PubMed

Metabolites. 2023 May 9;13(5):644. doi: 10.3390/metabo13050644.ABSTRACTUrine metabolomics is gaining traction as a means of identifying metabolic signatures associated with health and disease states. Thirty-one (31) late preterm (LP) neonates admitted to the neonatal intensive care unit (NICU) and 23 age-matched healthy LPs admitted to the maternity ward of a tertiary hospital were included in the study. Proton nuclear magnetic resonance (1H NMR) spectroscopy was employed for urine metabolomic analysis on the 1st and 3rd days of life of the neonates. The data were analyzed using univariate and multivariate statistical analysis. A unique metabolic pattern of enhanced metabolites was identified in the NICU-admitted LPs from the 1st day of life. Metabolic profiles were distinct in LPs presenting with respiratory distress syndrome (RDS). The discrepancies likely reflect differences in the gut microbiota, either due to variations in nutrient intake or as a result of medical interventions, such as the administration of antibiotics and other medications. Altered metabolites could potentially serve as biomarkers for identifying critically ill LP neonates or those at high risk for adverse outcomes later in life, including metabolic risks. The discovery of novel biomarkers may uncover potential targets for drug discovery and optimal periods for effective intervention, offering a personalized approach.PMID:37233686 | DOI:10.3390/metabo13050644

Metabolites. 2023 May 9;13(5):645. doi: 10.3390/metabo13050645.ABSTRACTCarob (Ceratonia siliqua) is an exceptional source of significant bioactive compounds with great economic importance in the Mediterranean region, where it is widely cultivated. Carob fruit is used for the production of a variety of products and commodities such as powder, syrup, coffee, flour, cakes, and beverages. There is growing evidence of the beneficial effects of carob and the products made from it on a range of health problems. Therefore, metabolomics could be used to explore the nutrient-rich compounds of carob. Sample preparation is a crucial step in metabolomics-based analysis and has a great impact on the quality of the data obtained. Herein, sample preparation of carob syrup and powder was optimized, to enable highly efficient metabolomics-based HILIC-MS/MS analysis. Pooled powder and syrup samples were extracted under different conditions by adjusting pH, solvent type, and sample weight to solvent volume ratio (Wc/Vs). The metabolomics profiles obtained were evaluated using the established criteria of total area and number of maxima. It was observed that the Wc/Vs ratio of 1:2 resulted in the highest number of metabolites, regardless of solvent type or pH. Aqueous acetonitrile with a Wc/Vs ratio of 1:2 satisfied all established criteria for both carob syrup and powder samples. However, when the pH was adjusted, basic aqueous propanol 1:2 Wc/Vs and acidic aqueous acetonitrile 1:2 Wc/Vs provided the best results for syrup and powder, respectively. We strongly believe that the current study could support the standardization of the metabolomics sample preparation process to enable more efficient LC-MS/MS carob analysis.PMID:37233685 | DOI:10.3390/metabo13050645

Metabolites. 2023 May 8;13(5):641. doi: 10.3390/metabo13050641.ABSTRACTThe underlying mechanisms of the development of unhealthy metabolic phenotypes in obese children and adolescents remain unclear. We aimed to screen the metabolomes of individuals with the unhealthy obesity phenotype and identify the potential metabolic pathways that could regulate various metabolic profiles of obesity in Chinese adolescents. A total of 127 adolescents aged 11-18 years old from China were investigated using a cross-sectional study. The participants were classified as having metabolically healthy obesity (MHO) or metabolically unhealthy obesity (MUO) based on the presence/absence of metabolic abnormalities defined by metabolic syndrome (MetS) and body mass index (BMI). Serum-based metabolomic profiling using gas chromatography-mass spectrometry (GC-MS) was undertaken on 67 MHO and 60 MUO individuals. ROC analyses showed that palmitic acid, stearic acid, and phosphate could predict MUO, and that glycolic acid, alanine, 3-hydroxypropionic acid, and 2-hydroxypentanoic acid could predict MHO (all p < 0.05) from selected samples. Five metabolites predicted MUO, 12 metabolites predicted MHO in boys, and only two metabolites predicted MUO in girls. Moreover, several metabolic pathways may be relevant in distinguishing the MHO and MUO groups, including the fatty acid biosynthesis, fatty acid elongation in mitochondria, propanoate metabolism, glyoxylate and dicarboxylate metabolism, and fatty acid metabolism pathways. Similar results were observed for boys except for phenylalanine, tyrosine and tryptophan biosynthesis, which had a high impact [0.098]. The identified metabolites and pathways could be efficacious for investigating the underlying mechanisms of the development of different metabolic phenotypes in obese Chinese adolescents.PMID:37233682 | DOI:10.3390/metabo13050641

