ReviewFrontiers in plant science2026
A review of flavonoids at the crossroads of plant defense: integrating biotic and abiotic stress tolerance through AI- and CRISPR/Cas-guided metabolic reprogramming.
Review in Frontiers in plant science, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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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.
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Authors and funding
9 authors.
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Abstract
Flavonoids are multifunctional phenylpropanoid-derived metabolites that occupy a central position in plant adaptation to environmental stress. Beyond their established roles in antioxidant protection, they contribute to defense against pathogens and herbivores, signaling processes, and physiological acclimation to adverse environmental conditions. Although flavonoid responses to individual biotic or abiotic stresses have been extensively investigated, considerably less attention has been given to how flavonoid-associated regulatory networks function when multiple stresses occur simultaneously. This gap is particularly important because crops in agricultural systems are routinely exposed to overlapping biotic and abiotic challenges that generate distinct physiological, transcriptional, and metabolic responses. This review synthesizes current knowledge of flavonoid biosynthesis, structure-activity relationships, and the regulatory mechanisms governing flavonoid accumulation under diverse stress conditions. Particular emphasis is placed on the reorganization of flavonoid-associated networks under combined stress, including signaling crosstalk, pathway competition, metabolic trade-offs, and flux allocation that collectively shape adaptive responses. This review further evaluates how artificial intelligence can support identification of regulatory targets and pathway bottlenecks, how integration with CRISPR/Cas technologies may facilitate more precise manipulation of flavonoid biosynthesis, and how iterative Design-Build-Test-Learn (DBTL) frameworks could improve predictive flavonoid engineering through continuous integration of computational prediction and experimental validation. By integrating advances in stress biology, computational prediction, genome engineering, and iterative DBTL frameworks, this review outlines a roadmap for predictive reprogramming of flavonoid networks under combined stress and the development of crops with improved resilience to increasingly complex environmental conditions.
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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.