Frequent de novo generation of HCV3a resistance-associated substitutions in Spain
Bibliographic record
Abstract
Background: HCV subtype 3a, responsible for approximately 17% of HCV infections in Spain, remains a difficult-to-treat genotype despite the availability of highly effective treatments based on direct-acting antivirals. Current treatment regimens often combine a NS5A inhibitor with NS5B inhibitor (sofosbuvir). Resistance-associated substitutions (RASs) can have a profound impact on treatment response, especially in cirrhotic patients, with NS5A variant Y93H of particular interest due to its substantial fold-decrease in susceptibility to all NS5A inhibitors. For this reason, it is of interest to evaluate the virus epidemic history for patterns that can be of public health relevance. Methods: We combine publicly available with newly generated HCV3a NS5A and NS5B sequence data to elucidate the international HCV3a migration network with a focus on the role of Spain. Bayesian phylogenetic inference methods were used to estimate the epidemiological relations between the sampled virus lineages and to reconstruct the historical transmission patterns. Migration rates between locations were inferred using a discrete phylogeographic model in which rates from and to locations can differ. Results: There were no clear associations between the sample’s origin and amino acid usage patterns for NS5B RASs S282T, C316N/Y and V321A and for NS5A RASs M28T/V and L31M/V, while Q30L and Y93H appear overrepresented in Pakistanian (p=0.009) and Spanish strains respectively (p=0.052). Reconstruction of ancestral sequences shows that the Y93H RAS is usually de novo generated on external branches, dispersed over the whole phylogeny. Thus there is no founder effect for Y93H, as opposed to what is seen for HCV1a NS3 variant Q80K. The strengths and intensities of migration links between locations vary between the NS5A and NS5B datasets. Spain acts as a sink for HCV3a in both datasets but while most HCV3a import into Spain originates from Germany according to the NS5A data, the NS5B data point towards UK as the main source. Virus movements from Spain are usually towards other European countries (in particular to Portugal and Germany) and English-speaking countries (the so-called Anglosphere, which encompasses the Australia, Canada, India, Pakistan, the UK and the USA). The inconsistencies in the dominant origin location of HCV3a migration into Spain across datasets point out that each genomic region represents a different sample from the epidemic, and its combined phylogeographical analyses create a complementary picture of relevant migration patterns. This illustrates the usefulness of incorporating data from multiple genomic regions, the added value of longer genomic regions, and the need for broader sampling strategies. Conclusions: Spain can become an important 'host-spot' region of Y93H dissemination in the future, due to frequent de novo generation of this NS5A variant. Furthermore, while the inferred higher-level migration patterns are robust to the available sampling for a genomic sub-region, the details of the migration links between Spain and other locations vary by dataset. Our results indicate a need for the analyses of larger genomic regions, and a worldwide sampling of the HCV3a epidemic to more reliably infer the most important sources of HCV3a in Spain.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".