NMR-based metabolomics to determine the fluctuation of metabolites in hydroponic purslane crops at different harvesting times

Unveiling the impact of harvest time on Dioscorea opposita Thunb. cv. Tiegun maturity by NMR-based metabolomics and LC-MS/MS analysis

BACKGROUND

Dioscorea opposita Thunb. cv. Tiegun maturity (DM) is an important factor influencing its quality. However, there are few studies on the impact of harvest time on its maturation. In the present study, a NMR-based metabolomics approach was applied to investigate the dynamic metabolic changes of D. opposita Thunb. cv. Tiegun at six different harvest stages: stage 1 (S1), stage 2 (S2), Stage 3 (S3), stage 4 (S4), stage 5 (S5) and stage 6 (S6).

RESULTS

Principal component analysis showed distinct segregation of samples obtained from S1, S2 and S3 compared to those derived from S4, S5 and S6. Interestingly, these samples from the two periods were obtained before and after frost, indicating that frost descent might be important for DM. Eight differential metabolites responsible for good separation of different groups were identified by the principal component analysis loading plot and partial least squares-discriminant analysis. In addition, quantitative analysis of these metabolites using liquid chromatography-tandem mass spectrometry determined the effects of harvest time on these metabolite contents, two of which, sucrose and allantoin, were considered as potential biomarkers to determine DM.

CONCLUSION

The present study demonstrated that NMR-based metabolomics approach could serve as a powerful tool to identify differential metabolites during harvesting processes, also offering a fresh insight into understanding the DM and the potential mechanism of quality formation. © 2024 Society of Chemical Industry.

Combined NMR and MS-based metabonomics and real-time PCR analyses reveal dynamic metabolic changes of Ganoderma lucidum during fruiting body growing

Ganoderma lucidum (G. lucidum) is a rare medicinal fungus with various beneficial properties. One of its main components, ganoderic acids (GAs), are important triterpenoids known for their sedative and analgesic, hepatoprotective, and anti-tumor activities. Understanding the growth and development of the G. lucidum fruiting body is crucial for determining the optimal time to harvest them. In this study, we used nuclear magnetic resonance (NMR) spectroscopy to systematically characterize the metabolites of G. lucidum at seven distinct developmental stages. We also measured the contents of seven kinds of GAs using LC-MS/MS. A total of 49 metabolites were detected in G. lucidum, including amino acids, sugars, organic acids and GAs. During the transition from the bud development period (I) to the budding period (II), we observed a rapid accumulation of glucose, tyrosine, nicotinamide ribotide, inosine and GAs. After the budding period, the contents of most metabolites decreased until the mature period (VII). In addition, the contents of GAs showed an initial raising, followed by a decline during the elongation period, except for GAF, which exhibited a rapid raise during the mature stage. We also detected the expression of several genes involved in GA synthesis, finding that most genes including 16 cytochrome P450 monooxygenase were all down-regulated during periods IV and VII compared to period I. These findings provide valuable insights into the dynamic metabolic profiles of G. lucidum throughout its growth stage, and it is recommended to harvest G. lucidum at period IV.

Exploring the chemical diversity of Capsicum chinense cultivars using NMR-based metabolomics and machine learning methods

The habanero pepper (Capsicum chinense) is a prominent spicy fruit integral to the historical, social, cultural, and economic fabric of the Yucatan peninsula in Mexico. This study leverages the power of 1H NMR spectroscopy coupled with machine learning algorithms to dissect the metabolomic profile of eleven C. chinense cultivars, including those grown by INIFAP (Habanero-Jaguar, Antillano-HRA 1-1, Antillano-HRA 7-1, Habanero-HAm-18A, Habanero-HC-23C, and Jolokia-NJolokia-22) and commercial hybrids (Habanero-Rey Votán, Habanero-Kabal, Balam, USAPR10117, and Rey Pakal). A total of fifty metabolites, encompassing sugars, amino acids, short-chain organic acids, and nucleosides, were identified from the 1H NMR spectra. The optimized machine learning model proficiently predicted the similarity percentage between the INIFAP-grown cultivars and commercial hybrids, thereby facilitating a comprehensive comparison. Biomarkers unique to each cultivar were delineated, revealing that the Habanero-Rey Votán cultivar is characterized by the highest concentration of sugars. In contrast, the Balam cultivar is rich in amino acids and short-chain organic acids, sharing a similar metabolomic profile with the Jolokia-NJolokia-22 cultivar. The findings of this study underscore the efficacy and reliability of NMR-based metabolomics as a robust tool for differentiating C. chinense cultivars based on their intricate chemical profiles. This approach not only contributes to the scientific understanding of the metabolomic diversity among habanero peppers but also holds potential implications for food science, agriculture, and the culinary arts.