{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T04:46:34Z","timestamp":1790052394009,"version":"4.0.1"},"reference-count":394,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,7,31]],"date-time":"2026-07-31T00:00:00Z","timestamp":1785456000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Plant Sci."],"abstract":"<jats:p>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\u2013Build\u2013Test\u2013Learn (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.<\/jats:p>","DOI":"10.3389\/fpls.2026.1865665","type":"journal-article","created":{"date-parts":[[2026,7,31]],"date-time":"2026-07-31T15:10:02Z","timestamp":1785510602000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["A review of flavonoids at the crossroads of plant defense: integrating biotic and abiotic stress tolerance through AI- and CRISPR\/Cas-guided metabolic reprogramming"],"prefix":"10.3389","volume":"17","author":[{"given":"Aiman","family":"Hina","sequence":"first","affiliation":[{"name":"Ministry of Agriculture (MOA) National Centre for Soybean Improvement, State Key Laboratory for Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University","place":["Nanjing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Asim","family":"Abbasi","sequence":"additional","affiliation":[{"name":"Seed Insect Management Laboratory, National Seed Research and Training Center, University of Agriculture","place":["Faisalabad, Pakistan"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Amna","family":"Chaudhry","sequence":"additional","affiliation":[{"name":"Department of Food Engineering and Biotechnology, Faculty of Agricultural, Environmental and Food Sciences, Free University of Bozen-Bolzano","place":["Bozen-Bolzano, Italy"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tayyaba","family":"Sanaullah","sequence":"additional","affiliation":[{"name":"Department of Botany, Government Sadiq College Women University","place":["Bahawalpur, Pakistan"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Muhammad","family":"Arshad","sequence":"additional","affiliation":[{"name":"Department of Entomology, University of Agriculture","place":["Faisalabad, Pakistan"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hafiza Mehwish","family":"Sarwar","sequence":"additional","affiliation":[{"name":"Department of Entomology, University of Agriculture","place":["Faisalabad, Pakistan"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sara","family":"Sarfaraz","sequence":"additional","affiliation":[{"name":"Department of Bioinformatics, Fatima Jinnah Women University","place":["Rawalpindi, Pakistan"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Benjamin","family":"Karikari","sequence":"additional","affiliation":[{"name":"Department of Agronomy and Horticulture, Institute of Agriculture and Natural Resources, University of Nebraska-Lincoln","place":["St, Lincoln, NE, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nyasha J.","family":"Kavhiza","sequence":"additional","affiliation":[{"name":"Department of Environmental Management, Institute of Environmental Engineering, RUDN University","place":["Moscow, Russia"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,7,31]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pcbi.1005807","article-title":"A machine learning approach for predicting CRISPR-Cas9 cleavage efficiencies and patterns underlying its mechanism of action","volume":"13","author":"Abadi","year":"2017","journal-title":"PloS Comput. 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