Survival strategies of aerobic methanotrophs under hypoxia in methanogenic lake sediments

Survival strategies of aerobic methanotrophs under hypoxia in methanogenic lake sediments | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Survival strategies of aerobic methanotrophs under hypoxia in methanogenic lake sediments Gafni Almog, Maxim Rubin-Blum, J. Colin Murrell, Hanni Vigderovich, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3790875/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 3 You are reading this latest preprint version Abstract Background Microbial methane oxidation, methanotrophy, plays a crucial role in mitigating the release of the potent greenhouse gas methane from aquatic systems. While aerobic methanotrophy is a well-established process in oxygen-rich environments, emerging evidence suggests their activity in hypoxic conditions. However, the adaptability of these methanotrophs to such environments has remained poorly understood. Here, we explored the genetic adaptability of aerobic methanotrophs to hypoxia in the methanogenic sediments of Lake Kinneret (LK). These LK methanogenic sediments, situated below the oxidic and sulfidic zones, were previously characterized by methane oxidation coupled with iron reduction via the involvement of aerobic methanotrophs. Results In order to explore the adaptation of the methanotrophs to hypoxia, we conducted two experiments using LK sediments as inoculum: i) an aerobic "classical" methanotrophic enrichment with ambient air employing DNA stable isotope probing (DNA-SIP) and ii) hypoxic methanotrophic enrichment with repeated spiking of 1% oxygen. Analysis of 16S rRNA gene amplicons revealed the enrichment of Methylococcales methanotrophs, being up to a third of the enriched community. Methylobacter , Methylogaea , and Methylomonas were prominent in the aerobic experiment, while hypoxic conditions enriched primarily Methylomonas . Using metagenomics sequencing of DNA extracted from these experiments, we curated five Methylococcales metagenome-assembled genomes (MAGs) and evaluated the genetic basis for their survival in hypoxic environments. A comparative analysis with an additional 62 Methylococcales genomes from various environments highlighted several core genetic adaptations to hypoxia found in most examined Methylococcales genomes, including high-affinity cytochrome oxidases, oxygen-binding proteins, fermentation-based methane oxidation, motility, and glycogen use. We also found that some Methylococcales, including LK Methylococcales, may denitrify, while metals and humic substances may also serve as electron acceptors alternative to oxygen. Outer membrane multi-heme cytochromes and riboflavin were identified as potential mediators for the utilization of metals and humic material. These diverse mechanisms suggest the ability of methanotrophs to thrive in ecological niches previously thought inhospitable for their growth. Conclusions Our study sheds light on the ability of enriched Methylococcales methanotrophs from methanogenic LK sediments to survive under hypoxia. Genomic analysis revealed a spectrum of genetic capabilities, potentially enabling these methanotrophs to function. The identified mechanisms, such as those enabling the use of alternative electron acceptors, expand our understanding of methanotroph resilience in diverse ecological settings. These findings contribute to the broader knowledge of microbial methane oxidation and have implications for understanding and potential contribution methanotrophs may have in mitigating methane emissions in various environmental conditions. lake sediment aerobic methanotrophy methanogenic zone hypoxia Methylomonas Methylobacter Figures Figure 1 Figure 2 Figure 3 Figure 4 Background Methane, a potent greenhouse gas, has more than threefold increased its atmospheric concentrations since the pre-industrial era. Around half of global methane emissions arise from natural inland waters production, including lake sediments, wetlands, rivers, and reservoirs 1 . Methane is consumed naturally by aerobic and anaerobic methanotrophs. Aerobic methanotrophy is observed in oxic environments and performed by aerobic bacterial methanotrophs, employing oxygen for methane activation and catalyze the oxidation of methane to methanol through the action of methane monooxygenase (MMO) 2 , 3 . Anaerobic oxidation of methane (AOM) can be linked to sulfate reduction through anaerobic archaeal methanotrophs (ANMEs) and sulfate-reducing bacteria 4 . This process effectively prevents the release of up to 90% of the produced methane in marine sediments, and can be efficient also in rich sulfate freshwater systems 5 . In sulfate-depleted environments such as most freshwater sediments, AOM can consume over 50% of the produced methane 6 and be coupled to other electron acceptors such as nitrate, nitrite, metal oxides, and humic substances 7 – 11 . Microorganisms participating in AOM have been identified not only as ANMEs but surprisingly also as those considered aerobic bacterial methanotrophs. These bacteria were observed to be active and involved in methane oxidation beneath the chemocline in the anoxic hypolimnion 12 , 13 and in certain freshwater lake sediments 14 – 17 . Certain methanotrophs employ unique mechanisms to obtain oxygen, such as the disproportionation of various molecules. For instance, Methylomirabilis (NC10) produces oxygen through the disproportionation of nitrite, utilizing it subsequently for the oxidation of methane 18 . Additionally, specific alphaproteobacterial methanotrophs utilize methanobactins to generate both oxygen and hydrogen through the disproportionation of water. 19 . Some bacterial methanotrophs exhibit versatility by utilizing alternative electron acceptors. Methylococcales bacteria Methylomonas denitrificans and Methylocaldum sp. have been experimentally shown to couple denitrification with methane oxidation 20 – 22 , while others, such as Methylomonas , Methylosinus and Methylococcus capsulatus demonstrate methane oxidation coupled to iron reduction 23 – 25 . The adaptation mechanisms, that enable such a switch however, remain poorly understood. Here we explore the remarkable genetic adaptability of Methylococcales to hypoxia in methanogenic sediments of Lake Kinneret (LK, Sea of Galilee), where our previous studies confirmed methane oxidation coupled to iron reduction (Fe-AOM) beneath the sulfate reduction zone in the iron rich methanogenic zone 26 . The mediation of this Fe-AOM process was proposed to involve archaea methanogens and bacterial methanotrophs 27 – 29 . In-depth analyses, including isotopes of specific fatty acid lipids, quantification of the functional gene pmoA , and metagenomic analysis, confirmed the involvement of Methylococcales-like methanotrophs in methane oxidation 15 , 27 , 28 . Aerobic methanotrophy was also shown to boost iron reduction in these sediments 25 , but the potential microbial mechanisms that may allow the methanotrophs to survive under hypoxia and stimulate iron reduction have remained unclear. We hypothesized that LK methanogenic sediments harbor Methylococcales with unique strategies for survival in oxygen-limited conditions, and this research aimed to identify the key mechnisems enabling it. In order to explore this adaptation to hypoxia we conducted two experiments using LK methanogenic sediments as inoculum: i) an aerobic "classic" methanotrophic enrichment with ambient air employing DNA stable isotope probing (DNA-SIP) and ii) hypoxic methanotrophic enrichment with repeated spiking of 1% oxygen. Using metagenomics sequencing of DNA extracted from these experiments, we evaluated the genetic basis for their survival by comparing Methylococcales metagenome-assembled genomes (MAGs) from LK to Methylococcales genomes from diverse lineages and environments. including the. We investigated the genomic potential associated with various survival mechanisms including oxygen acquisition strategies, metabolic versatility and use of alternative electron acceptors. Methods Sampling site LK is a monomictic lake in northern Israel, stratified usually between March and December, leading to anoxic hypolimnion 30 . The seasonal changes lead to variations in the geochemical porewater profiles in the sediments, primarily of methane, iron, and sulfur 26 . In the mixed period, the oxic-anoxic boundary reaches the sediment-water interface, affecting chemical profiles within the sediment. Sulfate in the sediment is depleted at around a depth of 10 to 20 cm depending on the stratification period and is followed by iron reduction. Methane concentration profiles generally increase with depth up to saturation levels, however, methane concentration profiles also hint at the presence of a "deep sink" correlated with an increase in concentrations of reduced iron 15 , 26 . We collected sediment as inoculum for both experiments from the deepest point of LK (station A) using a gravity corer, as previously described by Bar-Or et al. 2017 27 . The specific sediment used for the experiments originated from a depth of 25–40 cm below the sediment surface. Study design overview To achieve this study's aims, we designed and conducted two experiments intended mainly to enrich methanotrophs. Experiment 1 was -a "classical" enrichment with ambient air, combined with DNA-Stable Isotope Probing (DNA-SIP). This experiment focused on identifying the active methanotrophs thriving under oxic conditions (Fig. 1A). In the second experiment, we stimulated methanotroph growth under hypoxic conditions (Fig. 1B). Following enrichments, we used amplicon sequencing of the 16S rRNA gene to study microbial diversity. Using metagenomic sequencing of representative samples, we curated Methylococcales MAGs. We investigated functions involved in adaptation to anoxia in these MAGs, alongside other Metylococcales genomes, using various bioinformatic platforms (Fig. 1C). Figure 1: Experimental layout. The workflow of this research. Sediment cores were collected from the methanotrophic zone in Lake Kineret and used to initiate two enrichment experiments. (A) Experiment 1: Incubated with ambient air, representing a "classical" enrichment approach coupled with DNA-SIP analysis. (B) Experiment 2: Hypoxic enrichment by repeatedly introducing small amounts (spikes) of oxygen (1%) to maintain low-oxygen conditions. Extracted DNA was used for 16S rRNA amplicon sequencing, and representing samples were also used to assemble and bin Methylococcales MAGs. Genome-based metabolic comparative analyses were performed using different bioinformatic platforms (C). Experiment 1 - Ambient air enrichment and DNA stable isotope fingerprinting Sediment samples collected from LK in September 2019 were used to enrich methanotrophs and explore the active methane-utilizing ones. Approximately 5 g of homogenized sediment was added to 300 ml sealed serum vials with black rubber septa. The vials were pre-filled with modified DNMS medium 31 (1:10 w/v). Prior to inoculation, the headspace of each vial was purged with nitrogen gas for a brief period (e.g., 1 hr) to flush out any methane gas remaining in the sediment. Following this, the headspace was replaced with ambient air. Subsequently, either 12 C-methane (control) or 13 C-methane (used for DNA-SIP) was introduced at a concentration of 20% (v/v) as the sole carbon source. Incubations proceeded at 25°C in the dark on a 120 rpm rotary shaker for a total of 21 days. Triplicate incubations were performed for each treatment, with detailed information on sampling times provided in the Supplementary Information (SI, Table S1 ). A separate 5 g sediment sample (time-zero control) was preserved in a 15 ml Falcon tube at -20°C for subsequent DNA extraction. After incubation, sediment samples were carefully collected and processed for DNA extraction. Briefly, samples were centrifuged, supernatants discarded, and the resulting pellets were stored at -20°C. DNA was then extracted from 0.5 g aliquots of each sample (time-zero, 12 C-methane, and 13 C-methane) using a PowerSoil kit (Qiagen) following the manufacturer's instructions. To achieve sufficient DNA concentration for DNA-SIP analysis, seven aliquots from each sample were extracted and pooled. DNA quality and quantity were assessed using a NanoDrop Spectrophotometer (Thermo Fisher Scientific). For DNA-SIP, samples were further processed through CsCl density gradient using a Beckman Coulter ultracentrifuge and a VTi 65.2 rotor, operating at 44,100 rpm at 20°C for 65 hours, as previously outlined 32 . Twelve fractions were collected from each sample, and their DNA content and their density was determined using an AR200 digital hand-held refractometer (Reichert, Buffalo, NY, USA). These DNA fractions were recovered, purified by PEG-6000 precipitation, resuspended in 30 µl TE buffer, and the DNA quantity was validated using Fluorometer (Qubit, Invitrogen). Experiment 2 - Hypoxic enrichments This experiment attempted to enrich methanotrophs in LK sediments with low oxygen conditions following the methods of Vigderovich et al, 2023 25 . Sediment samples collected in March 2021 set as the starting material for the experiment. Each