The discovery of regional neurotoxicity-associated metabolic alterations induced by carbon quantum dots in brain of mice using a spatial metabolomics analysis.

BACKGROUND: Recently, carbon quantum dots (CQDs) have been widely used in various fields, especially in the diagnosis and therapy of neurological disorders, due to their excellent prospects. However, the associated inevitable exposure of CQDs to the environment and the public could have serious severe consequences limiting their safe application and sustainable development. RESULTS: In this study, we found that intranasal treatment of 5 mg/kg BW (20 µL/nose of 0.5 mg/mL) CQDs affected the distribution of multiple metabolites and associated pathways in the brain of mice through the airflow-assisted desorption electrospray ionization mass spectrometry imaging (AFADESI-MSI) technique, which proved effective in discovery has proven to be significantly alerted and research into tissue-specific toxic biomarkers and molecular toxicity analysis. The neurotoxic biomarkers of CQDs identified by MSI analysis mainly contained aminos, lipids and lipid-like molecules which are involved in arginine and proline metabolism, biosynthesis of unsaturated fatty acids, and glutamine and glutamate metabolism, etc. as well as related metabolic enzymes. The levels or expressions of these metabolites and enzymes changed by CQDs in different brain regions would induce neuroinflammation, organelle damage, oxidative stress and multiple programmed cell deaths (PCDs), leading to neurodegeneration, such as Parkinson's disease-like symptoms. This study enlightened risk assessments and interventions of QD-type or carbon-based nanoparticles on the nervous system based on toxic biomarkers regarding region-specific profiling of altered metabolic signatures. CONCLUSION: These findings provide information to advance knowledge of neurotoxic effects of CQDs and guide their further safety evaluation.

Physiotyping of Plants and Modeling the Soil Plant Atmospheric Continuum (SPAC).

This chapter presents a holistic and quantitative approach to the carbon cycle in plant systems biology. It includes (rapid) phenotyping and monitoring of physiological key interactions of plants with its respective soil and atmospheric environment (soil plant atmospheric continuum-SPAC). The approach aims at qualifying and quantifying key components of this microhabitat as influenced by a single plant or a local group of plants in order to contribute to a flux-based modelling approach. The toolset consists of plant biometry, gas exchange, metabolomics, ionomics, root exudate characterization as well as soil biological and physical-chemical characterization. The results are presented as a basic interaction and input-output model aka conceptual system model employing H. T. Odum-style plots based on empirical data.

Sample Preparation from Plant Tissue for Gas Chromatography-Mass Spectrometry (GC-MS)we.

Metabolites are intermediate products formed during metabolism. Metabolites play different roles, including providing energy, supporting structure, transmitting signals, catalyzing reactions, enhancing defense, and interacting with other species. Plant metabolomics research aims to detect precisely all metabolites found within tissues of plants through GC-MS. This chapter primarily focuses on extracting metabolites using chemicals such as methanol, chloroform, ribitol, MSTFA, and TMCS. The metabolic analysis method is frequently used according to the specific kind of sample or matrix being investigated and the analysis objective. Chromatography (LC, GC, and CE) with mass spectrometry and NMR spectroscopy is used in modern metabolomics to analyze metabolites from plant samples. The most frequently used method for metabolites analysis is the GC-MS. It is a powerful technique that combines gas chromatography's separation capabilities with mass spectrometry, offering detailed information, including structural identification of each metabolite. This chapter contains an easy-to-follow guide to extract plant-based metabolites. The current protocol provides all the information needed for extracting metabolites from a plant, precautions, and troubleshooting.

Studying Plant Specialized Metabolites Using Computational Metabolomics Strategies.

Plant specialized metabolites have diversified vastly over the course of plant evolution, and they are considered key players in complex interactions between plants and their environment. The chemical diversity of these metabolites has been widely explored and utilized in agriculture and crop enhancement, the food industry, and drug development, among other areas. However, the immensity of the plant metabolome can make its exploration challenging. Here we describe a protocol for exploring plant specialized metabolites that combines high-resolution mass spectrometry and computational metabolomics strategies, including molecular networking, identification of structural motifs, as well as prediction of chemical structures and metabolite classes.

iMPAQT reveals that adequate mitohormesis from TFAM overexpression leads to life extension in mice.

