Atherosclerosis, gut microbiome, and exercise in a meta-omics perspective: a literature review.

Background: Cardiovascular diseases are the leading cause of death worldwide, significantly impacting public health. Atherosclerotic cardiovascular diseases account for the majority of these deaths, with atherosclerosis marking the initial and most critical phase of their pathophysiological progression. There is a complex relationship between atherosclerosis, the gut microbiome's composition and function, and the potential mediating role of exercise. The adaptability of the gut microbiome and the feasibility of exercise interventions present novel opportunities for therapeutic and preventative approaches. Methodology: We conducted a comprehensive literature review using professional databases such as PubMed and Web of Science. This review focuses on the application of meta-omics techniques, particularly metagenomics and metabolomics, in studying the effects of exercise interventions on the gut microbiome and atherosclerosis. Results: Meta-omics technologies offer unparalleled capabilities to explore the intricate connections between exercise, the microbiome, the metabolome, and cardiometabolic health. This review highlights the advancements in metagenomics and metabolomics, their applications in research, and examines how exercise influences the gut microbiome. We delve into the mechanisms connecting these elements from a metabolic perspective. Metagenomics provides insight into changes in microbial strains post-exercise, while metabolomics sheds light on the shifts in metabolites. Together, these approaches offer a comprehensive understanding of how exercise impacts atherosclerosis through specific mechanisms. Conclusions: Exercise significantly influences atherosclerosis, with the gut microbiome serving as a critical intermediary. Meta-omics technology holds substantial promise for investigating the gut microbiome; however, its methodologies require further refinement. Additionally, there is a pressing need for more extensive cohort studies to enhance our comprehension of the connection among these element.

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.

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.

From the grapevine to the glass: A wine metabolomics tale by FT-ICR-MS.

Wine is one of the most consumed beverages around the world. Its unique characteristics arise from numerous processes, from the selection of grapevine varieties and grapes, the effect of the terroir and geographical origin, through the biochemical process of fermentation by microorganisms, until its aging. All molecules found in wine define its chemical fingerprint and can be used to tell the story of its origin, production, authenticity and quality. Wine's chemical composition can be characterized using an untargeted metabolomics approach based on extreme resolution mass spectrometry. Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR-MS) is currently the most powerful analytical technique to analyse such complex sample, providing the most comprehensive analysis of the chemical fingerprint of wine.

[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.

Metabolomics-driven investigation of plant defense response against pest and pathogen attack.

The advancement of metabolomics has assisted in the identification of various bewildering characteristics of the biological system. Metabolomics is a standard approach, facilitating crucial aspects of system biology with absolute quantification of metabolites using minimum samples, based on liquid/gas chromatography, mass spectrometry and nuclear magnetic resonance. The metabolome profiling has narrowed the wide gaps of missing information and has enhanced the understanding of a wide spectrum of plant-environment interactions by highlighting the complex pathways regulating biochemical reactions and cellular physiology under a particular set of conditions. This high throughput technique also plays a prominent role in combined analyses of plant metabolomics and other omics datasets. Plant metabolomics has opened a wide paradigm of opportunities for developing stress-tolerant plants, ensuring better food quality and quantity. However, despite advantageous methods and databases, the technique has a few limitations, such as ineffective 3D capturing of metabolites, low comprehensiveness, and lack of cell-based sampling. In the future, an expansion of plant-pathogen and plant-pest response towards the metabolite architecture is necessary to understand the intricacies of plant defence against invaders, elucidation of metabolic pathway operational during defence and developing a direct correlation between metabolites and biotic stresses. Our aim is to provide an overview of metabolomics and its utilities for the identification of biomarkers or key metabolites associated with biotic stress, devising improved diagnostic methods to efficiently assess pest and pathogen attack and generating improved crop varieties with the help of combined application of analytical and molecular tools.

Bayesian functional analysis for untargeted metabolomics data with matching uncertainty and small sample sizes.

Untargeted metabolomics based on liquid chromatography-mass spectrometry technology is quickly gaining widespread application, given its ability to depict the global metabolic pattern in biological samples. However, the data are noisy and plagued by the lack of clear identity of data features measured from samples. Multiple potential matchings exist between data features and known metabolites, while the truth can only be one-to-one matches. Some existing methods attempt to reduce the matching uncertainty, but are far from being able to remove the uncertainty for most features. The existence of the uncertainty causes major difficulty in downstream functional analysis. To address these issues, we develop a novel approach for Bayesian Analysis of Untargeted Metabolomics data (BAUM) to integrate previously separate tasks into a single framework, including matching uncertainty inference, metabolite selection and functional analysis. By incorporating the knowledge graph between variables and using relatively simple assumptions, BAUM can analyze datasets with small sample sizes. By allowing different confidence levels of feature-metabolite matching, the method is applicable to datasets in which feature identities are partially known. Simulation studies demonstrate that, compared with other existing methods, BAUM achieves better accuracy in selecting important metabolites that tend to be functionally consistent and assigning confidence scores to feature-metabolite matches. We analyze a COVID-19 metabolomics dataset and a mouse brain metabolomics dataset using BAUM. Even with a very small sample size of 16 mice per group, BAUM is robust and stable. It finds pathways that conform to existing knowledge, as well as novel pathways that are biologically plausible.

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.

Extensive targeted metabolomics analysis reveals the identification of major metabolites, antioxidants, and disease-resistant active pharmaceutical components in Camellia tuberculata (Camellia L.) seeds.

