Overexpression of geranyl diphosphate synthase 1 (NnGGPPS1) from Nelumbo nucifera enhances carotenoid and chlorophyll content and biomass.

As the traditional herb with pharmacological compounds in China, the key genes related with terpenoid biosynthesis are still unveiled in Nelumbo nucifera. Geranylgeranyl pyrophosphate synthase (GGPPS) is one of the key enzymes in terpenoids biosynthesis, synthesizing the common precursor of GGPP for downstream enzymes for generating various terpenoids. In this study, four NnGGPPS genes were isolated from N. nucifera. Sequence and phylogenetic analyses indicate that NnGGPPS1 and NnGGPPS2 belong to large subunit (LSU). Whereas NnGGPPS3 and NnGGPPS4 are classified as small subunit (SSU) of SSU Ⅱ and SSU I, respectively. Among four NnGGPPSs, only NnGGPPS1 and NnGGPPS2 can produce GGPP in bacterial pigment complementation assay. Combination analysis of subcellular localization and gene co-expression analysis (GCN) illustrates that NnGGPPS1 is the main transcript related with methylerythritol phosphate (MEP) pathway, abscisic acid (ABA) biosynthesis, carotenoid and chlorophyll biosynthesis and degradation. Overexpression of NnGGPPS1 improves the growth of transgenic tobacco, and increases carotenoids and chlorophyll contents. Moreover, NnGGPPS1 transgenic tobacco exhibits improved photosynthesis efficiency and ROS scavenging ability. The up-regulated expression of the key genes in MEP pathway, carotenoid biosynthesis and chlorophyll biosynthesis, result in the increase of metabolic flux in NnGGPPS1 transgenic lines. Furthermore, the elevated MEP-derived primary metabolites of carotenoid and chlorophyll was attributed to enhancement of plant biomass of NnGGPPS1 transgenic lines. Therefore, NnGGPPS1 plays a vital role in biosynthesis of carotenoid and chlorophyll.

Melatonin-induced myeloblastosis viral oncogene homologs alleviate fresh-cut lotus root browning during storage by attenuating flavonoid biosynthesis and reactive oxygen species.

BACKGROUND: Lotus roots (Nelumbo nucifera Gaertn.) are rich in nutrients and have ornamental and food value. However, browning has caused huge economic losses and security risks during the storage and harvesting of fresh-cut lotus. This study investigated the role of melatonin in inhibiting lotus browning, and illustrates its molecular mechanism. RESULTS: The application of melatonin effectively retarded the process of lotus browning, enhanced reactive oxygen species (ROS) scavenging enzyme activity, and inhibited the activity of polyphenol oxidase (PPO), and peroxidase (POD). Melatonin reduced flavonoid content, and decreased enzymatic activity in flavonoid biosynthesis. Transcriptome Sequencing (RNA-seq) was used to screen the genes regulated by exogenous melatonin when defending against fresh-cut lotus browning. Gene co-expression analysis (GCN) indicated that the transcription factors MYB5, MYB6, and MYB308, activated by melatonin, were negatively related to the expression of PPO and the genes related to flavonoid and phenylpropanoid biosynthesis. These myeloblastosis viral oncogene homologs (MYBs) were positively related to the expression of genes encoding the enzymes in glutathione metabolism. CONCLUSION: Melatonin retarded lotus browning by transcriptional suppression of key genes associated with flavonoid and phenylpropanoid biosynthesis through the stimulation of MYB5, MYB6, and MYB308. © 2023 Society of Chemical Industry.

Multi-dimensional variables and feature parameter selection for aboveground biomass estimation of potato based on UAV multispectral imagery.

Aboveground biomass (AGB) is an essential assessment of plant development and guiding agricultural production management in the field. Therefore, efficient and accurate access to crop AGB information can provide a timely and precise yield estimation, which is strong evidence for securing food supply and trade. In this study, the spectral, texture, geometric, and frequency-domain variables were extracted through multispectral imagery of drones, and each variable importance for different dimensional parameter combinations was computed by three feature parameter selection methods. The selected variables from the different combinations were used to perform potato AGB estimation. The results showed that compared with no feature parameter selection, the accuracy and robustness of the AGB prediction models were significantly improved after parameter selection. The random forest based on out-of-bag (RF-OOB) method was proved to be the most effective feature selection method, and in combination with RF regression, the coefficient of determination (R2) of the AGB validation model could reach 0.90, with root mean square error (RMSE), mean absolute error (MAE), and normalized RMSE (nRMSE) of 71.68 g/m2, 51.27 g/m2, and 11.56%, respectively. Meanwhile, the regression models of the RF-OOB method provided a good solution to the problem that high AGB values were underestimated with the variables of four dimensions. Moreover, the precision of AGB estimates was improved as the dimensionality of parameters increased. This present work can contribute to a rapid, efficient, and non-destructive means of obtaining AGB information for crops as well as provide technical support for high-throughput plant phenotypes screening.