机器学习模型揭示了多环芳如何影响环境细菌群落
Mingyu Gao1, Guogang Zheng2, Chaotang Lei1
1College of Environment, Zhejiang University of Technology, Hangzhou 310032, PR China.
The Science of the total environment
|October 24, 2024
概括
多环芳 (PAH) 在土壤,水和沉积物息地显著改变细菌群体. 机器学习确定了关键的细菌生物标志物和潜在的PAH降解微生物,揭示了尽管结构变化,功能弹性.
科学领域:
- 环境科学 环境科学
- 微生物学 微生物学
- 生态毒理学 生态毒理学
背景情况:
- 多环芳 (PAH) 是具有生态风险的普遍环境污染物.
- 了解PAH对各种细菌群落 (土壤,水,沉积物) 的影响至关重要,但具有挑战性.
研究的目的:
- 研究PAHs对不同环境息地的细菌群体结构和功能的影响.
- 使用机器学习模型识别潜在的PAH降解细菌.
主要方法:
- 重新分析了来自各种息地的1924年16S rRNA测序样本.
- 机器学习的应用,特别是随机森林模型,以分析细菌社区的转移和预测退化物种.
- 评估细菌群体在应对PAH污染时的功能.
主要成果:
- 在土壤,水和沉积物中,PAH污染显著改变了细菌社区结构.
- 蛋白质细菌在土壤和沉积物中的数量减少,但在水中增加.
- 随机森林模型在识别PAH影响时达到高准确度 (97.72100%) 在属级.
- 确定了70种对PAH敏感的生物标志物,包括潜在的降解物,如Bacillus和Flavobacterium.
- 细菌社区的功能在很大程度上不受PAH污染的影响.
结论:
- 氧化会诱导细菌群体结构的息地特异性变化.
- 机器学习有效地识别细菌生物标记物和潜在的PAH降解剂.
- 鉴定出的微生物候选物对未来的生物修复工作有价值.
- 细菌群体表现出功能稳定,尽管有PAHs引起的结构变化.
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