Liquid Chromatography-mass Spectrometry-based Metabolomics Research Articles

Application of molecular networking to improve the compound annotation in liquid chromatography-mass spectrometry-based metabolomics analysis: A case study of Bupleuri radix.

Compound annotation is always a challenging step in metabolomics studies. The molecular networking strategy has been developed recently to organize the relationship between compounds as a network based on their tandem mass (MS2) spectra similarity, which can be used to improve compound annotation in metabolomics analysis. This study used Bupleuri Radix from different geographic areas to evaluate the performance of molecular networking strategy for compound annotation in liquid chromatography-mass spectrometry (LC-MS)-based metabolomics. The Bupleuri Radix extract was analyzed by LC-quadrupole time-of-flight MS under MSe acquisition mode. After raw data preprocessing, the resulting dataset was used for statistical analysis, including principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA). The chemical makers related to the sample growth place were selected using variable importance in projection (VIP) > 2, fold change (FC) > 2, and p < 0.05. The molecular networking analysis was applied to conduct the compound annotation. The score plots of PCA showed that the samples were classified into two clusters depending on their growth place. Then, the PLS-DA model was constructed to explore the chemical changes of the samples further. Sixteen compounds were selected as chemical makers and tentatively annotated by the feature-based molecular networking (FBMN) analysis. The results showed that the molecular networking method fully exploits the MS information and is a promising tool for facilitating compound annotation in metabolomics studies. However, the software used for feature extraction influenced the results of library searching and molecular network construction, which need to be taken into account in future studies.

Identification of 1-Methylnicotinamide as a specific biomarker for the progression of cirrhosis to hepatocellular carcinoma

PurposeHepatocellular carcinoma (HCC) is a prevalent malignant tumor, often arising from hepatitis induced by the hepatitis B virus (HBV) in China. However, effective biomarkers for early diagnosis are lacking, leading to a 5-year overall survival rate of less than 20% among patients with advanced HCC. This study aims to identify serum biomarkers for early HCC diagnosis to enhance patient survival rates.MethodsWe established an independent cohort comprising 27 healthy individuals, 13 patients with HBV-induced cirrhosis, 13 patients with hepatitis B-type HCC, and 8 patients who progressed from cirrhosis to hepatocellular carcinoma during follow-up. Serum metabolic abnormalities during the progression from cirrhosis to HCC were studied using untargeted metabolomics. Liquid chromatography-mass spectrometry-based metabolomics methods characterized the subjects’ serum metabolic profiles. Partial least squares discriminant analysis (PLS-DA) was employed to elucidate metabolic profile changes during the progression from cirrhosis to HCC. Differentially expressed metabolites (DEMs) between cirrhosis and HCC groups were identified using the LIMMA package in the R language. Two machine learning algorithms, Least Absolute Shrinkage and Selection Operator (LASSO), and Random Forest Classifier (RF), were used to identify key metabolic biomarkers involved in the progression from cirrhosis to HCC. Key metabolic biomarkers were further validated using targeted metabolomics in a new independent validation cohort comprising 25 healthy individuals and 25 patients with early-stage hepatocellular carcinoma.ResultsA total of 155 serum metabolites were identified, of which 21/54 metabolites exhibited significant changes in HCC patients compared with cirrhosis patients and healthy individuals, respectively. PLS-DA clustering results demonstrated a significant change trend in the serum metabolic profile of patients with HBV-induced cirrhosis during the progression to HCC. Utilizing LASSO regression and RF algorithms, we confirmed 10 key metabolic biomarkers. Notably, 1-Methylnicotinamide (1-MNAM) exhibited a persistent and significant decrease in healthy individuals, cirrhosis, and HCC patients. Moreover, 1-MNAM levels in developing patients were significantly higher during the cirrhosis stage than in the HCC stage. Targeted metabolomic validation in an external cohort further confirmed the good diagnostic performance of 1-MNAM in early HCC detection.ConclusionOur findings imply that 1-MNAM may be a specific biomarker for the progression of cirrhosis to HCC.

Endometrial microbial dysbiosis and metabolic alteration promote the development of endometrial cancer.

