Search for Potential Biomarkers by UPLC/Q-TOF–MS Analysis of Dynamic Changes of Glycerophospholipid Constituents of RAW264.7 Cells Treated With NSAID

Transcriptomic and Metabolomic Analyses Reveal That Fullerol Improves Drought Tolerance in Brassica napus L

Carbon nanoparticles have potential threats to plant growth and stress tolerance. The polyhydroxy fullerene-fullerol (one of the carbon nanoparticles) could increase biomass accumulation in several plants subjected to drought; however, the underlying molecular and metabolic mechanisms governed by fullerol in improving drought tolerance in Brassica napus remain unclear. In the present study, exogenous fullerol was applied to the leaves of B. napus seedlings under drought conditions. The results of transcriptomic and metabolomic analyses revealed changes in the molecular and metabolic profiles of B. napus. The differentially expressed genes and the differentially accumulated metabolites, induced by drought or fullerol treatment, were mainly enriched in the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways related to carbohydrate metabolism (e.g., "carbon metabolism" and "galactose metabolism"), amino acid metabolism (e.g., "biosynthesis of amino acids" and "arginine and proline metabolism"), and secondary metabolite metabolism (e.g., "biosynthesis of secondary metabolites"). For carbohydrate metabolism, the accumulation of oligosaccharides (e.g., sucrose) was decreased, whereas that of monosaccharides (e.g., mannose and myo-inositol) was increased by drought. With regard to amino acid metabolism, under drought stress, the accumulation of amino acids such as phenylalanine and tryptophan decreased, whereas that of glutamate and proline increased. Further, for secondary metabolite metabolism, B. napus subjected to soil drying showed a reduction in phenolics and flavonoids, such as hyperoside and trans-3-coumaric acid. However, the accumulation of carbohydrates was almost unchanged in fullerol-treated B. napus subjected to drought. When exposed to water shortage, the accumulation of amino acids, such as proline, was decreased upon fullerol treatment. However, that of phenolics and flavonoids, such as luteolin and trans-3-coumaric acid, was enhanced. Our findings suggest that fullerol can alleviate the inhibitory effects of drought on phenolics and flavonoids to enhance drought tolerance in B. napus.

Effects of IC50 dose of retinol on metabolomics of RAW264.7 cells

Vitamin A is one of the most multifunctional vitamins in normal human physiology and is involved in several basic physiological processes from embryonic development to adulthood, such as embryogenesis, vision, immunity, cell differentiation, and proliferation. In this study, we conducted ¹ H- NMR to evaluate the metabolomic changes in RAW264.7 cells after treatment with retinol at an IC₅₀ dose to identify its effects on the differential metabolites and main metabolic pathways. Our results showed that the IC₅₀ dose (140 μM) of retinol affected the metabolism of RAW264.7 cells, with a total of 22 differential metabolites identified via ¹ H-NMR, including amino acids, sugars, organic acids, glutathione, glycerin, and creatine. Additionally, multiple metabolic pathways were affected by retinol treatment, including downregulation of amino acid biosynthesis, protein synthesis, and pyruvate metabolism. We speculate that the cytotoxicity of retinol at the IC₅₀ dose is attributed to mitochondrial dysfunction as a result of oxidative stress or lipid peroxidation. PRACTICAL APPLICATIONS: With the general improvement of people's living standards, people use dietary supplements to improve the level of retinol to prevent non-specific diseases. But there are more and more cases of acute or chronic poisoning caused by excessive intake of vitamin A. Therefore, it is necessary to study the toxicity of vitamin A, and more attention should be paid to the excessive intake of vitamin A. From the perspective of metabolomics, this experiment studies the adverse effects of high dose retinol through the changes of metabolites and metabolic pathways at the cellular level. This study will assist further analyses of the toxic mechanism of excessive retinol as fortified foods and nutrient supplementation.

1H-NMR-based metabolomics to investigate the effects of Phoenix dactylifera seed extracts in LPS-IFN-γ-induced RAW 264.7 cells

Inflammation has been revealed to play a central role in the onset and progression of many illnesses. Nuclear magnetic resonance (NMR) based metabolomics method was adopted to evaluate the effects of Phoenix dactylifera seeds, in particular the Algerian date variety of Deglet on the metabolome of the LPS-IFN-γ-induced RAW 264.7 cells. Variations in the extracellular and intracellular profiles emphasized the differences in the presence of tyrosine, phenylalanine, alanine, proline, asparagine, isocitrate, inosine and lysine. Principal component analysis (PCA) revealed noticeable clustering patterns between the treated and induced RAW cells based on the metabolic profile of the extracellular metabolites. However, the effects of treatment on the intracellular metabolites appears to be less distinct as suggested by the PCA and heatmap analyses. A clear group segregation was observed for the intracellular metabolites from the treated and induced cells based on the orthogonal partial least squares-discriminant analysis (OPLS-DA) score plot. Likewise, 11 of the metabolites in the treated cells were significantly different from those in the induced groups, including amino acids and succinate. The enrichment analysis demonstrated that treatment with Deglet seed extracts interfered with the energy and of amino acids metabolism. Overall, the obtained data reinforced the possible application of Deglet seeds as a functional food with anti-inflammatory properties.

