Machine learning reveals microbial interactions driving plastic degradation across plastisphere environments

Microplastic pollution fosters the development of distinct microbial biofilm communities, termed the plastisphere, that vary across environmental contexts. Here, we used 16S rRNA gene sequencing combined with machine learning (ML) approaches to explore plastisphere microbial diversity and the interactions between potential plastic-degrading bacteria (PDBs) and non-plastic-degrading bacteria (NDBs) across ocean, surface water, and wastewater habitats. Our findings reveal that wastewater plastispheres harbor the most diverse and compositionally even microbial communities, likely driven by complex nutrient loads, pollutant inputs, and high microbial seeding potential. Genus-level analysis of potential PDBs indicated habitat-specific taxa, including Pseudomonas, Acinetobacter, and Aquabacterium in wastewater, Flavobacterium and Alteromonas in ocean, and Psychrobacter and Novosphingobium in surface waters. Network analyses using Pearson's correlation and Random Forest modeling uncovered consistent co-occurrence patterns between potential PDBs and diverse NDB taxa such as Clostridium_sensu_stricto_5, Lachnospiraceae_UCG-001, and Cloacibacterium, suggesting potential facilitative interactions, including redox modulation, nutrient exchange, and biofilm support. ML tools proved effective in identifying key taxa and potential ecological interactions, but their application remains limited by taxonomic resolution, lack of functional validation, and insufficient integration of environmental metadata. These findings underscore the ecological complexity of plastisphere communities and the need for community-level approaches in plastic biodegradation research.