Dynamics of microbial populations mediating biogeochemical cycling in a freshwater lake

Abstract

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.

Keywords: 16S rRNA gene sequencing; Biogeochemical model; Metagenome-assembled genome.

Fig. 1

Distribution of genes (black lines, normalized relative abundance) and their correspondence with modeled processes (gray lines, relative rate) suggest that the model captures the major factors influencing the distribution of most genes in the lake, except genes involved in sulfur cycling. Modeled rates are identical to those published in a previous analysis, which were not calibrated to match gene distributions. Observations represent the following genes and corresponding processes: a nosZ genes with associated modeled processes heterotrophic and autotrophic denitrification, combined; b genes involved in iron reduction in Geobacter and Rhodoferax and modeled heterotrophic iron reduction; c dsrB genes and modeled heterotrophic sulfate reduction and autotrophic sulfide oxidation, combined; d pmoABC genes and modeled methane oxidation and nitrification, combined; e hoa genes and modeled nitrification; and f mxaG genes and modeled methane oxidation (using both oxygen and sulfate)

Fig. 2

a–i Distribution MAGs (bins) and matched OTUs within the water column. To match OTUs with MAGs, the MAG distribution (red) had to align with both the amplicon OTU (aOTU; black) and metagenomic OTU (mOTU; gray) distributions of the same sequence. From the most abundant OTUs, these OTUs matched the MAGs with a similar distribution and classification. The MAG characteristics, including similarity to cultured microorganisms with the same characteristic and presence of the genes in the MAG, support the role of these OTUs in the modeled process in the lake

Fig. 3

Percent change in modeled chemical species after removing denitrification coupled to methane and sulfur oxidation from the optimized model. After calibrating the model to match the chemical and gene distributions with the additional processes, denitrification coupled to methane and sulfur oxidation rate constants were set to zero, but all other parameters remained constant. Chemical concentrations were summed over all depths and time points. Removing these processes most substantially impacts iron speciation likely because of the competition with iron oxidation for nitrate

Fig. 4

a–t Dynamics of populations capable of mediating the modeled processes. The spatiotemporal distribution of OTUs (second and fourth column) and associated processes predicted by the model (first and third column, respectively). Each panel has a its own key to the right of the graph indicating the color coding specific to each graph for the relative abundance (percent of total) of each OTU or rate (μM y^-1) of each process. The model was not calibrated using OTU dynamics; thus, the relationship between the model and observations suggests that the availability of energy is an underlying factor driving the spatiotemporal dynamics of the most abundant and active microorganisms in the lake