Organic matter lability modifies the vertical structure of methane-related microbial communities in lake sediments

This is a self-archived version of an original article. This version may differ from the original in pagination and typographic details. Author(s): Title: Year: Version: Copyright: Rights: Rights url: Please cite the original version: CC BY 4.0 https://creativecommons.org/licenses/by/4.0/ Organic matter lability modifies the vertical structure of methane-related microbial communities in lake sediments © 2023 Rissanen et al. Published version Rissanen, Antti J.; Jilbert, Tom; Simojoki, Asko; Mangayil, Rahul; Aalto, Sanni L.; Khanongnuch, Ramita; Peura, Sari; Jäntti, Helena Rissanen, A. J., Jilbert, T., Simojoki, A., Mangayil, R., Aalto, S. L., Khanongnuch, R., Peura, S., & Jäntti, H. (2023). Organic matter lability modifies the vertical structure of methane-related microbial communities in lake sediments. Microbiology Spectrum, 11(5), Article e01955-23. https://doi.org/10.1128/spectrum.01955-23 2023 | Open Peer Review | Environmental Microbiology | Research Article Organic matter lability modifies the vertical structure of methane-related microbial communities in lake sediments Antti J. Rissanen,1,2 Tom Jilbert,3 Asko Simojoki,4 Rahul Mangayil,1 Sanni L. Aalto,5,6 Ramita Khanongnuch,1 Sari Peura,7 Helena Jäntti5 AUTHOR AFFILIATIONS See affiliation list on p. 12. ABSTRACT Eutrophication increases the input of labile, algae-derived, organic matter (OM) into lake sediments. This potentially increases methane (CH4) emissions from sediment to water through increased methane production rates and decreased methane oxidation efficiency in sediments. However, the effect of OM lability on the structure of methane oxidizing (methanotrophic) and methane producing (methanogenic) microbial communities in lake sediments is still understudied. We studied the vertical profiles of the sediment and porewater geochemistry and the microbial communities (16S rRNA gene amplicon sequencing) at five profundal stations of an oligo-mesotrophic, boreal lake (Lake Pääjärvi, Finland), varying in surface sediment OM sources (assessed via sediment C:N ratio). Porewater profiles of methane, dissolved inorganic carbon (DIC), acetate, iron, and sulfur suggested that sites with more autochthonous OM showed higher overall OM lability, which increased remineralization rates, leading to increased electron acceptor (EA) consumption and methane emissions from sediment to water. When OM lability increased, the abundance of anaerobic nitrite-reducing methano trophs (Candidatus Methylomirabilis) relative to aerobic methanotrophs (Methylococ cales) in the methane oxidation layer of sediment surface decreased, suggesting that Methylococcales were more competitive than Ca. Methylomirabilis under decreas ing redox conditions and increasing methane availability due to their more diverse metabolism (fermentation and anaerobic respiration) and lower affinity for methane. Furthermore, when OM lability increased, the abundance of methanotrophic community in the sediment surface layer, especially Ca. Methylomirabilis, relative to the methano genic community decreased. We conclude that increasing input of labile OM, subse quently affecting the redox zonation of sediments, significantly modifies the methane producing and consuming microbial community of lake sediments. IMPORTANCE Lakes are important natural emitters of the greenhouse gas methane (CH4). It has been shown that eutrophication, via increasing the input of labile organic matter (OM) into lake sediments and subsequently affecting the redox conditions, increases methane emissions from lake sediments through increased sediment methane production rates and decreased methane oxidation efficiency. However, the effect of organic matter lability on the structure of the methane-related microbial communities of lake sediments is not known. In this study, we show that, besides the activity, also the structure of lake sediment methane producing and consuming microbial community is significantly