Emerging evidence suggests that the endometrial microbiome plays important roles in the development of endometrial cancer (EC). Here, we evaluate stage-specific roles of microbial dysbiosis and metabolic disorders in patients with EC, patients with endometrial hyperplasia (EH), and patients afflicted with benign uterine conditions (CK). This prospective cohort study included 33 women with EC, 15 women with endometrial EH, and 15 women with benign uterine conditions (CK) from November 2022 to September 2023. Different typical endometrial samples were imaged with a scanning electron microscope and a transmission electron microscope. The endometrial microbiome was assessed by sequencing the V3-V4 region of the 16S rRNA gene and the ITS1 to fill the gap in relation to the study of the uterine fungal microbiome. Moreover, liquid chromatography-mass spectrometry-based metabolomics was used to identify and quantify metabolic changes among these groups. The endometrial microbiome revealed that there is a structural microbiome shift and an increase in the α-diversity in the EC and EH cases, distinguishable from the benign cases, especially the fungal community structure. The fungal microbiome from patients with EC and EH was altered relative to controls and dominated by Penicillium sp. By contrast, Sarocladium was more abundant in controls. Significant differences were observed in the composition and content of compounds between benign cases and EC, especially estradiol-like metabolism-related substances. Altered microbiota was correlated with the concentrations of interleukin-6 (IL-6), IL-11, transforming growth factor-beta, and β-glucuronidase activity especially the relative abundance increase of Penicillium sp. This study suggested that the endometrial microbiome is complicit in modulating the development of EC such as estrogen activity and a pro-inflammatory response. Our work provides a new insight into the endometrial microbiome from a perspective of stages, which opens up new avenues for EC prognosis and therapy.

Intercropping with maize and sorghum-induced saikosaponin accumulation in Bupleurum chinense DC. by liquid chromatography-mass spectrometry-based metabolomics.

Bupleuri Radix is an important medicinal plant, which has been used in China and other Asian countries for thousands of years. Cultivated Bupleurum chinense DC. (B. chinense) is the main commodity of Bupleuri Radix. The benefits of intercropping with various crops for B. chinense have been recognized; however, the influence of intercropping on the chemical composition of B. chinense is still unclear yet. In this study, intercropping with sorghum and maize exhibited little effect on the root length, root diameter, and single root mass of B. chinense. Only the intercropping with sorghum increased the root length of B. chinense slightly compared to the monocropping. In addition, 200 compounds were identified by UHPLC-Q-TOF-MS, and metabolomic combined with the Venn diagram and heatmap analysis showed apparent separation between the intercropped and monocropped B. chinense samples. Intercropping with sorghum and maize could both increase the saikosaponins, fatty acyls, and organic acids in B. chinense while decreasing the phospholipids. The influence of intercropping on the saikosaponin biosynthesis was probably related with the light intensity and hormone levels in B. chinense. Moreover, we found intercropping increased the anti-inflammatory activity of B. chinense. This study provides a scientific reference for the beneficial effect of intercropping mode of B. chinense.

Liquid Chromatography-Mass Spectrometry-Based Metabolomics Reveals Dynamic Metabolite Changes during Early Postmortem Aging of Donkey Meat.

Metabolic changes in donkey meat during the early postmortem period have not been previously reported. The LC-MS-based metabolomics technique was conducted to understand the metabolic profiles and identify the key metabolites of donkey meat in the first 48 h postmortem. The pH values showed a decreasing trend followed by an increasing trend. Shear force was the lowest at 4 h and the highest at 24 h (p < 0.05). For the metabolome, some candidate biomarker metabolites were identified, such as adenine, inosine, n-acetylhistidine, citric acid, isocitrate, and malic acid. Predominant metabolic pathways, such as citrate cycle (TCA cycle), alanine, aspartate and glutamate metabolism, and purine metabolism, were affected by aging time. Overabundant n-acetylhistidine was identified in LT, declined at 12 h postmortem aging, and then increased. This may explain the significantly lower pH at 12 h postmortem. Adenine was higher at 4 h postmortem, then declined. Decreased ADP may indicate a fast consumption of ATP and subsequent purine metabolism in donkey meat. The results of this study provided new insights into early postmortem aging of donkey meat quality.

Reducing Quantitative Uncertainty Caused by Data Processing in Untargeted Metabolomics.

