A prevaccination validated network that drives the breadth of the protective neutralizing antibody response following Dengue Vaccine TV003 immunization
Bibliographic record
Abstract
ABSTRACT Development of fully protective dengue virus (DV) vaccines has been problematic as infection with DV requires a broad antibody immune response that targets all 4 possible serotypes. Herein, we used an integrated systems vaccinology approach to identify prevaccination features that allow the development of fully protective DV-specific antibody responses. This approach allowed us to identify a transcription network in a subset of monocytes defined by the expression of CD68 and downstream of specific pro- and anti-inflammatory cytokines. Moreover, we identified metabolites as drivers of an immune response that induced neutralizing antibodies to the 4 DV serotypes. Specifically, PC/PE drove the production of TGF-B in CD68 low monocytes, which was a positive correlate of the protective antibody response. In contrast, primary and secondary bile acids triggered a proinflammatory response downstream of TGR5 signaling and inflammasome activation in CD68 high monocytes, which was associated to a non-protective antibody response. These features were validated in vitro in primary myeloid cells. Our results highlight the role of cell and systemic metabolism as regulators of protective immune responses to vaccination, and that systems vaccinology is a key tool to identify such mechanisms.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| 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) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".