A Community Multi-Omics Approach towards the Assessment of Surface Water Quality in an Urban River System

Abstract

A multi-omics approach was applied to an urban river system (the Brisbane River (BR), Queensland, Australia) in order to investigate surface water quality and characterize the bacterial population with respect to water contaminants. To do this, bacterial metagenomic amplicon-sequencing using Illumina next-generation sequencing (NGS) of the V5-V6 hypervariable regions of the 16S rRNA gene and untargeted community metabolomics using gas chromatography coupled with mass spectrometry (GC-MS) were utilized. The multi-omics data, in combination with fecal indicator bacteria (FIB) counts, trace metal concentrations (by inductively coupled plasma mass spectrometry (ICP-MS)) and in-situ water quality measurements collected from various locations along the BR were then used to assess the health of the river ecosystem. Sites sampled represented the transition from less affected (upstream) to polluted (downstream) environments along the BR. Chemometric analysis of the combined datasets indicated a clear separation between the sampled environments. Burkholderiales and Cyanobacteria were common key factors for differentiation of pristine waters. Increased sugar alcohol and short-chain fatty acid production was observed by Actinomycetales and Rhodospirillaceae that are known to form biofilms in urban polluted and brackish waters. Results from this study indicate that a multi-omics approach enables a deep understanding of the health of an aquatic ecosystem, providing insight into the bacterial diversity present and the metabolic output of the population when exposed to environmental contaminants.

Keywords: chemometrics; contaminated system; metabolomics; metagenomics; trace metals; urban river system.

Figure 1

Map of the Brisbane River (BR) and the selected sampling sites (BR₁–BR₅).

Figure 2

Bacterial order (top 17) profile of the BR sample sites. Note: ‘others’ represent orders less than 2% of the total sequence abundance.

Figure 3

Bacterial metagenomics similarity and uniqueness characterization based on (A) order; (B) family and (C) genus.

Figure 3

Bacterial metagenomics similarity and uniqueness characterization based on (A) order; (B) family and (C) genus.

Figure 4

PLS-DA plot of the identified metabolites. (A) PLS-DA Score Scatter plot; (B) PLS-DA Loading Scatter plot.

Figure 5

Metabolite similarity and uniqueness characterization.

Figure 6

PLS-DA plot of the metadata and multi-omics datasets based the Microbial Water Quality Assessment Category class assessment. (A) PLS-DA Score Scatter plot; (B) PLS-DA Loading Scatter plot.

Figure 7

PLS-DA plot of the metadata and multi-omics datasets based the Microbial Water Quality Assessment Category class assessment. (A) PLS-DA Score Scatter plot; (B) PLS-DA Loading Scatter plot.

Figure 8

PLS-DA plot of the metadata and multi-omics datasets based the Microbial Water Quality Assessment Category class and low salinity assessment. (A) PLS-DA Score Scatter plot; (B) PLS-DA Loading Scatter plot.