Key Laboratory of Tobacco Biology and Processing
Key Laboratory of Tobacco Biology and Processing
Gong D.,Chinese Academy of Agricultural Sciences |
Huang L.,Biomarker Technologies Corporation |
Xu X.,Chinese Academy of Agricultural Sciences |
Xu X.,Key Laboratory of Tobacco Biology and Processing |
And 5 more authors.
Molecular Breeding | Year: 2016
Tobacco (Nicotiana tabacum L., 2n = 48) is an important agronomic crop and model plant. Flue-cured tobacco is the most important type and accounts for approximately 80 % of tobacco production worldwide. The low genetic diversity of flue-cured tobacco impedes the construction of a high-density genetic linkage map using simple sequence repeat (SSR) markers and warrants the exploitation of single nucleotide polymorphic (SNP) markers from genomic regions. In this article, initially using specific locus-amplified fragment sequencing, we discovered 10,891 SNPs that were subsequently used as molecular markers for genetic map construction. Combined with SSR markers, a final high-density genetic map was generated containing 4215 SNPs and 194 SSRs distributed on 24 linkage groups (LGs). The genetic map was 2662.43 cM in length, with an average distance of 0.60 cM between adjacent markers. Furthermore, by mapping the SNP markers to the ancestral genomes of Nicotiana tomentosiformis and Nicotiana sylvestris, a large number of genome rearrangements were identified as occurring after the polyploidization event. Finally, using this novel integrated map and mapping population, two major quantitative trait loci (QTLs) were identified for flue-curing and mapped to the LG6 of tobacco. This is the first report of SNP markers and a SNP-based linkage map being developed in tobacco. The high-density genetic map and QTLs related to tobacco curing will support gene/QTL fine mapping, genome sequence assembly and molecular breeding in tobacco. © 2016, Springer Science+Business Media Dordrecht.
Sun J.,Key Laboratory of Tobacco Biology and Processing |
Sun J.,Jiangsu University |
Zhou X.,Key Laboratory of Tobacco Biology and Processing |
Zhou X.,Jiangsu University |
And 3 more authors.
Biochemical and Biophysical Research Communications | Year: 2016
Fast identification of moisture content in tobacco plant leaves plays a key role in the tobacco cultivation industry and benefits the management of tobacco plant in the farm. In order to identify moisture content of tobacco plant leaves in a fast and nondestructive way, a method involving Mahalanobis distance coupled with Monte Carlo cross validation(MD-MCCV) was proposed to eliminate outlier sample in this study. The hyperspectral data of 200 tobacco plant leaf samples of 20 moisture gradients were obtained using FieldSpc® 3 spectrometer. Savitzky-Golay smoothing(SG), roughness penalty smoothing(RPS), kernel smoothing(KS) and median smoothing(MS) were used to preprocess the raw spectra. In addition, Mahalanobis distance(MD), Monte Carlo cross validation(MCCV) and Mahalanobis distance coupled to Monte Carlo cross validation(MD-MCCV) were applied to select the outlier sample of the raw spectrum and four smoothing preprocessing spectra. Successive projections algorithm (SPA) was used to extract the most influential wavelengths. Multiple Linear Regression (MLR) was applied to build the prediction models based on preprocessed spectra feature in characteristic wavelengths. The results showed that the preferably four prediction model were MD-MCCV-SG (Rp2 = 0.8401 and RMSEP = 0.1355), MD-MCCV-RPS (Rp2 = 0.8030 and RMSEP = 0.1274), MD-MCCV-KS (Rp2 = 0.8117 and RMSEP = 0.1433), MD-MCCV-MS (Rp2 = 0.9132 and RMSEP = 0.1162). MD-MCCV algorithm performed best among MD algorithm, MCCV algorithm and the method without sample pretreatment algorithm in the eliminating outlier sample from 20 different moisture gradients of tobacco plant leaves and MD-MCCV can be used to eliminate outlier sample in the spectral preprocessing. © 2016 Elsevier Inc. All rights reserved.
Guo Y.,Chinese Academy of Agricultural Sciences |
Guo Y.,Key Laboratory of Tobacco Biology and Processing
Plant Molecular Biology | Year: 2013
The application of systems biology approaches has greatly facilitated the process of deciphering the molecular mechanisms underlying leaf senescence. Analyses of the leaf senescence transcriptome have identified some of the major biochemical events during senescence including protein degradation and nutrient remobilization. Proteomic studies have confirmed these findings and have suggested up-regulated energy metabolism during leaf senescence which might be important for cell viability maintenance. As a critical part of systems biology, studies involving transcription regulation networking and senescence-inducing signaling have deepened our understanding on the molecular regulation of leaf senescence. The important next steps towards a systems biological understanding of leaf senescence will be discussed. © 2012 Springer Science+Business Media Dordrecht.