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Updated: Aug 27, 2025

Composition and Distribution Analysis of Bioaerosols Under Different Environmental Conditions
Published on: January 7, 2019
Regionalization and Shaping Factors for Microbiomes and Core Resistomes in Atmospheric Particulate Matters
Ziyun Li1, Mei Li2,3, Shuhui Tong1
1State Key Laboratory of Microbial Technology, Shandong Universitygrid.27255.37, Qingdao, China.
Abstract:
Antimicrobial resistance (AMR) seriously threatens public health by reducing antibiotic effectiveness in curing bacterial infections. Atmospheric particulate matter (APM) is a common environmental hazard that affects human health by causing various diseases and disseminating bacterial pathogenesis, of which pathogenic bacteria and AMR are essential parts. The properties of APM microbiomes and resistomes, along with their shaping factors and mutual relationships, need further examination. To address this, we analyzed APMs collected from 13 cities within four clusters (North and South China, Inner Mongolia, and Tibet). Significant regionalization was found for both the microbiomes (P < 0.001) and core resistomes (P < 0.001) for APMs, with statistical analyses showing significant differences in different regions. Principal coordinate analysis (PCoA) and accompanying ANOSIM analyses showed that microbiomes and core resistomes followed the same regional subclustering hierarchy patterns. This finding, together with response analysis of APM microbiomes and core resistomes to environmental parameters that showed similar response patterns, as well as Procrustes analysis (M2 = 0.963, P < 0.05) between APM microbiomes and core resistomes, strongly suggested that APM microbiomes and core resistomes are correlated. Co-occurrence network analysis further revealed key taxa and antimicrobial resistance determinants in the interactions between APM microbiomes and core resistomes. Thus, it was concluded that APM microbiome and resistome compositions were highly regional, that environmental pollutants and APM levels impacted APM microbiomes and resistomes, and that microbiomes and resistomes in APMs are significantly correlated (P < 0.05). IMPORTANCE Bacteria associated with atmospheric particulate matter (APMs) can transmit over long distances. A large portion of these bacteria can potentially threaten human health. The antimicrobial resistance (AMR) of pathogenic bacteria carried by APMs prevents curing from infections. Therefore, both the pathogenic bacteria in APMs and their AMR are receiving more attention. The literature suggests a knowledge gap that exists for bacterial AMR and bacterial pathogenesis in APMs, including their distribution patterns, mutual relationships, and factors influencing their compositions. This work aimed to bridge this knowledge gap by studying APM samples collected from 13 cities. The results demonstrated that both bacteria and antibiotic resistance determinants were highly regional and that their composition patterns were significantly correlated, and influenced by the same group of environmental factors. This study thus determined the relationship between the two important aspects of bacterial pathogenesis in APMs and represents significant progress in understanding bacterial pathogenesis in APMs.
Insights
Atmospheric particulate matter (APM) carries regional bacteria and antimicrobial resistance (AMR) genes. These microbial and resistance patterns are linked and influenced by environmental factors, posing a public health concern.
Area of Science:
- Environmental Science
- Microbiology
- Public Health
Background:
- Antimicrobial resistance (AMR) is a global health threat, exacerbated by environmental factors.
- Atmospheric particulate matter (APM) harbors diverse microbial communities and resistance genes, with poorly understood regional variations and interrelationships.
Purpose of the Study:
- To investigate the regional characteristics and correlations of microbial communities (microbiomes) and antimicrobial resistance genes (resistomes) within APM.
- To identify environmental factors shaping APM microbiomes and resistomes.
Main Methods:
- Analysis of APM samples from 13 cities across four distinct regions.
- Utilized principal coordinate analysis (PCoA), ANOSIM, response analysis, Procrustes analysis, and co-occurrence network analysis.
- Statistical analyses were employed to assess regionalization and correlations.
Main Results:
- Significant regionalization was observed for both APM microbiomes and core resistomes (P < 0.001).
- Microbiomes and resistomes exhibited similar regional clustering patterns and responded similarly to environmental parameters.
- A significant correlation (P < 0.05) was found between APM microbiomes and resistomes, with key taxa and resistance determinants identified.
Conclusions:
- APM microbiome and resistome compositions are highly regional and significantly correlated.
- Environmental pollutants and APM levels influence the composition of APM microbiomes and resistomes.
- Understanding these relationships is crucial for assessing the public health risks associated with airborne bacteria and AMR.
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