Processing liquid chromatography-mass spectrometry-based metabolomics data using computational programs often introduces additional quantitative uncertainty, termed computational variation in a previous work. This work develops a computational solution to automatically recognize metabolic features with computational variation in a metabolomics data set. This tool, AVIR (short for "Accurate eValuation of alIgnment and integRation"), is a support vector machine-based machine learning strategy (https://github.com/HuanLab/AVIR). The rationale is that metabolic features with computational variation have a poor correlation between chromatographic peak area and peak height-based quantifications across the samples in a study. AVIR was trained on a set of 696 manually curated metabolic features and achieved an accuracy of 94% in a 10-fold cross-validation. When tested on various external data sets from public metabolomics repositories, AVIR demonstrated an accuracy range of 84%-97%. Finally, tested on a large-scale metabolomics study, AVIR clearly indicated features with computational variation and thus guided us to manually correct them. Our results show that 75.3% of the samples with computational variation had a relative intensity difference of over 20% after correction. This demonstrates the critical role of AVIR in reducing computational variation to improve quantitative certainty in untargeted metabolomics analysis.

Dysregulation of acyl carnitines, pentose phosphate pathway and arginine and ornithine metabolism are associated with decline in intrinsic capacity in Chinese older adults

BackgroundIntrinsic capacity is the combination of individual physical and mental abilities, reflecting the aging degree of the older adults. However, the mechanisms and metabolic characteristics of the decline in intrinsic capacity are still unclear.AimsTo identify metabolic signatures and associated pathways of decline in intrinsic capacity based on the metabolite features.MethodsWe recruited 70 participants aged 77.19 ± 8.31 years. The five domains of intrinsic capacity were assessed by Short Physical Performance Battery (for mobility), Montreal cognition assessment (for cognition), 30-Item Geriatric Depression Scale (for psychology), self-reported hearing/visual impairment (for sensory) and Nutritional risk screening (for vitality), respectively. The serum samples of participants were analyzed by liquid chromatography-mass spectrometry-based metabolomics, followed by metabolite set enrichment analysis and metabolic pathway analysis.ResultsThere were 50 participants with a decline in intrinsic capacity in at least one of the domains. A total of 349 metabolites were identified from their serum samples. Overall, 24 differential metabolites, 5 metabolite sets and 13 pathways were associated with the decline in intrinsic capacity.DiscussionOur results indicated that decline in intrinsic capacity had unique metabolomic profiles.ConclusionThe specific change of acyl carnitines was observed to be a feature of decline in intrinsic capacity. Dysregulation of the pentose phosphate pathway and of arginine and ornithine metabolism was strongly associated with the decline in intrinsic capacity.

Exploration of urine metabolic biomarkers for new-onset, untreated pediatric epilepsy: A gas and liquid chromatography mass spectrometry-based metabolomics study

ObjectiveThe discovery of objective indicators for recent epileptic seizures will help confirm the diagnosis of epilepsy and evaluate therapeutic effects. Past studies had shortcomings such as the inclusion of patients under treatment and those with various etiologies that could confound the analysis results significantly. We aimed to minimize such confounding effects and to explore the small molecule biomarkers associated with the recent occurrence of epileptic seizures using urine metabolomics. MethodsThis is a multicenter prospective study. Subjects included pediatric patients aged 2 to 12 years old with new-onset, untreated epilepsy, who had had the last seizure within 1 month before urine collection. Controls included healthy children aged 2 to 12 years old. Those with underlying or chronic diseases, acute illnesses, or recent administration of medications or supplements were excluded. Targeted metabolome analysis of spot urine samples was conducted using gas chromatography (GC)- and liquid chromatography (LC)-tandem mass spectrometry (MS/MS). ResultsWe enrolled 17 patients and 21 controls. Among 172 metabolites measured by GC/MS/MS and 41 metabolites measured by LC/MS/MS, only taurine was consistently reduced in the epilepsy group. This finding was subsequently confirmed by the absolute quantification of amino acids. No other metabolites were consistently altered between the two groups. ConclusionsUrine metabolome analysis, which covers a larger number of metabolites than conventional biochemistry analyses, found no consistently altered small molecule metabolites except for reduced taurine in epilepsy patients compared to healthy controls. Further studies with larger samples, subjects with different ages, expanded target metabolites, and the investigation of plasma samples are required.

