Project description:Korean ginseng (Panax ginseng Meyer) has long been cultivated as an important medicinal plant. Drought results from the moderate water loss, which primarily impairs the growth of ginseng and reduction of yield loss. However, basis of biological clues to understanding the accurate mechanisms related to drought stress in proteome level are still elusive. Therefore, we carried out label-free quantitative proteomic analysis using ginseng roots subjected to drought stress which was grown at less than 10% soil moisture for two weeks, compared with normal ginseng which was grown at 25% soil moisture. The acquired proteins were carried out label-free proteomic analysis using LC-MS/MS. This approach led to the identification of total 2,471 proteins, and out of 195 proteins showed significant modulation. Functional classification revealed that proteins related to secondary metabolites, calcium signaling, and photosynthesis were enriched in control sample (cluster_1), while proteins associated with stress responsive, redox, electron transport, and protein synthesis were mainly dominated in cluster_2 (drought stress condition). Taken together, our results provided an overview of the drought-induced proteomic changes in ginseng root, and it is correlated with physiological changes, contributing to reveal potential marker at proteome level in ginseng.
Project description:Next-generation sequencing (NGS) was performed to identify genes changed in ginseng upon Colletotrichum panacicola infection. The goal of the work is to find interesting genes involved in ginseng in response to fungi induction. The object is to reveal the molecular mechanism of ginseng disease development caused by Colletotrichum panacicola.
Project description:Korean ginseng is one of the most valuable medicinal plants worldwide. Yet, our understanding of ginseng proteomics is largely limited due to difficulties in extraction and resolution of ginseng proteins because of the presence of natural contaminants such as polysaccharides, phenols, and glycosides. Here, we compared four different protein extraction methods, namely, TCA/acetone, TCA/acetone–MeOH/chloroform, Phenol–TCA/acetone, and Phenol–MeOH/chloroform methods. Consequently, the TCA/acetone–MeOH/chloroform method displayed the highest extraction efficiency, thus it was used for the comparative proteome profiling of leaf, root, shoot, and fruit by a label–free quantitative proteomics approach. This approach led to the identification of 2,604 significantly modulated proteins among four tissues. We could pinpoint differential pathways and proteins associated with ginsenoside biosynthesis including the methylerythritol 4–phosphate (MEP) pathway, the mevalonate (MVA) pathway, UDP–glycosyltransferases (UGTs), and oxidoreductases (CYP450s). The current study reports an efficient and reproducible method for the isolation of proteins from a wide range of ginseng tissues and provides a detailed organ–based proteome map and a more comprehensive view of enzymatic alterations in ginsenoside biosynthesis.
Project description:Next generation sequencing (NGS) was performed to identify genes changed in ginseng upon Botrytis cinerea △BcSpd1 treatment. The goal of the work is to find interesting genes involved in ginseng in response to fungi induction. The object is to reveal the molecular mechanism of ginseng defense induced by Botrytis cinerea △BcSpd1 .
Project description:Salt stress is one of the major abiotic stresses affecting the yield of ginseng (Panax ginseng C. A. Meyer). The objective of this study was to identify proteins of ginseng, which is responsive in salt stress. In this direction, ginseng plants of different growth stages (3, 4 and 5 years), were grown in the hydroponic conditions and exposed to 5 ds/m salt concentration. The secreted proteins, collected from the water, at 0, 24, 72 and 120 hours after exposure were used for the proteome analysis using shotgun approaches. Through the shotgun proteomics, a total of 155 and 88 secreted proteins were identified by searching in two RNA-sequencing (RNA-seq) database, respectively.