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Investigating the impact of carbamazepine on tomato plant metabolism using genome-scale metabolic modelling

Srinivasan, S.; Raman, K.; Srivastava, S.

2025-02-07 plant biology
10.1101/2025.02.03.635865 bioRxiv
Show abstract

A comprehensive mechanistic analysis of emerging pharmaceutical pollutants stress response in plants is needed to understand its chronic impact on food-chain contamination and agricultural productivity. To unravel this at systems-level, the current study employs insights from green-liver concept and establishes the utility of constraint-based modelling approach for elucidating perturbations in a plants metabolism due to pharmaceutical stress. In this study, the stress response of an emerging recalcitrant anticonvulsant pollutant, carbamazepine (CBZ), was simulated in tomato crop under phototrophic conditions. For this, an updated genome-scale metabolic model of tomato leaf (CBZ_iSL3433) was developed and augmented with CBZ transformation reactions based on the green-liver concept. The model was able to capture energy and co-factor competition-induced biomass reduction in presence of CBZ stress. Further, the study provides an in silico mechanistic proof for abiotic stress response induced by CBZ in tomato with altered flux states in nutrient assimilation, synthesis of key precursors of leaf biomass and secondary metabolites. Additionally, to extend the applicability of model, potential ameliorative effects of biostimulants such as proline, spermine, glycerol, and ethanol were investigated through model predictions. Through systematic computational analysis, 154 significantly altered reactions were identified in the presence of CBZ stress, of which 92 % of reactions were ameliorated with biostimulants. Amino acid biosynthesis was found to be the most significantly altered pathway under CBZ stress in the presence of biostimulants. Overall, the proposed framework can aid in screening and developing rational strategies to maintain agricultural yields amid rising plant stress due to such anthropogenic pollutants.

Published in Scientific Reports (predicted rank #20) · training set

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