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In stratified lakes, methane oxidizing bacteria are critical methane converters that significantly reduce emissions of this greenhouse gas to the atmosphere. Efforts to better understand their ecology uncovered a surprising diversity, vertical structure, and seasonal succession. It is an open question how this diversity has to be considered in models of microbial methane oxidation. Likewise, it is unclear to what extent simple microbial traits related to the kinetics of the oxidation process and temperature optimum, suggested by previous studies, suffice to understand the observed ecology of methane oxidizing bacteria. Here we incorporate niche partitioning in a mechanistic model of seasonal lake mixing and microbial methane oxidation in a stratified lake. Can we model MOB diversity and niche partitioning based on differences in methane oxidation kinetics and temperature adaptation? We found that our model approach can closely reproduce diversity and niche preference patterns of methanotrophs that were observed in seasonally stratified lakes. We show that the combination of trait values resulting in coexisting methanotroph communities is limited to very confined regions within the parameter space of potential trait combinations. However, our model also indicates that the sequence of community assembly, and variations in the stratification and mixing behavior of the lake result in different stable combinations. A scenario analysis introducing variable mixing conditions showed that annual weather conditions and the pre-existing species also affect the developing stable methanotrophic species composition of the lake. Both, effect of pre-existing species and the environmental impact suggest that the MOB community in lakes may differ from year to year, and a stable community may never truly occur. The model further shows that there are always better-adapted species in the trait parameter space that would destabilize and replace an existing stable community. Thus, natural selection may drive trait values into the specific configurations observed in nature based on physiological limits and tradeoffs between traits., Frontiers in Environmental Science, 10, ISSN:2296-665X

The nitrogen (N) cycle is of global importance, as N is an essential element and a limiting nutrient in terrestrial and aquatic ecosystems. Excessive anthropogenic N fertilizer usage threatens sensitive downstream aquatic ecosystems. Although freshwater lake sediments remove N through various microbially mediated processes, few studies have investigated the microbial communities involved. In an integrated biogeochemical and microbiological study on a eutrophic and oligotrophic lake, we estimated N removal rates from pore water concentration gradients in sediments. Simultaneously, the abundance of different microbial N transformation genes was investigated using metagenomics on a seasonal and spatial scale. We observed that contrasting nutrient concentrations in sediments were associated with distinct microbial community compositions and significant differences in abundances of various N transformation genes. For both characteristics, we observed a more pronounced spatial than seasonal variability within each lake. The eutrophic Lake Baldegg showed a higher denitrification potential with higher nosZ gene (N2O reductase) abundances and higher nirS:nirK (nitrite reductase) ratios, indicating a greater capacity for complete denitrification. Correspondingly, this lake had a higher N removal efficiency. The oligotrophic Lake Sarnen, in contrast, had a higher potential for nitrification. Specifically, it harbored a high abundance of Nitrospira, including some with the potential for comammox. Our results demonstrate that knowledge of the genomic N transformation potential is important for interpreting N process rates and understanding how the lacustrine sedimentary N cycle responds to variations in trophic conditions., mSphere, 7 (1)

