Involvement of CBF in the fine-tuning of litchi flowering time and cold and drought stresses.

Litchi (Litchi chinensis) is an economically important fruit tree in southern China and is widely cultivated in subtropical regions. However, irregular flowering attributed to inadequate floral induction leads to a seriously fluctuating bearing. Litchi floral initiation is largely determined by cold temperatures, whereas the underlying molecular mechanisms have yet to be identified. In this study, we identified four CRT/DRE BINDING FACTORS (CBF) homologs in litchi, of which LcCBF1, LcCBF2 and LcCBF3 showed a decrease in response to the floral inductive cold. A similar expression pattern was observed for the MOTHER OF FT AND TFL1 homolog (LcMFT) in litchi. Furthermore, both LcCBF2 and LcCBF3 were found to bind to the promoter of LcMFT to activate its expression, as indicated by the analysis of yeast-one-hybrid (Y1H), electrophoretic mobility shift assays (EMSA), and dual luciferase complementation assays. Ectopic overexpression of LcCBF2 and LcCBF3 in Arabidopsis caused delayed flowering and increased freezing and drought tolerance, whereas overexpression of LcMFT in Arabidopsis had no significant effect on flowering time. Taken together, we identified LcCBF2 and LcCBF3 as upstream activators of LcMFT and proposed the contribution of the cold-responsive CBF to the fine-tuning of flowering time.

Methyl-inositol, γ-aminobutyric acid and other health benefit compounds in the aril of litchi.

The available components in the flesh of litchi seem insufficient to interpret its wide and significant physiological effects. Some unusual compounds, including myo-inositol, inositol methyl derivatives and γ-aminobutyric acid (GABA) were identified as main constituents in the flesh of litchi. Their concentrations varied among cultivars but remain relatively constant during development. Litchi flesh was shown to contain moderate myo-inositol (0.28-0.78 mg g(-1) FW), ascorbic acid (0.08-0.39 mg g(-1) FW) and phenolics (0.47-1.60 mg g(-1) FW), but abundant l-quebrachitol (1.6-6.4 mg g(-1) FW) and GABA (1.7-3.5 mg g(-1) FW). The concentration of GABA in the flesh of litchi was about 100 times higher than in other fruits. And l-quebrachitol is not a common component in fruits. The biological and physiological activities of inositols, inositol derivatives and GABA have been extensively documented. These compounds are probably important compositional characteristic contributing to the widely shown health benefits of litchi.

Transcriptomic analysis of floral initiation in litchi (Litchi chinensis Sonn.) based on de novo RNA sequencing.

KEY MESSAGE: Comparative transcriptome analysis of litchi ( Litchi chinensis Sonn.) buds at two developmental stages revealed multiple processes involving various phytohormones regulating floral initiation, and expression of numerous flowering-related genes. Floral initiation is a critical and complicated plant developmental process involving interactions of numerous endogenous and environmental factors, but little is known about the complex network regulating floral initiation in litchi (Litchi chinensis Sonn.). Illumina second-generation sequencing is an efficient method for obtaining massive transcriptional information resulting from phase changes in plant development. In this study, comparative transcriptomic analysis was performed with resting and emerging panicle stage buds, to gain further understanding of the molecular mechanisms involved in floral initiation in litchi. Abundance analysis identified 5,928 unigenes exhibiting at least twofold differences in expression between the two bud stages. Of these, 4,622 unigenes were up-regulated and 1,306 were down-regulated in panicle-emerging buds compared with resting buds. KEGG pathway enrichment analysis revealed that unigenes exhibiting differential expression were involved in the metabolism and signal transduction of various phytohormones. The expression levels of unigenes annotated as auxin, cytokinin, jasmonic acid, and salicylic acid biosynthesis were up-regulated, whereas those unigenes annotated as abscisic acid biosynthesis were down-regulated during floral initiation. In addition, 188 unigenes exhibiting sequence similarities to known flowering-related genes from other plants were differentially expressed during floral initiation. Thirteen genes were selected for confirmation of expression levels using quantitative-PCR. Our results provide abundant sequence resources for studying mechanisms underlying floral initiation in litchi and establish a platform for further studies of litchi and other evergreen fruit trees.

Identification of MV-generated ROS responsive EST clones in floral buds of Litchi chinensis Sonn.

KEY MESSAGE: A suppression subtractive hybridization library was constructed using inflorescence primordia of 'Nuomici' litchi to identify EST clones responsive to MV-generated ROS. 93 ESTs could be aligned as unique gene sequences in the inflorescence primordia of litchi. Litchi is an evergreen woody tree widely cultivated in subtropical and tropical regions. However, defective flowering is a pending problem of litchi production. Our previous study indicated that reactive oxygen species (ROS) induced by methyl viologen dichloride hydrate (MV) promotes flowering in litchi. In the present study, a suppression subtractive hybridization (SSH) library was constructed using inflorescence primordia of 'Nuomici' with the aim to find out ROS responsive clones during floral differentiation. 1856 Expressed sequence tag (EST) clones were randomly selected. Clones carrying single exogenous fragments were screened by reverse northern analysis to identify those responsive to MV-generated ROS. A total of 783 differentially expressed EST clones were identified as MV responsive cDNA and were subjected to sequencing. Among them, 26 clones were represented more than three times. 783 clones were aligned to 93 unique gene sequences. The unique genes were classified into 9 categories. 16 % of them were involved in transport facilitation, 11 % in transcription regulation, 4 % in stress response, 9 % in carbohydrate metabolism, 1 % in secondary metabolism, 14 % in intracellular signaling, and 25 % in other metabolism, while 9 % were genes with unknown functions and 11 % were genes with no match in the database.