affected by changes in the sediment organic matter lability. KEYWORDS greenhouse gas, freshwater, methanotroph, methanogen, 16S rRNA gene, eutrophication The concentration of atmospheric methane (CH4), a critical greenhouse gas, has increased substantially since industrialization, with current total emissions of 550– Month XXXX Volume 0 Issue 0 10.1128/spectrum.01955-23 1 Editor Sandi Orlic, Institut Ruder Boskovic, Zagreb, Croatia Ad Hoc Peer Reviewer Lisa Y. Stein, University of Alberta, Edmonton, Alberta, Canada Address correspondence to Antti J. Rissanen, [email protected]. The authors declare no conflict of interest. See the funding table on p. 12. Received 12 May 2023 Accepted 17 July 2023 Published 12 September 2023 Copyright © 2023 Rissanen et al. This is an openaccess article distributed under the terms of the Creative Commons Attribution 4.0 International license. Downloaded from https://journals.asm.org/journal/spectrum on 15 September 2023 by 130.234.242.76. 600 Tg/year (top-down estimates) of which approximately 40% stem from natural sources (1). Lakes are important natural emitters of CH4. The numerous lakes and ponds of the northern boreal zone, with annual emissions of ~16.5 Tg, are especially important contributors to the global CH4 budget (2, 3). Thus, knowledge of the factors affecting CH4 emissions from northern lakes is essential for accurate estimates and modeling of the global CH4 budget and its changes due to global change (e.g., eutrophication and climate warming). The CH4 emissions from lake sediments are controlled by the balance between methanogenesis and CH4 oxidation. Methanogenesis is the final step in anoxic organic matter (OM) degradation by methanogenic archaea that produce CH4 from acetate or from oxidation of H2 using CO2 as an electron acceptor (EA) (4). Some methanogens can also produce CH4 from other methyl compounds (e.g., methanol) (4, 5). In oxic surface sediments, CH4 is consumed through aerobic CH4 oxidation by methanotrophic bacteria (MOB) using O2 as an EA (6). In anoxic conditions, CH4 is consumed through anaerobic methane oxidation (AOM) by anaerobic methanotrophic archaea (ANME) utilizing various inorganic or organic compounds (7–9) or by bacteria within genus Candidatus Methylomirabilis utilizing NO2− as EAs (10). Furthermore, MOB belonging to Methylococcales have been shown to be capable of metabolizing CH4 in hypoxic and anoxic conditions via fermentation and anaerobic respiration of various EAs, including NO3−, NO2−, Fe3+, and organic EAs (11–19). Eutrophication was recently shown to increase CH4 emissions from lake sediments through lowered CH4 oxidation efficiency at increasing sediment methanogenesis rate (20). However, whether this phenomenon is due to change in the activity of metha nogens and methanotrophs or also due to change in their community structure and abundance is not known. In support of the latter hypothesis, the results from the recent studies comparing lakes with different trophic states suggest that the density of sediment methanogens is higher while that of methanotrophs is lower, in eutrophic lakes that contain more labile algae-based sediment OM and where CH4 fluxes from the sediment are higher than in oligotrophic and mesotrophic lakes (21–23). However, no previous study has simultaneously studied the change in both methanotrophic and methanogenic communities within a gradient of changing OM lability. The close spatial proximity of MOB and AOM-driving Ca. Methylomirabilis bacteria in lake sediments (24) also suggests that competition for CH4 exists between these groups. Indeed, a recent modeling study suggested that an increase in OM quantity decreases the abundance of Ca. Methylomirabilis while increasing the abundance of MOB (25). Furthermore, in the comparison [using the 16S rRNA gene dataset by Han et al. (21)] of sediment methanotroph communities between lakes with varying trophic status by van Grinsven et al. (22), Ca. Methylomirabilis sp. was as abundant as MOB in the sediments of an oligotrophic lake but had negligible