Project description:In this study, Pinus yunnanensis was used as the experimental material, with a total of 12 samples assigned into four groups: CK_12, CK_6, T5_12 and T9_6. TMT labeling quantitative proteomics technology was applied, and protein detection was performed using liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS). Raw mass spectrometry data were processed for quality control, protein identification and quantification. Differentially expressed proteins (DEPs) among groups were screened. Subsequent bioinformatics analyses were conducted, including GO functional enrichment, KEGG metabolic pathway enrichment, protein-protein interaction network analysis, hub node analysis and protein cluster analysis. This work systematically reveals the alterations in protein expression and underlying molecular regulatory mechanisms of Pinus yunnanensis under different treatments. A total of 319 proteins were identified in this experiment, and multiple sets of significantly differentially expressed proteins were obtained via intergroup comparison. The results provide valuable proteomic data for exploring the physiological and molecular mechanisms of Pinus yunnanensis.

| PXD079689 |

rhizosphere metagenome

Project description:Rhizosphere fungi of Pinus yunnanensis Raw sequence reads

| PRJNA892749 | ENA

the national Pinus yunnanensis seed base

Project description:the national Pinus yunnanensis seed base Raw sequence reads

| PRJNA1161222 | ENA

rhizosphere metagenome

Project description:Pinus yunnanensis rhizosphere bacteria Raw sequence reads

| PRJNA892260 | ENA

GeoChip data of soil microbial communities in the maize rhizosphere and bulk soils

Project description:Microbial communities in the rhizosphere make significant contributions to crop health and nutrient cycling. However, their ability to perform important biogeochemical processes remains uncharacterized. Important functional genes, which characterize the rhizosphere microbial community, were identified to understand metabolic capabilities in the maize rhizosphere using GeoChip 3.0-based functional gene array method.

2014-09-12 | GSE61338 | GEO

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