Characterization of the complete chloroplast genome of Wolffia arrhiza and comparative genomic analysis with relative Wolffia species.

Lemnoideae, commonly referred to as the duckweed, are aquatic plants found worldwide. Wolffia species are known for their extreme reduction in size and complexity, lacking both roots and leaves, and they hold the distinction of being the smallest plants among angiosperms. Interestingly, it belongs to the Araceae family, despite its apparent morphological differences from land plants in the same family. Traditional morphological methods have limitations in classifying these plants, making molecular-level information essential. The chloroplast genome of Wolffia arrhiza is revealed that a total length of 169,602 bp and a total GC content of 35.78%. It follows the typical quadripartite structure, which includes a large single copy (LSC, 92,172 bp) region, a small single copy (SSC, 13,686 bp) region, and a pair of inverted repeat (IR, 31,872 bp each) regions. There are 131 genes characterized, comprising 86 Protein-Coding Genes, 37 Transfer RNA (tRNA) genes, and 8 ribosomal RNA (rRNA) genes. Moreover, 48 simple sequence repeats and 32 long repeat sequences were detected. Comparative analysis between W. arrhiza and six other Lemnoideae species identified 12 hotspots of high nucleotide diversity. In addition, a phylogenetic analysis was performed using 14 species belonging to the Araceae family and one external species as an outgroup. This analysis unveiled W. arrhiza and Wolffia globosa as closely related sister species. Therefore, this research has revealed the complete chloroplast genome data of W. arrhiza, offering a more detailed understanding of its evolutionary position and phylogenetic categorization within the Lemnoideae subfamily.

Unraveling the maternal and paternal origins of allotetraploid Vigna reflexo-pilosa.

The genomic structures of Vigna hirtella Ridl. and Vigna trinervia (B.Heyne ex Wight & Arn.) Tateishi & Maxted, key ancestral species of the allotetraploid Vigna reflexo-pilosa var. glabra (Roxb.) N.Tomooka & Maxted, remain poorly understood. This study presents a comprehensive genomic comparison of these species to deepen our knowledge of their evolutionary trajectories. By comparing the genomic profiles of V. hirtella and V. trinervia with those of V. reflexo-pilosa, we investigate the complex genomic mechanisms underlying allopolyploid evolution within the genus Vigna. Comparison of the chloroplast genome revealed that V. trinervia is closely related to V. reflexo-pilosa. De novo assembly of the whole genome, followed by synteny analysis and Ks value calculations, confirms that V. trinervia is closely related to the A genome of V. reflexo-pilosa, and V. hirtella to its B genome. Furthermore, the comparative analyses reveal that V. reflexo-pilosa retains residual signatures of a previous polyploidization event, particularly evident in higher gene family copy numbers. Our research provides genomic evidence for polyploidization within the genus Vigna and identifies potential donor species of allotetraploid species using de novo assembly techniques. Given the Southeast Asian distribution of both V. hirtella and V. trinervia, natural hybridization between these species, with V. trinervia as the maternal ancestor and V. hirtella as the paternal donor, seems plausible.

Sophora genomes provide insight into the evolution of alkaloid metabolites along with small-scale gene duplication.

The genus Sophora (Fabaceae) includes medicinal plants that have been used in East Asian countries since antiquity. Sophora flavescens is a perennial herb indigenous to China, India, Japan, Korea, and Russia. Its dried roots have antioxidant, anti-inflammatory, antibacterial, apoptosis-modulating, and antitumor efficacy. The congeneric S. koreensis is endemic to Korea and its genome is less than half the size of that of S. flavescens. Nevertheless, this discrepancy can be used to assemble and validate the S. flavescens genome. A comparative genomic study of the two genomes can disclose the recent evolutionary divergence of the polymorphic phenotypic profiles of these species. Here, we used the PacBio sequencing platform to sequence and assemble the S. koreensis and S. flavescens genomes. We inferred that it was mainly small-scale duplication that occurred in S. flavescens. A KEGG analysis revealed pathways that might regulate the pharmacologically important secondary metabolites in S. flavescens and S. koreensis. The genome assemblies of Sophora spp. could be used in comparative genomics and data mining for various plant natural products.

Optimizing the Experimental Method for Stomata-Profiling Automation of Soybean Leaves Based on Deep Learning.