abundance in the sediments of mesoand eutrophic lakes, where MOB dominated the methanotrophic community. These differences suggest that MOB are more competitive in conditions of higher OM lability, when increased OM mineralization decreases redox potential and increases CH4 availability. This is possibly due to MOB, especially Methylococcales, having a lower affinity for CH4 and more diverse metabolism in hypoxic and anoxic conditions (incl. capability for fermentation and anaerobic respiration of various EAs as explained above) than Ca. Methylomirabilis (12, 15, 17, 18, 26–28). Hence, it could be expected that changes in the OM quality of lake sediments affect differently the abundances of MOB and Ca. Methylomirabilis sp. bacteria. In this study, we investigated how spatial variability in OM lability in a single lake affects the community structure of CH4 producing and consuming sedimentary microbes. Using 16S rRNA gene sequencing, we studied the variation in the metha nogenic and methanotrophic community in the uppermost sediment layers at five sites with naturally varying OM quality of surface sediment within an oligo-mesotro phic boreal Lake Pääjärvi, Southern Finland. To determine OM lability, we used bulk sediment C:N ratios, which have been shown to broadly reflect relative contributions Research Article Microbiology Spectrum Month XXXX Volume 0 Issue 0 10.1128/spectrum.01955-23 2 Downloaded from https://journals.asm.org/journal/spectrum on 15 September 2023 by 130.234.242.76. of (autochthonous) phytoplankton and (allochthonous) terrestrial sources to bulk sedimentary OM (29, 30). Higher C:N ratios indicate a greater proportion of terrestrial OM, which is considered less labile (less available for microbial degradation) due to a combination of primary chemical composition, degradation during transport and protection by aggregation with mineral material [(31) and references therein], whereas lower C:N indicates more labile, phytoplankton-derived material [(32) and references therein]. We hypothesized that under increasing OM lability, (i) the relative contribution of methanotrophs within the CH4 cycling community decreases while that of methano gens increases and (ii) the relative contribution of Ca. Methylomirabilis sp. bacteria within the CH4 oxidizing community decreases while that of MOB increases. MATERIALS AND METHODS Study lake Lake Pääjärvi is a NO3−-rich, oligo-mesotrophic lake in Southern Finland (61.04N, 25.08E; A = 13.4 km2, max. depth = 87 m, mean residence time = 3.3 yr). The water column circulates twice per year and is always well oxygenated (33). Field measurements of dissolved oxygen during this study (determined with a handheld YSI ODO probe) confirmed the presence of oxygen throughout the water column at all sediment sampling locations (Fig. S1). The hypolimnetic NO3− concentration, measured 2–5 cm above the sediment surface, is typically 46–75 µmol L−1 (34, 35). The large catchment area of Lake Pääjärvi (244 km2) is dominated by forests and agriculture. The nutrient concentrations of the water have increased since the 1970s (36). Porewater and sediment sampling Vertical profiles of porewater and sediment samples were collected from five stations in Lake Pääjärvi on 9 August 2017 using a handheld HTH/Kajak corer with plexiglass tubes (Table 1). This study uses data from the top-most 10 cm layer (i.e., 0–10 cm from the sediment-water interface surface). The study stations followed a water depth gradient, Station 1 being the shallowest and Station 5 the deepest (Table 1). The core tubes were pre-drilled with two vertical series of 4 mm holes (each at 2 cm resolu tion), and then taped, in preparation for porewater sampling with Rhizons (Rhizosphere research products, Wageningen, Netherlands). Rhizon sampling automatically filters the porewaters at 0.15 µm into attached 10 mL syringes under vacuum. One vertical series of samples was taken for analysis of dissolved CH4 and dissolved inorganic carbon (DIC). The syringes were pre-filled with 1 mL of 0.1M HNO3 to immediately convert