vail contained 7g of methanogenic sediments from LK, 50 mg of hematite (10mM final concentration) and anoxic filtered porewater (1:3 w/v), previously extracted from the same depth in a 60 ml serum vails sealed with black rubber septa. Hematite, an iron oxide mineral, can potentially serve as an alternative electron acceptor for methanotrophs under anoxic conditions 25 . The anoxic porewater further ensured the initial absence of oxygen within the vials. To establish these anoxic conditions, each vial underwent continuous nitrogen purging for about an hour. Following this, the vials were stored in the dark at 4°C for five days. After the anoxic period, the enrichment process was initiated. Vials were vigorously mixed (vortexed) and flushed again with nitrogen for 15 minutes to ensure the removal of any residual oxygen. Subsequently, 1.5 ml of air was introduced into each vial, resulting in a final headspace oxygen concentration of 1% (v/v). Two treatment groups were established CH 4 + O 2 treatment and N 2 + O 2 as Control. For the CH 4 + O 2 treatment, eleven vials received an additional 1.5 ml of 99.95% methane gas, resulting in a final headspace oxygen concentration of 5% (v/v) allowing methanotrophs to potentially uptake methane as an energy source. For the N 2 + O 2 Control, triplicate vials were supplemented with an additional 1.5 ml of 99.999% nitrogen gas instead of methane, resulting in a final headspace oxygen concentration of 5% (v/v), serving as a control for the effect of methane availability. The enrichment incubations proceeded at 25°C in the dark with the vials inverted to minimize headspace gas exchange. Oxygen consumption within the vials was monitored throughout the experiment using PSt6 sensors in designated vials. Vials were sacrificed at various time intervals, detailed in the Supplementary Information (Table S2 ) and the experiment lasted for 40 days. These samples were promptly stored at -80°C for subsequent DNA/RNA extraction. DNA was successfully extracted from all samples using a PowerSoil kit (Qiagen) following the manufacturer's instructions. However, RNA concentrations using Quant-ittTM RiboGreen RNA kit (ThermoFisher) were found to be below the detection limit. DNA library preparation and sequencing A total of thirty-seven samples underwent 16S rRNA gene sequencing, while three samples were also allocated for metagenome analysis. Comprehensive details regarding the samples utilized in the DNA-SIP and hypoxic experiments can be found in Tables S1 and S2, respectively. Sequencing of the V4 region of the 16S rRNA gene was executed using the primer pair 515f–806r 33 , 34 , and the procedure was conducted via Illumina sequencing at Hylabs, Rehovot, Israel. Metagenomic libraries were generated from three distinct DNA samples: one originating from the DNA-SIP experiment and two from the hypoxic experiment (refer to Tables S1 and S2 for detailed descriptions, respectively). Subsequently, the libraries underwent sequencing at Novogene, Singapore, with each sample producing 65–80 million 2x150 bp paired-end reads using Illumina NovaSeq. The library construction process employed the NEBNext® Ultra™ II DNA Library Prep Kit. 33 , 34 Bioinformatics For 16S rRNA gene amplicons, demultiplexed paired-end reads were analyzed within QIIME2 V2020.6 pipeline 35 . By applying the DADA2 pipeline 36 , implemented in QIIME2, reads were truncated according to their quality plots, chimeras were removed, and reads were merged and grouped into amplicon sequence variants (ASVs). Taxonomy was assigned to ASVs by Silva 138 99% classifier 37 . Beta diversity was visualized by Principal Coordinate Analysis (PCoA), in which the dissimilarity between samples was estimated with a Bray-Curtis distance matrix using the distance and ordination functions in the R package phyloseq 38 . Metagenomes were assembled using SPAdes V3.15 with –meta k = 21,33,66,99,127 parameters 39 , following adapter trimming and error correction with tadpole.sh, using the BBtools suite following read preparation with the BBtools suite (Bushnell, B, sourceforge.net/projects/bbmap/). Downstream mapping and binning of metagenome-assembled genomes (MAGs) were performed using DAStool, Vamb, Maxbin 2.0, and Metabat2 40–43 within the Atlas V2.9.1 pipeline 44 , using the genome dereplication nucleotide identity threshold of 0.975. MAG quality was verified using Checkm2 45 and QUAST 46 . Functional annotation was carried out using the SEED implemented in Viral Bioinformatics Resource Center (BV-BRC) server 47 , and key annotations were verified by BLASTing against the NCBI database. Additional Methylococcales genomes, a total of 62 genomes (a comprehensive list can be found in Table S3), were generated based on BLASTp against LK predicted membrane-bound particulate methane monooxygenase protein, using the BV-BRC platform 48 – 50 (max hit: 20, Evalue threshold: 0.0001). The average nucleotide identity (ANI) was calculated using pyani 51 (See Table S4 for their values). Multiheme cytochromes (MHCs) were assigned using the FEET pipeline ( https://github.com/McMahonLab/FEET.git ) 52 . In short, FEET first uses python to find MHCs using the following regular expressions for heme binding motifs: [CXCH], [CXXCH], [CXXXCH], and [CXXXXXXXXXXX[!=C]XXCH]. At least three [CXXCH] motifs and at least five total aforementioned motifs were required to call an MHC. Localization was predicted by Cello V2.5 ( http://cello.life.nctu.edu.tw/ ). Each protein sequence was then manually verified using the InterPro database ( https://www.ebi.ac.uk/InterPro/ ) 53 . Phylogenetic trees were generated with the BV-BRC platform 53 . Marker proteins that are universally conserved across the bacterial domain were extracted from genomes 54 . A hundred single-copy markers that were present in all genomes (See Table S5 for gene list) analyzed in this study were used for alignment with MUSCLE 55 . The randomized accelerated maximum likelihood (RAxML) tree was calculated 56 . Final representation of the tree was curated using Itol ( https://itol.embl.de/ ). Results and discussion Ambient oxygen selects for the enrichment of aerobic methanotrophs DNA-SIP experiments utilizing labeled 13 C-methane resulted in the isolation of 13 C-enriched DNA observed in fractions with a density ranging from 1.72 to 1.70 g ml − 1 , as illustrated in the labeled fraction (Fig. 2a). Analysis of the relative abundance of 16S rRNA genes revealed bacterial communities that differed between the labeled and unlabeled fractions, as evidenced by Principal Coordinates Analysis (PCoA) based on Bray–Curtis dissimilarity (Fig. 2b). Axis 1 and 2 explained 43.9% and 17.4% of the variation, respectively, indicating distinct communities in the labeled and unlabeled fractions, while 12 C-methane-fed communities clustered together. The use of the 12 C-methane-fed sample as a control accounted for differences in the guanine/cytosine (GC) content of DNA samples. Despite Methylococcales comprising less than 1% of the initial microbial population at time zero, enrichment during the experiment resulted in Methylococcales accounting for approximately one-third of the microbial community (refer to Figure S1 ). In the fractions fed with labeled 13 C-methane, Methylobacter emerged as the predominant taxon with a relative read abundance ranging from 18–31%, alongside the enrichment of other methanotrophs (Fig. 2c). This finding aligns with previous studies that identified Methylobacter as a dominant and active species in diverse natural environments 57 , including oxic 58 and anoxic freshwater lake sediments 59 , anoxic water columns 60 , and wetlands 61 . Other enriched Methylococcales species in this experiment included Methylomonas (5–8%) and Methylogaea (up to 3%) (Fig. 2c). In contrast, within the 12 C-methane-enriched cultures, Methylobacter exhibited a relative abundance of 6–10%, while the relative abundance of Methylomonas and Methylogaea varied significantly among biological triplicates, ranging from 4% to up to 20% (refer to Figure S1 ). Figure 2: Aerobic enrichment experiment employing DNA-SIP. (A) The average DNA concentration (ng/µl) for each fraction (n = 3 biological replicates) in the 13 C-methane-fed cultures (blue) and the 12 C-methane-fed cultures (colorless). Error bars indicate the standard error (standard deviation/n) of the DNA concentration, while the error bars for density are smaller than the symbol. (B) Principal Component Analysis (PCoA) based on Bray-Curtis dissimilarity illustrating microbial diversity in labeled DNA fractions (circles), unlabeled DNA fractions (triangles), and time zero samples that were not fractionated (squares) for both 13 C-methane-fed cultures (in blue) and 12 C-methane-fed cultures (colorless). (C) The average relative abundance of dominant Methylococcales in the labeled DNA fractions of the 13 C-methane-fed cultures. Hypoxic conditions select for Methylomonas We observed a notable adaptation of microbial communities to periodic spiking with 1% oxygen and methane, leading to a significant increase in oxygen consumption (0.69 ± 0.04 mg L-1 hr-1), in contrast to controls where the oxygen consumption was markedly lower (0.02 ± 0.01 mg L-1 hr-1) (Fig. 3a). Microbial communities in cultures deprived of methane exhibited clustering with those from non-enriched samples (time zero), indicating that the microbial community in these samples remained relatively consistent despite exposure to oxygen. These findings suggest that methane serves as the electron donor for oxygen respiration, especially considering the limited availability of alternative electron donors in these methanogenic sediments (Fig. 3a). Further analysis of the microbial community revealed the specific selection of microorganisms through periodic spiking with 1% oxygen (Fig. 3b), predominantly enriching for Methylomonas (Fig. 3c). These outcomes align with previous studies, reinforcing the notion that Methylomonas may exhibit better adaptation to low oxygen conditions, such as, better uptake of oxygen and methane, compared to Methylobacter 62 . Figure 3: Hypoxic enrichment experiment. (A) In vitro monitoring of oxygen levels (%) in bottles exposed to O 2 + CH 4 (blue) and O 2 + N 2 (green). (B) Principal Component Analysis illustrating microbial diversity exposed to O 2 + CH 4 (circles), O 2 + N 2 (triangles), and time zero samples without enrichment (squares). These are color-coded based on treatment, with blue, green, and gray representing O 2 + CH 4 , O 2 + N 2 , and time zero, respectively. (C) The average relative abundance of dominant Methylococcales spp. enriched in the O 2 + CH 4 treatment. Methylococcales lineages dominate enrichment cultures In this investigation, methanotrophs were selectively enriched from methanogenic LK sediments through two distinct enrichment experiments. The first experiment involved the use of ambient air coupled with DNA-stable isotope probing (DNA-SIP), while the second experiment included repeated injections of 1% oxygen to simulate hypoxic conditions. From both experiments, we identified five Methylococcales metagenome-assembled genomes (MAGs), detailed in Table 1 for genome statistics. The metagenomic analysis uncovered the enrichment of Methylomonas, Methylogaea , and Methylobacter (MAGs 1–5 respectively). The closest relatives to these lineages were Methylomonas sp. ZR1 63 , Methylogaea oryzae strain E10 64 and Methylobacter tundripaludum strain OWC-G53F 63 (see Fig. 4 and Table 2 for ANI values). For the hypoxic enrichments, two MAGs, namely, Methylomonas LK_4 and LK_5, which are related to Methylomonas sp. strain FW.007 65 . A detailed genomic comparison between all LK Methylococcales MAGs highlighting functions of interest is given below and summarized in Table 2. ADD Table 1 here Metabolic reconstruction of novel Lake Kinneret methanotrophs Genome-base metabolic reconstruction of the five MAGs affirmed the characteristic metabolic framework of Methylococcales methanotrophs (Table 2). The prediction of methane oxidation through the particulate methane monooxygenase was consistent across all LK Methylococcales lineages. With the exception of Methylogaea LK_2, which exclusively harbored the pxmABC genes encoding an alternative form of particulate methane monooxygenase potentially functioning under oxygen-limiting conditions 20 , the remaining LK Methylococcales lineages were predicted to possess one copy of the pmoCAB operon, encoding the canonical particulate methane monooxygenase. The genomes of Methylomonas LK_4 and LK_5 contained both pxmABC and pmoCAB operons, potentially broadening their range of affinities for methane and oxygen 66 . In addition, none of the genomes of the LK Methylococcales lineages encoded the soluble methane oxygenase. The lanthanide-dependent xoxF 67 was predicted to catalyze methanol oxidation in all LK Methylococcales lineages except Methylogaea , whereas the calcium-dependent mxaFI 68 was predicted only in the genome of LK Methylomonas and likely within the Methylogaea LK_2 genome (only mxaI was found). The potential secretion of methanol by methanotrophs into the environment, with subsequent consumption by syntrophic partners, indicative of