Stomatal observation and automatic stomatal detection are useful analyses of stomata for taxonomic, biological, physiological, and eco-physiological studies. We present a new clearing method for improved microscopic imaging of stomata in soybean followed by automated stomatal detection by deep learning. We tested eight clearing agent formulations based upon different ethanol and sodium hypochlorite (NaOCl) concentrations in order to improve the transparency in leaves. An optimal formulation-a 1:1 (v/v) mixture of 95% ethanol and NaOCl (6-14%)-produced better quality images of soybean stomata. Additionally, we evaluated fixatives and dehydrating agents and selected absolute ethanol for both fixation and dehydration. This is a good substitute for formaldehyde, which is more toxic to handle. Using imaging data from this clearing method, we developed an automatic stomatal detector using deep learning and improved a deep-learning algorithm that automatically analyzes stomata through an object detection model using YOLO. The YOLO deep-learning model successfully recognized stomata with high mAP (~0.99). A web-based interface is provided to apply the model of stomatal detection for any soybean data that makes use of the new clearing protocol.

Genome of the world's smallest flowering plant, Wolffia australiana, helps explain its specialized physiology and unique morphology.

Watermeal, Wolffia australiana, is the smallest known flowering monocot and is rich in protein. Despite its great potential as a biotech crop, basic research on Wolffia is in its infancy. Here, we generated the reference genome of a species of watermeal, W. australiana, and identified the genome-wide features that may contribute to its atypical anatomy and physiology, including the absence of roots, adaxial stomata development, and anaerobic life as a turion. In addition, we found evidence of extensive genome rearrangements that may underpin the specialized aquatic lifestyle of watermeal. Analysis of the gene inventory of this intriguing species helps explain the distinct characteristics of W. australiana and its unique evolutionary trajectory.

Database: web application for visualization of the cumulated RNAseq data against the salicylic acid (SA) and methyl jasmonate (MeJA) treatment of Arabidopsis thaliana.

BACKGROUND: Plants have adapted to survive under adverse conditions or exploit favorable conditions in response to their environment as sessile creatures. In a way of plant adaptation, plant hormones have been evolved to efficiently use limited resources. Plant hormones including auxin, jasmonic acid, salicylic acid, and ethylene have been studied to reveal their role in plant adaptation against their environment by phenotypic observation with experimental design such as mutation on hormone receptors and treatment / non-treatment of plant hormones along with other environmental conditions. With the development of Next Generation Sequencing (NGS) technology, it became possible to score the total gene expression of the sampled plants and estimate the degree of effect of plant hormones in gene expression. This allowed us to infer the signaling pathway through plant hormones, which greatly stimulated the study of functional genomics using mutants. Due to the continued development of NGS technology and analytical techniques, many plant hormone-related studies have produced and accumulated NGS-based data, especially RNAseq data have been stored in the sequence read archive represented by NCBI, EBI, and DDBJ. DESCRIPTION: Here, hormone treatment RNAseq data of Arabidopsis (Col0), wild-type genotype, were collected with mock, SA, and MeJA treatments. The genes affected by hormones were identified through a machine learning approach. The degree of expression of the affected gene was quantified, visualized in boxplot using d3 (data-driven-document), and the database was built by Django. CONCLUSION: Using this database, we created a web application ( http://pgl.gnu.ac.kr/hormoneDB/ ) that lists hormone-related or hormone-affected genes and visualizes the boxplot of the gene expression of selected genes. This web application eventually aids the functional genomics researchers who want to gather the cases of the gene responses by the hormones.

Soybean-VCF2Genomes: a database to identify the closest accession in soybean germplasm collection.

BACKGROUND: The development of next generation sequencer (NGS) and the analytical methods allowed the researchers to profile their samples more precisely and easier than before. Especially for agriculture, the certification of the genomic background of their plant materials would be important for the reliability of seed market and stable yield as well as for quarantine procedure. However, the analysis of NGS data is still difficult for non-computational researchers or breeders to verify their samples because majority of current softwares for NGS analysis require users to access unfamiliar Linux environment. MAIN BODY: Here, we developed a web-application, "Soybean-VCF2Genomes", http://pgl.gnu.ac.kr/soy_vcf2genome/ to map single sample variant call format (VCF) file against known soybean germplasm collection for identification of the closest soybean accession. Based on principal component analysis (PCA), we simplified genotype matrix for lowering computational burden while maintaining accurate clustering. With our web-application, users can simply upload single sample VCF file created by more than 10x resequencing strategy to find the closest samples along with linkage dendrogram of the reference genotype matrix. CONCLUSION: The information of the closest soybean cultivar will allow breeders to estimate relative germplasmic position of their query sample to determine soybean breeding strategies. Moreover, our VCF2Genomes scheme can be extended to other plant species where the whole genome sequences of core collection are publicly available.