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      Sequential Waves of Gene Expression in Patients with Clinically Defined Dengue Illnesses Reveal Subtle Disease Phases and Predict Disease Severity

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          Abstract

          Background

          Dengue virus (DENV) infection can range in severity from mild dengue fever (DF) to severe dengue hemorrhagic fever (DHF) or dengue shock syndrome (DSS). Changes in host gene expression, temporally through the progression of DENV infection, especially during the early days, remains poorly characterized. Early diagnostic markers for DHF are also lacking.

          Methodology/Principal Findings

          In this study, we investigated host gene expression in a cohort of DENV-infected subjects clinically diagnosed as DF (n = 51) and DHF (n = 13) from Maracay, Venezuela. Blood specimens were collected daily from these subjects from enrollment to early defervescence and at one convalescent time-point. Using convalescent expression levels as baseline, two distinct groups of genes were identified: the “early” group, which included genes associated with innate immunity, type I interferon, cytokine-mediated signaling, chemotaxis, and complement activity peaked at day 0–1 and declined on day 3–4; the second “late” group, comprised of genes associated with cell cycle, emerged from day 4 and peaked at day 5–6. The up-regulation of innate immune response genes coincided with the down-regulation of genes associated with viral replication during day 0–3. Furthermore, DHF patients had lower expression of genes associated with antigen processing and presentation, MHC class II receptor, NK and T cell activities, compared to that of DF patients. These results suggested that the innate and adaptive immunity during the early days of the disease are vital in suppressing DENV replication and in affecting outcome of disease severity. Gene signatures of DHF were identified as early as day 1.

          Conclusions/Significance

          Our study reveals a broad and dynamic picture of host responses in DENV infected subjects. Host response to DENV infection can now be understood as two distinct phases with unique transcriptional markers. The DHF signatures identified during day 1–3 may have applications in developing early molecular diagnostics for DHF.

          Author Summary

          The clinical outcome of DENV infection in humans can be DF or the more severe DHF and DSS. The individual's previous DENV exposure history, infecting serotypes, and host genetics are thought to be contributing factors to dengue disease severity. Our study contributed to the current dengue research field in the following ways: 1) Our study reveals the dynamics of host gene expression over each day post onset of symptoms. The gene transcription patterns enabled classification of dengue disease into 2 subtle phases: early acute and late acute. 2) The study identified gene markers differentiating severe dengue cases from non-severe cases with >90% accuracy. Taken together, our study offers insight into host responses in DENV-infected subjects and these results may be valuable for the future development of diagnostic tools for disease severity.

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          Most cited references35

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          Gene Ontology: tool for the unification of biology

          Genomic sequencing has made it clear that a large fraction of the genes specifying the core biological functions are shared by all eukaryotes. Knowledge of the biological role of such shared proteins in one organism can often be transferred to other organisms. The goal of the Gene Ontology Consortium is to produce a dynamic, controlled vocabulary that can be applied to all eukaryotes even as knowledge of gene and protein roles in cells is accumulating and changing. To this end, three independent ontologies accessible on the World-Wide Web (http://www.geneontology.org) are being constructed: biological process, molecular function and cellular component.
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            DAVID: Database for Annotation, Visualization, and Integrated Discovery.

            Functional annotation of differentially expressed genes is a necessary and critical step in the analysis of microarray data. The distributed nature of biological knowledge frequently requires researchers to navigate through numerous web-accessible databases gathering information one gene at a time. A more judicious approach is to provide query-based access to an integrated database that disseminates biologically rich information across large datasets and displays graphic summaries of functional information. Database for Annotation, Visualization, and Integrated Discovery (DAVID; http://www.david.niaid.nih.gov) addresses this need via four web-based analysis modules: 1) Annotation Tool - rapidly appends descriptive data from several public databases to lists of genes; 2) GoCharts - assigns genes to Gene Ontology functional categories based on user selected classifications and term specificity level; 3) KeggCharts - assigns genes to KEGG metabolic processes and enables users to view genes in the context of biochemical pathway maps; and 4) DomainCharts - groups genes according to PFAM conserved protein domains. Analysis results and graphical displays remain dynamically linked to primary data and external data repositories, thereby furnishing in-depth as well as broad-based data coverage. The functionality provided by DAVID accelerates the analysis of genome-scale datasets by facilitating the transition from data collection to biological meaning.
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              Model-based analysis of oligonucleotide arrays: expression index computation and outlier detection.