cross-feeding between methanotrophs and methylotrophs 69 , was considered highly likely. This inference is supported by the elevated relative abundance of Methylotenera in our enrichment experiments (refer to Figures S1 and S2 for microbial community relative abundance). The presence of the ribulose monophosphate pathway (RuMP) was identified in all LK Methylococcales lineages, evident through the presence of key genes such as those that encode the 3-hexulose-6-phosphate synthase ( hps ) and 6- phospho -3-hexuloisomerase ( phi ) 70 . The RuMP pathway appears to be the exclusive pathway for methane carbon assimilation, as the serine cycle was absent, lacking genes encoding key enzymes like malate thiokinase (both mtkAB ) and hydroxypyruvate reductase ( ghrB ). Except for Methylogaea , all LK Methylococcales lineages encoded the phosphogluconate dehydratase ( edd) and 2-dehydro-3-deoxy-phosphogluconate aldolase (eda) genes necessary for the Entner–Doudoroff (ED) variant of the RuMP cycle 71 . The energy-efficient Embden–Meyerhof–Parnas (EMP) variant 71 was predicted in all Methylococcales lineages, evidenced by the presence of non-ATP dependent pyrophosphate-dependent phosphofructokinase ( pfk ), triosephosphate isomerase ( tpi ), glyceraldehyde 3-phosphate dehydrogenase ( gapdh ), phosphoglycerate kinase ( pgk ), and enolase ( eno ) genes. ADD Table 2 here Adaptations to hypoxic conditions in Methylococcales To assess whether the genomic adaptations to hypoxic conditions are unique to LK Methylococcales, we compared their genomes and with an additional 62 Methylococcales genomes from diverse environments (Fig. 4). These genomes represent Methylococcales from groundwater, contaminated rivers, sewage systems, lake sediments, rice fields, volcano mud, and more (Supplementary Table S3). This comparison revealed a core set of mechanisms shared by most Methylococcales, including those from LK potentially enabling them to function effectively in hypoxic conditions. These include (i) Enhanced oxygen usage by cytochrome bd ubiquinol oxidase (found in 88% of analyzed genomes), allowing efficient oxygen respiration potentially sustaining growth at ≤ 3 nM molecular oxygen 72 . (ii) Enhancing respiration under hypoxia by oxygen-binding hemerythrin, by increasing the activity of pMMO 73 (present all of the analyzed genomes). (iii) Flagella-mediated motility across gradients to optimize oxygen and methane availability (present in 95% of the analyzed genomes) 74 . (iv) Alternative electron acceptors including nitric oxide reduction, as well as riboflavins (found in 70% and 89% of the genomes, respectively). These last electron acceptors, are soluble secreted electron shuttles mediating extracellular electron transfer (EET) 75 – 77 , which can be reversibly oxidized and reduced, carrying electrons between cells and insoluble electron acceptors such as manganese and iron oxides over large distances 71 , 75 , 78 . Manganese and iron oxides are highly abundant in methanogenic sediments 79 . Methylococcales can store carbohydrates and use alternative metabolic pathways to provide energy under oxygen limitation. Genes for glycogen synthesis and degradation were found in all of the genomes, likely allowing Methylococcales to conserve resources during periods of limited nutrients 80 , 81 . Additional metabolic alternative is the ability to produce fermentation products like succinate and acetate was found in 66% and 51% of genomes, respectively. The occurrence of alcohol dehydrogenases (present in 91% of genomes) and bidirectional hydrogenases (present in 99% of genomes) indicates potential for alcohol and hydrogen production 60 , 82 . While many metabolic mechanisms were shared among Methylococcales, we identified several less abundant mechanisms that were present in LK Methylococcales. These include (i) production of lactate (found in 21% of the genomes), predicted in Methylobacter LK_1 and Methylogaea LK_2 MAGs; (ii) Methane-dependent denitrification serves as a link between the carbon and nitrogen cycles 83 , 84 . While nitrous oxide reduction was found as a common trait, respiratory nitrate reductase and nitric oxide-forming nitrite reductase (with respective genes identified in 36% and 27% of the genomes) were observed as less prevalent mechanisms. However, despite being relatively uncommon among the Methylococcales, these mechanisms were found more abundant in LK Methylococcales and exhibited in Methylobacter LK_1, Methylomonas LK_3, and LK_5 MAGs; (iii) Outer membrane cytochromes (OMCs, found in 62% of the genomes) needed to reduce iron 24 , 59 , 85 , were found in Methylomonas LK_3, LK_4 and LK_5. Methylococcales appear to lack electrically conductive pili (e-pili) that can support EET 85 , as > 120 amino acid long PilA proteins in Methylococcales were longer than the canonical 60–90 amino acid-long e-pili 86 . Additional manual investigation of OMCs using the InterPro database confirmed that at least some of these sequences belong to the multiheme cytochrome superfamily with at least one copy of predicted as extracellular OMC (found in 55% of the genomes), and fewer OMCs were predicted as membrane-bound (found in 10% of the genomes). Others were either unrelated to OMCs or predicted to be hydroxylamine oxidoreductase or cytochromes c-552 involved in ammonia oxidation and nitrite reduction. Among LK Methylococcales, only Methylomonas LK_3 encoded a membrane-associated OMC, hinting at the possibility of EET in this organism, but this strategy to cope with oxygen limitation is not widespread in LK. Figure 4: Phylogenomic analysis and metabolic profiling. A phylogenomic tree along with the metabolic presence-absence profile of 67 Methylococcales genomes. A comprehensive list of the proteins is available in Table S7 and predicted OMC proteins are available in Table S8. Additional information regarding presence-absence of kye genes particulate methane monooxygenase pmoCAB operon and pxmABC operon, soluble methane monooxygenase (mmoXYBZDC), lanthanide-dependent methanol dehydrogenases (xoxF), and methanol dehydrogenase (mxaF) is available in Table S9. Notably, the five LK Methylococcales are highlighted in bold. The phylogenetic tree is built on 100 genes (refer to Table S5), and the taxa clustering percentage is based on 100 bootstrap resamples, consistently yielding values of 98 or higher (specific values not shown). Conclusions Our study highlights the potential prevalence and diversity of adaptive strategies utilized by methanotrophic bacteria in low-oxygen environments, specifically within LK sediments. These LK sediments, located 20 cm below the sediment water interface and 20 meters below the hypolimnion, were previously shown to be involve Methylococcales in methane oxidation and stimulation of iron reduction. We hypothesized that this environment harbors Methylococcales with unique strategies for survival. Through enrichment experiments, comparative metagenomics, and genomic analyses of diverse Methylococcales lineages, we propose several potential mechanisms enabling these organisms to thrive under oxygen-limited conditions. Our findings demonstrate that most Methylococcales, including those from LK, possess a set of traits enabling their survival in hypoxic environments: spanning effective usage of trace oxygen, motility for reaching optimal oxygen concentrations, glycogen storage, alternative energy generation using fermentation, and the use of alternative electron acceptors and possible EET mediated by riboflavins. Some less widespread functions employed by LK Methylococcales include lactate productionn, methane-dependent denitrification, and EET via outer membrane cytochromes (OMCs). Further studies are necessary to validate these potential strategies. Experimental examination of these potential adaptations may unveil additional layers of complexity in the survival strategies. Additionally, investigating whether these adaptations are expressed under hypoxic conditions is essential for understanding their role in hypoxia tolerance. This knowledge may contribute to a more comprehensive understanding of global methane cycling. Declarations Author information: Contributions : A.G., H.V., and O.S. conceived the project and designed the study, with contributions from W.E. and J.C.M. J.C.M. hosted A.G. in his research group, providing essential tools and scientific background for conducting DNA stable isotope probing, in collaboration with N.M.L. A.G. collected the data, and M.R.B. performed the bioinformatic analysis. A.G. wrote the first draft of the manuscript. All authors substantively revised the manuscript, and each author approves of the submitted version. Ethics declarations : Ethics approval and consent to participate: Not applicable. Consent for publication: Not applicable. Competing interests: The authors declare that they have no competing interests. Supplementary Information: Additional file 1. Supplementary Methods: Gases spiking list of events. Supplementary Figures S1 -S2. Figure S1 a: Relative abundance showing 16S rRNA of top 15 genera in the three time steps of the DNA-SIP experiment. Figure S1 b: Principal component analysis of the same samples. Figure S2 : Relative abundance showing 16S rRNA of top 10 genera from the hypoxic experiment. Additional file 2. Supplementary Tables S1–S8. Supplementary Table S1 : DNA-SIP sample list including sampling time, samples used for 16S rRNA gene sequencing and metagenome analysis. Supplementary Table S2 : Anoxic experiment sample list including sampling time, samples used for 16S rRNA gene sequencing and metagenome analysis. Supplementary Table S3: list of Methylococcales genomes used, including detailed information. Supplementary Table S4: list of average nucleotide identity (ANI) values between Methylococcales genomes. Supplementary Table S5: list of proteins used for phylogenetic tree construction. Supplementary Table S6: list of predicted proteins and their copy number as predicted in LK Methylococcales. Supplementary Table S7: list of predicted proteins used for the presence-absence list in Fig. 4. This includes protein type, location, and the name for each Methylococcales genome. Supplementary Table S8: list of predicted OMC proteins used for the presence-absence list in Fig. 4. This includes protein type, location, protein sequence, and the name of each Methylococcales genome. Funding: This research was funded by ERC-2018-COG (818450) and ISF (857–2016) grants awarded to OS. In addition, AG was supported by a post-doctoral scholarship of the Kreitman School, Ben Gurion University of the Negev. Author Contribution "A.G., H.V., and O.S. conceived the project and designed the study, with contributions from W.E. and J.C.M. J.C.M. hosted A.G. in his research group, providing essential tools and scientific background for conducting DNA stable isotope probing, in collaboration with N.M.L. A.G. collected the data, and M.R.B. performed the bioinformatic analysis. A.G. wrote the first draft of the manuscript. All authors substantively revised the manuscript, and each author approves of the submitted version. Availability of data and materials: The datasets generated and analyzed during the current study are available under NCBI BioProject ID PRJNA1041615. Additional data are available under the Supplementary information sections. References Johnson MS, Matthews E, Du J, Genovese V, Bastviken D. Methane Emission From Global Lakes: New Spatiotemporal Data and Observation-Driven Modeling of Methane Dynamics Indicates Lower Emissions. JGR Biogeosciences (2022). Kelly DP, Wood AP. The Chemolithotrophic Prokaryotes . The Prokaryotes (2006). 10.1007/0-387-30742-7_15 . Lawton TJ, Rosenzweig AC. Biocatalysts for methane conversion: big progress on breaking a small substrate. Curr Opin Chem Biol. 2016;35:142–9. Knittel K, Boetius A. Anaerobic oxidation of methane: Progress with an unknown process. Annu Rev Microbiol. 2009;63:311–34. Nordi KÁ, Thamdrup B, Schubert CJ. Anaerobic oxidation of methane in an iron-rich Danish freshwater lake sediment. Limnol Oceanogr. 2013;58:546–54. Segarra KEA, et al. High rates of anaerobic methane oxidation in freshwater wetlands reduce potential atmospheric methane emissions. Nat Commun. 2015;6:2–9. Haroon MF, et al. Anaerobic oxidation of methane coupled to nitrate reduction in a novel archaeal lineage. Nature. 2013;500:567–70. Raghoebarsing AA, et al. A microbial consortium couples anaerobic methane oxidation to denitrification. Nature. 2006;440:918–21. Nordi K á, Thamdrup B. Nitrate-dependent anaerobic methane oxidation in a freshwater sediment. Geochim. Cosmochim. Acta 132, 141–150 (2014). Ettwig KF et al. Archaea catalyze iron-dependent anaerobic oxidation of methane. Proc. Natl. Acad. Sci. U. S. A. 113, 12792–12796 (2016). Lu YZ, et al. Cr(VI) reduction coupled with anaerobic oxidation of methane in a laboratory reactor. Water Res. 2016;102:445–52. Blees J, et al. Micro-aerobic bacterial methane oxidation in the chemocline and anoxic water column of deep south-Alpine Lake Lugano (Switzerland). Limnol Oceanogr. 2014;59:311–24. Oswald K, et al. Aerobic gammaproteobacterial methanotrophs mitigate methane emissions from oxic and anoxic lake waters. Limnol Oceanogr. 