Abstract：
In this study, the toxicity induced by the alkylating agent methyl methanesulfonate (MMS) in Allium cepa L. was investigated. For this aim, bulbs were divided into 4 groups as control and application (100, 500 and 4000 µM MMS) and germinated for 72 h at 22-24 °C. At the end of the germination period root tips were collected and made ready for analysis by applying traditional preparation methods. Germination, root elongation, weight, mitotic index (MI) values, micronucleus (MN) and chromosomal abnormality (CAs) numbers, malondialdehyde (MDA) levels, superoxide dismutase (SOD) and catalase (CAT) activities and anatomical structures of bulbs were used as indicators to determine toxicity. Moreover the extent of DNA fragmentation induced by MMS was determined by comet assay. To confirm the DNA fragmentation induced by MMS, the DNA-MMS interaction was examined with molecular docking. Correlation and principal component analyses (PCA) were performed to examine the relationship between all parameters and understand the underlying structure and relationships among these parameters. In the present study, a deep neural network (DNN) with two hidden layers implemented in Matlab has been developed for the comparison of the estimated data with the real data. The effect of MDA levels, SOD and CAT activities at 4 different endpoints resulting from administration of various concentrations of MMS, including MN, MI, CAs and DNA damage, was attempted to be estimated by DNN model. It is assumed that the predicted results are in close agreement with the actual data. The effectiveness of the model was evaluated using 4 different metrics, MAE, MAPE, RMSE and R2, which together show that the model performs commendably. As a result, the highest germination, root elongation, weight gain and MI were measured in the control group. MMS application caused a decrease in all physiological parameters and an increase in cytogenetic (except MI) and biochemical parameters. MMS application caused an increase in antioxidant enzyme levels (SOD and CAT) up to a concentration of 500 µM and a decrease at 4000 µM. MMS application induced different types of CAs and anatomical damages in root meristem cells. The results of the comet assay showed that the severity of DNA fragmentation increased with increasing MMS concentration. Molecular docking analysis showed a strong DNA-MMS interaction. The results of correlation and PCA revealed significant positive and negative interactions between the studied parameters and confirmed the interactions of these parameters with MMS. It has been shown that the DNN model developed in this study is a valuable resource for predicting genotoxicity due to oxidative stress and lipid peroxidation. In addition, this model has the potential to help evaluate the genotoxicity status of various chemical compounds. At the end of the study, it was concluded that MMS strongly supports a versatile toxicity in plant cells and the selected parameters are suitable indicators for determining this toxicity.
摘要：
在这项研究中,研究了烷化剂甲磺酸甲酯（MMS）在洋葱中引起的毒性。为了这个目标,灯泡分为4组作为对照和应用（100、500和4000µMMMS），并在22-24°C下发芽72小时。在发芽期结束时，收集根尖，并通过应用传统的制备方法进行分析。萌发,根伸长,体重,有丝分裂指数（MI）值，微核（MN）和染色体异常（CA）数量，丙二醛（MDA）水平，以鳞茎的超氧化物歧化酶（SOD）和过氧化氢酶（CAT）活性和解剖结构为指标来确定毒性。此外，通过彗星测定法确定MMS诱导的DNA片段化程度。为了证实MMS诱导的DNA片段化，通过分子对接检查DNA-MMS相互作用。进行相关性和主成分分析（PCA）以检查所有参数之间的关系，并了解这些参数之间的潜在结构和关系。在本研究中,已开发出在Matlab中实现的具有两个隐藏层的深度神经网络（DNN），用于将估计数据与实际数据进行比较。MDA水平的影响，不同浓度的MMS在4个不同终点的SOD和CAT活性，包括MN,MI,CA和DNA损伤，试图通过DNN模型进行估计。假设预测结果与实际数据非常吻合。使用4种不同的指标对模型的有效性进行了评估，MAE,地图,RMSE和R2，它们共同表明该模型表现良好。因此,最高的发芽，根伸长,在对照组中测量体重增加和MI。MMS的应用导致所有生理参数的降低以及细胞遗传学（MI除外）和生化参数的增加。MMS的应用导致抗氧化酶水平（SOD和CAT）增加到500µM的浓度，而在4000µM时降低。MMS应用可诱导根分生组织细胞中不同类型的CA和解剖损伤。彗星测定的结果表明，随着MMS浓度的增加，DNA断裂的严重程度增加。分子对接分析表明DNA-MMS相互作用强。相关性和PCA的结果揭示了所研究参数之间的显着正和负相互作用，并证实了这些参数与MMS的相互作用。已经表明，在这项研究中开发的DNN模型是预测由于氧化应激和脂质过氧化引起的遗传毒性的宝贵资源。此外,该模型有可能帮助评估各种化合物的遗传毒性状态。在研究结束时,结论是，MMS强烈支持植物细胞中的多种毒性，所选参数是确定这种毒性的合适指标。
