Large-scale computational models of liver metabolism: How far from the clinics?

Nuclear receptors: pathophysiological mechanisms and drug targets in liver disease

Nuclear receptors (NRs) are ligand-dependent transcription factors required for liver development and function. As a consequence, NRs have emerged as attractive drug targets in a wide range of liver diseases. However, liver dysfunction and failure are linked to loss of hepatocyte identity characterised by deficient NR expression and activities. This might at least partly explain why several pharmacological NR modulators have proven insufficiently efficient to improve liver functionality in advanced stages of diseases such as metabolic dysfunction-associated steatotic liver disease (MASLD). In this perspective, we review the most recent advances in the hepatic NR field and discuss the contribution of multiomic approaches to our understanding of their role in the molecular organisation of an intricated transcriptional regulatory network, as well as in liver intercellular dialogues and interorgan cross-talks. We discuss the potential benefit of novel therapeutic approaches simultaneously targeting multiple NRs, which would not only reactivate the hepatic NR network and restore hepatocyte identity but also impact intercellular and interorgan interplays whose importance to control liver functions is further defined. Finally, we highlight the need of considering individual parameters such as sex and disease stage in the development of NR-based clinical strategies.

The Surreptitious Burden of Nonalcoholic Fatty Liver Disease in the Elderly in the Asia-Pacific Region: An Insight from the Global Burden of Disease Study 2019

Nonalcoholic fatty liver disease (NAFLD) represents a significant health threat worldwide. The aging population and a rise in metabolic syndrome in Asia might influence the epidemiology of NAFLD among the elderly. However, there is a lack of understanding of the burden and recommendations for NAFLD in this group. Our study sought to investigate the trends in the NAFLD burden among the elderly in the Asia-Pacific region. We employed data from the Global Burden of Disease 2019 study for an in-depth analysis of the prevalence and disability-adjusted life years (DALYs) along with age-standardized rate (ASR) associated with NAFLD in elderly populations (age 65-89 years) across the Asia-Pacific region, including the Southeast Asia (SEA) and Western Pacific (WP) regions, from 2010 to 2019. This study also examined the trends and disparities in NAFLD burden across different nations and sexes. In 2019, there were over 120 million cases of NAFLD in the elderly in the Asia-Pacific region. The ASR of prevalence was higher in SEA compared to WP (36,995.37 vs. 32,821.78 per 100,000). ASR of prevalence increased with annual percentage change (APC) +0.95% in the WP while it increased by +0.87% in SEA. During the study period, the ASR of DALYs decreased in SEA (APC -0.41%) but remained stable in the WP region. The burden of NAFLD in the elderly population in Asia-Pacific has increased, underscoring the timely intervention to tackle this high and rising burden.

A feedback loop between NONHSAT024276 and PTBP1 inhibits tumor progression and glycolysis in HCC by increasing the PKM1/PKM2 ratio

Hepatocellular carcinoma (HCC) is one of the most common malignancies with a hallmark of aberrant metabolism. The mechanism of long noncoding RNAs (lncRNAs) underlying the aggressive behaviors and glycolysis of HCC is poorly understood. In this study, we identified, via microarray, novel lncRNA NONHSAT024276 as a potential tumor suppressor in HCC. The downregulation of NONHSAT024276 closely correlated with larger tumor volume and higher aspartate transaminase levels. Functional experiments were performed to verify the role of NONHSAT024276 in HCC progression, and the negative effects of NONHSAT024276 expression on cell proliferation and migration were identified. Mechanistically, NONHSAT024276 directly bound to polypyrimidine tract-binding protein 1 (PTBP1), downregulating it and forming a feedback loop. Furthermore, NONHSAT024276 increased the ratio of M1 and M2 isoforms of pyruvate kinase (PKM1/PKM2) and also obstructed the PTBP1/PKM-mediated glycolysis. Finally, the rescue assays confirmed that NONHSAT024276 functioned in HCC via downregulating PTBP1 to increase the PKM1/PKM2 ratio. Hence, this study supported a model in which NONHSAT024276 downregulated PTBP1 and formed a feedback loop to increase the PKM1/PKM2 ratio to inhibit glycolysis and progression of HCC, opening new prospects for preventing or treating HCC.

