PubMed

Metabolites. 2024 Nov 20;14(11):645. doi: 10.3390/metabo14110645.ABSTRACTBackground/Objectives: Leflunomide, an isoxazole immunosuppressant, is widely used in the treatment of diseases such as rheumatoid arthritis (RA) and psoriatic arthritis (PsA) as well as lupus nephritis (LN). In recent years, clinical data have shown that some patients have obvious weight loss, liver injury, and other serious adverse reactions after taking leflunomide. However, the causes and mechanisms by which leflunomide reduces weight are unclear. Methods: Therefore, we used a mouse animal model to administer leflunomide, and we observed that the weight of mice in the leflunomide experimental group was significantly reduced (p < 0.01). In this animal experiment, a metabolomic method was used to analyze the livers of the mice in the experimental group and found that the main difference in terms of metabolic pathways was in the metabolism of aromatic amino acids, and it was confirmed that leflunomide can inhibit the limitations of phenylalanine, tyrosine, and tryptophan biosynthesis. Results: Our study revealed that leflunomide inhibited the activity of DAHPS in the gut microbiota, disrupting the metabolism of phenylalanine, tyrosine, and tryptophan, as well as the metabolism of carbohydrates and lipids. Leflunomide also increased endoplasmic reticulum stress by activating the PERK pathway, thereby promoting CHOP expression and increasing apoptosis-induced liver damage. Conclusions: These effects may be related to the observed weight loss induced by leflunomide.PMID:39590880 | DOI:10.3390/metabo14110645

Metabolites. 2024 Nov 20;14(11):641. doi: 10.3390/metabo14110641.ABSTRACTBackground: Diabetic kidney disease (DKD) is a major complication of diabetes leading to kidney failure. Methods: This study investigates lipid metabolism profiles of long-standing DKD (LDKD, diabetes duration > 10 years) by integrative analysis of available single-cell RNA sequencing and spatial multi-omics data (focusing on spatial continuity samples) from the Kidney Precision Medicine Project. Results: Two injured cell types, an injured thick ascending limb (iTAL) and an injured proximal tubule (iPT), were identified and significantly elevated in LDKD samples. Both iTAL and iPT exhibit increased lipid metabolic and biosynthetic activities and decreased lipid and fatty acid oxidative processes compared to TAL/PT cells. Notably, compared to PT, iPT shows significant upregulation of specific injury and fibrosis-related genes, including FSHR and BMP7. Meanwhile, comparing iTAL to TAL, inflammatory-related genes such as ANXA3 and IGFBP2 are significantly upregulated. Furthermore, spatial metabolomics analysis reveals regionally distributed clusters in the kidney and notably differentially expressed lipid metabolites, such as triglycerides, glycerophospholipids, and sphingolipids, particularly pronounced in the inner medullary regions. Conclusions: These findings provide an integrative description of the lipid metabolism landscape in LDKD, highlighting injury-associated cellular processes and potential molecular mechanisms.PMID:39590877 | DOI:10.3390/metabo14110641

Metabolites. 2024 Nov 19;14(11):638. doi: 10.3390/metabo14110638.ABSTRACTAlterations in metabolism caused by SARS-CoV-2 infection have been highlighted in various investigations and have been used to search for biomarkers in different biological matrices. However, the selected biomarkers vary greatly across studies. Our objective is to provide a robust selection of biomarkers, including results from different sample treatments in the analysis of volatile organic compounds (VOCs) present in urine samples from patients with COVID-19. Between September 2021 and May 2022, urine samples were collected from 35 hospitalized COVID-19 patients and 32 healthy controls. The samples were analyzed by headspace (HS) solid phase microextraction (SPME) coupled to gas chromatography-mass spectrometry (GC-MS). Analyses were conducted on untreated urine samples and on samples that underwent specific pretreatments: lyophilization and treatment with sulfuric acid. Partial Least Squares Linear Discriminant Analysis (PLS-LDA) and Subwindow Permutation Analysis (SPA) models were established to distinguish patterns between COVID-19 patients and healthy controls. The results identify compounds that are present in different proportions in urine samples from COVID-19 patients compared to those from healthy individuals. Analysis of urine samples using HS-SPME-GC-MS reveals differences between COVID-19 patients and healthy individuals. These differences are more pronounced when methods that enhance VOC formation are used. However, these pretreatments can cause reactions between sample components, creating additional products or removing compounds, so biomarker selection could be altered. Therefore, using a combination of methods may be more informative when evaluating metabolic alterations caused by viral infections and would allow for a better selection of biomarkers.PMID:39590874 | DOI:10.3390/metabo14110638

