Shared IgG Infection Signatures vs. Hemorrhage-Restricted IgA Clusters in Human Dengue: A Phenotype of Differential Class-Switch via TGFβ1

Phenotypic manifestations of infectious diseases are closely related to individual immune responses. Methods to extract information from patients’ own immune reactions would be of great use for both diagnosis and treatment. Dengue fever is one of the diseases that clinical aggravations could occur paradoxically after humoral immunity appears. This property makes dengue fever an excellent disease model to explore. A principal component analyses (PCAs)-based framework derived from a prior vaccination study was developed. The framework was verified by successful demonstrations of known IgG signatures from a Mexico Dengue data set. Afterward the pipeline was tested upon de novo IgG and IgA libraries of Dengue patients from southern Taiwan. We discovered four infection signatures within IgG repertoires, two of which were identical to previous reports. However, it was IgA but not IgG that could differentiate hemorrhagic from non-hemorrhagic patients. IgA repertoires were found more diversified among bleeders, from whom seven signature clusters were characterized. The expressions of transforming growth factor beta 1 (TGFβ1) and accordingly mediated class-switch activity of IgA were distinct only among the PCA-segregated bleeding group. In sum, intercontinental sharing of IgG signatures in dengue fever was demonstrated via a unified working flow. Differential regulation of IgA class-switch with associated diversity expansion plus existences of hemorrhage-restricted clusters were shown. The ability of the framework to find common IgG signatures would implicate applications to infections even from unknown pathogens. The clusters within IgA repertoires could offer perspectives to other IgA-related bleeding disorders such as Henoch-Schönlein purpura or IgA nephropathy. Substantiated grounds for IgA-specific effector function via TGFβ1-mediated class-switch would be a new factor to consider for infectious diseases.