Evidence map›Paper›PMID 40313722›Full record

ArticleFrontiers in plant science2025

Comprehensive transcriptomic and metabolomic analysis revealed drought tolerance regulatory pathways in upland cotton.

Fenglei Sun, Junhao Wang, Shiwei Geng, Yajun Liang, Zhaolong Gong, Ni Yang, Shuaishuai Qian, Nala Zhang, Xueyuan Li, Junduo Wang and 1 more

Abstract read
In one paragraph

Article in Frontiers in plant science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

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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

8 citing papers in PubMed.

  1. Natural variation inScience advances · 2026
    Article
  2. Article
  3. Article
  4. Review
  5. Review
  6. Comparative Metabolomic Analysis of Three MedicinalMolecules (Basel, Switzerland) · 2025
    Article
  7. Article
  8. Transcriptomic analysis ofFrontiers in plant science · 2025
    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

11 authors.

Fenglei Sun *Cotton Research Institute of Xinjiang Uygur Autonomous Region Academy of Agricultural Sciences/Xinjiang Key Laboratory of Cotton Genetic Improvement and Intelligent Production, Urumqi, Xinjiang, China.
Junhao Wang *Engineering Research Centre of Cotton, Ministry of Education/College of Agriculture, Xinjiang Agricultural University, Urumqi, Xinjiang, China.
Shiwei Geng *Cotton Research Institute of Xinjiang Uygur Autonomous Region Academy of Agricultural Sciences/Xinjiang Key Laboratory of Cotton Genetic Improvement and Intelligent Production, Urumqi, Xinjiang, China.
Yajun LiangCotton Research Institute of Xinjiang Uygur Autonomous Region Academy of Agricultural Sciences/Xinjiang Key Laboratory of Cotton Genetic Improvement and Intelligent Production, Urumqi, Xinjiang, China.
Zhaolong GongCotton Research Institute of Xinjiang Uygur Autonomous Region Academy of Agricultural Sciences/Xinjiang Key Laboratory of Cotton Genetic Improvement and Intelligent Production, Urumqi, Xinjiang, China.
Ni YangNational Key Laboratory of Crop Genetic Improvement, Hubei Hong Shan Laboratory, Huazhong Agricultural University, Wuhan, China.
Shuaishuai QianCotton Research Institute of Xinjiang Uygur Autonomous Region Academy of Agricultural Sciences/Xinjiang Key Laboratory of Cotton Genetic Improvement and Intelligent Production, Urumqi, Xinjiang, China.
Nala ZhangNational Key Laboratory of Crop Genetic Improvement, Hubei Hong Shan Laboratory, Huazhong Agricultural University, Wuhan, China.
Xueyuan LiCotton Research Institute of Xinjiang Uygur Autonomous Region Academy of Agricultural Sciences/Xinjiang Key Laboratory of Cotton Genetic Improvement and Intelligent Production, Urumqi, Xinjiang, China.
Junduo WangCotton Research Institute of Xinjiang Uygur Autonomous Region Academy of Agricultural Sciences/Xinjiang Key Laboratory of Cotton Genetic Improvement and Intelligent Production, Urumqi, Xinjiang, China.
Juyun ZhengCotton Research Institute of Xinjiang Uygur Autonomous Region Academy of Agricultural Sciences/Xinjiang Key Laboratory of Cotton Genetic Improvement and Intelligent Production, Urumqi, Xinjiang, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Cotton is a kind of cash crop widely planted in arid and semi-arid areas. In this study, we performed multi-omics analysis of two drought resistant extreme materials, Yumian 4 and C460, under drought stress. Transcriptome analysis showed that DY (post-drought stress Yumian 4) had more differentially expressed genes than DC (post-drought stress C460), and there were 10247 DEGs in the two comparison groups. Metabolomics analysis identified 1766 metabolites, which were divided into 12 classes. The up-regulated metabolites mainly included lipid accumulation, phenylpropanoid biosynthesis, and flavonoids. The combined transcriptome and metabolome analysis highlighted the importance of phenylpropanoid biosynthesis in enhancing drought tolerance. Combining the two omics analysis, it was found that the enrichment pathway of differential genes and differential metabolites is mainly in the phenylpropane biosynthesis pathway, which contains 23 related candidate genes. In summary, the results of multi-omics analysis of the two extreme drought resistance cotton materials showed that they enhanced drought resistance by affecting phenylpropanoid biosynthesis pathways. Promote the accumulation of osmotic substances. The results further deepen our understanding of the molecular mechanism of drought tolerance in cotton and provide new insights for molecular breeding of cotton.

Indexed as

cottondrought stressmetabolomicsmulti-omics analysistranscriptomics

Identifiers

PMID40313722
PMCPMC12044533

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