Functional Characterization of Nuclear Localization and Export Signals in Hepatitis C Virus Proteins and Their Role in the Membranous Web
Bibliographic record
Abstract
The hepatitis C virus (HCV) is a positive strand RNA virus of the Flavivirus family that replicates in the cytoplasm of infected hepatocytes. Previously, several nuclear localization signals (NLS) and nuclear export signals (NES) have been identified in HCV proteins, however, there is little evidence that these proteins travel into the nucleus during infection. We have recently shown that nuclear pore complex (NPC) proteins (termed nucleoporins or Nups) are present in the membranous web and are required during HCV infection. In this study, we identify a total of 11 NLS and NES sequences in various HCV proteins. We show direct interactions between HCV proteins and importin α5 (IPOA5/kapα1), importin β3 (IPO5/kap β3), and exportin 1 (XPO1/CRM1) both in-vitro and in cell culture. These interactions can be disrupted using peptides containing the specific NLS or NES sequences of HCV proteins. Moreover, using a synchronized infection system, we show that these peptides inhibit HCV infection during distinct phases of the HCV life cycle. The inhibitory effects of these peptides place them in two groups. The first group binds IPOA5 and inhibits infection during the replication stage of HCV life cycle. The second group binds IPO5 and is active during both early replication and early assembly. This work delineates the entire life cycle of HCV and the active involvement of NLS sequences during HCV replication and assembly. Given the abundance of NLS sequences within HCV proteins, our previous finding that Nups play a role in HCV infection, and the relocation of the NLS double-GFP reporter in HCV infected cells, this work supports our previous hypothesis that NPC-like structures and nuclear transport factors function in the membranous web to create an environment conducive to viral replication.
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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) | 0.000 | 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".