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Hepatocellular carcinoma (HCC) is one of the most difficult cancer types to treat. Liver cancer is often diagnosed at late stages and therapeutic treatment is frequently accompanied by development of multidrug resistance. This leads to poor outcomes for cancer patients. Understanding the fundamental molecular mechanisms leading to liver cancer development is crucial for developing new therapeutic approaches, which are more efficient in treating cancer. Mice with a liver specific UDP-glucose ceramide glucosyltransferase (UGCG) knockout (KO) show delayed diethylnitrosamine (DEN)-induced liver tumor growth. Accordingly, the rationale for our study was to determine whether UGCG overexpression is sufficient to drive cancer phenotypes in liver cells. We investigated the effect of UGCG overexpression (OE) on normal murine liver (NMuLi) cells. Increased UGCG expression results in decreased mitochondrial respiration and glycolysis, which is reversible by treatment with EtDO-P4, an UGCG inhibitor. Furthermore, tumor markers such as FGF21 and EPCAM are lowered following UGCG OE, which could be related to glucosylceramide (GlcCer) and lactosylceramide (LacCer) accumulation in glycosphingolipid-enriched microdomains (GEMs) and subsequently altered signaling protein phosphorylation. These cellular processes lead to decreased proliferation in NMuLi/UGCG OE cells. Our data show that increased UGCG expression itself does not induce pro-cancerous processes in normal liver cells, which indicates that increased GlcCer expression leads to different outcomes in different cancer types.
The emerging disciplines of lipidomics and metabolomics show great potential for the discovery of diagnostic biomarkers, but appropriate pre-analytical sample-handling procedures are critical because several analytes are prone to ex vivo distortions during sample collection. To test how the intermediate storage temperature and storage period of plasma samples from K3EDTA whole-blood collection tubes affect analyte concentrations, we assessed samples from non-fasting healthy volunteers (n = 9) for a broad spectrum of metabolites, including lipids and lipid mediators, using a well-established LC-MS-based platform. We used a fold change-based approach as a relative measure of analyte stability to evaluate 489 analytes, employing a combination of targeted LC-MS/MS and LC-HRMS screening. The concentrations of many analytes were found to be reliable, often justifying less strict sample handling; however, certain analytes were unstable, supporting the need for meticulous processing. We make four data-driven recommendations for sample-handling protocols with varying degrees of stringency, based on the maximum number of analytes and the feasibility of routine clinical implementation. These protocols also enable the simple evaluation of biomarker candidates based on their analyte-specific vulnerability to ex vivo distortions. In summary, pre-analytical sample handling has a major effect on the suitability of certain metabolites as biomarkers, including several lipids and lipid mediators. Our sample-handling recommendations will increase the reliability and quality of samples when such metabolites are necessary for routine clinical diagnosis.
Progranulin deficiency in mice is associated with deregulations of the scavenger receptor signaling of CD36/SCARB3 in immune disease models, and CD36 is a dominant receptor in taste bud cells in the tongue and contributes to the sensation of dietary fats. Progranulin-deficient mice (Grn−/−) are moderately overweight during middle age. We therefore asked if there was a connection between progranulin/CD36 in the tongue and fat taste preferences. By using unbiased behavioral analyses in IntelliCages and Phenomaster cages we showed that progranulin-deficient mice (Grn−/−) developed a strong preference of fat taste in the form of 2% milk over 0.3% milk, and for diluted MCTs versus tap water. The fat preference in the 7d-IntelliCage observation period caused an increase of 10% in the body weight of Grn−/− mice, which did not occur in the wildtype controls. CD36 expression in taste buds was reduced in Grn−/− mice at RNA and histology levels. There were no differences in the plasma or tongue lipids of various classes including sphingolipids, ceramides and endocannabinoids. The data suggest that progranulin deficiency leads to a lower expression of CD36 in the tongue resulting in a stronger urge for fatty taste and fatty nutrition.
