PubMed

Microorganisms. 2023 Jan 19;11(2):264. doi: 10.3390/microorganisms11020264.ABSTRACTThe probiotic Weizmannia coagulans (W. coagulans) BC2000 can increase the abundance of intestinal transforming ellagic acid (EA) bacteria and inhibit metabolic disorders caused by hyperlipidemia by activating liver autophagy. This study aimed to investigate the inhibitory effects of W. coagulans BC2000 and EA on hyperlipidemia-induced cholesterol metabolism disorders. C57BL/6J mice (n = 10 in each group) were fed a low-fat diet, high-fat diet (HFD), HFD supplemented with EA, HFD supplemented with EA and W. coagulans BC77, HFD supplemented with EA, and W. coagulans BC2000. EA and W. coagulans BC2000 supplementation prevented HFD-induced hypercholesterolemia and promoted fecal cholesterol excretion. Transcriptome analysis showed that primary bile acid biosynthesis in the liver was significantly activated by EA and W. coagulans BC2000 treatments. EA and W. coagulans BC2000 treatment also significantly increased the intestinal Eggerthellaceae abundance and the liver EA metabolites, iso-urolithin A, Urolithin A, and Urolithin B. Therefore, W. coagulans BC2000 supplementation promoted the intestinal transformation of EA, which led to the upregulation of liver bile synthesis, thus preventing hypercholesterolemia.PMID:36838229 | DOI:10.3390/microorganisms11020264

Metabolites. 2023 Feb 17;13(2):301. doi: 10.3390/metabo13020301.ABSTRACTMyopic retinopathy is an important cause of irreversible vision loss and blindness. As metabolomics has recently been successfully applied in myopia research, this study sought to characterize the serum metabolic profile of myopic retinopathy in children and adolescents (4-18 years) and to develop a diagnostic model that combines clinical and metabolic features. We selected clinical and serum metabolic data from children and adolescents at different time points as the training set (n = 516) and the validation set (n = 60). All participants underwent an ophthalmologic examination. Untargeted metabolomics analysis of serum was performed. Three machine learning (ML) models were trained by combining metabolic features and conventional clinical factors that were screened for significance in discrimination. The better-performing model was validated in an independent point-in-time cohort and risk nomograms were developed. Retinopathy was present in 34.2% of participants (n = 185) in the training set, including 109 (28.61%) with mild to moderate myopia. A total of 27 metabolites showed significant variation between groups. After combining Lasso and random forest (RF), 12 modelled metabolites (mainly those involved in energy metabolism) were screened. Both the logistic regression and extreme Gradient Boosting (XGBoost) algorithms showed good discriminatory ability. In the time-validation cohort, logistic regression (AUC 0.842, 95% CI 0.724-0.96) and XGBoost (AUC 0.897, 95% CI 0.807-0.986) also showed good prediction accuracy and had well-fitted calibration curves. Three clinical characteristic coefficients remained significant in the multivariate joint model (p < 0.05), as did 8/12 metabolic characteristic coefficients. Myopic retinopathy may have abnormal energy metabolism. Machine learning models based on metabolic profiles and clinical data demonstrate good predictive performance and facilitate the development of individual interventions for myopia in children and adolescents.PMID:36837920 | DOI:10.3390/metabo13020301

Metabolites. 2023 Feb 17;13(2):300. doi: 10.3390/metabo13020300.ABSTRACTTempe is fermented soybean from Java, Indonesia, that can serve as a functional food due to its high nutritional content and positive impact on health. Although the tempe fermentation process is known to affect its nutrient content, changes in the metabolite profile during tempe production have not been comprehensively examined. Thus, this research applied a metabolomics approach to investigate the metabolite profile in each step of tempe production, from soybean soaking to over-fermentation. Fourteen samples of raw soybeans, i.e., soaked soybeans (24 h), steamed soybeans, fungal fermented soybeans, and over-fermented soybeans (up to 72 h), were collected. Untargeted metabolomics by gas chromatography/mass spectrometry (GC-MS) was used to determine soybean transformations from various fermentation times and identify disparity-related metabolites. The results showed that soybeans samples clustered together on the basis of the different fermentation steps. The results also showed that sugar, sugar alcohol, organic acids, and amino acids, as well as fermentation time, contributed to the soybean metabolite profile transformations. During the fermentation of tempe, sugars and sugar alcohols accumulated at the beginning of the process before gradually decreasing as fermentation progressed. Specifically, at the beginning of the fermentation, gentiobiose, galactinol, and glucarate were accumulated, and several metabolites such as glutamine, 4-hydroxyphenylacetic acid, and homocysteine increased along with the progression of fermentation. In addition, notable isoflavones daidzein and genistein increased from 24 h of fermentation until 72 h. This is the first report that provides a complete description of the metabolic profile of the tempe production from soybean soaking to over-fermentation. Through this study, the dynamic changes at each step of tempe production were revealed. This information can be beneficial to the tempe industry for the improvement of product quality based on metabolite profiling.PMID:36837919 | DOI:10.3390/metabo13020300

