Unraveling Microplastic Effects on Gut Microbiota across Various Animals Using Machine Learning

Area of Science:

• Environmental Science
• Microbiology
• Toxicology

Background:

• Microplastics are persistent pollutants with widespread ecological impacts.
• Significant effects on animal gut microbiota are documented, but comparative analyses are lacking.
• Understanding factors driving microplastic-induced microbiota changes is crucial.

Purpose of the Study:

• To conduct a meta-analysis comparing microplastic effects on gut microbiota across diverse animal species.
• To identify key factors influencing microplastic toxicity on gut microbial communities.
• To discover potential biomarkers for microplastic exposure.

Main Methods:

• Compiled and analyzed 1352 gut microbiota samples from six animal categories.
• Employed machine learning for an in-depth meta-analysis.
• Utilized statistical models to identify critical exposure factors and biomarkers.

Main Results:

• Mice showed higher susceptibility to microplastics, with reduced diversity and altered microbial balance (e.g., increased Firmicutes/Bacteroidetes ratio).
• Earthworms and insects demonstrated greater microplastic degradation potential than mice.
• Machine learning identified exposure duration as the primary factor driving microplastic-induced gut microbiota alterations.
• Lactobacillus, Helicobacter, and Pseudomonas were identified as potential biomarkers.

Conclusions:

• Microplastic exposure poses significant health risks to animals, varying by species.
• Exposure duration is a critical determinant of microplastic impact on gut microbiota.
• Specific bacterial species can serve as indicators of microplastic toxicity in animal guts.