In response to exposure to toxic manmade chemicals, such as herbicides and environmental pollutants, plants have developed a three-phase metabolic system ('green liver model') similar to the detoxification mechanisms observed in mammals. Detoxification of toxic compounds in plants involves specific enzymes, such as cytochrome P-450 monooxygenases (CYP) and glutathione S-transferases (GST), which are expressed in response to toxic exposure. The goal of the study is to derive significant relationships between the structural features of toxic xenobiotic chemicals and the induced gene expression response in the model plant <i>Arabidopsis thaliana</i>. We obtained normalized gene expression data for all confirmed and putative GST and CYP genes in Arabidopsis plants exposed to a suite of 56 structurally diverse xenobiotic chemicals, including pesticides (e.g., fenclorim), herbicides (e.g., sulfometuron methyl), and persistent organic pollutants (e.g., phenanthrene). The quantitative structure relationship analysis (QSAR) was performed using the Alva software suite (Alvascience). More than five thousand molecular descriptors were generated for each compound using alvaDesc. alvaModel software was used to generate statistically significant models using gene expression data as endogenous variables, including the average level of expression of GSTs and CYPs, and the number of GSTs and CYPs overexpressed by exposure to the compounds. The best algorithm for model generation was shown to be the support vector machine (SVM). Though the training set generated good quality models with R2 values well above 0.8, validation of the model using the test set failed, producing poor R2 values. Future work includes further selection of relevant xenobiotic response genes and the use of multi-target QSAR methods, which would be able to include multiple endogenous variables (gene expression data).

Understanding the response to xenobiotics in <i>Arabidopsis thaliana</i> using QSAR analysis

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Understanding the response to xenobiotics in Arabidopsis thaliana using QSAR analysis