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.

AraDQ: an automated digital phenotyping software for quantifying disease symptoms of flood-inoculated Arabidopsis seedlings.

BACKGROUND: Plant scientists have largely relied on pathogen growth assays and/or transcript analysis of stress-responsive genes for quantification of disease severity and susceptibility. These methods are destructive to plants, labor-intensive, and time-consuming, thereby limiting their application in real-time, large-scale studies. Image-based plant phenotyping is an alternative approach that enables automated measurement of various symptoms. However, most of the currently available plant image analysis tools require specific hardware platform and vendor specific software packages, and thus, are not suited for researchers who are not primarily focused on plant phenotyping. In this study, we aimed to develop a digital phenotyping tool to enhance the speed, accuracy, and reliability of disease quantification in Arabidopsis. RESULTS: Here, we present the Arabidopsis Disease Quantification (AraDQ) image analysis tool for examination of flood-inoculated Arabidopsis seedlings grown on plates containing plant growth media. It is a cross-platform application program with a user-friendly graphical interface that contains highly accurate deep neural networks for object detection and segmentation. The only prerequisite is that the input image should contain a fixed-sized 24-color balance card placed next to the objects of interest on a white background to ensure reliable and reproducible results, regardless of the image acquisition method. The image processing pipeline automatically calculates 10 different colors and morphological parameters for individual seedlings in the given image, and disease-associated phenotypic changes can be easily assessed by comparing plant images captured before and after infection. We conducted two case studies involving bacterial and plant mutants with reduced virulence and disease resistance capabilities, respectively, and thereby demonstrated that AraDQ can capture subtle changes in plant color and morphology with a high level of sensitivity. CONCLUSIONS: AraDQ offers a simple, fast, and accurate approach for image-based quantification of plant disease symptoms using various parameters. Its fully automated pipeline neither requires prior image processing nor costly hardware setups, allowing easy implementation of the software by researchers interested in digital phenotyping of diseased plants.

Synthetic Studies toward 5,6,7,3',4'-Monomethoxytetrahydroxyflavones: Synthesis of Pedalitin.

During the synthetic studies toward 5,6,7,3',4'-monomethoxytetrahydroxyflavones, a concise pedalitin synthesis procedure was achieved. As previously reported, 6-hydroxy-2,3,4-trimethoxyacetophenone was prepared by Friedel-Crafts acylation of 1,4-dihydroxy-2,6-dimethoxybenzene with boron trifluoride diethyl etherate in acetic acid. When aldol condensation of 6-hydroxy-2,3,4-trimethoxyacetophenone 2b with vanillin was performed in basic conditions, it produced 2'-hydroxychalcone 3b, and, surprisingly, along with 3-hydroxyflavone 4 in a considerable amount. We propose that this oxidative cyclization is presumably due to the contribution of a quinone methide, likely to be subjected to aerobic oxidation. The chalcone was then subjected to oxidative cyclization with iodine in dimethyl sulfoxide to afford flavone 5 in good yield. To our delight, serial demethylation of the three methoxy groups at the 5-, 6-, and 3'-positions of 5 proceeded smoothly to produce pedalitin 1, under hydrogen bromide solution (30% in acetic acid). The crystal structures of 3-hydroxyflavone 4 and pedalitin tetraacetate 6 were unambiguously determined by X-ray crystallography.

Sanguisorba officinalis L. Ameliorates Hepatic Steatosis and Fibrosis by Modulating Oxidative Stress, Fatty Acid Oxidation, and Gut Microbiota in CDAHFD-Induced Mice.

