A pilot study optimizing metabolomic and lipidomic acquisition in serum for biomarker discovery in nonalcoholic fatty liver disease

Low sphingolipid levels predict poor survival in patients with alcohol-related liver disease

Background & Aims

Alcohol-related hepatitis (AH) and alcohol-related cirrhosis are grave conditions with poor prognoses. Altered hepatic lipid metabolism can impact disease development and varies between different alcohol-related liver diseases. Therefore, we aimed to investigate lipidomics and metabolomics at various stages of alcohol-related liver diseases and their correlation with survival.

Methods

Patients with newly diagnosed alcohol-related cirrhosis, who currently used alcohol (ALC-A), stable outpatients with decompensated alcohol-related cirrhosis with at least 8 weeks of alcohol abstinence (ALC), and patients with AH, were compared with each other and with healthy controls (HC). Circulating lipids and metabolites were analysed using HPLC and mass spectrometry.

Results

Forty patients with ALC, 95 with ALC-A, 30 with AH, and 42 HC provided plasma. Lipid levels changed according to disease severity, with generally lower levels in AH and cirrhosis than in the HC group; this was most pronounced for AH, followed by ALC-A. Nine out of 10 free fatty acids differed between cirrhosis groups by relative increases of 0.12-0.66 in ALC compared with the ALC-A group (p <0.0005). For metabolomics, total bile acids increased by 19.7, 31.3, and 80.4 in the ALC, ALC-A, and AH groups, respectively, compared with HC (all p <0.0001). Low sphingolipid ([d42:1] and [d41:1]) levels could not predict 180-day mortality (AUC = 0.73, p = 0.95 and AUC = 0.73, p = 0.95) more accurately than the model for end-stage liver disease score (AUC = 0.71), but did predict 90-day mortality (AUC d42:1 = 0.922, AUC d41:1 = 0.893; pd42:1 = 0.005, pd41:1 = 0.007) more accurately than the MELD score AUCMELD = 0.70, pMELD = 0.19).

Conclusions

Alcohol-related severe liver disease is characterised by low lipid levels progressing with severity of liver disease, especially low sphingomyelins, which also associate to poor prognoses.

Impact and implications

Lipidomics has the potential to diagnose and risk stratify patients with liver diseases. Lipidomics differed between patients with alcohol-related hepatitis and alcohol-related cirrhosis with and without recent alcohol use. Furthermore, lipidomics could predict short-term mortality and might be suitable as a prognostic tool in the future.

Clinical Trials Registration

Scientific Ethics Committee of the Capital Region of Denmark, journal no. H-21013476.

Investigation of metabolite alterations in the kidneys of methionine-choline-deficient mouse by mass spectrometry imaging

Methionine and choline both are essential nutrients which are needed for methyl group metabolism. A methionine-choline-deficient (MCD) diet leads to pathological changes in the kidney. The mechanism of the MCD diet is complex, and fundamental research is still required to provide a better understanding of the driving forces behind it. We evaluated the regional effects of the MCD diet on the metabolites of mouse kidney tissue using desorption electrospray ionization mass spectrometry imaging technology. A total of 20, 17, and 13 metabolites were significantly changed in the cortex, outer medulla, and inner medulla, respectively, of the mouse kidney tissue after the administration of the MCD diet. Among the discriminating metabolites, only three metabolites (guanidoacetic acid, serine, and nicotinamide riboside) were significantly increased, and all the other metabolites showed a significant decrease. The results showed that there were significant region-specific changes in the serine metabolism, carnitine metabolism, choline metabolism, and arginine metabolism. This study presents unique regional metabolic data, providing a more comprehensive understanding of the molecular characteristics of the MCD diet in the kidney.

A miniaturized comprehensive approach for total lipidome analysis and vitamin D metabolite quantification in human serum

The balance between the different lipid molecules present in biological fluids accurately reflects the health state of the organism and can be used by medical personnel to finely tune therapy to a single patient, a process known as precision medicine. In this work, we developed a miniaturized workflow for the analysis of different lipid classes at the intact level, as well as their fatty acid constituents, starting from human serum. Fatty acids were identified by using flow-modulated comprehensive gas chromatography coupled to mass spectrometry (FM-GC × GC-MS), and their relative amount as well as the ratio of specific FA classes was determined by using FM-GC × GC with a flame ionization detector. Ultra-high-performance liquid chromatography coupled to tandem mass spectrometry was used for the simultaneous quantification of vitamin D metabolites and assessment of different intact lipid classes. An MRM method was developed for the quantification of five vitamin D metabolites (vitamin D2, vitamin D3, 25-hydroxyvitamin D2, 25-hydroxyvitamin D3, 24R,25-dihydroxyvitamin D3), and validated in terms of LoD, LoQ, accuracy, and precision, also using a certified reference material. At the same time, a combination of SCAN, precursor ion scan, and neutral loss scan, in both positive and negative modes, was used for the identification of 81 intact lipid species, such as phospholipids, cholesteryl esters, and triacylglycerols, in less than 25 min. In order to easily monitor the lipid composition and speed up the identification process, a two-dimensional map of the lipidome was generated, by plotting the molecular weight of the identified molecules versus their retention time. Moreover, a relative quantification was performed within each lipid class identified. The combination of untargeted and targeted data could provide useful information about the pathophysiological condition of the organism and evaluate, in a tailored manner, an efficient action.

