Non-Heading Chinese Cabbage Database: An Open-Access Platform for the Genomics of Brassica campestris (syn. Brassica rapa) ssp. chinensis

High expression of ethylene response factor BcERF98 delays the flowering time of non-heading Chinese cabbage

MAIN CONCLUSION

BcERF98 is induced by ethylene signaling and inhibits the expression of BcFT by interacting with BcNF-YA2 and BcEIP9, thereby inhibiting plant flowering. Several stresses trigger the accumulation of ethylene, which then transmits the signal to ethylene response factors (ERFs) to participate in the regulation of plant development to adapt to the environment. This study clarifies the function of BcERF98, a homolog of AtERF98, in the regulation of plant flowering time mediated by high concentrations of ethylene. Results indicate that BcERF98 is a nuclear and the cell membrane-localized transcription factor and highly responsive to ethylene signaling. BcERF98 inhibits the expression of BcFT by interacting with BcEIP9 and BcNF-YA2, which are related to flowering time regulation, thereby participating in ethylene-mediated plant late flowering regulation. The results have enriched the theoretical knowledge of flowering regulation in non-heading Chinese cabbage (NHCC), providing the scientific basis and gene reserves for cultivating new varieties of NHCC with different flowering times.

QTL mapping and candidate gene analysis reveal two major loci regulating green leaf color in non-heading Chinese cabbage

KEY MESSAGE

Two major-effect QTL GlcA07.1 and GlcA09.1 for green leaf color were fine mapped into 170.25 kb and 191.41 kb intervals on chromosomes A07 and A09, respectively, and were validated by transcriptome analysis. Non-heading Chinese cabbage (NHCC) is a leafy vegetable with a wide range of green colors. Understanding the genetic mechanism behind broad spectrum of green may facilitate the breeding of high-quality NHCC. Here, we used F2 and F7:8 recombination inbred line (RIL) population from a cross between Wutacai (dark-green) and Erqing (lime-green) to undertake the genetic analysis and quantitative trait locus (QTL) mapping in NHCC. The genetic investigation of the F2 population revealed that the variation of green leaf color was controlled by two recessive genes. Six pigments associated with green leaf color, including total chlorophyll, chlorophyll a, chlorophyll b, total carotenoids, lutein, and carotene were quantified and applied for QTL mapping in the RIL population. A total of 7 QTL were detected across the whole genome. Among them, two major-effect QTL were mapped on chromosomes A07 (GlcA07.1) and A09 (GlcA09.1) corresponding to two QTL identified in the F2 population. The QTL GlcA07.1 and GlcA09.1 were further fine mapped into 170.25 kb and 191.41 kb genomic regions, respectively. By comparing gene expression level and gene annotation, BraC07g023810 and BraC07g023970 were proposed as the best candidates for GlcA07.1, while BraC09g052220 and BraC09g052270 were suggested for GlcA09.1. Two InDel molecular markers (GlcA07.1-BcGUN4 and GlcA09.1-BcSG1) associated with BraC07gA023810 and BraC09g052220 were developed and could effectively identify leaf color in natural NHCC accessions, suggesting their potential for marker-assisted leaf color selection in NHCC breeding.

LSAP: A Machine Learning Method for Leaf-Senescence-Associated Genes Prediction

Plant leaves, which convert light energy into chemical energy, serve as a major food source on Earth. The decrease in crop yield and quality is caused by plant leaf premature senescence. It is important to detect senescence-associated genes. In this study, we collected 5853 genes from a leaf senescence database and developed a leaf-senescence-associated genes (SAGs) prediction model using the support vector machine (SVM) and XGBoost algorithms. This is the first computational approach for predicting SAGs with the sequence dataset. The SVM-PCA-Kmer-PC-PseAAC model achieved the best performance (F1score = 0.866, accuracy = 0.862 and receiver operating characteristic = 0.922), and based on this model, we developed a SAGs prediction tool called “SAGs_Anno”. We identified a total of 1,398,277 SAGs from 3,165,746 gene sequences from 83 species, including 12 lower plants and 71 higher plants. Interestingly, leafy species showed a higher percentage of SAGs, while leafless species showed a lower percentage of SAGs. Finally, we constructed the Leaf SAGs Annotation Platform using these available datasets and the SAGs_Anno tool, which helps users to easily predict, download, and search for plant leaf SAGs of all species. Our study will provide rich resources for plant leaf-senescence-associated genes research.