Identifying optimal capsid duplication length for the stability of reporter flaviviruses
Bibliographic record
Abstract
Mosquito-transmitted flaviviruses cause widespread disease across the world. To provide better molecular tools for drug screens and pathogenesis studies, we report a new approach to produce stable NanoLuc-tagged flaviviruses, including dengue virus serotypes 1-4, Japanese encephalitis virus, yellow fever virus, West Nile virus, and Zika virus. Since the reporter gene is often engineered at the capsid gene region, the capsid sequence must be duplicated to flank the reporter gene; such capsid duplication is essential for viral replication. The conventional approach for stabilizing reporter flaviviruses has been to shorten or modify the duplicated capsid sequence to minimize homologous recombination. No study has examined the effects of capsid duplication length on reporter virus stability. Here we report an optimal length to stabilize reporter flaviviruses. These viruses were stable after ten rounds of cell culture passaging, and in the case of stable NanoLuc-tagged Zika virus (ZIKV C38), the virus replicated to 107 FFU/ml in cell culture and produced robust luciferase signal after inoculation in mosquitoes. Mechanistically, the optimal length of capsid duplication may contain all the cis-acting RNA elements required for viral RNA replication, thus reducing the selection pressure for recombination. Together, these data describe an improved method of constructing optimal reporter flaviviruses.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| 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".