Related Experiment Video
Updated: Feb 24, 2026
Studying Oxidative Stress Caused by the Mitis Group Streptococci in Caenorhabditis elegans
Published on: March 23, 2019
General Machine Learning Model, Review, and Experimental-Theoretic Study of Magnolol Activity in Enterotoxigenic
Yanli Deng1, Yong Liu2, Shaoxun Tang2
1National Research Center of Engineering Technology for Utilization of Botanical Functional Ingredients, Hunan Agricultural University, Changsha, Hunan 410128. China.
Abstract:
This study evaluated the antioxidative effects of magnolol based on the mouse model induced by Enterotoxigenic Escherichia coli (E. coli, ETEC). All experimental mice were equally treated with ETEC suspensions (3.45×109 CFU/ml) after oral administration of magnolol for 7 days at the dose of 0, 100, 300 and 500 mg/kg Body Weight (BW), respectively. The oxidative metabolites and antioxidases for each sample (organism of mouse) were determined: Malondialdehyde (MDA), Nitric Oxide (NO), Glutathione (GSH), Myeloperoxidase (MPO), Catalase (CAT), Superoxide Dismutase (SOD), and Glutathione Peroxidase (GPx). In addition, we also determined the corresponding mRNA expressions of CAT, SOD and GPx as well as the Total Antioxidant Capacity (T-AOC). The experiment was completed with a theoretical study that predicts a series of 79 ChEMBL activities of magnolol with 47 proteins in 18 organisms using a Quantitative Structure- Activity Relationship (QSAR) classifier based on the Moving Averages (MAs) of Rcpi descriptors in three types of experimental conditions (biological activity with specific units, protein target and organisms). Six Machine Learning methods from Weka software were tested and the best QSAR classification model was provided by Random Forest with True Positive Rate (TPR) of 0.701 and Area under Receiver Operating Characteristic (AUROC) of 0.790 (test subset, 10-fold crossvalidation). The model is predicting if the new ChEMBL activities are greater or lower than the average values for the magnolol targets in different organisms.
Insights
Magnolol demonstrates significant antioxidative effects against E. coli (ETEC) infections in mice by modulating oxidative stress markers. A QSAR model further predicts magnolol
Area of Science:
- Pharmacology and Toxicology
- Biochemistry
- Computational Chemistry
Background:
- Enterotoxigenic Escherichia coli (E. coli, ETEC) infections cause oxidative stress.
- Magnolol is a compound with potential therapeutic properties.
- Evaluating magnolol's antioxidative capacity is crucial for understanding its protective mechanisms.
Purpose of the Study:
- To investigate the antioxidative effects of magnolol in a mouse model of ETEC infection.
- To analyze the impact of magnolol on key oxidative stress biomarkers.
- To develop a Quantitative Structure-Activity Relationship (QSAR) model for predicting magnolol's bioactivity.
Main Methods:
- Mice were orally administered magnolol and then infected with ETEC.
- Oxidative metabolites (MDA, NO) and antioxidases (GSH, MPO, CAT, SOD, GPx) were measured.
- mRNA expression of antioxidant enzymes and Total Antioxidant Capacity (T-AOC) were determined.
- A QSAR model was built using ChEMBL data and machine learning (Random Forest) to predict magnolol's activities.
Main Results:
- Magnolol treatment modulated levels of MDA, NO, GSH, MPO, CAT, SOD, and GPx in ETEC-infected mice.
- mRNA expression of CAT, SOD, and GPx, along with T-AOC, were significantly affected by magnolol.
- The Random Forest QSAR model achieved an AUROC of 0.790, effectively predicting magnolol's bioactivity against various protein targets.
Conclusions:
- Magnolol exhibits notable antioxidative properties, mitigating E. coli (ETEC)-induced oxidative stress in vivo.
- The developed QSAR model provides a valuable tool for predicting magnolol's biological activities and guiding future drug discovery efforts.
- Magnolol holds promise as a therapeutic agent against infections associated with oxidative damage.
