RIG-I–like Receptor Triggering by Dengue Virus Drives Dendritic Cell Immune Activation and TH1 Differentiation
Bibliographic record
Abstract
Abstract Dengue virus (DENV) causes 400 million infections annually and is one of several viruses that can cause viral hemorrhagic fever, which is characterized by uncontrolled immune activation resulting in high fever and internal bleeding. Although the underlying mechanisms are unknown, massive cytokine secretion is thought to be involved. Dendritic cells (DCs) are the main target cells of DENV, and we investigated their role in DENV-induced cytokine production and adaptive immune responses. DENV infection induced DC maturation and secretion of IL-1β, IL-6, and TNF. Inhibition of DENV RNA replication abrogated these responses. Notably, silencing of RNA sensors RIG-I or MDA5 abrogated DC maturation, as well as cytokine responses by DENV-infected DCs. DC maturation was induced by type I IFN responses because inhibition of IFN-α/β receptor signaling abrogated DENV-induced DC maturation. Moreover, DENV infection of DCs resulted in CCL2, CCL3, and CCL4 expression, which was abrogated after RIG-I and MDA5 silencing. DCs play an essential role in TH cell differentiation, and we show that RIG-I and MDA5 triggering by DENV leads to TH1 polarization, which is characterized by high levels of IFN-γ. Notably, cytokines IL-6, TNF, and IFN-γ and chemokines CCL2, CCL3, and CCL4 have been associated with disease severity, endothelial dysfunction, and vasodilation. Therefore, we identified RIG-I and MDA5 as critical players in innate and adaptive immune responses against DENV, and targeting these receptors has the potential to decrease hemorrhagic fever in patients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".