Metabolites. 2023 Feb 17;13(2):299. doi: 10.3390/metabo13020299.ABSTRACTWe here characterize changes in metabolite patterns in glioblastoma patients undergoing surgery and concurrent chemoradiation using machine learning (ML) algorithms to characterize metabolic changes during different stages of the treatment protocol. We examined 105 plasma specimens (before surgery, 2 days after surgical resection, before starting concurrent chemoradiation, and immediately after chemoradiation) from 36 patients with isocitrate dehydrogenase (IDH) wildtype glioblastoma. Untargeted GC-TOF mass spectrometry-based metabolomics was used given its superiority in identifying and quantitating small metabolites; this yielded 157 structurally identified metabolites. Using Multinomial Logistic Regression (MLR) and GradientBoostingClassifier (GB Classifier), ML models classified specimens based on metabolic changes. The classification performance of these models was evaluated using performance metrics and area under the curve (AUC) scores. Comparing post-radiation to pre-radiation showed increased levels of 15 metabolites: glycine, serine, threonine, oxoproline, 6-deoxyglucose, gluconic acid, glycerol-alpha-phosphate, ethanolamine, propyleneglycol, triethanolamine, xylitol, succinic acid, arachidonic acid, linoleic acid, and fumaric acid. After chemoradiation, a significant decrease was detected in 3-aminopiperidine 2,6-dione. An MLR classification of the treatment phases was performed with 78% accuracy and 75% precision (AUC = 0.89). The alternative GB Classifier algorithm achieved 75% accuracy and 77% precision (AUC = 0.91). Finally, we investigated specific patterns for metabolite changes in highly correlated metabolites. We identified metabolites with characteristic changing patterns between pre-surgery and post-surgery and post-radiation samples. To the best of our knowledge, this is the first study to describe blood metabolic signatures using ML algorithms during different treatment phases in patients with glioblastoma. A larger study is needed to validate the results and the potential application of this algorithm for the characterization of treatment responses.PMID:36837918 | DOI:10.3390/metabo13020299

Metabolites. 2023 Feb 17;13(2):296. doi: 10.3390/metabo13020296.ABSTRACTColorectal cancer (CRC), one of the most prevalent and deadly cancers worldwide, generally evolves from adenomatous polyps. The understanding of the molecular mechanisms underlying this pathological evolution is crucial for diagnostic and prognostic purposes. Integrative systems biology approaches offer an optimal point of view to analyze CRC and patients with polyposis. The present study analyzed the association networks constructed from a publicly available array of 113 serum metabolites measured on a cohort of 234 subjects from three groups (66 CRC patients, 76 patients with polyposis, and 92 healthy controls), which concentrations were obtained via targeted liquid chromatography-tandem mass spectrometry. In terms of architecture, topology, and connectivity, the metabolite-metabolite association network of CRC patients appears to be completely different with respect to patients with polyposis and healthy controls. The most relevant nodes in the CRC network are those related to energy metabolism. Interestingly, phenylalanine, tyrosine, and tryptophan metabolism are found to be involved in both CRC and polyposis. Our results demonstrate that the characterization of metabolite-metabolite association networks is a promising and powerful tool to investigate molecular aspects of CRC.PMID:36837915 | DOI:10.3390/metabo13020296