Sex-dependent dynamics of metabolism in primary mouse hepatocytes

The liver is one of the most sexually dimorphic organs. The hepatic metabolic pathways that are subject to sexual dimorphism include xenobiotic, amino acid and lipid metabolism. Non-alcoholic fatty liver disease and hepatocellular carcinoma are among diseases with sex-dependent prevalence, progression and outcome. Although male and female livers differ in their abilities to metabolize foreign compounds, including drugs, sex-dependent treatment and pharmacological dynamics are rarely applied in all relevant cases. Therefore, it is important to consider hepatic sexual dimorphism when developing new treatment strategies and to understand the underlying mechanisms in model systems. We isolated primary hepatocytes from male and female C57BL6/N mice and examined the sex-dependent transcriptome, proteome and extracellular metabolome parameters in the course of culturing them for 96 h. The sex-specific gene expression of the general xenobiotic pathway altered and the female-specific expression of Cyp2b13 and Cyp2b9 was significantly reduced during culture. Sex-dependent differences of several signaling pathways increased, including genes related to serotonin and melatonin degradation. Furthermore, the ratios of male and female gene expression were inversed for other pathways, such as amino acid degradation, beta-oxidation, androgen signaling and hepatic steatosis. Because the primary hepatocytes were cultivated without the influence of known regulators of sexual dimorphism, these results suggest currently unknown modulatory mechanisms of sexual dimorphism in vitro. The large sex-dependent differences in the regulation and dynamics of drug metabolism observed during cultivation can have an immense influence on the evaluation of pharmacodynamic processes when conducting initial preclinical trials to investigate potential new drugs.

Computational Hypothesis: How Intra-Hepatic Functional Heterogeneity May Influence the Cascading Progression of Free Fatty Acid-Induced Non-Alcoholic Fatty Liver Disease (NAFLD)

Non-Alcoholic Fatty Liver Disease (NAFLD) is the most common type of chronic liver disease in developed nations, affecting around 25% of the population. Elucidating the factors causing NAFLD in individual patients to progress in different rates and to different degrees of severity, is a matter of active medical research. Here, we aim to provide evidence that the intra-hepatic heterogeneity of rheological, metabolic and tissue-regenerating capacities plays a central role in disease progression. We developed a generic mathematical model that constitutes the liver as ensemble of small liver units differing in their capacities to metabolize potentially cytotoxic free fatty acids (FFAs) and to repair FFA-induced cell damage. Transition from simple steatosis to more severe forms of NAFLD is described as self-amplifying process of cascading liver failure, which, to stop, depends essentially on the distribution of functional capacities across the liver. Model simulations provided the following insights: (1) A persistently high plasma level of FFAs is sufficient to drive the liver through different stages of NAFLD; (2) Presence of NAFLD amplifies the deleterious impact of additional tissue-damaging hits; and (3) Coexistence of non-steatotic and highly steatotic regions is indicative for the later occurrence of severe NAFLD stages.

An Early Stage Researcher's Primer on Systems Medicine Terminology

Background: Systems Medicine is a novel approach to medicine, that is, an interdisciplinary field that considers the human body as a system, composed of multiple parts and of complex relationships at multiple levels, and further integrated into an environment. Exploring Systems Medicine implies understanding and combining concepts coming from diametral different fields, including medicine, biology, statistics, modeling and simulation, and data science. Such heterogeneity leads to semantic issues, which may slow down implementation and fruitful interaction between these highly diverse fields. Methods: In this review, we collect and explain more than100 terms related to Systems Medicine. These include both modeling and data science terms and basic systems medicine terms, along with some synthetic definitions, examples of applications, and lists of relevant references. Results: This glossary aims at being a first aid kit for the Systems Medicine researcher facing an unfamiliar term, where he/she can get a first understanding of them, and, more importantly, examples and references for digging into the topic.