Sect. tuberculata plant belongs to the Camellia genus and is named for the "tuberculiform protuberance on the surface of the ovary and fruit". It is a species of great ornamental value and potential medicinal value. However, little has been reported on the metabolites of C. tuberculata seeds. Therefore, this study was conducted to investigate the metabolites of C. tuberculata seeds based on UPLC/ESI-Q TRAP-MS/MS with extensively targeted metabolomics. A total of 1611 metabolites were identified, including 107 alkaloids, 276 amino acids and derivatives, 283 flavonoids, 86 lignans and coumarins, 181 lipids, 68 nucleotides and derivatives, 101 organic acids, 190 phenolic acids, 10 quinones, 4 steroids, 17 tannins, 111 terpenoids, and 177 other metabolites. We compared the different metabolites in seeds between HKH, ZM, ZY, and LY. The 1311 identified different metabolites were classified into three categories. Sixty-three overlapping significant different metabolites were found, of which lignans and coumarins accounted for the largest proportion. The differentially accumulated metabolites were enriched in different metabolic pathways between HKH vs. LY, HKH vs. ZM, HKH vs. ZY, LY vs. ZY, ZM vs. LY and ZM vs. ZY, with the most abundant metabolic pathways being 4, 2, 4, 7, 7 and 5, respectively (p < 0.05). Moreover, among the top 20 metabolites in each subgroup comparison in terms of difference multiplicity 7, 8 and 13. ZM and ZY had the highest phenolic acid content. Ninety-six disease-resistant metabolites and 48 major traditional Chinese medicine agents were identified based on seven diseases. The results of this study will not only lead to a more comprehensive and in-depth understanding of the metabolic properties of C. tuberculata seeds, but also provide a scientific basis for the excavation and further development of its medicinal value.

Widely targeted metabolomics reveals differences in metabolites of Paeonia lactiflora cultivars.

INTRODUCTION: Paeonia lactiflora contains diverse active constituents and exhibits various pharmacological activities. However, only partial identification of biologically active substances from P. lactiflora has been achieved using low-throughput techniques. Here, the roots of P. lactiflora, namely, Fenyunu (CK), Dafugui (DFG), and Red Charm (HSML), were studied. The primary and secondary metabolites were investigated using ultrahigh-performance liquid chromatography-electrospray ionization-tandem mass spectrometry (UPLC-ESIMS/MS). METHODS: The chemical compounds and categories were detected using broadly targeted UPLC-MS/MS. Principal component analysis (PCA), orthogonal partial least-squares discriminant analysis (OPLS-DA), and hierarchical clustering analysis (HCA) were carried out for metabolites of different varieties of P. lactiflora. RESULTS: A total of 1237 compounds were detected and classified into 11 categories. HCA, PCA, and OPLS-DA of these metabolites indicated that each variety of P. lactiflora was clearly separated from the other groups. Differential accumulated metabolite analysis revealed that the three P. lactiflora varieties contained 116 differentially activated metabolites (DAMs) involved in flavonoid, flavone, and flavonol metabolism. KEGG pathway analysis revealed that, in 65 pathways, 336 differentially abundant metabolites (DMs) were enriched in the CK and DFG groups; moreover, the type and content of terpenoids were greater in the CK group than in the DFG group. The CK and HSML groups contained 457 DMs enriched in 61 pathways; the type and amount of flavonoids, terpenoids, and tannins were greater in the CK group than in the HSML group. The DFG and HSML groups contained 497 DMs enriched in 65 pathways; terpenoids and alkaloids were more abundant in the HSML variety than in the DFG variety. CONCLUSIONS: A total of 1237 compounds were detected, and the results revealed significant differences among the three P. lactiflora varieties. Among the three P. lactiflora varieties, phenolic acids and flavonoids composed the largest and most diverse category of metabolites, and their contents varied greatly. Therefore, CK is suitable for medicinal plant varieties, and DFG and HSML are suitable for ornamental plant varieties. Twelve proanthocyanidin metabolites likely determined the differences in color among the three varieties.

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.

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.

Metabolomics reveals the importance of metabolites in Mussaenda pubescens for antioxidant properties and quality traits.

Mussaenda pubescens (Mp) is a valuable medicinal plant that has traditionally been used for medicinal purposes or as a tea substitute. However, there are few studies on the comprehensive and dynamic evaluation of Mp metabolites. This study used an ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) approach and biochemical analysis to investigate substance changes in leaves at three different stages and elucidate the relationship between metabolites and antioxidant capacity. The findings showed that Mp leaves contained 957 metabolites, the majority of which were phenolic acids, lipids, and terpenoids. The metabolite profiling of Mp leaves was significantly influenced by their growth and development at different stages. A total of 317 differentially accumulated metabolites (DAMs) were screened, including 150 primary metabolites and 167 secondary metabolites, with 202 DAMs found in bud leaf vs. tender leaf, 54 DAMs in tender leaf vs. mature leaf, and 254 DAMs in bud leaf vs. mature leaf. Total phenolics, flavonoids, and anthocyanin concentrations decreased as Mp leaves grew and developed, whereas terpenoids increased significantly. The secondary metabolites also demonstrated a positive correlation with antioxidant activity. Phenolics, flavonoids, terpenoids, and anthocyanins were the primary factors influencing the antioxidant activity of leaves. These findings provide new insights into the metabolite formation mechanism, as well as the development and utilization of Mp tea.

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.

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.

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.