Integrated metabolomics and network pharmacology to reveal the lipid-lowering mechanisms of Qizha Shuangye granules in hyperlipidemic rats.

Qizha Shuangye granules (QSG) comprise six traditional Chinese herbal medicines (TCHMs), which have a long history of treating hyperlipidemia (HLP) in China. This study aimed to evaluate the potential lipid-lowering effects of QSG in an HLP rat model and investigate possible mechanisms. The HLP rat model was induced by a high-fat diet. Lipid-related indicators in serum were detected. Serum and liver metabolites were investigated using a liquid chromatography-mass spectrometry-based metabolomics approach. A herb-compound-target-metabolite (H-C-T-M) network was further constructed to reveal the possible molecular mechanism of QSG to alleviate HLP. The administration of QSG inhibited the HLP-induced changes in total cholesterol, triglyceride, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, and non-esterified fatty acid (NEFA) levels. Additionally, QSG significantly attenuated the liver histopathological changes induced by HLP. Metabolomic analysis showed the serum and liver metabolic disorders presented in HLP rats. QSG can reverse the abnormal metabolism caused by HLP. Through network pharmacology analysis, key proteins such as androgen receptor, 3-hydroxy-3-methylglutaryl-CoA reductase, and peroxisome proliferator-activated receptor-α were screened out, and they were speculated to be possible therapeutic targets for QSG to treat HLP. The present study integrated metabolomics and network pharmacology analysis to reveal the efficacy and possible mechanism of QSG in treating HLP, which provides a new reference for the research and development of QSG as a functional food. © 2023 Society of Chemical Industry.

Comparative untargeted and targeted metabonomics reveal discriminations in metabolite profiles between Mycoplasma capricolum subsp. capripneumoniae and Mycoplasma capricolum subsp. capricolum.

Mycoplasmas are among the smallest prokaryotic microbes that can grow and proliferate on non-living media. They have reduced genomes, which may be associated with a concomitant reduction in their metabolic capacity. Mycoplasma capricolum subsp. capripneumoniae (Mccp) and Mycoplasma capricolum subsp. capricolum (Mcc), both belong to the Mycoplasma mycoides cluster, are significant important pathogenic Mycoplasma species in veterinary research field. They share high degree of genome homology but Mcc grows markedly faster and has higher growth titer than Mccp. This study investigated the metabolites of these two pathogenic bacteria from the middle and late stages of the logarithmic growth phase through liquid chromatography-mass spectrometry-based metabolomics and targeted energy metabolomics. The multivariate analysis was conducted to identify significant differences between the two important Mycoplasma species. A total of 173 metabolites were identified. Of them, 33 and 34 metabolites involved in purine and pyrimidine, pyruvate metabolism, and amino acid synthesis were found to significantly differ in the middle and late stages, respectively. The abundance of fructose 1,6-bisphosphate, ADP, and pyruvate was higher in Mcc than in Mccp during the whole logarithmic period. Lactate was upregulated in slow-growing Mccp. The pH buffering agent N-[2-hydroxyethyl]piperazine-N'-[2-ethanesulfonic acid] added to media effectively prevented pH reduction and increase bacterial viability and protein biomass. The multivariate analysis revealed that the two Mycoplasma species significantly differed in glucose metabolism, growth factor transport and metabolism, cholesterol utilization, and environmental regulation. The study data are beneficial for understanding the metabolomic characteristics of these two crucial Mycoplasma species and shedding more light on mycoplasma metabolism, and serve as a resource for the pathogenesis and development of related vaccines.

Liquid chromatography-mass spectrometry-based metabolomics and fluxomics reveals the metabolic alterations in glioma U87MG multicellular tumor spheroids versus two-dimensional cell cultures.