Additional file 1: Supplementary Figure 1. Comparing microbial communities between WWTP locations to identify outliers. Graphs showing multivariate dispersion (A) and beta diversity (B) of microbial community composition at N=11 WWTP locations, based on metagenomic data (left-hand side) and metatranscriptomic data (right-hand side). For multivariate dispersion (A), NMDS plots were calculated based on Bray-Curtis dissimilarities. Lines (color-coded by location) link samples at each location to their centromere. For beta diversity (B), boxplots show the 25 % and 75 % percentiles and medians of Bray-Curtis dissimilarities. Points are color and symbol-coded by WWTP compartments: INF = inflow (sewage), DNF = denitrification bioreactor, NFC = nitrification bioreactor, EFF = effluent (treated water). In beta diversity based on metatranscriptomic data (lower row, right-hand side), significant differences between location “FD” and the other locations are indicated with asterisks (unpaired two-sample Wilcoxon test, * p < 0.05; ** p < 0.01; *** p < 0.001). Based on these results, location FD was excluded from further analysis. Supplementary Figure 2. Assessment of the variation caused by sampling processing (sequencing). NMDS plot based on Bray-Curtis dissimilarities derived from metatranscriptomic data, comparing microbial community composition across WWTP compartments and locations. The variation caused by sample processing is shown for one location, “ZR”, showing three sequencing replicates from the inflow (INF) (replicates indicated by yellow asterisks). Based on this comparison, we concluded that variation caused by sequencing was low. Compartments: INF = inflow (sewage), DNF = denitrification bioreactor, NFC = nitrification bioreactor, EFF = effluent (treated water). Supplementary Figure 3. Rarefaction curves for metagenomic (rDNA) and metatranscriptomic (rRNA) data. Curves showing the number of reads as a function of the number of OTUs identified (N=37 samples, i.e. one sample from each WWTP compartment (4) at each WWTP location (10), excluding 3 samples because of exceptionally low sequencing-depth). Samples are color-coded by compartment: INF = inflow (sewage), DNF = denitrification bioreactor, NFC = nitrification bioreactor, EFF = effluent (treated water). Supplementary Figure 4. Total number of rDNA and rRNA sequences. Boxplots showing the 25 % and 75 % percentiles and medians of the total number of rDNA (metagenomics) and rRNA (metatranscriptomics) sequences for (A) the total community and (B) the parasitic community, comparing prokaryotes (blue) and eukaryotes (yellow). Compartments: INF = inflow (sewage), DNF = denitrification bioreactor, NFC = nitrification bioreactor, EFF = effluent (treated water). Supplementary Figure 5. Microbial community structure and environmental factors across WWTPs. NMDS biplots based on Bray-Curtis dissimilarities showing microbial community composition across WWTP compartments and locations, in association with environmental data. Metagenomic and metatranscriptomic data are shown separately. Samples are color-coded and grouped (ellipses) by compartment. Significant environmental vectors are shown as arrows (* p < 0.05; ** p < 0.01; *** p < 0.001). Compartments: INF = inflow (sewage), DNF = denitrification bioreactor, NFC = nitrification bioreactor, EFF = effluent (treated water). Environmental vectors: DO = dissolved oxygen, DOC = dissolved organic carbon, HRT = Hydraulic retention time, TN = total nitrogen, TP = total phosphorus. Supplementary Figure 6. Microbial community structure and environmental factors in the separate WWTP compartments. NMDS biplots based on Bray-Curtis dissimilarities, showing microbial community composition in association with environmental data for each WWTP compartment. Metagenomic and metatranscriptomic data are shown separately. The distribution of the samples (symbol-coded by WWTP location) is visualized by the ellipses. Significant environmental vectors are shown as arrows. Compartments: INF = inflow (sewage), DNF = denitrification bioreactor, NFC = nitrification bioreactor, EFF = effluent (treated water). Environmental vectors: TN = total nitrogen. Supplementary Figure 7. Co-occurrence networks of parasitic orders in the four WWTP compartments. Networks showing correlations derived from co-occurrence network inferences for each WWTP compartment, based on metagenomic (first row) and metatranscriptomic (second row) data. Only associations that involve parasites are shown. Nodes represent genera grouped at the order level and trait level (red nodes: parasitic taxa; yellow nodes: free-living taxa), with node size proportional to the total number of reads for each order. Edges represent correlations between taxa (blue lines: positive correlations; red lines: negative correlations), with line thickness proportional to the number of genera per order involved. Compartments: INF = inflow (sewage), DNF = denitrification bioreactor, NFC = nitrification bioreactor, EFF = effluent. Abbreviations for Archaea: E = Euryarchaeota, O = Others. Abbreviations for bacteria: A = Actinobacteria, B = Bacteroidetes, C = Chloroflexi, F = Firmicutes, O = Others, Pl = Planctomycetes, Pr = Proteobacteria, T = Tenericutes, V = Verrucomicrobia. Abbreviations for Proteobacteria: Alpha = Alphaproteobacteria, Beta = Betaproteobacteria, Gamma = Gammaproteobacteria, Delta = Deltaproteobacteria, O = Others. Abbreviations Fungi: Ascomycota = A, Basidiomycota = B, Others = O. Abbreviations for protists: Ce = Cercozoa (*including Rosculus), Ci = Ciliophora, Co = Conosa, Db = Discoba, Di = Discosea, Ms = Mesomycetozoa, Mt = Metamonada, O = Others, S = Stramenopiles, T = Tubulinea. Supplementary Table 1. Microbial community composition after quality filtering. Total number and relative number (%) of ribosomal reads and OTUs in the metagenomic (rDNA) and metatranscriptomic (rRNA) data of 10 WWTP locations, for prokaryotes (bacteria and Archaea) and eukaryotes (protists, fungi and microscopic metazoa). Supplementary Table 2. Parasitic genera in WWTPs based on both metagenomic and metatranscriptomic data. Overview of all parasitic genera identified in the WWTP samples. Supplementary Table 3. Comparing the total number of rDNA and rRNA sequences. Pair-wise comparison of the total number of eukaryotic and prokaryotic sequences for the total community and the parasitic community, contrasting the inflow (INF) with the denitrification bioreactor (DNF), the denitrification bioreactor (DNF) with the nitrification bioreactor (NFC), and the nitrification bioreactor (NFC) with the effluent (EFF). Sign test (* p < 0.05; ** p < 0.01; *** p < 0.001). Supplementary Table 4. Comparing the abundance of parasitic protists between WWTP compartments. Pair-wise comparison of relative abundances of parasitic protist taxa in metagenomic and metatranscriptomic data, contrasting the inflow (INF) with the denitrification bioreactor (DNF), and the nitrification bioreactor (NFC) with the effluent (EFF). Sign test (* p < 0.05; ** p < 0.01; *** p < 0.001). Supplementary Table 5. Overview of the most numerous orders in WWTPs. Overview of the most numerous orders shown in Fig. 1. Numbers show their mean relative abundances across all compartments and locations (total N=40 samples) for both rDNA and rRNA data, as well as the absolute difference between these relative DNA and RNA abundances, per order.

