Characterizing an in vivo Mice Model for Studying Senescence in Hepatocytes

Senescence is a stress-induced durable cell cycle arrest. Senescent cells accumulate with age in most tissues of humans, primates, and rodents, as well as at the sites of tissue injury and remodeling [1]. Previous studies have shown that the accumulation of senescent cells in certain tissues, including white adipose, pancreas and liver leads to type 2 diabetes and the development of non-alcoholic fatty liver disease (NAFLD) [2, 3]. Moreover, a recent report demonstrated that P16-senescent cells suppress their hepatic fatty acid oxidation, switching their metabolism towards anabolism [4]. Hepatocytes are the major epithelial cells of the liver involved in regulating metabolism, detoxification, synthesis and storage [5]. However, the liver also contains several other cell types including the biliary epithelial cells (cholangiocytes), stellate cells, Kupffer cells and liver sinusoidal endothelial cells; each of these cell types possesses unique functions and work cooperatively to carry out the liver’s diverse roles [5]. Previous studies have generated models to study senescence in the liver by overexpressing or deleting senescence-related genes in the whole liver generating contradictory results that could be address by studying a model for every specific cell type of the liver. Considering that the impact of senescence specifically on hepatocyte function remains unclear, this project aims to characterize an in vivo senescence mice model created by p16 overexpression, a tumor suppressor and a major senescence marker. The transgene construct AAV8-TBG-eGFP-P16 was injected through the tail vein (AAV8-TBGeGFP injection served as the control group). In our model, P16 over-expression is restricted to hepatocytes since Adeno-Associated Virus 8 (AAV8) has hepatic tropism and TBG is a hepatocyte-specific promoter [6, 7].

One week after the AAV8 injection, we confirmed that almost all hepatocytes were infected by visualizing the GFP pattern under fluorescence microscope. Next, we assessed p16 expression at the mRNA level by using qRTPCR and at the protein level by using Western blot. We observed increased expression of p16 at both mRNA and protein level in the p16 group in comparison to the control group. Through senescence-associated beta-galactosidase (SA-𝛽-gal) staining (a widely used biomarker of senescence), we identified that most of hepatocytes were positive for this senescence marker in the p16 group while almost no hepatocytes were positive in the control group [8]. In addition, we carried out differential gene expression analysis using R package DESeq2 and gene set enrichment analysis (GSEA) on the RNA-sequencing data of control samples and p16 samples [9, 10]. This bioinformatic approach confirmed that senescence-related pathways (e.g. p16, p53) were upregulated in the p16 model; further suggesting that genes and pathways relating to proliferation (e.g. mitotic spindle, cyclin B) were downregulated. TNF𝛼 signaling through NF-𝜅B was enriched, which suggests the presence of senescence-associated secretory phenotype (SASP) [11]. These observations confirmed that the induction of senescence was successful in our model. We also noticed that oxidative phosphorylation, adipogenesis and bile acid metabolism were enriched, while glycolysis was downregulated in the p16 samples, suggesting that hepatocyte senescence has an influence on cellular bioenergetics, as well as lipid and sterol metabolism, and potentially leads to fat accumulation.

Using this model, we tried to understand how senescence induction in hepatocytes affects liver regeneration after 70% partial hepatectomy (PH). One week after tail vein injection of the construct, PH was performed and samples were collected at 0hr, 48hr, 72hr and 1 week after PH. Recovery of liver mass after PH was significantly slower in the P16 animals compared to controls, suggesting delayed regeneration. In samples collected at 48h, the time of maximal hepatocyte DNA synthesis in controls, GSEA revealed enrichment for proliferative markers (G2M checkpoint, mitotic spindle, E2F targets) relative to time 0 (pre-PH) samples. These findings were expected as PH imposes an enormous regenerative demand on the liver remnant. Compared to controls, however, the p16 group exhibited significant up-regulation of TNF signaling via NF-𝜅B, unfolded protein response, UV response, and hypoxia signaling, but down-regulation of peroxisome, bile acid metabolism, beta-catenin signaling, myogenesis and mitotic spindle. Thus, livers that were excessively populated by senescent hepatocytes at the time of PH developed increased cellular stress, dysregulated metabolism and reduced proliferative activity after PH.

Overall, our results show that we have a useful model to study senescence in vivo, and we plan to leverage this system to improve molecular understanding of how hepatocyte senescence impacts recovery from NAFLD and other types of liver injury that increase hepatocyte senescence. We expect to generate knowledge that will improve prevention, diagnosis and/or treatment of cirrhosis and liver failure, degenerative liver diseases that develop when regeneration of injured livers is defective.