Non-alcoholic fatty liver disease (NAFLD) is a leading cause of chronic liver diseases and encompasses non-alcoholic steatosis, steatohepatitis, and fibrosis. Sanguisorba officinalis L. (SO) roots have traditionally been used for their antioxidant properties and have beneficial effects on metabolic disorders, including diabetes and obesity. However, its effects on hepatic steatosis and fibrosis remain unclear. In this study, we explored the effects of a 95% ethanolic SO extract (SOEE) on NAFLD and fibrosis in vivo and in vitro. The SOEE was orally administered to C57BL/6J mice fed a choline-deficient, L-amino-acid-defined, high-fat diet for 10 weeks. The SOEE inhibited hepatic steatosis by modulating hepatic malondialdehyde levels and the expression of oxidative stress-associated genes, regulating fatty-acid-oxidation-related genes, and inhibiting the expression of genes that are responsible for fibrosis. The SOEE suppressed the deposition of extracellular matrix hydroxyproline and mRNA expression of fibrosis-associated genes. The SOEE decreased the expression of fibrosis-related genes in vitro by inhibiting SMAD2/3 phosphorylation. Furthermore, the SOEE restored the gut microbial diversity and modulated specific bacterial genera associated with NAFLD and fibrosis. This study suggests that SOEE might be the potential candidate for inhibiting hepatic steatosis and fibrosis by modulating oxidative stress, fatty acid oxidation, and gut microbiota composition.

Integrative analysis of metabolite and transcriptome reveals biosynthetic pathway and candidate genes for eupatilin and jaceosidin biosynthesis in Artemisia argyi.

Artemisia argyi (A. argyi) is a medicinal plant belonging to the Asteraceae family and Artemisia genus. Flavonoids abundant in A. argyi are associated with anti-inflammatory, anticancer, and antioxidative effects. Eupatilin and jaceosidin are representative polymethoxy flavonoids with medicinal properties significant enough to warrant the development of drugs using their components. However, the biosynthetic pathways and related genes of these compounds have not been fully explored in A. argyi. This study comprehensively analyzed the transcriptome data and flavonoids contents from four different tissues of A. argyi (young leaves, old leaves, trichomes collected from stems, and stems without trichomes) for the first time. We obtained 41,398 unigenes through the de-novo assembly of transcriptome data and mined promising candidate genes involved in the biosynthesis of eupatilin and jaceosidin using differentially expressed genes, hierarchical clustering, phylogenetic tree, and weighted gene co-expression analysis. Our analysis led to the identification of a total of 7,265 DEGs, among which 153 genes were annotated as flavonoid-related genes. In particular, we were able to identify eight putative flavone-6-hydroxylase (F6H) genes, which were responsible for providing a methyl group acceptor into flavone basic skeleton. Furthermore, five O-methyltransferases (OMTs) gene were identified, which were required for the site-specific O-methylation during the biosynthesis of eupatilin and jaceosidin. Although further validation would be necessary, our findings pave the way for the modification and mass-production of pharmacologically important polymethoxy flavonoids through genetic engineering and synthetic biological approaches.

The Impact of Light Wavelength and Darkness on Metabolite Profiling of Korean Ginseng: Evaluating Its Anti-Cancer Potential against MCF-7 and BV-2 Cell Lines.

Korean ginseng is a source of functional foods and medicines; however, its productivity is hindered by abiotic stress factors, such as light. This study investigated the impacts of darkness and different light wavelengths on the metabolomics and anti-cancer activity of ginseng extracts. Hydroponically-grown Korean ginseng was shifted to a light-emitting diodes (LEDs) chamber for blue-LED and darkness treatments, while white fluorescent (FL) light treatment was the control. MCF-7 breast cancer and lipopolysaccharide (LPS)-induced BV-2 microglial cells were used to determine chemo-preventive and neuroprotective potential. Overall, 53 significant primary metabolites were detected in the treated samples. The levels of ginsenosides Rb1, Rb2, Rc, Rd, and Re, as well as organic and amino acids, were significantly higher in the dark treatment, followed by blue-LED treatment and the FL control. The dark-treated ginseng extract significantly induced apoptotic signaling in MCF-7 cells and dose-dependently inhibited the NF-κB and MAP kinase pathways in LPS-induced BV-2 cells. Short-term dark treatment increased the content of Rd, Rc, Rb1, Rb2, and Re ginsenosides in ginseng extracts, which promoted apoptosis of MCF-7 cells and inhibition of the MAP kinase pathway in BV-2 microglial cells. These results indicate that the dark treatment might be effective in improving the pharmacological potential of ginseng.