Metabolites. 2023 Apr 30;13(5):622. doi: 10.3390/metabo13050622.ABSTRACTPositive results on animal health, feed efficiency, and milk's nutritional content have been obtained after oral administration of probiotics. The aim of the present study was therefore to evaluate the effect of dietary supplementation with high numbers of multispecies probiotic formulations on the milk metabolomic profiles of alkaline sphingomyelinase (alk-SMase) and alkaline phosphatase (ALP) in donkeys. Twenty animals were randomly allocated to receive either a normal diet (group B) or a supplemented diet (group A). Colostrum and milk samples were obtained within 48 h, at 15 days (supplementation start), and at 45 days after parturition. Different metabolomic profiles were observed between colostrum and milk, as were the concentrations of 12 metabolites that changed following 30 days of probiotic supplementation. Alk-SMase activity was found to be higher in donkey colostrum (vs. milk at 15 days); this enzyme, together with ALP, increased in milk after 30 days of probiotic supplementation. The results of the present study provide new insight into the complex changes in donkey colostrum and milk composition in the first 45 days of lactation and how the milk metabolome can be modulated by probiotic supplementation.PMID:37233663 | DOI:10.3390/metabo13050622

Metabolites. 2023 Apr 30;13(5):620. doi: 10.3390/metabo13050620.ABSTRACTActinomycetes are prolific producers of bioactive secondary metabolites. The prevalence of multidrug-resistant (MDR) pathogens has prompted us to search for potential natural antimicrobial agents. Herein, we report the isolation of rare actinobacteria from Egyptian soil. The strain was identified as Amycolatopsis keratiniphila DPA04 using 16S rRNA gene sequencing. Cultivation profiling, followed by chemical and antimicrobial evaluation of crude extracts, revealed the activity of DPA04 ISP-2 and M1 culture extracts against Gram-positive bacteria. Minimum inhibitory concentrations (MIC) values ranged from 19.5 to 39 µg/mL. Chemical analysis of the crude extracts using ultra-high performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UHPLC-QTOF) led to the identification of 45 metabolites of different chemical classes. In addition, ECO-0501 was identified in the cultures with significant antimicrobial activity. Multidrug resistance in Staphylococcus aureus is reported to be related to the multidrug efflux pump (MATE). ECO-0501 and its related metabolites were subjected to molecular docking studies against the MATE receptor as a proposed mechanism of action. ECO-0501 and its derivatives (AK_1 and N-demethyl ECO-0501) had better binding scores (-12.93, -12.24, and -11.92 kcal/mol) than the co-crystallized 4HY inhibitor (-8.99 kcal/mol) making them promising candidates as MATE inhibitors. Finally, our work established that natural products from this strain could be useful therapeutic tools for controlling infectious diseases.PMID:37233661 | DOI:10.3390/metabo13050620

