Dynamic changes in metabolic and lipidomic profiles of tea plants during drought stress and re-watering

Tea (Camellia sinensis L.), as an evergreen plant, needs a humid environment. Water deficit would diminish tea yield and quality. We analyzed the dynamic changes in the metabolite and lipid profiling of tea leaves under various drought conditions and re-watering to determine the metabolic changes in tea leaves responding to drought challenges. In all, 119 metabolites showed substantial alterations in drought-stressed tea plants, including sugars and sugar alcohols, amino acids, and tricarboxylic acid cycle intermediates and lipids. We detected 29 lipids and they were classified into phosphatidylglycerol (PG), phosphatidic acid (PA), sulfoquinovosyl-diacylglycerol (SQDG), phosphatidylcholine (PC), lyso-phosphatidylcholine (LysoPC), and phosphatidylinositol (PI). The levels of sugar, sugar alcohol, and sugar precursors may change as a response to drought stress. Compared with these metabolites, the membrane lipids showed more dynamic changes in tea under drought stresses. Furthermore, metabolic recovery was only partial, with the majority of the examined metabolites exhibiting significantly different levels between samples from re-watered and well-watered tea plants. The findings also showed that comprehensive metabolomic and lipidomic approaches were efficient in elucidating the impacts of drought stress on tea plant metabolism. Our findings are valuable for understanding the mechanisms behind drought tolerance in tea plants from the metabolism perspective and utilizing the compounds to improve the drought tolerance of tea plants.

Exploration of chemical markers using a metabolomics strategy and machine learning to study the different origins of Ixeris denticulata (Houtt.) Stebb

As a generally edible plant, Ixeris denticulata (Houtt.) Stebb is widely distributed in China. Its medicinal value has attracted much attention in recent years. However, the chemical markers that cause quality and taste differences in I. denticulata from different regions are currently unclear. In this study, samples from 8 different origins were collected and analysed by UPLC-Q-TOF/MS. A metabolomics data processing strategy and machine learning method were established to explore the reasons for the difference in quality and taste of different origins from the perspective of chemical composition. With the established strategy, 10 characteristic chemical markers were identified that could be used to distinguish the origins of I. denticulata. The strategy proposed in this study could provide a certain basis for quality control and reasonable consumption of I. denticulata and additional food and medicinal homologous species.

A holistic strategy for discovering structural analogues of drug residues in meat using characteristic structural fragments filtering by high-resolution Orbitrap mass spectrometry

A holistic strategy for discovering structural analogs was established using characteristic structural fragments filtering by high-resolution Orbitrap mass spectrometry and successfully employed for discovering potential hazards in meat. The mass spectrometry fragmentation mechanisms of 113 compounds (including sulphonamides, tetracyclines, benzimidazoles, steroid hormones, cephalosporins, β-blockers) were investigated and a new strategy for screening of characteristic fragment ions was proposed. To process the data acquired by two scan modes, firstly an integrated filtering strategy was conducted to facilitate the characterisation of multi-class drugs. The integrated filtering strategy was applied to reduce interference in the raw data, which could help extracting the MS1 characteristics of the homolog-type chemical substances and expand the screening of the compounds as effectively as possible. This strategy was based on a combination of nitrogen rule, neutral loss and multiple characteristic fragment ions filtering. The method was validated by rapid screening and identification of targeted compounds in spiked samples. Particularly, the successful detection of several new compounds indicated that this strategy had significant advantages over individual filtration methods and could be a promising method for screening and identifying newly homolog-type drug residues in complex samples.

Rapid Screening of Chemical Constituents in Rhizoma Anemarrhenae by UPLC-Q-TOF/MS Combined with Data Postprocessing Techniques

