Regular physical exercise has been investigated as a primary preventive measure of several chronic diseases and premature death. Moreover, it has been shown to synchronize responses across multiple organs. In particular, hepatic tissue has proven to be a descriptive matrix to monitor the effect of physical activity. In this study, we performed an untargeted metabolomics-based analysis of hepatic tissue extracts from rats that have undergone either lifelong or chronic exercise training. For this purpose, 56 hepatic samples were collected and were analyzed by UHPLC-TOF-MS in negative ionization mode. This approach involved untargeted metabolite detection on hepatic tissue extracts accompanied by an in-house retention time/accurate mass library enabling confident metabolite identification. Unsupervised (PCA) and supervised (OPLS-DA) multivariate analysis showed significant metabolic perturbation on a panel of 28 metabolites, including amino acids, vitamins, nucleotides, and sugars. The training regime employed in this study resulted in a probable acceleration of the bioenergetic processes (glycolysis, glycogen metabolism), promoted catabolism of purines, and supplied biosynthetic precursors via the pentose phosphate pathway and pentose and glucuronate interconversions. Overall, the applied methodology was able to discriminate the different training schedules based on the rat liver metabolome.
Objectives: This systematic review evaluates the effectiveness of fecal microbiota transplantation (FMT) in treating Clostridioides difficile infection (CDI) in mouse models using a metabolomics-based approach. Methods: A comprehensive search was conducted in three databases (PubMed, Scopus, Google Scholar) from 10 April 2024 to 17 June 2024. Out of the 460 research studies reviewed and subjected to exclusion criteria, only 5 studies met all the inclusion criteria and were analyzed. Results: These studies consistently showed that FMT effectively restored gut microbiota and altered metabolic profiles, particularly increasing short-chain fatty acids (SCFAs) and secondary bile acids, which inhibited C. difficile growth. FMT proved superior to antibiotic and probiotic treatments in re-establishing a healthy gut microbiome, as evidenced by significant changes in the amino acid and carbohydrate levels. Despite its promise, variability in the outcomes—due to factors such as immune status, treatment protocols, and donor microbiome differences—underscores the need for standardization. Rather than pursuing immediate standardization, the documentation of factors such as donor and recipient microbiome profiles, preparation methods, and administration details could help identify optimal configurations for specific contexts and patient needs. In all the studies, FMT was successful in restoring the metabolic profile in mice. Conclusions: These findings align with the clinical data from CDI patients, suggesting that FMT holds potential as a therapeutic strategy for gut health restoration and CDI management. Further studies could pave the way for adoption in clinical practice.
Shikonin and its enantiomer alkannin, which are natural products, have been extensively studied in vitro and in vivo for, among others, their antitumor activity. The investigation of the molecular pathways involved in their action is of interest, since they are not yet clearly defined. Metabolic profiling in cells can provide a picture of a cell's phenotype upon intervention, assisting in the elucidation of the mechanism of action. In this study, the cytotoxic effect of shikonin on a human hepatocarcinoma cell line was studied. Huh7 cells were treated with shikonin at 5 μM, and it was found that shikonin markedly inhibited cell growth. Metabolic profiling indicated alterations in the metabolic content of the cells and the culture media upon treatment, detecting the metabolic response of the cells. This study demonstrates the potential of metabolomics to improve knowledge on the mechanisms involved in shikonin's antitumor action.
Physical exercise can reduce adverse conditions during aging, while both exercise and aging act as metabolism modifiers. The present study investigates rat fecal and cecal metabolome alterations derived from exercise during rats' lifespan.Groups of rats trained life-long or for a specific period of time were under study. The training protocol consisted of swimming, 15-18 min per day, 3-5 days per week, with load of 4-0% of rat's weight. Fecal samples and cecal extracts were analyzed by targeted and untargeted metabolic profiling methods (GC-MS and LC-MS/MS). Effects of exercise and aging on the rats' fecal and cecal metabolome were observed.Fecal and cecal metabolomics are a promising field to investigate exercise biochemistry and age-related alterations.
Abstract Background Coronary artery disease (CAD) remains one of the leading causes of mortality and morbidity worldwide. As oxygen and nutrient supply to the myocardium significantly decrease during ischemic periods, important changes occur regarding myocardial intermediary energy metabolism. Metabolomics is an emerging field in systems biology, which quantifies metabolic changes in response to disease progression. This study aims to evaluate the diagnostic utility of plasma metabolomics-based biomarkers for determining the complexity and severity of CAD, as it is assessed via the SYNTAX score. Methods Corlipid is a prospective, non-interventional cohort trial empowered to enroll 1065 patients with no previous coronary intervention history, who undergo coronary angiography in University Hospital AHEPA, Thessaloniki. Venous blood samples are collected before coronary angiography. State-of the-art analytical methods are performed to calculate the serum levels of novel biomarkers: ceramides, acyl-carnitines, fatty acids, and proteins such as galectin-3, adiponectin, and the ratio of apolipoprotein B/apolipoprotein A1. Furthermore, all patients will be categorized based on the indication for coronary angiography (acute coronary syndrome, chronic coronary syndrome, preoperative coronary angiography) and on the severity of CAD using the SYNTAX score. Follow-up of 12 months after enrollment will be performed to record the occurrence of major adverse cardiovascular events. A risk prediction algorithm will be developed by combining clinical characteristics with established and novel biomarkers to identify patients at high risk for complex CAD based on their metabolite signatures. The first patient was enrolled in July 2019 and completion of enrollment is expected until May 2021. Discussion CorLipid is an ongoing trial aiming to investigate the correlation between metabolic profile and complexity of coronary artery disease in a cohort of patients undergoing coronary angiography with the potential to suggest a decision-making tool with high discriminative power for patients with CAD. To our knowledge, Corlipid is the first study aspiring to create an integrative metabolomic biomarkers-based algorithm by combining metabolites from multiple classes, involved in a wide range of pathways with well-established biochemical markers. Trial registration CorLipid trial registration: ClinicalTrials.gov number: NCT04580173. Registered 8 October 2020—Retrospectively registered, https://clinicaltrials.gov/ct2/show/NCT04580173 .
Lipidomics emerges as a promising research field with the potential to help in personalized risk stratification and improve our understanding on the functional role of individual lipid species in the metabolic perturbations occurring in coronary artery disease (CAD). This study aimed to utilize a machine learning approach to provide a lipid panel able to identify patients with obstructive CAD. In this posthoc analysis of the prospective CorLipid trial, we investigated the lipid profiles of 146 patients with suspected CAD, divided into two categories based on the existence of obstructive CAD. In total, 517 lipid species were identified, from which 288 lipid species were finally quantified, including glycerophospholipids, glycerolipids, and sphingolipids. Univariate and multivariate statistical analyses have shown significant discrimination between the serum lipidomes of patients with obstructive CAD. Finally, the XGBoost algorithm identified a panel of 17 serum biomarkers (5 sphingolipids, 7 glycerophospholipids, a triacylglycerol, galectin-3, glucose, LDL, and LDH) as totally sensitive (100% sensitivity, 62.1% specificity, 100% negative predictive value) for the prediction of obstructive CAD. Our findings shed light on dysregulated lipid metabolism's role in CAD, validating existing evidence and suggesting promise for novel therapies and improved risk stratification.
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