Metabolomics最新文献

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.

Background: Soccer is the most recognized sports worldwide. It is a fertile ground for the use of metabolomics analyses, considering the multifactorial nature of soccer's physical demands on the body. Although scientific studies have tried using it to better understand the impacts of soccer into different contexts of the sport, no systematic review is available on metabolomics analyses in soccer athletes subjected to physical exertion interventions.

Aim of review: Retrieve scientific articles that conducted metabolomics analyses on soccer athletes subjected to physical exertion interventions.

Key scientific concepts of review: Initially, 271 studies were screened, and 48 were retrieved for abstract analysis. Of these, 26 met the eligibility criteria, but 5 failed to meet inclusion criteria. The 21 studies included in this systematic review demonstrate that responses from physical training or acute exercise sessions, followed by the effects of soccer matches, have been the primary focus of researchers to date, highlighting alterations on metabolites from the energy metabolism, immunological pathway, purines, tryptophan/phenylalanine metabolism, as well as oxidative species and antioxidant capacity. Other studies suggest, albeit preliminarily, that organic metabolites have the potential to distinguish soccer players' performance and physical fitness, as well as provide valuable insights into diet, physical condition, training load, and recovery throughout the season. Despite metabolomics great potential to understand physiological alterations provoked by soccer as shown by the included studies, future studies should consider female athletes, explore the cause-and-effect relationship between metabolites and soccer performance more deeply, and examine the effects of different training periodizations on these markers.

Introduction: Outside of case-control settings, ethnicity specific changes in the human metabolome are understudied especially in community dwelling, ageing men. Characterising serum for age and ethnicity specific features can enable tailored therapeutics research and improve our understanding of the interplay between age, ethnicity, and metabolism in global populations.

Objective: A metabolomics approach was adopted to profile serum metabolomes in middle-aged and elderly men of different ethnicities from the Northwest of England, UK.

Methods: Serum samples from 572 men of White European (WE), South Asian (SA), and African-Caribbean (AC) ethnicities, ranging between 40 and 86 years were analysed. A combination of liquid chromatography (LC) and gas chromatography (GC) coupled to high-resolution mass spectrometry (MS) was used to generate the metabolomic profiles. Partial Least Squares Discriminant Analysis (PLS-DA) based classification models were built and validated using resampling via bootstrap analysis and permutation testing. Features were putatively annotated using public Human Metabolome Database (HMDB) and Golm Metabolite Database (GMD). Variable Importance in Projection (VIP) scores were used to determine features of interest, after which pathway enrichment analysis was performed.

Results: Using profiles from our analysis we classify subjects by their ethnicity with an average correct classification rate (CCR) of 90.53% (LC-MS data) and 85.58% (GC-MS data). Similar classification by age (< 60 vs. ≥ 60 years) returned CCRs of 90.20% (LC-MS) and 71.13% (GC-MS). VIP scores driven feature selection revealed important compounds from putatively annotated lipids (subclasses including fatty acids and carboxylic acids, glycerophospholipids, steroids), organic acids, amino acid derivatives as key contributors to the classifications. Pathway enrichment analysis using these features revealed statistically significant perturbations in energy metabolism (TCA cycle), N-Glycan and unsaturated fatty acid biosynthesis linked pathways amongst others.

Conclusion: We report metabolic differences measured in serum that can be attributed to ethnicity and age in healthy population. These results strongly emphasise the need to consider confounding effects of inherent metabolic variations driven by ethnicity of participants in population-based metabolic profiling studies. Interpretation of energy metabolism, N-Glycan and fatty acid biosynthesis should be carefully decoupled from the underlying differences in ethnicity of participants.

Introduction: Rapid detection and identification of pathogens and antimicrobial susceptibility is essential for guiding appropriate antimicrobial therapy and reducing morbidity and mortality associated with sepsis.

Objectives: The metabolic response of clinical isolates of Klebsiella oxytoca exposed to different concentrations of ciprofloxacin (the second generation of quinolones antibiotics) were studied in order to investigate underlying mechanisms associated with antimicrobial resistance (AMR).

Methods: Metabolomics investigations were performed using Fourier-transform infrared (FT-IR) spectroscopy as a metabolic fingerprinting approach combined with gas chromatography-mass spectrometry (GC-MS) for metabolic profiling.

Results: Our findings demonstrated that metabolic fingerprints provided by FT-IR analysis allowed for the differentiation of susceptible and resistant isolates. GC-MS analysis validated these findings, while also providing a deeper understanding of the metabolic alterations caused by exposure to ciprofloxacin. GC-MS metabolic profiling detected 176 metabolic features in the cellular extracts cultivated on BHI broth, and of these, 137 could be identified to Metabolomics Standards Initiative Level 2. Data analysis showed that 40 metabolites (30 Level 2 and 10 unknown) were differentiated between susceptible and resistant isolates. The identified metabolites belonging to central carbon metabolism; arginine and proline metabolism; alanine, aspartate and glutamate metabolism; and pyruvate metabolism. Univariate receiver operating characteristic (ROC) curve analyses revealed that six of these metabolites (glycerol-3-phosphate, O-phosphoethanolamine, asparagine dehydrate, maleimide, tyrosine, and alanine) have a crucial role in distinguishing susceptible from resistant isolates (AUC > 0.84) and contributing to antimicrobial resistance in K. oxtytoca.

Conclusion: Our study provides invaluable new insights into the mechanisms underlying development of antimicrobial resistance in K. oxytoca suggests potential therapeutic targets for prevention and identification of AMR in K. oxytoca infections.

Introduction: Inflammatory bowel diseases (IBDs) are chronic immune driven intestinal disorders with marked metabolic alteration. Mass spectrometry imaging (MSI) enables the direct visualization of biomolecules within tissues and facilitates the study of metabolic changes. Integrating multiple spatial information sources is a promising approach for discovering new biomarkers and understanding biochemical alteration within the context of the disease.

Objective: This study evaluates the metabolomic changes in gut tissue samples from a preclinical model of spontaneous colitis, the HLA-B27/hβ2m transgenic rat, to uncover disease biomarkers.

Methods: We applied MSI to study the biochemical profile of bowel samples from HLA-B27/hβ2m transgenic and WT control rats in an unbiased manner. Statistical comparison was used to identify discriminative features. Some features were annotated using LC-MS/MS. The significance of these discriminative features was evaluated based on their distribution within histological layers and the presence of immune infiltration.

Results: We identified spatially resolved changes in the metabolomic pattern of HLA-B27⁺ samples compared to WT controls. Out of the 275 discriminative features identified, 83 were annotated as metabolites. Two functional groups of discriminative metabolites were discussed as markers of gut barrier impairment and immune cell infiltration.

Conclusion: MS imaging's spatial dimension provides insights into disease mechanisms through the identification of spatially resolved biomarkers.