Evidence map›Paper›PMID 40275192›Full record

ArticleBMC plant biology2025

Identification of critical transition signal (CTS) to characterize regulated stochasticity during ABA-induced growth-to-defense transition.

Rasmieh Hamid, Bahman Panahi, Feba Jacob, Amir Ghaffar Shahriari

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Article in BMC plant biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1citing papers in PubMed
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1 · What the graph read from it

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3 · Its place in the literature

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1 citing paper in PubMed.

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5 · Who and what money

Authors and funding

4 authors.

Rasmieh HamidDepartment of Plant Breeding, Cotton Research Institute of Iran (CRII), Agricultural Research, Education and Extension Organization (AREEO), Gorgan, Iran.
Bahman PanahiDepartment of Genomics, Branch for Northwest & West region, Agricultural Research, Education and Extension Organization (AREEO), Agricultural Biotechnology Research Institute of Iran (ABRII), Tabriz, 5156915-598, Iran. panahi.lahroodi@gmail.com.
Feba JacobCentre for Plant Biotechnology and Molecular Biology, Kerala Agricultural University, Thrissur, India.
Amir Ghaffar ShahriariDepartment of Agriculture and Natural Resources, Higher Education Center of Eghlid, Eghlid, Iran. shahriari.ag@eghlid.ac.ir.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAbscisic acid (ABA) plays a central role in regulating plant responses to abiotic stress. It orchestrates a complex regulatory network that facilitates the transition from growth to defense. Understanding the molecular mechanisms underlying this ABA-induced transition from growth to defense is essential for elucidating plant adaptive strategies under environmental stress conditions.

resultsIn this study, we used a refined dynamic network biomarker (DNB) approach to quantitatively identify the critical transition signal (CTS) and characterize the regulated stochasticity during the ABA-induced transition from growth to defense in Arabidopsis thaliana. By integrating high-resolution time-series RNA-seq data with dynamic network analysis, we identified a set of DNB genes that serve as key molecular regulators of this transition. The critical transition phase was identified precisely at the ninth time point (6 h after treatment), which marks the crucial switch from a growth-dominated to a defense -oriented state. Gene Ontology (GO) enrichment analysis revealed a significant overrepresentation of defense-related biological processes, while STRING network analysis revealed strong functional interactions between DNB genes and differentially expressed genes (DEGs) and highlighted key regulatory hubs. In particular, key hub genes such as PIF4, TPS8, NIA1, and HSP90-5 were identified as potential master regulators of ABA-mediated defense activation, highlighting their importance for plant stress adaptation.

conclusionsBy integrating a network-driven transcriptomic analysis, this study provides new insights into the molecular basis of ABA-induced transitions from growth to defense. The identification of CTS provides a new perspective on regulated stochasticity in plant stress responses and provides a conceptual framework for improving crop stress resistance. In addition, the establishment of a comprehensive database of ABA-responsive defense genes represents a valuable resource for future research on plant adaptation and resilience.

Indexed as

Abscisic AcidArabidopsisPlant Growth RegulatorsSignal TransductionArabidopsis ProteinsGene Expression Regulation, PlantStress, PhysiologicalAbscisic AcidArabidopsis ProteinsPlant Growth RegulatorsAbscisic acid (ABA)Critical transition signal (CTS)Dynamic network biomarker (DNB)Growth-to-defense transitionPlant stress responseRNA-seqStochasticity in gene regulation

Identifiers

PMID40275192
PMCPMC12020100

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LicenceCC BY-NC-ND
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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 Socratic graph.