Systems-level investigation of liver transcriptome in physiology and pathology

Abstract

Metabolism is an integral part of cellular physiology, with the liver as the central organ for a wide range of metabolic functions and homeostasis. The liver, unlike other organs, has a remarkable capacity to regenerate after partial loss of its mass, thus maintaining a constant liver-to-body weight ratio to preserve homeostasis. In an injury-free liver, the turnover of regeneration is very slow, but in the presence of an injury or perturbation, the regenerative response is triggered as a reparative strategy. Perturbations interfering with liver functions and disturbing the homeostatic state have serious repercussions leading to metabolic disorders like hepatic steatosis, non-alcoholic fatty liver disease (NAFLD), non-alcoholic steatohepatitis (NASH) and hepatocellular carcinoma (HCC). Ageing is one risk factor that increases the susceptibility to these diseases. The regenerative ability of the liver has been used to treat these diseases in the form of liver transplantation and partial liver resection, but recurrence of the disease is often observed after a few years. Understanding the molecular network that controls liver function and its regenerative ability in health and disease is crucial for developing clinical applications. The emergence of high throughput omics technologies provides a scope to develop a systems-level understanding of the disease. In this direction, we attempt to understand the pathophysiology of the liver by adopting systems biology approach for omics data interpretation. Tissue homeostasis and regeneration depend on the reversible transitions between quiescence (G0) and cell proliferation. During regeneration, the liver needs to maintain the essential metabolic tasks along with the fulfilment of metabolic requirements for hepatocyte growth and division. To understand the regulatory mechanisms involved in balancing the liver function and proliferation demand after injury or resection, we analyzed RNA sequencing temporal data of liver regeneration after two-thirds partial hepatectomy (PH) using network inference and mathematical modelling approaches. The reconstruction of the dynamic regulatory network revealed the overall temporal coordination of metabolism, RNA splicing and cell cycle during liver regeneration. A temporal shift in the gene expression pattern corresponding to increased hepatocyte proliferation and decreased hepatocyte function is observed with HNF4A as a key transcriptional activator. Based on these key observations, we developed a mathematical model of the HNF4A regulatory circuit, which showed the emergence of different states corresponding to compensatory metabolism, proliferation, and epithelial-to-mesenchymal transition. We showed that a mutually exclusive behaviour emerges due to the bistable inactivation of HNF4A, which controls the initiation and termination of liver regeneration and different population-level behaviour. Through our approach of modelling a regulatory circuit from high-throughput gene expression data, we proposed a mechanistic explanation of different states observed in single-cell RNA sequencing data of liver regeneration. The functional impairment of the liver with ageing reduces its regenerative capability and predisposes it to NAFLD and HCC. Mapping the molecular network of the liver encompassing these physiological (ageing) and pathological conditions may help to understand the crosstalk of ageing with different liver diseases. We performed networklevel analyses by integrating mouse transcriptomic data with protein-protein interaction (PPI) network. A sample-wise analysis using network entropy measure was performed, which showed an increasing trend with ageing and helped to identify ageing genes based on local entropy changes. To gain further insights, we also integrated the differentially expressed genes (DEGs) between young and different age groups with the PPI network and identified core modules and nodes associated with ageing. Finally, we computed the network proximity of the ageing network with different networks of liver diseases and regeneration to quantify the effect of ageing. Our analysis revealed the complex interplay of immune, cancer signalling, and metabolic genes in the ageing liver. We found significant network proximities between ageing and NAFLD, HCC, liver damage conditions, and the early phase of liver regeneration with common nodes, including NLRP12, TRP53, GSK3B, CTNNB1, MAT1 and FASN. A common theme involving pathways in cancers connects ageing, regeneration and liver diseases. Overall, our study maps the network-level changes of ageing and their interconnections with the physiology and pathology of the liver. While the regulated process of liver regeneration is crucial for damage-induced repair, its dysregulation may lead to HCC, the most common type of liver cancer. Understanding the molecular pathogenesis of HCC sequentially from precancerous state to cancer may improve prognosis and treatment strategies. In this direction, we studied the transcriptomics data of tumour samples and their adjacent normal samples in different premalignant states from HCC patients undergoing transplantation or partial hepatectomy. A hierarchical approach was adopted to identify modules, pathways, and genes relevant to the prediction of disease-free survival (DFS). Modules of co-expressed genes were identified from (a) only tumour samples, (b) premalignant and tumour samples collectively (premalignant-totumour), and (c) all normal and premalignant samples and their association to patient clinical characteristics was studied. Modules and genes related to the cell cycle, immune system, ribosome, and liver metabolic pathways served as good predictors for DFS using tumour samples. DFS modules were also associated with treatment (transplantation and resection) given to patients. An overall decrease in liver function and immune pathways but an increase in cell cycle activity was observed. The progression from premalignant to tumour is accompanied by variations in the extent of downregulation of liver function and immune system and an increase in cell cycle activity, bringing about variability in patient outcomes. Interestingly, we showed that modules and genes based on normal and premalignant samples also serve as good predictors of DFS. An increase in immune and cell cycle activity was observed in premalignant conditions, which suggests that tumourmatched normal samples already contain multiple signatures relevant to predict the DFS. This analysis revealed a shift in immune activity from premalignant to tumour state. THBD, a classical marker for dendritic cells, is a good predictor of DFS at the premalignant stage. Further, cell cycle genes related to microtubules, kinetochores, and centromere are altered in the premalignant stage, which are DFS genes in tumour samples. This study captured the dynamic changes in gene expression of various biological processes in the stepwise progression of HCC. Although liver regeneration capacity has been applied as a clinical intervention tool in the form of liver transplantation, its functional stability determines the post-treatment outcome. Understanding the molecular mechanisms driving long-term stability (normal) or rejection of the transplanted graft may play an important role in improving the post-transplantation outcome. This may help identify molecular markers to predict post-operative rejection. We performed differential gene coexpression analysis of transcriptomic data from postoperative liver biopsies of normal and rejection patients. This analysis revealed the rewiring of gene coexpression patterns pertaining to liver function, immune pathways and cell cycle. The modules of immune and cell cycle genes showed intact within-module coexpression in rejection samples compared to normal samples. We identified EGR2, MTHFD2, CD52, and CD38 from immune module and RRM2, TOP2A, ZWINT, TYMS, MAD2L1, ANLN, PRC1, and CDKN3 from cell cycle module as novel features distinguishing normal and rejection samples which require further validation. Overall, this thesis attempts to generate systems-level insights into liver physiology and pathology that may have implications in clinical settings. The pathophysiology of the liver is studied using transcriptome data from experimental mouse models to data from HCC patients. The work on liver regeneration provided insights into regulatory mechanisms governing the balance between liver function and proliferation. The network-level analysis of liver ageing, regeneration and pathological conditions showed the network-level influence of ageing on different liver-associated conditions and helped to identify key candidate pathways and genes commonly dysregulated across these conditions. Further, the work on HCC patients helped to characterise the different trajectories for progression from premalignant to tumour state and predict DFS based on both premalignant and tumour samples. This provides a scope for early detection and prognostication of HCC patients. We also analysed the liver transplantation dataset to identify features distinguishing normal and rejection samples