PLC1 mediated Cycloastragenol-induced stomatal movement by regulating the production of NO in Arabidopsis thaliana.

BACKGROUND: Astragalus grows mainly in drought areas. Cycloastragenol (CAG) is a tetracyclic triterpenoid allelochemical extracted from traditional Chinese medicine Astragalus root. Phospholipase C (PLC) and Gα-submit of the heterotrimeric G-protein (GPA1) are involved in many biotic or abiotic stresses. Nitric oxide (NO) is a crucial gas signal molecule in plants. RESULTS: In this study, using the seedlings of Arabidopsis thaliana (A. thaliana), the results showed that low concentrations of CAG induced stomatal closure, and high concentrations inhibited stomatal closure. 30 µmol·L-1 CAG significantly increased the relative expression levels of PLC1 and GPA1 and the activities of PLC and GTP hydrolysis. The stomatal aperture of plc1, gpa1, and plc1/gpa1 was higher than that of WT under CAG treatment. CAG increased the fluorescence intensity of NO in guard cells. Exogenous application of c-PTIO to WT significantly induced stomatal aperture under CAG treatment. CAG significantly increased the relative expression levels of NIA1 and NOA1. Mutants of noa1, nia1, and nia2 showed that NO production was mainly from NOA1 and NIA1 by CAG treatment. The fluorescence intensity of NO in guard cells of plc1, gpa1, and plc1/gpa1 was lower than WT, indicating that PLC1 and GPA1 were involved in the NO production in guard cells. There was no significant difference in the gene expression of PLC1 in WT, nia1, and noa1 under CAG treatment. The gene expression levels of NIA1 and NOA1 in plc1, gpa1, and plc1/gpa1 were significantly lower than WT, indicating that PLC1 and GPA1 were positively regulating NO production by regulating the expression of NIA1 and NOA1 under CAG treatment. CONCLUSIONS: These results suggested that the NO accumulation was essential to induce stomatal closure under CAG treatment, and GPA1 and PLC1 acted upstream of NO.

Plant polysaccharide itself as hydrogen bond donor in a deep eutectic system-based mechanochemical extraction method.

A deep eutectic system (DESys) is formed when a hydrogen bond acceptor (HBA) is processed with polysaccharide (hydrogen bond donor, HBD) containing plant substance in water to dissolve, extract, and recover the polysaccharide directly, instead of using a traditional deep eutectic solvent (DES). The extraction efficiency is enhanced by the direct formation of the DESys, in a mechanochemical extraction (MCE) system. Key factors affecting the extraction efficiency were systematically studied and optimized. The effects of the DESys on the structure and physicochemical properties of polysaccharides were studied by several analytical techniques. The findings demonstrated that the direct DESys formation extraction efficiency was superior than that of traditional extraction methods while retaining physicochemical properties of polysaccharides. Moreover, the composition of polysaccharides extracted with this method is different from that obtained by conventional methods. The recovery and purification process of polysaccharides is simplified by eliminating the need for an additional HBD.

Hyperspectral technique combined with stacking and blending ensemble learning method for detection of cadmium content in oilseed rape leaves.

BACKGROUND: Oilseed rape, as one of the most important oil crops, is an important source of vegetable oil and protein for mankind. As a non-essential element for plant growth, heavy metal cadmium (Cd) is easily absorbed by plants. Cd will inhibit the photosynthesis of plants, destroy the cell structure, slow the growth of plants, and affect their development and yield. It is necessary to develop a method based on visible near-infrared (NIR) hyperspectral imaging (HSI) technology to quickly and nondestructively determine the Cd content in rape leaves. RESULTS: Two-layer estimation models were established by combining visible-NIR HSI with ensemble learning methods (stacking and blending). One layer used support vector regression, extreme learning machine, decision tree, and random forest (RF) as basic learners, and the other layer used support vector regression or RF as a meta learner. Different models were used to analyze the spectra of rape treated with five Cd concentrations to obtain the best prediction method. The results showed that the best model to predict Cd content was the stacking ensemble model with RF as the meta learner, with coefficient of determination for prediction of 0.9815 and root-mean-square error for prediction of 5.8969 mg kg-1 . A pseudo-color image was developed using this stacking model to visualize the content and distribution of Cd. CONCLUSION: The combination of visible-NIR HSI technology and the stacking ensemble learning method is a feasible method to detect the Cd content in rape leaves, which has the potential of being rapid and nondestructive. © 2022 Society of Chemical Industry.