Plastic marine debris represents a global threat for the marine environment, having serious consequences for the ocean, the wildlife and the human health. While the plastics distribution, fate, persistence and toxicity mechanisms for the marine fauna have been more studied in the last decade, small efforts have been devoted to identify and characterize marine microbes that colonize plastic and microplastic debris in the ocean, and their potential to degrade plastics. Here we review the knowledge on the microbial biodiversity and degradation mechanisms of marine plastic debris, and present data, based on metagenomic analyses, on the distribution patterns of genes potentially involved in microbially-mediated plastic degradation in coastal locations across the global ocean. Most studies on plastic-colonizing microbes have focused on seawater rather than sediment, with most studies underlining striking differences in composition between assemblages attached to plastic particles and those in the surrounding environment. The diversity of microbes attached to plastic is high, and the core epiplastic microbial assemblages include often hydrocarbon-degrading bacteria, as well as prokaryotic and eukaryotic phototrophs. Several marine microbes have shown to be able to degrade or deteriorate plastic in the laboratory, or to grow on plastic as the only source of carbon, while indirect evidences suggest that microbially-mediated degradation of recalcitrant plastics also occur in the ocean, though at very low rates. Metagenomic analyses show that plastic degradation-related genes are present in microbial assemblages in several coastal ocean sites, with relative abundance related to the magnitude of plastic pollution at each site. Further research is required to study microbial plastic-degraders in the marine ecosystem, to decipher and exploit the potential of microbial consortia to degrade or mineralize plastic compounds, and to better understand the fate and residence times of plastic waste in the ocean.
Recent Advances of Deep Learning in Bioinformatics and Computational Biology
