Bacterial community structure and metabolic potential in microbialite-forming mats from South Australian saline lakes

Abstract

Microbialites are sedimentary rocks created in association with benthic microorganisms. While they harbour complex microbial communities, Cyanobacteria perform critical roles in sediment stabilisation and accretion. Microbialites have been described from permanent and ephemeral saline lakes in South Australia; however, the microbial communities that generate and inhabit these biogeological structures have not been studied in detail. To address this knowledge gap, we investigated the composition, diversity and metabolic potential of bacterial communities from different microbialite-forming mats and surrounding sediments in five South Australian saline coastal lakes using 16S rRNA gene sequencing and predictive metagenome analyses. While Proteobacteria and Bacteroidetes were the dominant phyla recovered from the mats and sediments, Cyanobacteria were significantly more abundant in the mat samples. Interestingly, at lower taxonomic levels, the mat communities were vastly different across the five lakes. Comparative analysis of putative mat and sediment metagenomes via PICRUSt2 revealed important metabolic pathways driving the process of carbonate precipitation, including cyanobacterial oxygenic photosynthesis, ureolysis and nitrogen fixation. These pathways were highly conserved across the five examined lakes, although they appeared to be performed by distinct groups of bacterial taxa found in each lake. Stress response, quorum sensing and circadian clock were other important pathways predicted by the in silico metagenome analysis. The enrichment of CRISPR/Cas and phage shock associated genes in these cyanobacteria-rich communities suggests that they may be under selective pressure from viral infection. Together, these results highlight that a very stable ecosystem function is maintained by distinctly different communities in microbialite-forming mats in the five South Australian lakes and reinforce the concept that 'who' is in the community is not as critical as their net metabolic capacity.

Keywords: 16S rRNA gene amplicon sequencing; bacterial biodiversity; cyanobacteria; microbial mats; microbialites; saline and hypersaline lakes.

FIGURE 1

Geographic location of the sampling sites with conductivity gradient overlay (top), and site views and morphologies of sampled microbialite‐forming mats (bottom). The map image was generated in R statistical software using the package leaflet. LH = Lake Hamilton, SM = Sleaford Mere, DL = Deep Lake, CN = Coorong North, CS = Coorong South

FIGURE 2

Bacterial community structure in South Australian saline coastal lakes. Relative abundances of the major taxa contributing to the bacterial communities in mats and sediments, in order of increasing conductivity of lakes. SM = Sleaford Mere, CS = Coorong South, CN = Coorong North, DL = Deep Lake, LH = Lake Hamilton. Sediment samples SA4, SA19, SA7 and SA13 belong to lakes SM, CS, CN and LH, respectively. The four most abundant classes of the phylum Proteobacteria are presented. The low abundance phyla in each group with median relative abundance <2% were grouped as Other

FIGURE 3

Venn diagram showing the number of shared ASVs among mat samples from five lakes; gradient grey colour filling exhibits the differences in ASV counts among various regions

FIGURE 4

Principal coordinate analysis (PCoA) using Bray‐Curtis distance matrix generated from ASV profiles of mat samples. SM = Sleaford Mere, CS = Coorong South, CN = Coorong North, DL = Deep Lake, LH = Lake Hamilton

FIGURE 5

Abundances of the major genera contributing to the cyanobacteria community of mat samples from the South Australian saline lakes, in order of increasing conductivity. SM = Sleaford Mere, CS = Coorong South, CN = Coorong North, DL = Deep Lake, LH = Lake Hamilton. The abundance of ASVs were standardised to the median sequencing depth using the standf function in R. Prefix ‘Un‐’ stands for ‘Unclassified’. The shading represents the enrichment of the genera according to conductivity; blue: mesosaline, yellow: polysaline to hypersaline and orange: hypersaline

FIGURE 6

PICRUSt2‐predicted metabolic functions of lithified mats, soft mats and sediments. (a) Euler diagrams represent the number of shared ASVs, shared predicted KEGG‐annotated genes and shared predicted MetaCyc pathways between sediments and the mat group. (b) Principal coordinate analysis (PCoA) using Jaccard distance matrix generated from predicted KEGG gene profiles. (c) PCoA using Bray‐Curtis distance matrix generated from predicted KEGG gene profiles. The circles represent groups with significant pairwise PERMANOVA test: adjusted p = .003 for lithified mat group vs sediment group. (d) Venn diagrams represent the number of shared predicted KEGG‐annotated genes (left) and shared predicted MetaCyc pathways (right) among five PICRUSt2‐predicted microbialite‐associated metagenomes from the five examined lakes; gradient grey colour filling exhibits the differences in KEGG/pathway counts among various regions. SM = Sleaford Mere, CS = Coorong South, CN = Coorong North, DL = Deep Lake, LH = Lake Hamilton. (e) The abundance of selected KEGG‐annotated metabolic genes that were significantly more abundant in lithified mats compared to sediment controls based on DeSEq2 analysis. A full description of gene names is provided in Table S10