The global origins of resistance-associated variants in the non-structural proteins 5A and 5B of the hepatitis C virus
Bibliographic record
Abstract
New, costly, fast acting, therapies targeting the non-structural proteins 5A and 5B (NS5A and NS5B) regions of the hepatitis C virus (HCV) genome are curative in the majority of cases. Variants with certain mutations in the NS5A and NS5B regions of HCV have been shown to reduce susceptibility to direct-acting NS5A and NS5B therapy and are found in treatment naïve patients. Despite this, the ease with which these variants evolve is poorly known, as are their evolutionary and geographic origins. To address this crucial gap we inferred the evolutionary and geographic origins of resistance-associated variants (RAVs) in the HCV NS5A and NS5B regions of subtypes 1a, 1b, and 3a sequences available from global databases. We found that RAVs in the NS5A region of HCV, when prevalent, were widely dispersed throughout the phylogenetic tree of HCV with multiple independent origins and that these variants are globally distributed. In contrast, most of the NS5B C316N variants came from one of two clades in the phylogenetic tree of HCV subtype 1b. The presence of serine (S) at codon 218 of HCV NS5B appears to facilitate the evolution of the C316N RAV. Other NS5B RAVs did not arise very frequently in our data set, except for S556G in subtype 1b and with respect to geography NS5B RAVs were also globally distributed. The inferred distribution of RAVs in the NS5A region and frequency of their origin suggest a low fitness barrier without the need for co-evolution of compensatory mutations. A low fitness barrier may allow rapid selection of de novo resistance to NS5A inhibitors during therapy.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".