Soil sample DB Fungi
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA488356 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:soil sample XZ2019 Raw sequence reads-fungi
| PRJNA542409 | ENA
Project description:Many trees form ectomycorrhizal symbiosis with fungi. During symbiosis, the tree roots supply sugar to the fungi in exchange for nitrogen, and this process is critical for the nitrogen and carbon cycles in forest ecosystems. However, the extents to which ectomycorrhizal fungi can liberate nitrogen and modify the soil organic matter and the mechanisms by which they do so remain unclear since they have lost many enzymes for litter decomposition that were present in their free-living, saprotrophic ancestors. Using time-series spectroscopy and transcriptomics, we examined the ability of two ectomycorrhizal fungi from two independently evolved ectomycorrhizal lineages to mobilize soil organic nitrogen. Both species oxidized the organic matter and accessed the organic nitrogen. The expression of those events was controlled by the availability of glucose and inorganic nitrogen. Despite those similarities, the decomposition mechanisms, including the type of genes involved as well as the patterns of their expression, differed markedly between the two species. Our results suggest that in agreement with their diverse evolutionary origins, ectomycorrhizal fungi use different decomposition mechanisms to access organic nitrogen entrapped in soil organic matter. The timing and magnitude of the expression of the decomposition activity can be controlled by the below-ground nitrogen quality and the above-ground carbon supply.
2018-11-30 | GSE110485 | GEO
Project description:Alkaline soil sample for fungi genome sequencing
| PRJNA678600 | ENA
Project description:Purpose: RNA seq analysis were to compare and contrast the gene expression profile involved in the dedifferentiation of db/db islets in type 2 diabetes Methods: Islets of wild type, db/+ and db/db were purified using perfusion from 12 week old mice and RNA were isolated. Islated RNA were used in RNA seq to understand the expression pattern Results: Using an optimized data analysis workflow, we mapped about 10 million sequence reads per sample to the mouse genome (build mm9) and identified 16,014 transcripts WT, db/+ and db/db mice islets with TopHat workflow. Hierarchical clustering of differentially expressed genes uncovered there role in type 2 diabetes. Data analysis with TopHat workflows revealed a significant overlap yet provided complementary insights in transcriptome profiling. Conclusions: We characterised and identified genes involved in dedifferentiation of islets.
2018-05-08 | GSE107489 | GEO