Machine-learning meta-analysis reveals ethylene as a central
component of the molecular core in abiotic stress responses in
Arabidopsis

J 2025

Machine-learning meta-analysis reveals ethylene as a central component of the molecular core in abiotic stress responses in Arabidopsis

SANCHEZ-MUNOZ, Raul; Thomas DEPAEPE; Markéta ŠÁMALOVÁ; Jan HEJÁTKO; Isiah ZAPLANA et al.

Základní údaje

Originální název

Machine-learning meta-analysis reveals ethylene as a central component of the molecular core in abiotic stress responses in Arabidopsis

Autoři

SANCHEZ-MUNOZ, Raul; Thomas DEPAEPE; Markéta ŠÁMALOVÁ; Jan HEJÁTKO ; Isiah ZAPLANA a Dominique VAN DER STRAETEN

Vydání

Nature Communications, Nature Research, 2025, 2041-1723

Další údaje

Jazyk

angličtina

Typ výsledku

Článek v odborném periodiku

Obor

10611 Plant sciences, botany

Stát vydavatele

Německo

Utajení

není předmětem státního či obchodního tajemství

Odkazy

URL

Impakt faktor

Impact factor: 15.700 v roce 2024

Označené pro přenos do RIV

Ano

Kód RIV

RIV/00216224:14310/25:00143273

Organizační jednotka

Přírodovědecká fakulta

Klíčová slova anglicky

Abiotic; Dynamic networks; Plant hormones

Štítky

14314010, 143160, rivok

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 24. 3. 2026 09:34, Mgr. Eva Dubská

Anotace

V originále

Understanding how plants adapt their physiology to overcome severe and often multifactorial stress conditions in nature is vital in light of the climate crisis. This remains a challenge given the complex nature of the underlying molecular mechanisms. To provide a comprehensive picture of stress-mitigation mechanisms, an exhaustive analysis of publicly available stress-related transcriptomic data has been conducted. We combine a meta-analysis with an unsupervised machine-learning algorithm to identify a core of stress-related genes active at 1-6 h and 12-24 h of exposure in Arabidopsis thaliana shoots and roots. To ensure robustness and biological significance of the output, often lacking in meta-analyses, a triple validation is incorporated. We present a 'stress gene core': a set of key genes involved in plant tolerance to ten adverse environmental conditions and ethylene-precursor supplementation rather than individual conditions. Notably, ethylene plays a key regulatory role in this core, influencing gene expression and acting as a critical factor in stress tolerance. Additionally, the analysis provides insights into previously uncharacterized genes, key genes within large families, and gene expression dynamics, which are used to create biologically validated databases that can guide further abiotic stress research. These findings establish a strong framework for advancing multi-stress-resilient crops, paving the way for sustainable agriculture in the face of climate challenges.

Citovat

SANCHEZ-MUNOZ, Raul; Thomas DEPAEPE; Markéta ŠÁMALOVÁ; Jan HEJÁTKO; Isiah ZAPLANA a Dominique VAN DER STRAETEN. Machine-learning meta-analysis reveals ethylene as a central component of the molecular core in abiotic stress responses in Arabidopsis. Nature Communications. Nature Research, 2025, roč. 16, č. 1, s. 1-21. ISSN 2041-1723. Dostupné z: https://doi.org/10.1038/s41467-025-59542-3.

@article{2546734,
   author = {SanchezandMunoz, Raul and Depaepe, Thomas and Šámalová, Markéta and Hejátko, Jan and Zaplana, Isiah and Van Der Straeten, Dominique},
   article_number = {1},
   doi = {https://doi.org/10.1038/s41467-025-59542-3},
   keywords = {Abiotic; Dynamic networks; Plant hormones},
   language = {eng},
   issn = {2041-1723},
   journal = {Nature Communications},
   title = {Machine-learning meta-analysis reveals ethylene as a central component of the molecular core in abiotic stress responses in Arabidopsis},
   url = {https://www.nature.com/articles/s41467-025-59542-3},
   volume = {16},
   year = {2025}
}

TY  - JOUR
ID  - 2546734
AU  - Sanchez-Munoz, Raul - Depaepe, Thomas - Šámalová, Markéta - Hejátko, Jan - Zaplana, Isiah - Van Der Straeten, Dominique
PY  - 2025
TI  - Machine-learning meta-analysis reveals ethylene as a central component of the molecular core in abiotic stress responses in Arabidopsis
JF  - Nature Communications
VL  - 16
IS  - 1
SP  - 1-21
EP  - 1-21
PB  - Nature Research
SN  - 20411723
KW  - Abiotic
KW  - Dynamic networks
KW  - Plant hormones
UR  - https://www.nature.com/articles/s41467-025-59542-3
N2  - Understanding how plants adapt their physiology to overcome severe and often multifactorial stress conditions in nature is vital in light of the climate crisis. This remains a challenge given the complex nature of the underlying molecular mechanisms. To provide a comprehensive picture of stress-mitigation mechanisms, an exhaustive analysis of publicly available stress-related transcriptomic data has been conducted. We combine a meta-analysis with an unsupervised machine-learning algorithm to identify a core of stress-related genes active at 1-6 h and 12-24 h of exposure in Arabidopsis thaliana shoots and roots. To ensure robustness and biological significance of the output, often lacking in meta-analyses, a triple validation is incorporated. We present a 'stress gene core': a set of key genes involved in plant tolerance to ten adverse environmental conditions and ethylene-precursor supplementation rather than individual conditions. Notably, ethylene plays a key regulatory role in this core, influencing gene expression and acting as a critical factor in stress tolerance. Additionally, the analysis provides insights into previously uncharacterized genes, key genes within large families, and gene expression dynamics, which are used to create biologically validated databases that can guide further abiotic stress research. These findings establish a strong framework for advancing multi-stress-resilient crops, paving the way for sustainable agriculture in the face of climate challenges.
ER  -

SANCHEZ-MUNOZ, Raul; Thomas DEPAEPE; Markéta ŠÁMALOVÁ; Jan HEJÁTKO; Isiah ZAPLANA a Dominique VAN DER STRAETEN. Machine-learning meta-analysis reveals ethylene as a central component of the molecular core in abiotic stress responses in Arabidopsis. \textit{Nature Communications}. Nature Research, 2025, roč.~16, č.~1, s.~1-21. ISSN~2041-1723. Dostupné z: https://doi.org/10.1038/s41467-025-59542-3.

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