Metabolites. 2023 Feb 16;13(2):294. doi: 10.3390/metabo13020294.ABSTRACTThe harvesting period is a critical period for the accumulation of flavonoids in the leaves of the important medicinal plant Epimedium sagittatum. In this study, we conducted an experiment on E. sagittatum leaves sprayed with mineral elements with the aim of improving the quality of the herbal leafage during the harvesting period. We elucidated the changes in flavonoids (icariin, epimedin A, epimedin B, and epimedin C) in E. sagittatum leaves. The sum of main flavonoids content reached a maximum (11.74%) at 20 days after the high-concentration Fe2+ (2500 mg·L-1) treatment. We analyzed the FT-IR spectra characteristics of E. sagittatum leaf samples using the FT-IR technique, and constructed an OPLS-DA model and identified characteristic peaks to achieve differentiated identification of E. sagittatum. Further, widely untargeted metabolomic analysis identified different classes of metabolites. As the most important characteristic flavonoids, the relative contents of icariin, icaritin, icariside I, and icariside II were found to be up-regulated by high-Fe2+ treatment. Our experimental results demonstrate that high-concentration Fe2+ treatment is an effective measure to increase the flavonoids content in E. sagittatum leaves during the harvesting period, which can provide a scientific basis for the improvement of E. sagittatum leaf cultivation agronomic measures.PMID:36837913 | DOI:10.3390/metabo13020294

Metabolites. 2023 Feb 16;13(2):288. doi: 10.3390/metabo13020288.ABSTRACTChile peppers (Capsicum annuum L.) are good sources of vitamins and minerals that can be included in the diet to mitigate nutritional deficiencies. Metabolomics examines the metabolites involved in biological pathways to understand the genes related to complex phenotypes such as the nutritional quality traits. The current study surveys the different metabolites present in jalapeño ('NuMex Pumpkin Spice') and serrano ('NuMex LotaLutein') type chile peppers grown in New Mexico using a widely targeted metabolomics approach, with the 'NuMex LotaLutein' as control. A total of 1088 different metabolites were detected, where 345 metabolites were differentially expressed; 203 (59%) were downregulated and 142 (41%) were upregulated (i.e., relative metabolite content is higher in 'NuMex Pumpkin Spice'). The upregulated metabolites comprised mostly of phenolic acids (42), flavonoids (22), and organic acids (13). Analyses of principal component (PC) and orthogonal partial least squares demonstrated clustering based on cultivars, where at least 60% of variation was attributed to the first two PCs. Pathway annotation identified 89 metabolites which are involved in metabolic pathways and the biosynthesis of secondary metabolites. Altogether, metabolomics provided insights into the different metabolites present which can be targeted for breeding and selection towards the improvement of nutritional quality traits in Capsicum.PMID:36837906 | DOI:10.3390/metabo13020288

Metabolites. 2023 Feb 15;13(2):282. doi: 10.3390/metabo13020282.ABSTRACTMetabolite identification in untargeted metabolomics is complex, with the risk of false positive annotations. This work aims to use machine learning to successively predict the retention time (Rt) and the collision cross-section (CCS) of an open-access database to accelerate the interpretation of metabolomic results. Standards of metabolites were tested using liquid chromatography coupled with high-resolution mass spectrometry. In CCSBase and QSRR predictor machine learning models, experimental results were used to generate predicted CCS and Rt of the Human Metabolome Database. From 542 standards, 266 and 301 compounds were detected in positive and negative electrospray ionization mode, respectively, corresponding to 380 different metabolites. CCS and Rt were then predicted using machine learning tools for almost 114,000 metabolites. R2 score of the linear regression between predicted and measured data achieved 0.938 and 0.898 for CCS and Rt, respectively, demonstrating the models' reliability. A CCS and Rt index filter of mean error ± 2 standard deviations could remove most misidentifications. Its application to data generated from a toxicology study on tobacco cigarettes reduced hits by 76%. Regarding the volume of data produced by metabolomics, the practical workflow provided allows for the implementation of valuable large-scale databases to improve the biological interpretation of metabolomics data.PMID:36837901 | DOI:10.3390/metabo13020282

