Molecular classification of outcomes from dengue virus -3 infections

• Published in: Journal of clinical virology Vol. 64; pp. 97 - 106
• Main Authors: Brasier, Allan R., Zhao, Yingxin, Wiktorowicz, John E., Spratt, Heidi M., Nascimento, Eduardo J.M., Cordeiro, Marli T., Soman, Kizhake V., Ju, Hyunsu, Recinos, Adrian, Stafford, Susan, Wu, Zheng, Marques, Ernesto T.A., Vasilakis, Nikos
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 01.03.2015
• Subjects: Acute phase reaction; Adult; Allergy and Immunology; alpha-Macroglobulins - analysis; Biomarker pipeline; Biomarkers - analysis; Biomarkers - blood; Complement Activation; Dengue; Dengue - diagnosis; Dengue - genetics; Dengue - virology; Dengue virus; Dengue Virus - genetics; Early Diagnosis; Female; Humans; Infectious Disease; Male; Phenotype; Platelet Count; Proteomics; ROC Curve; Selected reaction monitoring; Severe Dengue - diagnosis; Severe Dengue - genetics; Severe Dengue - virology; Tropomyosin - analysis; Viral Load; Young Adult; Acute phase reaction; Selected reaction monitoring; Biomarker pipeline; Dengue
• ISSN: 1386-6532; 1873-5967; 1873-5967
• DOI: 10.1016/j.jcv.2015.01.011

•We conducted a biomarker study associated with complications of dengue infection.•We apply a novel liquid separations-proteomics profiling to detect dengue markers.•Differences in plasma proteins are measured in subjects with 3 infectious outcomes.•A machine learning classifier (random forest) accurately distinguishes disease outcomes.•Our results suggest that distinct pathophysiologic processes underly dengue fever outcomes. 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. 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. 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. 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.