PubMed

Diabetologia. 2023 Oct 27. doi: 10.1007/s00125-023-06027-x. Online ahead of print.ABSTRACTAIMS/HYPOTHESIS: The identification of people who are at high risk of developing type 2 diabetes is a key part of population-level prevention strategies. Previous studies have evaluated the predictive utility of omics measurements, such as metabolites, proteins or polygenic scores, but have considered these separately. The improvement that combined omics biomarkers can provide over and above current clinical standard models is unclear. The aim of this study was to test the predictive performance of genome, proteome, metabolome and clinical biomarkers when added to established clinical prediction models for type 2 diabetes.METHODS: We developed sparse interpretable prediction models in a prospective, nested type 2 diabetes case-cohort study (N=1105, incident type 2 diabetes cases=375) with 10,792 person-years of follow-up, selecting from 5759 features across the genome, proteome, metabolome and clinical biomarkers using least absolute shrinkage and selection operator (LASSO) regression. We compared the predictive performance of omics-derived predictors with a clinical model including the variables from the Cambridge Diabetes Risk Score and HbA1c.RESULTS: Among single omics prediction models that did not include clinical risk factors, the top ten proteins alone achieved the highest performance (concordance index [C index]=0.82 [95% CI 0.75, 0.88]), suggesting the proteome as the most informative single omic layer in the absence of clinical information. However, the largest improvement in prediction of type 2 diabetes incidence over and above the clinical model was achieved by the top ten features across several omic layers (C index=0.87 [95% CI 0.82, 0.92], Δ C index=0.05, p=0.045). This improvement by the top ten omic features was also evident in individuals with HbA1c <42 mmol/mol (6.0%), the threshold for prediabetes (C index=0.84 [95% CI 0.77, 0.90], Δ C index=0.07, p=0.03), the group in whom prediction would be most useful since they are not targeted for preventative interventions by current clinical guidelines. In this subgroup, the type 2 diabetes polygenic risk score was the major contributor to the improvement in prediction, and achieved a comparable improvement in performance when added onto the clinical model alone (C index=0.83 [95% CI 0.75, 0.90], Δ C index=0.06, p=0.002). However, compared with those with prediabetes, individuals at high polygenic risk in this group had only around half the absolute risk for type 2 diabetes over a 20 year period.CONCLUSIONS/INTERPRETATION: Omic approaches provided marginal improvements in prediction of incident type 2 diabetes. However, while a polygenic risk score does improve prediction in people with an HbA1c in the normoglycaemic range, the group in whom prediction would be most useful, even individuals with a high polygenic burden in that subgroup had a low absolute type 2 diabetes risk. This suggests a limited feasibility of implementing targeted population-based genetic screening for preventative interventions.PMID:37889320 | DOI:10.1007/s00125-023-06027-x

Bioinformatics. 2023 Oct 27:btad661. doi: 10.1093/bioinformatics/btad661. Online ahead of print.ABSTRACTThe analysis of stable isotope labeling experiments requires accurate, efficient, and reproducible quantification of mass isotopomer distributions (MIDs), which is not a core feature of general-purpose metabolomics software tools that are optimized to quantify metabolite abundance. Here we present PIRAMID, a MATLAB-based tool that addresses this need by offering a user-friendly, graphical user interface (GUI)-driven program to automate the extraction of isotopic information from mass spectrometry (MS) data sets. This tool can simultaneously extract ion chromatograms for various metabolites from multiple data files in common vendor-agnostic file formats, locate chromatographic peaks based on a targeted list of characteristic ions and retention times, and integrate MIDs for each target ion. These MIDs can be corrected for natural isotopic background based on the user-defined molecular formula of each ion. PIRAMID offers support for datasets acquired from low- or high-resolution (HR) MS, and single (MS) or tandem (MS/MS) instruments. It also enables the analysis of single or dual labeling experiments using a variety of isotopes (i.e., 2H, 13C, 15N, 18O, 34S).AVAILABILITY: MATLAB p-code files are freely available for non-commercial use and can be downloaded from https://mfa.vueinnovations.com/. Commercial licenses are also available.SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.PMID:37889279 | DOI:10.1093/bioinformatics/btad661

