Antigen-Driven Patterns of TCR Bias Are Shared across Diverse Outcomes of Human Hepatitis C Virus Infection
Bibliographic record
Abstract
Hepatitis C virus (HCV) infection causes significant morbidity and mortality worldwide. T cells play a central role in HCV clearance; however, there is currently little understanding of whether the disease outcome in HCV infection is influenced by the choice of TCR repertoire. TCR repertoires used against two immunodominant HCV determinants--the highly polymorphic, HLA-B*0801 restricted (1395)HSKKKCDEL(1403) (HSK) and the comparatively conserved, HLA-A*0101-restricted, (1435)ATDALMTGY(1443) (ATD)--were analyzed in clearly defined cohorts of HLA-matched, HCV-infected individuals with persistent infection and HCV clearance. In comparison with ATD, TCR repertoire selected against HSK was more narrowly focused, supporting reports of mutational escape in this epitope, in persistent HCV infection. Notwithstanding the Ag-driven divergence, T cell repertoire selection against either Ag was comparable in subjects with diverse disease outcomes. Biased T cell repertoires were observed early in infection and were evident not only in persistently infected individuals but also in subjects with HCV clearance, suggesting that these are not exclusively characteristic of viral persistence. Comprehensive clonal analysis of Ag-specific T cells revealed widespread use of public TCRs displaying a high degree of predictability in TRBV/TRBJ gene usage, CDR3 length, and amino acid composition. These public TCRs were observed against both ATD and HSK and were shared across diverse disease outcomes. Collectively, these observations indicate that repertoire diversity rather than particular Vβ segments are better associated with HCV persistence/clearance in humans. Notably, many of the anti-HCV TCRs switched TRBV and TRBJ genes around a conserved, N nucleotide-encoded CDR3 core, revealing TCR sequence mosaicism as a potential host mechanism to combat this highly variant virus.
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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) | 0.001 | 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".