Metabolites. 2024 Nov 18;14(11):637. doi: 10.3390/metabo14110637.ABSTRACTBACKGROUND/OBJECTIVES: Dynamic changes in components from colostrum to mature milk occur in any mammal. However, the time it takes to reach the mature milk stage differs between taxa and species, as do the final concentrations of all the components. The white rhinoceros belongs to the family Perissodactyla, of which the milk and milk metabolome of the domesticated Equidae have been studied to some detail. Metabolomic information on the colostrum and milk of the Rhinocerotidae is lacking.METHODS: Colostrum and milk were obtained from seven white rhinoceroses. Of note is that it was their first parturition and all followed the same diet, two factors known to affect colostrum composition and its changes during early lactation in domesticated mammals. Milk serum was prepared by the ultrafiltration of the milk samples. Untargeted 1N NMR spectra were processed with Topspin 3.2, calibration was carried out according to the alanine signal and the identification of signals was carried out with Chenomx and assignments in the literature. Statistical analysis of the data was carried out using MetaboAnalyst 6.0.RESULTS: The changes in the metabolites were followed during the first 7 days of lactation as well as on day 20. The amounts of amino acids and their derivatives, organic acids and lipid metabolites decreased over lactation, while carbohydrates and their derivatives increased. The colostrum phase ended on day 2, while the transition to mature milk seemed to be complete by day 7. From day 3 to 7, galactose metabolism and tyrosine metabolism were uprated. Of interest is the presence of the oligosaccharide 3'-sialyllactose on days 3 and 4 of lactation.CONCLUSIONS: Mainly the content of carbohydrates increased over lactation, specifically lactose. The 3'-sialyllactose content peaked on days 3 and 4 of lactation. The colostrum phase ended on day 2. The mature milk stage was reached by day 7. The galactose metabolism and tyrosine metabolism were uprated after day 3 of lactation.PMID:39590873 | DOI:10.3390/metabo14110637

Metabolites. 2024 Nov 15;14(11):628. doi: 10.3390/metabo14110628.ABSTRACTBackground/Objectives: Sorghum bicolor is a source of many bioactive components, such as polyphenols. Those components are present mainly in its bran, often removed in industrial processes through decortication. In that sense, this work aimed to analyze the polyphenol content, especially free flavonoids, from the bran of a newly developed variety compared to other commercially available varieties. Methods: The samples were white sorghum TDN® Sorgho, red sorghum Mini Sorgho, and the newly developed red sorghum RILN-156. First, decortication was conducted to obtain the bran samples, which were triturated and then sieved. The use of colorimetric methods allowed the general quantification of the polyphenolic components. First, the polyphenol content was extracted using 70% methanol. Then, the samples' total phenolic content, total flavonoid content, total tannin content, total anthocyanin content, and antioxidant potential were determined. To analyze the different components and identify the free flavonoids, an untargeted metabolomics analysis (with liquid chromatography coupled with mass spectrometer (LC/MS) and capillary electrophoresis coupled with a mass spectrometer (CE/MS)) was performed. Results: The results have shown that apart from anthocyanin and tannin, the newly developed variety, RILN-156, presented the highest concentration of polyphenolic content, including a higher antioxidant capacity. The exploratory analysis identified 19 flavonoids within the samples, with galangin and daidzein being the most abundant ones. Conclusions: These results show a promising finding for using this newly developed sorghum variety (RILN-156) industrially and further investigating its health benefits. They also elucidate the differences between colored sorghum within themselves and with white sorghum varieties.PMID:39590864 | DOI:10.3390/metabo14110628

