Schäfer Noemi, Rothhaar Paul, Heuss Christian, Neumann-Haefelin Christoph, Thimme Robert, Dietz Julia, Sarrazin Christoph, Schnitzler Paul, Merle Uta, Pérez-Del-Pulgar Sofía, Laketa Vibor, Lohmann Volker
Department of Infectious Diseases, Molecular Virology, Section Virus-Host Interactions, Heidelberg University, 69120 Heidelberg, Germany.
Department of Medicine II, Medical Center, Faculty of Medicine, University of Freiburg, 79110 Freiburg, Germany.
Viruses. 2024 Nov 30;16(12):1871. doi: 10.3390/v16121871.
The study of hepatitis C virus (HCV) replication in cell culture is mainly based on cloned viral isolates requiring adaptation for efficient replication in Huh7 hepatoma cells. The analysis of wild-type (WT) isolates was enabled by the expression of SEC14L2 and by inhibitors targeting deleterious host factors. Here, we aimed to optimize cell culture models to allow infection with HCV from patient sera. We used Huh7-Lunet cells ectopically expressing SEC14L2, CD81, and a GFP reporter with nuclear translocation upon cleavage by the HCV protease to study HCV replication, combined with a drug-based regimen for stimulation of non-modified wild-type isolates. RT-qPCR-based quantification of HCV infections using patient sera suffered from a high background in the daclatasvir-treated controls. We therefore established an automated image analysis pipeline based on imaging of whole wells and iterative training of a machine learning tool, using nuclear GFP localization as a readout for HCV infection. Upon visual validation of hits assigned by the automated image analysis, the method revealed no background in daclatasvir-treated samples. Thereby, infection events were found for 15 of 34 high titer HCV genotype (gt) 1b sera, revealing a significant correlation between serum titer and successful infection. We further show that transfection of viral RNA extracted from sera can be used in this model as well, albeit with so far limited efficiency. Overall, we generated a robust serum infection assay for gt1b isolates using semi-automated image analysis, which was superior to conventional RT-qPCR-based quantification of viral genomes.
丙型肝炎病毒（HCV）在细胞培养中的复制研究主要基于克隆的病毒分离株，这些分离株需要经过适应性改造才能在Huh7肝癌细胞中高效复制。通过SEC14L2的表达以及靶向有害宿主因子的抑制剂，实现了对野生型（WT）分离株的分析。在此，我们旨在优化细胞培养模型，以允许用患者血清中的HCV进行感染。我们使用异位表达SEC14L2、CD81和一种在被HCV蛋白酶切割后具有核转位功能的绿色荧光蛋白（GFP）报告基因的Huh7-Lunet细胞来研究HCV复制，并结合基于药物的方案来刺激未修饰的野生型分离株。使用患者血清进行基于逆转录定量聚合酶链反应（RT-qPCR）的HCV感染定量时，在接受达卡他韦治疗的对照中背景较高。因此，我们基于对整个孔的成像以及对机器学习工具的迭代训练，建立了一个自动图像分析流程，使用核GFP定位作为HCV感染的读数。在对自动图像分析分配的命中结果进行视觉验证后，该方法在达卡他韦治疗的样本中未发现背景。由此，在34份高滴度HCV基因1b型（gt1b）血清中的15份中发现了感染事件，揭示了血清滴度与成功感染之间存在显著相关性。我们还进一步表明，从血清中提取的病毒RNA转染也可用于该模型，尽管目前效率有限。总体而言，我们使用半自动图像分析为gt1b分离株生成了一种可靠的血清感染检测方法，该方法优于基于传统RT-qPCR的病毒基因组定量方法。
