CpG content in the Zika virus genome affects infection phenotypes in the adult brain and fetal lymph nodes
Bibliographic record
Abstract
Increasing the number of CpG dinucleotides in RNA viral genomes, while preserving the original amino acid composition, leads to impaired infection which does not cause disease. Beneficially, impaired infection evokes antiviral host immune responses providing a cutting-edge vaccine approach. For example, we previously showed that CpG-enriched Zika virus variants cause attenuated infection phenotypes and protect against lethal challenge in mice. While CpG recoding is an emerging and promising vaccine approach, little is known about infection phenotypes caused by recoded viruses in vivo, particularly in non-rodent species. Here, we used well-established mouse and porcine models to study infection phenotypes of the CpG-enriched neurotropic and congenital virus—Zika virus, directly in the target tissues—the brain and placenta. Specifically, we used the uttermost challenge and directly injected mice intracerebrally to compare infection phenotypes caused by wild-type and two CpG-recoded Zika variants and model the scenario where vaccine strains breach the blood-brain barrier. Also, we directly injected porcine fetuses to compare in utero infection phenotypes and model the scenario where recoded vaccine strains breach the placental barrier. While overall infection kinetics were comparable between wild-type and recoded virus variants, we found convergent phenotypical differences characterized by reduced pathology in the mouse brain and reduced replication of CpG-enriched variants in fetal lymph nodes. Next, using next-generation sequencing for the whole virus genome, we compared the stability of de novo introduced CpG dinucleotides during prolonged virus infection in the brain and placenta. Most de novo introduced CpG dinucleotides were preserved in sequences of recoded Zika viruses showing the stability of vaccine variants. Altogether, our study emphasized further directions to fine-tune the CpG recoding vaccine approach for better safety and can inform future immunization strategies.
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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".