This Article presents, for the first time to our knowledge, an untargeted nuclear magnetic resonance (NMR) metabolomic characterization of the polar intracellular metabolic adaptations of human adipose-derived mesenchymal stem cells during osteogenic differentiation. The use of mesenchymal stem cells (MSCs) for bone regeneration is a promising alternative to conventional bone grafts, and untargeted metabolomics may unveil novel metabolic information on the osteogenic differentiation of MSCs, allowing their behavior to be understood and monitored/guided toward effective therapies. Our results unveiled statistically relevant changes in the levels of just over 30 identified metabolites, illustrating a highly dynamic process with significant variations throughout the whole 21-day period of osteogenic differentiation, mainly involving amino acid metabolism and protein synthesis; energy metabolism and the roles of glycolysis, the tricarboxylic acid cycle, and oxidative phosphorylation; cell membrane metabolism; nucleotide metabolism (including the specific involvement of O-glycosylation intermediates and NAD+); and metabolic players in protective antioxidative mechanisms (such as glutathione and specific amino acids). Different metabolic stages are proposed and are supported by putative biochemical explanations for the metabolite changes observed. This work lays the groundwork for the use of untargeted NMR metabolomics to find potential metabolic markers of osteogenic differentiation efficacy.

The ability to improve the data quality of ion mobility-mass spectrometry (IM-MS) measurements is of great importance for enabling modular and efficient computational workflows and gaining better qualitative and quantitative insights from complex biological and environmental samples. We developed the PNNL PreProcessor, a standalone and user-friendly software housing various algorithmic implementations to generate new MS-files with enhanced signal quality and in the same instrument format. Different experimental approaches are supported for IM-MS based on Drift-Tube (DT) and Structures for Lossless Ion Manipulations (SLIM), including liquid chromatography (LC) and infusion analyses. The algorithms extend the dynamic range of the detection system, while reducing file sizes for faster and memory-efficient downstream processing. Specifically, multidimensional smoothing improves peak shapes of poorly defined low-abundance signals, and saturation repair reconstructs the intensity profile of high-abundance peaks from various analyte types. Other functionalities are data compression and interpolation, IM demultiplexing, noise filtering by low intensity threshold and spike removal, and exporting of acquisition metadata. Several advantages of the tool are illustrated, including an increase of 19.4% in lipid annotations and a two-times faster processing of LC-DT IM-MS data-independent acquisition spectra from a complex lipid extract of a standard human plasma sample. The software is freely available at https://omics.pnl.gov/software/pnnl-preprocessor.

Selenium (Se) is an essential trace element with important health roles due to the antioxidant properties of selenoproteins. To analyze the interplay between Se and gut microbiota, gut metabolomic profiles were determined in conventional (C) and microbiota depleted mice (Abx) after Se-supplementation (Abx-Se) by untargeted metabolomics, using an analytical multiplatform based on GC-MS and UHPLC-QTOF-MS (MassIVE ID MSV000087829). Gut microbiota profiling was performed by 16S rRNA gene amplicon sequencing. Significant differences in the levels of about 70% of the gut metabolites determined, including fatty acyls, glycerolipids, glycerophospholipids, and steroids, were found in Abx-Se compared to Abx, and only 30% were different between Abx-Se and C, suggesting an important effect of Se-supplementation on Abx mice metabolism. At genus level, the correlation analysis showed strong associations between metabolites and gut bacterial profiles. Likewise, higher abundance of Lactobacillus spp., a potentially beneficial genus enriched after Se-supplementation, was associated with higher levels of prenol lipids, phosphatidylglycerols (C-Se), steroids and diterpenoids (Abx-Se), and also with lower levels of fatty acids (Abx-Se). Thus, we observed a crucial interaction between Se intake-microbiota-metabolites, although further studies to clarify the specific mechanisms are needed. This is the first study about untargeted gut metabolomics after microbiota depletion and Se-supplementation.

Compulsivity is a key manifestation of inhibitory control deficit and a cardinal symptom of psychopathological conditions such as obsessive-compulsive and attention-deficit hyperactivity disorders, in which metabolic alterations have raised attention as putative biomarkers for early identification. The present study assessed the metabolic profile in a preclinical model of a compulsive phenotype of rats. We used the schedule-induced polydipsia (SIP) method to classify male Wistar rats into high drinkers (HDs) or low drinkers (LDs) according to their compulsive drinking rate developed by exposure to a fixed-time 60 s (FT-60) schedule of reinforcement with water available ad libitum during 20 sessions. Before and after SIP, blood samples were collected for subsequent serum analysis by nuclear magnetic resonance spectroscopy coupled to multivariate analysis. Although no differences existed in the pre-SIP set, the compulsive drinking behavior induced remarkable metabolic alterations: HD rats selected by SIP exhibited a hyperlipidemic, hypoglycemic, and hyperglutaminergic profile compared with their low-compulsive counterparts. Interestingly, these alterations were not attributable to the mere exposure to reward pellets because a control experiment did not show differences between HDs and LDs after 20 sessions of pellet consumption without intermittent reinforcement. Our results shed light toward the implication of dietary and metabolic factors underpinning the vulnerability to compulsive behaviors.

Metabolomics is increasingly applied to investigate diet-disease associations in nutrition research. However, studies of metabolite reproducibility are limited, which could hamper their use within epidemiologic studies. The objective of this study was to evaluate the metabolite reproducibility during 4 months in a free-living population. In the A-DIET Confirm study, fasting plasma and dietary data were collected once a month for 4 months. Metabolites were measured using liquid chromatography tandem mass spectrometry, and their reproducibility was estimated using the intraclass correlation coefficient (ICC). Regularized canonical correlation analysis (rCCA) was employed to examine the diet-metabolite associations. In total, 138 metabolites were measured, and median ICC values of 0.49 and 0.65 were found for amino acids and biogenic amines, respectively. Acylcarnitines, lysophosphatidylcholines, phosphatidylcholines, and sphingomyelins had median ICC values of 0.69, 0.66, 0.63, and 0.63, respectively. The median ICC for all metabolites was 0.62, and 54% of metabolites had ICC values ≥0.60. Additionally, the rCCA heat map revealed positive correlations between dairy/meat intake and specific lipids. In conclusion, more than half of the metabolites demonstrated good to excellent reproducibility. A single measurement per subject could appropriately reflect the metabolites' long-term concentration levels and may also be sufficient for assessing disease risk in epidemiologic studies. The study data are deposited in MetaboLights (MTBLS3428 (www.ebi.ac.uk/metabolights)).