Mitochondrial transcription factor A, TFAM, is essential for mitochondrial function. We examined the effects of overexpressing the TFAM gene in mice. Two types of transgenic mice were created: TFAM heterozygous (TFAM Tg) and homozygous (TFAM Tg/Tg) mice. TFAM Tg/Tg mice were smaller and leaner notably with longer lifespans. In skeletal muscle, TFAM overexpression changed gene and protein expression in mitochondrial respiratory chain complexes, with down-regulation in complexes 1, 3, and 4 and up-regulation in complexes 2 and 5. The iMPAQT analysis combined with metabolomics was able to clearly separate the metabolomic features of the three types of mice, with increased degradation of fatty acids and branched-chain amino acids and decreased glycolysis in homozygotes. Consistent with these observations, comprehensive gene expression analysis revealed signs of mitochondrial stress, with elevation of genes associated with the integrated and mitochondrial stress responses, including Atf4, Fgf21, and Gdf15. These found that mitohormesis develops and metabolic shifts in skeletal muscle occur as an adaptive strategy.

Analysis of the potential regulatory mechanisms of female and latent genital tuberculosis affecting ovarian reserve function using untargeted metabolomics.

Female and latent genital tuberculosis (FGTB and LGTB) in young women may lead to infertility by damaging ovarian reserve function, but the regulatory mechanisms remain unclear. In this study, we investigated the effects of FGTB and LGTB on ovarian reserve function and potential regulatory mechanisms by untargeted metabolomics of follicular fluid, aiming to provide insights for the clinical management and treatment approaches for afflicted women. We recruited 19 patients with FGTB, 16 patients with LGTB, and 16 healthy women as a control group. Clinical data analysis revealed that both the FGTB and LGTB groups had significantly lower ovarian reserve marker levels compared to the control group, including lower anti-Müllerian hormone levels (FGTB: 0.82 [0.6, 1.1] µg/L; LGTB: 1.57 [1.3, 1.8] µg/L vs. control: 3.29 [2.9, 3.5] µg/L), reduced antral follicular counts (FGTB: 6 [5.5, 9.5]; LGTB: 10.5 [7, 12.3] vs. control: 17 [14.5, 18]), and fewer retrieved oocytes (FGTB: 3 [2, 5]; LGTB: 8 [4, 8.3] vs. control: 14.5 [11.5, 15.3]). Conversely, these groups exhibited higher ovarian response marker levels, such as longer gonadotropin treatment days (FGTB: 12 [10.5, 12.5]; LGTB: 11 [10.8, 11.3] vs. control: 10 [8.8, 10]) and increased gonadotropin dosage requirements (FGTB: 3300 [3075, 3637.5] U; LGTB: 3037.5 [2700, 3225] U vs. control: 2531.25 [2337.5, 2943.8] U). All comparisons were statistically significant at P < 0.05. The results suggested that FGTB and LGTB have adverse effects on ovarian reserve and response. Untargeted metabolomic analysis identified 92 and 80 differential metabolites in the control vs. FGTB and control vs. LGTB groups, respectively. Pathway enrichment analysis revealed significant alterations in metabolic pathways in the FGTB and LGTB groups compared to the control group (P < 0.05), with specific changes noted in galactose metabolism, biotin metabolism, steroid hormone biosynthesis, and nicotinate and nicotinamide metabolism in the FGTB group, and caffeine metabolism, primary bile acid biosynthesis, steroid hormone biosynthesis, and glycerophospholipid metabolism in the LGTB group. The analysis of metabolic levels has revealed the potential mechanisms by which FGTB and LGTB affect ovarian reserve function, namely through alterations in metabolic pathways. The study emphasizes the importance of comprehending the metabolic alterations associated with FGTB and LGTB, which is of considerable relevance for the clinical management and therapeutic approaches in afflicted women.

Urinary metabolomic profiling of a cohort of Colombian patients with systemic lupus erythematosus.

