Evidence map›Paper›PMID 40307287›Full record

ArticleScientific data2025

An integrated transcriptome, metabolome, and microbiome dataset of Populus under nutrient-poor conditions.

Jiadong Wu, Dongyan He, Yue Wang, Sijia Liu, Yuxin Du, Haofei Wang, Shuxian Tan, Deqiang Zhang, Jianbo Xie

Abstract read
In one paragraph

Article in Scientific data, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Review
  2. Review
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Jiadong Wu *State Key Laboratory of Tree Genetics and Breeding, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, 100083, China.
Dongyan He *State Key Laboratory of Tree Genetics and Breeding, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, 100083, China.
Yue Wang *State Key Laboratory of Tree Genetics and Breeding, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, 100083, China.
Sijia LiuState Key Laboratory of Tree Genetics and Breeding, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, 100083, China.
Yuxin DuState Key Laboratory of Tree Genetics and Breeding, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, 100083, China.
Haofei WangState Key Laboratory of Tree Genetics and Breeding, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, 100083, China.
Shuxian TanState Key Laboratory of Tree Genetics and Breeding, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, 100083, China.
Deqiang ZhangState Key Laboratory of Tree Genetics and Breeding, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, 100083, China. DeqiangZhang@bjfu.edu.cn.ORCID http://orcid.org/0000-0002-8849-2366
Jianbo XieState Key Laboratory of Tree Genetics and Breeding, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, 100083, China. jbxie@bjfu.edu.cn.ORCID http://orcid.org/0000-0002-8650-7675

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

MetabolomeMicrobiotaPopulusTranscriptomeDatasets as TopicNutrientsPlant RootsRhizosphereRNA, Ribosomal, 16SSoil MicrobiologyNutrientsRNA, Ribosomal, 16S

Identifiers

PMID40307287
PMCPMC12043821

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.