Metabolites. 2023 Feb 15;13(2):280. doi: 10.3390/metabo13020280.ABSTRACTSalicornia perennis is a halophyte belonging to the botanical subfamily Salicornioideae that forms extensive perennial salt marsh patches. This subfamily has excellent potential, still unexplored, as a source of food, medicine, and phytoremediation. This study aimed to evaluate the lipophilic composition of the Salicornia perennis different organs inhabiting salt marshes of Ria de Aveiro under different stress regimes. For this purpose, the lipophilic content was extracted with hexane and subsequent GC-MS analysis of the extracts for each plant organ, which was collected in three different salt marshes of the Ria de Aveiro. High sugar content was detected in the stems, whereas in fruiting articles, the higher content was in fatty acids. Shorter-chain organic acids were concentrated in the stems and vegetative articles; waxes were detected in greater quantity in photosynthetic organs. More or less stressful environments induce changes in the ratio and composition of molecules, such as acclimatization and oxidative stress reduction strategies; for example, fatty acid content was higher in plants subjected to a higher stress regime. These data contribute to understand the metabolic pathways of the species under study, suggesting new research approaches to its potential as food, medicine, and phytoremediator.PMID:36837899 | DOI:10.3390/metabo13020280

Metabolites. 2023 Feb 14;13(2):277. doi: 10.3390/metabo13020277.ABSTRACTIn this study, a beetroot peel flour was made, and its in vitro antioxidant activity was determined in aqueous (BPFw) and ethanolic (BPFe) extracts. The influence of BPFw on breast cancer cell viability was also determined. A targeted betalain profile was obtained using high-resolution Q-Extractive Plus Orbitrap mass spectrometry (Obrtitrap-HRMS) alongside untargeted chemical profiling of BPFw using Ultra-High-Performance Liquid Chromatography with High-Resolution Mass Spectrometry (UHPLC-HRMS). BPFw and BPFe presented satisfactory antioxidant activities, with emphasis on the total phenolic compounds and ORAC results for BPFw (301.64 ± 0.20 mg GAE/100 g and 3032.78 ± 55.00 µmol T/100 g, respectively). The MCF-7 and MDA-MB-231 breast cancer cells presented reductions in viability when treated with BPFw, showing dose-dependent behavior, with MDA-MB-231 also showing time-dependent behavior. The chemical profiling of BPFw led to the identification of 9 betalains and 59 other compounds distributed amongst 28 chemical classes, with flavonoids and their derivates and coumarins being the most abundant. Three forms of betalain generated via thermal degradation were identified. However, regardless of thermal processing, the BPF still presented satisfactory antioxidant and anticancer activities, possibly due to synergism with other identified molecules with reported anticancer activities via different metabolic pathways.PMID:36837895 | DOI:10.3390/metabo13020277

Metabolites. 2023 Feb 14;13(2):275. doi: 10.3390/metabo13020275.ABSTRACTPistacia lentiscus L. is a medicinal plant that grows spontaneously throughout the Mediterranean basin and is traditionally used to treat diseases, including diabetes. The aim of this work consists of the evaluation of the α-glucosidase inhibitory effect (i.e., antidiabetic activity in vitro) of different extracts from the leaves, stem barks and fruits of P. lentiscus harvested on mountains and the littoral of Tizi-Ouzou in Algeria. Metabolomic profiling combined with a chemometric approach highlighted the variation of the antidiabetic properties of P. lentiscus according to the plant's part and origin. A multiblock OPLS analysis showed that the metabolites most involved in α-glucosidase inhibition activity were mainly found in the stem bark extracts. The highest inhibitory activity was found for the stem bark extracts, with averaged inhibition percentage values of 84.7% and 69.9% for the harvested samples from the littoral and mountain, respectively. On the other hand, the fruit extracts showed a lower effect (13.6%) at both locations. The UHPLC-ESI-HRMS characterization of the metabolites most likely responsible for the α-glucosidase-inhibitory activity allowed the identification of six compounds: epigallocatechin(4a>8)epigallocatechin (two isomers), (epi)gallocatechin-3'-O-galloyl-(epi)gallocatechin (two isomers), 3,5-O-digalloylquinic acid and dihydroxy benzoic acid pentoside.PMID:36837894 | DOI:10.3390/metabo13020275