Microbiol Spectr. 2023 Oct 27:e0054123. doi: 10.1128/spectrum.00541-23. Online ahead of print.ABSTRACTNew antibacterial agents and agent combinations are urgently needed to combat antimicrobial resistance. A multidimensional chemical library screening strategy was used to identify compounds in the National Cancer Institute (NCI) diversity set V library (1,593 compounds) with anti-methicillin-resistant Staphylococcus aureus (MRSA) activity. In this effort, library compounds were screened for anti-MRSA activity in both their original [un-metabolized (UM)] and human liver microsome-metabolized [post-metabolized (PM)] forms and in the absence and presence of sub-minimum inhibitory concentration (MIC) levels of cefoxitin. This strategy allows for the identification of intrinsically active agents, agents with active metabolites, and agents that can act synergistically with cefoxitin. Sixteen UM compounds with MICs ≤ 12.5 µM were identified. No agents with substantially enhanced activity after microsomal metabolism were found. Several agents showed significant apparent synergy with cefoxitin, and checkerboard assays were used to confirm synergy for four of these (celastrol, porfiromycin, 4-quinazolinediamine, and teniposide). A follow-up comparative screen in the absence and presence of 4-µM thymidine was used to identify three agents as likely folate/thymidine biosynthesis inhibitors. A liquid chromatography-mass spectrometry (LC-MS/MS) assay for deoxythymidine triphosphate (dTTP) was used to confirm these three as suppressing dTTP biosynthesis in MRSA. Bactericidal vs bacteriostatic activity was also evaluated. This study further demonstrates the utility of comparative library screening to identify novel bioactive agents with interesting synergies and biological activities. The identification of several folate/thymidine biosynthesis inhibitors from this small screen indicates that this pathway is a viable target for new drug discovery efforts. IMPORTANCE New antibacterial agents are urgently needed to counter increasingly resistant bacteria. One approach to this problem is library screening for new antibacterial agents. Library screening efforts can be improved by increasing the information content of the screening effort. In this study, we screened the National Cancer Institute diversity set V against methicillin-resistant Staphylococcus aureus (MRSA) with several enhancements. One of these is to screen the library before and after microsomal metabolism as means to identify potential active metabolites. A second enhancement is to screen the library in the absence and presence of sub-minimum inhibitory concentration levels of another antibiotic, such as cefoxitin in this study. This identified four agents with synergistic activity with cefoxitin out of 16 agents with good MRSA activity alone. Finally, active agents from this effort were counter-screened in the presence of thymidine, which quickly identified three folate/thymidine biosynthesis inhibitors, and also screened for bactericidal vs bacteriostatic activity.PMID:37888993 | DOI:10.1128/spectrum.00541-23

Toxics. 2023 Oct 1;11(10):833. doi: 10.3390/toxics11100833.ABSTRACTInorganic arsenic in drinking water is prioritized as a top environmental contaminant by the World Health Organization, with over 230 million people potentially being exposed. Arsenic toxicity has been well documented and is associated with a plethora of human diseases, including diabetes, as established in numerous animal and epidemiological studies. Our previous study revealed that arsenic exposure leads to the inhibition of nuclear receptors, including LXR/RXR. To this end, FXR is a nuclear receptor central to glucose and lipid metabolism. However, limited studies are available for understanding arsenic exposure-FXR interactions. Herein, we report that FXR knockout mice developed more profound glucose intolerance than wild-type mice upon arsenic exposure, supporting the regulatory role of FXR in arsenic-induced glucose intolerance. We further exposed mice to arsenic and tested if GW4064, a FXR agonist, could improve glucose intolerance and dysregulation of hepatic proteins and serum metabolites. Our data showed arsenic-induced glucose intolerance was remarkably diminished by GW4064, accompanied by a significant ratio of alleviation of dysregulation in hepatic proteins (83%) and annotated serum metabolites (58%). In particular, hepatic proteins "rescued" from arsenic toxicity by GW4064 featured members of glucose and lipid utilization. For instance, the expression of PCK1, a candidate gene for diabetes and obesity that facilitates gluconeogenesis, was repressed under arsenic exposure in the liver, but revived with the GW4064 supplement. Together, our comprehensive dataset indicates FXR plays a key role and may serve as a potential therapeutic for arsenic-induced metabolic disorders.PMID:37888683 | DOI:10.3390/toxics11100833

