Vaccines Bearing Engineered HIV-1 gp41 Transmembrane and Cytoplasmic-tail Domains Elicit Rare Reactivities Targeting the C-terminal Subdomain of the MPER
Bibliographic record
Abstract
Abstract Numerous vaccine strategies have failed to induce neutralizing (Nt) antibodies (Abs) against the HIV-1 gp41 MPER, the target of several broadly NtAbs. Factors contributing to this include an incomplete understanding of native MPER structure, and restricted/transient exposure of key Nt sites. Here, we describe novel MPER DNA and MPER-peptide liposome vaccines (PLVs) that best elicit Abs against N- and C-terminal (N- & C-) subdomains of the MPER; these subdomains are targeted by the 2F5 and 4E10/10E8 NtAbs, respectively. DNA-prime, PLV-boost immunization of rabbits and guinea pigs (GPs) with vaccines that optimize exposure of the N-terminal subdomain, elicited low-level 2F5-like reactivities that were moderately elevated with boosting. Co-immunization with the vaccines increased durability of these responses, with preliminary results showing moderate Nt activity from GPs (but not rabbits; however, GP pre-immune sera exhibited high Nt background). DNA and PLV vaccines were optimized to expose both the N- and C-subdomains. DNA vaccines were engineered to (i) encode an N-terminal coiled-coil to support a trimeric structure, (ii) express the native gp41 TMD, and (iii) truncate the cytoplasmic tail. Lipids and peptides for PLVs were designed to optimally expose the C-subdomain. Prime-boost and co-immunization of rabbits with the DNA vaccines and mixed N- and C-subdomain PLVs elicited Abs targeting both Nt sites of the MPER, but no Nt activity. Similar immunizations are underway in GPs. Together, our results illustrate the importance of (i) the TMD in eliciting Abs against the C-subdomain, (ii) co-immunization, and (iii) perhaps of GPs as a model, for eliciting NtAb responses.
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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 | 0.001 |
| 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".