Environmental processes and health implications potentially mediated by dust-borne bacteria

Abstract

Understanding microbial migration and survival mechanisms in dust events (DEs) can elucidate genetic and metabolic exchange between environments and help predict the atmospheric pathways of ecological and health-related microbial stressors. Dust-borne microbial communities have been previously characterized, but the impact and interactions between potentially active bacteria within transported communities remain limited. Here, we analysed samples collected during DEs in Israel, using amplicon sequencing of the 16S rRNA genes and transcripts. Different air trajectories and wind speeds were associated not only with the genomic microbial community composition variations but also with specific 16S rRNA bacterial transcripts. Potentially active dust-borne bacteria exhibited positive interactions, including carbon and nitrogen cycling, biotransformation of heavy metals, degradation of organic compounds, biofilm formation, and the presence of pathogenic taxa. This study provides insights into the potential interactive relationships and survival strategies of microorganisms within the extreme dust environment.

FIGURE 1

Bacterial community composition in dust samples collected during springtime in the East Mediterranean. (A) Principal coordinate analysis of the bacterial 16S rRNA gene and 16S rRNA ASVs, representing the genomic and transcriptional fingerprint of the dust microbiome, was conducted using Weighted UniFrac metrics. (B) Venn diagram illustrating the shared microbes among all sampled dust events. (C) and (D) diagrams present the shared microbes in the day and night samples, respectively. (E) The top 13 abundant phyla, ranked by average abundance, as represented in percentages for each dust sample. A detailed description of DNA/RNA extraction is given in the Supporting Information file. ASV, amplified sequence variant.

FIGURE 2

Relative abundance and interactions of bioactive dust‐borne bacteria. (A) A comparison of abundances of the top five phyla in dust events based on 16S rRNA gene (DNA) and 16S rRNA (cDNA) libraries, (B) a heatmap presenting the predominant potentially active genera within the top five phyla in day and night samples, and (C) a visualization of the interactions between different genera of the microbial community. The network analysis was constructed using Spearman correlation coefficients between genera. Nodes represent genera and edges represent correlations between nodes (Barberán et al., ; Varsadiya et al., 2021). A detailed description of the methodology is given in the Supporting Information file.