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Recapitulation of HDV infection in a fully permissive hepatoma cell line allows efficient drug evaluation.

ABSTRACT: Hepatitis delta virus (HDV) depends on the helper function of hepatitis B virus (HBV), which provides the envelope proteins for progeny virus secretion. Current infection-competent cell culture models do not support assembly and secretion of HDV. By stably transducing HepG2 cells with genes encoding the NTCP-receptor and the HBV envelope proteins we produce a cell line (HepNB2.7) that allows continuous secretion of infectious progeny HDV following primary infection. Evaluation of antiviral drugs shows that the entry inhibitor Myrcludex B (IC50: 1.4?nM) and interferon-? (IC50: 28?IU/ml, but max. 60-80% inhibition) interfere with primary infection. Lonafarnib inhibits virus secretion (IC50: 36?nM) but leads to a substantial intracellular accumulation of large hepatitis delta antigen and replicative intermediates, accompanied by the induction of innate immune responses. This work provides a cell line that supports the complete HDV replication cycle and presents a convenient tool for antiviral drug evaluation.

SUBMITTER: Lempp FA

PROVIDER: S-EPMC6531471 | biostudies-literature | 2019 May

REPOSITORIES: biostudies-literature

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Recapitulation of HDV infection in a fully permissive hepatoma cell line allows efficient drug evaluation.

Lempp Florian A FA Schlund Franziska F Rieble Lisa L Nussbaum Lea L Link Corinna C Zhang Zhenfeng Z Ni Yi Y Urban Stephan S

Nature communications 20190522 1

Hepatitis delta virus (HDV) depends on the helper function of hepatitis B virus (HBV), which provides the envelope proteins for progeny virus secretion. Current infection-competent cell culture models do not support assembly and secretion of HDV. By stably transducing HepG2 cells with genes encoding the NTCP-receptor and the HBV envelope proteins we produce a cell line (HepNB2.7) that allows continuous secretion of infectious progeny HDV following primary infection. Evaluation of antiviral drugs ...[more]

PMID: 31118422

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Project description:The erythropoietin-producing hepatoma A2 receptor (EphA2) is a tyrosine kinase overexpressed by tumor stroma and cancer cells. A high expression level of EphA2 predicts poor prognosis, correlating with disease progression and metastasis. Therefore, EphA2 is a relevant therapeutic target for human cancer. Antibodies, selectively bound to EphA2, can induce rapid receptor phosphorylation that results in antibody internalization and degradation. This internalization mechanism has been exploited with the development of antibody-drug conjugates (ADCs) for cancer chemotherapy. In this study, we used PET imaging to study the pharmacokinetics and tumor delivery of a panel of anti-EphA2 monoclonal antibodies (mAbs) with and without drug conjugates. Methods: A library of human anti-EphA2 mAbs were screened and evaluated for EphA2 internalization rate, binding affinity, epitope binding, and hydrophobicity. We chose 3 of these antibodies, denoted as 1C1, 3B10, and 2H7, which recognize different epitopes, for further evaluation. ADCs were generated by S239C mutation to give a ratio of 2 drug molecules per antibody. Native mAbs and ADCs were characterized, after conjugation to a DFO chelator and 89Zr radiolabeling, in assays including cell uptake, internalization, hydrophobicity, and in vivo imaging using PET. Results: All 3 mAbs had high affinities for EphA2 but exhibited different internalization rates following the order of 1C1 > 3B10 > 2H7. Internalization rate is only 1 factor that affects in vitro cell uptake and in vivo tumor accumulation. Interestingly, the hydrophobicity of the mAbs, which followed the order of 2H7 > 1C1 > 3B10, had a strong correlation with in vivo tumor uptake measured by PET, with the least hydrophobic antibody, 3B10, showing the highest tumor uptake. ADC significantly reduced the in vivo uptake of all 3 mAbs. Conclusion: Tumor uptake of mAb is a complex process that is affected by multiple parameters, including internalization, hydrophobicity, and chemical modification. Our results suggest that the addition of drug molecules to mAb increases the clearance of the mAb presumably due to the increased hydrophobicity. Understanding the complexity of antibody-based tumor delivery may help improve ADC engineering for better tumor targeting and reduced side effects.

Project description:BACKGROUND: Altered networks of gene regulation underlie many complex conditions, including cancer. Inferring gene regulatory networks from high-throughput microarray expression data is a fundamental but challenging task in computational systems biology and its translation to genomic medicine. Although diverse computational and statistical approaches have been brought to bear on the gene regulatory network inference problem, their relative strengths and disadvantages remain poorly understood, largely because comparative analyses usually consider only small subsets of methods, use only synthetic data, and/or fail to adopt a common measure of inference quality. METHODS: We report a comprehensive comparative evaluation of nine state-of-the art gene regulatory network inference methods encompassing the main algorithmic approaches (mutual information, correlation, partial correlation, random forests, support vector machines) using 38 simulated datasets and empirical serous papillary ovarian adenocarcinoma expression-microarray data. We then apply the best-performing method to infer normal and cancer networks. We assess the druggability of the proteins encoded by our predicted target genes using the CancerResource and PharmGKB webtools and databases. RESULTS: We observe large differences in the accuracy with which these methods predict the underlying gene regulatory network depending on features of the data, network size, topology, experiment type, and parameter settings. Applying the best-performing method (the supervised method SIRENE) to the serous papillary ovarian adenocarcinoma dataset, we infer and rank regulatory interactions, some previously reported and others novel. For selected novel interactions we propose testable mechanistic models linking gene regulation to cancer. Using network analysis and visualization, we uncover cross-regulation of angiogenesis-specific genes through three key transcription factors in normal and cancer conditions. Druggabilty analysis of proteins encoded by the 10 highest-confidence target genes, and by 15 genes with differential regulation in normal and cancer conditions, reveals 75% to be potential drug targets. CONCLUSIONS: Our study represents a concrete application of gene regulatory network inference to ovarian cancer, demonstrating the complete cycle of computational systems biology research, from genome-scale data analysis via network inference, evaluation of methods, to the generation of novel testable hypotheses, their prioritization for experimental validation, and discovery of potential drug targets.

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Recapitulation of HDV infection in a fully permissive hepatoma cell line allows efficient drug evaluation.

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Recapitulation of HDV infection in a fully permissive hepatoma cell line allows efficient drug evaluation.

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