2016;61:S101–18. Beck DAC et al. A metagenomic insight into freshwater methane-utilizing communities and evidence for cooperation between the Methylococcaceae and the Methylophilaceae. PeerJ 2013, 1–23 (2013). Bar-Or I, Ben-Dov E, Kushmaro A, Eckert W, Sivan O. Methane-related changes in prokaryotes along geochemical profiles in sediments of Lake Kinneret (Israel). Biogeosciences. 2015;12:2847–60. Martinez-Cruz K, et al. Anaerobic oxidation of methane by aerobic methanotrophs in sub-Arctic lake sediments. Sci Total Environ. 2017;607–608:23–31. Su G, Zopfi J, Niemann H, Lehmann MF. Multiple Groups of Methanotrophic Bacteria Mediate Methane Oxidation in Anoxic Lake Sediments. Front Microbiol 13, (2022). Ettwig KF, et al. Nitrite-driven anaerobic methane oxidation by oxygenic bacteria. Nature. 2010;464:543–8. Dershwitz P, et al. Oxygen generation via water splitting by a novel biogenic metal ion-binding compound. Appl Environ Microbiol. 2021;87:1–14. Kits KD, Klotz MG, Stein LY. Methane oxidation coupled to nitrate reduction under hypoxia by the Gammaproteobacterium Methylomonas denitrificans, sp. nov. type strain FJG1. Environ Microbiol. 2015;17:3219–32. Orata FD, Kits KD, Stein LY. Complete genome sequence of Methylomonas denitrificans strain FJG1, an obligate aerobic methanotroph that can couple methane oxidation with denitrification. Genome Announc. 2018;6:1–2. Dang CC et al. Heavy metal reduction coupled to methane oxidation: Mechanisms, recent advances and future perspectives. J Hazard Mater 405, (2021). Zheng Y, et al. Methane-Dependent Mineral Reduction by Aerobic Methanotrophs under Hypoxia. Environ Sci Technol Lett. 2020. 10.1021/acs.estlett.0c00436 . Tanaka K, et al. Extracellular electron transfer via outer membrane cytochromes in a methanotrophic bacterium methylococcus capsulatus (Bath). Front Microbiol. 2018;9:1–7. Vigderovich H, et al. Aerobic methanotrophy increases the net iron reduction in methanogenic lake sediments. Front Microbiol. 2023;14:1–17. Sivan O, et al. Geochemical evidence for iron-mediated anaerobic oxidation of methane. Limnol Oceanogr. 2011;56:1536–44. Bar-Or I, et al. Iron-Coupled Anaerobic Oxidation of Methane Performed by a Mixed Bacterial-Archaeal Community Based on Poorly Reactive Minerals. Environ Sci Technol. 2017;51:12293–301. Elul M, et al. Metagenomic insights into the metabolism of microbial communities that mediate iron and methane cycling in Lake Kinneret sediments. Biogeosciences Discuss. 2020;1–24. 10.5194/bg-2020-329 . Vigderovich H, et al. Long-term incubations provide insight into the mechanisms of anaerobic oxidation of methane in methanogenic lake sediments. Biogeosciences. 2022;19:2313–31. Adler M, Eckert W, Sivan O. Quantifying rates of methanogenesis and methanotrophy in Lake Kinneret sediments (Israel) using pore-water profiles. Limnol Oceanogr. 2011;56:1525–35. Farhan Ul Haque M, Crombie AT, Murrell JC. Novel facultative Methylocella strains are active methane consumers at terrestrial natural gas seeps. Microbiome. 2019;7:1–17. Neufeld JD, et al. DNA stable-isotope probing. Nat Protoc. 2007;2:860–6. Parada AE, Needham DM, Fuhrman JA. Every base matters: Assessing small subunit rRNA primers for marine microbiomes with mock communities, time series and global field samples. Environ Microbiol. 2016;18:1403–14. Apprill A, Mcnally S, Parsons R, Weber L. Minor revision to V4 region SSU rRNA 806R gene primer greatly increases detection of SAR11 bacterioplankton. Aquat Microb Ecol. 2015;75:129–37. Bolyen E, et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat Biotechnol. 2019;37:852–7. Callahan BJ, et al. DADA2: High-resolution sample inference from Illumina amplicon data. Nat Methods. 2016;13:581–3. Quast C, et al. The SILVA ribosomal RNA gene database project: Improved data processing and web-based tools. Nucleic Acids Res. 2013;41:590–6. McMurdie PJ, Holmes S, Phyloseq. An R Package for Reproducible Interactive Analysis and Graphics of Microbiome Census Data. PLoS ONE 8, (2013). Prjibelski A, Antipov D, Meleshko D, Lapidus A, Korobeynikov A. Using SPAdes De Novo Assembler. Curr Protoc Bioinforma. 2020;70:1–29. Wu YW, Simmons BA, Singer SW. MaxBin 2.0: An automated binning algorithm to recover genomes from multiple metagenomic datasets. Bioinformatics. 2016;32:605–7. Sieber CMK, et al. Recovery of genomes from metagenomes via a dereplication, aggregation and scoring strategy. Nat Microbiol. 2018;3:836–43. Kang DD et al. MetaBAT 2: An adaptive binning algorithm for robust and efficient genome reconstruction from metagenome assemblies. PeerJ 2019, 1–13 (2019). Nissen JN, et al. Improved metagenome binning and assembly using deep variational autoencoders. Nat Biotechnol. 2021;39:555–60. Kieser S, Brown J, Zdobnov EM, Trajkovski M, McCue LA. ATLAS: A Snakemake workflow for assembly, annotation, and genomic binning of metagenome sequence data. BMC Bioinformatics. 2020;21:1–8. Chklovski A, Parks DH, Woodcroft BJ, Tyson GW. CheckM2: a rapid, scalable and accurate tool for assessing microbial genome quality using machine learning. bioRxiv. 2022. doi.org/10.1101/2022.07.11.499243 . Gurevich A, Saveliev V, Vyahhi N, Tesler G. QUAST: Quality assessment tool for genome assemblies. Bioinformatics 29, 1072–1075 (2013). Olson RD, et al. Introducing the Bacterial and Viral Bioinformatics Resource Center (BV-BRC): a resource combining PATRIC, IRD and ViPR. Nucleic Acids Res. 2023;51:D678–89. Altschul SF. BLAST Algorithm. eLS 1–4 (2005). 10.1038/npg.els.0005253 . Boratyn GM, et al. BLAST: a more efficient report with usability improvements. Nucleic Acids Res. 2013;41:29–33. O’Leary NA, et al. Reference sequence (RefSeq) database at NCBI: Current status, taxonomic expansion, and functional annotation. Nucleic Acids Res. 2016;44:D733–45. Pritchard L, Glover RH, Humphris S, Elphinstone JG, Toth IK. Genomics and taxonomy in diagnostics for food security: Soft-rotting enterobacterial plant pathogens. Anal Methods. 2016;8:12–24. Olmsted CN, et al. Environmental predictors of electroactive bacterioplankton in small boreal lakes. Environ Microbiol. 2023;25:705–20. Paysan-Lafosse T, et al. InterPro in 2022. Nucleic Acids Res. 2023;51:D418–27. Davis JJ, et al. PATtyFams: Protein families for the microbial genomes in the PATRIC database. Front Microbiol. 2016;7:1–12. Edgar RC. Multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res 32. 2004;MUSCLE:1792–7. Stamatakis A, Hoover P, Rougemont J. A rapid bootstrap algorithm for the RAxML web servers. Syst Biol. 2008;57:758–71. Chistoserdova L. Methylotrophs in natural habitats: current insights through metagenomics. Appl Microbiol Biotechnol. 2015;99:5763–79. Dumont MG, Pommerenke B, Casper P. Using stable isotope probing to obtain a targeted metatranscriptome of aerobic methanotrophs in lake sediment. Environ Microbiol Rep. 2013;5:757–64. He R, et al. Metabolic flexibility of aerobic methanotrophs under anoxic conditions in Arctic lake sediments. ISME J. 2021;1–13. 10.1038/s41396-021-01049-y . Rissanen AJ, et al. Vertical stratification patterns of methanotrophs and their genetic controllers in water columns of oxygen-stratified boreal lakes. FEMS Microbiol Ecol. 2021;97:1–16. Smith GJ, et al. Members of the genus methylobacter are inferred to account for the majority of aerobic methane oxidation in oxic soils from a freshwater wetland. MBio. 2018;9:1–17. Islam MM, Le T, Daggumati SR, Saha R. Investigation of microbial community interactions between Lake Washington methanotrophs using genome-scale metabolic modeling. PeerJ 2020, 1–28 (2020). Guo W, et al. Genome-scale revealing the central metabolic network of the fast growing methanotroph Methylomonas sp. ZR1. World J Microbiol Biotechnol. 2021;37:1–17. Geymonat E, Ferrando L, Tarlera SE. Methylogaea oryzae gen. nov., sp. nov., a mesophilic methanotroph isolated from a rice paddy field. Int J Syst Evol Microbiol. 2011;61:2568–72. Zhang Y, Kitajima M, Whittle AJ, Liu WT. Benefits of genomic insights and CRISPR-Cas signatures to monitor potential pathogens across drinking water production and distribution systems. Front Microbiol. 2017;8:1–15. Tavormina PL, et al. Abundance and distribution of diverse membrane-bound monooxygenase (Cu-MMO) genes within the Costa Rica oxygen minimum zone. Environ Microbiol Rep. 2013;5:414–23. Vekeman B, et al. Genome Characteristics of Two Novel Type I Methanotrophs Enriched from North Sea Sediments Containing Exclusively a Lanthanide-Dependent XoxF5-Type Methanol Dehydrogenase. Microb Ecol. 2016;72:503–9. Keltjens JT, Pol A, Reimann J. Op Den Camp, H. J. M. PQQ-dependent methanol dehydrogenases: Rare-earth elements make a difference. Appl Microbiol Biotechnol. 2014;98:6163–83. Krause SMB et al. Lanthanide-dependent cross-feeding of methane-derived carbon is linked by microbial community interactions. Proc. Natl. Acad. Sci. U. S. A. 114, 358–363 (2017). Kalyuzhnaya MG, Gomez OA, Murrell JC. The Methane-Oxidizing Bacteria (Methanotrophs) . Taxonomy, Genomics and Ecophysiology of Hydrocarbon-Degrading Microbes (2019). 10.1007/978-3-030-14796-9_10 . Kalyuzhnaya MG, et al. Highly efficient methane biocatalysis revealed in a methanotrophic bacterium. Nat Commun. 2013;4:1–7. Stolpera DA, Revsbech NP, Canfield DE. Aerobic growth at nanomolar oxygen concentrations. Proc. Natl. Acad. Sci. U. S. A. 107, 18755–18760 (2010). Chen KHC, et al. Bacteriohemerythrin bolsters the activity of the particulate methane monooxygenase (pMMO) in Methylococcus capsulatus (Bath). J Inorg Biochem. 2012;111:10–7. Oshkin IY et al. Thriving in Wetlands: Ecophysiology of the Spiral-Shaped Methanotroph Methylospira mobilis as Revealed by the Complete Genome Sequence. (2019). Von Canstein H, Ogawa J, Shimizu S, Lloyd JR. Secretion of flavins by Shewanella species and their role in extracellular electron transfer. Appl Environ Microbiol. 2008;74:615–23. Coursolle D, Baron DB, Bond DR, Gralnick JA. The Mtr respiratory pathway is essential for reducing flavins and electrodes in Shewanella oneidensis. J Bacteriol. 2010;192:467–74. Fuller SJ, et al. Extracellular electron transport-mediated fe(iii) reduction by a community of alkaliphilic bacteria that use flavins as electron shuttles. Appl Environ Microbiol. 2014;80:128–37. Watanabe K, Manefield M, Lee M, Kouzuma A. Electron shuttles in biotechnology. Curr Opin Biotechnol. 2009;20:633–41. Scholtysik G, et al. Geochemical focusing and burial of sedimentary iron, manganese, and phosphorus during lake eutrophication. Limnol Oceanogr. 2022;67:768–83. Strong PJ, Kalyuzhnaya M, Silverman J, Clarke WP. A methanotroph-based biorefinery: Potential scenarios for generating multiple products from a single fermentation. Bioresour Technol. 2016;215:314–23. Liu LY, et al. Biological conversion of methane to polyhydroxyalkanoates: Current advances, challenges, and perspectives. Environ Sci Ecotechnology. 2020;2:100029. Gilman A et al. Oxygen-limited metabolism in the methanotroph Methylomicrobium buryatense 5GB1C. PeerJ 2017, (2017). Dimitri Kits K, Campbell DJ, Rosana AR, Stein L. Y. Diverse electron sources support denitrification under hypoxia in the obligate methanotroph Methylomicrobium album strain BG8. Front Microbiol. 2015;6:1–11. Kits KD, Klotz MG, Stein LY. Methane oxidation coupled to nitrate reduction under hypoxia by the Gammaproteobacterium Methylomonas denitrificans, sp. nov. type strain FJG1. Environ Microbiol. 2015;17:3219–32. Li B, et al. Iron oxides act as an alternative electron acceptor for aerobic methanotrophs in anoxic lake sediments. Water Res. 2023;234:119833. Holmes DE, Dang Y, Walker DJF, Lovley DR. The electrically conductive pili of Geobacter s pecies are a recently evolved feature for extracellular electron transfer. (2016). 10.1099/mgen.0.000072 . Unsectioned Paragraphs only Table1.xlsx only Table2.xlsx Supplementary Files onlyTable1.xlsx onlyTable2.xlsx SI21.3.24.docx SITables.xlsx SI21.3.24.docx SITables.xlsx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 24 Apr, 2024 Submission checks completed at journal 28 Mar, 2024 First submitted to journal 26 Mar, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3790875","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":291449008,"identity":"b3b94a93-f269-4a5b-b775-ebe7d7659c22","order_by":0,"name":"Gafni Almog","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9klEQVRIiWNgGAWjYBACNnYILQcmGRugwgkFuLXwM0NoY7AGhBYD3FokmyF0YgOKFgY8WgwOcyc+/PLLLn07e/vzB4w7ahMb2A8/YHiAVwvvZmPZvuTcnT1nDBsYzxxPbOBJM8DrMKCWbdKSPcy5G27kAB3Wdgzowhz8frGHaKlPN7iR/hCihf8Nfi0gWyQ//DicYHAjAeiwtprEBgkCtoD9wthw3BDklxmJZw4Yt0k8MziAV8vx3o0Pf/ypljdnb3/w4eOOOtl+/uSHD39U4NYCAsy8bdCISGA4zMAGpA/g1wCMwh9/4HFXR0jxKBgFo2AUjEAAAFYjWEBdtxEIAAAAAElFTkSuQmCC","orcid":"","institution":"Ben-Gurion University of the Negev","correspondingAuthor":true,"prefix":"","firstName":"Gafni","middleName":"","lastName":"Almog","suffix":""},{"id":291449009,"identity":"dd28977f-720b-4223-abcb-8cf85c56a95f","order_by":1,"name":"Maxim Rubin-Blum","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Maxim","middleName":"","lastName":"Rubin-Blum","suffix":""},{"id":291449010,"identity":"fefb1c9a-ffa8-4707-9cfd-ddfb699a820e","order_by":2,"name":"J. Colin Murrell","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"J.","middleName":"Colin","lastName":"Murrell","suffix":""},{"id":291449011,"identity":"6d85eccc-19b2-4584-add2-fc5b843d6427","order_by":3,"name":"Hanni Vigderovich","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Hanni","middleName":"","lastName":"Vigderovich","suffix":""},{"id":291449012,"identity":"6d026e5b-520b-4d65-9c7e-c7cefc5a21b7","order_by":4,"name":"Werner Eckert","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Werner","middleName":"","lastName":"Eckert","suffix":""},{"id":291449013,"identity":"3dc7042e-2759-4082-ba66-a30255e582a8","order_by":5,"name":"Nasmille Larke-Mejía","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Nasmille","middleName":"","lastName":"Larke-Mejía","suffix":""},{"id":291449014,"identity":"8db2293b-793c-4da3-9906-90129822627e","order_by":6,"name":"Orit Sivan","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Orit","middleName":"","lastName":"Sivan","suffix":""}],"badges":[],"createdAt":"2023-12-22 08:44:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3790875/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3790875/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":54931662,"identity":"f6a7551f-f877-4f88-9993-cac42c1e71da","added_by":"auto","created_at":"2024-04-18 18:48:58","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":92863,"visible":true,"origin":"","legend":"","description":"","filename":"Figures1300.