Multicellular tumor spheroids (MCTSs) that reconstitute the metabolic characteristics of in vivo tumor tissue may facilitate the discovery of molecular biomarkers and effective anticancer therapies. However, little is known about how cancer cells adapt their metabolic changes in complex three-dimensional (3D) microenvironments. Here, using the two-dimensional (2D) cell model as control, the metabolic phenotypes of glioma U87MG multicellular tumor spheroids were systematically investigated based on static metabolomics and dynamic fluxomics analysis. A liquid chromatography-mass spectrometry-based global metabolomics and lipidomics approach was adopted to survey the cellular samples from 2D and 3D culture systems, revealing marked molecular differences between them. Then, by means of metabolomic pathway analysis, the metabolic pathways altered in glioma MCTSs were found using 13 C6 -glucose as a tracer to map the metabolic flux of glycolysis, the tricarboxylic acid (TCA) cycle, de novo nucleotide synthesis, and de novo lipid biosynthesis in the MCTS model. We found nine metabolic pathways as well as glycerolipid, glycerophospholipid and sphingolipid metabolism to be predominantly altered in glioma MCTSs. The reduced nucleotide metabolism, amino acid metabolism and glutathione metabolism indicated an overall lower cellular activity in MCTSs. Through dynamic fluxomics analysis in the MCTS model, we found that cells cultured in MCTSs exhibited increased glycolysis activity and de novo lipid biosynthesis activity, and decreased the TCA cycle and de novo purine nucleotide biosynthesis activity. Our study highlights specific, altered biochemical pathways in MCTSs, emphasizing dysregulation of energy metabolism and lipid metabolism, and offering novel insight into metabolic events in glioma MCTSs.

Metabolomic analyses uncover an inhibitory effect of niclosamide on mitochondrial membrane potential in cholangiocarcinoma cells.

Niclosamide is an oral anthelminthic drug that has been used for treating tapeworm infections. Its mechanism involves the disturbance of mitochondrial membrane potential that in turn inhibits oxidative phosphorylation leading to ATP depletion. To date, niclosamide has been validated as the potent anti-cancer agent against several cancers. However, the molecular mechanisms underlying the effects of niclosamide on the liver fluke Opisthorchis viverrini (Ov)-associated cholangiocarcinoma (CCA) cell functions remain to be elucidated. The aims of this study were to investigate the effects of niclosamide on CCA cell proliferation and on metabolic phenoconversion through the alteration of metabolites associated with mitochondrial function in CCA cell lines. The inhibitory effect of niclosamide on CCA cells was determined using SRB assay. A mitochondrial membrane potential using tetramethylrhodamine, ethyl ester-mitochondrial membrane potential (TMRE-MMP) assay was conducted. Liquid chromatography-mass spectrometry-based metabolomics was employed to investigate the global metabolic changes upon niclosamide treatment. ATP levels were measured using CellTiter-Glo® luminescent cell viability assay. NAD metabolism was examined by the NAD+/NADH ratio. Niclosamide strongly inhibited CCA cell growth and reduced the MMP of CCA cells. An orthogonal partial-least square regression analysis revealed that the effects of niclosamide on suppressing cell viability and MMP of CCA cells were significantly associated with an increase in niacinamide, a precursor in NAD synthesis that may disrupt the electron transport system leading to suppression of NAD+/NADH ratio and ATP depletion. Our findings unravel the mode of action of niclosamide in the energy depletion that could potentially serve as the promising therapeutic strategy for CCA treatment.

Exploring the in vitro anti-inflammatory activity of gross saponins of Tribulus terrestris L. fruit by using liquid chromatography-mass spectrometry-based cell metabolomics approach.

Our previous studies confirmed the efficacy of gross saponins of Tribulus terrestris L. fruit in treating cerebral ischemia. This study aimed to investigate the related mechanisms in vitro. The lipopolysaccharide-induced BV2 cells model was constructed and treated with gross saponins at different concentrations to explore its anti-inflammatory activity. The cell metabolite changes were tracked by liquid chromatography-mass spectrometry (LC-MS)-based metabolomics, and the metabolic biomarkers and related metabolic pathways were analyzed. Molecular biochemistry analysis was further used to verify the relevant inflammatory pathways. The results showed that the saponins reduced nitric oxide release and the secretion of tumor necrosis factor-alpha, interleukin-1β, and interleukin-6 from lipopolysaccharide-induced BV2 cells. Metabolic perturbations occurred in lipopolysaccharide-treated BV2 cells, which could be reversed by drug treatment via mainly regulating glycerophospholipid metabolism, tryptophan metabolism, purine metabolism pathways, etc. The western blot analysis demonstrated that saponin could suppress the activation of the inflammatory-related signaling pathway. The present study explored the in vitro anti-inflammatory mechanism of gross saponins of Tribulus terrestris L. fruit using an LC-MS-based cell metabolomics approach, which confirms the great potential of LC-MS for drug efficacy evaluation and can be applied in other herbal medicine-related analyses.

