Diverse roles of heterogeneous nuclear ribonucleoproteins in viral life cycle
Bibliographic record
Abstract
Understanding the host-virus interactions helps to decipher the viral replication strategies and pathogenesis. Viruses have limited genetic content and rely significantly on their host cell to establish a successful infection. Viruses depend on the host for a broad spectrum of cellular RNA-binding proteins (RBPs) throughout their life cycle. One of the major RBP families is the heterogeneous nuclear ribonucleoproteins (hnRNPs) family. hnRNPs are typically localized in the nucleus, where they are forming complexes with pre-mRNAs and contribute to many aspects of nucleic acid metabolism. hnRNPs contain RNA binding motifs and frequently function as RNA chaperones involved in pre-mRNA processing, RNA splicing, and export. Many hnRNPs shuttle between the nucleus and the cytoplasm and influence cytoplasmic processes such as mRNA stability, localization, and translation. The interactions between the hnRNPs and viral components are well-known. They are critical for processing viral nucleic acids and proteins and, therefore, impact the success of the viral infection. This review discusses the molecular mechanisms by which hnRNPs interact with and regulate each stage of the viral life cycle, such as replication, splicing, translation, and assembly of virus progeny. In addition, we expand on the role of hnRNPs in the antiviral response and as potential targets for antiviral drug research and development.
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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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".