Metabolomics最新文献

Introduction and objective: Rumex sanguineus, a traditional medicinal plant of the Polygonaceae family, is gaining popularity as an edible resource. However, despite its historical and nutritional significance, its chemical composition remains poorly understood. To deepen the understanding of the of Rumex sanguineus composition, an in-depth analysis using non-targeted, mass spectrometry-based metabolomics was performed.  METHODS: Rumex roots, stems and leaves samples were analyzed by UHPLC-HRMS and subsequently subjected to feature-based molecular networking.

Results and conclusion: Overall, 347 primary and specialized metabolites grouped into 8 biochemical classes were annotated. Most of these metabolites (60%) belong to the polyphenols and anthraquinones classes. To investigate potential' toxicity due to the presence of anthraquinones, the amount of emodin was quantified with analytical standard, revealing higher accumulation in leaves compared to stems and roots. This highlights the need for thorough metabolomic studies to understand both beneficial and harmful compounds, especially in plants with historical medicinal use transitioning to modern culinary use.

Introduction: Hemodynamic forces play a crucial role in modulating endothelial cell (EC) behavior, significantly influencing blood vessel responses. While traditional in vitro studies often explore ECs under static conditions, ECs are exposed to various hemodynamic forces in vivo. This study investigates how wall shear stress (WSS) influences EC metabolism, focusing on the interplay between WSS and key metabolic pathways.

Objectives: The aim of this study is to examine the effects of WSS on EC metabolism, specifically evaluating its impact on central carbon metabolism and glycolysis using transcriptomics and tracer metabolomics approaches.

Methods: ECs were exposed to WSS, and transcriptomic analysis was performed to assess gene expression changes related to metabolic pathways. Tracer metabolomics was used to track metabolic fluxes, focusing on glutamine and glycolytic metabolism. Additionally, chemical inhibition of glutamate dehydrogenase was conducted to evaluate its role in EC fitness under WSS.

Results: Transcriptomic data revealed upregulation of glutamine and glutamate pathways, alongside downregulation of glycolytic activity in ECs exposed to WSS. Tracer metabolomics confirmed that WSS promotes glutamine anaplerosis into the Krebs cycle, while decreasing glycolytic metabolism. Suppression of glutamate dehydrogenase impaired EC fitness under WSS conditions.

Conclusion: Our findings illuminate that ECs subjected to WSS exhibit a preference for glutamine as a key nutrient source for central carbon metabolism pathways, indicating diminished reliance on glycolysis. This study elucidates the nutritional predilections and regulatory mechanisms governing EC metabolism under WSS in vitro, underscoring the pivotal role of physical stimuli in shaping EC metabolic responses.

Introduction: ZenoTOF is new class of high-resolution mass spectrometer that combines resolution and sensitivity. This mass spectrometer is well designed to perform metabolomics.

Methods: In this context, we compared the performance of ZenoTOF 7600 system (Sciex) with QTOF6520 (Agilent Technologies) through the leaf metabolome analysis of two Coffea species, namely C. anthonyi and C. arabica.

Results: Both species were used to compare both TOF systems. Our results showed that the ZenoTOF 7600 system provided more features (3146 vs 2326 metabolites) and more nodes (1410 vs 379 metabolites) by molecular network in only one injection.

Conclusion: These performances were attributed to the scan speed and sensitivity of the ZenotTOF and demonstrates its added value in the context of metabolomics.

Introduction: Breath Volatile organic compounds (VOCs) are promising biomarkers for clinical purposes due to their unique properties. Translation of VOC biomarkers into the clinic depends on identification and validation: a challenge requiring collaboration, well-established protocols, and cross-comparison of data. Previously, we developed a breath collection and analysis method, resulting in 148 breath-borne VOCs identified.

Objectives: To develop a complementary analytical method for the detection and identification of additional VOCs from breath. To develop and implement upgrades to the methodology for identifying features determined to be "on-breath" by comparing breath samples against paired background samples applying three metrics: standard deviation, paired t-test, and receiver-operating-characteristic (ROC) curve.