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3790875/v1/14482d3d863d3319415d14b4.jpg"},{"id":54931664,"identity":"9369f18a-f208-4bd4-8fdb-48b23c5a2156","added_by":"auto","created_at":"2024-04-18 18:48:58","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":85523,"visible":true,"origin":"","legend":"","description":"","filename":"Figures2300.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3790875/v1/3ea751eea6d85986dd1bc2d7.jpg"},{"id":54931665,"identity":"d42d523d-3356-47df-842f-9d16f990f997","added_by":"auto","created_at":"2024-04-18 18:48:58","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":79477,"visible":true,"origin":"","legend":"","description":"","filename":"Figures3300.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3790875/v1/16ef66ae54a5a6debfebc4f7.jpg"},{"id":54931667,"identity":"508e0b85-0cb3-41e3-b6c6-9417dd2e0b9e","added_by":"auto","created_at":"2024-04-18 18:48:58","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":184268,"visible":true,"origin":"","legend":"","description":"","filename":"Figures4300.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3790875/v1/947e8a1f2159330564ca476e.jpg"},{"id":54986880,"identity":"cb364f47-445c-4a52-92eb-3b685636ab9b","added_by":"auto","created_at":"2024-04-19 15:47:08","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":834877,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3790875/v1/4de5db08-0b6f-4822-bf8b-fce5574d785b.pdf"},{"id":54931663,"identity":"b0dfbc63-2ebe-496e-b33e-018f192c75df","added_by":"auto","created_at":"2024-04-18 18:48:58","extension":"xlsx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":11611,"visible":true,"origin":"","legend":"","description":"","filename":"onlyTable1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3790875/v1/322276517c73a3490654b24c.xlsx"},{"id":54931666,"identity":"824ed0b5-8a2b-4d3b-a7fd-dfc611514dc7","added_by":"auto","created_at":"2024-04-18 18:48:58","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":11312,"visible":true,"origin":"","legend":"","description":"","filename":"onlyTable2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3790875/v1/beae00ec302ee8cb25fab91c.xlsx"},{"id":54931670,"identity":"992fcdff-1835-48a1-8e6d-1276258fc3a9","added_by":"auto","created_at":"2024-04-18 18:48:59","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":321932,"visible":true,"origin":"","legend":"","description":"","filename":"SI21.3.24.docx","url":"https://assets-eu.researchsquare.com/files/rs-3790875/v1/cdb3b4448fcf193331304d7b.docx"},{"id":54931669,"identity":"5263ba7b-fff7-4603-81bf-4e9a2cd1517e","added_by":"auto","created_at":"2024-04-18 18:48:59","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":463343,"visible":true,"origin":"","legend":"","description":"","filename":"SITables.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3790875/v1/b6406aa07efe01ba7adf430b.xlsx"},{"id":54932139,"identity":"e2cbd7f7-5698-4997-9512-c058a8745c3c","added_by":"auto","created_at":"2024-04-18 18:56:59","extension":"docx","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":321932,"visible":true,"origin":"","legend":"","description":"","filename":"SI21.3.24.docx","url":"https://assets-eu.researchsquare.com/files/rs-3790875/v1/db0f590bc4cd71c74b3fdea3.docx"},{"id":54931672,"identity":"aba071bd-e0ed-4d7c-8c5b-d7cca7f27aa0","added_by":"auto","created_at":"2024-04-18 18:48:59","extension":"xlsx","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":463343,"visible":true,"origin":"","legend":"","description":"","filename":"SITables.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3790875/v1/cc33e93e8a3da010e29dc4a5.xlsx"}],"financialInterests":"","formattedTitle":"\u003cp\u003eSurvival strategies of aerobic methanotrophs under hypoxia in methanogenic lake sediments\u003c/p\u003e","fulltext":[{"header":"Background","content":"\u003cp\u003eMethane, a potent greenhouse gas, has more than threefold increased its atmospheric concentrations since the pre-industrial era. Around half of global methane emissions arise from natural inland waters production, including lake sediments, wetlands, rivers, and reservoirs \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Methane is consumed naturally by aerobic and anaerobic methanotrophs. Aerobic methanotrophy is observed in oxic environments and performed by aerobic bacterial methanotrophs, employing oxygen for methane activation and catalyze the oxidation of methane to methanol through the action of methane monooxygenase (MMO) \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Anaerobic oxidation of methane (AOM) can be linked to sulfate reduction through anaerobic archaeal methanotrophs (ANMEs) and sulfate-reducing bacteria \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. This process effectively prevents the release of up to 90% of the produced methane in marine sediments, and can be efficient also in rich sulfate freshwater systems \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. In sulfate-depleted environments such as most freshwater sediments, AOM can consume over 50% of the produced methane \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e and be coupled to other electron acceptors such as nitrate, nitrite, metal oxides, and humic substances \u003csup\u003e\u003cspan additionalcitationids=\"CR8 CR9 CR10\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eMicroorganisms participating in AOM have been identified not only as ANMEs but surprisingly also as those considered aerobic bacterial methanotrophs. These bacteria were observed to be active and involved in methane oxidation beneath the chemocline in the anoxic hypolimnion \u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e and in certain freshwater lake sediments \u003csup\u003e\u003cspan additionalcitationids=\"CR15 CR16\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Certain methanotrophs employ unique mechanisms to obtain oxygen, such as the disproportionation of various molecules. For instance, \u003cem\u003eMethylomirabilis\u003c/em\u003e (NC10) produces oxygen through the disproportionation of nitrite, utilizing it subsequently for the oxidation of methane \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. Additionally, specific alphaproteobacterial methanotrophs utilize methanobactins to generate both oxygen and hydrogen through the disproportionation of water. \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Some bacterial methanotrophs exhibit versatility by utilizing alternative electron acceptors. Methylococcales bacteria \u003cem\u003eMethylomonas denitrificans\u003c/em\u003e and \u003cem\u003eMethylocaldum\u003c/em\u003e sp. have been experimentally shown to couple denitrification with methane oxidation \u003csup\u003e\u003cspan additionalcitationids=\"CR21\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e, while others, such as \u003cem\u003eMethylomonas\u003c/em\u003e, \u003cem\u003eMethylosinus\u003c/em\u003e and \u003cem\u003eMethylococcus capsulatus\u003c/em\u003e demonstrate methane oxidation coupled to iron reduction \u003csup\u003e\u003cspan additionalcitationids=\"CR24\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. The adaptation mechanisms, that enable such a switch however, remain poorly understood.\u003c/p\u003e \u003cp\u003eHere we explore the remarkable genetic adaptability of Methylococcales to hypoxia in methanogenic sediments of Lake Kinneret (LK, Sea of Galilee), where our previous studies confirmed methane oxidation coupled to iron reduction (Fe-AOM) beneath the sulfate reduction zone in the iron rich methanogenic zone \u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. The mediation of this Fe-AOM process was proposed to involve archaea methanogens and bacterial methanotrophs \u003csup\u003e\u003cspan additionalcitationids=\"CR28\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. In-depth analyses, including isotopes of specific fatty acid lipids, quantification of the functional gene \u003cem\u003epmoA\u003c/em\u003e, and metagenomic analysis, confirmed the involvement of Methylococcales-like methanotrophs in methane oxidation \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e,\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. Aerobic methanotrophy was also shown to boost iron reduction in these sediments \u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e, but the potential microbial mechanisms that may allow the methanotrophs to survive under hypoxia and stimulate iron reduction have remained unclear.\u003c/p\u003e \u003cp\u003eWe hypothesized that LK methanogenic sediments harbor Methylococcales with unique strategies for survival in oxygen-limited conditions, and this research aimed to identify the key mechnisems enabling it. In order to explore this adaptation to hypoxia we conducted two experiments using LK methanogenic sediments as inoculum: i) an aerobic \"classic\" methanotrophic enrichment with ambient air employing DNA stable isotope probing (DNA-SIP) and ii) hypoxic methanotrophic enrichment with repeated spiking of 1% oxygen. Using metagenomics sequencing of DNA extracted from these experiments, we evaluated the genetic basis for their survival by comparing Methylococcales metagenome-assembled genomes (MAGs) from LK to Methylococcales genomes from diverse lineages and environments. including the. We investigated the genomic potential associated with various survival mechanisms including oxygen acquisition strategies, metabolic versatility and use of alternative electron acceptors.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSampling site\u003c/h2\u003e \u003cp\u003eLK is a monomictic lake in northern Israel, stratified usually between March and December, leading to anoxic hypolimnion \u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. The seasonal changes lead to variations in the geochemical porewater profiles in the sediments, primarily of methane, iron, and sulfur \u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. In the mixed period, the oxic-anoxic boundary reaches the sediment-water interface, affecting chemical profiles within the sediment. Sulfate in the sediment is depleted at around a depth of 10 to 20 cm depending on the stratification period and is followed by iron reduction. Methane concentration profiles generally increase with depth up to saturation levels, however, methane concentration profiles also hint at the presence of a \"deep sink\" correlated with an increase in concentrations of reduced iron \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. We collected sediment as inoculum for both experiments from the deepest point of LK (station A) using a gravity corer, as previously described by Bar-Or et al. 2017 \u003csup\u003e27\u003c/sup\u003e. The specific sediment used for the experiments originated from a depth of 25\u0026ndash;40 cm below the sediment surface.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eStudy design overview\u003c/h2\u003e \u003cp\u003eTo achieve this study's aims, we designed and conducted two experiments intended mainly to enrich methanotrophs. Experiment 1 was -a \"classical\" enrichment with ambient air, combined with DNA-Stable Isotope Probing (DNA-SIP). This experiment focused on identifying the active methanotrophs thriving under oxic conditions (Fig.\u0026nbsp;1A). In the second experiment, we stimulated methanotroph growth under hypoxic conditions (Fig.\u0026nbsp;1B). Following enrichments, we used amplicon sequencing of the 16S rRNA gene to study microbial diversity. Using metagenomic sequencing of representative samples, we curated Methylococcales MAGs. We investigated functions involved in adaptation to anoxia in these MAGs, alongside other Metylococcales genomes, using various bioinformatic platforms (Fig.\u0026nbsp;1C).