all DIC to CO2. A known volume of N2 gas headspace was injected into the syringes after sampling, and the samples were shaken to equilibrate the dissolved gases with the headspace. The subsamples of the headspace were then extracted into 3 mL Exetainers (Labco Limited, Lampeter, UK) and stored at room temperature (RT) until analysis [for full details see (37)]. The second vertical series was taken for S and Fe (by inductively coupled plasma atomic emission spectroscopy, ICP-OES) and short chain organic acid analysis. Short chain organic acid subsamples were stored frozen at −20°C until analysis. The subsamples for ICP-OES were acidified with 1 M HNO3 and stored at RT. After porewater sampling, the sediment cores were sliced into plastic bags at a resolution of 1 cm. The subsamples of 400‒500 µL wet sediment were collected from each slice and stored frozen at −20°C for DNA-based molecular microbiological analyses. The remaining wet sediment samples were stored frozen at −20°C under N2 until further processing. Porewater and sediment bulk geochemical analysis The S and Fe concentrations in the porewater samples were determined by ICP-OES (Thermo iCAP 6000, Thermo Fisher Scientific, Waltham, MA, USA). In this system, S is expected to be dominated by sulfate (SO42-) and Fe by Fe2+, although in each case, other forms are possible. The porewater CH4 and DIC concentrations were determined Research Article Microbiology Spectrum Month XXXX Volume 0 Issue 0 10.1128/spectrum.01955-23 3 Downloaded from https://journals.asm.org/journal/spectrum on 15 September 2023 by 130.234.242.76. by gas chromatography (GC) as described in Jilbert et al. (37). Briefly, the sample vials (Exetainers) were pressurized with helium (He) to 2.0 bar before loading into the GC (Agilent technologies 7890B GC system, Agilent Technologies, Santa Clara, Ca, USA). CH4 was determined by a flame-ionization detector (FID) and CO2 by a thermal conductivity detector (TCD). The instrument simultaneously measures N2 and O2 + Ar (in TCD), from which a 100% sum can be calculated for the estimation of CH4 and CO2 concentrations in the original sample, in ppm by volume. For a full description of the calculations, see Jilbert et al. (37). Diffusive fluxes of methane across the sediment-water-interface were estimated using Fick’s Law: F=φ∙D θ2 ∂C ∂z where F = flux in μmol m−2 d−1, D = diffusion coefficient of CH4 in freshwater, based on a value of 1.67 × 10−9 m2 s−1 at 25°C and adjusted downward according to bottom water temperature, using Eq. 4.57 in Boudreau (38); φ = volume fraction of total porosity and θ = tortuosity, as related by θ2 = 1‒ ln(φ2); and ∂C/∂z is the partial differential gradient estimated from the finite difference gradient ΔC/Δz, the concentration gradient of methane between the uppermost porewater sample and the overlying water in the sediment core tube. Sediment samples were freeze-dried, grounded in an agate mortar, and weighed into tin cups for carbon (C) and nitrogen (N) content determinations, which were determined using an elemental analyzer (LECO TruSpec Micro, LECO Corp., St. Joseph, MI, USA). In accordance with extensive previous studies on Finnish lake sediments (39, 40), acidification was not applied prior to the determinations. High levels of organic acidity from Finnish river catchments (41) maintain low annual mean pH values in most lakes and, therefore, there is a negligible occurrence of carbonates in lake sediments. Hence, our total C data is considered equivalent to organic C (Corg), and total N is considered equivalent to organic N (Norg). Porewater short chain organic acids were analyzed using high performance liquid chromatography (HPLC) equipped with Shodex SUGAR column (300 mm × 8 mm, Phenomenex, Torrance, CA, USA), autosampler (SIL-20AC HT, Shimadzu, Kioto, Japan), refractive index detector (RID-10A, Shimadzu), and 0.01 M H2SO4 as the mobile phase. The HPLC samples were prepared as described in Salmela et al. (42). The identification and quantification of the liquid metabolites were conducted using external