Changes in the Structure of the Microbial Community Associated with Nannochloropsis salina following Treatments with Antibiotics and Bioactive Compounds

Abstract

Open microalgae cultures host a myriad of bacteria, creating a complex system of interacting species that influence algal growth and health. Many algal microbiota studies have been conducted to determine the relative importance of bacterial taxa to algal culture health and physiological states, but these studies have not characterized the interspecies relationships in the microbial communities. We subjected Nanochroloropsis salina cultures to multiple chemical treatments (antibiotics and quorum sensing compounds) and obtained dense time-series data on changes to the microbial community using 16S gene amplicon metagenomic sequencing (21,029,577 reads for 23 samples) to measure microbial taxa-taxa abundance correlations. Short-term treatment with antibiotics resulted in substantially larger shifts in the microbiota structure compared to changes observed following treatment with signaling compounds and glucose. We also calculated operational taxonomic unit (OTU) associations and generated OTU correlation networks to provide an overview of possible bacterial OTU interactions. This analysis identified five major cohesive modules of microbiota with similar co-abundance profiles across different chemical treatments. The Eigengenes of OTU modules were examined for correlation with different external treatment factors. This correlation-based analysis revealed that culture age (time) and treatment types have primary effects on forming network modules and shaping the community structure. Additional network analysis detected Alteromonadeles and Alphaproteobacteria as having the highest centrality, suggesting these species are "keystone" OTUs in the microbial community. Furthermore, we illustrated that the chemical tropodithietic acid, which is secreted by several species in the Alphaproteobacteria taxon, is able to drastically change the structure of the microbiota within 3 h. Taken together, these results provide valuable insights into the structure of the microbiota associated with N. salina cultures and how these structures change in response to chemical perturbations.

Keywords: algae; correlation network; microbiota.

Figure 1

Clustering of microbial diversity (β-diversity) of the starting microbiota with the samples from different chemical treatments at 3 and 24 h. Jackknifing of the UPGMA tree displays the robustness of clustering of the microbiota from the 3 h from 24 h samples. Bootstrap values are shown at the nodes of the tree, indicating percentage of jackknifed data supporting the node. Samples are named as following: chemical name (e.g., GLUCOSE), H/L (high/low concentration, e.g., L) and treatment time (e.g., 3), sample (GLUCOSEL3, treated with low amount of glucose for 3 h). Samples from 3 h (blue) treatment that were most similar to the starting microbiota are highlighted with a red vertical red bar.

Figure 2

Association of modules with external treatment factors. (A) OTUs associated with treatment–time with respect to their membership significance belonging to the yellow module. (B) Eigengene adjacencies heatmap identifying modules (row) that significantly associated with chemical treatments (column). Negative correlations (green) and positive correlations (red) indicate high adjacency (DiLeo et al., 2011), while white (zero) indicates low adjacency.

Figure 3

Relative abundance of OTUs at the family level (indicated by different colors) in identified modules. (A) Blue module (total OTUs 40 annotated at family level, 127 OTUs unassigned at family level are not shown) was dominated by Flavobacteriaceae (25%, n = 10) and Alteromonadaceae (20%, n = 8). (B) Brown module (14 OTUs annotated, 63 unassigned) was dominated by Rhodobacteraceae (92%, n = 13). (C) Yellow module (total OTUs 11 annotated at family level) is comprised of Alteromonadaceae (72%, n = 8), Rhodobacteraceae (9.1%, n = 1), and Moraxellaceae (9.1%, n = 1). (D) Turquoise module (10 OTUs assigned to 8 families, 876 unassigned OTUs) is comprised of 8 families. These data showed that Rhodobacteraceae was significantly enriched in the brown module (one-sided Fisher's exact test, P < 0.0001) and Alteromonadaceae was significantly enriched in the yellow module (one-sided Fisher's exact test, P < 0.05).

Figure 4

Topology of the microbial community associated with N. salina. The identified modules were colored according to module names (Blue, Brown, Turquoise, and Yellow). The whole network contains 1000 nodes representing bacterial OTUs and 68,675 edges, showing correlations between the OTUs with an edge weight cutoff of 0.20 in the WGCNA network (DiLeo et al., 2011). OTUs were annotated at the family level. Rhodobacteraceae and Alteromonadaceae are shown as squares and triangles, respectively. OTUs from families other than Rhodobacteraceae and Alteromonadaceae are displayed as circle.

Figure 5

Family-level taxonomic composition of TDA-perturbed microbiota. Triplicate samples are grouped by TDA concentration and exposure time. Taxonomic affiliations of OTUs are shown at the family level (see legend colors) and TDA treatment concentrations are indicated.

Figure 6

Microbiota were shifted by TDA addition in a dose-dependent manner within 3 h treatment based on weight-Unifrac PCoA with respect to treatment time and TDA concentration. TDA (500 nM)-treated microbiota shifted the microbiota structure away from 3 h-microbiota cluster toward the 24 h-group.