Lipid nanoparticles (LNP) induce activation and maturation of antigen presenting cells in young and aged individuals
Bibliographic record
Abstract
Abstract Despite the overwhelming success of mRNA-based vaccine in protecting against SARS-CoV-2 infection and reducing disease severity and hospitalization, little is known about the role lipid nanoparticles (LNP) play in initiating immune response. In this report we studied the adjuvantive impact of empty LNP with no mRNA cargo (eLNP) on anti-viral pathways and immune function of cells from young and aged individuals. We found that eLNP induced maturation of monocyte derived dendritic cells by measuring the expression of CD40, CD80, HLA-DR and production of cytokines including IFN-α,IL-6, IFN-γ, IL-12, and IL-21. Flow cytometry analysis of specific dendritic cell subsets showed that eLNP can induce CD40 expression and cytokine production in cDC1, cDC2 and monocytes. Empty LNP (eLNP) effects on dendritic cells and monocytes coincided with induction pIRF7 and pTBK1, which are both important in mitigating innate immune signaling. Interestingly our data show that in response to eLNP stimulus at 6 and 24 hrs, aged individuals have decreased CD40 expression and reduced IFN- γ output compared to young adults. Furthermore, we show that cDC1, cDC2, and CD14 dim CD16 + monocytes from healthy aged individuals have dysregulated anti-viral signaling response to eLNP stimulation as measured by the defect in type I IFN production, phosphorylation of IRF7, TBK-1, and immune function like phagocytosis. These data showed a novel function of eLNP in eliciting DC maturation and innate immune signaling pathways and that some of these functions are impaired in older individuals providing some suggestion of why older individuals (> 65 yrs of age) respond display lower immune responses and adverse events to SARS-CoV-2 mRNA-based vaccines.
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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) | 0.001 | 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".