Elucidating Novel Hepatitis C Virus–Host Interactions Using Combined Mass Spectrometry and Functional Genomics Approaches
Bibliographic record
Abstract
More than 170 million people worldwide are infected with the hepatitis C virus (HCV), for which future therapies are expected to rely upon a combination of oral antivirals. For a rapidly evolving virus like HCV, host-targeting antivirals are an attractive option. To decipher the role of novel HCV-host interactions, we used a proteomics approach combining immunoprecipitation of viral-host protein complexes coupled to mass spectrometry identification and functional genomics RNA interference screening of HCV partners. Here, we report the proteomics analyses of protein complexes associated with Core, NS2, NS3/4A, NS4B, NS5A, and NS5B proteins. We identified a stringent set of 98 human proteins interacting specifically with one of the viral proteins. The overlap with previous virus-host interaction studies demonstrates 24.5% shared HCV interactors overall (24/98), illustrating the reliability of the approach. The identified human proteins show enriched Gene Ontology terms associated with the endoplasmic reticulum, transport proteins with a major contribution of NS3/4A interactors, and transmembrane proteins for Core interactors. The interaction network emphasizes a high degree distribution, a high betweenness distribution, and high interconnectivity of targeted human proteins, in agreement with previous virus-host interactome studies. The set of HCV interactors also shows extensive enrichment for known targets of other viruses. The combined proteomic and gene silencing study revealed strong enrichment in modulators of HCV RNA replication, with the identification of 11 novel cofactors among our set of specific HCV partners. Finally, we report a novel immune evasion mechanism of NS3/4A protein based on its ability to affect nucleocytoplasmic transport of type I interferon-mediated signal transducer and activator of transcription 1 nuclear translocation. The study revealed highly stringent association between HCV interactors and their functional contribution to the viral replication cycle and pathogenesis.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".