In situ environment rather than substrate type dictates microbial community structure of biofilms in a cold seep system

Abstract

Using microscopic and molecular techniques combined with computational analysis, this study examined the structure and composition of microbial communities in biofilms that formed on different artificial substrates in a brine pool and on a seep vent of a cold seep in the Red Sea to test our hypothesis that initiation of the biofilm formation and spreading mode of microbial structures differs between the cold seep and the other aquatic environments. Biofilms on different substrates at two deployment sites differed morphologically, with the vent biofilms having higher microbial abundance and better structural features than the pool biofilms. Microbes in the pool biofilms were more taxonomically diverse and mainly composed of various sulfate-reducing bacteria whereas the vent biofilms were exclusively dominated by sulfur-oxidizing Thiomicrospira. These results suggest that the redox environments at the deployment sites might have exerted a strong selection on microbes in the biofilms at two sites whereas the types of substrates had limited effects on the biofilm development.

Figure 1. Percentage of microbial taxa classified based on 16S rRNA gene sequences.

Biofilms were developed on different substrate materials within the brine pool (BP-biofilms) and on a seeping vent (SV-biofilms). The 16S rRNA amplicons of the seeping water, BP-biofilms and SV-biofilms were pyrosequenced and classified by comparing with the SILVA database at (a) phylum and (b) genus levels. Minor group represents the sum of all phyla or genera with a proportion of less than 1% and 3%, respectively, for all 13 samples. Refer to Table 3 for sample ID.

Figure 2. Principal coordinates analysis (PCoA) of microbial communities of seeping water and biofilms.

The microbial communities were revealed based on 16S rRNA gene pyrosequencing. PCoA plot of PC1 and PC2 is shown. Sample IDs are described in Table 3.

Figure 3. Ordination analysis (CANOCO) of biofilm microbial abundance in relation to site, substrate material and plate position.

Percentage of 16S rRNA pyrosequencing reads assigned to (a) phylum and (b) genus levels was used as the ‘species data’ for RDA and CCA analyses, respectively. Correlations between environmental variables (marked by red triangles; site: vent and pool; material type: Al, PVC and PS; and orientation: in and out) and the first two canonical axes are represented by the length and angle of the blue triangles (environmental factor vectors). Biofilm samples were denoted with open circles. Forward selection with Monte Carlo permutation tests was applied to build the parsimonious model, which identified site as the major influential factor significantly contributing to the variations in the biofilm microbial communities. At the genus level, the lower axis minimum fit was set to the variance explained by the first axis to identify genera which were affected most severely in the model.

Figure 4. Percentage of 16S rRNA gene pyrosequencing reads assigned to different groups of sulfate-reducing (SRB) and sulfur-oxidizing (SOB) bacteria in different biofilms.

Inset shows the abundance of total SRB and SOB. Taxonomic classification was based on comparison of sequences within the SILVA database.

Figure 5. Phylogenetic trees based on sequences of dsrB gene obtained from the BP-biofilm and SW samples, respectively.

The trees were constructed using Neighbor-Joining method. Bootstrap values of > 50% based on 500 resamplings are indicated by numbers at nodes. Each clone sequence (OTU) is represented by sample ID followed by clone number in bold. Number in bracket indicates number of clones assigned to the same OTU. Reference sequence is represented by strain name followed by accession number in parenthesis. The isolation source of the reference clone sequence is indicated by a superscript of ‘W’ for water, ‘S’ for sediment, and ‘E’ for environmental sample.