Yield and Metabolite Production of Pelargonium sidoides DC. in Response to Irrigation and Nitrogen Management

Plant secondary metabolic responses to global climate change: A meta-analysis in medicinal and aromatic plants

Plant secondary metabolites (SMs) play crucial roles in plant-environment interactions and contribute greatly to human health. Global climate changes are expected to dramatically affect plant secondary metabolism, yet a systematic understanding of such influences is still lacking. Here, we employed medicinal and aromatic plants (MAAPs) as model plant taxa and performed a meta-analysis from 360 publications using 1828 paired observations to assess the responses of different SMs levels and the accompanying plant traits to elevated carbon dioxide (eCO₂ ), elevated temperature (eT), elevated nitrogen deposition (eN) and decreased precipitation (dP). The overall results showed that phenolic and terpenoid levels generally respond positively to eCO₂ but negatively to eN, while the total alkaloid concentration was increased remarkably by eN. By contrast, dP promotes the levels of all SMs, while eT exclusively exerts a positive influence on the levels of phenolic compounds. Further analysis highlighted the dependence of SM responses on different moderators such as plant functional types, climate change levels or exposure durations, mean annual temperature and mean annual precipitation. Moreover, plant phenolic and terpenoid responses to climate changes could be attributed to the variations of C/N ratio and total soluble sugar levels, while the trade-off supposition contributed to SM responses to climate changes other than eCO₂ . Taken together, our results predicted the distinctive SM responses to diverse climate changes in MAAPs and allowed us to define potential moderators responsible for these variations. Further, linking SM responses to C-N metabolism and growth-defence balance provided biological understandings in terms of plant secondary metabolic regulation.

An Exploration of Pepino (Solanum muricatum) Flavor Compounds Using Machine Learning Combined with Metabolomics and Sensory Evaluation

Flavor is one of the most important characteristics that directly determines the popularity of a food. Moreover, the flavor of fruits is determined by the interaction of multiple metabolic components. Pepino, an emerging horticultural crop, is popular for its unique melon-like flavor. We analyzed metabolomics data from three different pepino growing regions in Haidong, Wuwei, and Jiuquan and counted the status of sweetness, acidity, flavor, and overall liking ratings of pepino fruit in these three regions by sensory panels. The metabolomics and flavor ratings were also integrated and analyzed using statistical and machine learning models, which in turn predicted the sensory panel ratings of consumers based on the chemical composition of the fruit. The results showed that pepino fruit produced in the Jiuquan region received the highest ratings in sweetness, flavor intensity, and liking, and the results with the highest contribution based on sensory evaluation showed that nucleotides and derivatives, phenolic acids, amino acids and derivatives, saccharides, and alcohols were rated in sweetness (74.40%), acidity (51.57%), flavor (56.41%), and likability (33.73%) dominated. We employed 14 machine learning strategies trained on the discovery samples to accurately predict the outcome of sweetness, sourness, flavor, and liking in the replication samples. The Radial Sigma SVM model predicted with better accuracy than the other machine learning models. Then we used the machine learning models to determine which metabolites influenced both pepino flavor and consumer preference. A total of 27 metabolites most important for pepino flavor attributes to distinguish pepino originating from three regions were screened. Substances such as N-acetylhistamine, arginine, and caffeic acid can enhance pepino‘s flavor intensity, and metabolites such as glycerol 3-phosphate, aconitic acid, and sucrose all acted as important variables in explaining the liking preference. While glycolic acid and orthophosphate inhibit sweetness and enhance sourness, sucrose has the opposite effect. Machine learning can identify the types of metabolites that influence fruit flavor by linking metabolomics of fruit with sensory evaluation among consumers, which conduces breeders to incorporate fruit flavor as a trait earlier in the breeding process, making it possible to select and release fruit with more flavor.

Scopoletin: a review of its source, biosynthesis, methods of extraction, and pharmacological activities

Scopoletin, also known as 6-methoxy-7 hydroxycoumarin, is one of the naturally occurring coumarin commonly found in many edible plants and plays an important role in human health. Despite the various potential pharmacological properties, the biosynthesis process, method of extraction, and mechanism of action on this compound have not been documented well. In this current review, the biosynthesis pathway, distribution of scopoletin in the plant kingdom, and extraction techniques are elaborated. The in vitro, in vivo, and in silico pharmacological studies are also discussed on antioxidant, antimicrobial, anticancer, anti-inflammation, and neuroprotective aspects of scopoletin. This study may help to understand the benefit of scopoletin containing plants and would be beneficial for the prevention and treatment of diseases.