A Comparative Study of Flavonoids and Carotenoids Revealed Metabolite Responses for Various Flower Colorations Between Nicotiana tabacum L. and Nicotiana rustica L.

Tobacco is a model plant for studying flower coloration. Flavonoids and carotenoids were reported to contribute to the flower color in many plants. We investigated the mechanism underlying flower color formation in tobacco by comparing the profiling flavonoids and carotenoids between various species Nicotiana tabacum L. and Nicotiana rustica L., as their flowers commonly presented red (pink) and yellow (orange), respectively. The metabolomes were conducted by UPLC-ESI-MS/MS system. The main findings were as follows: (1) A total of 31 flavonoids and 36 carotenoids were identified in all four cultivars involved in N. tabacum and N. rustica. (2) Flavonoids and carotenoids tended to concentrate in the red flowers (N. tabacum) and yellow flowers (N. rustica), respectively. (3) About eight flavonoids and 12 carotenoids were primarily screened out for metabolic biomarkers, such as the robust biomarker involving kaempferol-3-o-rut, quercetin-glu, rutin, lutein, and ß-carotene. This is the first research of systematic metabolome involving both flavonoids and carotenoids in tobacco flower coloration. The metabolic mechanism concluded that flavonoids and carotenoids mainly contributed to red (pink) and yellow (orange) colors of the tobacco flowers, respectively. Our finding will provide essential insights into characterizing species and modifying flower color in tobacco breeding through genetic improvement or regulation of featured metabolic synthesis.

Ramie Yield Estimation Based on UAV RGB Images.

Timely and accurate crop growth monitoring and yield estimation are important for field management. The traditional sampling method used for estimation of ramie yield is destructive. Thus, this study proposed a new method for estimating ramie yield based on field phenotypic data obtained from unmanned aerial vehicle (UAV) images. A UAV platform carrying RGB cameras was employed to collect ramie canopy images during the whole growth period. The vegetation indices (VIs), plant number, and plant height were extracted from UAV-based images, and then, these data were incorporated to establish yield estimation model. Among all of the UAV-based image data, we found that the structure features (plant number and plant height) could better reflect the ramie yield than the spectral features, and in structure features, the plant number was found to be the most useful index to monitor the yield, with a correlation coefficient of 0.6. By fusing multiple characteristic parameters, the yield estimation model based on the multiple linear regression was obviously more accurate than the stepwise linear regression model, with a determination coefficient of 0.66 and a relative root mean square error of 1.592 kg. Our study reveals that it is feasible to monitor crop growth based on UAV images and that the fusion of phenotypic data can improve the accuracy of yield estimations.

Expression profiling of cadmium response genes in ramie (Boehmeria nivea L.) root.

Ramie (Boehmeria nivea), a perennial herb belongs to Urticaceae family, is a rapid growth and high biomass crop with highly tolerant and accumulative to heavy metals. However, the gene expression and regulation caused by cadmium (Cd) in ramie has not been well studied. In the present study, a gene expression database of ramie root in the absence (control) or presence of 100 µM Cd was established. Solexa high-throughput sequencing technology showed that 3,654,395 and 3,572,333 tags have been obtained from control and Cd treatment respectively. In total, 3887 genes were detected with significant differential expression levels, in which 2883 genes were up-regulated and 1004 genes were down-regulated. Gene ontology and pathway-based analyses were performed to determine and further to understand the biological functions of those differentially expressed genes. Fifteen genes were selected and their expression levels were confirmed by quantitative RT-PCR, and twelve of them showed consistent expression patterns with the digital gene expression data. Results on these expression profiling of genes lay the basis for biotechnological modification of new transgenic plants with improved phytoremediation capacity.