RESUMO
The flavivirus non-structural protein 1 (NS1) is secreted from infected cells into the circulation and the serum levels correlate with disease severity. The effect of secreted NS1 (sNS1) on non-infected mammalian immune cells is largely unknown. Here, we expressed recombinant sNS1 proteins of tick-borne encephalitis virus (TBEV) and West Nile virus (WNV) and investigated their effects on dendritic cell (DC) effector functions. Murine bone marrow-derived DCs (BMDCs) showed reduced surface expression of co-stimulatory molecules and decreased release of pro-inflammatory cytokines when treated with sNS1 of TBEV or WNV prior to poly(I:C) stimulation. Transcriptional profiles of BMDCs that were sNS1-exposed prior to poly(I:C) stimulation showed two gene clusters that were downregulated by TBEV or WNV sNS1 and that were associated with innate and adaptive immune responses. Functionally, both sNS1 proteins modulated the capacity for BMDCs to induce specific T-cell responses as indicated by reduced IFN-γ levels in both CD4+ and CD8+ T cells after BMDC co-cultivation. In human monocyte-derived DCs, poly(I:C)-induced upregulation of co-stimulatory molecules and cytokine responses were even more strongly impaired by TBEV sNS1 or WNV sNS1 pretreatment than in the murine system. Our findings indicate that exogenous flaviviral sNS1 proteins interfere with DC-mediated stimulation of T cells, which is crucial for the initiation of cell-mediated adaptive immune responses in human flavivirus infections. Collectively, our data determine soluble flaviviral NS1 as a virulence factor responsible for a dampened immune response to flavivirus infections. IMPORTANCE The effective initiation of protective host immune responses controls the outcome of infection, and dysfunctional T-cell responses have previously been associated with symptomatic human flavivirus infections. We demonstrate that secreted flavivirus NS1 proteins modulate innate immune responses of uninfected bystander cells. In particular, sNS1 markedly reduced the capacity of dendritic cells to stimulate T-cell responses upon activation. Hence, by modulating cellular host responses that are required for effective antigen presentation and initiation of adaptive immunity, sNS1 proteins may contribute to severe outcomes of flavivirus disease.
RESUMO
BACKGROUND: Several studies have been performed to study transcriptome profiles after dengue virus infections with partly different results. Due to slightly different settings of the individual studies, different genes and enriched gene sets are reported in these studies. The main aim of this network meta-analysis was to aggregate a selection of these studies to identify genes and gene sets that are more generally associated with dengue virus infection, i.e. with less dependence on the individual study settings. METHODS: We performed network meta-analysis by different approaches using publicly available gene expression data of five selected studies from the Gene Expression Omnibus database. The study network includes dengue fever (DF), hemorrhagic fever (DHF), shock syndrome (DSS) patients as well as convalescent and healthy control individuals. After data merging and missing value imputation, study-specific batch effects were removed. Pairwise differential expression analysis and subsequent gene-set enrichment analysis were performed between the five study groups. Furthermore, mutual information networks were derived from the top genes of each group comparison, and the separability between the three patient groups was studied by machine learning models. RESULTS: From the 10 possible pairwise group comparisons in the study network, six genes (IFI27, TPX2, CDT1, DTL, KCTD14 and CDCA3) occur with a noticeable frequency among the top listed genes of each comparison. Thus, there is an increased evidence that these genes play a general role in dengue virus infections. IFI27 and TPX2 have also been highlighted in the context of dengue virus infection by other studies. A few of the identified gene sets from the network meta-analysis overlap with findings from the original studies. Mutual information networks yield additional genes for which the observed pairwise correlation is different between the patient groups. Machine learning analysis shows a moderate separability of samples from the DF, DHF and DSS groups (accuracy about 80%). CONCLUSIONS: Due to an increased sample size, the network meta-analysis could reveal additional genes which are called differentially expressed between the studied groups and that may help to better understand the molecular basis of this disease.