Project description:Next generation sequencing (NGS) was performed to identify genes changed in ginseng upon Botrytis cinerea infection. The goal of the work is to find interesting genes involved in medical plant in response to fungi infection. The object is to reveal the molecular mechanism of medical plant defense.
2022-11-02 | GSE179805 | GEO
Project description:Intraspecific phylogeny and genomic resources development for an important medical plant Dioscorea nipponica, based on low-coverage whole genome sequencing data
Project description:Primary objectives: The primary objective is to investigate circulating tumor DNA (ctDNA) via deep sequencing for mutation detection and by whole genome sequencing for copy number analyses before start (baseline) with regorafenib and at defined time points during administration of regorafenib for treatment efficacy in colorectal cancer patients in terms of overall survival (OS).
Primary endpoints: circulating tumor DNA (ctDNA) via deep sequencing for mutation detection and by whole genome sequencing for copy number analyses before start (baseline) with regorafenib and at defined time points during administration of regorafenib for treatment efficacy in colorectal cancer patients in terms of overall survival (OS).
Project description:In this study, it is noticeable that 32 tea-specific miRNAs were confirmed on the base of genome survey, using deep sequencing and microarray hybridization, and many miRNAs might associate with secondary metabolites synthesis.
Project description:Dioscorea tuber undergoes multiple morphological and bio-chemical changes during its 9 month growth period. A stage specific gel free analysis was done to understand the proteomic changes associated with tuber development and assign markers. On the basis of morphological traits the tuber life cycle was divided into four developmental stages namely; root initiation (S1), vegetative growth (S2), new tuber initiation (S3) and tuber maturation (S4) which was validated by principal component analysis (PCA). The first most comprehensive data set was generated by using the pooled genome information from Dioscorea + Solanum + Viridateplantae as reference set identifying 78.2% of the total 3,681 proteins. The over-representation analysis of proteins using PANTHER and KEGG MAPPER revealed both expected and novel biological processes relevant to each developmental stage. A high abundance of the enzymes of ascorbate-glutathione cycle, carbohydrate metabolism, Glycolysis, TCA cycle was detected during tuber degradation and formation. The Glycolytic and starch biosynthesis pathway were re-constructed using the information derived from the proteome data. Novel transcription factors (14) associated with oxidative stress tolerance were identified in D.alata proteome. In conclusion, the data set comprehensively describes the proteome of Dioscorea tuber and provided growth specific markers (APx, MDHAR, invertase for degradation and sucrose synthase for formation) that would pave the way to a systematic study of the tuber. The study provides information that may influence the direction of research for improving the productivity of this under-utilized crop.
Project description:Ustilago maydis is an important plant pathogen causing corn-smut disease and an effective biotechnological production host. The lack of a comprehensive metabolic overview hinders a full understanding of the organism’s environmental adaptation and a full use of its metabolic potential. Here, we report the first genome scale metabolic model (GSMM) of Ustilago maydis (iUma22) for the simulation of metabolic activities. iUma22 was reconstructed from sequencing and annotation using PathwayTools, the biomass equation was derived from literature values and from the codon composition. The final model contains over 25% of annotated genes (6,909) in the sequenced genome. Substrate utilization was corrected by Biolog-Phenotype arrays and exponential batch cultivations were used to test growth predictions. The growth data revealed a metabolic phenotype shift at high glucose uptake rates and the model allowed its quantification. A pan-genome of four different U. maydis strains revealed missing metabolic pathways in iUma22. The new model allows studies of metabolic adaptations to different environmental niches as well as for biotechnological applications.