Toxins (Basel). 2023 Oct 6;15(10):601. doi: 10.3390/toxins15100601.ABSTRACTBACKGROUND: Plant-derived pyrrolizidine alkaloids (PAs) in feed cause metabolic disturbances in farm animals resulting in high economic losses worldwide. The molecular pathways affected by these PAs in cells and tissues are not yet fully understood. The objective of the study was to examine the dose-dependent effects of orally applied PAs derived from tansy ragwort in midlactation dairy cows.METHODS: Twenty Holstein dairy cows were treated with target exposures of 0, 0.47, 0.95 and 1.91 mg of total PA/kg of body weight/d in control, PA1, PA2 and PA3, respectively, for 28 days. Liver tissue biopsy and plasma and milk samples were taken at day 28 of treatment to assess changes in metabolic pathways. A targeted metabolomics approach was performed to detect the metabolite profiles in all compartments.RESULTS: The PA-affected metabolite profiling in liver tissue, plasma and milk revealed changes in three substrate classes: acylcarnitines (ACs), phosphatidylcholines (PCs) and sphingomyelins (SMs). In addition, in the plasma, amino acid concentrations were affected by PA exposure.CONCLUSIONS: PA exposure disturbed liver metabolism at many sites, especially devastating pathways related to energy metabolism and to amino acid utilization, most likely based on mitochondrial oxidative stress. The effects on the milk metabolite profile may have consequences for milk quality.PMID:37888632 | DOI:10.3390/toxins15100601

Chemphyschem. 2023 Oct 27:e202300596. doi: 10.1002/cphc.202300596. Online ahead of print.ABSTRACTHeterocyclic thiones have recently been identified as reversible covalent warheads, consistent with their mild electrophilic nature. Little is known so far about their mechanism of action in labelling nucleophilic sidechains, especially cysteines. The vast number of tractable cysteines promotes a wide range of target proteins to examine; however, our focus was put on functional cysteines. We chose the main protease of SARS-CoV-2 harboring Cys145 at the active site that is a structurally characterized and clinically validated target of covalent inhibitors. We screened an in-house, cysteine-targeting covalent inhibitor library which resulted in several covalent fragment hits with benzoxazole, benzothiazole and benzimidazole cores. Thione derivatives and Michael acceptors were selected for further investigations with the objective of exploring the mechanism of inhibition of the thiones and using the thoroughly characterized Michael acceptors for benchmarking our studies. Classical and hybrid quantum mechanical/molecular mechanical (QM/MM) molecular dynamics simulations were carried out that revealed a new mechanism of covalent cysteine labelling by thione derivatives, which was supported by QM and free energy calculations and by a wide range of experimental results. Our study shows that the molecular recognition step plays a crucial role in the overall binding of both sets of molecules.PMID:37888491 | DOI:10.1002/cphc.202300596

Mar Drugs. 2023 Sep 22;21(10):502. doi: 10.3390/md21100502.ABSTRACTThe prevalence of gout and the adverse effects of current synthetic anti-gout drugs call for new natural and effective xanthine oxidase (XOD) inhibitors to target this disease. Based on our previous finding that an edible seaweed Pterocladiella capillacea extract inhibits XOD, XOD-inhibitory and anti-inflammatory activities were used to evaluate the anti-gout potential of different P. capillacea extract fractions. Through affinity ultrafiltration coupled with liquid chromatography tandem mass spectrometry (LC-MS/MS), feature-based molecular networking (FBMN), and database mining of multiple natural products, the extract's bioactive components were traced and annotated. Through molecular docking and ADMET analysis, the possibility and drug-likeness of the annotated XOD inhibitors were predicted. The results showed that fractions F4, F6, F4-2, and F4-3 exhibited strong XOD inhibition activity, among which F4-3 reached an inhibition ratio of 77.96% ± 4.91% to XOD at a concentration of 0.14 mg/mL. In addition, the P. capillacea extract and fractions also displayed anti-inflammatory activity. Affinity ultrafiltration LC-MS/MS analysis and molecular networking showed that out of the 20 annotated compounds, 8 compounds have been previously directly or indirectly reported from seaweeds, and 4 compounds have been reported to exhibit anti-gout activity. Molecular docking and ADMET showed that six seaweed-derived compounds can dock with the XOD activity pocket and follow the Lipinski drug-like rule. These results support the value of further investigating P. capillacea as part of the development of anti-gout drugs or related functional foods.PMID:37888437 | DOI:10.3390/md21100502

