Multivalent Human Cytomegalovirus Glycoprotein B Nucleoside-Modified mRNA Vaccines Demonstrate a Greater Breadth in T cell but not Antibody Responses
Bibliographic record
Abstract
Abstract Human cytomegalovirus (HCMV) remains the most common congenital infection and infectious complication in immunocompromised patients. The most successful HCMV vaccine to-date, an HCMV glycoprotein B (gB) subunit vaccine adjuvanted with MF59, achieved 50% efficacy against primary HCMV infection. A previous study demonstrated that gB/MF59 vaccinees were less frequently infected with HCMV gB genotype strains most similar to the vaccine strain than strains encoding genetically distinct gB genotypes, suggesting strain-specific immunity accounted for the limited efficacy. To determine whether vaccination with multiple HCMV gB genotypes could increase the breadth of anti-HCMV gB humoral and cellular responses, we immunized 18 female rabbits with monovalent (gB-1), bivalent (gB-1+gB-3), or pentavalent (gB-1+gB-2+gB-3+gB-4+gB-5) gB lipid nanoparticle-encapsulated nucleoside-modified RNA (mRNA-LNP) vaccines. The multivalent vaccine groups did not demonstrate higher magnitude or breadth of the IgG response to the gB ectodomain or cell-associated gB compared to that of monovalent vaccine. Also, the multivalent vaccines did not show an increase in the breadth of neutralization activity and antibody-dependent cellular phagocytosis against HCMV strains encoding distinct gB genotypes. Yet, peripheral blood mononuclear cell-derived T cell responses elicited by multivalent vaccines were of a higher magnitude compared to that of monovalent vaccinated animals against a vaccine-mismatched gB genotype at peak immunogenicity. Our data suggests that inclusion of multivalent gB antigens is beneficial to increase the magnitude of T cell response but not an effective strategy to increase the breadth of anti-HCMV gB antibody responses. Further studies are required to validate whether the multivalent gB mRNA vaccines could effectively increase the T cell response breadth.
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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) | 0.002 | 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".