Allan R Brasier1, Yingxin Zhao2, John E Wiktorowicz3, Heidi M Spratt4, Eduardo J M Nascimento5, Marli T Cordeiro6, Kizhake V Soman7, Hyunsu Ju8, Adrian Recinos9, Susan Stafford10, Zheng Wu10, Ernesto T A Marques11, Nikos Vasilakis12. 1. Department of Internal Medicine, University of Texas Medical Branch, Galveston, TX, United States; Sealy Center for Molecular Medicine, UTMB, United States; Institute for Translational Sciences, UTMB, United States. Electronic address: arbrasie@utmb.edu. 2. Department of Internal Medicine, University of Texas Medical Branch, Galveston, TX, United States; Sealy Center for Molecular Medicine, UTMB, United States; Institute for Translational Sciences, UTMB, United States. 3. Sealy Center for Molecular Medicine, UTMB, United States; Institute for Translational Sciences, UTMB, United States; Department of Biochemistry and Molecular Biology, UTMB, United States. 4. Sealy Center for Molecular Medicine, UTMB, United States; Institute for Translational Sciences, UTMB, United States; Department Preventive Medicine and Community Health, UTMB, United States. 5. Department of Infectious Diseases and Microbiology and Immunology, University of Pittsburgh, United States. 6. Laboratorio de Virologia e Terapie Experimental do Centro de Pesquisas Aggeu Magalhaes-CPqAM, Fiocruz, Recife, Pernambuco, Brazil. 7. Sealy Center for Molecular Medicine, UTMB, United States; Department of Biochemistry and Molecular Biology, UTMB, United States. 8. Department Preventive Medicine and Community Health, UTMB, United States. 9. Department of Internal Medicine, University of Texas Medical Branch, Galveston, TX, United States. 10. Biomolecular Resource Facility, UTMB, United States. 11. Laboratorio de Virologia e Terapie Experimental do Centro de Pesquisas Aggeu Magalhaes-CPqAM, Fiocruz, Recife, Pernambuco, Brazil; Department of Infectious Diseases and Microbiology and Immunology, University of Pittsburgh, United States. 12. Department of Pathology and Center for Biodefense and Emerging Infectious Diseases, University of Texas Medical Branch, Galveston, TX, United States; Center for Tropical Diseases, University of Texas Medical Branch, Galveston, TX, United States; Institute for Human Infection and Immunity, University of Texas Medical Branch, Galveston, TX, United States.
Abstract
OBJECTIVES: Dengue virus (DENV) infection is a significant risk to over a third of the human population that causes a wide spectrum of illness, ranging from sub-clinical disease to intermediate syndrome of vascular complications called dengue fever complicated (DFC) and severe, dengue hemorrhagic fever (DHF). Methods for discriminating outcomes will impact clinical trials and understanding disease pathophysiology. STUDY DESIGN: We integrated a proteomics discovery pipeline with a heuristics approach to develop a molecular classifier to identify an intermediate phenotype of DENV-3 infectious outcome. RESULTS: 121 differentially expressed proteins were identified in plasma from DHF vs dengue fever (DF), and informative candidates were selected using nonparametric statistics. These were combined with markers that measure complement activation, acute phase response, cellular leak, granulocyte differentiation and viral load. From this, we applied quantitative proteomics to select a 15 member panel of proteins that accurately predicted DF, DHF, and DFC using a random forest classifier. The classifier primarily relied on acute phase (A2M), complement (CFD), platelet counts and cellular leak (TPM4) to produce an 86% accuracy of prediction with an area under the receiver operating curve of >0.9 for DHF and DFC vs DF. CONCLUSIONS: Integrating discovery and heuristic approaches to sample distinct pathophysiological processes is a powerful approach in infectious disease. Early detection of intermediate outcomes of DENV-3 will speed clinical trials evaluating vaccines or drug interventions.
OBJECTIVES:Dengue virus (DENV) infection is a significant risk to over a third of the human population that causes a wide spectrum of illness, ranging from sub-clinical disease to intermediate syndrome of vascular complications called dengue fever complicated (DFC) and severe, dengue hemorrhagic fever (DHF). Methods for discriminating outcomes will impact clinical trials and understanding disease pathophysiology. STUDY DESIGN: We integrated a proteomics discovery pipeline with a heuristics approach to develop a molecular classifier to identify an intermediate phenotype of DENV-3 infectious outcome. RESULTS: 121 differentially expressed proteins were identified in plasma from DHF vs dengue fever (DF), and informative candidates were selected using nonparametric statistics. These were combined with markers that measure complement activation, acute phase response, cellular leak, granulocyte differentiation and viral load. From this, we applied quantitative proteomics to select a 15 member panel of proteins that accurately predicted DF, DHF, and DFC using a random forest classifier. The classifier primarily relied on acute phase (A2M), complement (CFD), platelet counts and cellular leak (TPM4) to produce an 86% accuracy of prediction with an area under the receiver operating curve of >0.9 for DHF and DFC vs DF. CONCLUSIONS: Integrating discovery and heuristic approaches to sample distinct pathophysiological processes is a powerful approach in infectious disease. Early detection of intermediate outcomes of DENV-3 will speed clinical trials evaluating vaccines or drug interventions.
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