standards. Molecular microbiological analyses DNA was extracted from the frozen sediment samples using DNeasy PowerSoil Kit (Qiagen, Hilden, Germany). DNA concentration was measured using a Qubit 2.0 Fluorometer and a dsDNA HS Assay Kit (Thermo Fisher Scientific, Waltham, MA, USA). PCR and 16S rRNA gene amplicon sequencing was performed commercially by the Foundation for the Promotion of Health and Biomedical Research of Valencia Region TABLE 1 C:N ratios of the surface sediment [0–1 cm layer and 0–2 cm layer (i.e., average of 0–1 cm and 0–2 cm layers)], sediment porewater acetate and dissolved inorganic carbon (DIC) concentrations, and Shannon diversity index of prokaryotic diversity in the sediment (average +/−SD within the 0–10 cm layer) as well as the estimated CH4 emissions from sediment to water (based on porewater CH4 profiles) at the study stationsb Station Depth (m) C:N (0–1) C:N (0–2) Acetate (µmol L−1) SD DIC (µmol L−1) SD CH4 flux (µmol m−2 d−1) Shannon SD 3 52 14.74 14.73 9.3 8.5 553.4 269.3 280.6 6.71 0.16 2 22 13.40 13.78 5.3 7.3 589.7 322.6 99.5 6.80 0.21 4 60 12.90 13.38 15.4 1.3 675.8 140.0 1,148.2 6.93 0.11 1 14 12.78 13.25 13.7 2.5 900.8 93.0 1,262.6a7.01 0.10 5 80 12.54 12.50 14.4 0.8 965.4 144.8 4,324.8a6.99 0.13 aCH4 flux estimates for stations 1 and 5 should be considered as maximum estimates because the sampling resolution was lower at the top of the core (due to geometry of the core in the tube) and, therefore, the gradient at the sediment-water-interface may be less steep in reality (see Fig. 1). bStudy stations are organized in the order of increasing organic matter lability based on decreasing surface sediment C:N ratios. See full vertical profiles (for the 0–10 cm layer) of C:N ratio, DIC, acetate, and Shannon diversity index in Fig. S2A through D, respectively. Research Article Microbiology Spectrum Month XXXX Volume 0 Issue 0 10.1128/spectrum.01955-23 4 Downloaded from https://journals.asm.org/journal/spectrum on 15 September 2023 by 130.234.242.76. (FISABIO, Valencia, Spain). In the PCR reactions, the V4 region of the bacterial and archaeal 16S rRNA genes was simultaneously targeted using primer pair 515FB (5′- GTGYCAGCMGCCGCGGTAA-3´)/806FB (5′-GGACTACNVGGGTWTCTAAT-3´) (43, 44). PCR, library preparation, and paired-end sequencing (Illumina MiSeq, Illumina, San Diego, CA, USA) were performed as previously described (45), except that, in PCR reactions, approximately 15 ng of DNA were used. The quality assessment of the raw sequence reads, merging of paired-end reads, alignment, chimera removal, preclustering, taxonomic classification (using Silva database 132), and removal of chloroplast, mitochondria, and eukaryote sequences, and division of 16S rRNA gene sequences into operational taxonomic units (OTUs) at 97% similar ity level was conducted as described in Rissanen et al. (11) (the detailed description is available also in Supplementary Methods). Singleton OTUs (OTUs with only one sequence) were removed, and the data were then normalized by subsampling to the same size, 77,340 sequences. One sample, representing the layer 8–9 cm depth at Station 3, was discarded from the analyses since it had only ~10,000 joined sequence reads. Good coverage was 0.95–0.97 in each library confirming that sequence variation was adequately covered. Prokaryotic diversity was assessed via calculation of the Shannon diversity index for each library. To test the hypotheses, this study focused specifically on the relative abundance (% of prokaryotic 16S rRNA genes) of known aerobic (46) and anaerobic methanotrophic bacteria (10), anaerobic methanotrophic archaea (47) as well as methanogenic archaea (5, 48). Statistical analyses The relationship between microbial variables and the OM quality of sediment was analyzed using the Spearman’s rank correlation test. Data showing significant correla tions (P < 0.05) were further studied using linear and non-linear regression analyses. The microbial variables included the Shannon diversity index and the relative abundances (percentage of 16S rRNA genes) of