Apolipoprotein E Mediates Evasion From Hepatitis C Virus Neutralizing Antibodies
Bibliographic record
Abstract
BACKGROUND & AIMS: Efforts to develop an effective vaccine against hepatitis C virus (HCV) have been hindered by the propensity of the virus to evade host immune responses. HCV particles in serum and in cell culture associate with lipoproteins, which contribute to viral entry. Lipoprotein association has also been proposed to mediate viral evasion of the humoral immune response, though the mechanisms are poorly defined. METHODS: We used small interfering RNAs to reduce levels of apolipoprotein E (apoE) in cell culture-derived HCV-producing Huh7.5-derived hepatoma cells and confirmed its depletion by immunoblot analyses of purified viral particles. Before infection of naïve hepatoma cells, we exposed cell culture-derived HCV strains of different genotypes, subtypes, and variants to serum and polyclonal and monoclonal antibodies isolated from patients with chronic HCV infection. We analyzed the interaction of apoE with viral envelope glycoprotein E2 and HCV virions by immunoprecipitation. RESULTS: Through loss-of-function studies on patient-derived HCV variants of several genotypes and subtypes, we found that the HCV particle apoE allows the virus to avoid neutralization by patient-derived antibodies. Functional studies with human monoclonal antiviral antibodies showed that conformational epitopes of envelope glycoprotein E2 domains B and C were exposed after depletion of apoE. The level and conformation of virion-associated apoE affected the ability of the virus to escape neutralization by antibodies. CONCLUSIONS: In cell-infection studies, we found that HCV-associated apoE helps the virus avoid neutralization by antibodies against HCV isolated from chronically infected patients. This method of immune evasion poses a challenge for the development of 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".