A mechanobiological mathematical model of liver metabolism

The liver plays a complex role in metabolism and detoxification, and better tools are needed to understand its function and to develop liver-targeted therapies. In this study, we establish a mechanobiological model of liver transport and hepatocyte biology to elucidate the metabolism of urea and albumin, the production/detoxification of ammonia, and consumption of oxygen and nutrients. Since hepatocellular shear stress (SS) can influence the enzymatic activities of liver, the effect of SS on the urea and albumin synthesis are empirically modeled through the mechanotransduction mechanisms. The results demonstrate that the rheology and dynamics of the sinusoid flow can significantly affect liver metabolism. We show that perfusate rheology and blood hematocrit can affect urea and albumin production by changing hepatocyte mechanosensitive metabolism. The model can also simulate enzymatic diseases of the liver such as hyperammonemia I, hyperammonemia II, hyperarginemia, citrollinemia, and argininosuccinicaciduria, which disrupt the urea metabolism and ammonia detoxification. The model is also able to predict how aggregate cultures of hepatocytes differ from single cell cultures. We conclude that in vitro perfusable devices for the study of liver metabolism or personalized medicine should be designed with similar morphology and fluid dynamics as patient liver tissue. This robust model can be adapted to any type of hepatocyte culture to determine how hepatocyte viability, functionality, and metabolism are influenced by liver pathologies and environmental conditions.

Non-alcoholic fatty liver disease and Atherosclerosis at a crossroad: The overlap of a theory of change and bioinformatics

Atherosclerosis (ATH) and non-alcoholic fatty liver disease (NAFLD) are medical conditions that straddle a communal epidemiology, underlying mechanism and a clinical syndrome that has protean manifestations, touching every organ in the body. These twin partners, ATH and NAFLD, are seemingly straightforward and relatively simple topics when considered alone, but their interdependence calls for more thought. The study of the mutual relationship of NAFLD and ATH should involve big data analytics approaches, given that they encompass a constellation of diseases and are related to several recognized risk factors and health determinants and calls to an explicit theory of change, to justify intervention. Research studies on the “association between aortic stiffness and liver steatosis in morbidly obese patients”, published recently, sparsely hypothesize new mechanisms of disease, claiming the “long shadow of NAFLD” as a risk factor, if not as a causative factor of arterial stiffness and ATH. This statement is probably overreaching the argument and harmful for the scientific credence of this area of medicine. Despite the verification that NAFLD and cardiovascular disease are strongly interrelated, current evidence is that NAFLD may be a useful indicator for flagging early arteriosclerosis, and not a likely causative factor. Greater sustainable contribution by precision medicine tools, by validated bioinformatics approaches, is needed for substantiating conjectures, assumptions and inferences related to the management of big data and addressed to intervention for behavioral changes within an explicit theory of change.

Multi-scale models of lung fibrosis

The architectural complexity of the lung is crucial to its ability to function as an organ of gas exchange; the branching tree structure of the airways transforms the tracheal cross-section of only a few square centimeters to a blood-gas barrier with a surface area of tens of square meters and a thickness on the order of a micron or less. Connective tissue comprised largely of collagen and elastic fibers provides structural integrity for this intricate and delicate system. Homeostatic maintenance of this connective tissue, via a balance between catabolic and anabolic enzyme-driven processes, is crucial to life. Accordingly, when homeostasis is disrupted by the excessive production of connective tissue, lung function deteriorates rapidly with grave consequences leading to chronic lung conditions such as pulmonary fibrosis. Understanding how pulmonary fibrosis develops and alters the link between lung structure and function is crucial for diagnosis, prognosis, and therapy. Further information gained could help elaborate how the healing process breaks down leading to chronic disease. Our understanding of fibrotic disease is greatly aided by the intersection of wet lab studies and mathematical and computational modeling. In the present review we will discuss how multi-scale modeling has facilitated our understanding of pulmonary fibrotic disease as well as identified opportunities that remain open and have produced techniques that can be incorporated into this field by borrowing approaches from multi-scale models of fibrosis beyond the lung.

