Incorporation of Ebola glycoprotein into HIV particles facilitates dendritic cell and macrophage targeting and enhances HIV-specific immune responses
Bibliographic record
Abstract
The development of an effective vaccine against HIV infection remains a global priority. Dendritic cell (DC)-based HIV immunotherapeutic vaccine is a promising approach which aims at optimizing the HIV-specific immune response using primed DCs to promote and enhance both the cellular and humoral arms of immunity. Since the Ebola virus envelope glycoprotein (EboGP) has strong DC-targeting ability, we investigated whether EboGP is able to direct HIV particles towards DCs efficiently and promote potent HIV-specific immune responses. Our results indicate that the incorporation of EboGP into non-replicating virus-like particles (VLPs) enhances their ability to target human monocyte-derived dendritic cells (MDDCs) and monocyte-derived macrophages (MDMs). Also, a mucin-like domain deleted EboGP (EboGPΔM) can further enhanced the MDDCs and MDMs-targeting ability. Furthermore, we investigated the effect of EboGP on HIV immunogenicity in mice, and the results revealed a significantly stronger HIV-specific humoral immune response when immunized with EboGP-pseudotyped HIV VLPs compared with those immunized with HIV VLPs. Splenocytes harvested from mice immunized with EboGP-pseudotyped HIV VLPs secreted increased levels of macrophage inflammatory proteins-1α (MIP-1α) and IL-4 upon stimulation with HIV Env and/or Gag peptides compared with those harvested from mice immunized with HIV VLPs. Collectively, this study provides evidence for the first time that the incorporation of EboGP in HIV VLPs can facilitate DC and macrophage targeting and induce more potent immune responses against HIV.
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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".