Metabolites. 2024 Nov 14;14(11):624. doi: 10.3390/metabo14110624.ABSTRACTOBJECTIVES: This study investigated metabolic changes associated with bovine leukemia virus (BLV) infection in dairy cows, focusing on pre-parturition alterations.METHODS: Metabolite identification in serum and urine samples was performed using a targeted metabolomics method, employing the TMIC Prime kit in combination with flow injection analysis and liquid chromatography-tandem mass spectrometry.RESULTS: Of 145 cows examined, 42 (28.9%) were BLV-seropositive. Around 38% of infected cows showed high somatic cell counts indicative of subclinical mastitis, with 15 experiencing additional health issues such as ketosis, milk fever, and lameness. Despite these conditions, no significant differences in milk yield or composition were observed between the infected and control groups. Metabolomic analysis conducted at -8 and -4 weeks prepartum revealed significant metabolic differences between BLV-infected and healthy cows. At -8 weeks, 30 serum metabolites were altered, including sphingomyelins, lysophosphatidylcholines, amino acids, and acylcarnitines, suggesting disruptions in membrane integrity, energy metabolism, and immune function indicative of early neoplastic transformations. By -4 weeks, the number of altered metabolites decreased to 17, continuing to reflect metabolic disruptions in cows with leukemia. Multivariate analysis highlighted distinct metabolic profiles between infected and control cows, identifying key discriminating metabolites such as choline, aspartic acid, phenylalanine, and arginine. Urine metabolomics revealed significant prepartum shifts in metabolites related to glucose, asymmetric dimethylarginine, and pyruvic acid, among others.CONCLUSIONS: The research confirmed metabolomics' efficacy in defining a BLV infection metabolic profile, elucidating leukosis-associated metabolic disruptions. This approach facilitates the identification of BLV-infected cows and enhances understanding of infection pathophysiology, providing a foundation for advanced management and intervention strategies in dairy herds. The study underscores the profound impact of leukosis on metabolic processes and highlights urine metabolomics' utility in non-invasively detecting BLV infection, offering the potential for improved herd health management.PMID:39590860 | DOI:10.3390/metabo14110624

Metabolites. 2024 Nov 14;14(11):623. doi: 10.3390/metabo14110623.ABSTRACTBackground: Plants constantly produce primary and secondary metabolites, and a significant fraction of these are volatile organic compounds (VOCs). Factors including the life stage of the plant, temperature, environment, and stress influence the abundance and types of VOCs emitted. The analysis of VOCs released by plants during different stages or with different conditions provides insight into plant metabolism and stress responses. Collecting the VOC profiles of plants in vivo makes it possible to obtain a representative sample of the entire plant volatilome under controlled conditions with minimal invasiveness. In addition, in vivo sampling can also be used to compare the impacts of different environmental conditions or stressors on plants, i.e., the presence/absence of a pest or amount of nitrogen in soil. Methods: In this study, an in vivo plant sampling technique is introduced and validated using active sampling and thermal desorption (TD) tubes with comprehensive two-dimensional gas chromatography coupled to a time-of-flight mass spectrometer (TD-GC×GC-TOFMS). The purpose of this work is to highlight a novel technique to analyze headspace secondary plant metabolites with a minimal invasiveness. Results: It was concluded that in vivo active sampling onto TD tubes provides a wider global coverage of compounds and larger peak areas when compared to extraction by solid-phase microextraction (SPME). Additionally, the Horwitz ratio of active sampling onto TD tubes was 0.893, demonstrating this technique to be a reliable and reproducible method. Lastly, a variety of plants were sampled to assess the versatility of this technique across various plant species with different sizes and volatile profiles. Hundreds of compounds were measured with this analysis, including terpenes, aldehydes, ketones, terpenoids, and alcohols. Conclusions: This novel in vivo active sampling method provides an additional technique for extracting and analyzing volatile secondary plant metabolites.PMID:39590859 | DOI:10.3390/metabo14110623

