An Extensive Antigenic Footprint Underpins Immunodominant TCR Adaptability against a Hypervariable Viral Determinant
Bibliographic record
Abstract
Mutations in T cell epitopes are implicated in hepatitis C virus (HCV) persistence and can impinge on vaccine development. We recently demonstrated a narrow bias in the human TCR repertoire targeted at an immunodominant, but highly mutable, HLA-B*0801-restricted epitope ((1395)HSKKKCDEL(1403) [HSK]). To investigate if the narrow TCR repertoire facilitates CTL escape, structural and biophysical studies were undertaken, alongside comprehensive functional analysis of T cells targeted at the natural variants of HLA-B*0801-HSK in different HCV genotypes and quasispecies. Interestingly, within the TCR-HLA-B*0801-HSK complex, the TCR contacts all available surface-exposed residues of the HSK determinant. This broad epitope coverage facilitates cross-genotypic reactivity and recognition of common mutations reported in HCV quasispecies, albeit to a varying degree. Certain mutations did abrogate T cell reactivity; however, natural variants comprising these mutations are reportedly rare and transient in nature, presumably due to fitness costs. Overall, despite a narrow bias, the TCR accommodated frequent mutations by acting like a blanket over the hypervariable epitope, thereby providing effective viral immunity. Our findings simultaneously advance the understanding of anti-HCV immunity and indicate the potential for cross-genotype HCV vaccines.
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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 | 0.000 |
| 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) | 0.001 | 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".