Depletion of the enzyme cofactor, tetrahydrobiopterin (BH4), in T-cells was shown to prevent their proliferation upon receptor stimulation in models of allergic inflammation in mice, suggesting that BH4 drives autoimmunity. Hence, the clinically available BH4 drug (sapropterin) might increase the risk of autoimmune diseases. The present study assessed the implications for multiple sclerosis (MS) as an exemplary CNS autoimmune disease. Plasma levels of biopterin were persistently low in MS patients and tended to be lower with high Expanded Disability Status Scale (EDSS). Instead, the bypass product, neopterin, was increased. The deregulation suggested that BH4 replenishment might further drive the immune response or beneficially restore the BH4 balances. To answer this question, mice were treated with sapropterin in immunization-evoked autoimmune encephalomyelitis (EAE), a model of multiple sclerosis. Sapropterin-treated mice had higher EAE disease scores associated with higher numbers of T-cells infiltrating the spinal cord, but normal T-cell subpopulations in spleen and blood. Mechanistically, sapropterin treatment was associated with increased plasma levels of long-chain ceramides and low levels of the poly-unsaturated fatty acid, linolenic acid (FA18:3). These lipid changes are known to contribute to disruptions of the blood–brain barrier in EAE mice. Indeed, RNA data analyses revealed upregulations of genes involved in ceramide synthesis in brain endothelial cells of EAE mice (LASS6/CERS6, LASS3/CERS3, UGCG, ELOVL6, and ELOVL4). The results support the view that BH4 fortifies autoimmune CNS disease, mechanistically involving lipid deregulations that are known to contribute to the EAE pathology.
Small molecule biomarker discovery: Proposed workflow for LC-MS-based clinical research projects
(2023)
Mass spectrometry focusing on small endogenous molecules has become an integral part of biomarker discovery in the pursuit of an in-depth understanding of the pathophysiology of various diseases, ultimately enabling the application of personalized medicine. While LC-MS methods allow researchers to gather vast amounts of data from hundreds or thousands of samples, the successful execution of a study as part of clinical research also requires knowledge transfer with clinicians, involvement of data scientists, and interactions with various stakeholders.
The initial planning phase of a clinical research project involves specifying the scope and design, and engaging relevant experts from different fields. Enrolling subjects and designing trials rely largely on the overall objective of the study and epidemiological considerations, while proper pre-analytical sample handling has immediate implications on the quality of analytical data. Subsequent LC-MS measurements may be conducted in a targeted, semi-targeted, or non-targeted manner, resulting in datasets of varying size and accuracy. Data processing further enhances the quality of data and is a prerequisite for in-silico analysis. Nowadays, the evaluation of such complex datasets relies on a mix of classical statistics and machine learning applications, in combination with other tools, such as pathway analysis and gene set enrichment. Finally, results must be validated before biomarkers can be used as prognostic or diagnostic decision-making tools. Throughout the study, quality control measures should be employed to enhance the reliability of data and increase confidence in the results.
The aim of this graphical review is to provide an overview of the steps to be taken when conducting an LC-MS-based clinical research project to search for small molecule biomarkers.
Endocannabinoids are important lipid-signaling mediators. Both protective and deleterious effects of endocannabinoids in the cardiovascular system have been reported but the mechanistic basis for these contradicting observations is unclear. We set out to identify anti-inflammatory mechanisms of endocannabinoids in the murine aorta and in human vascular smooth muscle cells (hVSMC). In response to combined stimulation with cytokines, IL-1β and TNFα, the murine aorta released several endocannabinoids, with anandamide (AEA) levels being the most significantly increased. AEA pretreatment had profound effects on cytokine-induced gene expression in hVSMC and murine aorta. As revealed by RNA-Seq analysis, the induction of a subset of 21 inflammatory target genes, including the important cytokine CCL2 was blocked by AEA. This effect was not mediated through AEA-dependent interference of the AP-1 or NF-κB pathways but rather through an epigenetic mechanism. In the presence of AEA, ATAC-Seq analysis and chromatin-immunoprecipitations revealed that CCL2 induction was blocked due to increased levels of H3K27me3 and a decrease of H3K27ac leading to compacted chromatin structure in the CCL2 promoter. These effects were mediated by recruitment of HDAC4 and the nuclear corepressor NCoR1 to the CCL2 promoter. This study therefore establishes a novel anti-inflammatory mechanism for the endogenous endocannabinoid AEA in vascular smooth muscle cells. Furthermore, this work provides a link between endogenous endocannabinoid signaling and epigenetic regulation.
Background: Hyperhomocysteinemia is considered a possible contributor to the complex pathology of Alzheimer’s disease (AD). For years, researchers in this field have discussed the apparent detrimental effects of the endogenous amino acid homocysteine in the brain. In this study, the roles of hyperhomocysteinemia driven by vitamin B deficiency, as well as potentially beneficial dietary interventions, were investigated in the novel AppNL-G-F knock-in mouse model for AD, simulating an early stage of the disease. Methods: Urine and serum samples were analyzed using a validated LC-MS/MS method and the impact of different experimental diets on cognitive performance was studied in a comprehensive behavioral test battery. Finally, we analyzed brain samples immunohistochemically in order to assess amyloid-β (Aβ) plaque deposition. Results: Behavioral testing data indicated subtle cognitive deficits in AppNL-G-F compared to C57BL/6J wild type mice. Elevation of homocysteine and homocysteic acid, as well as counteracting dietary interventions, mostly did not result in significant effects on learning and memory performance, nor in a modified Aβ plaque deposition in 35-week-old AppNL-G-F mice. Conclusion: Despite prominent Aβ plaque deposition, the AppNL-G-F model merely displays a very mild AD-like phenotype at the investigated age. Older AppNL-G-F mice should be tested in order to further investigate potential effects of hyperhomocysteinemia and dietary interventions.
