Integrative analysis of metabolomics, transcriptomics and proteomics to study mechanisms that regulate lentiviral vector hepatocyte transduction

肝移植是治疗许多先天性和获得性疾病的唯一方法。然而，多发性肝病被认为是单基因疾病，可能通过靶向肝细胞进行基因治疗。因此，肝脏是开发新的基因治疗方法的重要靶器官。一个挑战是肝细胞通常是静止的；因此，重组慢病毒载体(LV)可能比其他基因转移载体具有优势，能够转导未分裂的细胞，并整合到细胞基因组中，允许长期转基因表达。关键是，无论具体应用如何，建立有效的LV转导对于成功的肝脏基因治疗是必不可少的。该项目的重点将是研究LV转导对细胞代谢和肝细胞内信号通路的潜在影响。转录组学和蛋白质组学分别测量基因和蛋白质的变化，而代谢组学允许同时测量大量细胞代谢物。机器学习可以用来整合所有这三种技术产生的数据，并将被用来确定病毒引起的大规模变化，旨在识别影响/调节肝细胞有效基因转移的新靶点、途径和机制。

Liver transplantation is the only cure for many congenital and acquired diseases. However, multiple liver diseases are thought to be single-gene disorders that may be amenable to gene therapy via targeting of liver hepatocytes. The liver, therefore, represents an important target organ for developing new gene therapy approaches. One challenge is that hepatocytes are generally quiescent; thus a recombinant lentiviral vector (LV) may have the advantage over other gene transfer vectors in being able to transduce non-dividing cells, and also to integrate in the cell genome permitting long-term transgene expression. Crucially, irrespective of the specific application, establishing efficient LV transduction will be imperative for successful liver gene therapy. The specific focus of this project will be to study potential effects of LV transduction on cellular metabolism and signalling pathways in liver cells. Whereas transcriptomics and proteomics measure changes in genes and proteins, respectively, metabolomics allows the concurrent measurement of large numbers of cellular metabolites. Machine learning can be used to integrate data generated from all three technologies, and will be used to determine large-scale changes induced by the virus aiming to identify novel targets, pathways and mechanisms that affect/regulate efficient gene transfer in hepatocytes.