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"1\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabb\" border=\"1\"\u003e \u003ccolgroup cols=\"1\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFigure 1: Experimental layout.\u003c/p\u003e \u003cp\u003eThe workflow of this research. Sediment cores were collected from the methanotrophic zone in Lake Kineret and used to initiate two enrichment experiments. (A) Experiment 1: Incubated with ambient air, representing a \"classical\" enrichment approach coupled with DNA-SIP analysis. (B) Experiment 2: Hypoxic enrichment by repeatedly introducing small amounts (spikes) of oxygen (1%) to maintain low-oxygen conditions. Extracted DNA was used for 16S rRNA amplicon sequencing, and representing samples were also used to assemble and bin Methylococcales MAGs. Genome-based metabolic comparative analyses were performed using different bioinformatic platforms (C).\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eExperiment 1 - Ambient air enrichment and DNA stable isotope fingerprinting\u003c/h2\u003e \u003cp\u003eSediment samples collected from LK in September 2019 were used to enrich methanotrophs and explore the active methane-utilizing ones. Approximately 5 g of homogenized sediment was added to 300 ml sealed serum vials with black rubber septa. The vials were pre-filled with modified DNMS medium \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e (1:10 w/v). Prior to inoculation, the headspace of each vial was purged with nitrogen gas for a brief period (e.g., 1 hr) to flush out any methane gas remaining in the sediment. Following this, the headspace was replaced with ambient air. Subsequently, either \u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003eC-methane (control) or \u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003eC-methane (used for DNA-SIP) was introduced at a concentration of 20% (v/v) as the sole carbon source. Incubations proceeded at 25\u0026deg;C in the dark on a 120 rpm rotary shaker for a total of 21 days. Triplicate incubations were performed for each treatment, with detailed information on sampling times provided in the Supplementary Information (SI, Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). A separate 5 g sediment sample (time-zero control) was preserved in a 15 ml Falcon tube at -20\u0026deg;C for subsequent DNA extraction.\u003c/p\u003e \u003cp\u003eAfter incubation, sediment samples were carefully collected and processed for DNA extraction. Briefly, samples were centrifuged, supernatants discarded, and the resulting pellets were stored at -20\u0026deg;C. DNA was then extracted from 0.5 g aliquots of each sample (time-zero, \u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003eC-methane, and \u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003eC-methane) using a PowerSoil kit (Qiagen) following the manufacturer's instructions. To achieve sufficient DNA concentration for DNA-SIP analysis, seven aliquots from each sample were extracted and pooled. DNA quality and quantity were assessed using a NanoDrop Spectrophotometer (Thermo Fisher Scientific).\u003c/p\u003e \u003cp\u003eFor DNA-SIP, samples were further processed through CsCl density gradient using a Beckman Coulter ultracentrifuge and a VTi 65.2 rotor, operating at 44,100 rpm at 20\u0026deg;C for 65 hours, as previously outlined \u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. Twelve fractions were collected from each sample, and their DNA content and their density was determined using an AR200 digital hand-held refractometer (Reichert, Buffalo, NY, USA). These DNA fractions were recovered, purified by PEG-6000 precipitation, resuspended in 30 \u0026micro;l TE buffer, and the DNA quantity was validated using Fluorometer (Qubit, Invitrogen).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eExperiment 2 - Hypoxic enrichments\u003c/h2\u003e \u003cp\u003eThis experiment attempted to enrich methanotrophs in LK sediments with low oxygen conditions following the methods of Vigderovich et al, 2023 \u003csup\u003e25\u003c/sup\u003e. Sediment samples collected in March 2021 set as the starting material for the experiment. Each vail contained 7g of methanogenic sediments from LK, 50 mg of hematite (10mM final concentration) and anoxic filtered porewater (1:3 w/v), previously extracted from the same depth in a 60 ml serum vails sealed with black rubber septa. Hematite, an iron oxide mineral, can potentially serve as an alternative electron acceptor for methanotrophs under anoxic conditions \u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. The anoxic porewater further ensured the initial absence of oxygen within the vials. To establish these anoxic conditions, each vial underwent continuous nitrogen purging for about an hour. Following this, the vials were stored in the dark at 4\u0026deg;C for five days. After the anoxic period, the enrichment process was initiated. Vials were vigorously mixed (vortexed) and flushed again with nitrogen for 15 minutes to ensure the removal of any residual oxygen. Subsequently, 1.5 ml of air was introduced into each vial, resulting in a final headspace oxygen concentration of 1% (v/v). Two treatment groups were established CH\u003csub\u003e4\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;O\u003csub\u003e2\u003c/sub\u003e treatment and N\u003csub\u003e2\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;O\u003csub\u003e2\u003c/sub\u003e as Control. For the CH\u003csub\u003e4\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;O\u003csub\u003e2\u003c/sub\u003e treatment, eleven vials received an additional 1.5 ml of 99.95% methane gas, resulting in a final headspace oxygen concentration of 5% (v/v) allowing methanotrophs to potentially uptake methane as an energy source. For the N\u003csub\u003e2\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;O\u003csub\u003e2\u003c/sub\u003e Control, triplicate vials were supplemented with an additional 1.5 ml of 99.999% nitrogen gas instead of methane, resulting in a final headspace oxygen concentration of 5% (v/v), serving as a control for the effect of methane availability. The enrichment incubations proceeded at 25\u0026deg;C in the dark with the vials inverted to minimize headspace gas exchange. Oxygen consumption within the vials was monitored throughout the experiment using PSt6 sensors in designated vials. Vials were sacrificed at various time intervals, detailed in the Supplementary Information (Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e) and the experiment lasted for 40 days. These samples were promptly stored at -80\u0026deg;C for subsequent DNA/RNA extraction. DNA was successfully extracted from all samples using a PowerSoil kit (Qiagen) following the manufacturer's instructions. However, RNA concentrations using Quant-ittTM RiboGreen RNA kit (ThermoFisher) were found to be below the detection limit.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eDNA library preparation and sequencing\u003c/h2\u003e \u003cp\u003eA total of thirty-seven samples underwent 16S rRNA gene sequencing, while three samples were also allocated for metagenome analysis. Comprehensive details regarding the samples utilized in the DNA-SIP and hypoxic experiments can be found in Tables S1 and S2, respectively. Sequencing of the V4 region of the 16S rRNA gene was executed using the primer pair 515f\u0026ndash;806r \u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e,\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e, and the procedure was conducted via Illumina sequencing at Hylabs, Rehovot, Israel.\u003c/p\u003e \u003cp\u003eMetagenomic libraries were generated from three distinct DNA samples: one originating from the DNA-SIP experiment and two from the hypoxic experiment (refer to Tables S1 and S2 for detailed descriptions, respectively). Subsequently, the libraries underwent sequencing at Novogene, Singapore, with each sample producing 65\u0026ndash;80\u0026nbsp;million 2x150 bp paired-end reads using Illumina NovaSeq.\u0026nbsp;The library construction process employed the NEBNext\u0026reg; Ultra\u0026trade; II DNA Library Prep Kit.\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e,\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eBioinformatics\u003c/h2\u003e \u003cp\u003eFor 16S rRNA gene amplicons, demultiplexed paired-end reads were analyzed within QIIME2 V2020.6 pipeline \u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. By applying the DADA2 pipeline \u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e, implemented in QIIME2, reads were truncated according to their quality plots, chimeras were removed, and reads were merged and grouped into amplicon sequence variants (ASVs). Taxonomy was assigned to ASVs by Silva 138 99% classifier \u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. Beta diversity was visualized by Principal Coordinate Analysis (PCoA), in which the dissimilarity between samples was estimated with a Bray-Curtis distance matrix using the distance and ordination functions in the R package phyloseq \u003csup\u003e38\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eMetagenomes were assembled using SPAdes V3.15 with \u0026ndash;meta k\u0026thinsp;=\u0026thinsp;21,33,66,99,127 parameters \u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e, following adapter trimming and error correction with tadpole.sh, using the BBtools suite following read preparation with the BBtools suite (Bushnell, B, sourceforge.net/projects/bbmap/). Downstream mapping and binning of metagenome-assembled genomes (MAGs) were performed using DAStool, Vamb, Maxbin 2.0, and Metabat2 \u003csup\u003e40\u0026ndash;43\u003c/sup\u003e within the Atlas V2.9.1 pipeline \u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e, using the genome dereplication nucleotide identity threshold of 0.975. MAG quality was verified using Checkm2 \u003csup\u003e45\u003c/sup\u003e and QUAST \u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. Functional annotation was carried out using the SEED implemented in Viral Bioinformatics Resource Center (BV-BRC) server \u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e, and key annotations were verified by BLASTing against the NCBI database.\u003c/p\u003e \u003cp\u003eAdditional Methylococcales genomes, a total of 62 genomes (a comprehensive list can be found in Table S3), were generated based on BLASTp against LK predicted membrane-bound particulate methane monooxygenase protein, using the BV-BRC platform \u003csup\u003e\u003cspan additionalcitationids=\"CR49\" citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e (max hit: 20, Evalue threshold: 0.0001). The average nucleotide identity (ANI) was calculated using pyani \u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e (See Table S4 for their values).\u003c/p\u003e \u003cp\u003eMultiheme cytochromes (MHCs) were assigned using the FEET pipeline (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/McMahonLab/FEET.git\u003c/span\u003e\u003cspan address=\"https://github.com/McMahonLab/FEET.git\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) \u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e. In short, FEET first uses python to find MHCs using the following regular expressions for heme binding motifs: [CXCH], [CXXCH], [CXXXCH], and [CXXXXXXXXXXX[!=C]XXCH]. At least three [CXXCH] motifs and at least five total aforementioned motifs were required to call an MHC. Localization was predicted by Cello V2.5 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://cello.life.nctu.edu.tw/\u003c/span\u003e\u003cspan address=\"http://cello.life.nctu.edu.tw/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Each protein sequence was then manually verified using the InterPro database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ebi.ac.uk/InterPro/\u003c/span\u003e\u003cspan address=\"https://www.ebi.ac.uk/InterPro/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) \u003csup\u003e\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003ePhylogenetic trees were generated with the BV-BRC platform \u003csup\u003e\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e. Marker proteins that are universally conserved across the bacterial domain were extracted from genomes \u003csup\u003e\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e. A hundred single-copy markers that were present in all genomes (See Table S5 for gene list) analyzed in this study were used for alignment with MUSCLE \u003csup\u003e\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e. The randomized accelerated maximum likelihood (RAxML) tree was calculated \u003csup\u003e\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e. Final representation of the tree was curated using Itol (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://itol.embl.de/\u003c/span\u003e\u003cspan address=\"https://itol.embl.de/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results and discussion","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eAmbient oxygen selects for the enrichment of aerobic methanotrophs\u003c/h2\u003e \u003cp\u003eDNA-SIP experiments utilizing labeled \u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003eC-methane resulted in the isolation of \u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003eC-enriched DNA observed in fractions with a density ranging from 1.72 to 1.70 g ml\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, as illustrated in the labeled fraction (Fig.