Metabolites. 2023 Apr 30;13(5):618. doi: 10.3390/metabo13050618.ABSTRACTPeritoneal cancers present significant clinical challenges with poor prognosis. Understanding the role of cancer cell metabolism and cancer-promoting metabolites in peritoneal cancers can provide new insights into the mechanisms that drive tumor progression and can identify novel therapeutic targets and biomarkers for early detection, prognosis, and treatment response. Cancer cells dynamically reprogram their metabolism to facilitate tumor growth and overcome metabolic stress, with cancer-promoting metabolites such as kynurenines, lactate, and sphingosine-1-phosphate promoting cell proliferation, angiogenesis, and immune evasion. Targeting cancer-promoting metabolites could also lead to the development of effective combinatorial and adjuvant therapies involving metabolic inhibitors for the treatment of peritoneal cancers. With the observed metabolomic heterogeneity in cancer patients, defining peritoneal cancer metabolome and cancer-promoting metabolites holds great promise for improving outcomes for patients with peritoneal tumors and advancing the field of precision cancer medicine. This review provides an overview of the metabolic signatures of peritoneal cancer cells, explores the role of cancer-promoting metabolites as potential therapeutic targets, and discusses the implications for advancing precision cancer medicine in peritoneal cancers.PMID:37233659 | DOI:10.3390/metabo13050618

Metabolites. 2023 Apr 29;13(5):614. doi: 10.3390/metabo13050614.ABSTRACTNuclear magnetic resonance (NMR)-based metabolomics is a valuable tool for identifying biomarkers and understanding the underlying metabolic changes associated with various diseases. However, the translation of metabolomics analysis to clinical practice has been limited by the high cost and large size of traditional high-resolution NMR spectrometers. Benchtop NMR, a compact and low-cost alternative, offers the potential to overcome these limitations and facilitate the wider use of NMR-based metabolomics in clinical settings. This review summarizes the current state of benchtop NMR for clinical applications where benchtop NMR has demonstrated the ability to reproducibly detect changes in metabolite levels associated with diseases such as type 2 diabetes and tuberculosis. Benchtop NMR has been used to identify metabolic biomarkers in a range of biofluids, including urine, blood plasma and saliva. However, further research is needed to optimize the use of benchtop NMR for clinical applications and to identify additional biomarkers that can be used to monitor and manage a range of diseases. Overall, benchtop NMR has the potential to revolutionize the way metabolomics is used in clinical practice, providing a more accessible and cost-effective way to study metabolism and identify biomarkers for disease diagnosis, prognosis, and treatment.PMID:37233655 | DOI:10.3390/metabo13050614

Metabolites. 2023 Apr 28;13(5):611. doi: 10.3390/metabo13050611.ABSTRACTNuclear magnetic resonance (NMR)-based metabolomics, which comprehensively measures metabolites in biological systems and investigates their response to various perturbations, is widely used in research to identify biomarkers and investigate the pathogenesis of underlying diseases. However, further applications of high-field superconducting NMR for medical purposes and field research are restricted by its high cost and low accessibility. In this study, we applied a low-field, benchtop NMR spectrometer (60 MHz) employing a permanent magnet to characterize the alterations in the metabolic profile of fecal extracts obtained from dextran sodium sulfate (DSS)-induced ulcerative colitis model mice and compared them with the data acquired from high-field NMR (800 MHz). Nineteen metabolites were assigned to the 60 MHz 1H NMR spectra. Non-targeted multivariate analysis successfully discriminated the DSS-induced group from the healthy control group and showed high comparability with high-field NMR. In addition, the concentration of acetate, identified as a metabolite with characteristic behavior, could be accurately quantified using a generalized Lorentzian curve fitting method based on the 60 MHz NMR spectra.PMID:37233652 | DOI:10.3390/metabo13050611