Hyperhomocysteinemia has been suggested potentially to contribute to a variety of pathologies, such as Alzheimer’s disease (AD). While the impact of hyperhomocysteinemia on AD has been investigated extensively, there are scarce data on the effect of AD on hyperhomocysteinemia. The aim of this in vivo study was to investigate the kinetics of homocysteine (HCys) and homocysteic acid (HCA) and effects of AD-like pathology on the endogenous levels. The mice received a B-vitamin deficient diet for eight weeks, followed by the return to a balanced control diet for another eight weeks. Serum, urine, and brain tissues of AppNL-G-F knock-in and C57BL/6J wild type mice were analyzed for HCys and HCA using LC-MS/MS methods. Hyperhomocysteinemic levels were found in wild type and knock-in mice due to the consumption of the deficient diet for eight weeks, followed by a rapid normalization of the levels after the return to control chow. Hyperhomocysteinemic AppNL-G-F mice had significantly higher HCys in all matrices, but not HCA, compared to wild type control. Higher serum concentrations were associated with elevated levels in both the brain and in urine. Our findings confirm a significant impact of AD-like pathology on hyperhomocysteinemia in the AppNL-G-F mouse model. The immediate normalization of HCys and HCA after the supply of B-vitamins strengthens the idea of a B-vitamin intervention as a potentially preventive treatment option for HCys-related disorders such as AD.
The evaluation of pharmacological data using machine learning requires high data quality. Therefore, data preprocessing, that is, cleaning analytical laboratory errors, replacing missing values or outliers, and transforming data adequately before actual data analysis, is crucial. Because current tools available for this purpose often require programming skills, preprocessing tools with graphical user interfaces that can be used interactively are needed. In collaboration between data scientists and experts in bioanalytical diagnostics, a graphical software package for data preprocessing called pguIMP is proposed, which contains a fixed sequence of preprocessing steps to enable reproducible interactive data preprocessing. As an R-based package, it also allows direct integration into this data science environment without requiring any programming knowledge. The implementation of contemporary data processing methods, including machine-learning-based imputation techniques, ensures the generation of corrected and cleaned bioanalytical data sets that preserve data structures such as clusters better than is possible with classical methods. This was evaluated on bioanalytical data sets from lipidomics and drug research using k-nearest-neighbors-based imputation followed by k-means clustering and density-based spatial clustering of applications with noise. The R package provides a Shiny-based web interface designed to be easy to use for non–data analysis experts. It is demonstrated that the spectrum of methods provided is suitable as a standard pipeline for preprocessing bioanalytical data in biomedical research domains. The R package pguIMP is freely available at the comprehensive R archive network (https://cran.r-project.org/web/packages/pguIMP/index.html).
Introduction: Arachidonoyl ethanolamide (AEA) and 2-arachidonoyl glycerol (2-AG) are central lipid mediators of the endocannabinoid system. They are highly relevant due to their involvement in a wide variety of inflammatory, metabolic or malign diseases. Further elucidation of their modes of action and use as biomarkers in an easily accessible matrix, like blood, is restricted by their susceptibility to deviations during blood sampling and physiological co-dependences, which results in high variability of reported concentrations in low ng/mL ranges.
Objectives: The objective of this review is the identification of critical parameters during the pre-analytical phase and proposal of minimum requirements for reliable determination of endocannabinoids (ECs) in blood samples.
Methods: Reported physiological processes influencing the EC concentrations were put into context with published pre-analytical research and stability data from bioanalytical method validation.
Results: The cause for variability in EC concentrations is versatile. In part, they are caused by inter-individual factors like sex, metabolic status and/or diurnal changes. Nevertheless, enzymatic activity in freshly drawn blood samples is the main reason for changing concentrations of AEA and 2-AG, besides additional non-enzymatic isomerization of the latter.
Conclusion: Blood samples for EC analyses require immediate processing at low temperatures (>0 °C) to maintain sample integrity. Standardization of the respective blood tube or anti-coagulant, sampling time point, applied centrifugal force and complete processing time can further decrease variability caused by sample handling. Nevertheless, extensive characterization of study participants is needed to reduce distortion of clinical data caused by co-variables and facilitate research on the endocannabinoid system.