Temporal Dynamics of the Transcriptional Response to Dengue Virus Infection in Nicaraguan Children

Dengue is the most prevalent mosquito-borne human illness worldwide. The ability to predict disease severity during the earliest days of the illness is a long-sought, but unachieved goal. We examined human genome-wide transcript abundance patterns in daily peripheral blood mononuclear cell (PBMC) samples from 41 children hospitalized with dengue virus (DENV) infection in Nicaragua, as well as 8 healthy control subjects. Nine patients had primary dengue fever (DF1), 11 had dengue fever with serologic evidence of prior DENV infection, i.e., secondary dengue fever (DF2), 12 had dengue hemorrhagic fever (DHF), and 9 had dengue shock syndrome (DSS). We identified 2,092 genes for which transcript abundance differed significantly between patients on days 3–6 of fever and healthy subjects (FDR<1%). Prior DENV infection explained the greatest amount of variation in gene expression among patients. The number of differentially expressed genes was greatest on fever day 3 in patients with DF1, while the number in patients with DF2 or DHF/DSS was greatest on day 5. Genes associated with the mitotic cell cycle and B cell differentiation were expressed at higher levels, and genes associated with signal transduction and cell adhesion were expressed at lower levels, in patients versus healthy controls. On fever day 3, a set of interferon-stimulated gene transcripts was less abundant in patients who subsequently developed DSS than in other patient groups (p<0.05, ranksum). Patients who later developed DSS also had higher levels of transcripts on day 3 associated with mitochondrial function (p<0.01, ranksum). These day 3 transcript abundance findings were not evident on subsequent fever days. In conclusion, we identified differences in the timing and magnitude of human gene transcript abundance changes in DENV patients that were associated with serologic evidence of prior infection and with disease severity. Some of these differential features may predict the outcome of DENV infection.