Metabolic phenotyping of patients with Advanced Chronic Liver Disease for better characterization of Cirrhosis Regression.

BACKGROUND & AIMS: Regression of cirrhosis has been observed in patients with viral and non-viral etiologies of liver disease in whom the underlying cause of liver injury was effectively suppressed. However, the understanding of the factors contributing to reversibility of fibrosis and cirrhosis is limited. Our aims were to assess clinical factors, perform genotyping of known variants, and comprehensive metabolic phenotyping to characterize the regression of fibrosis in patients with compensated advanced chronic liver disease (cACLD). METHODS: In a case-control pilot study with 81 cACLD patients, we compared individuals exhibiting histological or clinical evidence of cACLD regression ("regressors"; n=44) with those showing no improvement ("non-regressors"; n=37) after a minimum of 24 months of successful therapy of the cause of liver disease. Data were validated using an external validation cohort (n=30). RESULTS: Regardless of the cause of cACLD, the presence of obesity (OR 0.267 95%CI:0.072-0.882; P=0.049), high liver stiffness (OR 0.960, 95%CI:0.925-0.995; P=0.032), and carriage of GCKR variant rs1260326 (OR 0.148, 95%CI:0.030-0.773; P=0.019) are associated with a reduced likelihood of fibrosis regression in a subgroup of 60 ACLD patients genotyped for known genetic variants. Using liver tissue transcriptomics, we identified metabolic pathways differentiating regressors from non-regressors, with top pathways associated to lipid metabolism -especially fatty acids, bile acids, phospholipids, triacylglycerides (biosynthesis), and the carnitine shuttle. In the entire discovery cohort, we further measured metabolites within the defined pathways, which led to identifying 33 circulating markers differentiating regressors from non-regressors after etiological therapy. The validation cohort confirmed 14 of the differentially expressed markers. CONCLUSIONS: We identified and validated a group of lipid biomarkers associated with regression of fibrosis that could be used as non-invasive biomarker for detecting regression of fibrosis in cACLD.

Transcriptomics-driven metabolic pathway analysis reveals similar alterations in lipid metabolism in mouse MASH model and human.

BACKGROUND: Metabolic dysfunction-associated steatotic liver disease (MASLD) is a prevalent chronic liver disease worldwide, and can rapidly progress to metabolic dysfunction-associated steatohepatitis (MASH). Accurate preclinical models and methodologies are needed to understand underlying metabolic mechanisms and develop treatment strategies. Through meta-analysis of currently proposed mouse models, we hypothesized that a diet- and chemical-induced MASH model closely resembles the observed lipid metabolism alterations in humans. METHODS: We developed transcriptomics-driven metabolic pathway analysis (TDMPA), a method to aid in the evaluation of metabolic resemblance. TDMPA uses genome-scale metabolic models to calculate enzymatic reaction perturbations from gene expression data. We performed TDMPA to score and compare metabolic pathway alterations in MASH mouse models to human MASH signatures. We used an already-established WD+CCl4-induced MASH model and performed functional assays and lipidomics to confirm TDMPA findings. RESULTS: Both human MASH and mouse models exhibit numerous altered metabolic pathways, including triglyceride biosynthesis, fatty acid beta-oxidation, bile acid biosynthesis, cholesterol metabolism, and oxidative phosphorylation. We confirm a significant reduction in mitochondrial functions and bioenergetics, as well as in acylcarnitines for the mouse model. We identify a wide range of lipid species within the most perturbed pathways predicted by TDMPA. Triglycerides, phospholipids, and bile acids are increased significantly in mouse MASH liver, confirming our initial observations. CONCLUSIONS: We introduce TDMPA, a methodology for evaluating metabolic pathway alterations in metabolic disorders. By comparing metabolic signatures that typify human MASH, we show a good metabolic resemblance of the WD+CCl4 mouse model. Our presented approach provides a valuable tool for defining metabolic space to aid experimental design for assessing metabolism.