Metabolites. 2023 Feb 14;13(2):271. doi: 10.3390/metabo13020271.ABSTRACTThe gut-liver axis has been recognized as a potential pathway in which dietary factors may contribute to liver disease in people living with HIV (PLWH). The objective of this study was to explore associations between dietary quality, the fecal microbiome, the metabolome, and liver health in PLWH from the Miami Adult Studies on HIV (MASH) cohort. We performed a cross-sectional analysis of 50 PLWH from the MASH cohort and utilized the USDA Healthy Eating Index (HEI)-2015 to measure diet quality. A Fibrosis-4 Index (FIB-4) score < 1.45 was used as a strong indication that advanced liver fibrosis was not present. Stool samples and fasting blood plasma samples were collected. Bacterial composition was characterized using 16S rRNA sequencing. Metabolomics in plasma were determined using gas and liquid chromatography/mass spectrometry. Statistical analyses included biomarker identification using linear discriminant analysis effect size. Compared to participants with FIB-4 ≥ 1.45, participants with FIB-4 < 1.45 had higher intake of dairy (p = 0.006). Fibrosis-4 Index score was inversely correlated with seafood and plant protein HEI component score (r = -0.320, p = 0.022). The relative abundances of butyrate-producing taxa Ruminococcaceae, Roseburia, and Lachnospiraceae were higher in participants with FIB-4 < 1.45. Participants with FIB-4 < 1.45 also had higher levels of caffeine (p = 0.045) and related metabolites such as trigonelline (p = 0.008) and 1-methylurate (p = 0.023). Dietary components appear to be associated with the fecal microbiome and metabolome, and liver health in PLWH. Future studies should investigate whether targeting specific dietary components may reduce liver-related morbidity and mortality in PLWH.PMID:36837890 | DOI:10.3390/metabo13020271

Metabolites. 2023 Feb 13;13(2):268. doi: 10.3390/metabo13020268.ABSTRACTCadmium (Cd) is a toxic heavy metal, which will lead to ecosystem contamination, threatening the life of grazing animals. Goats are an important grazing animal biomarker to evaluate Cd toxicity, but the effect of short-term and high-concentration Cd toxicity on goat liver function and its latent mechanism is still unclear. A total of ten male Guizhou black goats were randomly divided into two groups: CON group, sterilized tap water (no CdCl2), and Cd group (20 mg Cd·kg-1·BW, CdCl2⋅2.5H2O). The test lasted for 30 days. In this study, we found that Cd poisoning in drinking water affected significantly the distribution of Cd in the goat offal and tissues, and damaged the goat's immune function of the liver. With a metabolomics approach, 59 metabolites were identified. Metabolomics analysis suggested that Cd affected lipid and amino acid metabolism of the goat liver. Collectively, our results confirmed the effect of Cd on liver function and liver metabolism, and provided insights on the molecular basis for early warnings of Cd poisoning in goats.PMID:36837887 | DOI:10.3390/metabo13020268

Metabolites. 2023 Feb 13;13(2):267. doi: 10.3390/metabo13020267.ABSTRACTBoth genetic and non-genetic factors are important in the pathophysiology of non-alcoholic fatty liver disease (NAFLD). The aim of our study was to identify novel metabolites and pathways associated with NAFLD by including both genetic and non-genetic factors in statistical analyses. We genotyped six genetic variants in the PNPLA3, TM6SF2, MBOAT7, GCKR, PPP1R3B, and HSD17B13 genes reported to be associated with NAFLD. Non-targeted metabolomic profiling was performed from plasma samples. We applied a previously validated fatty liver index to identify participants with NAFLD. First, we associated the six genetic variants with 1098 metabolites in 2 339 men without NAFLD to determine the effects of the genetic variants on metabolites, and then in 2 535 men with NAFLD to determine the joint effects of genetic variants and non-genetic factors on metabolites. We identified several novel metabolites and metabolic pathways, especially for PNPLA3, GCKR, and PPP1R38 variants relevant to the pathophysiology of NAFLD. Importantly, we showed that each genetic variant for NAFLD had a specific metabolite signature. The plasma metabolite signature was unique for each genetic variant, suggesting that several metabolites and different pathways are involved in the risk of NAFLD. The FLI index reliably identifies metabolites for NAFLD in large population-based studies.PMID:36837886 | DOI:10.3390/metabo13020267