Systemic lupus erythematosus (SLE) is an autoimmune and multisystem disease with a high public health impact. Lupus nephritis (LN), commonly known as renal involvement in SLE, is associated with a poorer prognosis and increased rates of morbidity and mortality in patients with SLE. Identifying new urinary biomarkers that can be used for LN prognosis or diagnosis is essential and is part of current active research. In this study, we applied an untargeted metabolomics approach involving liquid and gas chromatography coupled with mass spectrometry to urine samples collected from 17 individuals with SLE and no kidney damage, 23 individuals with LN, and 10 clinically healthy controls (HCs) to identify differential metabolic profiles for SLE and LN. The data analysis revealed a differentially abundant metabolite expression profile for each study group, and those metabolites may act as potential differential biomarkers of SLE and LN. The differential metabolic pathways found between the LN and SLE patients with no kidney involvement included primary bile acid biosynthesis, branched-chain amino acid synthesis and degradation, pantothenate and coenzyme A biosynthesis, lysine degradation, and tryptophan metabolism. Receiver operating characteristic curve analysis revealed that monopalmitin, glycolic acid, and glutamic acid allowed for the differentiation of individuals with SLE and no kidney involvement and individuals with LN considering high confidence levels. While the results offer promise, it is important to recognize the significant influence of medications and other external factors on metabolomics studies. This impact has the potential to obscure differences in metabolic profiles, presenting a considerable challenge in the identification of disease biomarkers. Therefore, experimental validation should be conducted with a larger sample size to explore the diagnostic potential of the metabolites found as well as to examine how treatment and disease activity influence the identified chemical compounds. This will be crucial for refining the accuracy and effectiveness of using urine metabolomics for diagnosing and monitoring lupus and lupus nephritis.

Blood-Based Multiomics-Guided Detection of a Precancerous Pancreatic Tumor.

Over a decade ago, longitudinal multiomics analysis was pioneered for early disease detection and individually tailored precision health interventions. However, high sample processing costs, expansive multiomics measurements along with complex data analysis have made this approach to precision/personalized medicine impractical. Here we describe in a case report, a more practical approach that uses fewer measurements, annual sampling, and faster decision making. We also show how this approach offers promise to detect an exceedingly rare and potentially fatal condition before it fully manifests. Specifically, we describe in the present case report how longitudinal multiomics monitoring (LMOM) helped detect a precancerous pancreatic tumor and led to a successful surgical intervention. The patient, enrolled in an annual blood-based LMOM since 2018, had dramatic changes in the June 2021 and 2022 annual metabolomics and proteomics results that prompted further clinical diagnostic testing for pancreatic cancer. Using abdominal magnetic resonance imaging, a 2.6 cm lesion in the tail of the patient's pancreas was detected. The tumor fluid from an aspiration biopsy had 10,000 times that of normal carcinoembryonic antigen levels. After the tumor was surgically resected, histopathological findings confirmed it was a precancerous pancreatic tumor. Postoperative omics testing indicated that most metabolite and protein levels returned to patient's 2018 levels. This case report illustrates the potentials of blood LMOM for precision/personalized medicine, and new ways of thinking medical innovation for a potentially life-saving early diagnosis of pancreatic cancer. Blood LMOM warrants future programmatic translational research with the goals of precision medicine, and individually tailored cancer diagnoses and treatments.

Tongue-Coating Microbial and Metabolic Characteristics in Halitosis.

Halitosis is a common oral condition, which leads to social embarrassment and affects quality of life. Cumulative evidence has suggested the association of tongue-coating microbiome with the development of intraoral halitosis. The dynamic variations of tongue-coating microbiota and metabolites in halitosis have not been fully elucidated. Therefore, the present study aimed to determine the tongue-coating microbial and metabolic characteristics in halitosis subjects without other oral diseases using metagenomics and metabolomics analysis. The participants underwent oral examination, halitosis assessment, and tongue-coating sample collection for the microbiome and metabolome analysis. It was found that the microbiota richness and diversity were significantly elevated in the halitosis group. Furthermore, species from Actinomyces, Prevotella, Veillonella, and Solobacterium were significantly more abundant in the halitosis group. However, the Rothia and Streptococcus species exhibited opposite tendencies. Eleven Kyoto Encyclopedia of Genes and Genomes pathways were significantly enriched in the halitosis tongue coatings, including cysteine and methionine metabolism. Functional genes related to sulfur, indole, skatole, and cadaverine metabolic processes (such as serA, metH, metK and dsrAB) were identified to be more abundant in the halitosis samples. The metabolome analysis revealed that indole-3-acetic, ornithine, and L-tryptophan were significantly elevated in the halitosis samples. Furthermore, it was observed that the values of volatile sulfur compounds and indole-3-acetic abundances were positively correlated. The multiomics analysis identified the metagenomic and metabolomic characteristics to differentiate halitosis from healthy individuals using the least absolute shrinkage and selection operator logistic regression and random forest classifier. A total of 19 species and 39 metabolites were identified as features in halitosis patients, which included indole-3-acetic acid, Bacillus altitudinis, Candidatus Saccharibacteria, and Actinomyces species. In conclusion, an evident shift in microbiome and metabolome characteristics was observed in the halitosis tongue coating, which may have a potential etiological significance and provide novel insights into the mechanism for halitosis.

