Particle association of Enterococcus sp. increases growth rates and simulated persistence in water columns of varying light attenuation and turbulent diffusivity.

Predicting water quality and the human health risks associated with sewage-derived microbes requires understanding the fate and transport of these contaminants. Sewage-derived pathogen risks are typically assessed and monitored by measuring concentrations of fecal indicating bacteria (FIB), like Enterococcus sp. Previous research demonstrated that a high fraction of FIB is particle-associated, which can alter FIB dynamics within secondary water bodies. In this study, we experimentally quantified the effect of particle association on dark, temperature- and light-dependent growth and sinking rates of enterococci. Particle association significantly increased dark growth rates, light-dependent growth rates (i.e. decreased mortality), and sinking rates, relative to free-living enterococci. Simulations using a novel, 1-dimensional model parameterized by these rates indicate greater persistence (T90) for particle-associated enterococci in water bodies across a wide range of diffuse attenuation coefficients of light (Kd) and turbulent diffusivity (D) values. In addition, persistence of both fractions increased in simulated turbid and turbulent waters, compared to clear and/or quiescent conditions. Simulated persistence of both fractions also increased when enterococci discharges occurred later in a diel cycle (towards sunset, as opposed to sunrise), especially for the free-living population, because later discharges under our model conditions allowed both fractions to mix deeper before inactivation via sunlight. Model sensitivity testing revealed that T90 variability was greatest when dark growth rates were altered, suggesting that future empirical studies should focus on quantifying these rates for free-living and particle-associated sewage-derived microbes. Despite greater sensitivity of T90 to variability in dark growth rates, omitting light-dependent growth rates from simulations dramatically influenced T90 values. Our results demonstrate that particle association can increase enterococci persistence in receiving waters and highlight the importance of incorporating particle association in future water quality models.

Dynamics of microbial populations mediating biogeochemical cycling in a freshwater lake.

BACKGROUND: Microbial processes are intricately linked to the depletion of oxygen in in-land and coastal water bodies, with devastating economic and ecological consequences. Microorganisms deplete oxygen during biomass decomposition, degrading the habitat of many economically important aquatic animals. Microbes then turn to alternative electron acceptors, which alter nutrient cycling and generate potent greenhouse gases. As oxygen depletion is expected to worsen with altered land use and climate change, understanding how chemical and microbial dynamics impact dead zones will aid modeling efforts to guide remediation strategies. More work is needed to understand the complex interplay between microbial genes, populations, and biogeochemistry during oxygen depletion. RESULTS: Here, we used 16S rRNA gene surveys, shotgun metagenomic sequencing, and a previously developed biogeochemical model to identify genes and microbial populations implicated in major biogeochemical transformations in a model lake ecosystem. Shotgun metagenomic sequencing was done for one time point in Aug., 2013, and 16S rRNA gene sequencing was done for a 5-month time series (Mar.-Aug., 2013) to capture the spatiotemporal dynamics of genes and microorganisms mediating the modeled processes. Metagenomic binning analysis resulted in many metagenome-assembled genomes (MAGs) that are implicated in the modeled processes through gene content similarity to cultured organism and the presence of key genes involved in these pathways. The MAGs suggested some populations are capable of methane and sulfide oxidation coupled to nitrate reduction. Using the model, we observe that modulating these processes has a substantial impact on overall lake biogeochemistry. Additionally, 16S rRNA gene sequences from the metagenomic and amplicon libraries were linked to processes through the MAGs. We compared the dynamics of microbial populations in the water column to the model predictions. Many microbial populations involved in primary carbon oxidation had dynamics similar to the model, while those associated with secondary oxidation processes deviated substantially. CONCLUSIONS: This work demonstrates that the unique capabilities of resident microbial populations will substantially impact the concentration and speciation of chemicals in the water column, unless other microbial processes adjust to compensate for these differences. It further highlights the importance of the biological aspects of biogeochemical processes, such as fluctuations in microbial population dynamics. Integrating gene and population dynamics into biogeochemical models has the potential to improve predictions of the community response under altered scenarios to guide remediation efforts.