Metabolic impact of partial hepatectomy in the non-alcoholic steatohepatitis animal model of methionine-choline deficient diet

Amino acid metabolomics and machine learning for assessment of post-hepatectomy liver regeneration

Objective

Amino acid (AA) metabolism plays a vital role in liver regeneration. However, its measuring utility for post-hepatectomy liver regeneration under different conditions remains unclear. We aimed to combine machine learning (ML) models with AA metabolomics to assess liver regeneration in health and non-alcoholic steatohepatitis (NASH).

Methods

The liver index (liver weight/body weight) was calculated following 70% hepatectomy in healthy and NASH mice. The serum levels of 39 amino acids were measured using ultra-high performance liquid chromatography-tandem mass spectrometry analysis. We used orthogonal partial least squares discriminant analysis to determine differential AAs and disturbed metabolic pathways during liver regeneration. The SHapley Additive exPlanations algorithm was performed to identify potential AA signatures, and five ML models including least absolute shrinkage and selection operator, random forest, K-nearest neighbor (KNN), support vector regression, and extreme gradient boosting were utilized to assess the liver index.

Results

Eleven and twenty-two differential AAs were identified in the healthy and NASH groups, respectively. Among these metabolites, arginine and proline metabolism were commonly disturbed metabolic pathways related to liver regeneration in both groups. Five AA signatures were identified, including hydroxylysine, L-serine, 3-methylhistidine, L-tyrosine, and homocitrulline in healthy group, and L-arginine, 2-aminobutyric acid, sarcosine, beta-alanine, and L-cysteine in NASH group. The KNN model demonstrated the best evaluation performance with mean absolute error, root mean square error, and coefficient of determination values of 0.0037, 0.0047, 0.79 and 0.0028, 0.0034, 0.71 for the healthy and NASH groups, respectively.

Conclusion

The KNN model based on five AA signatures performed best, which suggests that it may be a valuable tool for assessing post-hepatectomy liver regeneration in health and NASH.

Hepatectomy-Induced Alterations in Hepatic Perfusion and Function - Toward Multi-Scale Computational Modeling for a Better Prediction of Post-hepatectomy Liver Function

Liver resection causes marked perfusion alterations in the liver remnant both on the organ scale (vascular anatomy) and on the microscale (sinusoidal blood flow on tissue level). These changes in perfusion affect hepatic functions via direct alterations in blood supply and drainage, followed by indirect changes of biomechanical tissue properties and cellular function. Changes in blood flow impose compression, tension and shear forces on the liver tissue. These forces are perceived by mechanosensors on parenchymal and non-parenchymal cells of the liver and regulate cell-cell and cell-matrix interactions as well as cellular signaling and metabolism. These interactions are key players in tissue growth and remodeling, a prerequisite to restore tissue function after PHx. Their dysregulation is associated with metabolic impairment of the liver eventually leading to liver failure, a serious post-hepatectomy complication with high morbidity and mortality. Though certain links are known, the overall functional change after liver surgery is not understood due to complex feedback loops, non-linearities, spatial heterogeneities and different time-scales of events. Computational modeling is a unique approach to gain a better understanding of complex biomedical systems. This approach allows (i) integration of heterogeneous data and knowledge on multiple scales into a consistent view of how perfusion is related to hepatic function; (ii) testing and generating hypotheses based on predictive models, which must be validated experimentally and clinically. In the long term, computational modeling will (iii) support surgical planning by predicting surgery-induced perfusion perturbations and their functional (metabolic) consequences; and thereby (iv) allow minimizing surgical risks for the individual patient. Here, we review the alterations of hepatic perfusion, biomechanical properties and function associated with hepatectomy. Specifically, we provide an overview over the clinical problem, preoperative diagnostics, functional imaging approaches, experimental approaches in animal models, mechanoperception in the liver and impact on cellular metabolism, omics approaches with a focus on transcriptomics, data integration and uncertainty analysis, and computational modeling on multiple scales. Finally, we provide a perspective on how multi-scale computational models, which couple perfusion changes to hepatic function, could become part of clinical workflows to predict and optimize patient outcome after complex liver surgery.

CE-MS for metabolomics: Developments and applications in the period 2018-2020

Capillary electrophoresis-mass spectrometry (CE-MS) is now a mature analytical technique in metabolomics, notably for the efficient profiling of polar and charged metabolites. Over the past few years, (further) progress has been made in the design of improved interfacing techniques for coupling CE to MS; also, in the development of CE-MS approaches for profiling metabolites in volume-restricted samples, and in strategies that further enhance the metabolic coverage. In this article, which is a follow-up of a previous review article covering the years 2016-2018 (Electrophoresis 2019, 40, 165-179), the main (technological) developments in CE-MS methods and strategies for metabolomics are discussed covering the literature from July 2018 to June 2020. Representative examples highlight the utility of CE-MS in the fields of biomedical, clinical, microbial, plant and food metabolomics. A complete overview of recent CE-MS-based metabolomics studies is given in a table, which provides information on sample type and pretreatment, capillary coatings, and MS detection mode. Finally, some general conclusions and perspectives are given.

Methionine metabolism in chronic liver diseases: an update on molecular mechanism and therapeutic implication

As one of the bicyclic metabolic pathways of one-carbon metabolism, methionine metabolism is the pivot linking the folate cycle to the transsulfuration pathway. In addition to being a precursor for glutathione synthesis, and the principal methyl donor for nucleic acid, phospholipid, histone, biogenic amine, and protein methylation, methionine metabolites can participate in polyamine synthesis. Methionine metabolism disorder can aggravate the damage in the pathological state of a disease. In the occurrence and development of chronic liver diseases (CLDs), changes in various components involved in methionine metabolism can affect the pathological state through various mechanisms. A methionine-deficient diet is commonly used for building CLD models. The conversion of key enzymes of methionine metabolism methionine adenosyltransferase (MAT) 1 A and MAT2A/MAT2B is closely related to fibrosis and hepatocellular carcinoma. In vivo and in vitro experiments have shown that by intervening related enzymes or downstream metabolites to interfere with methionine metabolism, the liver injuries could be reduced. Recently, methionine supplementation has gradually attracted the attention of many clinical researchers. Most researchers agree that adequate methionine supplementation can help reduce liver damage. Retrospective analysis of recently conducted relevant studies is of profound significance. This paper reviews the latest achievements related to methionine metabolism and CLD, from molecular mechanisms to clinical research, and provides some insights into the future direction of basic and clinical research.

Hypothalamus-pituitary-adrenal Axis in Glucolipid metabolic disorders

With the change of life style, glucolipid metabolic disorders (GLMD) has become one of the major chronic disorders causing public health and clinical problems worldwide. Previous studies on GLMD pay more attention to peripheral tissues. In fact, the central nervous system (CNS) plays an important role in controlling the overall metabolic balance. With the development of technology and the in-depth understanding of the CNS, the relationship between neuro-endocrine-immunoregulatory (NEI) network and metabolism had been gradually illustrated. As the hub of NEI network, hypothalamus-pituitary-adrenal (HPA) axis is important for maintaining the balance of internal environment in the body. The relationship between HPA axis and GLMD needs to be further studied. This review focuses on the role of HPA axis in GLMD and reviews the research progress on drugs for GLMD, with the hope to provide the direction for exploring new drugs to treat GLMD by taking the HPA axis as the target and improve the level of prevention and control of GLMD.