Predicting and optimizing reactive oxygen species metabolism in Punica granatum L. through machine learning: role of exogenous GABA on antioxidant enzyme activity under drought and salinity stress

Abstract

Background Drought and salinity stress have been proposed as the main environmental factors threatening food security, as they adversely affect crops' agricultural productivity. As a potential solution, the application of plant growth regulators to enhance drought and salinity tolerance has gained considerable attention. γ-aminobutyric acid (GABA) is a four-carbon non-protein amino acid that accumulates in plants as a response to stressful conditions. This study focused on a comparative assessment of several machine learning (ML) regression models, including radial basis function, generalized regression neural network (GRNN), random forest (RF), and support vector regression (SVR) to develop predictive models for assessing the effect of different concentrations of GABA (0, 10, 20, and 40 mM) on various physio-biochemical traits during periods of drought, salinity, and combined stress conditions. The physio-biochemical traits included antioxidant enzyme activities (superoxide dismutase, SOD; peroxidase, POD; catalase, CAT; and ascorbate peroxidase, APX), protein content, malondialdehyde (MDA) levels, and hydrogen peroxide (H2O2) levels. The non‑dominated sorting genetic algorithm‑II (NSGA‑II) was employed for optimizing the superior prediction model. Results The GRNN model outperformed the other ML algorithms and was therefore selected for optimization by NSGA-II. The GRNN-NSGA-II model revealed that treatment with GABA at concentrations of 20.90 mM and 20.54 mM, under combined drought and salinity stress conditions at 20.86 and 20.72 days post-treatment, respectively, could result in the maximum values for protein content (by 0.80 and 0.69), APX activity (by 50.63 and 51.51), SOD activity (by 0.54 and 0.53), POD activity (by 1.53 and 1.72), CAT activity (by 4.42 and 5.66), as well as lower MDA levels (by 0.12 and 0.15) and H2O2 levels (by 0.44 and 0.55), respectively, in the ‘Atabaki’ and ‘Rabab’ cultivars. Conclusions This study demonstrates that the GRNN-NSGA-II model, as an advanced ML algorithm with a strong predictive ability for outcomes in combined stressful environmental conditions, provides valuable insights into the significant factors influencing such multifactorial processes.

Gamma amino butyric acid (GABA) application modulated the morpho-physiological and yield traits of fragrant rice under well-watered and drought conditions

“…Its multifaceted functions extend to the modulation of gene expression, particularly in response to environmental stresses such as drought [ 14 ]. Specifically, GABA treatment upregulates genes involved in reactive oxygen species (ROS) scavenging, such as superoxide dismutase (SOD), catalase (CAT), and ascorbate peroxidase (APX), enhancing the antioxidant defense system in drought-stressed plants [ 15 ]. Additionally, genes related to the biosynthesis of osmolytes like proline and glycine betaine are upregulated by GABA application under drought stress [ 16 ].…”

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“…Numerous ML models effectively forecast and refine plant tissue culture procedures across various studies [32][33][34][35][36][37]. ML has demonstrated significant potential in enhancing various aspects of in vitro culture systems, providing improvements over traditional statistical methods.…”

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“…Numerous ML models effectively forecast and refine plant tissue culture procedures across various studies [32][33][34][35][36][37]. ML has demonstrated significant potential in enhancing various aspects of in vitro culture systems, providing improvements over traditional statistical methods.…”