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Natural-selected plastics biodegradation species and enzymes in landfills

Summary

Landfills contain billions of tons of plastic waste that can take centuries to decompose naturally. This research discovered that landfill microorganisms have evolved to break down plastics through natural selection. Using advanced computer analysis of microbial DNA, scientists identified thousands of potential plastic-degrading enzymes that could be engineered for industrial applications to help clean up plastic pollution.

Background

Plastic pollution is a critical environmental threat with approximately 11 billion metric tons projected to accumulate by 2050. Landfills serve as major repositories for nearly half of global plastic waste, containing naturally selected microbial communities with potential plastic-degrading capabilities. Enzymatic degradation offers promising approaches for plastic waste management and bioremediation.

Objective

To identify and characterize plastic-degrading microorganisms and enzymes from landfill systems globally using metagenomics and machine learning approaches. The study aimed to expand the catalog of plastic-degrading bioresources beyond traditional database annotations.

Results

Identified 24 enzymes for plastic biodegradation through traditional annotation and 978,107 candidate plastic-degrading genes via machine learning. Analysis yielded 712 putative plastic-degrading proteins hosted by 150 high-quality MAGs, with landfills showing enrichment in hydrolases, amidases, proteases, and carboxylesterases. Three archaeal MAGs possessing plastic-degrading genes were also discovered.

Conclusion

Landfills represent valuable reservoirs of naturally selected plastic-degrading microorganisms and enzymes. The integration of metagenomics and machine learning successfully expanded the catalog of plastic-degrading bioresources by 35-fold compared to traditional methods, providing foundations for biocatalysis engineering and in situ bioremediation strategies.
  • Published in:PNAS Nexus,
  • Study Type:Metagenomic Survey and Machine Learning Analysis,
  • Source: PMID: 40104686, DOI: 10.1093/pnasnexus/pgaf066
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