Masking the 5′ terminal nucleotides of the hepatitis C virus genome by an unconventional microRNA-target RNA complex
Bibliographic record
Abstract
Hepatitis C virus subverts liver-specific microRNA, miR-122, to upregulate viral RNA abundance in both infected cultured cells and in the liver of infected chimpanzees. These findings have identified miR-122 as an attractive antiviral target. Thus, it is imperative to know whether a distinct functional complex exists between miR-122 and the viral RNA versus its normal cellular target mRNAs. Toward this goal, effects on viral RNA abundance of mutated miR-122 duplex molecules, bound at each of the two target sites in the viral genome, were compared to effects on microRNA- or siRNA-mediated regulation of reporter target mRNAs. It was found that miR-122 formed an unusual microRNA complex with the viral RNA that is distinct from miR-122 complexes with reporter mRNAs. Notably, miR-122 forms an oligomeric complex in which one miR-122 molecule binds to the 5' terminus of the hepatitis C virus (HCV) RNA with 3' overhanging nucleotides, masking the 5' terminal sequences of the HCV genome. Furthermore, specific internal nucleotides as well as the 3' terminal nucleotides in miR-122 were absolutely required for maintaining HCV RNA abundance but not for microRNA function. Both miR-122 molecules utilize similar internal nucleotides to interact with the viral genome, creating a bulge and tail in the miR-122 molecules, revealing tandemly oriented oligomeric RNA complexes. These findings suggest that miR-122 protects the 5' terminal viral sequences from nucleolytic degradation or from inducing innate immune responses to the RNA terminus. Finally, this remarkable microRNA-mRNA complex could be targeted with compounds that inactivate miR-122 or interfere with this unique RNA structure.
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 | 0.000 | 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 | 0.000 |
| 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".