integration, and information visualization. Finally, we outline how research in this direction to derive more robust molecular biomarkers may lead to predictive and personalized medicine. Indiana Center for Systems Biology and Personalized Medicine (CSBPM) was founded in 2007 as an IUPUI signature center by Dr. Jake Chen and his colleagues in the Indiana University School of Informatics, School of Medicine, and School of Science. CSBPM is the only research center in the State of Indiana with the primary goal of pursuing predictive and personalized medicine. CSBPM currently consists of eleven faculty members from the School of Medicine, School of Science, School of Engineering, School of Informatics, and Indiana University Simon Cancer Center. The primary mission of the center is to foster the development and use of systems biology and computational modeling techniques to address challenges in future genome-based medicine. The ultimate goal of the center is to shorten the discovery-to-practice gap between integrative ―Omics‖ biology studies—including genomics, transcriptomics, proteomics, and metabolomics—and predictive and personalized medicine applications.
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    MicroRNA Expression Profiling of Human Respiratory Epithelium Affected by Invasive Candida Infection
    (Public Library of Science, 2015) Muhammad, Syed Aun; Fatima, Nighat; Syed, Nawazish-I.-Husain; Wu, Xiaogang; Yang, X. Frank; Chen, Jake Yue; IU School of Informatics and Computing
    Invasive candidiasis is potentially life-threatening systemic fungal infection caused by Candida albicans (C. albicans). Candida enters the blood stream and disseminate throughout the body and it is often observed in hospitalized patients, immunocompromised individuals or those with chronic diseases. This infection is opportunistic and risk starts with the colonization of C. albicans on mucocutaneous surfaces and respiratory epithelium. MicroRNAs (miRNAs) are small non-coding RNAs which are involved in the regulation of virtually every cellular process. They regulate and control the levels of mRNA stability and post-transcriptional gene expression. Aberrant expression of miRNAs has been associated in many disease states, and miRNA-based therapies are in progress. In this study, we investigated possible variations of miRNA expression profiles of respiratory epithelial cells infected by invasive Candida species. For this purpose, respiratory epithelial tissues of infected individuals from hospital laboratory were accessed before their treatment. Invasive Candida infection was confirmed by isolation of Candia albicans from the blood cultures of the same infected individuals. The purity of epithelial tissues was assessed by flow cytometry (FACSCalibur cytometer; BD Biosciences, Heidelberg, Germany) using statin antibody (S-44). TaqMan quantitative real-time PCR (in a TaqMan Low Density Array format) was used for miRNA expression profiling. MiRNAs investigated, the levels of expression of 55 miRNA were significantly altered in infected tissues. Some miRNAs showed dramatic increase (miR-16-1) or decrease of expression (miR-17-3p) as compared to control. Gene ontology enrichment analysis of these miRNA-targeted genes suggests that Candidal infection affect many important biological pathways. In summary, disturbance in miRNA expression levels indicated the change in cascade of pathological processes and the regulation of respiratory epithelial functions following invasive Candidal infection. These findings contribute to our understanding of host cell response to Candidal systemic infections.
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    A new approach to construct pathway connected networks and its application in dose responsive gene expression profiles of rat liver regulated by 2,4DNT
    (BMC, 2010-12-01) Chowbina, Sudhir; Deng, Youping; Ai, Junmei; Wu, Xiaogang; Guan, Xin; Wilbanks, Mitchell S.; Escalon, Barbara Lynn; Meyer, Sharon A.; Perkins, Edward J.; Chen, Jake Yue; BioHealth Informatics, School of Informatics and Computing
    Military and industrial activities have lead to reported release of 2,4-dinitrotoluene (2,4DNT) into soil, groundwater or surface water. It has been reported that 2,4DNT can induce toxic effects on humans and other organisms. However the mechanism of 2,4DNT induced toxicity is still unclear. Although a series of methods for gene network construction have been developed, few instances of applying such technology to generate pathway connected networks have been reported. Results Microarray analyses were conducted using liver tissue of rats collected 24h after exposure to a single oral gavage with one of five concentrations of 2,4DNT. We observed a strong dose response of differentially expressed genes after 2,4DNT treatment. The most affected pathways included: long term depression, breast cancer regulation by stathmin1, WNT Signaling; and PI3K signaling pathways. In addition, we propose a new approach to construct pathway connected networks regulated by 2,4DNT. We also observed clear dose response pathway networks regulated by 2,4DNT. Conclusions We developed a new method for constructing pathway connected networks. This new method was successfully applied to microarray data from liver tissue of 2,4DNT exposed animals and resulted in the identification of unique dose responsive biomarkers in regards to affected pathways.