Metabolites. 2024 Nov 14;14(11):622. doi: 10.3390/metabo14110622.ABSTRACTBackground/Objectives: Targeted metabolomics is often criticized for the limited metabolite coverage that it offers. Indeed, most targeted assays developed or used by researchers measure fewer than 200 metabolites. In an effort to both expand the coverage and improve the accuracy of metabolite quantification in targeted metabolomics, we decided to develop a comprehensive liquid chromatography-tandem mass spectrometry (LC-MS/MS) assay that could quantitatively measure more than 700 metabolites in serum or plasma. Methods: The developed assay makes use of chemical derivatization followed by reverse phase LC-MS/MS and/or direct flow injection MS (DFI-MS) in both positive and negative ionization modes to separate metabolites. Multiple reaction monitoring (MRM), in combination with isotopic standards and multi-point calibration curves, is used to detect and absolutely quantify the targeted metabolites. The assay has been adapted to a 96-well plate format to enable automated, high-throughput sample analysis. Results: The assay (called MEGA) is able to detect and quantify 721 metabolites in serum/plasma, covering 20 metabolite classes and many commonly used clinical biomarkers. The limits of detection were determined to range from 1.4 nM to 10 mM, recovery rates were from 80% to 120%, and quantitative precision was within 20%. LC-MS/MS metabolite concentrations of the NIST® SRM®1950 plasma standard were found to be within 15% of NMR quantified levels. The MEGA assay was further validated in a large dietary intervention study. Conclusions: The MEGA assay should make comprehensive quantitative metabolomics much more affordable, accessible, automatable, and applicable to large-scale clinical studies.PMID:39590858 | DOI:10.3390/metabo14110622

Metabolites. 2024 Nov 14;14(11):621. doi: 10.3390/metabo14110621.ABSTRACTChronobiology is the field of study focused on understanding the temporal patterns of biological functions, specifically examining the regular cycles or oscillations in these processes [...].PMID:39590857 | DOI:10.3390/metabo14110621

Metabolites. 2024 Nov 12;14(11):619. doi: 10.3390/metabo14110619.ABSTRACTBackground: Strains XG04 and XGT2 of Lentinus edodes (Berk.) Singer demonstrate a high degree of genomic similarity, with XGT2 representing a systematic selection of XG04 and exhibiting enhanced phenotypic traits. Methods: An investigation into the differences between these strains was conducted using untargeted metabolomics to identify potential causal factors. Five exogenous inducers were assessed for their relationship with the observed phenotypes, and their impacts on fruiting body characteristics were analyzed. Results: Notably, the exogenous inducer rolipram, at a concentration of 0.4%, was found to increase cAMP expression levels in L. edodes primordia, which subsequently affected gill development, leading to the formation of gill-free fruiting bodies. Morphological differences between the two strains were evident; XG04 exhibited a spherical morphology with absent gills, rendering it commercially unviable, whereas XGT2 displayed a thicker cap and a more robust stipe, maintaining its characteristic umbrella shape. Conclusions: As the concentration of rolipram increased, both cap retraction and gill reduction in XGT2 occurred in a dose-dependent manner. The endogenous cAMP levels in the fruiting bodies were measured before and after rolipram treatment, revealing that the cap retraction and gill reduction in XGT2 progressed in a dose-dependent manner alongside increasing cAMP expression levels. Furthermore, a positive correlation was observed between cAMP levels and rolipram concentration. This study provides a foundation for improving the quality and productivity of mushroom cultivation by manipulating fruiting body characteristics through external stimuli.PMID:39590855 | DOI:10.3390/metabo14110619

