Application of Stable Isotope Labels for Metabolomics in Studies in Fatty Liver Disease

Acute aerobic exercise reveals FAHFAs distinguish the metabolomes of overweight and normal weight runners

Background

Responses of the metabolome to acute aerobic exercise may predict maximum oxygen consumption (VO₂max) and longer-term outcomes, including the development of diabetes and its complications.

Methods

Serum samples were collected from overweight/obese trained (OWT) and normal-weight trained (NWT) runners prior to and immediately after a supervised 90-minute treadmill run at 60% VO₂max (NWT = 14, OWT = 11) in a cross-sectional study. We applied a liquid chromatography high-resolution–mass spectrometry–based untargeted metabolomics platform to evaluate the effect of acute aerobic exercise on the serum metabolome.

Results

NWT and OWT metabolic profiles shared increased circulating acylcarnitines and free fatty acids (FFAs) with exercise, while intermediates of adenine metabolism, inosine, and hypoxanthine were strongly correlated with body fat percentage and VO₂max. Untargeted metabolomics-guided follow-up quantitative lipidomic analysis revealed that baseline levels of fatty acid esters of hydroxy fatty acids (FAHFAs) were generally diminished in the OWT group. FAHFAs negatively correlated with visceral fat mass and HOMA-IR. Strikingly, a 4-fold decrease in FAHFAs was provoked by acute aerobic running in NWT participants, an effect that negatively correlated with circulating IL-6; these effects were not observed in the OWT group. Machine learning models based on a preexercise metabolite profile that included FAHFAs, FFAs, and adenine intermediates predicted VO₂max.

Conclusion

These findings in overweight human participants and healthy controls indicate that exercise-provoked changes in FAHFAs distinguish normal-weight from overweight participants and could predict VO₂max. These results support the notion that FAHFAs could modulate the inflammatory response, fuel utilization, and insulin resistance.

Trial registration

ClinicalTrials.gov, NCT02150889.

Funding

NIH DK091538, AG069781, DK098203, TR000114, UL1TR002494.

Hypoxia-Inducible Factors as Key Players in the Pathogenesis of Non-alcoholic Fatty Liver Disease and Non-alcoholic Steatohepatitis

Non-alcoholic fatty liver disease (NAFLD) and its more severe form non-alcoholic steatohepatitis (NASH) are a major public health concern with high and increasing global prevalence, and a significant disease burden owing to its progression to more severe forms of liver disease and the associated risk of cardiovascular disease. Treatment options, however, remain scarce, and a better understanding of the pathological and physiological processes involved could enable the development of new therapeutic strategies. One process implicated in the pathology of NAFLD and NASH is cellular oxygen sensing, coordinated largely by the hypoxia-inducible factor (HIF) family of transcription factors. Activation of HIFs has been demonstrated in patients and mouse models of NAFLD and NASH and studies of activation and inhibition of HIFs using pharmacological and genetic tools point toward important roles for these transcription factors in modulating central aspects of the disease. HIFs appear to act in several cell types in the liver to worsen steatosis, inflammation, and fibrosis, but may nevertheless improve insulin sensitivity. Moreover, in liver and other tissues, HIF activation alters mitochondrial respiratory function and metabolism, having an impact on energetic and redox homeostasis. This article aims to provide an overview of current understanding of the roles of HIFs in NAFLD, highlighting areas where further research is needed.

Investigating metabolism by mass spectrometry: From steady state to dynamic view

Metabolism is the set of life-sustaining reactions in organisms. These biochemical reactions are organized in metabolic pathways, in which one metabolite is converted through a series of steps catalyzed by enzymes in another chemical compound. Metabolic reactions are categorized as catabolic, the breaking down of metabolites to produce energy, and/or anabolic, the synthesis of compounds that consume energy. The balance between catabolism of the preferential fuel substrate and anabolism defines the overall metabolism of a cell or tissue. Metabolomics is a powerful tool to gain new insights contributing to the identification of complex molecular mechanisms in the field of biomedical research, both basic and translational. The enormous potential of this kind of analyses consists of two key aspects: (i) the possibility of performing so-called targeted and untargeted experiments through which it is feasible to verify or formulate a hypothesis, respectively, and (ii) the opportunity to run either steady-state analyses to have snapshots of the metabolome at a given time under different experimental conditions or dynamic analyses through the use of labeled tracers. In this review, we will highlight the most important practical (e.g., different sample extraction approaches) and conceptual steps to consider for metabolomic analysis, describing also the main application contexts in which it is used. In addition, we will provide some insights into the most innovative approaches and progress in the field of data analysis and processing, highlighting how this part is essential for the proper extrapolation and interpretation of data.

Implementation of liquid chromatography-high resolution mass spectrometry methods for untargeted metabolomic analyses of biological samples: A tutorial

Untargeted metabolomics is now widely recognized as a useful tool for exploring metabolic changes taking place in biological systems under different conditions. By its nature, this is a highly interdisciplinary field of research, and mastering all of the steps comprised in the pipeline can be a challenging task, especially for those researchers new to the topic. In this tutorial, we aim to provide an overview of the most widely adopted methods of performing LC-HRMS-based untargeted metabolomics of biological samples. A detailed protocol is provided in the Supplementary Information for rapidly implementing a basic screening workflow in a laboratory setting. This tutorial covers experimental design, sample preparation and analysis, signal processing and data treatment, and, finally, data analysis and its biological interpretation. Each section is accompanied by up-to-date literature to guide readers through the preparation and optimization of such a workflow, as well as practical information for avoiding or fixing some of the most frequently encountered pitfalls.