Methods: A thermal desorption (TD)-gas chromatography (GC)-mass spectrometry (MS)-based analytical method utilizing a PEG phase GC column was developed for the detection of biologically relevant VOCs. The multi-step VOC identification methodology was upgraded through several developments: candidate VOC grouping schema, ion abundance correlation based spectral library creation approach, hybrid alkane-FAMES retention indexing, relative retention time matching, along with additional quality checks. In combination, these updates enable highly accurate identification of breath-borne VOCs, both on spectral and retention axes.

Results: A total of 621 features were statistically determined as on-breath by at least one metric (standard deviation, paired t-test, or ROC). A total of 38 on-breath VOCs were able to be confidently identified from comparison to chemical standards.

Conclusion: The total confirmed on-breath VOCs is now 186. We present an updated methodology for high-confidence VOC identification, and a new set of VOCs commonly found on-breath.

Introduction: Burkholderia thailandensis E264 is a non-pathogenic soil bacterium that produces rhamnolipids (RLs), which are utilised in various fields. Although studies have illustrated changes in RLs congeners in response to environmental factors, studies on the influence of temperature on the RLs congeners produced by B. thailandensis E264 are scarce.

Objective: It was hypothesised that RL congeners will be distributed differently at different temperature, which caused the produced RL to have different properties. This brought about the idea of a tailored production of RL for specific application through temperature control. Thus, this study aimed to investigate the distribution of RLs congeners by B. thailandensis E264 in response to different temperatures.

Methodology: B. thailandensis E264 was grown at three different temperatures (25 °C, 30 °C, and 37 °C) for nine days and subjected to metabolomic analysis using liquid chromatography quadrupole time-of-flight mass spectrometry (LC-QToF-MS).

Results: The findings indicated that temperature significantly affected the metabolomic distribution of B. thailandensis E264, with mono-rhamno-mono-lipid and mono-rhamno-di-lipid being the predominant metabolites at 37 °C and 30 °C, with relative abundances of 64.1% and 65.3%, respectively. In comparison, di-rhamno-di-lipid was detected at 25 °C with an overall relative abundance of 77.7%.

Conclusion: This investigation showed that changing the cultivation temperature of the non-pathogenic B. thailandensis E264 produces diverse rhamnolipid congeners, which could enable the targeted synthesis of specific RLs for various applications and increase the market value of biosurfactants.

Introduction: Tree bacterial diseases are a threat in forestry due to their increasing incidence and severity. Understanding tree defence mechanisms requires evaluating metabolic changes arising during infection. Metabolite extraction affects the chemical diversity of the samples and, therefore, the biological relevance of the data. Metabolite extraction has been standardized for several biological models. However, little information is available regarding how it influences wood extract's chemical diversity.

Objectives: This study aimed to develop a methodological approach to obtain extracts from different tree species with the highest reproducibility and chemical diversity possible, to ensure proper coverage of the trees' metabolome.

Methods: A full factorial design was used to evaluate the effect of solvent type, extraction temperature and number of extraction cycles on the metabolic profile, chemical diversity and antibacterial activity of four tree species.

Results: Solvent, temperature and their interaction significantly affected the extracts' chemical diversity, while the number of extraction cycles positively correlated with yield and antibacterial activity. Although 60% of the features were recovered in all the tested conditions, differences in the presence and abundance of specific chemical classes per tree were observed, including organooxygen compounds, prenol lipids, carboxylic acids, and flavonoids.

Conclusions: Each tree species has a unique metabolic profile, which means that no single protocol is universally effective. Extraction at 50 °C for three cycles using 80% methanol or chloroform/methanol/water showed the best results and is suggested for studying wood metabolome. These observations highlight the need to tailor extraction protocols to each tree species to ensure comprehensive metabolome coverage for metabolic profiling.

Introduction: Metabolomics is the comprehensive study of small molecules in biological systems. It has recently garnered attention for its wide variety of applications such as diseases, drug treatments, agriculture, and more. As the interest in metabolomics grow, meeting the demands of cutting-edge research requires software tools that not only advance analytical capabilities, but also prioritize user-friendly features.

