Comparative transcriptome analysis of persimmon somatic mutants (Diospyros kaki) identifies regulatory networks for fruit maturation and size.

Bud sports in fruit crops often result in new cultivars with unique traits, such as distinct fruit size and color, compared to their parent plants. This study investigates the phenotypic differences and gene expression patterns in Tonewase and Ohtanenashi persimmon bud sports compared to those in their parent, Hiratanenashi, based on RNA-seq data. Tonewase is characterized by early maturation, whereas Ohtanenashi is noted for its larger fruit size. Despite the importance of these traits in determining fruit quality, their molecular bases in persimmons have been understudied. We compared transcriptome-level differences during fruit development between the bud sport samples and their original cultivar. Comprehensive transcriptome analyses identified 15,814 differentially expressed genes and 26 modules via weighted gene co-expression network analysis. Certain modules exhibited unique expression patterns specific to the different cultivars during fruit development, likely contributing to the phenotypic differences observed. Specifically, M11, M16, M22, and M23 were uniquely expressed in Tonewase, whereas M13 and M24 showed distinct patterns in Ohtanenashi. By focusing on genes with distinct expression profiles, we aimed to uncover the genetic basis of cultivar-specific traits. Our findings suggest that changes in the expression of genes associated with ethylene and cell wall pathways may drive Tonewase's earlier maturation, whereas genes related to the cell cycle within the M24 module appear crucial for Ohtanenashi's larger fruit size. Additionally, ethylene and transcription factor genes within this module may contribute to the increased fruit size observed. This study elucidates the differences in transcriptomic changes during fruit development between the two bud sport samples and their original cultivar, enhancing our understanding of the genetic determinants influencing fruit size and maturation.

Non-destructive assessment of cannabis quality during drying process using hyperspectral imaging and machine learning.

Cannabis sativa L. is an industrially valuable plant known for its cannabinoids, such as cannabidiol (CBD) and Δ9-tetrahydrocannabinol (THC), renowned for its therapeutic and psychoactive properties. Despite its significance, the cannabis industry has encountered difficulties in guaranteeing consistent product quality throughout the drying process. Hyperspectral imaging (HSI), combined with advanced machine learning technology, has been used to predict phytochemicals that presents a promising solution for maintaining cannabis quality control. We examined the dynamic changes in cannabinoid compositions under diverse drying conditions and developed a non-destructive method to appraise the quality of cannabis flowers using HSI and machine learning. Even when the relative weight and water content remained constant throughout the drying process, drying conditions significantly influenced the levels of CBD, THC, and their precursors. These results emphasize the importance of determining the exact drying endpoint. To develop HSI-based models for predicting cannabis quality indicators, including dryness, precursor conversion of CBD and THC, and CBD : THC ratio, we employed various spectral preprocessing methods and machine learning algorithms, including logistic regression (LR), support vector machine (SVM), k-nearest neighbor (KNN), random forest (RF), and Gaussian naïve Bayes (GNB). The LR model demonstrated the highest accuracy at 94.7-99.7% when used in conjunction with spectral pre-processing techniques such as multiplicative scatter correction (MSC) or Savitzky-Golay filter. We propose that the HSI-based model holds the potential to serve as a valuable tool for monitoring cannabinoid composition and determining optimal drying endpoint. This tool offers the means to achieve uniform cannabis quality and optimize the drying process in the industry.

Rotundifuran Induces Ferroptotic Cell Death and Mitochondria Permeability Transition in Lung Cancer Cells.

Rotundifuran (RF), a potent anti-inflammatory and anti-cancer compound, is a natural compound predominantly present in Vitex Rotundifolia. Herein, we investigated the effects of RF on the growth of lung cancer cells. Our findings suggested that RF inhibits cell growth, highlighting its potential as a therapeutic agent for cancer treatment. Interestingly, we observed that cell growth inhibition was not due to apoptosis, as caspases were not activated and DNA fragmentation did not occur. Furthermore, we found that intracellular vacuoles and autophagy were induced, but RF-induced cell death was not affected when autophagy was inhibited. This prompted us to investigate other possible mechanisms underlying cell growth inhibition. Through a cDNA chip analysis, we confirmed changes in the expression of ferroptosis-related genes and observed lipid peroxidation. We further examined the effect of ferroptosis inhibitors and found that they alleviated cell growth inhibition induced by RF. We also observed the involvement of calcium signaling, ROS accumulation, and JNK signaling in the induction of ferroptosis. Our findings suggested that RF is a potent anti-cancer drug and further studies are needed to validate its clinal use.