Soil plastispheres as hotpots of antibiotic resistance genes and potential pathogens

Abstract

In the Anthropocene, increasing pervasive plastic pollution is creating a new environmental compartment, the plastisphere. How the plastisphere affects microbial communities and antibiotic resistance genes (ARGs) is an issue of global concern. Although this has been studied in aquatic ecosystems, our understanding of plastisphere microbiota in soil ecosystems remains poor. Here, we investigated plastisphere microbiota and ARGs of four types of microplastics (MPs) from diverse soil environments, and revealed effects of manure, temperature, and moisture on them. Our results showed that the MPs select for microbial communities in the plastisphere, and that these plastisphere communities are involved in diverse metabolic pathways, indicating that they could drive diverse ecological processes in the soil ecosystem. The relationship within plastisphere bacterial zero-radius operational taxonomic units (zOTUs) was predominantly positive, and neutral processes appeared to dominate community assembly. However, deterministic processes were more important in explaining the variance in ARGs in plastispheres. A range of potential pathogens and ARGs were detected in the plastisphere, which were enriched compared to the soil but varied across MPs and soil types. We further found that the addition of manure and elevation of soil temperature and moisture all enhance ARGs in plastispheres, and potential pathogens increase with soil moisture. These results suggested that plastispheres are habitats in which an increased potential pathogen abundance is spatially co-located with an increased abundance of ARGs under global change. Our findings provided new insights into the community ecology of the microbiome and antibiotic resistome of the soil plastisphere.

Fig. 1. Experimental design.

Three microcosm experiments (a, b, and c) and one field experiment (d) were performed in this study. In the first experiment (a), bacterial communities from 30 μm glass bead as a negative control and different size of PE (30, 200, and 1500 μm) were analyzed to exclude the mesh bag itself caused the selection for bacteria in the plastisphere. In the second experiment (b), we studied the responses of bacterial communities to different microplastics (PVC, PA, PE, and PS) plastisphere from diverse soil environments, identified the bacterial community assembly process in plastisphere, and examined the compositions of pathogens and antibiotic resistance genes as well as their relationships with the bacterial communities in plastisphere. In the third experiment (c), effects of manure, temperature, and moisture on the microbiome and antibiotic resistome in the PE plastisphere were studied. In the fourth experiment (d), the composition, function, and resistome of microbial communities in different plastispheres and glass bead were revealed by metagenomic analysis.

Fig. 2. Structure of bacterial community.

Principal coordinates analysis (PCoA) presented the distribution of bacterial communities from different substrates (soil, glass bead (GB: 30 μm) and polyethylene microplastics (PE: 30, 200, and 1500 μm)) based on the Bray Curtis (a) and Weighted Unifrac (b) distances. Different shapes and colors represented different types of samples. The variation explained by the PCoA axes was listed in parentheses. The PERMANOVA was used to test significant difference (significant level p < 0.05). Boxplots revealed the distance of bacterial communities between each substrate and soil sample (c: Bray Curtis and d: Weighted Unifrac), which reflected the similarity of bacterial community between each substrate and soil sample. Significance of results was evaluated using pairwise PERMANOVA and labeled using different letters. center line, median; box limits, first and third quartiles; whiskers, 1.5× interquartile range.

Fig. 3. Relationships between prevalent bacterial zOTUs.

Co-occurrence networks of different plastisphere (a, b, c, and d) and soil (e) bacterial zOTUs. The size of the circle indicated the relative abundance of the zOTUs.

Fig. 4. Fit of neutral model for plastisphere.

Each point represented a bacterial zOTUs, and different colors indicated zOTUs that occur more or less frequently than predicted by neutral model. The predicted occurrence frequency was shown as a solid blue line and dashed lines indicated the 95% confidence interval around the neutral model. The R² indicated the fit to neutral model, and Nm indicated the metacommunity size times immigration.

Fig. 5. Functional prediction, potential pathogen and ARGs.

a Metabolic pathways of bacterial communities in different plastispheres predicted by Tax4Fun2. The asterisk indicated significant difference between different plastispheres (significant level: p < 0.05). b Ratio of potential pathogens/bacteria (mean ± SE; n = 5) detected in different plastisphere and soil samples. The different letter indicated significant difference between different samples from the same soil environment. c Relative abundance of ARGs (mean; n = 5) in different plastisphere and soil samples.

Fig. 6. The contribution of deterministic and stochastic processes acting on bacterial communities to the change of ARGs in the plastisphere.

Procrustes test revealed the significant correlation between ARG profiles and bacterial communities from deterministic process (a) and stochastic process (b), respectively. c Partial redundancy analysis differentiated effects of deterministic process and stochastic process on the variations in ARGs.

Fig. 7. Effects of manure, temperature and moisture on ARGs and potential pathogen.

a Relative abundance of ARGs (mean; n = 5) in the plastisphere and soil sample from different treatments. b Ratio of potential pathogens/bacteria (mean ± SE; n = 5) detected in the plastisphere and soil sample from different treatments. The different letter indicated significant difference between different samples (significant level: p < 0.05), and the “***” indicated p < 0.001.

Fig. 8. Bubble Plot revealing profiles of the 30 most abundant KEGG pathways from different substrates (PVC, PA, PE, PS, GB, and soil).

The abundance of KEGG pathway was represented as RPKM. The profiles were prepared by assembling the identified KOs into broader functional categories at different functional levels. PVC polyvinyl chloride, PA polyamide, PE polyethylene, PS polystyrene, and GB glass bead.