Hepatitis C Virus Protein Interaction Network Analysis Based on Hepatocellular Carcinoma

MicroRNA let-7b inhibits hepatitis C virus and induces apoptosis in human hepatoma cells

BACKGROUND

Small non-coding RNAs have emerged as essential modulators of viral infections such as hepatitis C virus (HCV). Cellular miRNAs directly regulate the viral infectivity and indirectly by targeting virus-host factors. The current study investigates the inhibitory effect of let-7b miRNA on HCV replication in the Hepatocarcinoma cell line (Huh7.5).

METHODS AND RESULTS

The algorithm-based search revealed that let-7b, a high score microRNA, has target sequences on the HCV genome. The Huh7.5 cells were stably transduced with let-7b lentiviral vectors (Huh7.5/let-7b) and mock (Huh7.5/scrambled). The expression of the let-7b level was assessed by real-time PCR assay and Red fluorescence microscope. A dual-luciferase assay was conducted to evaluate the liver-specific let-7b and HCV genome interaction. In the next step, for establishing HCVcc, Full-length HCV-RNA was transduced to naïve Huh7.5, Huh7.5/scrambled, and Huh7.5/let-7b cells. The results of in silico analysis and dual-luciferase reporter assay exhibited a specific interaction of HCV-NS5B and let-7b. Real-time PCR analysis revealed that in contrast to infected naïve Huh7.5 cells and Huh7.5/scrambled, a significant decrease in HCV-RNA load was seen in Huh7.5/let-7b cells. On the other hand, the Flow Cytometry test showed that let-7b could significantly induce the apoptosis pathway in Huh7.5/let-7b.

CONCLUSIONS

The results also suggest that let-7b, as a target of the HCV genome, potentially reduces HCV replication and raises cell apoptosis rate. We suggest that let-7b directly downregulates HCV replication and may serve as a unique antiviral therapy.

Hepatitis C virus associated hepatocellular carcinoma

Liver cancer is a global problem and hepatocellular carcinoma (HCC) accounts for about 85% of this cancer. In the USA, etiologies and risk factors for HCC include chronic hepatitis C virus (HCV) infection, diabetes, non-alcoholic steatohepatitis (NASH), obesity, excessive alcohol drinking, exposure to tobacco smoke, and genetic factors. Chronic HCV infection appears to be associated with about 30% of HCC. Chronic HCV infection induces multistep changes in liver, involving metabolic disorders, steatosis, cirrhosis and HCC. Liver carcinogenesis requires initiation of neoplastic clones, and progression to clinically diagnose malignancy. Tumor progression associates with profound exhaustion of tumor-antigen-specific CD8⁺T cells, and accumulation of PD-1^hi CD8⁺T cells and Tregs. In this chapter, we provide a brief description of HCV and environmental/genetic factors, immune regulation, and highlight mechanisms of HCV associated HCC. We also underscore HCV treatment and recent paradigm of HCC progression, highlighted the current treatment and potential future therapeutic opportunities.

Impact of IL28 Genotypes and Modeling the Interactions of HCV Core Protein on Treatment of Hepatitis C

BACKGROUND

Mutations in the core CVR region of hepatitis C virus (HCV) and polymorphisms of interleukin 28B (IL28B) are associated with progression toward liver disease and in response to therapy. In addition, interactions of the core protein with some cell interactors can be related to HCV liver damage.

AIM

This study aimed to evaluate the effect of core mutations as well as IL28B polymorphism on clinical features, sustained virological response (SVR) in 1a and 3a HCV genotypes amongst Iranian HCV infected patients, and the impact of mutations on core protein properties, antigenic properties, and interactions with HCV inhibitors, using several bioinformatics tools.

METHODS

Seventy-nine Iranian patients infected with HCV genotypes 1a and 3a and diagnosed with chronic active hepatitis were examined. Plasma viral RNA was used to amplify and sequence the HCV Core gene; also, HCV viral load, molecular genotyping, and the liver enzymes were determined for all samples. The sequencing results were analyzed by several reliable bioinformatics tools to determine the physicochemical properties, B cell epitopes, post-modification changes, and secondary/tertiary structures; and evaluate the interactions with 4 drugs by docking method.

RESULT

There were some substitutions in core CVR related to ALT and AST enzymes that can lead to HCV advanced liver disease. The most prevalent mutation for 3a genotypes was a substitution in aa 162 (I to V) while we did not find any mutation in 1a responder group. Polymorphism of the rs8099917 showed that the majority of patients had TG heterozygous and carried CT genotype at the rs12979860. Analysis indicated several phosphorylation sits for core protein as well as two important disulfide bonds. Immunogenic prediction showed that core protein can strongly induce the immune system. Interaction analysis, using the docking method revealed two potential interactors (Vitronectin and SETD2).

CONCLUSION

Generally, mutations in all core CVR regions in all patients showed a relationship between such substitutions and higher liver enzymes that can result in advanced liver disease progression in HCV infected patients. Furthermore, immunoinformatics analysis determined the possible immunodominant regions to be considered in HCV vaccine designs. Furthermore, no association between SVR and IL28B polymorphism was shown. In silico analysis determined modification sites, structures, B-cell epitopes of core protein and interactions with several interactors can lead to persistent HCV infection in the cell and the progress of liver diseases.

Leveraging Experimental Details for an Improved Understanding of Host-Pathogen Interactome

An increasing proportion of curated host-pathogen interaction (HPI) information is becoming available in interaction databases. These data represent detailed, experimentally-verified, molecular interaction data, which may be used to better understand infectious diseases. By their very nature, HPIs are context dependent, where the outcome of two proteins as interacting or not depends on the precise biological conditions studied and approaches used for identifying these interactions. The associated biology and the technical details of the experiments identifying interacting protein molecules are increasing being curated using defined curation standards but are overlooked in current HPI network modeling. Given the increase in data size and complexity, awareness of the process and variables included in HPI identification and curation, and their effect on data analysis and interpretation is crucial in understanding pathogenesis. We describe the use of HPI data for network modeling, aspects of curation that can help researchers to more accurately model specific infection conditions, and provide examples to illustrate these principles. © 2018 by John Wiley & Sons, Inc.