Analysis of plasma metabolomes from 11 309 subjects in five population-based cohorts.

Plasma metabolomics holds potential for precision medicine, but limited information is available to compare the performance of such methods across multiple cohorts. We compared plasma metabolite profiles after an overnight fast in 11,309 participants of five population-based Swedish cohorts (50-80 years, 52% women). Metabolite profiles were uniformly generated at a core laboratory (Metabolon Inc.) with untargeted liquid chromatography mass spectrometry and a comprehensive reference library. Analysis of a second sample obtained one year later was conducted in a subset. Of 1629 detected metabolites, 1074 (66%) were detected in all cohorts while only 10% were unique to one cohort, most of which were xenobiotics or uncharacterized. The major classes were lipids (28%), xenobiotics (22%), amino acids (14%), and uncharacterized (19%). The most abundant plasma metabolome components were the major dietary fatty acids and amino acids, glucose, lactate and creatinine. Most metabolites displayed a log-normal distribution. Temporal variability was generally similar to clinical chemistry analytes but more pronounced for xenobiotics. Extensive metabolite-metabolite correlations were observed but mainly restricted to within each class. Metabolites were broadly associated with clinical factors, particularly body mass index, sex and renal function. Collectively, our findings inform the conduct and interpretation of metabolite association and precision medicine studies.

Untargeted metabolomics reveals that declined PE and PC in obesity may be associated with prostate hyperplasia.

BACKGROUND: Recent studies have shown that obesity may contribute to the pathogenesis of benign prostatic hyperplasia (BPH). However, the mechanism of this pathogenesis is not fully understood. METHODS: A prospective case-control study was conducted with 30 obese and 30 nonobese patients with BPH. Prostate tissues were collected and analyzed using ultra performance liquid chromatography ion mobility coupled with quadrupole time-of-flight mass spectrometry (UPLC-IMS-Q-TOF). RESULTS: A total of 17 differential metabolites (3 upregulated and 14 downregulated) were identified between the obese and nonobese patients with BPH. Topological pathway analysis indicated that glycerophospholipid (GP) metabolism was the most important metabolic pathway involved in BPH pathogenesis. Seven metabolites were enriched in the GP metabolic pathway. lysoPC (P16:0/0:0), PE (20:0/20:0), PE (24:1(15Z)/18:0), PC (24:1(15Z)/14:0), PC (15:0/24:0), PE (24:0/18:0), and PC (16:0/18:3(9Z,12Z,15Z)) were all significantly downregulated in the obesity group, and the area under the curve (AUC) of LysoPC (P-16:0/0/0:0) was 0.9922. The inclusion of the seven differential metabolites in a joint prediction model had an AUC of 0.9956. Thus, both LysoPC (P-16:0/0/0:0) alone and the joint prediction model demonstrated good predictive ability for obesity-induced BPH mechanisms. CONCLUSIONS: In conclusion, obese patients with BPH had a unique metabolic profile, and alterations in PE and PC in these patients be associated with the development and progression of BPH.

Tackling new psychoactive substances through metabolomics: UHPLC-HRMS study on natural and synthetic opioids in male and female murine models.

Novel psychoactive substances (NPS) represent a broad class of drugs new to the illicit market that often allow passing drug-screening tests. They are characterized by a variety of structures, rapid transience on the drug scene and mostly unknown metabolic profiles, thus creating an ever-changing scenario with evolving analytical targets. The present study aims at developing an indirect screening strategy for NPS monitoring, and specifically for new synthetic opioids (NSOs), based on assessing changes in endogenous urinary metabolite levels as a consequence of the systemic response following their intake. The experimental design involved in-vivo mice models: 16 animals of both sex received a single administration of morphine or fentanyl. Urine was collected before and after administration at different time points; the samples were then analysed with an untargeted metabolomics LC-HRMS workflow. According to our results, the intake of opioids resulted in an elevated energy demand, that was more pronounced on male animals, as evidenced by the increase in medium and long chain acylcarnitines levels. It was also shown that opioid administration disrupted the pathways related to catecholamines biosynthesis. The observed alterations were common to both morphine and fentanyl: this evidence indicate that they are not related to the chemical structure of the drug, but rather on the drug class. The proposed strategy may reinforce existing NPS screening approaches, by identifying indirect markers of drug assumption.