In stratified lakes, methane-oxidizing bacteria (MOB) are strongly mitigating methane fluxes to the atmosphere by consuming methane entering the water column from the sediments. MOB communities in lakes are diverse and vertically structured, but their spatio-temporal dynamics along the water column as well as physico-chemical parameters and interactions with other bacterial species that drive the community assembly have so far not been explored in depth. Here, we present a detailed investigation of the MOB and bacterial community composition and a large set of physico-chemical parameters in a shallow, seasonally stratified, and sub-alpine lake. Four highly resolved vertical profiles were sampled in three different years and during various stages of development of the stratified water column. Non-randomly assembled MOB communities were detected in all compartments. We could identify methane and oxygen gradients and physico-chemical parameters like pH, light, available copper and iron, and total dissolved nitrogen as important drivers of the MOB community structure. In addition, MOB were well-integrated into a bacterial-environmental network. Partial redundancy analysis of the relevance network of physico-chemical variables and bacteria explained up to 84% of the MOB abundances. Spatio-temporal MOB community changes were 51% congruent with shifts in the total bacterial community and 22% of variance in MOB abundances could be explained exclusively by the bacterial community composition. Our results show that microbial interactions may play an important role in structuring the MOB community along the depth gradient of stratified lakes., Frontiers in Microbiology, 11, ISSN:1664-302X

In lakes, large amounts of methane are produced in anoxic sediments. Methane-oxidizing bacteria effectively convert this potent greenhouse gas into biomass and carbon dioxide. These bacteria are present throughout the water column, where methane concentrations can range from nanomolar to millimolar. In this study, we tested the hypothesis that methanotroph assemblages in a seasonally stratified freshwater lake are adapted to the contrasting methane concentrations in the epi- and hypolimnion. We further hypothesized that lake overturn would change the apparent methane oxidation kinetics as more methane becomes available in the epilimnion. In addition to the change in the methane oxidation kinetics, we investigated changes in the transcription of genes encoding methane monooxygenase, the enzyme responsible for the first step of methane oxidation, with metatranscriptomics. Using laboratory incubations of the natural microbial communities, we show that the half-saturation constant (Km) for methane – the methane concentration at which half the maximum methane oxidation rate is reached – was 20 times higher in the hypolimnion than in the epilimnion during stable stratification. During lake overturn, however, the kinetic constants in the epi- and hypolimnion converged along with a change in the transcriptionally active methanotroph assemblage. Conventional particulate methane monooxygenase appeared to be responsible for methane oxidation under different methane concentrations. Our results suggest that methane availability is one important factor for creating niches for methanotroph assemblages with well-adapted methane oxidation kinetics. This rapid selection and succession of adapted lacustrine methanotroph assemblages allowed the previously reported high removal efficiency of methane transported to the epilimnion to be maintained – even under rapidly changing conditions during lake overturn. Consequently, only a small fraction of methane stored in the anoxic hypolimnion is emitted to the atmosphere.