Metabolic role of the hepatic valine/3-hydroxyisobutyrate (3-HIB) pathway in fatty liver disease

BACKGROUND

The valine (branched-chain amino acid) metabolite 3-hydroxyisobutyrate (3-HIB), produced by 3-Hydroxyisobutyryl-CoA Hydrolase (HIBCH), is associated with insulin resistance and type 2 diabetes, but implicated tissues and cellular mechanisms are poorly understood. We hypothesized that HIBCH and 3-HIB regulate hepatic lipid accumulation.

METHODS

HIBCH mRNA in human liver biopsies ("Liver cohort") and plasma 3-HIB ("CARBFUNC" cohort) were correlated with fatty liver and metabolic markers. Human Huh7 hepatocytes were supplemented with fatty acids (FAs) to induce lipid accumulation. Following HIBCH overexpression, siRNA knockdown, inhibition of PDK4 (a marker of FA β-oxidation) or 3-HIB supplementation, we performed RNA-seq, Western blotting, targeted metabolite analyses and functional assays.

FINDINGS

We identify a regulatory feedback loop between the valine/3-HIB pathway and PDK4 that shapes hepatic FA metabolism and metabolic health and responds to 3-HIB treatment of hepatocytes. HIBCH overexpression increased 3-HIB release and FA uptake, while knockdown increased cellular respiration and decreased reactive oxygen species (ROS) associated with metabolic shifts via PDK4 upregulation. Treatment with PDK4 inhibitor lowered 3-HIB release and increased FA uptake, while increasing HIBCH mRNA. Implicating this regulatory loop in fatty liver, human cohorts show positive correlations of liver fat with hepatic HIBCH and PDK4 expression (Liver cohort) and plasma 3-HIB (CARBFUNC cohort). Hepatocyte 3-HIB supplementation lowered HIBCH expression and FA uptake and increased cellular respiration and ROS.

INTERPRETATION

These data implicate the hepatic valine/3-HIB pathway in mechanisms of fatty liver, reflected in increased plasma 3-HIB concentrations, and present possible targets for therapeutic intervention.

FUNDING

Funding was provided by the Research Council of Norway (263124/F20), the University of Bergen, the Western Norway Health Authorities, Novo Nordisk Scandinavia AS, the Trond Mohn Foundation and the Norwegian Diabetes Association.

Plasma Metabolomic and Lipidomic Profiling of Metabolic Dysfunction-Associated Fatty Liver Disease in Humans Using an Untargeted Multiplatform Approach

Metabolic dysfunction-associated fatty liver disease (MAFLD) is a complex disorder that is implicated in dysregulations in multiple biological pathways, orchestrated by interactions between genetic predisposition, metabolic syndromes and environmental factors. The limited knowledge of its pathogenesis is one of the bottlenecks in the development of prognostic and therapeutic options for MAFLD. Moreover, the extent to which metabolic pathways are altered due to ongoing hepatic steatosis, inflammation and fibrosis and subsequent liver damage remains unclear. To uncover potential MAFLD pathogenesis in humans, we employed an untargeted nuclear magnetic resonance (NMR) spectroscopy- and high-resolution mass spectrometry (HRMS)-based multiplatform approach combined with a computational multiblock omics framework to characterize the plasma metabolomes and lipidomes of obese patients without (n = 19) or with liver biopsy confirmed MAFLD (n = 63). Metabolite features associated with MAFLD were identified using a metabolome-wide association study pipeline that tested for the relationships between feature responses and MAFLD. A metabolic pathway enrichment analysis revealed 16 pathways associated with MAFLD and highlighted pathway changes, including amino acid metabolism, bile acid metabolism, carnitine shuttle, fatty acid metabolism, glycerophospholipid metabolism, arachidonic acid metabolism and steroid metabolism. These results suggested that there were alterations in energy metabolism, specifically amino acid and lipid metabolism, and pointed to the pathways being implicated in alerted liver function, mitochondrial dysfunctions and immune system disorders, which have previously been linked to MAFLD in human and animal studies. Together, this study revealed specific metabolic alterations associated with MAFLD and supported the idea that MAFLD is fundamentally a metabolism-related disorder, thereby providing new perspectives for diagnostic and therapeutic strategies.

Evaluation of Untargeted Metabolomic Strategy for the Discovery of Biomarker of Breast Cancer

Discovery of disease biomarker based on untargeted metabolomics is informative for pathological mechanism studies and facilitates disease early diagnosis. Numerous of metabolomic strategies emerge due to different sample properties or experimental purposes, thus, methodological evaluation before sample analysis is essential and necessary. In this study, sample preparation, data processing procedure and metabolite identification strategy were assessed aiming at the discovery of biomarker of breast cancer. First, metabolite extraction by different solvents, as well as the necessity of vacuum-dried and re-dissolution, was investigated. The extraction efficiency was assessed based on the number of eligible components (components with MS/MS data acquired), which was more reasonable for metabolite identification. In addition, a simplified data processing procedure was proposed involving the OPLS-DA, primary screening for eligible components, and secondary screening with constraints including VIP, fold change and p value. Such procedure ensured that only differential candidates were subjected to data interpretation, which greatly reduced the data volume for database search and improved analysis efficiency. Furthermore, metabolite identification and annotation confidence were enhanced by comprehensive consideration of mass and MS/MS errors, isotope similarity, fragmentation match, and biological source confirmation. On this basis, the optimized strategy was applied for the analysis of serum samples of breast cancer, according to which the discovery of differential metabolites highly encouraged the independent biomarkers/indicators used for disease diagnosis and chemotherapy evaluation clinically. Therefore, the optimized strategy simplified the process of differential metabolite exploration, which laid a foundation for biomarker discovery and studies of disease mechanism.