Steatotic liver disease, in which fat accumulates in the liver, is one of the most prevalent liver diseases worldwide and it is important to develop relevant animal models to help us understand its mechanisms. We aimed to assess the suitability of animal models for studying steatotic liver disease in humans. We developed an approach that evaluates how genes affect the metabolism or the chemical reactions and processes that occur in the body. We used it to compare a mouse model of the disease with human observations. Our results showed that there are significant changes in fat and energy metabolism in the mouse model. These observations match with changes observed in humans, suggesting it is a good model for studying human disease. Our findings could advance our understanding of the disease as well as help define strategies for its treatment.

Characterisation of hepatic lipid signature distributed across the liver zonation using mass spectrometry imaging.

Background & Aims: Lipid metabolism plays an important role in liver pathophysiology. The liver lobule asymmetrically distributes oxygen and nutrition, resulting in heterogeneous metabolic functions. Periportal and pericentral hepatocytes have different metabolic functions, which lead to generating liver zonation. We developed spatial metabolic imaging using desorption electrospray ionisation mass spectrometry to investigate lipid distribution across liver zonation with high reproducibility and accuracy. Methods: Fresh frozen livers from healthy mice with control diet were analysed using desorption electrospray ionisation mass spectrometry imaging. Imaging was performed at 50 µm × 50 µm pixel size. Regions of interest (ROIs) were manually created by co-registering with histological data to determine the spatial hepatic lipids across liver zonation. The ROIs were confirmed by double immunofluorescence. The mass list of specific ROIs was automatically created, and univariate and multivariate statistical analysis were performed to identify statistically significant lipids across liver zonation. Results: A wide range of lipid species was identified, including fatty acids, phospholipids, triacylglycerols, diacylglycerols, ceramides, and sphingolipids. We characterised hepatic lipid signatures in three different liver zones (periportal zone, midzone, and pericentral zone) and validated the reproducibility of our method for measuring a wide range of lipids. Fatty acids were predominantly detected in the periportal region, whereas phospholipids were distributed in both the periportal and pericentral zones. Interestingly, phosphatidylinositols, PI(36:2), PI(36:3), PI(36:4), PI(38:5), and PI(40:6) were located predominantly in the midzone (zone 2). Triacylglycerols and diacylglycerols were detected mainly in the pericentral region. De novo triacylglycerol biosynthesis appeared to be the most influenced pathway across the three zones. Conclusions: The ability to accurately assess zone-specific hepatic lipid distribution in the liver could lead to a better understanding of lipid metabolism during the progression of liver disease. Impact and Implications: Zone-specific hepatic lipid metabolism could play an important role in lipid homoeostasis during disease progression. Herein, we defined the zone-specific references of hepatic lipid species in the three liver zones using molecular imaging. The de novo triacylglycerol biosynthesis was highlighted as the most influenced pathway across the three zones.

Metabolic Pathway Analysis: Advantages and Pitfalls for the Functional Interpretation of Metabolomics and Lipidomics Data.

Over the past decades, pathway analysis has become one of the most commonly used approaches for the functional interpretation of metabolomics data. Although the approach is widely used, it is not well standardized and the impact of different methodologies on the functional outcome is not well understood. Using four publicly available datasets, we investigated two main aspects of topological pathway analysis, namely the consideration of non-human native enzymatic reactions (e.g., from microbiota) and the interconnectivity of individual pathways. The exclusion of non-human native reactions led to detached and poorly represented reaction networks and to loss of information. The consideration of connectivity between pathways led to better emphasis of certain central metabolites in the network; however, it occasionally overemphasized the hub compounds. We proposed and examined a penalization scheme to diminish the effect of such compounds in the pathway evaluation. In order to compare and assess the results between different methodologies, we also performed over-representation analysis of the same datasets. We believe that our findings will raise awareness on both the capabilities and shortcomings of the currently used pathway analysis practices in metabolomics. Additionally, it will provide insights on various methodologies and strategies that should be considered for the analysis and interpretation of metabolomics data.