Transcriptome and Metabolome Comparison of Smooth and Rough Citrus limon L. Peels Grown on Same Trees and Harvested in Different Seasons.

Background: Farmers harvest two batches fruits of Lemons (Citrus limon L. Burm. f.) i.e., spring flowering fruit and autumn flowering fruit in dry-hot valley in Yunnan, China. Regular lemons harvested in autumn have smooth skin. However, lemons harvested in spring have rough skin, which makes them less attractive to customers. Furthermore, the rough skin causes a reduction in commodity value and economical losses to farmers. This is a preliminary study that investigates the key transcriptomic and metabolomic differences in peels of lemon fruits (variety Yuning no. 1) harvested 30, 60, 90, 120, and 150 days after flowering from the same trees in different seasons. Results: We identified 5,792, 4,001, 3,148, and 5,287 differentially expressed genes (DEGs) between smooth peel (C) and rough peel (D) 60, 90, 120, and 150 days after flowering, respectively. A total of 1,193 metabolites differentially accumulated (DAM) between D and C. The DEGs and DAMs were enriched in the mitogen-activated protein kinase (MAPK) and plant hormone signaling, terpenoid biosynthesis, flavonoid, and phenylalanine biosynthesis, and ribosome pathways. Predominantly, in the early stages, phytohormonal regulation and signaling were the main driving force for changes in peel surface. Changes in the expression of genes associated with asymmetric cell division were also an important observation. The biosynthesis of terpenoids was possibly reduced in rough peels, while the exclusive expression of cell wall synthesis-related genes could be a possible reason for the thick peel of the rough-skinned lemons. Additionally, cell division, cell number, hypocotyl growth, accumulation of fatty acids, lignans and coumarins- related gene expression, and metabolite accumulation changes were major observations. Conclusion: The rough peels fruit (autumn flowering fruit) and smooth peels fruit (spring flowering fruit) matured on the same trees are possibly due to the differential regulation of asymmetric cell division, cell number regulation, and randomization of hypocotyl growth related genes and the accumulation of terpenoids, flavonoids, fatty acids, lignans, and coumarins. The preliminary results of this study are important for increasing the understanding of peel roughness in lemon and other citrus species.

Effect of lemon water soluble extract on hyperuricemia in a mouse model.

Lemon is a healthy fruit with high medicinal value. This study found that lemon water soluble extract (LET) can reduce uric acid levels in mice with potassium oxonate induced hyperuricemia. Histopathological analysis suggested that LET caused little damage to the kidneys of mice. It affected mABCG2 and mGLUT9 mRNA expression only in hyperuricemic mice, but not in healthy mice. Our further results show that potassium citrate, rather than citric acid, is the main ingredient in LET with a hypouricemic effect. This study also indicates that lemon does have unique medicinal value for the treatment of hyperuricemia, and that potassium citrate has the potential to be developed as a drug for hyperuricemia. Lowering uric acid through LET and potassium citrate may directly promote the degradation of excessive uric acid in patients with hyperuricemia.

Identification of Genes Associated with Lemon Floral Transition and Flower Development during Floral Inductive Water Deficits: A Hypothetical Model.

Water deficit is a key factor to induce flowering in many woody plants, but reports on the molecular mechanisms of floral induction and flowering by water deficit are scarce. Here, we analyzed the morphology, cytology, and different hormone levels of lemon buds during floral inductive water deficits. Higher levels of ABA were observed, and the initiation of floral bud differentiation was examined by paraffin sections analysis. A total of 1638 differentially expressed genes (DEGs) were identified by RNA sequencing. DEGs were related to flowering, hormone biosynthesis, or metabolism. The expression of some DEGs was associated with floral induction by real-time PCR analysis. However, some DEGs may not have anything to do with flowering induction/flower development; they may be involved in general stress/drought response. Four genes from the phosphatidylethanolamine-binding protein family were further investigated. Ectopic expression of these genes in Arabidopsis changed the flowering time of transgenic plants. Furthermore, the 5' flanking region of these genes was also isolated and sequence analysis revealed the presence of several putative cis-regulatory elements, including basic elements and hormone regulation elements. The spatial and temporal expression patterns of these promoters were investigated under water deficit treatment. Based on these findings, we propose a model for citrus flowering under water deficit conditions, which will enable us to further understand the molecular mechanism of water deficit-regulated flowering in citrus. HIGHLIGHT: Based on gene activity during floral inductive water deficits identified by RNA sequencing and genes associated with lemon floral transition, a model for citrus flowering under water deficit conditions is proposed.

Enlarge the training set based on inter-class relationship for face recognition from one image per person.

In some large-scale face recognition task, such as driver license identification and law enforcement, the training set only contains one image per person. This situation is referred to as one sample problem. Because many face recognition techniques implicitly assume that several (at least two) images per person are available for training, they cannot deal with the one sample problem. This paper investigates principal component analysis (PCA), Fisher linear discriminant analysis (LDA), and locality preserving projections (LPP) and shows why they cannot perform well in one sample problem. After that, this paper presents four reasons that make one sample problem itself difficult: the small sample size problem; the lack of representative samples; the underestimated intra-class variation; and the overestimated inter-class variation. Based on the analysis, this paper proposes to enlarge the training set based on the inter-class relationship. This paper also extends LDA and LPP to extract features from the enlarged training set. The experimental results show the effectiveness of the proposed method.