methanotrophs and methanogens (both total and different taxonomic groups), while C:N ratio of the surface sediment (0–1 and 0–2 cm layer) was used as a proxy for the OM quality of sedimenting OM (i.e., decreasing C:N ratio indicates increasing lability of sedimenting OM). Furthermore, sediment porewa ter DIC concentration was used as a further indicator of sediment OM lability in the correlation analyses (i.e., increasing DIC indicates increasing OM mineralization due to increased OM lability). We acknowledge that the relative abundances of microbes do not predict their absolute abundances. However, ratios of the relative abundances of different organisms to each other are robust against variations in their absolute abundances. Therefore, besides relative abundances, we also analyzed abundance ratios of microbes, such as the ratio of Ca. Methylomirabilis/Methylococcales, Metha notrophs/Methanogens, and Ca. Methylomirabilis/Methanogens. Correlation analyses were done using IBM SPSS Statistics 26 (IBM SPSS Statistics for Windows, Version 26.0, IBM Corp., Armonk, NY, USA), while the regression analyses were done using Minitab software (Minitab Statistical Software for Windows, Version 16.2.0.0, Minitab Inc., PA, USA). To determine the best-fitting models to the experimental data, along with their 95% confidence intervals (CIs), the parameter values of models were adjusted so as to minimize the squared deviation between the data values and the fitted values (S) and via checking the normality of residuals (P-value > 0.05). RESULTS AND DISCUSSION Sediment OM quality, vertical porewater profiles, and prokaryotic diversity Stations were ordered according to increasing OM lability as determined by decreasing C:N ratio in surface sediment as follows: Station 3, Station 2, Station 4, Station 1, and Station 5 (Table 1; Fig. S2A). This ordering did not follow the depth gradient, and therefore likely represents the heterogeneity of sedimentation of autochthonous and allochthonous OM within the lake. Surface sediment values were used here due to Research Article Microbiology Spectrum Month XXXX Volume 0 Issue 0 10.1128/spectrum.01955-23 5 Downloaded from https://journals.asm.org/journal/spectrum on 15 September 2023 by 130.234.242.76. assumed minimum overprinting of sediment diagenetic processes on the C:N ratio; hence, the values should represent the C:N ratio (lability) of the OM sedimenting to the lake bottom at the stations. Using C:N ratio of either the 0–1 cm layer or the average of 0–1 cm and 0–2 cm layers had no impact on the order of stations in terms of OM lability (Table 1; Fig. S2A). Hence, the possible mixing of surface sediment by bioturbation did not affect the major pattern in the OM lability between stations. The range of observed molar C:N ratios is quite narrow (approx 12.5–14.7 for the 0–1 cm interval) indicating an overall dominance of allochthonous terrestrial OM according to the end-member values of Goñi et al. (29), but small differences in the contribution of autochthonous OM appear to strongly influence the overall OM lability. Evidence for an OM lability gradient was shown in porewater DIC and acetate data. Average concentration of DIC within the 0–10 cm layer increased when there was a decrease in surface sediment C:N (Table 1, Fig. S2B) (ρ = −1.00, P < 0.0001, for both C:N ratio of 0–1 cm layer and average of C:N ratio of 0–1 and 0–2 cm layers). Although not significantly correlated with surface sediment C:N ratio (ρ = −0.6, P =0.285, for both C:N ratio of 0–1 cm layer and average of C:N ratio of 0–1 and 0–2 cm layers), acetate was also higher in stations with lower (i.e., Stations 4, 1, and 5) than with higher surface sediment C:N (Stations 3 and 2) (Table 1; Fig. S2C). Lactate was also detected but only within the sediments of Station 1, which was among the stations with the lowest surface sediment C:N ratio (Fig. S2C). These results suggest that OM remineralization rates (producing DIC, acetate, and lactate) were higher in stations with lower surface sediment C:N, which reflects their