Interactions Between Hepatitis C Virus and Proprotein Convertase Subtilisin/Kexin Type 9
Bibliographic record
Abstract
Hepatitis C virus (HCV) is a small enveloped positive-sense single-stranded RNA virus that infects 2-3% of the world population. The majority of infected people develop chronic hepatitis, which results in severe liver damages. No HCV vaccine has been developed and the current antiviral regimens have some limitations such as high costs and not being effective in some difficult-to-treat patients.\nProprotein convertase subtilisin/kexin type 9 (PCSK9) is a serine protease primarily produced in the liver. Its gene expression can be regulated by several transcription factors, such as sterol-regulatory element binding proteins (SREBPs), hepatocyte nuclear factor (HNF)-1, forkhead box O3 (FoxO3) and specificity protein 1 (Sp1). PCSK9 plays an important role in lipid homeostasis through facilitating the degradation of the low-density lipoprotein receptor (LDLR). It also exerts an antiviral effect on HCV. It can suppress HCV entry by reducing HCV receptors LDLR and cluster of differentiation 81. Although PCSK9 has been shown to inhibit HCV replication, the underlying mechanism has not been thoroughly characterized. Besides, the effects of PCSK9 on HCV translation and virion assembly/secretion have not been studied.\nSince PCSK9 can regulate lipid levels and the HCV life cycle is closely connected with lipid metabolism, I hypothesized that PCSK9 has an inhibitory effect on the HCV life cycle. I first showed that PCSK9 does not affect HCV translation or virion assembly/secretion. However, an inhibitory effect of PCSK9 on HCV replication is shown by overexpressing or knocking down PCSK9 in HCV replicon cells. Then I demonstrated that PCSK9-induced LDLR degradation is not involved in HCV replication regulation using gain-of-function (D374Y) or loss-of-function (Δaa. 31-52) PCSK9 mutants for LDLR degradation. Moreover, the auto-cleavage of PCSK9 affects HCV replication since only uncleaved proPCSK9 suppresses HCV replication and cleaved PCSK9 does not have effect on HCV replication. Next, I found that PCSK9 can interact with several HCV proteins including NS5A. The PCSK9 interacting region of NS5A is aa. 95-215 in domain I. The interaction between PCSK9 and NS5A inhibits NS5A dimerization and HCV RNA binding to NS5A. Considering that NS5A dimerization and RNA binding activity of NS5A are required for HCV replication, the interaction between PCSK9 and NS5A could be a mechanism of the inhibitory effect of PCSK9 on HCV replication. \nSince interferon (IFN) produced by innate immune system is important to clear viral infection and PCSK9 can inhibit HCV infection, I further hypothesized that PCSK9 affects HCV infection through regulating IFN production. I showed that PCSK9 suppresses IFNβ expression at the transcription and protein levels. The inhibitory effect of PCSK9 on IFNβ promoter/enhancer activity is mediated by positive regulatory domain IV in the IFNβ enhancer region where the activating transcription factor-2 (ATF-2)/c-Jun complex can bind. I demonstrated an interaction between PCSK9 and ATF-2. This interaction reduces ATF-2/c-Jun dimerization and ATF-2/c-Jun binding to IFNβ enhancer, which could explain how PCSK9 inhibits IFNβ expression. This is a novel function of PCSK9.\nHCV can differently modulate transcription factors involved in PCSK9 expression including SREBPs, HNF-1 and FoxO3, but how HCV regulates PCSK9 expression remains unknown. In this study, I demonstrated that HCV can up-regulate PCSK9 promoter activity in the context of HCV infection and in HCV replicon cells. Among HCV viral proteins, NS2, NS3, NS3-4A, NS5A and NS5B enhance, and p7 or NS4B decreases PCSK9 promoter activity. I also showed that transcription factors SREBP-1c, HNF-1α and Sp1 increase PCSK9 promoter activity in HCV replicon cells, whereas SREBP-1a, HNF-1β and FoxO3 have an inhibitory effect.\nIn conclusion, I showed complex interactions between HCV and PCSK9. On one hand, HCV up-regulates PCSK9 promoter activity. On the other hand, PCSK9 inhibits HCV replication via the interaction with NS5A. It also suppresses IFNβ expression via the interaction with ATF-2, which may regulate HCV infection. This research advances the understanding of the regulation of PCSK9 and the effects of PCSK9 on viral infection and IFN expression, and may help to optimize anti-HCV treatments.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".