Hepatitis C Virus Envelope Glycoprotein Fitness Defines Virus Population Composition following Transmission to a New Host
Bibliographic record
Abstract
Genetic variability is a hallmark of RNA virus populations. However, transmission to a new host often results in a marked decrease in population diversity. This genetic bottlenecking is observed during hepatitis C virus (HCV) transmission and can arise via a selective sweep or through the founder effect. To model HCV transmission, we utilized chimeric SCID/Alb-uPA mice with transplanted human hepatocytes and infected them with a human serum HCV inoculum. E1E2 glycoprotein gene sequences in the donor inoculum and recipient mice were determined following single-genome amplification (SGA). In independent experiments, using mice with liver cells grafted from different sources, an E1E2 variant undetectable in the source inoculum was selected for during transmission. Bayesian coalescent analyses indicated that this variant arose in the inoculum pretransmission. Transmitted variants that established initial infection harbored key substitutions in E1E2 outside HVR1. Notably, all posttransmission E1E2s had lost a potential N-linked glycosylation site (PNGS) in E2. In lentiviral pseudoparticle assays, the major posttransmission E1E2 variant conferred an increased capacity for entry compared to the major variant present in the inoculum. Together, these data demonstrate that increased envelope glycoprotein fitness can drive selective outgrowth of minor variants posttransmission and that loss of a PNGS is integral to this improved phenotype. Mathematical modeling of the dynamics of competing HCV variants indicated that relatively modest differences in glycoprotein fitness can result in marked shifts in virus population composition. Overall, these data provide important insights into the dynamics and selection of HCV populations during transmission.
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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 | 0.001 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".