Department of Biology, Institute of Science, Giresun University, Giresun, Türkiye.
Department of Biology, Faculty of Science and Art, Giresun University, Giresun, Türkiye.
Environ Sci Pollut Res Int. 2023 Nov;30(55):117952-117969. doi: 10.1007/s11356-023-30465-0. Epub 2023 Oct 24.
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.
在这项研究中，研究了烷化剂甲磺酸甲酯（MMS）在洋葱（Allium cepa L.）中的毒性。为此，将鳞茎分为 4 组，分别为对照组和应用组（100、500 和 4000 μM MMS），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准备方法进行分析。发芽、根伸长、重量、有丝分裂指数（MI）值、微核（MN）和染色体异常（CAs）数量、丙二醛（MDA）水平、超氧化物歧化酶（SOD）和过氧化氢酶（CAT）活性以及鳞茎的解剖结构被用作确定毒性的指标。此外，通过彗星试验确定 MMS 诱导的 DNA 片段化程度。为了确认 MMS 诱导的 DNA 片段化，用分子对接研究了 DNA-MMS 相互作用。相关性和主成分分析（PCA）用于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，使用 Matlab 中实现的具有两个隐藏层的深度神经网络（DNN）对比较真实数据的估计数据进行了研究。试图通过 DNN 模型估计 MDA 水平、SOD 和 CAT 活性在 4 个不同终点的影响，这些终点是由不同浓度的 MMS 引起的，包括 MN、MI、CAs 和 DNA 损伤。假设预测结果与实际数据非常吻合。使用 4 种不同的指标，即 MAE、MAPE、RMSE 和 R2 来评估模型的有效性，这些指标共同表明模型表现出色。结果表明，在对照组中，发芽率、根伸长率、重量增加和 MI 最高。MMS 的应用导致所有生理参数降低，细胞遗传学（除 MI 外）和生化参数增加。MMS 的应用导致抗氧化酶水平（SOD 和 CAT）在 500 μM 时增加，在 4000 μM 时减少。MMS 的应用诱导根尖细胞中的不同类型的 CAs 和解剖损伤。彗星试验的结果表明，随着 MMS 浓度的增加，DNA 片段化的严重程度增加。分子对接分析表明 DNA-MMS 之间存在强烈的相互作用。相关性和 PCA 的结果表明，研究参数之间存在显著的正相互作用和负相互作用，并证实了这些参数与 MMS 的相互作用。研究表明，本研究中开发的 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有价值的资源。此外，该模型有可能帮助评估各种化学化合物的遗传毒性状态。研究结束时得出结论，MMS 强烈支持植物细胞的多功能毒性，所选参数是确定这种毒性的合适指标。