Objectives: In response to this need, we present two new computer programs, A-SIMA: Advanced-Software for Interactive Metabolite Analysis and A-MAP: A Multivariate Analysis Program. These tools aim to introduce new capabilities for metabolite identification and data analysis, and thereby advancing the computational methodology in NMR-based metabolomics.

Methods: A-SIMA is designed with an easy-to-use graphical user interface which allows users to perform metabolite identification on 1D and 2D NMR data effortlessly with complete control over the identification process. Similarly, A-MAP facilitates multivariate statistical analysis of metabolite data through a straightforward process. It offers analysis options such as Principal Component Analysis and Orthogonal Partial Least Squares-Discriminant Analysis using regions of interests as inputs.

Results: Both A-SIMA and A-MAP are pre-built in the POKY suite, available at https://poky.clas.ucdenver.edu , with tutorial videos on YouTube for guidance on not only the programs, but also installation. The POKY suite is a software program for NMR biomolecular analysis. With the addition of these programs in POKY, researchers and professionals can experience a fully integrated process for every step of their metabolite analysis. Data can also be easily exported from these programs to be applied elsewhere.

Conclusion: The introduction of A-SIMA and A-MAP can be promising tools that can lead significant advancements in metabolomics research. These tools offer enhanced capabilities for metabolite analysis and statistical modelling in a user-friendly manner. Their integration into the POKY suite ensures accessibility, usability, and efficiency.

Introduction: Metabolic disorders are a global health concern, necessitating the development of drugs with fewer side effects and more efficacy. Traditional Indian medicine uses Tinospora cordifolia and Tinospora sinensis, but their metabolite fingerprints and impact on geographical location remains unknown.

Objective: The present study aimed to identify metabolite fingerprints from T. cordifolia and T. sinensis species from different geographic locations and also to identify potential quality markers for treating metabolic disorders.

Methods: Non-targeted metabolite fingerprinting of T. cordifolia and T. sinensis was performed using HPLC-QTOF-MS/MS analysis. Network pharmacology, molecular docking and molecular dynamics simulation analysis were performed to identify potential quality markers, hub targets, and key pathways associated with metabolic disorders.

Results: In this study, six potential marker compounds and twenty-five differential compounds were identified between T. cordifolia and T. sinensis. Based on geography, five and one metabolite marker compounds were identified in T. cordifolia and T. sinensis respectively. Network pharmacology, molecular docking, and molecular dynamics simulation analysis revealed trans piceid, crustecdysone in T. cordifolia, and gallic acid in T. sinensis as potential quality markers against metabolic disorder related hub targets.

Conclusion: Integration of non-targeted metabolomics and network pharmacology approach deciphers the pharmacological mechanism of action in terms of identifying potential quality markers from Tinospora species that can be used against metabolic disorders. However, further research is required to validate these findings in in vitro and in vivo studies for better assertion.

Background: Due to the high prevalence of myopia, there is a growing need for the identification of myopia intervention mechanisms and targets. Metabolomics has been gradually used to investigate changes in myopia tissue metabolites over the last few years, but the potential physiological and pathological roles of amino acids and their downstream metabolites discovered by metabolomics in myopia are not fully understood.

Aim of review: Aim to explore the possible relationship between amino acid metabolism and the occurrence and development of myopia, we collected a total of 21 experimental studies related to myopia metabolomics. Perform pathway analysis using MetaboAnalyst online software. We have identified over 20 amino acids that may be associated with the development of myopia. Among them, 19 types of amino acids are common amino acids. We discussed their possible mechanisms affecting myopia and proposed future prospects for treating myopia.

Key scientific concepts of review: Our analysis results show that metabolomics research on myopia involves many important amino acids. We have collected literature and found that research on amino acid metabolism in myopia mainly focuses on downstream small molecule substances. Amino acids and their downstream metabolites affect the development of myopia by participating in important biochemical processes such as oxidative stress, glucose metabolism, and lipid metabolism. Enzymes, receptors, and cytokines that regulate amino acid metabolism may become potential targets for myopia treatment.