Microbial Dynamics on Different Microplastics in Coastal Urban Aquatic Ecosystems: The Critical Roles of Extracellular Polymeric Substances

Abstract

Microplastics (MPs) serve as carriers for microbial community colonization, forming unique ecosystems known as plastispheres in urban aquatic ecosystems. However, interactions among microbes, extracellular polymeric substances (EPS), and MPs remain poorly understood. This study investigates microbial consortia and their EPS secretion behaviors across various plastispheres at two representative coastal urban water sites. Permutational multivariate analysis of variance revealed that MP type significantly influenced microbial community structures in reservoir environments (R² = 0.60, p < 0.001), highlighting the pronounced impact of MP types in high-quality urban waters. Specific microbial phyla and genera were identified as key contributors to EPS compositional variations across different plastispheres. Hierarchical partitioning results identified Acidobacteria, Nitrospirae, and Planctomycetes as influential phyla positively affecting EPS composition. Spearman correlation analysis pinpointed Robiginitialea (positive correlation) and Fimbriiglobus (negative correlation) as critical genera influencing EPS dynamics. Moreover, EPS-related gene abundance corresponded closely with observed EPS compositional differences. Dominant genes associated with protein biosynthesis included xapD in reservoirs and glnA in bays, while glmS and eno were predominant for polysaccharide biosynthesis in bays. This research advances our understanding of microbial-EPS-MP interactions in urban water systems, offering critical insights into ecological remediation and risk assessment of MP pollution.

Keywords: extracellular polymeric substances (EPS); metagenomic sequencing; microbial community; microplastics (MPs); temporal succession.

1

Comparative analysis of microbial community structure and diversity across aquatic environments. (a-b) biodiversity metrics: (a) Reservoir α-diversity indices; (b) Bay α-diversity indices. (c,d) Phylum-level taxonomic stacking diagrams, (e,f) heatmaps of top 40 genera: (c,e) Freshwater reservoir communities; (d,f) Brackish bay communities. ▲: mean value. Δ: statistical outlier. Missing reservoir data labeled with “*”: Excluded due to insufficient microbial biomass (<10 ng μL^–1 DNA). PERMANOVA: *** p < 0.001.

2

Community assemblies on MPs in (a) the freshwater reservoir and (b) the brackish bay. (c) VPA for explaining the relative abundance of microorganisms with explanatory variables (nutrient availability, physicochemical parameters, native microorganisms, and temporal progression).

3

Dynamics of EPS on MPs across aquatic environments: (a) Freshwater reservoir and (b) Brackish bay. (c) VPA quantifying ecological drivers of EPS variance. (d) Phylum-level contributions to EPS secretion through hierarchical partitioning analysis.

4

Genus-level associations between EPS components and microbial taxa. (a) Heatmap of Spearman correlations (ρ). Significant relationships (**p < 0.01, *p < 0.05, |ρ| > 0.8) between EPS components and microbial genera. Red and blue asterisks denote the genera with the highest number of significantly positive and negative correlations; (b) Correlation network. Nodes: Taxa grouped by interaction type (red: positive-correlation specialists; green: MP-dependent regulators; blue: negative-correlation specialists). Edges: Positive (solid red) and negative (dashed blue) associations. Node width ∝ connectivity degree; edge thickness ∝ |ρ|.

5

Relative abundances of functional genes related to protein biosynthesis, polysaccharide biosynthesis, and quorum sensing, respectively. Samples were collected on Day 60.