Expression of a Chromoplast-Specific Lycopene ß-Cyclase Gene (CYC-B) Is Implicated in Carotenoid Accumulation and Coloration in the Loquat.

Carotenoids are the principal pigments in the loquat. Although the metabolic pathway of plant carotenoids has been extensively investigated, few studies have been explored the regulatory mechanisms of loquat carotenoids because knowledge of the loquat genome is incomplete. The chromoplast-specific lycopene ß-cyclase gene (CYC-B) could catalyze cyclization of lycopene to ß-carotene. In this study, the differential accumulation patterns of loquat with different colors were analyzed and virus-induced gene silencing (VIGS) was utilized in order to verify CYC-B gene function. Using a cloning strategy of homologous genes, a CYC-B gene orthologue was successfully identified from the loquat. At a later stage of maturation, CYC-B gene expression and carotenoids concentrations in the 'Dawuxing' variety were higher than in 'Chuannong 1-5-9', possibly leading to the difference in pulp coloration of loquat. Interference of CYC-B gene expression in the loquat demonstrated clear visual changes. The green color in negative control fruits became yellow, while TRV2-CYC-B silenced fruits remained green. CYC-B gene expression and total carotenoid content in the pulp decreased by 32.5% and 44.1%, respectively. Furthermore, multiple key genes in the carotenoid metabolic pathway synergistically responded to downregulation of CYC-B gene expression. In summary, we provide direct evidences that CYC-B gene is involved in carotenoid accumulation and coloration in the loquat.

[Discrimination of varieties of paddy based on Vis/NIR spectroscopy combined with chemometrics].

A simple, fast and non-destructive method based on visible/near infrared reflectance (Vis/NIR) spectroscopy and chemometrics was put forward for discriminating varieties of paddy. Firstly, A field spectroradiometer was used for collecting spectra in the wavelength range from 325 to 1 025 nm. The Vis/NIR spectra were acquired from 150 samples of five varieties of paddy. Secondly, original spectral data were decomposed as low-frequency wavelet coefficients and high-frequency wavelet coefficients by wavelet transform (WT) at first level. High-frequency wavelet coefficients were deleted as they contained too many noise, so the reconstructed signals from low-frequency wavelet coefficients were used as replacer of original spectral data. Thirdly, principal component analysis (PCA) compressed the above data into several new variables that were the linear combination of original spectral variables. The analysis suggested that the first four PCs (principle components) could account for 99.89% of the original spectral information, it means that the four PCs could explain most variation of original variables. In order to set up the model for discriminating varieties of paddy, the four diagnostic PCs were applied as inputs of back propagation artificial neural network (BP-ANN), and the values of varieties of different paddy were applied as the outputs of BP-ANN. The threshold of error was set as 0.2, the optimal structure of BP-ANN was three layers with nodes as 4-9-3. The whole 150 samples were randomly divided into two parts, one of which that consisted of 100 samples was used to model, and the other one containing 50 samples was used to predict. This model has been used to predict the varieties of 50 unknown samples, and the discrimination rate 96% has been obtained. It proved that the model was very reliable and practicable. In short, it is feasible to discriminate varieties of paddy based on visible/near infrared reflectance (Vis/NIR) spectroscopy and chemometrics.