Dendritic Cell Inhibition Is Connected to Exhaustion of CD8+ T Cell Polyfunctionality during Chronic Hepatitis C Virus Infection
Bibliographic record
Abstract
Although chronic viral infections have evolved mechanisms to interfere with aspects of pathogen recognition by dendritic cells (DCs), the role that these APCs play in virus-specific T cell exhaustion is unclear. Herein we report that NS3-dependent suppression of Toll/IL-1 domain-containing adapter-inducing IFN-beta- and IFN-beta promoter stimulator-1- but not MyD88-coupled pathogen-recognition receptor-induced synthesis of proinflammatory cytokines (IL-12 and TNF-alpha) from DCs by hepatitis C virus (HCV) is a distinctive feature of a subgroup of chronically infected patients. The result is decreased CD8(+) T cell polyfunctional capacities (production of IFN-gamma, IL-2, TNF-alpha, and CD107a mobilization) that is confined to HCV specificities and that relates to the extent to which HCV inhibits DC responses in infected subjects, despite comparable plasma viral load, helper T cell environments, and inhibitory programmed death 1 receptor/ligand signals. Thus, subjects in whom pathogen-recognition receptor signaling in DCs was intact exhibited enhanced polyfunctionality (i.e., IL-2-secretion and CD107a). In addition, differences between HCV-infected patients in the ability of CD8(+) T cells to activate multiple functions in response to HCV did not apply to CD8(+) T cells specific for other immune-controlled viruses (CMV, EBV, and influenza). Our findings identify reversible virus evasion of DC-mediated innate immunity as an additional important factor that impacts the severity of polyfunctional CD8(+) T cell exhaustion during a chronic viral infection.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".