Metabolites. 2023 Apr 28;13(5):610. doi: 10.3390/metabo13050610.ABSTRACTYams are economic and medicinal crops with a long growth cycle, spanning between 9-11 months due to their prolonged tuber dormancy. Tuber dormancy has constituted a major constraint in yam production and genetic improvement. In this study, we performed non-targeted comparative metabolomic profiling of tubers of two white yam genotypes, (Obiaoturugo and TDr1100873), to identify metabolites and associated pathways that regulate yam tuber dormancy using gas chromatography-mass spectrometry (GC-MS). Yam tubers were sampled between 42 days after physiological maturity (DAPM) till tuber sprouting. The sampling points include 42-DAPM, 56-DAPM, 87DAPM, 101-DAPM, 115-DAPM, and 143-DAPM. A total of 949 metabolites were annotated, 559 in TDr1100873 and 390 in Obiaoturugo. A total of 39 differentially accumulated metabolites (DAMs) were identified across the studied tuber dormancy stages in the two genotypes. A total of 27 DAMs were conserved between the two genotypes, whereas 5 DAMs were unique in the tubers of TDr1100873 and 7 DAMs were in the tubers of Obiaoturugo. The differentially accumulated metabolites (DAMs) spread across 14 major functional chemical groups. Amines and biogenic polyamines, amino acids and derivatives, alcohols, flavonoids, alkaloids, phenols, esters, coumarins, and phytohormone positively regulated yam tuber dormancy induction and maintenance, whereas fatty acids, lipids, nucleotides, carboxylic acids, sugars, terpenoids, benzoquinones, and benzene derivatives positively regulated dormancy breaking and sprouting in tubers of both yam genotypes. Metabolite set enrichment analysis (MSEA) revealed that 12 metabolisms were significantly enriched during yam tuber dormancy stages. Metabolic pathway topology analysis further revealed that six metabolic pathways (linoleic acid metabolic pathway, phenylalanine metabolic pathway, galactose metabolic pathway, starch and sucrose metabolic pathway, alanine-aspartate-glutamine metabolic pathways, and purine metabolic pathway) exerted significant impact on yam tuber dormancy regulation. This result provides vital insights into molecular mechanisms regulating yam tuber dormancy.PMID:37233651 | DOI:10.3390/metabo13050610

Metabolites. 2023 Apr 28;13(5):609. doi: 10.3390/metabo13050609.ABSTRACTMetabolomic analysis methods were employed to determine biomarkers for various chronic kidney diseases (CKDs). Modern analytical methods were developed and applied successfully to find a specific metabolomic profile in urine samples from CKD and Balkan endemic nephropathy (BEN) patients. The aim was to explore a specific metabolomic profile defined by feasible/easy-to-identify molecular markers. Urine samples were collected from patients with CKDs and BEN, and from healthy subjects from endemic and nonendemic areas in Romania. Metabolomic analysis of urine samples, extracted by the liquid-liquid extraction (LLE) method, was performed by gas chromatography-mass spectrometry (GC-MS). The statistical exploration of the results was performed through a principal component analysis (PCA) evaluation. Urine samples were statistically analyzed using a classification based on six types of metabolites. Most urinary metabolites are distributed in the center of a loading plot, meaning that these compounds do not represent significant markers for BEN. One of the most frequent and higher-concentration urinary metabolites in BEN patients was p-Cresol, a phenolic compound that implies a severe injury of the renal filtration function. The presence of p-Cresol was associated with protein-bound uremic toxins, which have specific functional groups such as indole and phenyl. In prospective studies for future investigation, prevention, and disease treatment, we suggest a larger sample size, sample extraction using other methods, and analysis using other chromatography techniques coupled with mass spectrometry, which can generate a more significant data set for statistical analysis.PMID:37233650 | DOI:10.3390/metabo13050609

Metabolites. 2023 Apr 28;13(5):607. doi: 10.3390/metabo13050607.ABSTRACTBipolar disorder (BD) is a brain disorder that causes changes in a person's mood, energy, and ability to function. It has a prevalence of 60 million people worldwide, and it is among the top 20 diseases with the highest global burden. The complexity of this disease, including diverse genetic, environmental, and biochemical factors, and diagnoses based on the subjective recognition of symptoms without any clinical test of biomarker identification create significant difficulties in understanding and diagnosing BD. A 1H-NMR-based metabolomic study applying chemometrics of serum samples of Serbian patients with BD (33) and healthy controls (39) was explored, providing the identification of 22 metabolites for this disease. A biomarker set including threonine, aspartate, gamma-aminobutyric acid, 2-hydroxybutyric acid, serine, and mannose was established for the first time in BD serum samples by an NMR-based metabolomics study. Six identified metabolites (3-hydroxybutyric acid, arginine, lysine, tyrosine, phenylalanine, and glycerol) are in agreement with the previously determined NMR-based sets of serum biomarkers in Brazilian and/or Chinese patient samples. The same established metabolites (lactate, alanine, valine, leucine, isoleucine, glutamine, glutamate, glucose, and choline) in three different ethnic and geographic origins (Serbia, Brazil, and China) might have a crucial role in the realization of a universal set of NMR biomarkers for BD.PMID:37233648 | DOI:10.3390/metabo13050607