Community effort endorsing multiscale modelling, multiscale data science and multiscale computing for systems medicine

Systems medicine holds many promises, but has so far provided only a limited number of proofs of principle. To address this road block, possible barriers and challenges of translating systems medicine into clinical practice need to be identified and addressed. The members of the European Cooperation in Science and Technology (COST) Action CA15120 Open Multiscale Systems Medicine (OpenMultiMed) wish to engage the scientific community of systems medicine and multiscale modelling, data science and computing, to provide their feedback in a structured manner. This will result in follow-up white papers and open access resources to accelerate the clinical translation of systems medicine.

Integration of probabilistic regulatory networks into constraint-based models of metabolism with applications to Alzheimer's disease

Background

Mathematical models of biological networks can provide important predictions and insights into complex disease. Constraint-based models of cellular metabolism and probabilistic models of gene regulatory networks are two distinct areas that have progressed rapidly in parallel over the past decade. In principle, gene regulatory networks and metabolic networks underly the same complex phenotypes and diseases. However, systematic integration of these two model systems remains a fundamental challenge.

Results

In this work, we address this challenge by fusing probabilistic models of gene regulatory networks into constraint-based models of metabolism. The novel approach utilizes probabilistic reasoning in BN models of regulatory networks serves as the “glue” that enables a natural interface between the two systems. Probabilistic reasoning is used to predict and quantify system-wide effects of perturbation to the regulatory network in the form of constraints for flux variability analysis. In this setting, both regulatory and metabolic networks inherently account for uncertainty. Applications leverage constraint-based metabolic models of brain metabolism and gene regulatory networks parameterized by gene expression data from the hippocampus to investigate the role of the HIF-1 pathway in Alzheimer’s disease. Integrated models support HIF-1A as effective target to reduce the effects of hypoxia in Alzheimer’s disease. However, HIF-1A activation is far less effective in shifting metabolism when compared to brain metabolism in healthy controls.

Conclusions

The direct integration of probabilistic regulatory networks into constraint-based models of metabolism provides novel insights into how perturbations in the regulatory network may influence metabolic states. Predictive modeling of enzymatic activity can be facilitated using probabilistic reasoning, thereby extending the predictive capacity of the network. This framework for model integration is generalizable to other systems.

Electronic supplementary material

The online version of this article (10.1186/s12859-019-2872-8) contains supplementary material, which is available to authorized users.

Machine-readable Yin-Yang imbalance: traditional Chinese medicine syndrome computer modeling based on three-dimensional noninvasive cardiac electrophysiology imaging

Objectives

The principal diagnostic methods of traditional Chinese medicine (TCM) are inspection, auscultation and olfaction, inquiry, and pulse-taking. Treatment by syndrome differentiation is likely to be subjective. This study was designed to provide a basic theory for TCM diagnosis and establish an objective means of evaluating the correctness of syndrome differentiation.

Methods

We herein provide the basic theory of TCM syndrome computer modeling based on a noninvasive cardiac electrophysiology imaging technique. Noninvasive cardiac electrophysiology imaging records the heart’s electrical activity from hundreds of electrodes on the patient’s torso surface and therefore provides much more information than 12-lead electrocardiography. Through mathematical reconstruction algorithm calculations, the reconstructed heart model is a machine-readable description of the underlying mathematical physics model that reveals the detailed three-dimensional (3D) electrophysiological activity of the heart.

Results

From part of the simulation results, the imaged 3D cardiac electrical source provides dynamic information regarding the heart’s electrical activity at any given location within the 3D myocardium.

Conclusions

This noninvasive cardiac electrophysiology imaging method is suitable for translating TCM syndromes into a computable format of the underlying mathematical physics model to offer TCM diagnosis evidence-based standards for ensuring correct evaluation and rigorous, scientific data for demonstrating its efficacy.

