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The thematic synthesis “Optimized use of antibiotics and behavior changes” summarises the NRP 72 research that developed and tested interventions aimed at reducing and optimising antibiotic usage. It formulates detailed conclusions and recommendations in this area, which have been drafted in collaboration with researchers and numerous stakeholders.

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.