Al Haj Ishak Al Ali Roukaya, Mondamert Leslie, Berjeaud Jean-Marc, Jandry Joelle, Crépin Alexandre, Labanowski Jérôme
Institute of Chemistry, Materials and Natural Resources of Poitiers, UMR CNRS 7285, University of Poitiers, 86000 Poitiers, France.
Ecology and Biology of Interactions, UMR CNRS 7267, University of Poitiers, 86000 Poitiers, France.
Microorganisms. 2023 May 24;11(6):1375. doi: 10.3390/microorganisms11061375.
The release of a wide variety of persistent chemical contaminants into wastewater has become a growing concern due to their potential health and environmental risks. While the toxic effects of these pollutants on aquatic organisms have been extensively studied, their impact on microbial pathogens and their virulence mechanisms remains largely unexplored. This research paper focuses on the identification and prioritization of chemical pollutants that increase bacterial pathogenicity, which is a public health concern. In order to predict how chemical compounds, such as pesticides and pharmaceuticals, would affect the virulence mechanisms of three bacterial strains ( K12, H103, and serovar. Typhimurium), this study has developed quantitative structure-activity relationship (QSAR) models. The use of analysis of variance (ANOVA) functions assists in developing QSAR models based on the chemical structure of the compounds, to predict their effect on the growth and swarming behavior of the bacterial strains. The results showed an uncertainty in the created model, and that increases in virulence factors, including growth and motility of bacteria, after exposure to the studied compounds are possible to be predicted. These results could be more accurate if the interactions between groups of functions are included. For that, to make an accurate and universal model, it is essential to incorporate a larger number of compounds of similar and different structures.
由于各种持久性化学污染物可能带来健康和环境风险，其向废水中的排放已日益引起关注。虽然这些污染物对水生生物的毒性作用已得到广泛研究，但其对微生物病原体及其毒力机制的影响在很大程度上仍未得到探索。本研究论文聚焦于确定增加细菌致病性的化学污染物并对其进行优先级排序，这是一个公共卫生问题。为了预测农药和药物等化合物如何影响三种细菌菌株（K12、H103和鼠伤寒血清型）的毒力机制，本研究开发了定量构效关系（QSAR）模型。使用方差分析（ANOVA）函数有助于基于化合物的化学结构开发QSAR模型，以预测其对细菌菌株生长和群集行为的影响。结果表明所创建的模型存在不确定性，并且有可能预测出接触所研究化合物后细菌的毒力因子增加情况，包括细菌的生长和运动能力。如果纳入函数组之间的相互作用，这些结果可能会更准确。为此，要建立一个准确且通用的模型，纳入更多具有相似和不同结构的化合物至关重要。