Salmon Food-Specific Compounds and Their Metabolites Increase in Human Plasma and Are Associated with Cardiometabolic Health Indicators Following a Mediterranean-Style Diet Intervention

BackgroundNutrimetabolomics allows for the comprehensive analysis of foods and human biospecimens to identify biomarkers of intake and begin to probe their associations with health. Salmon contains hundreds of compounds that may provide cardiometabolic benefits. ObjectivesWe used untargeted metabolomics to identify salmon food-specific compounds (FSCs) and their predicted metabolites that were found in plasma after a salmon-containing Mediterranean-style (MED) diet intervention. Associations between changes in salmon FSCs and changes in cardiometabolic health indicators (CHIs) were also explored. MethodsFor this secondary analysis of a randomized, crossover, controlled feeding trial, 41 participants consumed MED diets with 2 servings of salmon per week for 2 5-wk periods. CHIs were assessed, and fasting plasma was collected pre- and postintervention. Plasma, salmon, and 99 MED foods were analyzed using liquid chromatography-mass spectrometry-based metabolomics. Compounds were characterized as salmon FSCs if detected in all salmon replicates but none of the other foods. Metabolites of salmon FSCs were predicted using machine learning. For salmon FSCs and metabolites found in plasma, linear mixed-effect models were used to assess change from pre- to postintervention and associations with changes in CHIs. ResultsRelative to the other 99 MED foods, there were 508 salmon FSCs with 237 unique metabolites. A total of 143 salmon FSCs and 106 metabolites were detected in plasma. Forty-eight salmon FSCs and 30 metabolites increased after the intervention (false discovery rate <0.05). Increases in 2 annotated salmon FSCs and 2 metabolites were associated with improvements in CHIs, including total cholesterol, low-density lipoprotein cholesterol, triglycerides, and apolipoprotein B. ConclusionsA data-driven nutrimetabolomics strategy identified salmon FSCs and their predicted metabolites that were detectable in plasma and changed after consumption of a salmon-containing MED diet. Findings support this approach for the discovery of compounds in foods that may serve, upon further validation, as biomarkers or act as bioactive components influential to health.The trials supporting this work were registered at NCT02573129 (Mediterranean-style diet intervention) and NCT05500976 (ongoing clinical trial).

BoxCar data-dependent acquisition improves the MS/MS coverage in liquid chromatography-mass spectrometry-based metabolomics analysis

Tandem mass spectrometry (MS2) information is always applied for compound identification or annotation in liquid chromatography-mass spectrometry (LC-MS)-based untargeted metabolomics study. Due to the complex chemical composition of the biological sample, acquiring MS2 spectra that cover all components is still challenging. Data-dependent acquisition (DDA) is commonly applied to obtain the MS2 data, but intensity-based trigger criteria restrict low abundance ions for fragmentation. BoxCar DDA was introduced on an LC- quadrupole time of flight (Q-TOF) MS in this study to improve the MS2 coverage of conventional DDA. BoxCar DDA with sub-mass ranges of 50, 25, and 10 was applied to perform sample analysis, respectively. The performance of multi-parallel collision-induced dissociation MS (MSe), DDA, and BoxCar DDA was compared. The results showed BoxCar DDA significantly increased the MS2 coverage and improved the signal quality. The MS2 coverage increased with the reduction of the sub-mass range. BoxCar (10) DDA exhibited the highest coverage of MS2 information. The signal quality acquired from DDA is better than that obtained under MSe, and the BoxCar (10) also showed superior for ions with low intensity. The plant material of gross saponin of Tribulus Terrestris L. fruit (GSTTF) was detected using the established BoxCar DDA, and the informative MS2 data was used to perform compound identification. The newly developed BoxCar DDA method could provide comprehensive MS2 data of analyte, which will not only be helpful to increase the confidence of compound identification in untargeted metabolomics but also facilitate ion pair detection in pseudotargeted analysis.

Unlocking Potentially Therapeutic Phytochemicals in Capadulla (Doliocarpus dentatus) from Guyana Using Untargeted Mass Spectrometry-Based Metabolomics.

