Pseudostellaria heterophylla improves intestinal microecology through modulating gut microbiota and metabolites in mice
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Abstract BACKGROUND Pseudostellaria heterophylla is a Chinese medicine and healthy edible that is widely used to for its immunomodulatory, antioxidant, antidiabetic and antitussive properties. However, the potential function of P . heterophylla in intestinal microecology remains unclear. In this study, we investigated the impact of P . heterophylla on immune functions and evaluated its potential to regulate the gut microbiota and metabolome. RESULTS The results showed that P . heterophylla significantly increased the content of red blood cells, total antioxidant capacity and expression of immune factors, and decreased platelet counts when compared to the control under cyclophosphamide injury. In addition, P . heterophylla altered the diversity and composition of the gut bacterial community; increased the abundance of potentially beneficial Akkermansia , Roseburia , unclassified Clostridiaceae , Mucispirillum , Anaeroplasma and Parabacteroides ; and decreased the relative abundance of pathogenic Cupriavidus and Staphylococcus in healthy mice. Metabolomic analyses showed that P . heterophylla significantly increased the content of functional oligosaccharides, common oligosaccharides, vitamins and functional substances. Probiotics and pathogens were regulated by metabolites across 11 pathways in the bacterial–host co‐metabolism network. CONCLUSION We demonstrated that P . heterophylla increased the abundance of probiotics and decreased pathogens, and further stimulated host microbes to produce beneficial secondary metabolites for host health. Our studies highlight the role of P . heterophylla in gut health and provide new insights for the development of traditional Chinese medicine in the diet. © 2024 Society of Chemical Industry.Keywords:
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A large part of metabolomics research relies on experiments involving mouse models, which are usually 6 to 20 weeks of age. However, in this age range mice undergo dramatic developmental changes. Even small age differences may lead to different metabolomes, which in turn could increase inter-sample variability and impair the reproducibility and comparability of metabolomics results. In order to learn more about the variability of the murine plasma metabolome, we analyzed male and female C57BL/6J, C57BL/6NTac, 129S1/SvImJ, and C3HeB/FeJ mice at 6, 10, 14, and 20 weeks of age, using targeted metabolomics (BIOCRATES AbsoluteIDQ™ p150 Kit). Our analysis revealed high variability of the murine plasma metabolome during adolescence and early adulthood. A general age range with minimal variability, and thus a stable metabolome, could not be identified. Age-related metabolomic changes as well as the metabolite profiles at specific ages differed markedly between mouse strains. This observation illustrates the fact that the developmental timing in mice is strain specific. We therefore stress the importance of deliberate strain choice, as well as consistency and precise documentation of animal age, in metabolomics studies.
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Objectives: Metabolomics is a new approach to precisely phenotype individuals and identify potentially novel biomarkers. Although GC- and LC-MS/MS based metabolomics has been established to determine the metabolome in plants, these techniques have not yet been evaluated in human plasma. We therefore aimed to analyze the reproducibility of measurements under various conditions.
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Metabolomics detects and quantifies the low molecular weight molecules, known as metabolites (constituents of the metabolome), produced by active, living cells under different conditions and times in their life cycles. NMR is playing an important role in metabolomics because of its ability to observe mixtures of small molecules in living cells or in cell extracts. Metabolomics, a new technology, is a promising tool for food processors, food quality and safety laboratories, food chain providers, and also plant breeders. Metabolomics involves the rapid, high throughput characterization of the small molecule metabolites found in an organism. Metabolome is closely tied to the genotype of an organism, its physiology and its environment and offers a unique opportunity to look at genotype-phenotype relationships. Metabolomics is increasingly being used in a variety of health applications including pharmacology, pre-clinical drug trials, toxicology, transplant monitoring, newborn screening and clinical chemistry. However, a key limitation to metabolomics is the fact that the human metabolome is not at all well characterized.
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Sugarcane is essential for global sugar production and its compressed juice is a key raw material for industrial products. Sugarcane juice includes various metabolites with abundances and compositional balances influencing product qualities and functionalities. Therefore, understanding the characteristic features of the sugarcane metabolome is important. However, sugarcane compositional variability and stability, even in pretreatment processes for nuclear magnetic resonance (NMR)-based metabolomic studies, remains elusive. The objective of this study is to evaluate sugarcane juice metabolomic variability affected by centrifugation, filtration, and thermal pretreatments, as well as the time-course changes for determining optimal conditions for NMR-based metabolomic approach. The pretreatment processes left the metabolomic compositions unchanged, indicating that these pretreatments are compatible with one another and the studied metabolomes are comparable. The thermal processing provided stability to the metabolome for more than 32 h at room temperature. Based on the determined analytical conditions, we conducted an NMR-based metabolomic study to discriminate the differences in the harvest period and allowed for successfully identifying the characteristic metabolome. Our findings denote that NMR-based sugarcane metabolomics enable us to provide an opportunity to collect a massive amount of data upon collaboration between multiple researchers, resulting in the rapid construction of useful databases for both research purposes and industrial use.
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The metabolome of a biological system refers to the complement of all low molecular weight (<1,500 daltons) metabolites in that system (Fig. 1). As biological changes in a system are thought to be amplified at the level of the metabolome, metabolites have been coined ‘the canaries of the genome’. Metabolomics refers to the quantitative analysis of the metabolome. Whilst the measurement and quantification of individual or small numbers of metabolites is well established in biochemistry, metabolomics differs from more targeted analyses in the number of classes of metabolites being detected, the range of analytical techniques being employed and the need for advanced signal processing and bioinformatics tools.
Different organisms are likely to contain variable numbers of metabolites. For example, well-characterised prokaryotic systems, such as E. coli, are estimated to contain approximately 750 metabolites (1). On the other hand, individual eukaryotic cells may contain between 4,000 and 20,000 metabolites (2), while estimates of all metabolites in the plant and fungal kingdoms, which are characterised by having complex secondary metabolism, range into the hundreds of thousands (3). The number of metabolites in specific cell, tissue and biofluid samples of metazoan organisms may also vary markedly. For example, the Human Metabolome Project (http://www.hmdb.ca/) has identified and quantified 6,826 metabolites in human tissues and biofluids. Of these, 3,970 have been identified in serum, while other biofluids, such as urine and cerebrospinal fluid, contain a comparatively simpler composition (472 and 360 metabolites, respectively) (4).
In common with some other ‘-omics’ approaches, metabolomics employs and is highly dependent on diverse analytical approaches (summarised in Fig. 2), including mass spectrometry (MS), nuclear magnetic resonance spectroscopy (NMR) and Fourier Transform infrared spectroscopy. Of these approaches, MS-based techniques have developed most rapidly and are increasingly being deployed in metabolomics analyses (Table 1). This article provides a short overview of MS-based metabolomics and provides a starting point for scientists considering exploiting this rapidly emerging field.
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