higher sediment OM lability. According to visual analysis of the porewater data, the differences in the vertical profiles of CH4, S (assumed to be mostly SO42−) and Fe (assumed to be mostly Fe2+ from reduction of solid-phase Fe oxides) between the stations followed broadly the differences in OM lability (Fig. 1). CH4 concentrations increased from the sediment surface downward with depth at each station (Fig. 1A through E). However, compared to other stations, they remained at very low levels at surface 0–4 cm layer at Stations 3 and 2 with the lowest sediment OM lability, while the highest surface sediment CH4 concentrations were observed at Station 5 with the highest OM lability (Fig. 1A through E). This pattern generally agrees with the comparison of sediment CH4 profiles between eutrophic (labile sediment OM) and oligotrophic (less labile OM) lakes by van Grinsven et al. (22). This suggests that active zones of methanogenesis extend closer to the sediment surface, and CH4 oxidation takes place in a thinner surface layer in stations with higher sediment OM lability than in those with lower OM lability (Fig. 1A through E). In addition, S (i.e., SO42−, see above) and Fe (Fe2+, see above) profiles suggested that the zone of reduction of SO42− and Fe oxides were located deeper from the sediment surface in the stations with less labile OM, as seen in S accumulation zone extending deeper and Fe accumulation zone starting deeper from the sediment surface in stations with less labile OM (Fig. 1F through O). As the water column was well oxygenated at each station (Fig. S1), the differences in the porewater profiles of CH4, S, and Fe between stations are not explained by differences in oxygen availability in the water overlying the sediment but are very likely driven by the differences in the sediment OM lability. Altogether, the porewater profiles suggested a higher rate of OM processing, higher consumption of O2 and alternative EAs, lower redox potential, and subsequently higher rate of methanogen esis in the stations with higher sediment OM lability (Fig. 1). This agrees with previous results on the comparison of lakes with different trophic status (22, 23). In accordance, the modeled diffusive methane emissions from sediment to water were generally higher in the stations with higher sediment OM lability (Table 1) (ρ = −0.90, P < 0.05, for both C:N ratio of 0–1 cm layer and average of C:N ratio of 0–1 and 0–2 cm layers), which agrees with the results by van Grinsven et al. (22). The magnitudes of estimated methane emissions from sediment to water, 99.5–4324.8 µmol m−2 d−1 (Table 1), agree well with the range of previously measured data for boreal lakes, i.e., 300–6562.5 µmol m−2 d−1 (49). Prokaryotic diversity, assessed via analysis of Shannon diversity index, increased when sediment OM lability increased (i.e., when C:N ratio decreased) (Table 1; Fig. S2D) (ρ = −0.90, P < 0.05, for both C:N ratio of 0–1 cm layer and average of C:N ratio of 0–1 and Research Article Microbiology Spectrum Month XXXX Volume 0 Issue 0 10.1128/spectrum.01955-23 6 Downloaded from https://journals.asm.org/journal/spectrum on 15 September 2023 by 130.234.242.76. 0–2 cm layers). This result agrees with those from soil ecosystems showing negative correlation between bacterial diversity and soil C:N ratio (50). This can be explained by high resource quality (i.e., labile OM with low C:N ratio) leading to a greater variety of resources for bacterial communities, which enhances their diversity by promoting greater niche differentiation (50, 51). Hence, variation in the prokaryotic diversity further highlights the differences in OM lability between stations. Methanotrophic community Aerobic MOB in the order Methylococcales as well as anaerobic methanotrophs in genera Ca. Methylomirabilis sp. (within bacterial phylum NC10) and Ca. Methanoperedens sp. (also known as ANME 2D archaea) were the most abundant groups of methanotrophs present in the studied sediments (Fig. 2A through E). This agrees with a previous study on boreal lake sediments (52). Aerobic MOB in the family Methylacidiphilaceae (i.e., Verrucomicrobial methanotrophs) were rare, while alphaproteobacterial MOB were not detected (Fig. 2A through E). The relative abundance of both Methylococcales and Ca. Methylomirabilis sp. generally peaked in the surface 0–3 cm layer at each station, while Ca. Methylomirabilis was occasionally observed also at deeper depths (Fig. 2A through E). In contrast to Methylococcales and Ca. Methylomirabilis sp., Ca. Methanoperedens FIG 1 Concentrations of CH4 (A-E), Fe (F-J), and S (K-O) in the porewater at different depths of sediments (incl. water overlying the sediment) at the five study stations shown in the order of increasing OM lability (from left to right; St3, St2, St4, St1, and St5) based on surface sediment C:N ratio (see Table 1). Depth 0 cm (dashed line) indicates the sediment-water interface. Research Article Microbiology Spectrum Month XXXX Volume 0 Issue 0 10.1128/spectrum.01955-23 7 Downloaded from https://journals.asm.org/journal/spectrum on 15 September 2023 by 130.234.242.76. sp. archaea had peaks in its relative abundance only at deeper layers, clearly below the sediment surface (i.e., below 5 cm depth) (Fig. 2A through E). In the deep layers, Ca. Methanoperedens sp. archaea can potentially use a wide variety of EAs in AOM, i.e., SO42−, Fe3+ minerals, and organic compounds (8, 47, 53–56), but as the scope of this study is focused on Ca. Methylomirabilis sp. and MOB, the putative role of Ca. Methanoperedens sp. archaea in the biogeochemical processes of the study lake is not considered further. The CH4 profiles in porewater show clearly depleted CH4 concentrations near the sediment surface (Fig. 1A through E), suggesting that abundant Methylococcales and Ca. Methylomirabilis sp. act as a crucial filter in the top 0–3 cm sediment layer, reducing CH4 fluxes from sediment to the overlying water column (Fig. 2A through E). The overlap in the vertical distribution of Methylococcales and Ca. Methylomirabilis sp. agrees with previous results from lake sediments and suggests competition between these groups (22, 24), as further indicated by a recent modeling study (25). Therefore, we tested the hypothesis that the relative contribution of Ca. Methylomirabilis sp. and MOB within the CH4 oxidizing community is sensitive to OM lability. In this analysis, we specifically focused on the top 0–3 cm sediment layer, as based on depleted CH4 concentrations and high methanotroph relative abundances at that layer, it is considered to be the key CH4 filter layer in reducing CH4 emissions from sediment to water (Fig. 1A through E and 2A through E). In partial support for the hypothesis, we found that within that layer, the relative abundance of Ca. Methylomirabilis decreased, when OM lability increased, i.e., Ca. Methylomirabilis correlated positively with sediment C:N ratio (Table 2; Fig. 3A), while the relative abundance of Methylococcales was not correlated with the C:N ratio (Table 2). Consequently, the ratio of Ca. Methylomirabilis to Methylococcales decreased alongside when OM lability increased (C:N decreased) (Table 2; Fig. 3B). As it is challenging to quantify the relative abundance of taxonomic groups in a layer (i.e., 0–3 cm) consisting of multiple sublayers (i.e., 0–1 cm, 0–2 cm, and 0–3 cm), we increased the robustness in our results by carrying out these analyses both with the average relative abundance and the maximum relative abundance of Methylococcales and Ca. Methylomirabilis within the 0–3 cm sediment layer, with both choices giving similar results for the change in Ca. Methylomirabilis and ratio of Ca. Methylomirabilis to Methylococcales alongside the OM FIG 2 Relative abundance (percentage of prokaryotic 16S rRNA gene reads) of methanotrophs (A-E) and methanogens (F-J) at different depths in the sediment at the five study stations shown in the order of increasing OM lability (from left to right; St3, St2, St4, St1, and St5) based on the surface sediment C:N ratio (see Table 1). 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