J Fungi (Basel). 2023 Oct 13;9(10):1011. doi: 10.3390/jof9101011.ABSTRACTMetarhizium anisopliae, an entomopathogenic fungus, has been widely used for the control of agricultural and forestry pests. However, sporulation degeneration occurs frequently during the process of successive culture, and we currently lack a clear understanding of the underlying mechanisms. In this study, the metabolic profiles of M. anisopliae were comparatively analyzed based on the metabolomics approach of gas chromatography-mass spectrometry (GC-MS). A total of 74 metabolites were detected in both normal and degenerate strains, with 40 differential metabolites contributing significantly to the model. Principal component analysis (PCA) and potential structure discriminant analysis (PLS-DA) showed a clear distinction between the sporulation of normal strains and degenerate strains. Specifically, 23 metabolites were down-regulated and 17 metabolites were up-regulated in degenerate strains compared to normal strains. The KEGG enrichment analysis identified 47 significant pathways. Among them, the alanine, aspartate and glutamate metabolic pathways and the glycine, serine and threonine metabolism had the most significant effects on sporulation, which revealed that significant changes occur in the metabolic phenotypes of strains during sporulation and degeneration processes. Furthermore, our subsequent experiments have substantiated that the addition of amino acids could improve M. anisopliae's spore production. Our study shows that metabolites, especially amino acids, which are significantly up-regulated or down-regulated during the sporulation and degeneration of M. anisopliae, may be involved in the sporulation process of M. anisopliae, and amino acid metabolism (especially glutamate, aspartate, serine, glycine, arginine and leucine) may be an important part of the sporulation mechanism of M. anisopliae. This study provides a foundation and technical support for rejuvenation and production improvement strategies for M. anisopliae.PMID:37888267 | DOI:10.3390/jof9101011

J Cardiovasc Dev Dis. 2023 Oct 21;10(10):438. doi: 10.3390/jcdd10100438.ABSTRACTBACKGROUND: Progressive myocardial remodeling (MR) in chronic heart failure (CHF) leads to aggravation of systolic dysfunction (SD) and clinical manifestations. Identification of metabolomic markers of these processes may help in the search for new therapeutic approaches aimed at achieving reversibility of MR and improving prognosis in patients with CHF.METHODS: To determine the relationship between plasma acylcarnitine (ACs) levels, MR parameters and clinical characteristics, in patients with CHF of ischemic etiology (n = 79) and patients with coronary heart disease CHD (n = 19) targeted analysis of 30 ACs was performed by flow injection analysis mass spectrometry.RESULTS: Significant differences between cohorts were found for the levels of 11 ACs. Significant positive correlations (r > 0.3) between the medium- and long-chain ACs (MCACs and LCACs) and symptoms (CHF NYHA functional class (FC); r = 0.31-0.39; p < 0.05); negative correlation (r = -0.31-0.34; p < 0.05) between C5-OH and FC was revealed. Positive correlations of MCACs and LCACs (r = 0.31-0.48; p < 0.05) with the left atrium size and volume, the right atrium volume, right ventricle, and the inferior vena cava sizes, as well as the pulmonary artery systolic pressure level were shown. A negative correlation between C18:1 and left ventricular ejection fraction (r = -0.31; p < 0.05) was found. However, a decrease in levels compared to referent values of ACs with medium and long chain lengths was 50% of the CHF-CHD cohort. Carnitine deficiency was found in 6% and acylcarnitine deficiency in 3% of all patients with chronic heart disease.CONCLUSIONS: ACs may be used in assessing the severity of the clinical manifestations and MR. ACs are an important locus to study in terms of altered metabolic pathways in patients with CHF of ischemic etiology and SD. Further larger prospective trials are warranted and needed to determine the potential benefits to treat patients with CV diseases with aberrate AC levels.PMID:37887885 | DOI:10.3390/jcdd10100438