Metabolites. 2023 Apr 28;13(5):605. doi: 10.3390/metabo13050605.ABSTRACTThe assessment, management, and prognostication of spinal cord injury (SCI) mainly rely upon observer-based ordinal scales measures. 1H nuclear magnetic resonance (NMR) spectroscopy provides an effective approach for the discovery of objective biomarkers from biofluids. These biomarkers have the potential to aid in understanding recovery following SCI. This proof-of-principle study determined: (a) If temporal changes in blood metabolites reflect the extent of recovery following SCI; (b) whether changes in blood-derived metabolites serve as prognostic indicators of patient outcomes based on the spinal cord independence measure (SCIM); and (c) whether metabolic pathways involved in recovery processes may provide insights into mechanisms that mediate neural damage and repair. Morning blood samples were collected from male complete and incomplete SCI patients (n = 7) following injury and at 6 months post-injury. Multivariate analyses were used to identify changes in serum metabolic profiles and were correlated to clinical outcomes. Specifically, acetyl phosphate, 1,3,7-trimethyluric acid, 1,9-dimethyluric acid, and acetic acid significantly related to SCIM scores. These preliminary findings suggest that specific metabolites may serve as proxy measures of the SCI phenotype and prognostic markers of recovery. Thus, serum metabolite analysis combined with machine learning holds promise in understanding the physiology of SCI and aiding in prognosticating outcomes following injury.PMID:37233646 | DOI:10.3390/metabo13050605

Metabolites. 2023 Apr 27;13(5):602. doi: 10.3390/metabo13050602.ABSTRACTThe microbial metabolite indolepropionic acid (IPA) and related indolic metabolites, including indolecarboxylic acid (ICA), indolelactic acid (ILA), indoleacetic acid (IAA), indolebutyric acid (IBA), indoxylsulfate (ISO4), and indole, were determined in human plasma, plasma ultrafiltrate (UF), and saliva. The compounds were separated on a 150 × 3 mm column of 3 μm Hypersil C18 eluted with a mobile phase of 80% pH 5 0.01 M sodium acetate containing 1.0 g/L of tert-butylammonium chloride/20% acetonitrile and then detected fluorometrically. Levels of IPA in human plasma UF and of ILA in saliva are reported for the first time. The determination of IPA in plasma UF enables the first report of free plasma IPA, the presumed physiologically active pool of this important microbial metabolite of tryptophan. Plasma and salivary ICA and IBA were not detected, consistent with the absence of any prior reported values. Observed levels or limits of detection for other indolic metabolites usefully supplement limited prior reports.PMID:37233643 | DOI:10.3390/metabo13050602

Metabolites. 2023 Apr 27;13(5):596. doi: 10.3390/metabo13050596.ABSTRACTPesticides constitute a category of chemical products intended specifically for the control and mitigation of pests. With their constant increase in use, the risk to human health and the environment has increased proportionally due to occupational and environmental exposure to these compounds. The use of these chemicals is associated with several toxic effects related to acute and chronic toxicity, such as infertility, hormonal disorders and cancer. The present work aimed to study the metabolic profile of individuals occupationally exposed to pesticides, using a metabolomics tool to identify potential new biomarkers. Metabolomics analysis was carried out on plasma and urine samples from individuals exposed and non-exposed occupationally, using liquid chromatography coupled with mass spectrometry (UPLC-MS). Non-targeted metabolomics analysis, using principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA) or partial least squares discriminant orthogonal analysis (OPLS-DA), demonstrated good separation of the samples and identified 21 discriminating metabolites in plasma and 17 in urine. The analysis of the ROC curve indicated the compounds with the greatest potential for biomarkers. Comprehensive analysis of the metabolic pathways influenced by exposure to pesticides revealed alterations, mainly in lipid and amino acid metabolism. This study indicates that the use of metabolomics provides important information about complex biological responses.PMID:37233637 | DOI:10.3390/metabo13050596

