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From microbiome shifts to plastizyme discovery: Integrating in situ, in vitro, and in silico approaches to study microplastic contamination
Original title: Van microbioomverschuivingen tot plastizyme-ontdekking: Integratie van in situ-, in vitro- en in silico-benaderingen om microplasticvervuiling te bestuderen
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This Dutch-language review examined freshwater ecosystem contamination by microplastics, covering sources from illegal dumping and wastewater treatment plant effluent, and discussed enzymatic degradation by plastizyme-producing microbes as a potential remediation approach. In situ microbiome analysis was identified as a key tool for discovering plastic-degrading microbial communities.
Plastic pollution is a growing global concern, and microplastics (MPs) which are products of plastic breakdown, are now omnipresent, found across all environments on Earth. Freshwater ecosystems such as lakes, ponds and rivers are vital resources to our existence and at particular risk of MPs pollution due to human activities. These include illegal waste dumping, WWTP effluent release into rivers, atmospheric deposition of textile fibers close to urban areas, road surface and agricultural run-offs. Microbes are metabolically flexible and can adapt to use versatile carbon sources including plastic polymers. This adaptability can be harnessed to support broader plastic bioremediation efforts. In my doctoral work, I focused on host-associated bacteria of Daphnia, a common freshwater zooplankton invertebrate that uses filter-feeding to browse water column and sediment for particles including MPs. As such, I aimed to investigate the potential consequences of MPs presence on Daphnia's epibionts with particular focus on microfibers, the most common MP type in freshwater ecosystems. Using a combination of in-situ Daphnia population sampling and in-vitro lab exposures, I studied both the ecological effects of MPs and the microbial potential for plastic biodegradation. Additionally, I aimed to make plastizyme screening more accurate and efficent and to that end, I developed a new in-silico tool that integrates state-of-the-art machine learning (ML) methods into existing workflows.
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