\u0026nbsp;2a). Analysis of the relative abundance of 16S rRNA genes revealed bacterial communities that differed between the labeled and unlabeled fractions, as evidenced by Principal Coordinates Analysis (PCoA) based on Bray\u0026ndash;Curtis dissimilarity (Fig.\u0026nbsp;2b). Axis 1 and 2 explained 43.9% and 17.4% of the variation, respectively, indicating distinct communities in the labeled and unlabeled fractions, while \u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003eC-methane-fed communities clustered together. The use of the \u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003eC-methane-fed sample as a control accounted for differences in the guanine/cytosine (GC) content of DNA samples.\u003c/p\u003e \u003cp\u003eDespite Methylococcales comprising less than 1% of the initial microbial population at time zero, enrichment during the experiment resulted in Methylococcales accounting for approximately one-third of the microbial community (refer to Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). In the fractions fed with labeled \u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003eC-methane, \u003cem\u003eMethylobacter\u003c/em\u003e emerged as the predominant taxon with a relative read abundance ranging from 18\u0026ndash;31%, alongside the enrichment of other methanotrophs (Fig.\u0026nbsp;2c). This finding aligns with previous studies that identified \u003cem\u003eMethylobacter\u003c/em\u003e as a dominant and active species in diverse natural environments \u003csup\u003e\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e, including oxic \u003csup\u003e\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e and anoxic freshwater lake sediments \u003csup\u003e\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/sup\u003e, anoxic water columns \u003csup\u003e\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u003c/sup\u003e, and wetlands \u003csup\u003e\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u003c/sup\u003e. Other enriched Methylococcales species in this experiment included \u003cem\u003eMethylomonas\u003c/em\u003e (5\u0026ndash;8%) and \u003cem\u003eMethylogaea\u003c/em\u003e (up to 3%) (Fig.\u0026nbsp;2c). In contrast, within the \u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003eC-methane-enriched cultures, \u003cem\u003eMethylobacter\u003c/em\u003e exhibited a relative abundance of 6\u0026ndash;10%, while the relative abundance of \u003cem\u003eMethylomonas\u003c/em\u003e and \u003cem\u003eMethylogaea\u003c/em\u003e varied significantly among biological triplicates, ranging from 4% to up to 20% (refer to Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabc\" border=\"1\"\u003e \u003ccolgroup cols=\"1\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFigure 2: Aerobic enrichment experiment employing DNA-SIP.\u003c/p\u003e \u003cp\u003e(A) The average DNA concentration (ng/\u0026micro;l) for each fraction (n\u0026thinsp;=\u0026thinsp;3 biological replicates) in the \u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003eC-methane-fed cultures (blue) and the \u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003eC-methane-fed cultures (colorless). Error bars indicate the standard error (standard deviation/n) of the DNA concentration, while the error bars for density are smaller than the symbol. (B) Principal Component Analysis (PCoA) based on Bray-Curtis dissimilarity illustrating microbial diversity in labeled DNA fractions (circles), unlabeled DNA fractions (triangles), and time zero samples that were not fractionated (squares) for both \u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003eC-methane-fed cultures (in blue) and \u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003eC-methane-fed cultures (colorless). (C) The average relative abundance of dominant Methylococcales in the labeled DNA fractions of the \u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003eC-methane-fed cultures.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eHypoxic conditions select for Methylomonas\u003c/h2\u003e \u003cp\u003eWe observed a notable adaptation of microbial communities to periodic spiking with 1% oxygen and methane, leading to a significant increase in oxygen consumption (0.69\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04 mg L-1 hr-1), in contrast to controls where the oxygen consumption was markedly lower (0.02\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01 mg L-1 hr-1) (Fig.\u0026nbsp;3a). Microbial communities in cultures deprived of methane exhibited clustering with those from non-enriched samples (time zero), indicating that the microbial community in these samples remained relatively consistent despite exposure to oxygen. These findings suggest that methane serves as the electron donor for oxygen respiration, especially considering the limited availability of alternative electron donors in these methanogenic sediments (Fig.\u0026nbsp;3a).\u003c/p\u003e \u003cp\u003eFurther analysis of the microbial community revealed the specific selection of microorganisms through periodic spiking with 1% oxygen (Fig.\u0026nbsp;3b), predominantly enriching for \u003cem\u003eMethylomonas\u003c/em\u003e (Fig.\u0026nbsp;3c).\u003c/p\u003e \u003cp\u003eThese outcomes align with previous studies, reinforcing the notion that \u003cem\u003eMethylomonas\u003c/em\u003e may exhibit better adaptation to low oxygen conditions, such as, better uptake of oxygen and methane, compared to \u003cem\u003eMethylobacter\u003c/em\u003e \u003csup\u003e\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabd\" border=\"1\"\u003e \u003ccolgroup cols=\"1\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFigure 3: Hypoxic enrichment experiment.\u003c/p\u003e \u003cp\u003e(A) In vitro monitoring of oxygen levels (%) in bottles exposed to O\u003csub\u003e2\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;CH\u003csub\u003e4\u003c/sub\u003e (blue) and O\u003csub\u003e2\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;N\u003csub\u003e2\u003c/sub\u003e (green). (B) Principal Component Analysis illustrating microbial diversity exposed to O\u003csub\u003e2\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;CH\u003csub\u003e4\u003c/sub\u003e (circles), O\u003csub\u003e2\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;N\u003csub\u003e2\u003c/sub\u003e (triangles), and time zero samples without enrichment (squares). These are color-coded based on treatment, with blue, green, and gray representing O\u003csub\u003e2\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;CH\u003csub\u003e4\u003c/sub\u003e, O\u003csub\u003e2\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;N\u003csub\u003e2\u003c/sub\u003e, and time zero, respectively. (C) The average relative abundance of dominant Methylococcales spp. enriched in the O\u003csub\u003e2\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;CH\u003csub\u003e4\u003c/sub\u003e treatment.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e\u003cem\u003eMethylococcales lineages dominate enrichment cultures\u003c/em\u003e\u003c/h2\u003e \u003cp\u003eIn this investigation, methanotrophs were selectively enriched from methanogenic LK sediments through two distinct enrichment experiments. The first experiment involved the use of ambient air coupled with DNA-stable isotope probing (DNA-SIP), while the second experiment included repeated injections of 1% oxygen to simulate hypoxic conditions. From both experiments, we identified five Methylococcales metagenome-assembled genomes (MAGs), detailed in Table\u0026nbsp;1 for genome statistics. The metagenomic analysis uncovered the enrichment of \u003cem\u003eMethylomonas, Methylogaea\u003c/em\u003e, and \u003cem\u003eMethylobacter\u003c/em\u003e (MAGs 1\u0026ndash;5 respectively). The closest relatives to these lineages were \u003cem\u003eMethylomonas\u003c/em\u003e sp. ZR1 \u003csup\u003e63\u003c/sup\u003e, \u003cem\u003eMethylogaea oryzae\u003c/em\u003e strain E10 \u003csup\u003e64\u003c/sup\u003e and \u003cem\u003eMethylobacter tundripaludum\u003c/em\u003e strain OWC-G53F \u003csup\u003e\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e\u003c/sup\u003e (see Fig.\u0026nbsp;4 and Table\u0026nbsp;2 for ANI values). For the hypoxic enrichments, two MAGs, namely, \u003cem\u003eMethylomonas\u003c/em\u003e LK_4 and LK_5, which are related to \u003cem\u003eMethylomonas\u003c/em\u003e sp. strain FW.007 \u003csup\u003e65\u003c/sup\u003e. A detailed genomic comparison between all LK Methylococcales MAGs highlighting functions of interest is given below and summarized in Table\u0026nbsp;2.\u003c/p\u003e \u003cp\u003eADD Table\u0026nbsp;1 here\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eMetabolic reconstruction of novel Lake Kinneret methanotrophs\u003c/h2\u003e \u003cp\u003eGenome-base metabolic reconstruction of the five MAGs affirmed the characteristic metabolic framework of Methylococcales methanotrophs (Table\u0026nbsp;2). The prediction of methane oxidation through the particulate methane monooxygenase was consistent across all LK Methylococcales lineages. With the exception of \u003cem\u003eMethylogaea\u003c/em\u003e LK_2, which exclusively harbored the \u003cem\u003epxmABC\u003c/em\u003e genes encoding an alternative form of particulate methane monooxygenase potentially functioning under oxygen-limiting conditions \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e, the remaining LK Methylococcales lineages were predicted to possess one copy of the \u003cem\u003epmoCAB\u003c/em\u003e operon, encoding the canonical particulate methane monooxygenase. The genomes of Methylomonas LK_4 and LK_5 contained both \u003cem\u003epxmABC\u003c/em\u003e and \u003cem\u003epmoCAB\u003c/em\u003e operons, potentially broadening their range of affinities for methane and oxygen \u003csup\u003e\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e\u003c/sup\u003e. In addition, none of the genomes of the LK Methylococcales lineages encoded the soluble methane oxygenase.\u003c/p\u003e \u003cp\u003eThe lanthanide-dependent \u003cem\u003exoxF\u003c/em\u003e \u003csup\u003e\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e\u003c/sup\u003e was predicted to catalyze methanol oxidation in all LK Methylococcales lineages except \u003cem\u003eMethylogaea\u003c/em\u003e, whereas the calcium-dependent \u003cem\u003emxaFI\u003c/em\u003e \u003csup\u003e\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e\u003c/sup\u003e was predicted only in the genome of LK \u003cem\u003eMethylomonas\u003c/em\u003e and likely within the \u003cem\u003eMethylogaea\u003c/em\u003e LK_2 genome (only mxaI was found). The potential secretion of methanol by methanotrophs into the environment, with subsequent consumption by syntrophic partners, indicative of cross-feeding between methanotrophs and methylotrophs \u003csup\u003e\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e\u003c/sup\u003e, was considered highly likely. This inference is supported by the elevated relative abundance of \u003cem\u003eMethylotenera\u003c/em\u003e in our enrichment experiments (refer to Figures \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e and S2 for microbial community relative abundance).\u003c/p\u003e \u003cp\u003eThe presence of the ribulose monophosphate pathway (RuMP) was identified in all LK Methylococcales lineages, evident through the presence of key genes such as those that encode the 3-hexulose-6-phosphate synthase (\u003cem\u003ehps\u003c/em\u003e) and 6-\u003cem\u003ephospho\u003c/em\u003e-3-hexuloisomerase (\u003cem\u003ephi\u003c/em\u003e) \u003csup\u003e\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e\u003c/sup\u003e. The RuMP pathway appears to be the exclusive pathway for methane carbon assimilation, as the serine cycle was absent, lacking genes encoding key enzymes like malate thiokinase (both \u003cem\u003emtkAB\u003c/em\u003e) and hydroxypyruvate reductase (\u003cem\u003eghrB\u003c/em\u003e). Except for \u003cem\u003eMethylogaea\u003c/em\u003e, all LK Methylococcales lineages encoded the phosphogluconate dehydratase (\u003cem\u003eedd)\u003c/em\u003e and 2-dehydro-3-deoxy-phosphogluconate aldolase \u003cem\u003e(eda)\u003c/em\u003e genes necessary for the Entner\u0026ndash;Doudoroff (ED) variant of the RuMP cycle \u003csup\u003e\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e\u003c/sup\u003e. The energy-efficient Embden\u0026ndash;Meyerhof\u0026ndash;Parnas (EMP) variant \u003csup\u003e\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e\u003c/sup\u003e was predicted in all Methylococcales lineages, evidenced by the presence of non-ATP dependent pyrophosphate-dependent phosphofructokinase (\u003cem\u003epfk\u003c/em\u003e), triosephosphate isomerase (\u003cem\u003etpi\u003c/em\u003e), glyceraldehyde 3-phosphate dehydrogenase (\u003cem\u003egapdh\u003c/em\u003e), phosphoglycerate kinase (\u003cem\u003epgk\u003c/em\u003e), and enolase (\u003cem\u003eeno\u003c/em\u003e) genes.