A GYS2/p53 Negative Feedback Loop Restricts Tumor Growth in HBV-Related Hepatocellular Carcinoma

Hepatocellular carcinogenesis is attributed to the reprogramming of cellular metabolism as a consequence of the alteration in metabolite-related gene regulation. Identifying the mechanism of aberrant metabolism is of great potential to provide novel targets for the treatment of hepatocellular carcinoma (HCC). Here, we demonstrated that glycogen synthase 2 (GYS2) restricted tumor growth in hepatitis B virus-related HCC via a negative feedback loop with p53. Expression of GYS2 was significantly downregulated in HCC and correlated with decreased glycogen content and unfavorable patient outcomes. GYS2 overexpression suppressed, whereas GYS2 knockdown facilitated cell proliferation in vitro and tumor growth in vivo via modulating p53 expression. GYS2 competitively bound to MDM2 to prevent p53 from MDM2-mediated ubiquitination and degradation. Furthermore, GYS2 enhanced the p300-induced acetylation of p53 at K373/382, which in turn inhibited the transcription of GYS2 in the support of HBx/HDAC1 complex. In summary, our findings suggest that GYS2 serves as a prognostic factor and functions as a tumor suppressor in HCC. The newly identified HBx/GYS2/p53 axis is responsible for the deregulation of glycogen metabolism and represents a promising therapeutic target for the clinical management of HCC. SIGNIFICANCE: We elucidated the clinical significance, biological function, and regulation of the HBx/GYS2/p53 axis, which supplement the understanding of tumor glycogen metabolism and provide potential prognostic and therapeutic targets for HCC treatment.Graphical Abstract: http://cancerres.aacrjournals.org/content/canres/79/3/534/F1.large.jpg.

Sex differences in hepatic one-carbon metabolism

Background

There are large differences between men and women of child-bearing age in the expression level of 5 key enzymes in one-carbon metabolism almost certainly caused by the sex hormones. These male-female differences in one-carbon metabolism are greatly accentuated during pregnancy. Thus, understanding the origin and consequences of sex differences in one-carbon metabolism is important for precision medicine.

Results

We have created a mathematical model of hepatic one-carbon metabolism based on the underlying physiology and biochemistry. We use the model to investigate the consequences of sex differences in gene expression. We give a mechanistic understanding of observed concentration differences in one-carbon metabolism and explain why women have lower S-andenosylmethionine, lower homocysteine, and higher choline and betaine. We give a new explanation of the well known phenomenon that folate supplementation lowers homocysteine and we show how to use the model to investigate the effects of vitamin deficiencies, gene polymorphisms, and nutrient input changes.

Conclusions

Our model of hepatic one-carbon metabolism is a useful platform for investigating the mechanistic reasons that underlie known associations between metabolites. In particular, we explain how gene expression differences lead to metabolic differences between males and females.

Electronic supplementary material

The online version of this article (doi:10.1186/s12918-018-0621-7) contains supplementary material, which is available to authorized users.

Personalized therapy when tackling nonalcoholic fatty liver disease: a focus on sex, genes, and drugs

INTRODUCTION

Nonalcoholic fatty liver disease (NAFLD) is the most frequent liver disease in the world. It describes a term for a group of hepatic diseases including steatosis, fibrosis, and cirrhosis that can finally lead to hepatocellular carcinoma. There are many factors influencing NAFLD initiation and progression, such as obesity, dyslipidemia, insulin resistance, genetic factors, and hormonal changes. However, there is also lean-NAFLD which is not associated with obesity. NAFLD is considered to be a sexually dimorphic disease. In most cases, men have a higher prevalence for the disease compared to premenopausal women. Areas covered: In this review, we first summarize the NAFLD disease epidemiology, pathology, and diagnosis. We describe NAFLD progression with the focus on sexual and genetic differences for disease development and pharmacological treatment. Personalized treatment for multifactorial NAFLD is discussed in consideration of different factors, including genetics, gender and sex. Expert opinion: The livers of female and male NAFLD patients have different metabolic capacities which influence the metabolism of all drugs applied to such patients. This aspect is not yet sufficiently taken into account. The liver computational models might quicken the pace toward assessing personalized disease progression and treatment options.