Insects. 2023 Oct 3;14(10):800. doi: 10.3390/insects14100800.ABSTRACTPlenty of freshwater species, especially macroinvertebrates that are essential to the provision of numerous ecosystem functions, encounter higher mortality due to acute hypoxia. However, within the family Chironomidae, a wide range of tolerance to hypoxia/anoxia is displayed. Propsilocerus akamusi depends on this great tolerance to become a dominant species in eutrophic lakes. To further understand how P. akamusi responds to acute hypoxic stress, we used multi-omics analysis in combination with histomorphological characteristics and physiological indicators. Thus, we set up two groups-a control group (DO 8.4 mg/L) and a hypoxic group (DO 0.39 mg/L)-to evaluate enzyme activity and the transcriptome, metabolome, and histomorphological characteristics. With blue-black chromatin, cell tightness, cell membrane invagination, and the production of apoptotic vesicles, tissue cells displayed typical apoptotic features in the hypoxic group. Although lactate dehydrogenase (LDH), alcohol dehydrogenase (ADH), catalase (CAT), and Na+/K+ -ATPase (NKA) activities were dramatically enhanced under hypoxic stress, glycogen content, and superoxide dismutase (SOD) activities were significantly reduced compared to the control group. The combined analysis of the transcriptome and metabolome, which further demonstrated, in addition to carbohydrates, including glycogen, the involvement of energy metabolism pathways, including fatty acid, protein, trehalose, and glyoxylate cycles, provided additional support for the aforementioned findings. Lactate is the end product of glycogen degradation, and HIF-1 plays an important role in promoting glycogenolysis in acute hypoxic conditions. However, we discovered that the ethanol tested under hypoxic stress likely originates from the symbiodinium of P. akamusi. These results imply that some parameters related to energy metabolism, antioxidant enzyme activities, and histomorphological features may be used as biomarkers of eutrophic lakes in Chironomus riparius larvae. The study also provides a scientific reference for assessing toxicity and favoring policies to reduce their impact on the environment.PMID:37887812 | DOI:10.3390/insects14100800

Metabolites. 2023 Oct 23;13(10):1104. doi: 10.3390/metabo13101104.ABSTRACTSouth Africa is rich in diverse medicinal plants, and it is reported to have over 35% of the global Helichrysum species, many of which are utilized in traditional medicine. Various phytochemical studies have offered valuable insights into the chemistry of Helichrysum plants, hinting at bioactive components that define the medicinal properties of the plant. However, there are still knowledge gaps regarding the size and diversity of the Helichrysum chemical space. As such, continuous efforts are needed to comprehensively characterize the phytochemistry of Helichrysum, which will subsequently contribute to the discovery and exploration of Helichrysum-derived natural products for drug discovery. Thus, reported herein is a computational metabolomics work to comprehensively characterize the metabolic landscape of the medicinal herb Helichrysum splendidum, which is less studied. Metabolites were methanol-extracted and analyzed on a liquid chromatography-tandem mass spectrometry (LC-MS/MS) system. Spectral data were mined using molecular networking (MN) strategies. The results revealed that the metabolic map of H. splendidum is chemically diverse, with chemical superclasses that include organic polymers, benzenoids, lipid and lipid-like molecules, alkaloids, and derivatives, phenylpropanoids and polyketides. These results point to a vastly rich chemistry with potential bioactivities, and the latter was demonstrated through computationally assessing the binding of selected metabolites with CDK-2 and CCNB1 anti-cancer targets. Molecular docking results showed that flavonoids (luteolin, dihydroquercetin, and isorhamnetin) and terpenoids (tiliroside and silybin) interact strongly with the CDK-2 and CCNB1 targets. Thus, this work suggests that these flavonoid and terpenoid compounds from H. splendidum are potentially anti-cancer agents through their ability to interact with these proteins involved in cancer pathways and progression. As such, these actionable insights are a necessary step for further exploration and translational studies for H. splendidum-derived compounds for drug discovery.PMID:37887429 | DOI:10.3390/metabo13101104