Doliocarpus dentatus is thought to have a wide variety of therapeutic phytochemicals that allegedly improve libido and cure impotence. Although a few biomarkers have been identified with potential antinociceptive and cytotoxic properties, an untargeted mass spectrometry-based metabolomics approach has never been undertaken to identify therapeutic biofingerprints for conditions, such as erectile dysfunction, in men. This study executes a preliminary phytochemical screening of the woody vine of two ecotypes of D. dentatus with renowned differences in therapeutic potential for erectile dysfunction. Liquid chromatography-mass spectrometry-based metabolomics was used to screen for flavonoids, terpenoids, and other chemical classes found to contrast between red and white ecotypes. Among the metabolite chemodiversity found in the ecotype screens, using a combination of GNPS, MS-DIAL, and SIRIUS, approximately 847 compounds were annotated at levels 2 to 4, with the majority of compounds falling under lipid and lipid-like molecules, benzenoids and phenylpropanoids, and polyketides, indicative of the contributions of the flavonoid, shikimic acid, and terpenoid biosynthesis pathways. Despite the extensive annotation, we report on 138 tentative compound identifications of potentially therapeutic compounds, with 55 selected compounds at a level-2 annotation, and 22 statistically significant therapeutic biomarkers, the majority of which were polyphenols. Epicatechin methyl gallate, catechin gallate, and proanthocyanidin A2 had the greatest significant differences and were also relatively abundant among the red and white ecotypes. These putatively identified compounds reportedly act as antioxidants, neutralizing damaging free radicals, and lowering cell oxidative stress, thus aiding in potentially preventing cellular damage and promoting overall well-being, especially for treating erectile dysfunction (ED).

The metabolomic plasma profile of patients with Duchenne muscular dystrophy: providing new evidence for its pathogenesis

BackgroundDuchenne muscular dystrophy (DMD) is a fatal genetic muscle-wasting disease that affects 1 in 5000 male births with no current cure. Despite great progress has been made in the research of DMD, its underlying pathological mechanism based on the metabolomics is still worthy of further study. Therefore, it is necessary to gain a deeper understanding of the mechanisms or pathogenesis underlying DMD, which may reveal potential therapeutic targets and/or biomarkers.ResultsPlasma samples from 42 patients with DMD from a natural history study and 40 age-matched healthy volunteers were subjected to a liquid chromatography-mass spectrometry-based non-targeted metabolomics approach. Acquired metabolic data were evaluated by principal component analysis, partial least squares-discriminant analysis, and metabolic pathway analysis to explore distinctive metabolic patterns in patients with DMD. Differentially expressed metabolites were identified using publicly available and integrated databases. By comparing the DMD and healthy control groups, 25 differential metabolites were detected, including amino acids, unsaturated fatty acids, carnitine, lipids, and metabolites related to the gut microbiota. Correspondingly, linoleic acid metabolism, D-glutamine and D-glutamate metabolism, glycerophospholipid metabolism, and alanine, aspartate, and glutamate metabolism were significantly altered in patients with DMD, compared with those of healthy volunteers.ConclusionsOur study demonstrated the abnormal metabolism of amino acids, energy, and lipids in patients with DMD, consistent with pathological features, such as recurrent muscle necrosis and regeneration, interstitial fibrosis, and fat replacement. Additionally, we found that metabolites of intestinal flora were disordered in DMD patients, providing support for treatment of intestinal microbia disturbance in DMD diseases. Our study provides a new research strategy for understanding the pathogenesis of DMD.

Predicting lameness in dairy cattle using untargeted liquid chromatography–mass spectrometry-based metabolomics and machine learning

