DNA metabarcoding and its potential in microbial risk assessment in waste sorting plants

Area of Science:

• Environmental microbiology
• Occupational health
• Molecular biology

Background:

• Waste handling poses risks from exposure to hazardous microorganisms.
• Molecular techniques enable detailed microbial community analysis and pathogen identification.
• Accurate risk assessment in occupational settings relies on understanding bioaerosol composition.

Purpose of the Study:

• To characterize the microbiome in personal air samples from waste sorting plants (WSPs).
• To identify potential human pathogens present in the WSP working environment.
• To assess the utility of microbial metabarcoding for occupational pathogen detection.

Main Methods:

• High-throughput sequencing was employed to analyze full-shift personal air samples.
• Microbial community composition was assessed using alpha and beta diversity metrics.
• Taxonomic profiling identified dominant genera and potential human pathogens.

Main Results:

• Significant variations in microbial community composition were observed within and between WSPs.
• Seasonality did not significantly impact microbial community structure.
• Dominant fungi included Cladosporium sp., and dominant bacteria included Aerococcus sp.
• Potential human pathogens such as Aspergillus sp., Fusarium sp., and Enterobacteriaceae were detected.
• Some detected bacteria exhibited potential for drug resistance.

Conclusions:

• Personal air samples from WSPs harbor a diverse microbiome, including potential human pathogens.
• Microbial metabarcoding is effective for characterizing bioaerosols and detecting critical pathogens in workplaces.
• Findings support enhanced risk assessment strategies for waste handling environments.