Periodical
Unraveling Microplastic Effects on Gut Microbiota across Various Animals Using Machine Learning
| العنوان: | Unraveling Microplastic Effects on Gut Microbiota across Various Animals Using Machine Learning |
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| المؤلفون: | Yin, Lingzi, Yang, Minghao, Teng, Anqi, Ni, Can, Wang, Pandeng, Tang, Shaojun |
| المصدر: | ACS Nano; January 2025, Vol. 19 Issue: 1 p369-380, 12p |
| مستخلص: | Microplastics, rapidly expanding and durable pollutant, have been shown to significantly impact gut microbiota across a spectrum of animal species. However, comprehensive analyses comparing microplastic effects on gut microbiota among these species are still limited, and the critical factors driving these effects remain to be clarified. To address these issues, we compiled 1352 gut microbiota samples from six animal categories, employing machine learning to conduct an in-depth meta-analysis. Our study revealed that mice, compared with other animals, not only exhibit a heightened susceptibility to the toxic effects of microplastics─evidenced by decreased gut microbiota diversity, increased Firmicutes/Bacteroidetesratios, destabilized microbial networks, and disruption in the equilibrium of beneficial and harmful bacteria─but also possess limited potential to degrade microplastics, unlike earthworms and insects. Furthermore, machine learning models confirmed that exposure duration is the key factor driving changes induced by microplastics in gut microbiota. We also identified Lactobacillus, Helicobacter, and Pseudomonasas potential biomarkers for detecting microplastic toxicity in the animal gut. Overall, these findings provide valuable insights into the health risks and driving factors associated with microplastic exposure across multiple animal species. |
| قاعدة البيانات: | Supplemental Index |
| تدمد: | 19360851 1936086X |
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| DOI: | 10.1021/acsnano.4c07885 |