LiverSex Computational Model: Sexual Aspects in Hepatic Metabolism and Abnormalities

The liver is to date the best example of a sexually dimorphic non-reproductive organ. Over 1,000 genes are differentially expressed between sexes indicating that female and male livers are two metabolically distinct organs. The spectrum of liver diseases is broad and is usually prevalent in one or the other sex, with different contributing genetic and environmental factors. It is thus difficult to predict individual's disease outcomes and treatment options. Systems approaches including mathematical modeling can aid importantly in understanding the multifactorial liver disease etiology leading toward tailored diagnostics, prognostics and therapy. The currently established computational models of hepatic metabolism that have proven to be essential for understanding of non-alcoholic fatty liver disease (NAFLD) and hepatocellular carcinoma (HCC) are limited to the description of gender-independent response or reflect solely the response of the males. Herein we present LiverSex, the first sex-based multi-tissue and multi-level liver metabolic computational model. The model was constructed based on in silico liver model SteatoNet and the object-oriented modeling. The crucial factor in adaptation of liver metabolism to the sex is the inclusion of estrogen and androgen receptor responses to respective hormones and the link to sex-differences in growth hormone release. The model was extensively validated on literature data and experimental data obtained from wild type C57BL/6 mice fed with regular chow and western diet. These experimental results show extensive sex-dependent changes and could not be reproduced in silico with the uniform model SteatoNet. LiverSex represents the first large-scale liver metabolic model, which allows a detailed insight into the sex-dependent complex liver pathologies, and how the genetic and environmental factors interact with the sex in disease appearance and progression. We used the model to identify the most important sex-dependent metabolic pathways, which are involved in accumulation of triglycerides representing initial steps of NAFLD. We identified PGC1A, PPARα, FXR, and LXR as regulatory factors that could become important in sex-dependent personalized treatment of NAFLD.

Computational Modeling in Liver Surgery

The need for extended liver resection is increasing due to the growing incidence of liver tumors in aging societies. Individualized surgical planning is the key for identifying the optimal resection strategy and to minimize the risk of postoperative liver failure and tumor recurrence. Current computational tools provide virtual planning of liver resection by taking into account the spatial relationship between the tumor and the hepatic vascular trees, as well as the size of the future liver remnant. However, size and function of the liver are not necessarily equivalent. Hence, determining the future liver volume might misestimate the future liver function, especially in cases of hepatic comorbidities such as hepatic steatosis. A systems medicine approach could be applied, including biological, medical, and surgical aspects, by integrating all available anatomical and functional information of the individual patient. Such an approach holds promise for better prediction of postoperative liver function and hence improved risk assessment. This review provides an overview of mathematical models related to the liver and its function and explores their potential relevance for computational liver surgery. We first summarize key facts of hepatic anatomy, physiology, and pathology relevant for hepatic surgery, followed by a description of the computational tools currently used in liver surgical planning. Then we present selected state-of-the-art computational liver models potentially useful to support liver surgery. Finally, we discuss the main challenges that will need to be addressed when developing advanced computational planning tools in the context of liver surgery.

Computational modelling of genome-scale metabolic networks and its application to CHO cell cultures

Genome-scale metabolic models (GEMs) have become increasingly important in recent years. Currently, GEMs are the most accurate in silico representation of the genotype-phenotype link. They allow us to study complex networks from the systems perspective. Their application may drastically reduce the amount of experimental and clinical work, improve diagnostic tools and increase our understanding of complex biological phenomena. GEMs have also demonstrated high potential for the optimisation of bio-based production of recombinant proteins. Herein, we review the basic concepts, methods, resources and software tools used for the reconstruction and application of GEMs. We overview the evolution of the modelling efforts devoted to the metabolism of Chinese Hamster Ovary (CHO) cells. We present a case study on CHO cell metabolism under different amino acid depletions. This leads us to the identification of the most influential as well as essential amino acids in selected CHO cell lines.