Microbiome response in an urban river system is dominated by seasonality over wastewater treatment upgrades

Abstract

Background: Microorganisms such as coliform-forming bacteria are commonly used to assess freshwater quality for drinking and recreational use. However, such organisms do not exist in isolation; they exist within the context of dynamic, interactive microbial communities which vary through space and time. Elucidating spatiotemporal microbial dynamics is imperative for discriminating robust community changes from ephemeral ecological trends, and for improving our overall understanding of the relationship between microbial communities and ecosystem health. We conducted a seven-year (2013-2019) microbial time-series investigation in the Chicago Area Waterways (CAWS): an urban river system which, in 2016, experienced substantial upgrades to disinfection processes at two wastewater reclamation plants (WRPs) that discharge into the CAWS and improved stormwater capture, to improve river water quality and reduce flooding. Using culture-independent and culture-dependent approaches, we compared CAWS microbial ecology before and after the intervention.

Results: Examinations of time-resolved beta distances between WRP-adjacent sites showed that community similarity measures were often consistent with the spatial orientation of site locations to one another and to the WRP outfalls. Fecal coliform results suggested that upgrades reduced coliform-associated bacteria in the effluent and the downstream river community. However, examinations of whole community changes through time suggest that the upgrades did little to affect overall riverine community dynamics, which instead were overwhelmingly driven by yearly patterns consistent with seasonality.

Conclusions: This study presents a systematic effort to combine 16S rRNA gene amplicon sequencing with traditional culture-based methods to evaluate the influence of treatment innovations and systems upgrades on the microbiome of the Chicago Area Waterway System, representing the longest and most comprehensive characterization of the microbiome of an urban waterway yet attempted. We found that the systems upgrades were successful in improving specific water quality measures immediately downstream of wastewater outflows. Additionally, we found that the implementation of the water quality improvement measures to the river system did not disrupt the overall dynamics of the downstream microbial community, which remained heavily influenced by seasonal trends. Such results emphasize the dynamic nature of microbiomes in open environmental systems such as the CAWS, but also suggest that the seasonal oscillations remain consistent even when perturbed.

Keywords: 16S rRNA gene sequencing; Dynamics; Fecal coliform; Microbiome; Wastewater.

Fig. 1

A Alpha diversity of CAWS samples by sample type. The distribution of Shannon diversity indices for each sample type is consolidated for all seven sampling years (2013–2019). This box and whisker plots demonstrate the quartile range and outliers for each distribution. Statistical groupings are designated by the letters above the boxplots. Significance was assessed using paired Wilcoxon tests with Benjamini–Hochberg multiple testing corrections. B Beta diversity analysis of CAWS samples by sample type. PCoA plot based on the unweighted UniFrac distance matrix showing clustering patterns of different sample types. C Aggregated seasonal plots of beta distances as a function of month, for each sample type. The dots connected by solid black lines represent the mean beta distance between each sample community and a fixed baseline community of the same sample type and site. The blue density bands describe the distribution of beta distances to a baseline community, for each particular month. The dashed line signifies the mean beta distance value of all samples relative to its baseline community for a given sample type, and represents the null expectations given that seasonality plays no role in the community. As no collections were conducted in December, January, and February (axis labeled in red), the values of these months were interpolated using spline regression

Fig. 2

A, B Differential abundance plots of water and effluent samples, comparing samples collected in March to samples collected in August. Positive differential values indicate the top ten ASVs that were differentially abundant in March samples compared to August, and negative values indicate the top ten ASVs that were differentially abundant in August samples relative to March. C, D Log ratios of March-associated ASVs to August-associated ASVs as a function of month in water and effluent samples. Red points indicate the mean value of each month. Blue lines are best-fit curves of the data using a local polynomial regression fitting method (loess) with 95% confidence intervals

Fig. 3

A Simplified map of the Calumet and O’Brien regions of the CAWS. Subsetted sites used in the following analyses are described in this map. B Unweighted UniFrac PCoA plots of effluent sample, and water and sediment samples from upstream, immediate downstream, and further downstream relative to WRPs. Plots are separated by region and by intervention period. C Unweighted UniFrac distances between pairs of sites, matched by time point. Statistical comparisons between pre- and post- intervention distances within each pair are denoted using brackets, with n.s. indicating non-significance (p > 0.05). Statistical groupings of comparisons across pairs are designated by the letters above the boxplots. Significance was assessed using t-tests with Benjamini–Hochberg multiple-testing corrections

Fig. 4

Log-transformed fecal coliform concentrations as a function of time in effluent and downstream river samples of Calumet and O’Brien WRPs. Plots on the left represent effluent samples directly from WRPs, plots in the center represent samples of river sites immediately downstream from WRPs, and plots on the right represent samples of river sites further downstream of the WRPs. The dotted line represents 400 CFUs per 100 mL, a concentration set as the EPA standard for recreational waters. The p-values represent results of bootstrapping simulations testing for differences in median values of coliform concentrations between pre-intervention time points (before 2016) and post-intervention time points (after 2016)