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    Pathway and network analysis in proteomics
    (Elsevier, 2014-12-07) Wu, Xiaogang; Hasan, Mohammad Al; Chen, Jake Yue; Department of BioHealth Informatics, School of Informatics and Computing
    Proteomics is inherently a systems science that studies not only measured protein and their expressions in a cell, but also the interplay of proteins, protein complexes, signaling pathways, and network modules. There is a rapid accumulation of Proteomics data in recent years. However, Proteomics data are highly variable, with results sensitive to data preparation methods, sample condition, instrument types, and analytical methods. To address the challenge in Proteomics data analysis, we review current tools being developed to incorporate biological function and network topological information. We categorize these tools into four types: tools with basic functional information and little topological features (e.g., GO category analysis), tools with rich functional information and little topological features (e.g., GSEA), tools with basic functional information and rich topological features (e.g., Cytoscape), and tools with rich functional information and rich topological features (e.g., PathwayExpress). We first review the potential application of these tools to Proteomics; then we review tools that can achieve automated learning of pathway modules and features, and tools that help perform integrated network visual analytics.
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    Predictive and Personalized Medicine with Systems Biology Solutions
    (Office of the Vice Chancellor for Research, 2011-04-08) Wu, Xiaogang; Chen, Jake Yue
    Systems biology refers to the use of systems engineering and systems science techniques to the understanding of biological systems. At Indiana Center for Systems Biology and Personalized Medicine (ICSBPM), we are particularly interested in developing systems biology techniques that can help shorten the gaps between basic biomedical research and clinical applications of genome sciences toward predictive and personalized medicine. In the past several years, ICSBPM has developed many critical informatics resources for the systems biology and personalized medicine community. The database and software tools that we developed have promoted systems biology and personalized medicine research communities at the national scale. These tools include: HPD, an integrated human pathway database and analysis tool (Chowbina et al., in BMC Bioinformatics 2009, 10(S11): S5); HAPPI, a human annotated and predicted protein interaction database (Chen et al., in BMC Genomics 2009, 10(S1):S16); HIP2, a Database of Healthy Human Individual's Integrated Plasma Proteome (Saha et al., in BMC Medical Genomics 2008, 1(1):12); PEPPI, a Peptidomic Database of Protein Isoforms (Zhou et al., in BMC bioinformatics 2010, 11(S6), S7); ProteoLens, a multi-scale network visualization and data mining tool (Huan et al., in BMC bioinformatics 2008, 9(S9):S5); GeneTerrain, a visual exploration tool for network-organized expression panel biomarker development (You et al., in Information Visualization 2010, 9(1)), and C-Maps, comprehensive molecular connectivity maps between disease-specific proteins and drugs (Li et al., in PLoS Computational Biology, 5(7), e1000450). These tools has been demonstrated to help improve tumor classifications, understand cancer biological systems at the systems scale, tackle biomarker discovery challenges, and facilitate clinical adoption of predictive models developed from computational techniques. We hope that our experience and resources can cement collaborative translational medicine research towards predictive and personalized medicine applications.
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    TOWARDS A PATHWAY MODELING APPROACH TO ALZHEIMER’S DISEASE DRUG DISCOVERY
    (Office of the Vice Chancellor for Research, 2012-04-13) Ibrahim, Sara; Capouch, Don; Chandorkar, Sujay; Chen, Jake Yue; Saykin, Andrew J.; Wu, Xiaogang; Huang, Hui
    Network pharmacology has emerged as a new topic of study in recent years. Molecular connectivity maps between drugs and genes/proteins in specific disease contexts can be particularly valuable, since the functional approach with these maps helps researchers gain global perspectives on both the therapeutic and toxicological profiles of drugs. To assess drug pharmacological effects, we assume that “ideal” drugs for a patient can treat or prevent the disease by modulating gene expression profiles of this patient to the similar level with those in healthy people. Starting from this hypothesis, we build comprehensive disease-gene-drug connectivity relationships with drug-protein directionality (inhibit/activate) information based on a computational connectivity maps (CMaps) platform. In this work, we develop a novel approach based on integrative pathway modeling. Using Alzheimer’s disease (AD) as an example, we identify and rank AD-related drugs/compounds with their overall drug-protein “connectivity map” profile. First, we retrieve AD-associated proteins through the CMaps platform by using “Alzheimer’s disease” as a query term. Second, we retrieve AD-related pathways by using those AD-associated proteins as input and searching in the Human Pathway Database (HPD) and the PubMed. Third, we integrate the AD-related pathways into unified pathway models, from which we categorize the pharmaceutical effects of candidate drugs on all AD-associated proteins as either “therapeutic” or “toxic” (Figure 1). Finally, we transform the integrated pathways into network models and rank drugs based on the network topological features of drug targets, drug-affecting genes/proteins, and curated AD-associated proteins. We demonstrate that our approach can help identify AD drug candidates with significant therapeutic potentials with small toxic side effects. The case study correlates very well with the existing pharmacology of AD drugs and highlights the significance of the CMaps platform. Ongoing studies towards this direction also have the potential of changing future process of AD drug development. 1Indiana University School of Medicine.