Lameness in dairy cattle is a highly prevalent condition that impacts on the health and welfare of dairy cows. Prompt detection and implementation of effective treatment is important for managing lameness. However, major limitations are associated with visual assessment of lameness, which is the most commonly used method to detect lameness. The aims of this study were to investigate the use of metabolomics and machine learning to develop novel methods to detect lameness. Untargeted metabolomics using liquid chromatography-mass spectrometry (LC-MS) alongside machine learning models and a stability selection method were utilized to evaluate the predictive accuracy of differences in the metabolomics profile of first-lactation dairy cows before (during the transition period) and at the time of lameness (based on visual assessment using the 0-3 scale of the Agriculture and Horticulture Development Board). Urine samples were collected from 2 cohorts of dairy heifers and stored at -86°C before analysis using LC-MS. Cohort 1 (n = 90) cows were recruited as current first-lactation cows with weekly mobility scores recorded over a 4-mo timeframe, from which newly lame and nonlame cows were identified. Cohort 2 (n = 30) cows were recruited within 3 wk before calving, and lameness events (based on mobility score) were recorded through lactation until a minimum of 70 d in milk (DIM). All cows were matched paired by DIM ± 14 d. The median DIM at lameness identification was 187.5 and 28.5 for cohort 1 and 2, respectively. The best performing machine learning models predicted lameness at the time of lameness with an accuracy of between 81 and 82%. Using stability selection, the prediction accuracy at the time of lameness was 80 to 81%. For samples collected before and after calving, the best performing machine learning model predicted lameness with an accuracy of 71 and 75%, respectively. The findings from this study demonstrate that untargeted LC-MS profiling combined with machine learning methods can be used to predict lameness as early as before calving and before observable changes in gait in first-lactation dairy cows. The methods also provide accuracies for detecting lameness at the time of observable changes in gait of up to 82%. The findings demonstrate that these methods could provide substantial advancements in the early prediction and prevention of lameness risk. Further external validation work is required to confirm these findings are generalizable; however, this study provides the basis from which future work can be conducted.

The altered metabolic pathways of diffuse large B-cell lymphoma not otherwise specified

Altered metabolic fingerprints of Diffuse large B-cell lymphoma, not otherwise specified (DLBCL NOS) may offer novel opportunities to identify new biomarkers and improve the understanding of its pathogenesis. This study aimed to investigate the modified metabolic pathways in extranodal, germinal center B-cell (GCB) and non-GCB DLBCL NOS from the head and neck. Formalin-fixed paraffin-embedded (FFPE) tissues from eleven DLBCL NOS classified according to Hans’ algorithm using immunohistochemistry, and five normal lymphoid tissues (LT) were analyzed by high-performance liquid chromatography-mass spectrometry-based untargeted metabolomics. Partial Least Squares Discriminant Analysis showed that GCB and non-GCB DLBCL NOS have a distinct metabolomics profile, being the former more similar to normal lymphoid tissues. Metabolite pathway enrichment analysis indicated the following altered pathways: arachidonic acid, tyrosine, xenobiotics, vitamin E metabolism, and vitamin A. Our findings support that GCB and non-GCB DLBCL NOS has a distinct metabolomic profile, in which GCB possibly shares more metabolic similarities with LT than non-GCB DLBCL NOS.

The metabolomic profiling of total fat and fat distribution in a multi-cohort study of women and men

Currently studies aiming for the comprehensive metabolomics profiling of measured total fat (%) as well as fat distribution in both sexes are lacking. In this work, bioimpedance analysis was applied to measure total fat (%) and fat distribution (trunk to leg ratio). Liquid chromatography-mass spectrometry-based untargeted metabolomics was employed to profile the metabolic signatures of total fat (%) and fat distribution in 3447 participants from three Swedish cohorts (EpiHealth, POEM and PIVUS) using a discovery-replication cross-sectional study design. Total fat (%) and fat distribution were associated with 387 and 120 metabolites in the replication cohort, respectively. Enriched metabolic pathways for both total fat (%) and fat distribution included protein synthesis, branched-chain amino acids biosynthesis and metabolism, glycerophospholipid metabolism and sphingolipid metabolism. Four metabolites were mainly related to fat distribution: glutarylcarnitine (C5-DC), 6-bromotryptophan, 1-stearoyl-2-oleoyl-GPI (18:0/18:1) and pseudouridine. Five metabolites showed different associations with fat distribution in men and women: quinolinate, (12Z)-9,10-dihydroxyoctadec-12-enoate (9,10-DiHOME), two sphingomyelins and metabolonic lactone sulfate. To conclude, total fat (%) and fat distribution were associated with a large number of metabolites, but only a few were exclusively associated with fat distribution and of those metabolites some were associated with sex*fat distribution. Whether these metabolites mediate the undesirable effects of obesity on health outcomes remains to be further investigated.