Comparison of the immunogenicity of mRNA-encoded and protein HIV-1 Env–ferritin nanoparticle designs
Bibliographic record
Abstract
ABSTRACT Nucleoside-modified mRNA technology has revolutionized vaccine development with the success of mRNA COVID-19 vaccines. We used modified mRNA technology for the design of envelopes (Env) to induce HIV-1 broadly neutralizing antibodies (bnAbs). However, unlike SARS-CoV-2 neutralizing antibodies that are readily made, HIV-1 bnAb induction is disfavored by the immune system because of the rarity of bnAb B cell precursors and the cross-reactivity of bnAbs targeting certain Env epitopes with host molecules, thus requiring optimized immunogen design. The use of protein nanoparticles (NPs) has been reported to enhance B cell germinal center responses to HIV-1 Env. Here, we report our experience with the expression of Env–ferritin NPs compared with membrane-bound Env gp160 when encoded by modified mRNA. We found that well-folded Env–ferritin NPs were a minority of the protein expressed by an mRNA design and were immunogenic at 20 µg but minimally immunogenic in mice at 1 µg dose in vivo and were not expressed well in draining lymph nodes (LNs) following intramuscular immunization. In contrast, mRNA encoding gp160 was more immunogenic than mRNA encoding Env–NP at 1 µg dose and was expressed well in draining LN following intramuscular immunization. Thus, analysis of mRNA expression in vitro and immunogenicity at low doses in vivo are critical for the evaluation of mRNA designs for optimal immunogenicity of HIV-1 immunogens. IMPORTANCE An effective HIV-1 vaccine that induces protective antibody responses remains elusive. We have used mRNA technology for designs of HIV-1 immunogens in the forms of membrane-bound full-length envelope gp160 and envelope ferritin nanoparticle. Here, we demonstrated in a mouse model that the membrane-bound form induced a better response than envelope ferritin nanoparticle because of higher in vivo protein expression. The significance of our research is in highlighting the importance of analysis of mRNA design expression and low-dose immunogenicity studies for HIV-1 immunogens before moving to vaccine clinical trials.
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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".