Metabolites. 2024 Nov 12;14(11):617. doi: 10.3390/metabo14110617.ABSTRACTBACKGROUND: In 2019, SARS-CoV-2, the novel coronavirus, entered the world scene, presenting a global health crisis with a broad spectrum of clinical manifestations. Recognizing the significance of metabolomics as the omics closest to symptomatology, it has become a useful tool for predicting clinical outcomes. Several metabolomic studies have indicated variations in the metabolome corresponding to different disease severities, highlighting the potential of metabolomics to unravel crucial insights into the pathophysiology of SARS-CoV-2 infection.METHODS: The PRISMA guidelines were followed for this scoping review. Three major scientific databases were searched: PubMed, the Directory of Open Access Journals (DOAJ), and BioMed Central, from 2020 to 2024. Initially, 2938 articles were identified and vetted with specific inclusion and exclusion criteria. Of these, 42 articles were retrieved for analysis and summary.RESULTS: Metabolites were identified that were repeatedly noted to change with COVID-19 and its severity. Phenylalanine, glucose, and glutamic acid increased with severity, while tryptophan, proline, and glutamine decreased, highlighting their association with COVID-19 severity. Additionally, pathway analysis revealed that phenylalanine, tyrosine and tryptophan biosynthesis, and arginine biosynthesis were the most significantly impacted pathways in COVID-19 severity.CONCLUSIONS: COVID-19 severity is intricately linked to significant metabolic alterations that span amino acid metabolism, energy production, immune response modulation, and redox balance.PMID:39590853 | DOI:10.3390/metabo14110617

Metabolites. 2024 Nov 12;14(11):616. doi: 10.3390/metabo14110616.ABSTRACTBackground/Objectives: To investigate the crisping mechanism of broad bean-based crisping diets on Nile Tilapia. Methods: Four crisping diets were designed to feed 360 fish for 90 days, and multiomics analyses were employed. Results: Our results indicated that the designed crisping diets for Nile tilapia can effectively make tilapia muscle crispy. The ingestion of broad bean-based diets induced metabolic reprogramming dominated by glycolytic metabolism inhibition in fish, and metabolic reprogramming is the initiator of muscle structural remodeling. Among these, glucose is the main DAMP to be recognized by cellular PRRs, activating further immune response and oxidative stress and finally resulting in muscle change. Conclusions: Based on our results of multiomics, pck2, and ldh played main roles in crisping molecular mechanisms in driving the initial metabolic reprogram. Moreover, the addition of the crisping package further activated the ErbB signaling pathway and downstream MAPK signaling pathway to strengthen immune response, promoting muscle fiber development and growth. Our study delved into the effects of crisping formula diet on the liver of Nile tilapia at the molecular level, providing theoretical guidance for the nutritional regulation of crispy Nile tilapia.PMID:39590852 | DOI:10.3390/metabo14110616

Metabolites. 2024 Nov 11;14(11):614. doi: 10.3390/metabo14110614.ABSTRACTBackground: Diabetes is a devastating disease that causes millions of deaths. Fermented rice bran (FRB), made by fermenting rice bran with Aspergillus kawachii and a mixture of lactic acid bacteria, was hypothesized to b able to improve diabetes-related symptoms. This study aimed to investigate the effects of FRB supplementation in mitigating type 2 diabetes symptoms and identifying FRB bioactive compounds. Methods: In this study, KK-Ay mice (4 w.o. male) were used as a model for type 2 diabetes. Mice were divided into three different groups. The first group received a control diet, the second received a 12.5% non-fermented rice bran (RB) supplemented diet, and the last group was fed a 12.5% FRB-supplemented diet. Supplementation was done for 4 weeks. Results: FRB supplementation lowered the blood glucose level, OGTT, HOMA-IR, total cholesterol, liver RAGE protein, and glucokinase in KK-Ay mice. Metabolome analysis of RB and FRB showed that fermentation increased bioactive compounds in rice bran, such as GABA, L-theanine, and carnitine. It also increased the levels of various free amino acids while converting some amino acids such as arginine, tyrosine, and tryptophan into other metabolites. Conclusions: This research showed the potency of FRB supplementation as a preventive agent against type 2 diabetes.PMID:39590850 | DOI:10.3390/metabo14110614

Metabolites. 2024 Nov 11;14(11):612. doi: 10.3390/metabo14110612.ABSTRACTCONTEXT: Schinus terebinthifolia Raddi is used in Brazilian folk medicine due to the wound healing and antiseptic properties of its bark, and its fruit are used as a condiment. However, the aerial parts of this plant have been studied and present some bioactive compounds as well.OBJECTIVES: The aim of this study was to investigate the variation in volatile and non-volatile composition of S. terebinthifolia leaves using untargeted metabolomics.MATERIAL AND METHODS: The leaves of four trees were collected over one year; ethanolic extracts were analyzed by UHPLC-MS and fresh leaves were analyzed by GC-MS using HS-SPME. The data were processed using online software.RESULTS: The results suggest seasonality interfered little with the chemical composition of leaves. On the other hand, the sex of the plant clearly determined the chemical composition of both volatile and non-volatile compounds.DISCUSSION AND CONCLUSIONS: Chemical variability between plants with male and female flowers is fundamental information for the standardized use of its leaves. Compounds with important biological activities were putatively identified, confirming the potential use of S. terebinthifolia leaves as a source of bioactive compounds, reducing waste and increasing economic gains for local farmers throughout the year.PMID:39590848 | DOI:10.3390/metabo14110612