              Recent advances in cDNA and oligonucleotide DNA arrays have made it possible to measure the abundance of mRNA transcripts for many genes simultaneously. The analysis of such experiments is nontrivial because of large data size and many levels of variation introduced at different stages of the experiments. The analysis is further complicated by the large differences that may exist among different probes used to interrogate the same gene. However, an attractive feature of high-density oligonucleotide arrays such as those produced by photolithography and inkjet technology is the standardization of chip manufacturing and hybridization process. As a result, probe-specific biases, although significant, are highly reproducible and predictable, and their adverse effect can be reduced by proper modeling and analysis methods. Here, we propose a statistical model for the probe-level data, and develop model-based estimates for gene expression indexes. We also present model-based methods for identifying and handling cross-hybridizing probes and contaminating array regions. Applications of these results will be presented elsewhere.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS Negl Trop Dis
                PLoS Negl Trop Dis
                plos
                plosntds
                PLoS Neglected Tropical Diseases
                Public Library of Science (San Francisco, USA )
                1935-2727
                1935-2735
                July 2013
                11 July 2013
                : 7
                : 7
                : e2298
                Affiliations
                [1 ]Naval Medical Research Center, Silver Spring, Maryland, United States of America
                [2 ]Henry M. Jackson Foundation for the Advancement of Military Medicine, Rockville, Maryland, United States of America
                [3 ]US Naval Medical Research Unit No. 6, Lima, Peru
                [4 ]Laboratorio Regional de Diagnóstico e Investigación del Dengue y otras Enfermedades Virales (LARDIDEV), Instituto de Investigaciones Biomédicas de la Universidad de Carabobo (BIOMED-UC), Maracay, Venezuela
                [5 ]Division of Retrovirology, Walter Reed Army Institute of Research, Silver Spring, Maryland, United States of America
                [6 ]Department of Entomology, University of California at Davis, Davis, California, United States of America
                [7 ]US Naval Medical Research Unit No. 2 – Pacific, American Embassy Singapore, Singapore
                [8 ]Division of Malaria Vaccine Development, Walter Reed Army Institute of Research, Silver Spring, Maryland, United States of America
                Oxford University Clinical Research Unit, Vietnam
                Author notes

                None of the authors has a financial or personal conflict of interest related to this study. The corresponding author had full access to all data in the study and final responsibility for the decision to submit this publication.

                Conceived and designed the experiments: PJB CFO TJK. Performed the experiments: MTV ZW. Analyzed the data: PS JG MTV ZW. Contributed reagents/materials/analysis tools: GC GS IB SV DEC. Wrote the paper: PS JG. Critical review of the manuscript: JG GC BMF ACM CR PJB ESH TJK TWS. Project Coordinator: ESH TJK. Field coordinator: GC.

                Article
                PNTD-D-13-00165
                10.1371/journal.pntd.0002298
                3708824
                23875036
                74b6ca44-47a2-4b94-87cc-b63e6cbd8014
                Copyright @ 2013

                This is an open-access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.

                History
                : 28 January 2013
                : 23 May 2013
                Page count
                Pages: 14
                Funding
                This study was funded by the US Department of Defense Global Emerging Infections Surveillance and Response System (DoD-GEIS), a division of the Armed Forces Health Surveillance Center, WORK UNIT NUMBER: 847705.82000.25GB.B0016. The work was also supported by MIDRP funding: S0147_07_LI. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Biology
                Genomics
                Genome Expression Analysis
                Microbiology
                Virology
                Emerging Viral Diseases

                Infectious disease & Microbiology
                Infectious disease & Microbiology

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