1H-NMR Metabolomics Analysis of the Effects of Sulfated Polysaccharides from Masson Pine Pollen in RAW264.7 Macrophage Cells

Many polysaccharides have been shown to be bioactive, with the addition of sulfate often enhancing or altering this bioactivity. In previous studies, masson pine pollen polysaccharides, to include a sulfate derivative, have been shown to promote macrophage proliferation similarly to LPS. However, the exact metabolic mechanisms promoting this proliferation remain unclear. In this study, RAW264.7 macrophage cells were treated with a purified masson pine pollen polysaccharide (PPM60-D), a sulfate derivative (SPPM60-D), or LPS. Proliferation levels at a variety of concentrations were examined using MTT assay, with optimal concentration used when performing metabolomic analysis via ¹H nuclear magnetic resonance (¹H-NMR). This process resulted in the identification of thirty-five intracellular metabolites. Subsequent multivariate statistical analysis showed that both LPS and SPPM60-D promote RAW264.7 proliferation by promoting aerobic respiration processes and reducing processes associated with glycolysis. While some insight was gained regarding the mechanistic differences between SPPM60-D and LPS, the specific mechanisms governing the effect of SPPM60 on RAW264.7 cells will require further elucidation. These findings show that both LPS and SPPM60-D effectively promote RAW264.7 proliferation and may have beneficial uses in maintaining cellular vitality or inhibiting cancer.

Metabolomic profiling of rat urine after oral administration of the prescription antipyretic Hao Jia Xu Re Qing Granules by UPLC/Q-TOF-MS

Hao Jia Xu Re Qing Granules (HJ), is an effective clinically used antipyretic based on traditional Chinese medicine. Although its antipyretic therapeutic effectiveness is obvious, its therapeutic mechanism has not been comprehensively explored yet. In this research, we first identified potential biomarkers which may be relevant for the antipyretic effect of HJ based on urine metabolomics using ultra-performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF-MS). A rat model of fever was established using the yeast-induced febrile response. Total-ion-current metabolic profiles of different groups were acquired and the data were processed by multivariate statistical analysis-partial least-squares discriminant analysis. As envisioned, the results revealed changes of urine metabolites related to the antipyretic effect. Fourteen potential biomarkers were selected from the urine samples based on the results of Student's t-test, "shrinkage t", variable importance in projection and partial least-squares discriminant analysis. N-Acetylleucine, kynurenic acid, indole-3-ethanol, nicotinuric acid, pantothenic acid and tryptophan were the most significant biomarkers found in the urine samples, and may be crucially related to the antipyretic effect of HJ. Consequently, we propose the hypothesis that the significant antipyretic effect the HJ may be related to the inhibition of tryptophan metabolism. This research thus provides strong theoretical support and further direction to explain the antipyretic mechanism of HJ, laying the foundation for future studies.

Classification of samples from NMR-based metabolomics using principal components analysis and partial least squares with uncertainty estimation

Recent progress in metabolomics has been aided by the development of analysis techniques such as gas and liquid chromatography coupled with mass spectrometry (GC-MS and LC-MS) and nuclear magnetic resonance (NMR) spectroscopy. The vast quantities of data produced by these techniques has resulted in an increase in the use of machine algorithms that can aid in the interpretation of this data, such as principal components analysis (PCA) and partial least squares (PLS). Techniques such as these can be applied to biomarker discovery, interlaboratory comparison, and clinical diagnoses. However, there is a lingering question whether the results of these studies can be applied to broader sets of clinical data, usually taken from different data sources. In this work, we address this question by creating a metabolomics workflow that combines a previously published consensus analysis procedure ( https://doi.org/10.1016/j.chemolab.2016.12.010 ) with PCA and PLS models using uncertainty analysis based on bootstrapping. This workflow is applied to NMR data that come from an interlaboratory comparison study using synthetic and biologically obtained metabolite mixtures. The consensus analysis identifies trusted laboratories, whose data are used to create classification models that are more reliable than without. With uncertainty analysis, the reliability of the classification can be rigorously quantified, both for data from the original set and from new data that the model is analyzing. Graphical abstract ᅟ.