Rhizoma Anemarrhenae, a famous traditional Chinese medicine (TCM), is the dried rhizome of Anemarrhena asphodeloides Bge. (Anemarrhena Bunge of Liliaceae). The medicine presents anti-inflammatory, antipyretic, sedative, and diuretic effects. The chemical constituents of Rhizoma Anemarrhenae are complex and diverse, mainly including steroidal saponins, flavonoids, phenylpropanoids, benzophenones, and alkaloids. In this study, UPLC-Q-TOF/MS was used in combination with data postprocessing techniques, including characteristic fragments filter and neutral loss filter, to rapidly classify and identify the five types of substances in Rhizoma Anemarrhenae. On the basis of numerous literature reviews and according to the corresponding characteristic fragments produced by different types of compounds in combination with neutral loss filtering, we summarized the fragmentation patterns of the main five types of compounds and successfully screened and identified 32 chemical constituents in Rhizoma Anemarrhenae. The components included 18 steroidal saponins, 6 flavonoids, 4 phenylpropanoids, 2 alkaloids, and 2 benzophenones. The method established in this study provided necessary data for the study on the pharmacological effects of Rhizoma Anemarrhenae and also provided the basis for the chemical analysis and quality control of TCMs to promote the development of a method for chemical research on TCMs.

Studies on effect of Ginkgo biloba L. leaves in acute gout with hyperuricemia model rats by using UPLC-ESI-Q-TOF/MS metabolomic approach

The therapeutic effects of GBE on acute gout with hyperuricemia rats were investigated by using a metabolomic approach.

An integrated strategy for the rapid extraction and screening of phosphatidylcholines and lysophosphatidylcholines using semi-automatic solid phase extraction and data processing technology

This study attempts to establish a comprehensive strategy for the rapid extraction and screening of phosphatidylcholines (PCs) and lysophosphatidylcholines (LysoPCs) in biological samples using semi-automatic solid phase extraction (SPE) and data processing technology based on ultra-performance liquid chromatography-quadrupole-time of flight-mass spectrometry (UPLC-Q-TOF-MS). First, the Ostro sample preparation method (i.e., semi-automatic SPE) was compared with the Bligh-Dyer method in terms of substance coverage, reproducibility and sample preparation time. Meanwhile, the screening method for PCs and LysoPCs was built through mass range screening, mass defect filtering and diagnostic fragments filtering. Then, the Ostro sample preparation method and the aforementioned screening method were combined under optimal conditions to establish a rapid extraction and screening platform. Finally, this developed method was validated and applied to the preparation and data analysis of tissue samples. Through a systematic evaluation, this developed method was shown to provide reliable and high-throughput experimental results and was suitable for the preparation and analysis of tissue samples. Our method provides a novel strategy for the rapid extraction and analysis of functional phospholipids. In addition, this study will promote further study of phospholipids in disease research.

Screening, verification, and optimization of biomarkers for early prediction of cardiotoxicity based on metabolomics

Drug-induced cardiotoxicity seriously affects human health and drug development. However, many conventional detection indicators of cardiotoxicity exhibit significant changes only after the occurrence of severe heart injuries. Therefore, we investigated more sensitive and reliable indicators for the evaluation and prediction of cardiotoxicity. We created rat cardiotoxicity models in which the toxicity was caused by doxorubicin (20 mg/kg), isoproterenol (5 mg/kg), and 5-fluorouracil (125 mg/kg). We collected data from rat plasma samples based on metabolomics using ultra-performance liquid chromatography quadrupole time-of-flight mass spectrometry. Following multivariate statistical and integration analyses, we selected 39 biomarker ions of cardiotoxicity that predict cardiotoxicity earlier than biochemical analysis and histopathological assessment. Because drugs with different toxicities may cause similar metabolic changes compared with other noncardiotoxic models (hepatotoxic and nephrotoxic models), we obtained 10 highly specific biomarkers of cardiotoxicity. We subsequently used a support vector machine (SVM) to develop a predictive model to verify and optimize the exclusive biomarkers. l-Carnitine, 19-hydroxydeoxycorticosterone, LPC (14:0), and LPC (20:2) exhibited the strongest specificities. The prediction rate of the SVM model is as high as 90.0%. This research provides a better understanding of drug-induced cardiotoxicity in drug safety evaluations and secondary development and demonstrates novel ideas for verification and optimization of biomarkers via metabolomics.

A urinary metabolomics study of the metabolic dysfunction and the regulation effect of citalopram in rats exposed to chronic unpredictable mild stress

This is the first attempt to combine the exploration of biomarkers of depression and evaluating the effect of citalopram by a metabolomics method, and then use the method to access the depression status according to the changed metabolome.