The Multiples Fates of the Flavivirus RNA Genome During Pathogenesis
Bibliographic record
Abstract
genus comprises many viruses (including dengue, Zika, West Nile and yellow fever viruses) which constitute important public health concerns worldwide. For several of these pathogens, neither antivirals nor vaccines are currently available. In addition to this unmet medical need, flaviviruses are of particular interest since they constitute an excellent model for the study of spatiotemporal regulation of RNA metabolism. Indeed, with no DNA intermediate or nuclear step, the flaviviral life cycle entirely relies on the cytoplasmic fate of a single RNA species, namely the genomic viral RNA (vRNA) which contains all the genetic information necessary for optimal viral replication. From a single open reading frame, the vRNA encodes a polyprotein which is processed to generate the mature viral proteins. In addition to coding for the viral polyprotein, the vRNA serves as a template for RNA synthesis and is also selectively packaged into newly assembled viral particles. Notably, vRNA translation, replication and encapsidation must be tightly coordinated in time and space via a fine-tuned equilibrium as these processes cannot occur simultaneously and hence, are mutually exclusive. As such, these dynamic processes involve several vRNA secondary and tertiary structures as well as RNA modifications. Finally, the vRNA can be detected as a foreign molecule by cytosolic sensors which trigger upon activation antiviral signaling pathways and the production of antiviral factors such as interferons and interferon-stimulated genes. However, to create an environment favorable to infection, flaviviruses have evolved mechanisms to dampen these antiviral processes, notably through the production of a specific vRNA degradation product termed subgenomic flavivirus RNA (sfRNA). In this review, we discuss the current understanding of the fates of flavivirus vRNA and how this is regulated at the molecular level to achieve an optimal replication within infected cells.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".