Physiological responses and variation in secondary metabolite content among Thai holy basil cultivars (Ocimum tenuiflorum L.) grown under controlled environmental conditions in a plant factory

Phenolic content discrimination in Thai holy basil using hyperspectral data analysis and machine learning techniques

Hyperspectral imaging has emerged as a powerful tool for the non-destructive assessment of plant properties, including the quantification of phytochemical contents. Traditional methods for antioxidant analysis in holy basil (Ocimum tenuiflorum L.) are time-consuming, while hyperspectral imaging has the potential to rapidly observe holy basil. In this study, we employed hyperspectral imaging combined with machine learning techniques to determine the levels of total phenolic contents in Thai holy basil. Spectral data were acquired from 26 holy basil cultivars at different growth stages, and the total phenolic contents of the samples were measured. To extract the characteristics of the spectral data, we used 22 statistical features in both time and frequency domains. Relevant features were selected and combined with the corresponding total phenolic content values to develop a neural network model for classifying the phenolic content levels into 'low' and 'normal-to-high' categories. The neural network model demonstrated high performance, achieving an area under the receiver operating characteristic curve of 0.8113, highlighting its effectiveness in predicting phenolic content levels based on the spectral data. Comparative analysis with other machine learning techniques confirmed the superior performance of the neural network approach. Further investigation revealed that the model exhibited increased confidence in predicting the phenolic content levels of older holy basil samples. This study exhibits the potential of integrating hyperspectral imaging, feature extraction, and machine learning techniques for the rapid and non-destructive assessment of phenolic content levels in holy basil. The demonstrated effectiveness of this approach opens new possibilities for screening antioxidant properties in plants, facilitating efficient decision-making processes for researchers based on comprehensive spectral data.

Metabolomics and physio-chemical analyses of mulberry plants leaves response to manganese deficiency and toxicity reveal key metabolites and their pathways in manganese tolerance

Introduction

Manganese (Mn) plays a pivotal role in plant growth and development. Aside aiding in plant growth and development, Mn as heavy metal (HM) can be toxic in soil when applied in excess. Morus alba is an economically significant plant, capable of adapting to a range of environmental conditions and possessing the potential for phytoremediation of contaminated soil by HMs. The mechanism by which M. alba tolerates Mn stresses remains obscure.

Methods

In this study, Mn concentrations comprising sufficiency (0.15 mM), higher regimes (1.5 mM and 3 mM), and deficiency (0 mM and 0.03 mM), were applied to M. alba in pot treatment for 21 days to understand M. alba Mn tolerance. Mn stress effects on the net photosynthetic rate (Pn), stomatal conductance (Gs), transpiration rate (Tr), intercellular CO2 concentration (Ci), chlorophyll content, plant morphological traits, enzymatic and non-enzymatic parameters were analyzed as well as metabolome signatures via non-targeted LC-MS technique.

Results

Mn deficiency and toxicity decrease plant biomass, Pn, Ci, Gs, Tr, and chlorophyll content. Mn stresses induced a decline in the activities of catalase (CAT) and superoxide dismutase (SOD), while peroxidase (POD) activity, and leaf Mn content, increased. Soluble sugars, soluble proteins, malondialdehyde (MDA) and proline exhibited an elevation in Mn deficiency and toxicity concentrations. Metabolomic analysis indicates that Mn concentrations induced 1031 differentially expressed metabolites (DEMs), particularly amino acids, lipids, carbohydrates, benzene and derivatives and secondary metabolites. The DEMs are significantly enriched in alpha-linolenic acid metabolism, biosynthesis of unsaturated fatty acids, galactose metabolism, pantothenate and CoA biosynthesis, pentose phosphate pathway, carbon metabolism, etc.

Discussion and conclusion

The upregulation of Galactinol, Myo-inositol, Jasmonic acid, L-aspartic acid, Coproporphyrin I, Trigonelline, Pantothenol, and Pantothenate and their significance in the metabolic pathways makes them Mn stress tolerance metabolites in M. alba. Our findings reveal the fundamental understanding of DEMs in M. alba's response to Mn nutrition and the metabolic mechanisms involved, which may hold potential significance for the advancement of M. alba genetic improvement initiatives and phytoremediation programs.

The impact of boron nutrient supply in mulberry (Morus alba) response to metabolomics, enzyme activities, and physiological parameters

Boron (B) is essential for normal and healthy plant growth. Therefore, Boron stress is a common abiotic stress that limits plant growth and productivity. However, how mulberry copes with boron stress remains unclear. In this study, seedlings of the Morus alba cultivar, Yu-711, were treated with five different concentrations of boric acid (H₃BO₃), including deficient (0 and 0.02 mM), sufficient (0.1 mM) and toxic (0.5 and 1 mM) levels. Physiological parameters, enzymatic activities and non-targeted liquid chromatography-mass spectrometry (LC-MS) technique were employed to evaluate the effects of boron stress on the net photosynthetic rate (Pn), chlorophyll content, stomatal conductance (Gs), transpiration rate (Tr), intercellular CO₂ concentration (Ci) and metabolome signatures. Physiological analysis revealed that Boron deficiency and toxicity induced a decline in P_n, C_i, G_s, T_r, and chlorophyll content. Also, enzymatic activities, including catalase (CAT) and superoxide dismutase (SOD), decreased, while POD activity increased in response to Boron stress. Osmotic substances such as soluble sugars, soluble proteins, and proline (PRO) presented elevated levels under all Boron concentrations. Metabolome analysis indicated that differential metabolites, including amino acids, secondary metabolites, carbohydrates, and lipids, played a key role in Yu-711's response to Boron stress. These metabolites were mainly involved in amino acid metabolism, biosynthesis of other secondary metabolites, lipid metabolism, metabolism of cofactors and vitamins, and metabolism of other amino acids pathways. Our findings reveal the various metabolites pathways in mulberry response to boron nutrient supply and may serve as fundamental knowledge in breeding resistance mulberry plants, so that it can cope with climate changes.