Evidence map›Paper›PMID 36061617›Full record

ArticleHorticulture research2022

An eco-evo-devo genetic network model of stress response.

Li Feng, Tianyu Dong, Peng Jiang, Zhenyu Yang, Ang Dong, Shang-Qian Xie, Christopher H Griffin, Rongling Wu

Abstract read
In one paragraph

Article in Horticulture research, 2022. 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

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

8 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Graph statistics theory of individualized quantitative genetics under haplotype-resolved genome assembly.Proceedings of the National Academy of Sciences of the United States of America · 2026
    Article
  5. Article
  6. Article
  7. Review
  8. 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

8 authors.

Li FengCenter for Computational Biology, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China.
Tianyu DongCenter for Computational Biology, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China.
Peng JiangCenter for Computational Biology, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China.
Zhenyu YangCenter for Computational Biology, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China.
Ang DongCenter for Computational Biology, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China.
Shang-Qian XieKey Laboratory of Ministry of Education for Genetics and Germplasm Innovation of Tropical Special Trees and Ornamental Plants, College of Forestry, Hainan University, Haikou 570228, China.
Christopher H GriffinApplied Research Laboratory, The Pennsylvania State University, University Park, PA 16802, USA.
Rongling WuCenter for Computational Biology, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The capacity of plants to resist abiotic stresses is of great importance to agricultural, ecological and environmental sustainability, but little is known about its genetic underpinnings. Existing genetic tools can identify individual genetic variants mediating biochemical, physiological, and cellular defenses, but fail to chart an overall genetic atlas behind stress resistance. We view stress response as an eco-evo-devo process by which plants adaptively respond to stress through complex interactions of developmental canalization, phenotypic plasticity, and phenotypic integration. As such, we define and quantify stress response as the developmental change of adaptive traits from stress-free to stress-exposed environments. We integrate composite functional mapping and evolutionary game theory to reconstruct omnigenic, information-flow interaction networks for stress response. Using desert-adapted Euphrates poplar as an example, we infer salt resistance-related genome-wide interactome networks and trace the roadmap of how each SNP acts and interacts with any other possible SNPs to mediate salt resistance. We characterize the previously unknown regulatory mechanisms driving trait variation; i.e. the significance of a SNP may be due to the promotion of positive regulators, whereas the insignificance of a SNP may result from the inhibition of negative regulators. The regulator-regulatee interactions detected are not only experimentally validated by two complementary experiments, but also biologically interpreted by their encoded protein-protein interactions. Our eco-evo-devo model of genetic interactome networks provides an approach to interrogate the genetic architecture of stress response and informs precise gene editing for improving plants' capacity to live in stress environments.

Identifiers

PMID36061617
PMCPMC9433980

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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.