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Dengue virus (DENV) is the most prevalent arthropod-borne viral disease in humans. DENV causes a spectrum of illness ranging from mild to potentially severe complications. Dendritic cells (DCs) play a critical role in initiating and regulating highly effective antiviral immune response that include linking innate and adaptive immune responses. This study was conducted to comparatively characterize in detail the relative proportion, phenotypic changes, and maturation profile of subsets of both myeloid DCs (mDCs) and plasmacytoid DCs (pDCs) in children with dengue fever (DF), dengue hemorrhagic fever (DHF) and for purposes of control healthy individuals. The mDCs (Lin-CD11c+CD123lo), the pDCs (Lin-CD11c-CD123+) and the double negative (DN) subset (Lin-/HLA-DR+/CD11c-CD123-) were analyzed by polychromatic flow cytometry. The data were first analyzed on blood samples collected from DENV-infected patients at various times post-infection. Results showed that the relative proportion of mDCs were significantly decreased which was associated with an increase in disease severity in samples from DENV-infected patients. While there was no significant difference in the relative proportion of pDCs between healthy and DENV-infected patients, there was a marked increase in the DN subset. Analysis of the kinetics of changes of pDCs showed that there was an increase but only during the early febrile phase. Additionally, samples from patients during acute disease showed marked decreases in the relative proportion of CD141+ and CD16+ mDC subsets that were the major mDC subsets in healthy individuals. In addition, there was a significant decrease in the level of CD33-expressing mDCs in DENV patients. While the pDCs showed an up-regulation of maturation profile during acute DENV infection, the mDCs showed an alteration of maturation status. This study suggests that different relative proportion and phenotypic changes as well as alteration of maturation profile of DC subsets may play a critical role in the dengue pathogenesis and disease outcome.
Pubmed ID: 30001408
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Software for single-cell flow cytometry analysis. Its functions include management, display, manipulation, analysis and publication of the data stream produced by flow and mass cytometers.
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