Fish nodaviruses are causative agents of viral nervous necrosis causing high mortality in cultured marine fishes around the world. The first successful isolation of fish nodavirus was made with SSN-1 cells, which are persistently infected with snakehead retrovirus (SnRV). In the present study, a BF-2 cell line persistently infected with SnRV (PI-BF-2) was established to evaluate the influence of SnRV on the production of fish nodavirus. The PI-BF-2 cells were slightly more slender than BF-2 cells, but no difference was observed in propagation rate between both cell lines. No difference was observed in production of SnRV between PI-BF-2 and SSN-1 cell lines. Although both PI-BF-2 and BF-2 cell lines showed no cytopathic effect (CPE) after inoculation of striped jack nervous necrosis virus (SJNNV) and redspotted grouper nervous necrosis virus (RGNNV), these fish nodaviruses could be amplified in BF-2 cells, and moreover, production of fish nodaviruses in the PI-BF-2 cell line was more than 40 times higher than in BF-2 cells. Thus, it was concluded that BF-2 cell permissiveness to fish nodaviruses was enhanced by persistent infection with SnRV. Furthermore, homologous cDNA to genomic RNA of SJNNV was detected from both PI-BF-2 and SSN-1 cell lines persistently infected with SnRV. The amount of nodavirus cDNA in SJNNV-inoculated PI-BF-2 cells was clearly lower than that in SJNNV-inoculated SSN-1 cells.

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.

Key message: Herein, a method has been proposed for selecting optimal cross pairs based on the genetic potential of progeny in interpopulation crosses, considering different genetic effects among populations. In the breeding programs for self-pollinating plants, genetic improvements in multiple traits can be challenging when relying solely on a single biparental population, and interpopulation crosses are employed to integrate favorable alleles from multiple biparental populations to overcome this limitation. In this context, it is crucial to consider distinct genetic effects in different populations. In this study, we used a selection method based on the usefulness criterion (UC) to identify cross pairs suitable for interpopulation crosses. We expanded this approach to enhance breeding programs by accounting for varying genetic backgrounds within the genomic selection framework. Using the medicinal plant perilla as the study material, we performed simulations to compare the efficacy of selection based on the estimated genotypic values with that of selection based on UC. Our findings demonstrate that the proposed method is effective in facilitating the simultaneous improvement of multiple traits, particularly by considerably increasing the genetic gains among the top-performing individuals in the population. Furthermore, we provide guidelines for implementing interpopulation crosses, including recommendations for the optimal generation for crossing and an appropriate reference generation for calculating UC. The results obtained in this study offer valuable insights for small-scale breeding programs aimed at simultaneously enhancing multiple traits through interpopulation crosses and can be applied to a wide range of crops, including neglected and underutilized species.

Intercropping is a promising strategy to improve productivity and sustainability in agricultural systems, but designing effective genotype combinations remains a major challenge owing to the rapid increase in possible pairings as the number of candidate genotypes increases. This creates a practical bottleneck because field evaluation of all combinations is infeasible under realistic resource constraints. Here, we propose a framework that integrates genomic prediction and Bayesian optimization to support efficient decision-making for intercropping system design. Using genome-wide marker data from sorghum and soybean, we simulated intercropping performance across 5,214 genotype pairs under certain genetic architectures, including variation in heritability, correlations between direct and indirect genetic effects, and the contribution of pair-specific interactions. Genomic prediction models incorporating direct and indirect genetic effects substantially improved prediction accuracy compared with models based on direct genetic effects alone, and inclusion of specific mixing ability further enhanced the performance under high-heritability conditions. When coupled with Bayesian optimization, the models rapidly identified superior genotype pairs, requiring fewer evaluation cycles than random or prediction-only search strategies. Acquisition functions that account for predicted uncertainty were most effective in complex scenarios involving interaction effects or negative correlations between direct and indirect effects. These results demonstrate that combining genomic prediction with Bayesian optimization can substantially reduce the experimental burden associated with intercropping design, while improving the efficiency of identifying high-performing genotype pairs. The proposed framework provides a practical approach for prioritizing candidate mixtures in breeding and field evaluation, and contributes to the development of data-driven strategies for sustainable agricultural systems. Highlights: A data-driven framework was developed to optimize genotype pairs in intercropping. Modeling indirect effects improved prediction accuracy across genotype pairs. Pair-specific interactions enhanced prediction under high-heritability conditions. Bayesian optimization identified superior pairs under limited evaluation capacity. The framework reduces field-testing requirements for intercropping system design.

Red perilla is an important medicinal plant used in Kampo medicine, and the development of elite varieties of this species is urgently necessary. Medicinal compounds are generally considered target traits in medicinal plant breeding; however, selection based on the phenotypes of the compounds (i.e., conventional selection) is expensive and time-consuming. Here, we proposed genomic selection (GS) and marker-assisted selection (MAS) as suitable selection methods for medicinal plants, and evaluated the effectiveness of GS and MAS in red 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 to evaluate the accuracy of genomic prediction (GP). We found that GP had sufficiently high accuracy for all traits, confirming that GS was an effective method for perilla breeding. Moreover, the three populations showed varying degrees of segregation, suggesting that use of these populations in breeding may yield simultaneous enhancements of different target traits. This study can contribute to research on the genetic mechanisms of the major medicinal compounds of red perilla as well as the breeding efficiency of this medicinal plant.

In the breeding programs of self-pollinated plants, achieving genetic improvement in multiple traits can be challenging when relying solely on a single biparental population. Interpopulation crosses are employed to integrate favorable alleles from multiple biparental populations to overcome this limitation. In this context, it is crucial to consider the distinct genetic effects in different populations. In this study, we utilized a selection method based on the usefulness criterion (UC) to identify cross pairs suitable for interpopulation crosses. We expanded this approach to enhance breeding programs accounting for varying genetic backgrounds within the genomic selection framework. Using the medicinal plant red perilla as the study material, we conducted simulations to compare the efficacy of selection based on estimated genomic breeding values with that based on UC. Our findings demonstrate that the proposed method is effective in facilitating the simultaneous improvement of multiple traits, particularly by considerably increasing genetic gains among the top-performing individuals in the population. Furthermore, we provide guidelines for implementing interpopulation crosses, including recommendations for the optimal generation for crossing and the appropriate reference generation for calculating the UC. The results obtained in this study offer valuable insights for small-scale breeding programs aimed at simultaneously enhancing multiple traits through inter-population crosses and are applicable to a wide range of crops, including neglected and underutilized species. Key Message Herein, a method has been proposed for selecting optimal cross pairs based on the genetic potential of progeny in inter-population crosses, considering different genetic effects among populations.