Gut microbiota composition and metabolic characteristics in patients with Craniopharyngioma.

BACKGROUND: Emerging evidence suggests that the gut microbiota is associated with various intracranial neoplastic diseases. It has been observed that alterations in the gut microbiota are present in gliomas, meningiomas, and pituitary neuroendocrine tumors (Pit-NETs). However, the correlation between gut microbiota and craniopharyngioma (CP), a rare embryonic malformation tumor in the sellar region, has not been previously mentioned. Consequently, this study aimed to investigate the gut microbiota composition and metabolic patterns in CP patients, with the goal of identifying potential therapeutic approaches. METHODS: We enrolled 15 medication-free and non-operated patients with CP and 15 healthy controls (HCs), conducting sequential metagenomic and metabolomic analyses on fecal samples to investigate changes in the gut microbiota of CP patients. RESULTS: The composition of gut microbiota in patients with CP compared to HCs show significant discrepancies at both the genus and species levels. The CP group exhibits greater species diversity. And the metabolic patterns between the two groups vary markedly. CONCLUSIONS: The gut microbiota composition and metabolic patterns in patients with CP differ significantly from the healthy population, presenting potential new therapeutic opportunities.

The influence of maternal prepregnancy weight and gestational weight gain on the umbilical cord blood metabolome: a case-control study.

BACKGROUND: Maternal overweight/obesity and excessive gestational weight gain (GWG) are frequently reported to be risk factors for obesity and other metabolic disorders in offspring. Cord blood metabolites provide information on fetal nutritional and metabolic health and could provide an early window of detection of potential health issues among newborns. The aim of the study was to explore the impact of maternal prepregnancy overweight/obesity and excessive GWG on cord blood metabolic profiles. METHODS: A case control study including 33 pairs of mothers with prepregnancy overweight/obesity and their neonates, 30 pairs of mothers with excessive GWG and their neonates, and 32 control mother-neonate pairs. Untargeted metabolomic profiling of umbilical cord blood samples were performed using UHPLC‒MS/MS. RESULTS: Forty-six metabolites exhibited a significant increase and 60 metabolites exhibited a significant reduction in umbilical cord blood from overweight and obese mothers compared with mothers with normal body weight. Steroid hormone biosynthesis and neuroactive ligand‒receptor interactions were the two top-ranking pathways enriched with these metabolites (P = 0.01 and 0.03, respectively). Compared with mothers with normal GWG, in mothers with excessive GWG, the levels of 63 metabolites were increased and those of 46 metabolites were decreased in umbilical cord blood. Biosynthesis of unsaturated fatty acids was the most altered pathway enriched with these metabolites (P < 0.01). CONCLUSIONS: Prepregnancy overweight and obesity affected the fetal steroid hormone biosynthesis pathway, while excessive GWG affected fetal fatty acid metabolism. This emphasizes the importance of preconception weight loss and maintaining an appropriate GWG, which are beneficial for the long-term metabolic health of offspring.

NMR Metabolomics of Primary Ovarian Cancer Cells in Comparison to Established Cisplatin-Resistant and -Sensitive Cell Lines.

Cancer cell lines are frequently used in metabolomics, such as in vitro tumor models. In particular, A2780 cells are commonly used as a model for ovarian cancer to evaluate the effects of drug treatment. Here, we compare the NMR metabolomics profiles of A2780 and cisplatin-resistant A2780 cells with those of cells derived from 10 patients with high-grade serous ovarian carcinoma (collected during primary cytoreduction before any chemotherapeutic treatment). Our analysis reveals a substantial similarity among all primary cells but significant differences between them and both A2780 and cisplatin-resistant A2780 cells. Notably, the patient-derived cells are closer to the resistant A2780 cells when considering the exo-metabolome, whereas they are essentially equidistant from A2780 and A2780-resistant cells in terms of the endo-metabolome. This behavior results from dissimilarities in the levels of several metabolites attributable to the differential modulation of underlying biochemical pathways. The patient-derived cells are those with the most pronounced glycolytic phenotype, whereas A2780-resistant cells mainly diverge from the others due to alterations in a few specific metabolites already known as markers of resistance.

