An integrated transcriptome, metabolome, and microbiome dataset of Populus under nutrient-poor conditions
Abstract The rhizosphere microbiota recruited by plants contributes significantly to maintaining host productivity and resisting stress. However, the genetic mechanisms by which plants regulate this recruitment process remain largely unclear. Here, we generated a comprehensive dataset, including 27...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-04-01
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| Series: | Scientific Data |
| Online Access: | https://doi.org/10.1038/s41597-025-05029-1 |
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| author | Jiadong Wu Dongyan He Yue Wang Sijia Liu Yuxin Du Haofei Wang Shuxian Tan Deqiang Zhang Jianbo Xie |
| author_facet | Jiadong Wu Dongyan He Yue Wang Sijia Liu Yuxin Du Haofei Wang Shuxian Tan Deqiang Zhang Jianbo Xie |
| author_sort | Jiadong Wu |
| collection | DOAJ |
| description | Abstract The rhizosphere microbiota recruited by plants contributes significantly to maintaining host productivity and resisting stress. However, the genetic mechanisms by which plants regulate this recruitment process remain largely unclear. Here, we generated a comprehensive dataset, including 27 root transcriptomes, 27 root metabolomes, and 54 bulk or rhizosphere soil 16S rRNA amplicons across nine poplar species from four sections grown in nutrient-poor natural soil, along with eleven growth phenotype data. We provided a thorough description of this dataset, followed by a comprehensive co-expression network analysis example that broke down the wall of the four-way relationship between plant gene-metabolite-microbe-phenotype, thus identifying the links between plant gene expression, metabolite accumulation, growth behavior, and rhizosphere microbiome variation under nutrient-poor conditions. Overall, this dataset enhances our understanding of plant and microbe interactions, offering valuable strategies and novel insights for resolving how plants regulate rhizosphere microbial compositions and functions, thereby improving host fitness, which will benefit future research. |
| format | Article |
| id | doaj-art-7f0c2788ea244e489accbb6e4fd31da1 |
| institution | OA Journals |
| issn | 2052-4463 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Data |
| spelling | doaj-art-7f0c2788ea244e489accbb6e4fd31da12025-08-20T01:48:50ZengNature PortfolioScientific Data2052-44632025-04-0112111110.1038/s41597-025-05029-1An integrated transcriptome, metabolome, and microbiome dataset of Populus under nutrient-poor conditionsJiadong Wu0Dongyan He1Yue Wang2Sijia Liu3Yuxin Du4Haofei Wang5Shuxian Tan6Deqiang Zhang7Jianbo Xie8State Key Laboratory of Tree Genetics and Breeding, College of Biological Sciences and Technology, Beijing Forestry UniversityState Key Laboratory of Tree Genetics and Breeding, College of Biological Sciences and Technology, Beijing Forestry UniversityState Key Laboratory of Tree Genetics and Breeding, College of Biological Sciences and Technology, Beijing Forestry UniversityState Key Laboratory of Tree Genetics and Breeding, College of Biological Sciences and Technology, Beijing Forestry UniversityState Key Laboratory of Tree Genetics and Breeding, College of Biological Sciences and Technology, Beijing Forestry UniversityState Key Laboratory of Tree Genetics and Breeding, College of Biological Sciences and Technology, Beijing Forestry UniversityState Key Laboratory of Tree Genetics and Breeding, College of Biological Sciences and Technology, Beijing Forestry UniversityState Key Laboratory of Tree Genetics and Breeding, College of Biological Sciences and Technology, Beijing Forestry UniversityState Key Laboratory of Tree Genetics and Breeding, College of Biological Sciences and Technology, Beijing Forestry UniversityAbstract The rhizosphere microbiota recruited by plants contributes significantly to maintaining host productivity and resisting stress. However, the genetic mechanisms by which plants regulate this recruitment process remain largely unclear. Here, we generated a comprehensive dataset, including 27 root transcriptomes, 27 root metabolomes, and 54 bulk or rhizosphere soil 16S rRNA amplicons across nine poplar species from four sections grown in nutrient-poor natural soil, along with eleven growth phenotype data. We provided a thorough description of this dataset, followed by a comprehensive co-expression network analysis example that broke down the wall of the four-way relationship between plant gene-metabolite-microbe-phenotype, thus identifying the links between plant gene expression, metabolite accumulation, growth behavior, and rhizosphere microbiome variation under nutrient-poor conditions. Overall, this dataset enhances our understanding of plant and microbe interactions, offering valuable strategies and novel insights for resolving how plants regulate rhizosphere microbial compositions and functions, thereby improving host fitness, which will benefit future research.https://doi.org/10.1038/s41597-025-05029-1 |
| spellingShingle | Jiadong Wu Dongyan He Yue Wang Sijia Liu Yuxin Du Haofei Wang Shuxian Tan Deqiang Zhang Jianbo Xie An integrated transcriptome, metabolome, and microbiome dataset of Populus under nutrient-poor conditions Scientific Data |
| title | An integrated transcriptome, metabolome, and microbiome dataset of Populus under nutrient-poor conditions |
| title_full | An integrated transcriptome, metabolome, and microbiome dataset of Populus under nutrient-poor conditions |
| title_fullStr | An integrated transcriptome, metabolome, and microbiome dataset of Populus under nutrient-poor conditions |
| title_full_unstemmed | An integrated transcriptome, metabolome, and microbiome dataset of Populus under nutrient-poor conditions |
| title_short | An integrated transcriptome, metabolome, and microbiome dataset of Populus under nutrient-poor conditions |
| title_sort | integrated transcriptome metabolome and microbiome dataset of populus under nutrient poor conditions |
| url | https://doi.org/10.1038/s41597-025-05029-1 |
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