Alcoholic hepatitis (AH) is the most severe form of alcoholic liver disease for which there is no efficacious treatment aiding most patients. AH manifests differently in individuals, with some patients showing debilitating symptoms more so than others. Previous studies showed significant metabolic dysregulation associated with AH. Therefore, we sought to analyze how the activity of metabolic pathways differed in the liver of patients with varying degrees of AH severity. We utilized a genome-scale metabolic modeling approach that allowed for integration of a generic human cellular metabolic model with specific RNA-seq data corresponding to healthy and multiple liver disease states to predict the metabolic fluxes within each disease state. Additionally, we performed a systems-level analysis of the transcriptomic data and predicted metabolic flux data to identify the regulatory and functional differences in liver metabolism with increasing severity of AH. Our results provide unique insights into the sequential dysregulation of the solute transport mechanisms underlying the glutathione metabolic pathway with increasing AH disease severity. We propose targeting of the solute transporters in the glutathione pathway to mimic the flux activity of the healthy liver state as a potential therapeutic intervention for AH.
Liver regeneration, which leads to the re-establishment of organ mass, follows a specifically organized set of biological processes acting on various time and length scales. Computational models of liver regeneration largely focused on incorporating molecular and signaling detail have been developed by multiple research groups in the recent years. These modeling efforts have supported a synthesis of disparate experimental results at the molecular scale. Incorporation of tissue and organ scale data using noninvasive imaging methods can extend these computational models towards a comprehensive accounting of multiscale dynamics of liver regeneration. For instance, microscopy-based imaging methods provide detailed histological information at the tissue and cellular scales. Noninvasive imaging methods such as ultrasound, computed tomography and magnetic resonance imaging provide morphological and physiological features including volumetric measures over time. In this review, we discuss multiple imaging modalities capable of informing computational models of liver regeneration at the organ-, tissue- and cellular level. Additionally, we discuss available software and algorithms, which aid in the analysis and integration of imaging data into computational models. Such models can be generated or tuned for an individual patient with liver disease. Progress towards integrated multiscale models of liver regeneration can aid in prognostic tool development for treating liver disease.
Rapid breakdown of hepatic glycogen stores into glucose plays an important role during intense physical exercise to maintain systemic euglycemia. Hepatic glycogenolysis is governed by several different liver-intrinsic and systemic factors such as hepatic zonation, circulating catecholamines, hepatocellular calcium signaling, hepatic neuroanatomy, and the central nervous system (CNS). Of the factors regulating hepatic glycogenolysis, the extent of lobular innervation varies significantly between humans and rodents. While rodents display very few autonomic nerve terminals in the liver, nearly every hepatic layer in the human liver receives neural input. In the present study, we developed a multi-scale, multi-organ model of hepatic metabolism incorporating liver zonation, lobular scale calcium signaling, hepatic innervation, and direct and peripheral organ-mediated communication between the liver and the CNS. We evaluated the effect of each of these governing factors on the total hepatic glucose output and zonal glycogenolytic patterns within liver lobules during simulated physical exercise. Our simulations revealed that direct neuronal stimulation of the liver and an increase in circulating catecholamines increases hepatic glucose output mediated by mobilization of intracellular calcium stores and lobular scale calcium waves. Comparing simulated glycogenolysis between human-like and rodent-like hepatic innervation patterns (extensive vs. minimal) suggested that propagation of calcium transients across liver lobules acts as a compensatory mechanism to improve hepatic glucose output in sparsely innervated livers. Interestingly, our simulations suggested that catecholamine-driven glycogenolysis is reduced under portal hypertension. However, increased innervation coupled with strong intercellular communication can improve the total hepatic glucose output under portal hypertension. In summary, our modeling and simulation study reveals a complex interplay of intercellular and multi-organ interactions that can lead to differing calcium dynamics and spatial distributions of glycogenolysis at the lobular scale in the liver.
Recent experimental investigations of liver homeostatic renewal have identified high replication capacity hepatocyte populations as the primary maintainers of liver mass. However, the molecular and cellular processes controlling liver homeostatic renewal remain unknown. To address this problem, we developed and analyzed a mathematical model describing cellular network interactions underlying liver homeostatic renewal. Model simulation results demonstrate that without feedback control, basic homeostatic renewal is not robust to disruptions, leading to tissue loss under persistent/repetitive insults. Consequently, we extended our basic model to incorporate putative regulatory interactions and investigated how such interactions may confer robustness on the homeostatic renewal process. We utilized a Design of Experiments approach to identify the combination of feedback interactions that yields a cell network model of homeostatic renewal capable of maintaining liver mass robustly during persistent/repetitive injury. Simulations of this robust model indicate that repeated injury destabilizes liver homeostasis within several months, which differs from epidemiological observations of a much slower decay of liver function occurring over several years. To address this discrepancy, we extended the model to include feedback control by liver nonparenchymal cells. Simulations and analysis of the final multicellular feedback control network suggest that achieving robust liver homeostatic renewal requires intrinsic stability in a hepatocellular network combined with feedback control by nonparenchymal cells.
Hepatocytes make up over 80% of liver mass and carry out all essential liver functions such as protein synthesis and detoxification, among others. Recent studies have shown distinct metabolic functions of proliferating and non‐proliferating hepatocytes as a compensatory mechanism during liver regeneration. However, this mechanism has not been studied in the case of alcoholic liver disease (ALD), which is known to disrupt the regenerative capacity and alter the metabolic processes of the liver. We seek to determine if alcohol consumption alters the balance in metabolic functionality between proliferating and non‐proliferating hepatocyte populations during liver regeneration. In the present study, rats were pair‐fed either a chronic ethanol diet or a carbohydrate control diet per Lieber DeCarli calorie‐matched liquid diet protocol. We performed 70% partial hepatectomy (PHx) and liver samples were collected at the baseline and at 24 hours post PHx. Single hepatocytes were isolated by laser capture microdissection (LCM) where the subset of proliferating hepatocytes were identified by Ki67 staining. Single‐cell RNA‐sequencing (scRNA‐seq) was employed for the purpose of identifying transcriptional differences across hepatocyte subtypes (i.e. PHx, diet, proliferation). Principal component analysis (PCA) of all samples showed distinct separation of cells into clusters based on liver regeneration (baseline and PHx samples), but not on diet (control and ethanol) and cellular proliferation (Ki67‐positive vs. negative staining). PCA of baseline samples showed separation of proliferating and non‐proliferating hepatocytes in the ethanol group. We characterized metabolic differences between hepatocyte subtypes using a computational model of genome‐scale metabolic reaction activities. We found that the metabolic compartmentalization separating proliferating from non‐proliferating hepatocytes in the carbohydrate group is absent in the ethanol group. Several functional differentiating factors arose favoring reactive oxygen species detoxification and steroid metabolism in proliferating hepatocytes from ethanol‐adapted animals at baseline, which were absent post PHx. Furthermore, proliferating cells from the carbohydrate group showed higher reaction activity in CoA synthesis, NAD metabolism and inositol phosphate metabolic pathways as compared to their non‐proliferating counterparts, both at baseline and following PHx. Our results also indicate that these pathways are also active in the non‐proliferating hepatocytes following PHx in the ethanol group. Taken together, our combinatorial transcriptomic analysis and computational modeling showed distinct metabolic pathways differentiating proliferating from non‐proliferating hepatocytes at baseline, which is absent in hepatocytes following PHx in the ethanol group. These results point towards an alternative mechanism of metabolic compensation in ethanol‐fed animals such that similar metabolic pathways are active in both proliferating and non‐proliferating hepatocytes. This lack of metab...
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