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.
|Original language||English (US)|
|Journal||International journal of environmental research and public health|
|State||Published - Mar 14 2017|
Bibliographical noteFunding Information:
The authors would like to acknowledge the assistance and support provided by the CSIRO’s Microbiology Water Quality Sciences Group, in particular the support of J. Sidhu and A. Palmer for their assistance in collecting water samples used in this study. The authors would also like to thank and acknowledge the financial support of the CSIRO Land andWater business unit. Amplicon sequence data were processed and analyzed using the resources of the Minnesota Supercomputing Institute.
© 2017 by the authors. Licensee MDPI, Basel, Switzerland.
- Contaminated system
- Trace metals
- Urban river system