Contribution of a mutational bias in hepatitis C virus replication to the genetic barrier in the development of drug resistance
Bibliographic record
Abstract
The development of resistance to direct-acting antivirals (DAAs) targeting the hepatitis C virus (HCV) can compromise therapy. However, mechanisms that determine prevalence and frequency of resistance-conferring mutations remain elusive. Here, we studied the fidelity of the HCV RNA-dependent RNA polymerase NS5B in an attempt to link the efficiency of mismatch formation with genotypic changes observed in vivo. Enzyme kinetic measurements revealed unexpectedly high error rates (approximately 10(-3) per site) for G:U/U:G mismatches. The strong preference for G:U/U:G mismatches over all other mistakes correlates with a mutational bias in favor of transitions over transversions. Deep sequencing of HCV RNA samples isolated from 20 treatment-naïve patients revealed an approximately 75-fold difference in frequencies of the two classes of mutations. A stochastic model based on these results suggests that the bias toward transitions can also affect the selection of resistance-conferring mutations. Collectively, the data provide strong evidence to suggest that the nature of the nucleotide change can contribute to the genetic barrier in the development of resistance to DAAs.
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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".