Metabolites. 2023 Apr 25;13(5):591. doi: 10.3390/metabo13050591.ABSTRACTAs one of the OMICS in systems biology, metabolomics defines the metabolome and simultaneously quantifies numerous metabolites that are final or intermediate products and effectors of upstream biological processes. Metabolomics provides accurate information that helps determine the physiological steady state and biochemical changes during the aging process. To date, reference values of metabolites across the adult lifespan, especially among ethnicity groups, are lacking. The "normal" reference values according to age, sex, and race allow the characterization of whether an individual or a group deviates metabolically from normal aging, encompass a fundamental element in any study aimed at understanding mechanisms at the interface between aging and diseases. In this study, we established a metabolomics reference database from 20-100 years of age from a biracial sample of community-dwelling healthy men and women and examined metabolite associations with age, sex, and race. Reference values from well-selected healthy individuals can contribute to clinical decision-making processes of metabolic or related diseases.PMID:37233632 | DOI:10.3390/metabo13050591

Metabolites. 2023 Apr 25;13(5):589. doi: 10.3390/metabo13050589.ABSTRACTColorectal cancer (CRC) is one of the most common and lethal diseases among all types of cancer, and metabolites play a significant role in the development of this complex disease. This study aimed to identify potential biomarkers and targets in the diagnosis and treatment of CRC using high-throughput metabolomics. Metabolite data extracted from the feces of CRC patients and healthy volunteers were normalized with the median normalization and Pareto scale for multivariate analysis. Univariate ROC analysis, the t-test, and analysis of fold changes (FCs) were applied to identify biomarker candidate metabolites in CRC patients. Only metabolites that overlapped the two different statistical approaches (false-discovery-rate-corrected p-value < 0.05 and AUC > 0.70) were considered in the further analysis. Multivariate analysis was performed with biomarker candidate metabolites based on linear support vector machines (SVM), partial least squares discrimination analysis (PLS-DA), and random forests (RF). The model identified five biomarker candidate metabolites that were significantly and differently expressed (adjusted p-value < 0.05) in CRC patients compared to healthy controls. The metabolites were succinic acid, aminoisobutyric acid, butyric acid, isoleucine, and leucine. Aminoisobutyric acid was the metabolite with the highest discriminatory potential in CRC, with an AUC equal to 0.806 (95% CI = 0.700-0.897), and was down-regulated in CRC patients. The SVM model showed the most substantial discrimination capacity for the five metabolites selected in the CRC screening, with an AUC of 0.985 (95% CI: 0.94-1).PMID:37233630 | DOI:10.3390/metabo13050589

Metabolites. 2023 Apr 24;13(5):588. doi: 10.3390/metabo13050588.ABSTRACTMetabolomic approaches, such as in clinical applications of living individuals, have shown potential use for solving questions regarding the past when applied to archaeological material. Here, we study for the first time the potential of this Omic approach as applied to metabolites extracted from archaeological human dentin. Dentin obtained from micro sampling the dental pulp of teeth of victims and non-victims of Yersinia pestis (plague) from a 6th century Cambridgeshire site are used to evaluate the potential use of such unique material for untargeted metabolomic studies on disease state through liquid chromatography hyphenated to high-resolution mass spectrometry (LC-HRMS). Results show that small molecules of both likely endogenous and exogenous sources are preserved for a range of polar and less polar/apolar metabolites in archaeological dentin; however, untargeted metabolomic profiles show no clear differentiation between healthy and infected individuals in the small sample analysed (n = 20). This study discusses the potential of dentin as a source of small molecules for metabolomic assays and highlights: (1) the need for follow up research to optimise sampling protocols, (2) the requirements of studies with larger sample numbers and (3) the necessity of more databases to amplify the positive results achievable with this Omic technique in the archaeological sciences.PMID:37233629 | DOI:10.3390/metabo13050588