Comprehensive Metabolomic Analysis of Human Heart Tissue Enabled by Parallel Metabolite Extraction and High-Resolution Mass Spectrometry.

The heart contracts incessantly and requires a constant supply of energy, utilizing numerous metabolic substrates, such as fatty acids, carbohydrates, lipids, and amino acids, to supply its high energy demands. Therefore, a comprehensive analysis of various metabolites is urgently needed for understanding cardiac metabolism; however, complete metabolome analyses remain challenging due to the broad range of metabolite polarities, which makes extraction and detection difficult. Herein, we implemented parallel metabolite extractions and high-resolution mass spectrometry (MS)-based methods to obtain a comprehensive analysis of the human heart metabolome. To capture the diverse range of metabolite polarities, we first performed six parallel liquid-liquid extractions (three monophasic, two biphasic, and one triphasic) of healthy human donor heart tissue. Next, we utilized two complementary MS platforms for metabolite detection: direct-infusion ultrahigh-resolution Fourier-transform ion cyclotron resonance (DI-FTICR) and high-resolution liquid chromatography quadrupole time-of-flight tandem MS (LC-Q-TOF-MS/MS). Using DI-FTICR MS, 9644 metabolic features were detected where 7156 were assigned a molecular formula and 1107 were annotated by accurate mass assignment. Using LC-Q-TOF-MS/MS, 21,428 metabolic features were detected where 285 metabolites were identified based on fragmentation matching against publicly available libraries. Collectively, 1340 heart metabolites were identified in this study, which span a wide range of polarities including polar (benzenoids, carbohydrates, and nucleosides) as well as nonpolar (phosphatidylcholines, acylcarnitines, and fatty acids) compounds. The results from this study will provide critical knowledge regarding the selection of appropriate extraction and MS detection methods for the analysis of the diverse classes of human heart metabolites.

Multiple-matrix metabolomics analysis for the distinct detection of colorectal cancer and adenoma.

OBJECTIVES: Although colorectal cancer (CRC) is the leading cause of cancer-related morbidity and mortality, current diagnostic tests for early-stage CRC and colorectal adenoma (CRA) are suboptimal. Therefore, there is an urgent need to explore less invasive screening procedures for CRC and CRA diagnosis. METHODS: Untargeted gas chromatography-mass spectrometry (GC-MS) metabolic profiling approach was applied to identify candidate metabolites. We performed metabolomics profiling on plasma samples from 412 subjects including 200 CRC patients, 160 CRA patients and 52 normal controls (NC). Among these patients, 45 CRC patients, 152 CRA patients and 50 normal controls had their fecal samples tested simultaneously. RESULTS: Differential metabolites were screened in the adenoma-carcinoma sequence. Three diagnostic models were further developed to identify cancer group, cancer stage, and cancer microsatellite status using those significant metabolites. The three-metabolite-only classifiers used to distinguish the cancer group always keeps the area under the receiver operating characteristic curve (AUC) greater than 0.7. The AUC performance of the classifiers applied to discriminate CRC stage is generally greater than 0.8, and the classifiers used to distinguish microsatellite status of CRC is greater than 0.9. CONCLUSION: This finding highlights potential early-driver metabolites in CRA and early-stage CRC. We also find potential metabolic markers for discriminating the microsatellite state of CRC. Our study and diagnostic model have potential applications for non-invasive CRC and CRA detection.

[Metabolomics of quality formation of different cultivars of Peucedanum praeruptorum].