Metabolites. 2024 Nov 10;14(11):609. doi: 10.3390/metabo14110609.ABSTRACTBackground/Objectives: Metabolic profiling of tissue samples via liquid-state nuclear magnetic resonance (NMR) requires the extraction of polar metabolites in a suitable deuterated solvent. Such methods often prioritise metabolite recovery over protein removal due to the relatively low sensitivity of NMR metabolomics and the routine use of methods able to supress residual protein signals. However, residual protein may impact metabolite integrity and the metabolite stability after NMR sample preparation is often overlooked. This study aimed to investigate the effect of residual protein contamination in rodent brain extracts and identify a reproducible extraction method that optimises metabolite recovery while ensuring sample stability. Methods: The performance of acetonitrile/water (50-100% MeCN), methanol/water (50-100% MeOH), and methanol/water/chloroform (MeOH/H2O/CHCl3) were assessed for extraction efficiency, reproducibility, residual protein contamination, and metabolite stability up to eight hours post NMR sample preparation. Results: Aspartate and glutamate deuteration were observed in 50% MeCN, 50% MeOH, and 67% MeOH extractions along with the conversion of N-acetyl aspartate to aspartate and acetate in 50% MeCN and 50% MeOH extractions. Both observations correlated with residual protein contamination and, thus, are a result of inadequate protein precipitation, as confirmed by ultrafiltration. MeOH/H2O/CHCl3 extraction preserved the stability of these metabolites while maintaining good extraction efficiency and reproducibility. Conclusions: Thus, we recommend MeOH/H2O/CHCl3 extraction for untargeted brain NMR metabolic profiling due to its effective protein precipitation and reliable performance. Nonetheless, the performance of detecting metabolites prone to oxidation such as ascorbate and glutathione is not improved by this method.PMID:39590845 | DOI:10.3390/metabo14110609

Metabolites. 2024 Nov 8;14(11):606. doi: 10.3390/metabo14110606.ABSTRACTThis review presents the latest research on chromatography-based metabolomics for bioorganic research of honey, considering targeted, suspect, and untargeted metabolomics involving metabolite profiling and metabolite fingerprinting. These approaches give an insight into the metabolic diversity of different honey varieties and reveal different classes of organic compounds in the metabolic profiles, among which, key metabolites such as biomarkers and bioactive compounds can be highlighted. Chromatography-based metabolomics strategies have significantly impacted different aspects of bioorganic research, including primary areas such as botanical origins, honey origin traceability, entomological origins, and honey maturity. Through the use of different tools for complex data analysis, these strategies contribute to the detection, assessment, and/or correlation of different honey parameters and attributes. Bioorganic research is mainly focused on phytochemicals and their transformation, but the chemical changes that can occur during the different stages of honey formation remain a challenge. Furthermore, the latest user- and environmentally friendly sample preparation methods and technologies as well as future perspectives and the role of chromatography-based metabolomic strategies in honey characterization are discussed. The objective of this review is to summarize the latest metabolomics strategies contributing to bioorganic research onf honey, with emphasis on the (i) metabolite analysis by gas and liquid chromatography techniques; (ii) key metabolites in the obtained metabolic profiles; (iii) formation and accumulation of biogenic volatile and non-volatile markers; (iv) sample preparation procedures; (v) data analysis, including software and databases; and (vi) conclusions and future perspectives. For the present review, the literature search strategy was based on the PRISMA guidelines and focused on studies published between 2019 and 2024. This review outlines the importance of metabolomics strategies for potential innovations in characterizing honey and unlocking its full bioorganic potential.PMID:39590842 | DOI:10.3390/metabo14110606

