Decoding multifactorial stress responses in plants: lessons from omics integration

Clicks: 1
ID: 323059
2026
Free PDF
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal

Ranked #271 of 280 articles by views in Journal of experimental botany

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 280 in total.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
Abstract Plants in natural and agricultural environments are continuously exposed to multiple simultaneous abiotic stresses whose combined effects markedly differ from those of individual stresses. Despite this, our mechanistic understanding of plant stress biology still largely relies on reductionist frameworks based on single or pairwise interactions. Here, we argue that plant responses to increasing stress complexity are fundamentally non-additive and involve emergent molecular, metabolic, and physiological states that require a shift towards integrative and predictive approaches. We synthesize recent advances showing how signaling and metabolic networks are rewired under multifactorial stress combination (MFSC), leading to nonlinear behaviors and context-dependent regulation. We highlight the central role of multi-omics integration in uncovering system-level responses and discuss how emerging computational frameworks, including machine learning, can transform high-dimensional datasets into predictive models linking molecular states to plant performance. We further evaluate the limitations of current experimental systems, emphasizing the gap between controlled-environment studies and field conditions, where stress dynamics, microbiome interactions, and genotype × environment complexity shape plant responses. Together, we propose a roadmap towards a predictive science of plant resilience under MFSC, with direct implications for crop improvement in the context of climate change.
Reference Key
openalex_W7171941469 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Laia Molinero-Domingo, Lidia S. Pascual, Rosa M. Rivero, Ron Mittler, Sara I. Zandalinas
Journal Journal of experimental botany
Year 2026
DOI
10.1093/jxb/erag389
URL
https://doi.org/10.1093/jxb/erag389
Keywords Keywords not found

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.