Key Factors Governing Microbial Community in Extremely Acidic Mine Drainage (pH <3)

Abstract

The microbial community of acid mine drainage (AMD) fascinates researchers by their adaption and roles in shaping the environment. Molecular surveys have recently helped to enhance the understanding of the distribution, adaption strategy, and ecological function of microbial communities in extreme AMD environments. However, the interactions between the environment and microbial community of extremely acidic AMD (pH <3) from different mining areas kept unanswered questions. Here, we measured physicochemical parameters and profiled the microbial community of AMD collected from four mining areas with different mineral types to provide a better understanding of biogeochemical processes within the extremely acidic water environment. The prominent physicochemical differences across the four mining areas were in SO₄ ^2-, metal ions, and temperature, and distinct microbial diversity and community assemblages were also discovered in these areas. Mg²⁺ and SO₄ ^2- were the predominant factors determining the microbial structure and prevalence of dominant taxa in AMD. Leptospirillum, Ferroplasma, and Acidithiobacillus were abundant but showed different occurrence patterns in AMD from different mining areas. More diverse communities and functional redundancy were identified in AMD of polymetallic mining areas compared with AMD of copper mining areas. Functional prediction revealed iron, sulfur, nitrogen, and carbon metabolisms driven by microorganisms were significantly correlated with Mg²⁺ and SO₄ ^2-, Ca²⁺, temperature, and Fe²⁺, which distinguish microbial communities of copper mine AMD from that of polymetallic mine AMD. In summary, microbial diversity, composition, and metabolic potential were mainly shaped by Mg²⁺ and SO₄ ^2- concentrations of AMD, suggesting that the substrate concentrations may contribute to the distinct microbiological profiles of AMD from different mining areas. These findings highlight the microbial community structure in extremely acidic AMD forming by types of minerals and the interactions of physicochemical parameters and microbiology, providing more clues of the microbial ecological function and adaptation mechanisms in the extremely acidic environment.

Keywords: acid mine drainage; biogeochemical function potential; co-occurrence; microbial diversity; mineralogy.

Figure 1

Differences in physicochemical properties of acid mine drainage (AMD) from different mining areas. (A) Principal components analysis of AMD samples. (B) Hierarchical clustering of samples based on all environmental factors (k=2, Euclidean distance and average clustering method was used) and heatmaps for important environmental factors. T represents temperature; units for ion concentrations are mgL⁻¹. (C) Anosim analysis of differences between groups and within groups. Here, group means cluster generated by hierarchical clustering. Contribution of environmental variables to PCA1 (D) and PCA2 (E) of PCA analysis.

Figure 2

Dissimilarities of AMD microbial communities of four mining areas. (A) Alpha-diversity of AMD shown by the Shannon diversity index. (B) Principal coordinates analysis based on Bray–Curtis distance between samples. (C) Venn analysis of zero-radius operational taxonomic units (zOTUs) detected in four AMDs. (D) Relative abundance of microorganisms detected in AMD samples at the phylum level. (E) Top 50 taxa, at the genus level, in four AMD microbial communities. ^*p < 0.05, ^**p < 0.01, ^***p < 0.001.

Figure 3

Environmental factors determining AMD microbial community structure. (A) db-RDA of microbial communities. Abundance data were Hellinger-standardized. (B) Variation partition of key contributing factors on dissimilarity of microbial communities. (C) Pearson correlations (p<0.01) between abundant taxa and most important environmental factors. (D) Heatmaps of Pearson correlations between predicted functions of microbial communities and determining environmental factors in AMD. ^*p<0.05, ^**p<0.01, ^***p<0.001.

Figure 4

Co-occurrence network analysis. Colors represent prevalence patterns of nodes. mining area (MZ), Dabaoshan (DBS), Monywa (MYW), and Zijinshan (ZJS) mean nodes occurred exclusively in that mining area. All represented nodes are presented in all mining areas.