Metabolites. 2023 Oct 21;13(10):1101. doi: 10.3390/metabo13101101.ABSTRACTMetabolomics provides a unique snapshot into the world of small molecules and the complex biological processes that govern the human, animal, plant, and environmental ecosystems encapsulated by the One Health modeling framework. However, this "molecular snapshot" is only as informative as the number of metabolites confidently identified within it. The spectral similarity (SS) score is traditionally used to identify compound(s) in mass spectrometry approaches to metabolomics, where spectra are matched to reference libraries of candidate spectra. Unfortunately, there is little consensus on which of the dozens of available SS metrics should be used. This lack of standard SS score creates analytic uncertainty and potentially leads to issues in reproducibility, especially as these data are integrated across other domains. In this work, we use metabolomic spectral similarity as a case study to showcase the challenges in consistency within just one piece of the One Health framework that must be addressed to enable data science approaches for One Health problems. Here, using a large cohort of datasets comprising both standard and complex datasets with expert-verified truth annotations, we evaluated the effectiveness of 66 similarity metrics to delineate between correct matches (true positives) and incorrect matches (true negatives). We additionally characterize the families of these metrics to make informed recommendations for their use. Our results indicate that specific families of metrics (the Inner Product, Correlative, and Intersection families of scores) tend to perform better than others, with no single similarity metric performing optimally for all queried spectra. This work and its findings provide an empirically-based resource for researchers to use in their selection of similarity metrics for GC-MS identification, increasing scientific reproducibility through taking steps towards standardizing identification workflows.PMID:37887426 | DOI:10.3390/metabo13101101

Metabolites. 2023 Oct 20;13(10):1099. doi: 10.3390/metabo13101099.ABSTRACTNepeta nuda L. is a medicinal plant enriched with secondary metabolites serving to attract pollinators and deter herbivores. Phenolics and iridoids of N. nuda have been extensively investigated because of their beneficial impacts on human health. This study explores the chemical profiles of in vitro shoots and wild-grown N. nuda plants (flowers and leaves) through metabolomic analysis utilizing gas chromatography and mass spectrometry (GC-MS). Initially, we examined the differences in the volatiles' composition in in vitro-cultivated shoots comparing them with flowers and leaves from plants growing in natural environment. The characteristic iridoid 4a-α,7-β,7a-α-nepetalactone was highly represented in shoots of in vitro plants and in flowers of plants from nature populations, whereas most of the monoterpenes were abundant in leaves of wild-grown plants. The known in vitro biological activities encompassing antioxidant, antiviral, antibacterial potentials alongside the newly assessed anti-inflammatory effects exhibited consistent associations with the total content of phenolics, reducing sugars, and the identified metabolic profiles in polar (organic acids, amino acids, alcohols, sugars, phenolics) and non-polar (fatty acids, alkanes, sterols) fractions. Phytohormonal levels were also quantified to infer the regulatory pathways governing phytochemical production. The overall dataset highlighted compounds with the potential to contribute to N. nuda bioactivity.PMID:37887424 | DOI:10.3390/metabo13101099

Metabolites. 2023 Oct 19;13(10):1095. doi: 10.3390/metabo13101095.ABSTRACTRecently, a clinical blood metabogram was developed as a fast, low-cost and reproducible test that allows the implementation of metabolomics in clinical practice. The components of the metabogram are functionally related groups of blood metabolites associated with humoral regulation, the metabolism of lipids, carbohydrates and amines, lipid intake into the organism, and liver function, thereby providing clinically relevant information. It is known that the gut microbiota affects the blood metabolome, and the components of the blood metabolome may affect the composition of the gut microbiota. Therefore, before using the metabogram in the clinic, the link between the metabogram components and the level of gut microorganisms should be established. For this purpose, the metabogram and microbiota data were obtained in this work for the same individuals. Metabograms of blood plasma were obtained by direct mass spectrometry of blood plasma, and the gut microbiome was determined by a culture-based method and real-time polymerase chain reaction (PCR). This study involved healthy volunteers and individuals with varying degrees of deviation in body weight (n = 44). A correlation analysis determined which metabogram components are linked to which gut microorganisms and the strength of this link. Moreover, diagnostic parameters (sensitivity, specificity and accuracy) confirmed the capacity of metabogram components to be used for diagnosing gut microbiota alterations. Therefore, the obtained results allow the use of the metabogram in a clinical setting, taking into account its relationship with gut microbiota.PMID:37887420 | DOI:10.3390/metabo13101095