This study aims to reveal the quality formation of different cultivars of Peucedanum praeruptorum based on the metabolic differences and provide a theoretical basis for the development and utilization of this medicinal herb. The non-target metabonomics analysis based on ultra-high performance liquid chromatography tandem mass spectrometry(UHPLC-MS/MS) was conducted for six cultivars(YS, H, LZ, LY, LX, and Z) of P. praeruptorum of the same origin and at the same development stage. The principal component analysis, orthogonal partial least squares discriminant analysis, and univariate statistical analysis were carried out to screen the differential metabolites of different cultivars. The potential biomarkers associated with quality formation were predicted based on the mass-to-charge ratio, Kyoto Encyclopedia of Genes and Genomes pathway enrichment, information of relevant literature, and correlation analysis. The results showed that metabolites differed significantly among the six cultivars, and 571 and 465 differential metabolites were obtained in the positive and negative ion modes, respectively. From the differential metabolites, 22 potential biomarkers related to quality formation were predicted, which involved 9 metabolic pathways, including phenylalanine, tyrosine and tryptophan biosynthesis, biosynthesis of phenylpropanoids, and biosynthesis of plant hormones. Compared with the YS cultivar, other cultivars showed decreased concentrations of psoralen, imperatorin, and luvangetin and increased concentrations of 7-hydroxycoumarine, esculetin, columbianetin, and jasmonic acid, which were involved in the biosynthesis of phenylpropanoids. The concentrations of 2-succinylbenzoate, heraclenol, and L-tyrosine involved in other metabolic pathways decreased, especially in the Z and H cultivars. Therefore, regulating the biosynthesis of phenylpropanoids is one of the key mechanisms for improving the cultivar quality of P. praeruptorum. The Z and H cultivars have better quality and metabolic processes than other cultivars and thus can be used for the screening and breeding of high-quality germplasm.

[Non-targeted metabolomics of spleen and liver metabolism in mice treated with Pruni Semen processed with different methods].

Ultra performance liquid chromatography-quadrupole time-of-flight mass spectrometry(UPLC-Q-TOF-MS) was employed to investigate the impacts of Pruni Semen processed with different methods(raw and fried) on the liver and spleen metabolism in mice. A total of 24 male mice were randomly assigned to three groups: raw Pruni Semen group, fried Pruni Semen group, and control(deionized water) group. Mice in the three groups were orally administrated with 0.01 g·mL~(-1) Pruni Semen decoction or deionized water for one week. After that, the liver and spleen tissues were collected, and liquid chromatography-mass spectrometry(LC-MS)-based metabolomic analysis was carried out to investigate the impact of Pruni Semen on the liver and spleen metabolism in mice. Compared with thte control group, the raw Pruni Semen group showed up-regulation of 11 metabolites and down-regulation of 57 metabolites in the spleen(P<0.05), as well as up-regulation of 15 metabolites and down-regulation of 58 metabolites in the liver(P<0.05). The fried Pruni Semen group showed up-regulation of 31 metabolites and down-regulation of 10 metabolites in the spleen(P<0.05), along with up-regulation of 26 metabolites and down-regulation of 61 metabolites in the liver(P<0.05). The differential metabolites identified in the raw Pruni Semen group were primarily associated with alanine, aspartate, and glutamate metabolism, purine metabolism, amino sugar and nucleotide sugar metabolism, and D-glutamine and D-glutamate metabolism. The differential metabolites identified in the fried Pruni Semen group predominantly involved riboflavin metabolism, amino sugar and nucleotide sugar metabolism, purine metabolism, alanine, aspartate, and glutamate metabolism, D-glutamine and D-glutamate metabolism, and glutathione metabolism. The findings suggest that both raw and fried Pruni Semen have the potential to modulate the metabolism of the liver and spleen in mice by influencing the glutamine and glutamate metabolism.

Differences of Typical Wuyi Rock Tea in Taste and Nonvolatiles Profile Revealed by Multisensory Analysis and LC-MS-Based Metabolomics.

Wuyi Rock tea, specifically Shuixian and Rougui, exhibits distinct sensory characteristics. In this study, we investigated the sensory and metabolite differences between Shuixian and Rougui. Quantitative description analysis revealed that Rougui exhibited higher intensity in bitter, thick, harsh, and numb tastes, while Shuixian had stronger salty and umami tastes. Nontargeted metabolomics identified 151 compounds with 66 compounds identified as key differential metabolites responsible for metabolic discrimination. Most of the catechins and flavonoids were enriched in Rougui tea, while epigallocatechin-3,3'-di-O-gallate, epigallocatechin-3,5-di-O-gallate, gallocatechin-3,5-di-O-gallate, isovitexin, and theaflavanoside I were enriched in Shuixian tea. Catechins, kaempferol, quercetin, and myricetin derivatives were positively correlated with bitter taste and numb sensation. Sour taste was positively correlated to organic acids. Amino acids potentially contributed to salty and umami tastes. These results provide further insights into the taste characteristics and the relationship between taste attributes and specific metabolites in Wuyi Rock tea.