Tree peony (Paeonia section Moutan DC.) seeds are an excellent source of beneficial natural compounds that promote health, and they contain high levels of alpha-linolenic acid (ALA). In recent years, tree peony has been emerging as an oil crop. Therefore, combined analysis of the transcriptome and proteome of tree peony (P. ostii) seeds at 25, 32, 39, 53, 67, 81, 88, 95, and 109 days after pollination (DAP) was conducted to better understand the transcriptional and translational regulation of seed development and oil biosynthesis. A total of 38,482 unigenes and 2841 proteins were identified. A total of 26,912 differentially expressed genes (DEGs) and 592 differentially expressed proteins (DEPs) were clustered into three groups corresponding to the rapid growth, seed inclusion enrichment and conversion, and late dehydration and mature stages of seed development. Fifteen lipid metabolism pathways were identified at both the transcriptome and proteome levels. Pathway enrichment analysis revealed that a period of rapid fatty acid biosynthesis occurred at 53–88 DAP. Furthermore, 211 genes and 35 proteins associated with the fatty acid metabolism pathway, 63 genes and 11 proteins associated with the biosynthesis of unsaturated fatty acids (UFAs), and 115 genes and 24 proteins associated with ALA metabolism were identified. Phylogenetic analysis revealed that 16 putative fatty acid desaturase (FAD)-encoding genes clustered into four FAD groups, eight of which exhibited the highest expression at 53 DAP, suggesting that they play an important role in ALA accumulation. RT-qPCR analysis indicated that the temporal expression patterns of oil biosynthesis genes were largely similar to the RNA-seq results. The expression patterns of fatty acid metabolism- and seed development-related proteins determined by MRM were also highly consistent with the results obtained in the proteomic analysis. Correlation analysis indicated significant differences in the number and abundance of DEGs and DEPs but a high level of consistency in expression patterns and metabolic pathways. The results of the present study represent the first combined transcriptomic and proteomic analysis of tree peony seeds and provide insight into tree peony seed development and oil accumulation.

Streptococcus suis (S. suis), more specifically serotype 2, is a bacterial pathogen that threatens the lives of pigs and humans. Like many other pathogens, S. suis exhibits quorum sensing (QS) system-controlled virulence factors, such as biofilm formation that complicates treatment. Therefore, impairing the QS involving LuxS/AI-2 cycle in S. suis, may be a promising alternative strategy for overcoming S. suis infections. In this study, we investigated paeoniflorin (PF), a monoterpenoid glycoside compound extracted from peony, as an inhibitor of S. suis LuxS/AI-2 system. At a sub-minimal inhibitory concentration (MIC) (1/16 MIC; 25 μg/ml), PF significantly reduced biofilm formation by S. suis through inhibition of extracellular polysaccharide (EPS) production, without affecting bacterial growth. Moreover, evidence was brought that PF reduces AI-2 activity in S. suis biofilm. Molecular docking indicated that LuxS may be the target of PF. Monitoring LuxS enzymatic activity confirmed that PF had a partial inhibitory effect. Finally, we showed that the use of PF in a mouse model can relieve S. suis infections. This study highlighted the anti-biofilm potential of PF against S. suis, and brought evidence that it may as an inhibitor of the LuxS/AI-2 system to prevent S. suis biofilm-related infections. PF can thus be used as a new type of natural biofilm inhibitor for clinical application.

Melatonin (MT) plays a number of key roles in regulating plant growth and secondary metabolite accumulation. Prunella vulgaris is an important traditional Chinese herbal medicinal plant which is used for the treatment of lymph, goiter, and mastitis. However, the effect of MT on the yield and medicinal component content of P. vulgaris remains still unclear. In this research, we have examined the influence of different concentrations of MT (0, 50, 100, 200, 400 μM) on the physiological characteristics, secondary metabolite contents, and yield of P. vulgaris biomass. The results showed that 50–200 μM MT treatment had a positive effect on P. vulgaris. MT treatment at 100 μM greatly increased the activities of superoxide dismutase and peroxidase, the contents of soluble sugar and proline, and obviously decreased the relative electrical conductivity, the contents of malondialdehyde and hydrogen peroxide of leaves. Furthermore, it markedly promoted the growth and development of the root system, increased the content of photosynthetic pigments, improved the performance of photosystems I and II and the coordination of both photosystems, and enhanced the photosynthetic capacity of P. vulgaris. In addition, it significantly increased the dry mass of whole plant and spica and promoted the accumulation of total flavonoids, total phenolics, caffeic acid, ferulic acid, rosmarinic acid, and hyperoside in the spica of P. vulgaris. These findings demonstrated that the application of MT could effectively activate the antioxidant defense system of P. vulgaris, protect the photosynthetic apparatus from photooxidation damage, and improve the photosynthetic capacity and the root absorption capacity, thereby promoting the yield and accumulation of secondary metabolites in P. vulgaris.

Tree peony is a unique traditional flower in China, with large, fragrant, and colorful flowers. However, a relatively short and concentrated flowering period limits the applications and production of tree peony. A genome-wide association study (GWAS) was conducted to accelerate molecular breeding for the improvement of flowering phenology traits and ornamental phenotypes in tree peony. A diverse panel of 451 tree peony accessions was phenotyped for 23 flowering phenology traits and 4 floral agronomic traits over 3 years. Genotyping by sequencing (GBS) was used to obtain a large number of genome-wide single-nucleotide polymorphisms (SNPs) (107 050) for the panel genotypes, and 1047 candidate genes were identified by association mapping. Eighty-two related genes were observed during at least 2 years for flowering, and seven SNPs repeatedly identified for multiple flowering phenology traits over multiple years were highly significantly associated with five genes known to regulate flowering time. We validated the temporal expression profiles of these candidate genes and highlighted their possible roles in the regulation of flower bud differentiation and flowering time in tree peony. This study shows that GWAS based on GBS can be used to identify the genetic determinants of complex traits in tree peony. The results expand our understanding of flowering time control in perennial woody plants. Identification of markers closely related to these flowering phenology traits can be used in tree peony breeding programs for important agronomic traits.

Background: The study of plant phenotype by deep learning has received increased interest in recent years, which impressive progress has been made in the fields of plant breeding. Deep learning extremely relies on a large amount of training data to extract and recognize target features in the field of plant phenotype classification and recognition tasks. However, for some flower cultivars identification tasks with a huge number of cultivars, it is difficult for traditional deep learning methods to achieve better recognition results with limited sample data. Thus, a method based on metric learning for flower cultivars identification is proposed to solve this problem. Results: We added center loss to the classification network to make inter-class samples disperse and intra-class samples compact, the script of ResNet18, ResNet50, and DenseNet121 were used for feature extraction. To evaluate the effectiveness of the proposed method, a public dataset Oxford 102 Flowers dataset and two novel datasets constructed by us are chosen. For the method of joint supervision of center loss and L₂-softmax loss, the test accuracy rate is 91.88%, 97.34%, and 99.82% across three datasets, respectively. Feature distribution observed by T-distributed stochastic neighbor embedding (T-SNE) verifies the effectiveness of the method presented above. Conclusions: An efficient metric learning method has been described for flower cultivars identification task, which not only provides high recognition rates but also makes the feature extracted from the recognition network interpretable. This study demonstrated that the proposed method provides new ideas for the application of a small amount of data in the field of identification, and has important reference significance for the flower cultivars identification research.