Metabolites. 2024 Nov 8;14(11):603. doi: 10.3390/metabo14110603.ABSTRACTThe circular RNA circH19 has been implicated in the regulation of gene expression and various biological processes, including obesity. Objectives: This study aimed to elucidate the metabolic changes in plasma after circH19 knockdown in a diet-induced obese (DIO) mouse model. Methods: Plasma samples were collected following the intervention and subjected to non-targeted metabolomics analysis using liquid chromatography-mass spectrometry (LC-MS). Metabolic profiling was performed to identify and quantify metabolites, followed by multivariate statistical analysis to discern differential metabolic signatures. Results: A total of 1250 features were quantified, resulting in the upregulation of 564 metabolites and the downregulation of 686 metabolites in the circH19 knockdown group compared to the control mice. Metabolic pathway analysis revealed disruptions in lipid metabolism, amino acid turnover, and energy production pathways. Notably, the intervention led to a substantial decrease in circulating lipids and alterations in the plasma amino acid profile, indicative of an impact on protein catabolism and anabolic processes. The observed shifts in lipid and amino acid metabolism suggest potential therapeutic avenues for obesity and related metabolic disorders. Conclusions: The circH19 knockdown in DIO mice led to significant alterations in plasma metabolites, highlighting its potential role in the regulation of obesity and metabolic disorders.PMID:39590839 | DOI:10.3390/metabo14110603

Metabolites. 2024 Nov 7;14(11):602. doi: 10.3390/metabo14110602.ABSTRACTBackground: Untargeted lipidomics using collision-induced dissociation-based tandem mass spectrometry (CID-MS/MS) is essential for biological and clinical applications. However, annotation confidence still relies on manual curation by analytical chemists, despite the development of various software tools for automatic spectral processing based on rule-based fragment annotations. Methods: In this study, we present a novel machine learning model, MS2Lipid, for the prediction of known lipid subclasses from MS/MS queries, providing an orthogonal approach to existing lipidomics software programs in determining the lipid subclass of ion features. We designed a new descriptor, MCH (mode of carbon and hydrogen), to increase the specificity of lipid subclass prediction in nominal mass resolution MS data. Results: The model, trained with 6760 and 6862 manually curated MS/MS spectra for the positive and negative ion modes, respectively, classified queries into one or several of 97 lipid subclasses, achieving an accuracy of 97.4% in the test set. The program was further validated using various datasets from different instruments and curators, with the average accuracy exceeding 87.2%. Using an integrated approach with molecular spectral networking, we demonstrated the utility of MS2Lipid by annotating microbiota-derived esterified bile acids, whose abundance was significantly increased in fecal samples of obese patients in a human cohort study. This suggests that the machine learning model provides an independent criterion for lipid subclass classification, enhancing the annotation of lipid metabolites within known lipid classes. Conclusions: MS2Lipid is a highly accurate machine learning model that enhances lipid subclass annotation from MS/MS data and provides an independent criterion.PMID:39590838 | DOI:10.3390/metabo14110602

