Dynamic transcriptome and network-based analysis of yellow leaf mutant Ginkgo biloba

Abstract

Background: Golden leaf in autumn is a prominent feature of deciduous tree species like Ginkgo biloba L., a landscape tree widely cultivated worldwide. However, little was known about the molecular mechanisms of leaf yellowing, especially its dynamic regulatory network. Here, we performed a suite of comparative physiological and dynamic transcriptional analyses on the golden-leaf cultivar and the wild type (WT) ginkgo to investigate the underlying mechanisms of leaf yellowing across different seasons. Results: In the present study, we used the natural bud mutant cultivar with yellow leaves “Wannianjin” (YL) as materials. Physiological analysis revealed that higher ratios of chlorophyll a to chlorophyll b and carotenoid to chlorophyll b caused the leaf yellowing of YL. On the other hand, dynamic transcriptome analyses showed that genes related to chlorophyll metabolism played key a role in leaf coloration. Genes encoding non-yellow coloring 1 (NYC1), NYC1-like (NOL), and chlorophyllase (CLH) involved in the degradation of chlorophyll were up-regulated in spring. At the summer stage, down-regulated HEMA encoding glutamyl-tRNA reductase functioned in chlorophyll biosynthesis, while CLH involved in chlorophyll degradation was up-regulated, causing a lower chlorophyll accumulation. In carotenoid metabolism, genes encoding zeaxanthin epoxidase (ZEP) and 9-cis-epoxy carotenoid dioxygenase (NCED) showed significantly different expression levels in the WT and YL. Moreover, the weighted gene co-expression network analysis (WGCNA) suggested that the most associated transcriptional factor, which belongs to the AP2/ERF-ERF family, was engaged in regulating pigment metabolism. Furthermore, quantitative experiments validated the above results. Conclusions: By comparing the golden-leaf cultivar and the wide type of ginkgo across three seasons, this study not only confirm the vital role of chlorophyll in leaf coloration of YL but also provided new insights into the seasonal transcriptome landscape and co-expression network. Our novel results pinpoint candidate genes for further wet-bench experiments in tree species.