Metabolites. 2023 Oct 19;13(10):1094. doi: 10.3390/metabo13101094.ABSTRACTCold is an important environmental stress affecting the growth, productivity, and geographic distribution of tree species. Oaks are important for environmental conservation and wood supplies. Oak metabolites respond to low temperatures (LTs). In this study, the physiological and metabolic responses of two oak species to cold stress were investigated and compared. The field observations and physiological responses showed that Quercus wutaishanica was more cold-tolerant than Q. acutissima. After frost, the one-year-old twigs of Q. wutaishanica had higher survival rates, accumulated more soluble sugar and protein, and exhibited higher superoxide dismutase (SOD) activity than those of Q. acutissima. Untargeted metabolomics identified 102 and 78 differentially accumulated metabolites in Q. acutissima and Q. wutaishanica, respectively, when the leaves were subjected to LTs (4 °C for 24 h). The carbohydrate and flavonoid metabolites contributed to the cold tolerance of both oak species. Succinate, an intermediate in the citric acid cycle, was significantly inhibited by LTs, a potential energy conservation strategy. Unlike Q. acutissima, Q. wutaishanica underwent metabolic reprogramming that significantly increased the contents of phosphatidylcholine, gallic acid, oxidized glutathione, shikimate, and phenylpyruvate under LTs. Our data provide a reference for characterizing the mechanisms involved in the response of oak species to cold temperatures and enhancing the cold tolerance of forest trees.PMID:37887419 | DOI:10.3390/metabo13101094

Metabolites. 2023 Oct 18;13(10):1092. doi: 10.3390/metabo13101092.ABSTRACTOral hormonal contraceptive users carry the risk of venous thrombosis and increased mortality. This study aimed to comprehensively profile the serum metabolome of participants using a combination of drospirenone (DRSP) and ethinyl estradiol (EE) containing oral contraceptives (COCs). The MxP Quant 500 kit for liquid chromatography mass tandem spectrometry (LC-MS/MS) was used to analyse the 22 controls and 44 COC users (22 on a low EE dose (DRSP/20EE) and 22 on a higher EE dose (DRSP/30EE)). The kit's results were compared to our internally developed untargeted and targeted metabolomics methods previously applied to this cohort. Of the 630 metabolites included in the method, 277 provided desirable results (consistently detected above their detection limits), and of these, 5 had p-values < 0.05, including betaine, glutamine, cortisol, glycine, and choline. Notably, these variations were observed between the control and COC groups, rather than among the two COC groups. Partial least squares-discriminant analysis revealed 49 compounds with VIP values ≥ 1, including amino acids and their derivatives, ceramides, phosphatidylcholines, and triglycerides, among others. Ten differential compounds were consistent with our previous studies, reinforcing the notion of COCs inducing a prothrombotic state and increased oxidative stress. Although only a limited number of compounds were deemed usable, these were quantified with high reliability and facilitated the identification of meaningful biological differences among the sample groups. In addition to substantiating known drug-induced variations, new hypotheses were also generated.PMID:37887417 | DOI:10.3390/metabo13101092

Metabolites. 2023 Oct 17;13(10):1088. doi: 10.3390/metabo13101088.ABSTRACTThe Animal Metabolite Database (AMDB, https://amdb.online) is a freely accessible database with built-in statistical analysis tools, allowing one to browse and compare quantitative metabolomics data and raw NMR and MS data, as well as sample metadata, with a focus on the metabolite concentrations rather than on the raw data itself. AMDB also functions as a platform for the metabolomics community, providing convenient deposition and exchange of quantitative metabolomic data. To date, the majority of the data in AMDB relate to the metabolite content of the eye lens and blood of vertebrates, primarily wild species from Siberia, Russia and laboratory rodents. However, data on other tissues (muscle, heart, liver, brain, and more) are also present, and the list of species and tissues is constantly growing. Typically, every sample in AMDB contains concentrations of 60-90 of the most abundant metabolites, provided in nanomoles per gram of wet tissue weight (nmol/g). We believe that AMDB will become a widely used tool in the community, as typical metabolite baseline concentrations in tissues of animal models will aid in a wide variety of fundamental and applied scientific fields, including, but not limited to, animal modeling of human diseases, assessment of medical formulations, and evolutionary and environmental studies.PMID:37887413 | DOI:10.3390/metabo13101088

