A highly contiguous genome assembly of red perilla (Perilla frutescens) domesticated in Japan

DEGAP: Dynamic elongation of a genome assembly path

Genome assembly remains to be a major task in genomic research. Despite the development over the past decades of different assembly software programs and algorithms, it is still a great challenge to assemble a complete genome without any gaps. With the latest DNA circular consensus sequencing (CCS) technology, several assembly programs can now build a genome from raw sequencing data to contigs; however, some complex sequence regions remain as unresolved gaps. Here, we present a novel gap-filling software, DEGAP (Dynamic Elongation of a Genome Assembly Path), that resolves gap regions by utilizing the dual advantages of accuracy and length of high-fidelity (HiFi) reads. DEGAP identifies differences between reads and provides 'GapFiller' or 'CtgLinker' modes to eliminate or shorten gaps in genomes. DEGAP adopts an iterative elongation strategy that automatically and dynamically adjusts parameters according to three complexity factors affecting the genome to determine the optimal extension path. DEGAP has already been successfully applied to decipher complex genomic regions in several projects and may be widely employed to generate more gap-free genomes.

Comparative Metabolomics Analysis of Different Perilla Varieties Provides Insights into Variation in Seed Metabolite Profiles and Antioxidant Activities

Perilla seeds are essential functional foods and key ingredients in traditional medicine. Herein, we investigated the variation in phytochemical profiles and antioxidant activities of twelve different perilla seeds. The seeds showed significant variations in total phenolic and flavonoid contents ranging from 16.92 to 37.23 mg GAE/g (GAE, gallic acid equivalent) and 11.6 to 19.52 mg CAE/g (CAE, catechin equivalent), respectively. LC-QqQ-MS (liquid chromatography triple quadrupole tandem mass spectrometry)-based widely targeted metabolic profiling identified a total of 975 metabolites, including 68-269 differentially accumulated metabolites (DAMs). Multivariate analyses categorized the seeds into four groups based on the seed coat and leaf colors. Most key bioactive DAMs, including flavonoids (quercetin-3'-O-glucoside, prunin, naringenin, naringenin chalcone, butin, genistin, kaempferol-3-O-rutinoside, etc.), amino acids (valine, lysine, histidine, glutamine, threonine, etc.), and vitamins (B1, B3, B6, U, etc.) exhibited the highest relative content in PL3 (brown seed, purple leaf), PL1 (white seed, green-purple leaf), and PL4 (white seed, green leaf) groups, respectively. Meanwhile, key differentially accumulated phenolic acids showed a higher relative content in PL1 and PL4 than in other groups. Both seeds exhibited high antioxidant activities, although those of PL2 (brown seed, green leaf) group seeds were the lowest. Our results may facilitate the comprehensive use of perilla seeds in food and pharmaceutical industries.

Assessing the Potential for Genome-Assisted Breeding in Red Perilla Using Quantitative Trait Locus Analysis and Genomic Prediction

Red perilla is an important medicinal plant used in Kampo medicine. The development of elite varieties of this species is urgently required. Medicinal compounds are generally considered target traits in medicinal plant breeding; however, selection based on compound phenotypes (i.e., conventional selection) is expensive and time consuming. Here, we propose genomic selection (GS) and marker-assisted selection (MAS), which use marker information for selection, as suitable selection methods for medicinal plants, and we evaluate the effectiveness of these methods in perilla breeding. Three breeding populations generated from crosses between one red and three green perilla genotypes were used to elucidate the genetic mechanisms underlying the production of major medicinal compounds using quantitative trait locus analysis and evaluating the accuracy of genomic prediction (GP). We found that GP had a sufficiently high accuracy for all traits, confirming that GS is an effective method for perilla breeding. Moreover, the three populations showed varying degrees of segregation, suggesting that using these populations in breeding may simultaneously enhance multiple target traits. This study contributes to research on the genetic mechanisms of the major medicinal compounds of red perilla, as well as the breeding efficiency of this medicinal plant.

Development of an SNP marker set for marker-assisted backcrossing using genotyping-by-sequencing in tetraploid perilla

High-quality molecular markers are essential for marker-assisted selection to accelerate breeding progress. Compared with diploid species, recently diverged polyploid crop species tend to have highly similar homeologous subgenomes, which is expected to limit the development of broadly applicable locus-specific single-nucleotide polymorphism (SNP) assays. Furthermore, it is particularly challenging to make genome-wide marker sets for species that lack a reference genome. Here, we report the development of a genome-wide set of kompetitive allele specific PCR (KASP) markers for marker-assisted recurrent selection (MARS) in the tetraploid minor crop perilla. To find locus-specific SNP markers across the perilla genome, we used genotyping-by-sequencing (GBS) to construct linkage maps of two F2 populations. The two resulting high-resolution linkage maps comprised 2326 and 2454 SNP markers that spanned a total genetic distance of 2133 cM across 16 linkage groups and 2169 cM across 21 linkage groups, respectively. We then obtained a final genetic map consisting of 22 linkage groups with 1123 common markers from the two genetic maps. We selected 96 genome-wide markers for MARS and confirmed the accuracy of markers in the two F2 populations using a high-throughput Fluidigm system. We confirmed that 91.8% of the SNP genotyping results from the Fluidigm assay were the same as the results obtained through GBS. These results provide a foundation for marker-assisted backcrossing and the development of new varieties of perilla.

Unlocking secrets of nature's chemists: Potential of CRISPR/Cas-based tools in plant metabolic engineering for customized nutraceutical and medicinal profiles

Plant species have evolved diverse metabolic pathways to effectively respond to internal and external signals throughout their life cycle, allowing adaptation to their sessile and phototropic nature. These pathways selectively activate specific metabolic processes, producing plant secondary metabolites (PSMs) governed by genetic and environmental factors. Humans have utilized PSM-enriched plant sources for millennia in medicine and nutraceuticals. Recent technological advances have significantly contributed to discovering metabolic pathways and related genes involved in the biosynthesis of specific PSM in different food crops and medicinal plants. Consequently, there is a growing demand for plant materials rich in nutrients and bioactive compounds, marketed as "superfoods". To meet the industrial demand for superfoods and therapeutic PSMs, modern methods such as system biology, omics, synthetic biology, and genome editing (GE) play a crucial role in identifying the molecular players, limiting steps, and regulatory circuitry involved in PSM production. Among these methods, clustered regularly interspaced short palindromic repeats-CRISPR associated protein (CRISPR/Cas) is the most widely used system for plant GE due to its simple design, flexibility, precision, and multiplexing capabilities. Utilizing the CRISPR-based toolbox for metabolic engineering (ME) offers an ideal solution for developing plants with tailored preventive (nutraceuticals) and curative (therapeutic) metabolic profiles in an ecofriendly way. This review discusses recent advances in understanding the multifactorial regulation of metabolic pathways, the application of CRISPR-based tools for plant ME, and the potential research areas for enhancing plant metabolic profiles.