Metabolites. 2023 Feb 11;13(2):263. doi: 10.3390/metabo13020263.ABSTRACTThe SARS-CoV-2 virus primarily infects salivary glands suggesting a change in the saliva metabolite profile; this shift may be used as a monitoring instrument during SARS-CoV-2 infection. The present study aims to determine the salivary metabolomic profile of patients with and post-SARS-CoV-19 infection. Patients were without (PCR-), with SARS-CoV-2 (PCR+), or post-SARS-CoV-2 infection. Unstimulated whole saliva was collected, and the 1H spectra were acquired in a 500 MHz Bruker nuclear magnetic resonance spectrometer at 25 °C. They were subjected to multivariate analysis using principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA), as well as univariate analysis through t-tests (SPSS 20.0, IL, USA), with a significance level of p < 0.05. A distinction was found when comparing PCR- subjects to those with SARS-CoV-2 infection. When comparing the three groups, the PLS-DA cross-validation presented satisfactory accuracy (ACC = 0.69, R2 = 0.39, Q2 = 0.08). Seventeen metabolites were found in different proportions among the groups. The results suggested the downregulation of major amino acid levels, such as alanine, glutamine, histidine, leucine, lysine, phenylalanine, and proline in the PCR+ group compared to the PCR- ones. In addition, acetate, valerate, and capronic acid were higher in PCR- patients than in PCR+. Sucrose and butyrate were higher in post-SARS-CoV-2 infection compared to PCR-. In general, a reduction in amino acids was observed in subjects with and post-SARS-CoV-2 disease. The salivary metabolomic strategy NMR-based was able to differentiate between non-infected individuals and those with acute and post-SARS-CoV-19 infection.PMID:36837882 | DOI:10.3390/metabo13020263

Metabolites. 2023 Feb 10;13(2):259. doi: 10.3390/metabo13020259.ABSTRACTThis study investigated the effects of maternal nutrition on the plasma metabolome of Nellore bulls in the rearing and finishing phases, and metabolic differences between these phases. For this study, three nutritional approaches were used in 126 cows during pregnancy: NP-(control) mineral supplementation; PP-protein-energy supplementation in the final third; and FP-protein-energy supplementation during the entire pregnancy. We collected blood samples from male offspring in the rearing (450 ± 28 days old) and finishing phases (660 ± 28 days old). The blood was processed, and from plasma samples, we performed the targeted metabolome analysis (AbsoluteIDQ® p180 Kit). Multiple linear regression, principal component analysis (PCA), repeated measures analysis over time, and an enrichment analysis were performed. PCA showed an overlap of treatments and time clusters in the analyses. We identified significant metabolites among the treatments (rearing phase = six metabolites; finishing phase = three metabolites) and over time (21 metabolites). No significant metabolic pathways were found in the finishing phase, however, we found significant pathways in the rearing phase (Arginine biosynthesis and Histidine metabolism). Thus, prenatal nutrition impacted on plasma metabolome of bulls during the rearing and finishing phase and the different production stages showed an effect on the metabolic levels of bulls.PMID:36837878 | DOI:10.3390/metabo13020259

Metabolites. 2023 Feb 10;13(2):258. doi: 10.3390/metabo13020258.ABSTRACTMany unique chemical metabolites with significant antitumor activities have been isolated from Garcinia species and have become a leading hotspot of antitumor research in recent years. The aim of this study was to identify bioactive compounds from different plant parts (leaf, branch, stem bark, fruit, and seed) of G. xanthochymus through combining LC-MS-based metabolomics with cytotoxicity assays. As a result, 70% methanol seed extract exerted significant cytotoxic effects on five human cancer cell types (HL-60, A549, SMMC-7721, MDA-MB-231, and SW480). LC-MS-based metabolomics analysis was used, including principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA), in order to identify 12 potential markers from seed extract that may relate to bioactivity. LC-MS guidance isolated the markers to obtain three compounds and identified new isopentenyl phloroglucinols (1-3, named garxanthochin A-C), using spectroscopic methods. Among them, garxanthochin B (2) demonstrated moderate inhibitory activities against five human cancer cell types, with IC50 values of 14.71~24.43 μM. These findings indicate that G. xanthochymus seed has significant cytotoxic activity against cancer cells and garxanthochin B has potential applications in the development of antitumor-led natural compounds.PMID:36837877 | DOI:10.3390/metabo13020258