Metabolites. 2023 Oct 17;13(10):1086. doi: 10.3390/metabo13101086.ABSTRACTGrowing evidence indicates that metabolites and energy metabolism play an active rather than consequential role in regulating cellular fate. Cardiac development requires dramatic metabolic remodeling from relying primarily on glycolysis in pluripotent stem cells (PSCs) to oxidizing a wide array of energy substrates to match the high bioenergetic demands of continuous contraction in the developed heart. However, a detailed analysis of how remodeling of energy metabolism contributes to human cardiac development is lacking. Using dynamic multiple reaction monitoring metabolomics of central carbon metabolism, we evaluated temporal changes in energy metabolism during human PSC 3D cardiac lineage specification. Significant metabolic remodeling occurs during the complete differentiation, yet temporal analysis revealed that most changes occur during transitions from pluripotency to mesoderm (day 1) and mesoderm to early cardiac (day 5), with limited maturation of cardiac metabolism beyond day 5. Real-time metabolic analysis demonstrated that while hPSC cardiomyocytes (hPSC-CM) showed elevated rates of oxidative metabolism compared to PSCs, they still retained high glycolytic rates, confirming an immature metabolic phenotype. These observations support the opportunity to metabolically optimize the differentiation process to support lineage specification and maturation of hPSC-CMs.PMID:37887411 | DOI:10.3390/metabo13101086

Metabolites. 2023 Oct 13;13(10):1079. doi: 10.3390/metabo13101079.ABSTRACTIn this investigation, we outline the applications of a data mining technique known as Subgroup Discovery (SD) to the analysis of a sample size-limited metabolomics-based dataset. The SD technique utilized a supervised learning strategy, which lies midway between classificational and descriptive criteria, in which given the descriptive property of a dataset (i.e., the response target variable of interest), the primary objective was to discover subgroups with behaviours that are distinguishable from those of the complete set (albeit with a differential statistical distribution). These approaches have, for the first time, been successfully employed for the analysis of aromatic metabolite patterns within an NMR-based urinary dataset collected from a small cohort of patients with the lysosomal storage disorder Niemann-Pick class 1 (NPC1) disease (n = 12) and utilized to distinguish these from a larger number of heterozygous (parental) control participants. These subgroup discovery strategies discovered two different NPC1 disease-specific metabolically sequential rules which permitted the reliable identification of NPC1 patients; the first of these involved 'normal' (intermediate) urinary concentrations of xanthurenate, 4-aminobenzoate, hippurate and quinaldate, and disease-downregulated levels of nicotinate and trigonelline, whereas the second comprised 'normal' 4-aminobenzoate, indoxyl sulphate, hippurate, 3-methylhistidine and quinaldate concentrations, and again downregulated nicotinate and trigonelline levels. Correspondingly, a series of five subgroup rules were generated for the heterozygous carrier control group, and 'biomarkers' featured in these included low histidine, 1-methylnicotinamide and 4-aminobenzoate concentrations, together with 'normal' levels of hippurate, hypoxanthine, quinolinate and hypoxanthine. These significant disease group-specific rules were consistent with imbalances in the combined tryptophan-nicotinamide, tryptophan, kynurenine and tyrosine metabolic pathways, along with dysregulations in those featuring histidine, 3-methylhistidine and 4-hydroxybenzoate. In principle, the novel subgroup discovery approach employed here should also be readily applicable to solving metabolomics-type problems of this nature which feature rare disease classification groupings with only limited patient participant and sample sizes available.PMID:37887404 | DOI:10.3390/metabo13101079

Metabolites. 2023 Oct 13;13(10):1075. doi: 10.3390/metabo13101075.ABSTRACTThe untargeted metabolomics analysis of biological samples with nuclear magnetic resonance (NMR) provides highly complex data containing various signals from different molecules. To use these data for classification, e.g., in the context of food authentication, machine learning methods are used. These methods are usually applied as a black box, which means that no information about the complex relationships between the variables and the outcome is obtained. In this study, we show that the random forest-based approach surrogate minimal depth (SMD) can be applied for a comprehensive analysis of class-specific differences by selecting relevant variables and analyzing their mutual impact on the classification model of different truffle species. SMD allows the assignment of variables from the same metabolites as well as the detection of interactions between different metabolites that can be attributed to known biological relationships.PMID:37887402 | DOI:10.3390/metabo13101075