Evidence map›Paper›PMID 41459456›Full record

ReviewFrontiers in plant science2025

LEA proteins and ABA signaling: reciprocal regulation in stress adaptation.

Cuihong Hao, Xinxin Zhan, Ning Guo, Jing Liu, Dayong Cui

Abstract readReview
In one paragraph

Review 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 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. TheInternational journal of molecular sciences · 2026
    Article
  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

5 authors.

Cuihong HaoShandong Engineering Research Center of Rose Breeding Technology and Germplasm Innovation, School of Life Sciences, Qilu Normal University, Jinan, China.
Xinxin ZhanKey Laboratory of Biological Resources and Ecology of Pamirs Plateauin Xinjiang Uygur Autonomous Region, College of Life and Geographic Sciences, Kashi University, Kashi, China.
Ning GuoShandong Engineering Research Center of Rose Breeding Technology and Germplasm Innovation, School of Life Sciences, Qilu Normal University, Jinan, China.
Jing LiuShandong Engineering Research Center of Rose Breeding Technology and Germplasm Innovation, School of Life Sciences, Qilu Normal University, Jinan, China.
Dayong CuiShandong Engineering Research Center of Rose Breeding Technology and Germplasm Innovation, School of Life Sciences, Qilu Normal University, Jinan, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Challenging environmental conditions are major factors that severely affect plant growth and limit agricultural productivity. To mitigate these stresses, plants have evolved various adaptive mechanisms. Among these, Late Embryogenesis Abundant (LEA) proteins play a pivotal role in responding to abiotic stresses and participate in a reciprocal regulatory network with the abscisic acid (ABA) signaling pathway. However, the precise molecular mechanisms underlying this reciprocity and the full composition of this network require systematic integration. This review synthesizes recent advances to propose a novel "ABA-LEA feedback loop" model and presents a comprehensive analysis of the classification into seven groups, structural features, molecular functions and mechanisms by which LEA proteins contribute to plant stress resistance. Special emphasis is placed on the intricate interplay between LEA proteins and the ABA signaling pathway, encompassing both the ABA-dependent regulation of

Indexed as

abiotic stressabscisic acidLEA proteinsregulatory networkstress resistance mechanism

Identifiers

PMID41459456
PMCPMC12739472

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

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.