Metabolites. 2023 Feb 9;13(2):252. doi: 10.3390/metabo13020252.ABSTRACTThis work focused on the metabolomic profiling of the conditioned medium (FS03CM) produced by an anaerobic bacterium closely related to Terrisporobacter spp. to identify potential antimicrobial metabolites. The metabolome of the conditioned medium was profiled by two-channel Chemical Isotope Labelling (CIL) LC-MS. The detected metabolites were identified or matched by conducting a library search using different confidence levels. Forty-eight significantly changed metabolites were identified with high confidence after the growth of isolate FS03 in cooked meat glucose starch (CMGS) medium. Some of the secondary metabolites identified with known antimicrobial activities were 4-hydroxyphenyllactate, 3-hydroxyphenylacetic acid, acetic acid, isobutyric acid, valeric acid, and tryptamine. Our findings revealed the presence of different secondary metabolites with previously reported antimicrobial activities and suggested the capability of producing antimicrobial metabolites by the anaerobic bacterium FS03.PMID:36837871 | DOI:10.3390/metabo13020252

Metabolites. 2023 Feb 8;13(2):246. doi: 10.3390/metabo13020246.ABSTRACTIn plant biology, metabolomics is often used to quantitatively assess small molecules, metabolites, and their intermediates in plants. Metabolomics has frequently been applied to detect metabolic alterations in plants exposed to various biotic and abiotic stresses, including pesticides. The widespread use of pesticides and agrochemicals in intensive crop production systems is a serious threat to the functionality and sustainability of agroecosystems. Pesticide accumulation in soil may disrupt soil-plant relationships, thereby posing a pollution risk to agricultural output. Application of metabolomic techniques in the assessment of the biological consequences of pesticides at the molecular level has emerged as a crucial technique in exposome investigations. State-of-the-art metabolomic approaches such as GC-MS, LC-MS/MS UHPLC, UPLC-IMS-QToF, GC/EI/MS, MALDI-TOF MS, and 1H-HR-MAS NMR, etc., investigating the harmful effects of agricultural pesticides have been reviewed. This updated review seeks to outline the key uses of metabolomics related to the evaluation of the toxicological impacts of pesticides on agronomically important crops in exposome assays as well as bench-scale studies. Overall, this review describes the potential uses of metabolomics as a method for evaluating the safety of agricultural chemicals for regulatory applications. Additionally, the most recent developments in metabolomic tools applied to pesticide toxicology and also the difficulties in utilizing this approach are discussed.PMID:36837865 | DOI:10.3390/metabo13020246

Metabolites. 2023 Feb 7;13(2):242. doi: 10.3390/metabo13020242.ABSTRACTLimited data are available on the effects of pregnancy on the maternal metabolome. Therefore, the objective of this study was to use metabolomics analysis to determine pathways impacted by pregnancy followed by targeted confirmatory analysis to provide more powerful conclusions about metabolic alterations during pregnancy. Forty-seven pregnant women, 18-50 years of age were included in this study, with each subject serving as their own control. Plasma samples were collected between 25 and 28 weeks gestation and again ≥3 months postpartum for metabolomics analysis utilizing an HILIC/UHPLC/MS/MS assay with confirmatory targeted specific concentration analysis for 10 of the significantly altered amino acids utilizing an LC/MS assay. Principle component analysis (PCA) on metabolomics data clearly separated pregnant and postpartum groups and identified outliers in a preliminary assessment. Of the 980 metabolites recorded, 706 were determined to be significantly different between pregnancy and postpartum. Pathway analysis revealed three significantly impacted pathways, arginine biosynthesis (p = 2 × 10-5 and FDR = 1 × 10-3), valine, leucine, and isoleucine metabolism (p = 2 × 10-5 and FDR = 2 × 10-3), and xanthine metabolism (p = 4 × 10-5 and FDR = 4 × 10-3). Of these we focused analysis on arginine biosynthesis and branched-chain amino acid (BCAA) metabolism due to their clinical importance and interconnected roles in amino acid metabolism. In the confirmational analysis, 7 of 10 metabolites were confirmed as significant and all 10 confirmed the direction of change of concentrations observed in the metabolomics analysis. The data support an alteration in urea nitrogen disposition and amino acid metabolism during pregnancy. These changes could also impact endogenous nitric oxide production and contribute to diseases of pregnancy. This study provides evidence for changes in both the ammonia-urea nitrogen and the BCAA metabolism taking place during pregnancy.PMID:36837861 | DOI:10.3390/metabo13020242