Metabolites. 2024 Nov 7;14(11):600. doi: 10.3390/metabo14110600.ABSTRACTBackground: Ovarian cancer (OC) remains one of the most lethal cancers among women due to most cases going undiagnosed until later stages. The early detection and treatment of this malignancy provides the best prognosis, but the lack of an accurate and sensitive screening tool combined with ambiguous symptoms hinders these diagnoses. In contrast, screening for cervical cancer via Papanicolaou (Pap) tests is a widespread practice that greatly reduces the cancer's mortality rates. Interestingly, previous studies show evidence of OC cells in Pap tests, suggesting that proteins, and potentially lipids, shed from ovarian tumors end up in the cervix. The goal of this study is to evaluate the practicality of using Pap tests as biospecimens for OC-screening-related metabolomics. Methods: To evaluate the effectiveness of using residual Pap test samples as biospecimens for potential metabolomics work, 29 Pap test samples, collected from women over the age of 50 with normal cytology and no visible blood contamination, were first obtained from the University of Minnesota, with IRB approval. These samples were centrifuged to recover the cell pellets from the supernatants. The cell pellets underwent a biphasic extraction, followed by an RP-LC-MS analysis, while the supernatants underwent two separate extractions and analyses, including RP-LC-MS and HILIC-LC-MS. Non-targeted features were detected in the range of 220-1000 m/z to determine the sensitivity and scope of the various extraction and analytical workflows, as well as evaluating residual Pap test samples as viable metabolomics biospecimens. Results: The biphasic extraction and subsequent RP-LC-MS analysis of the isolated cell pellets from all 29 samples yielded informative, exploratory data, highlighting the potential of using residual Pap test samples as biospecimens for metabolomics, specifically lipidomics, studies. Each sample was analyzed in both the positive and negative ion mode, yielding the detection of 7318 in the positive ion mode and 3733 in the negative ion mode. Using multiple reference libraries, 22.85% and 36.19% of these features were annotated in the positive and negative ion mode, respectively. Among these detected features, 453 unique lipids, representative of 20 different lipid subclasses, were annotated in all 29 samples. Of the various lipid subclasses represented from the detected lipids, ceramides, triacylglycerols, hexosylceramides, and phosphatidylcholines contributed to over half (53.3%) of the detected lipids at 16.2%, 13.0%, 12.8%, and 11.3%, respectively. Conclusions: The detection of these 453 common lipids across all patients establishes a relative lipidome baseline for women over the age of 50 with normal cervical cytology. This exploratory study is the first investigation to utilize residual Pap test samples as biospecimens in a metabolomics/lipidomics workflow.PMID:39590836 | DOI:10.3390/metabo14110600

Metabolites. 2024 Nov 7;14(11):598. doi: 10.3390/metabo14110598.ABSTRACTBackground: Long COVID-19 has been characterized by the presence of symptoms lasting longer than 4 weeks after the acute infection. The pathophysiology of clinical manifestations still lacks knowledge. Objective: The objective of this paper was to evaluate metabolite abundance in the saliva of long COVID patients 60 days after hospital discharge. Methods: A convenience sample was composed of 30 post-discharge patients with long COVID and seven non-COVID-19 controls. All COVID-19 patients were evaluated by demographic characteristics, spirometry, 6 min walk test (6mWT), Saint George Respiratory Questionnaire (SGRQ), and body composition. Metabolomics was performed on saliva. Results: The long COVID-19 patients were 60.4 ± 14.3 years-old, and 66% male. Their lean body mass was 30.7 ± 7.3 kg and fat mass, 34.4 ± 13.7 kg. Spirometry evaluation showed forced vital capacity (FVC) of 3.84 ± 0.97 L with 96.0 ± 14.0% of the predicted value, and forced expiratory volume in the first second (FEV1) of 3.11 ± 0.83 L with 98.0 ± 16.0 of the predicted value. The long COVID-19 patients had reduced maximal inspiratory (90.1 ± 31.6 cmH2O) and maximal expiratory (97.3 ± 31.0 cmH2O) pressures. SGRQ showed domain symptoms of 32.3 ± 15.2, domain activities of 41.9 ± 25.6, and domain impact 13.7 ± 11.4, with a mean of 24.3 ± 14.9%. Physical capacity measured by distance covered in the 6mWT was 418.2 ± 130 m with a 73.3% (22.3-98.1) predictive value. The control group consisted of 44.1 ± 10.7-year-old men with a body mass index of 26.5 ± 1.66 Kg/m2. Metabolomics revealed 19 differentially expressed metabolites; expression was lower in 16 metabolites, and 2 metabolites were absent in the COVID-19 patients compared to controls. Calenduloside G methyl ester (p = 0.03), Gly Pro Lys (p = 0.0001), and creatine (p = 0.0001) expressions were lower in patients than controls. Conclusions: Long COVID-19 patients present less abundance of calenduloside G methyl ester, Gly Pro Lys, and creatine in saliva than healthy controls. Lower creatine abundance may be related to reduced physical capacity and fatigue.PMID:39590834 | DOI:10.3390/metabo14110598