Plant Stress (Sep 2024)
Green vanguards: Harnessing the power of plant antioxidants, signal catalysts, and genetic engineering to combat reactive oxygen species under multiple abiotic stresses
Abstract
The resilience of plants to concurrent abiotic stresses—such as drought, salinity, extreme temperatures, heavy metals, and elevated CO2 levels—is paramount in the era of climate change. Reactive oxygen species (ROS), traditionally perceived as mere byproducts of metabolic processes, serve a dual role: as crucial signaling molecules that facilitate plant adaptation and as deleterious agents causing cellular damage when excessively accumulated. In this review, we highlighted the intricate equilibrium that plants maintain through both enzymatic and non-enzymatic antioxidant defenses to mitigate ROS-mediated oxidative stress, emphasizing the sophisticated strategies plants deploy to counteract a spectrum of combined abiotic stresses. Some plant species, however, exhibit insufficient enhancement of their intrinsic antioxidant defenses to counterbalance stress-induced ROS accumulation and consequent oxidative damage. Consequently, we explored the pivotal role of diverse signaling molecules in further strengthening antioxidant defenses, offering profound insights into bolstering plant resilience. Furthermore, the advent of genetic engineering technologies unveils novel avenues for crop improvement, with the strategic overexpression of antioxidant genes such as SOD, APX, CAT, GPX, DHAR, GR, and GST showing immense potential in fortifying plants against oxidative challenges imposed by multiple abiotic stresses. Future perspectives entail deepening our understanding of the molecular mechanisms governing ROS generation and scavenging, investigating the synergistic effects of co-expressing antioxidant genes, and elucidating the interactions between endogenous plant hormones and exogenously applied signaling molecules. We advocate for integrative research methodologies, combining field experiments, controlled environmental studies, and computational modeling, to bridge the gap between laboratory discoveries and practical agricultural applications.