Metabolites. 2023 Apr 24;13(5):587. doi: 10.3390/metabo13050587.ABSTRACTVisceral adipose tissue (VAT) metabolic fingerprints differ according to body mass index (BMI) and glycemic status. Glucagon-like peptide 1 (GLP-1), glucose-dependent insulinotropic polypeptide (GIP) and glucagon are gut-associated hormones that play an important role in regulating energy and glucose homeostasis, although their metabolic actions in VAT are still poorly characterized. Our aim was to assess whether GLP-1, GIP and glucagon influence the VAT metabolite profile. To achieve this goal, VAT harvested during elective surgical procedures from individuals (N = 19) with different BMIs and glycemic statuses was stimulated with GLP-1, GIP or glucagon, and culture media was analyzed using proton nuclear magnetic resonance. In the VAT of individuals with obesity and prediabetes, GLP-1 shifted its metabolic profile by increasing alanine and lactate production while also decreasing isoleucine consumption, whereas GIP and glucagon decreased lactate and alanine production and increased pyruvate consumption. In summary, GLP-1, GIP and glucagon were shown to distinctively modulate the VAT metabolic profile depending on the subject's BMI and glycemic status. In VAT from patients with obesity and prediabetes, these hormones induced metabolic shifts toward gluconeogenesis suppression and oxidative phosphorylation enhancement, suggesting an overall improvement in AT mitochondrial function.PMID:37233628 | DOI:10.3390/metabo13050587

Metabolites. 2023 Apr 24;13(5):585. doi: 10.3390/metabo13050585.ABSTRACTUntargeted metabolomics of moderately resistant wild tomato species Solanum cheesmaniae revealed an altered metabolite profile in plant leaves in response to Alternaria solani pathogen. Leaf metabolites were significantly differentiated in non-stressed versus stressed plants. The samples were discriminated not only by the presence/absence of specific metabolites as distinguished markers of infection, but also on the basis of their relative abundance as important concluding factors. Annotation of metabolite features using the Arabidopsis thaliana (KEGG) database revealed 3371 compounds with KEGG identifiers belonging to biosynthetic pathways including secondary metabolites, cofactors, steroids, brassinosteroids, terpernoids, and fatty acids. Annotation using the Solanum lycopersicum database in PLANTCYC PMN revealed significantly upregulated (541) and downregulated (485) features distributed in metabolite classes that appeared to play a crucial role in defense, infection prevention, signaling, plant growth, and plant homeostasis to survive under stress conditions. The orthogonal partial least squares discriminant analysis (OPLS-DA), comprising a significant fold change (≥2.0) with VIP score (≥1.0), showed 34 upregulated biomarker metabolites including 5-phosphoribosylamine, kaur-16-en-18-oic acid, pantothenate, and O-acetyl-L-homoserine, along with 41 downregulated biomarkers. Downregulated metabolite biomarkers were mapped with pathways specifically known for plant defense, suggesting their prominent role in pathogen resistance. These results hold promise for identifying key biomarker metabolites that contribute to disease resistive metabolic traits/biosynthetic routes. This approach can assist in mQTL development for the stress breeding program in tomato against pathogen interactions.PMID:37233626 | DOI:10.3390/metabo13050585

Mar Drugs. 2023 May 19;21(5):308. doi: 10.3390/md21050308.ABSTRACTNatural Products (NP) are essential for the discovery of novel drugs and products for numerous biotechnological applications. The NP discovery process is expensive and time-consuming, having as major hurdles dereplication (early identification of known compounds) and structure elucidation, particularly the determination of the absolute configuration of metabolites with stereogenic centers. This review comprehensively focuses on recent technological and instrumental advances, highlighting the development of methods that alleviate these obstacles, paving the way for accelerating NP discovery towards biotechnological applications. Herein, we emphasize the most innovative high-throughput tools and methods for advancing bioactivity screening, NP chemical analysis, dereplication, metabolite profiling, metabolomics, genome sequencing and/or genomics approaches, databases, bioinformatics, chemoinformatics, and three-dimensional NP structure elucidation.PMID:37233502 | DOI:10.3390/md21050308