Evidence of CD4+ T cell-mediated immune pressure on the Hepatitis C virus genome
Bibliographic record
Abstract
Abstract Hepatitis C virus (HCV)-specific T cell responses are critical for immune control of infection. Viral adaptation to these responses, via mutations within regions of the virus targeted by CD8 + T cells, is associated with viral persistence. However, identifying viral adaptation to HCV-specific CD4 + T cell responses has been difficult although key to understanding anti-HCV immunity. In this context, HCV sequence and host genotype from a single source HCV genotype 1B cohort (n = 63) were analyzed to identify viral changes associated with specific human leucocyte antigen (HLA) class II alleles, as these variable host molecules determine the set of viral peptides presented to CD4 + T cells. Eight sites across the HCV genome were associated with HLA class II alleles implicated in infection outcome in this cohort (p ≤ 0.01; Fisher’s exact test). We extended this analysis to chronic HCV infection (n = 351) for the common genotypes 1A and 3A. Variation at 38 sites across the HCV genome were associated with specific HLA class II alleles with no overlap between genotypes, suggestive of genotype-specific T cell targets, which has important implications for vaccine design. Here we show evidence of HCV adaptation to HLA class II-restricted CD4 + T cell pressure across the HCV genome in chronic HCV infection without a priori knowledge of CD4 + T cell epitopes.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".