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Plastic‐Microbial BioRemediation DB : A Curated Database for Multi‐Omics Applications
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This study presents the Plastic-Microbial BioRemediation (Plastic-MBR) database, a curated multi-omics resource integrating genetic and enzymatic data related to putative plastic-degrading microorganisms. Application to metagenomic datasets from plastic-contaminated soil and river water successfully identified numerous putative plastic-degrading genes across diverse microbial taxa, supporting in silico screening for bioremediation candidates.
Plastic pollution is a major environmental challenge, with millions of tonnes produced annually and accumulating in ecosystems, causing long-term harm. Conventional disposal methods, such as landfilling and incineration, are often inadequate, emphasising the need for sustainable solutions like bioremediation. However, the bacterial biodiversity involved in plastic biodegradation remains poorly understood. To address this gap, we present the Plastic-Microbial BioRemediation (Plastic-MBR) database, a curated multi-omics resource that integrates publicly available genetic and enzymatic data related to putative plastic-degrading microorganisms. This database supports in silico analyses of metagenomic data from plastic-contaminated environments and comparative genomics, aiming to identify microbial taxa with potential plastic-degrading functions. We validated the functionality of the Plastic-MBR database by applying it to metagenomic datasets from plastic-contaminated soil and river water, successfully identifying numerous putative plastic-degrading genes across diverse microbial taxa. These results support the use of the Plastic-MBR database as a tool to identify candidate bacteria for future experimental validation, strain isolation, and functional studies, ultimately contributing to a deeper understanding of microbial potential in plastic bioremediation. While this study focuses on database development and computational validation, future studies will be essential to confirm and translate these genomic predictions into effective bioremediation strategies.
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