Somatic Mutations in Fruit Trees: Causes, Detection Methods, and Molecular Mechanisms.

Somatic mutations are genetic changes that occur in non-reproductive cells. In fruit trees, such as apple, grape, orange, and peach, somatic mutations are typically observed as "bud sports" that remain stable during vegetative propagation. Bud sports exhibit various horticulturally important traits that differ from those of their parent plants. Somatic mutations are caused by internal factors, such as DNA replication error, DNA repair error, transposable elements, and deletion, and external factors, such as strong ultraviolet radiation, high temperature, and water availability. There are several methods for detecting somatic mutations, including cytogenetic analysis, and molecular techniques, such as PCR-based methods, DNA sequencing, and epigenomic profiling. Each method has its advantages and limitations, and the choice of method depends on the research question and the available resources. The purpose of this review is to provide a comprehensive understanding of the factors that cause somatic mutations, techniques used to identify them, and underlying molecular mechanisms. Furthermore, we present several case studies that demonstrate how somatic mutation research can be leveraged to discover novel genetic variations. Overall, considering the diverse academic and practical value of somatic mutations in fruit crops, especially those that require lengthy breeding efforts, related research is expected to become more active.

Translating CO[Formula: see text] variability in a plant growth system into plant dynamics.

Plant growth occurs owing to the continuous interactions between environmental and genetic factors, and the analysis of plant growth provides crucial information on plant responses. Recent agronomic and analytical methodologies for plant growth require various channels for capturing broader and more dynamic plant traits. In this study, we provide a method of non-invasive growth analyses by translating CO[Formula: see text] variability around a plant. We hypothesized that the cumulative coefficient of variation (CCV) of plant-driven ambient CO[Formula: see text] variation in a plant growth system could yield a numerical indicator that is connected to the plant growth dynamics. Using the system outside-plant growth system-plant coupled dynamic model, we found that the CCV could translate dynamic plant growth under environmental and biophysical constraints. Furthermore, we experimentally demonstrated the application of CCV by using non-airtight growth chamber systems. Our findings may enrich plant growth information channels and assist growers or researchers to analyze plant growth comprehensively.

Genetic Diversity and Association Analysis for Carotenoid Content among Sprouts of Cowpea (Vigna unguiculata L. Walp).

The development and promotion of biofortified foods plants are a sustainable strategy for supplying essential micronutrients for human health and nutrition. We set out to identify quantitative trait loci (QTL) associated with carotenoid content in cowpea sprouts. The contents of carotenoids, including lutein, zeaxanthin, and ß-carotene in sprouts of 125 accessions were quantified via high-performance liquid chromatography. Significant variation existed in the profiles of the different carotenoids. Lutein was the most abundant (58 ± 12.8 mg/100 g), followed by zeaxanthin (14.7 ± 3.1 mg/100 g) and ß-carotene (13.2 ± 2.9 mg/100 g). A strong positive correlation was observed among the carotenoid compounds (r ≥ 0.87), indicating they can be improved concurrently. The accessions were distributed into three groups, following their carotenoid profiles, with accession C044 having the highest sprout carotenoid content in a single cluster. A total of 3120 genome-wide SNPs were tested for association analysis, which revealed that carotenoid biosynthesis in cowpea sprouts is a polygenic trait controlled by genes with additive and dominance effects. Seven loci were significantly associated with the variation in carotenoid content. The evidence of variation in carotenoid content and genomic regions controlling the trait creates an avenue for breeding cowpea varieties with enhanced sprouts carotenoid content.