Ammonia oxidation decreases the pH in wastewaters where alkalinity is limited relative to total ammonia. The activity of ammonia oxidizing bacteria (AOB), however, typically decreases with pH and often ceases completely in slightly acidic wastewaters. Nevertheless, nitrification at low pH has been reported in reactors treating human urine, but it has been unclear which organisms are involved. In this study, we followed the population dynamics of ammonia oxidizing organisms and reactor performance in synthetic fully hydrolyzed urine as the pH decreased over time in response to a decrease in the loading rate. Populations of the β-proteobacterial Nitrosomonas europaea lineage were abundant at the initial pH close to 6, but the growth of a possibly novel Nitrosococcus-related AOB genus decreased the pH to the new level of 2.2, challenging the perception that nitrification is inhibited entirely at low pH values, or governed exclusively by β-proteobacterial AOB or archaea. With the pH shift, nitrite oxidizing bacteria were not further detected, but nitrous acid (HNO2) was still removed through chemical decomposition to nitric oxide (NO) and nitrate. The growth of acid-tolerant γ-proteobacterial AOB should be prevented, by keeping the pH above 5.4, which is a typical pH limit for the N. europaea lineage. Otherwise, the microbial community responsible for high-rate nitrification can be lost, and strong emissions of hazardous volatile nitrogen compounds such as NO are likely., Environmental Science & Technology, 51 (12), ISSN:0013-936X, ISSN:1520-5851

The presence of predators can impact a variety of organisms within the ecosystem, including microorganisms. Because the effects of fish predators and their phenotypic differences on microbial communities have not received much attention, we tested how the presence/absence, genotype, and plasticity of the predatory three-spine stickleback (Gasterosteus aculeatus) influence aquatic microbes in outdoor mesocosms. We reared lake and stream stickleback genotypes on contrasting food resources to adulthood, and then added them to aquatic mesocosm ecosystems to assess their impact on the planktonic bacterial community. We also investigated whether the effects of fish persisted following the removal of adults, and the subsequent addition of a homogenous juvenile fish population. The presence of adult stickleback increased the number of bacterial OTUs and altered the size structure of the microbial community, whereas their phenotype affected bacterial community composition. Some of these effects were detectable after adult fish were removed from the mesocosms, and after juvenile fish were placed in the tanks, most of these effects disappeared. Our results suggest that fish can have strong short-term effects on microbial communities that are partially mediated by phenotypic variation of fish.

The presence of predators can impact a variety of organisms within the ecosystem, including microorganisms. Because the effects of fish predators and their phenotypic differences on microbial communities have not received much attention, we tested how the presence/absence, genotype, and plasticity of the predatory three-spine stickleback (Gasterosteus aculeatus) influence aquatic microbes in outdoor mesocosms. We reared lake and stream stickleback genotypes on contrasting food resources to adulthood, and then added them to aquatic mesocosm ecosystems to assess their impact on the planktonic bacterial community. We also investigated whether the effects of fish persisted following the removal of adults, and the subsequent addition of a homogenous juvenile fish population. The presence of adult stickleback increased the number of bacterial OTUs and altered the size structure of the microbial community, whereas their phenotype affected bacterial community composition. Some of these effects were detectable after adult fish were removed from the mesocosms, and after juvenile fish were placed in the tanks, most of these effects disappeared. Our results suggest that fish can have strong short-term effects on microbial communities that are partially mediated by phenotypic variation of fish., Ecology and Evolution, 7 (5), ISSN:2045-7758