\u003c/p\u003e \u003cp\u003eADD Table\u0026nbsp;2 here\u003c/p\u003e \u003cp\u003e \u003cem\u003eAdaptations to hypoxic conditions in\u003c/em\u003e Methylococcales\u003c/p\u003e \u003cp\u003eTo assess whether the genomic adaptations to hypoxic conditions are unique to LK Methylococcales, we compared their genomes and with an additional 62 Methylococcales genomes from diverse environments (Fig.\u0026nbsp;4). These genomes represent Methylococcales from groundwater, contaminated rivers, sewage systems, lake sediments, rice fields, volcano mud, and more (Supplementary Table S3).\u003c/p\u003e \u003cp\u003eThis comparison revealed a core set of mechanisms shared by most Methylococcales, including those from LK potentially enabling them to function effectively in hypoxic conditions. These include (i) Enhanced oxygen usage by cytochrome bd ubiquinol oxidase (found in 88% of analyzed genomes), allowing efficient oxygen respiration potentially sustaining growth at \u0026le;\u0026thinsp;3 nM molecular oxygen \u003csup\u003e\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e\u003c/sup\u003e. (ii) Enhancing respiration under hypoxia by oxygen-binding hemerythrin, by increasing the activity of pMMO \u003csup\u003e\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e\u003c/sup\u003e (present all of the analyzed genomes). (iii) Flagella-mediated motility across gradients to optimize oxygen and methane availability (present in 95% of the analyzed genomes) \u003csup\u003e\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e\u003c/sup\u003e. (iv) Alternative electron acceptors including nitric oxide reduction, as well as riboflavins (found in 70% and 89% of the genomes, respectively). These last electron acceptors, are soluble secreted electron shuttles mediating extracellular electron transfer (EET) \u003csup\u003e\u003cspan additionalcitationids=\"CR76\" citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e\u003c/sup\u003e, which can be reversibly oxidized and reduced, carrying electrons between cells and insoluble electron acceptors such as manganese and iron oxides over large distances \u003csup\u003e\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e,\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e,\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e\u003c/sup\u003e. Manganese and iron oxides are highly abundant in methanogenic sediments \u003csup\u003e\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eMethylococcales can store carbohydrates and use alternative metabolic pathways to provide energy under oxygen limitation. Genes for glycogen synthesis and degradation were found in all of the genomes, likely allowing Methylococcales to conserve resources during periods of limited nutrients \u003csup\u003e\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e,\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e\u003c/sup\u003e. Additional metabolic alternative is the ability to produce fermentation products like succinate and acetate was found in 66% and 51% of genomes, respectively. The occurrence of alcohol dehydrogenases (present in 91% of genomes) and bidirectional hydrogenases (present in 99% of genomes) indicates potential for alcohol and hydrogen production \u003csup\u003e\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e,\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eWhile many metabolic mechanisms were shared among Methylococcales, we identified several less abundant mechanisms that were present in LK Methylococcales. These include (i) production of lactate (found in 21% of the genomes), predicted in \u003cem\u003eMethylobacter\u003c/em\u003e LK_1 and \u003cem\u003eMethylogaea\u003c/em\u003e LK_2 MAGs; (ii) Methane-dependent denitrification serves as a link between the carbon and nitrogen cycles \u003csup\u003e\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e,\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e\u003c/sup\u003e. While nitrous oxide reduction was found as a common trait, respiratory nitrate reductase and nitric oxide-forming nitrite reductase (with respective genes identified in 36% and 27% of the genomes) were observed as less prevalent mechanisms. However, despite being relatively uncommon among the Methylococcales, these mechanisms were found more abundant in LK Methylococcales and exhibited in Methylobacter LK_1, Methylomonas LK_3, and LK_5 MAGs; (iii) Outer membrane cytochromes (OMCs, found in 62% of the genomes) needed to reduce iron \u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e,\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e,\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e\u003c/sup\u003e, were found in \u003cem\u003eMethylomonas\u003c/em\u003e LK_3, LK_4 and LK_5. Methylococcales appear to lack electrically conductive pili (e-pili) that can support EET \u003csup\u003e\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e\u003c/sup\u003e, as \u0026gt;\u0026thinsp;120 amino acid long PilA proteins in Methylococcales were longer than the canonical 60\u0026ndash;90 amino acid-long e-pili \u003csup\u003e\u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e86\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAdditional manual investigation of OMCs using the InterPro database confirmed that at least some of these sequences belong to the multiheme cytochrome superfamily with at least one copy of predicted as extracellular OMC (found in 55% of the genomes), and fewer OMCs were predicted as membrane-bound (found in 10% of the genomes). Others were either unrelated to OMCs or predicted to be hydroxylamine oxidoreductase or cytochromes c-552 involved in ammonia oxidation and nitrite reduction. Among LK Methylococcales, only \u003cem\u003eMethylomonas\u003c/em\u003e LK_3 encoded a membrane-associated OMC, hinting at the possibility of EET in this organism, but this strategy to cope with oxygen limitation is not widespread in LK.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabe\" border=\"1\"\u003e \u003ccolgroup cols=\"1\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFigure 4: Phylogenomic analysis and metabolic profiling.\u003c/p\u003e \u003cp\u003eA phylogenomic tree along with the metabolic presence-absence profile of 67 Methylococcales genomes. A comprehensive list of the proteins is available in Table S7 and predicted OMC proteins are available in Table S8. Additional information regarding presence-absence of kye genes particulate methane monooxygenase \u003cem\u003epmoCAB\u003c/em\u003e operon and \u003cem\u003epxmABC\u003c/em\u003e operon, soluble methane monooxygenase (mmoXYBZDC), lanthanide-dependent methanol dehydrogenases (xoxF), and methanol dehydrogenase (mxaF) is available in Table S9. Notably, the five LK Methylococcales are highlighted in bold. The phylogenetic tree is built on 100 genes (refer to Table S5), and the taxa clustering percentage is based on 100 bootstrap resamples, consistently yielding values of 98 or higher (specific values not shown).\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eOur study highlights the potential prevalence and diversity of adaptive strategies utilized by methanotrophic bacteria in low-oxygen environments, specifically within LK sediments. These LK sediments, located 20 cm below the sediment water interface and 20 meters below the hypolimnion, were previously shown to be involve Methylococcales in methane oxidation and stimulation of iron reduction. We hypothesized that this environment harbors Methylococcales with unique strategies for survival. Through enrichment experiments, comparative metagenomics, and genomic analyses of diverse Methylococcales lineages, we propose several potential mechanisms enabling these organisms to thrive under oxygen-limited conditions. Our findings demonstrate that most Methylococcales, including those from LK, possess a set of traits enabling their survival in hypoxic environments: spanning effective usage of trace oxygen, motility for reaching optimal oxygen concentrations, glycogen storage, alternative energy generation using fermentation, and the use of alternative electron acceptors and possible EET mediated by riboflavins. Some less widespread functions employed by LK Methylococcales include lactate productionn, methane-dependent denitrification, and EET via outer membrane cytochromes (OMCs). Further studies are necessary to validate these potential strategies. Experimental examination of these potential adaptations may unveil additional layers of complexity in the survival strategies. Additionally, investigating whether these adaptations are expressed under hypoxic conditions is essential for understanding their role in hypoxia tolerance. This knowledge may contribute to a more comprehensive understanding of global methane cycling.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eAuthor information:\u003c/h2\u003e \u003cp\u003e \u003cem\u003eContributions\u003c/em\u003e:\u003c/p\u003e \u003cp\u003eA.G., H.V., and O.S. conceived the project and designed the study, with contributions from W.E. and J.C.M. J.C.M. hosted A.G. in his research group, providing essential tools and scientific background for conducting DNA stable isotope probing, in collaboration with N.M.L. A.G. collected the data, and M.R.B. performed the bioinformatic analysis. A.G. wrote the first draft of the manuscript. All authors substantively revised the manuscript, and each author approves of the submitted version.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003e \u003cb\u003eEthics declarations\u003c/b\u003e:\u003c/h2\u003e \u003cp\u003e \u003cstrong\u003eEthics approval and consent to participate:\u003c/strong\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication:\u003c/strong\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eCompeting interests:\u003c/strong\u003e \u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eSupplementary Information:\u003c/h2\u003e \u003cp\u003e \u003cem\u003eAdditional file 1.\u003c/em\u003e \u003c/p\u003e \u003cp\u003eSupplementary Methods: Gases spiking list of events. Supplementary Figures \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e-S2. Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003ea: Relative abundance showing 16S rRNA of top 15 genera in the three time steps of the DNA-SIP experiment. Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eb: Principal component analysis of the same samples. Figure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e: Relative abundance showing 16S rRNA of top 10 genera from the hypoxic experiment.\u003c/p\u003e \u003cp\u003e \u003cem\u003eAdditional file 2.\u003c/em\u003e \u003c/p\u003e \u003cp\u003eSupplementary Tables S1\u0026ndash;S8. Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e: DNA-SIP sample list including sampling time, samples used for 16S rRNA gene sequencing and metagenome analysis. Supplementary Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e: Anoxic experiment sample list including sampling time, samples used for 16S rRNA gene sequencing and metagenome analysis. Supplementary Table S3: list of Methylococcales genomes used, including detailed information. Supplementary Table S4: list of average nucleotide identity (ANI) values between Methylococcales genomes. Supplementary Table S5: list of proteins used for phylogenetic tree construction. Supplementary Table S6: list of predicted proteins and their copy number as predicted in LK Methylococcales. Supplementary Table S7: list of predicted proteins used for the presence-absence list in Fig.\u0026nbsp;4. This includes protein type, location, and the name for each Methylococcales genome. Supplementary Table S8: list of predicted OMC proteins used for the presence-absence list in Fig.\u0026nbsp;4. This includes protein type, location, protein sequence, and the name of each Methylococcales genome.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e \u003cp\u003eThis research was funded by ERC-2018-COG (818450) and ISF (857\u0026ndash;2016) grants awarded to OS. In addition, AG was supported by a post-doctoral scholarship of the Kreitman School, Ben Gurion University of the Negev.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003e\"A.G., H.V., and O.S. conceived the project and designed the study, with contributions from W.E. and J.C.M. J.C.M. hosted A.G. in his research group, providing essential tools and scientific background for conducting DNA stable isotope probing, in collaboration with N.M.L. A.G. collected the data, and M.R.B. performed the bioinformatic analysis. A.G. wrote the first draft of the manuscript. All authors substantively revised the manuscript, and each author approves of the submitted version.\u003c/p\u003e\u003ch2\u003eAvailability of data and materials:\u003c/h2\u003e \u003cp\u003eThe datasets generated and analyzed during the current study are available under NCBI BioProject ID PRJNA1041615. Additional data are available under the Supplementary information sections.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eJohnson MS, Matthews E, Du J, Genovese V, Bastviken D. Methane Emission From Global Lakes: New Spatiotemporal Data and Observation-Driven Modeling of Methane Dynamics Indicates Lower Emissions. JGR Biogeosciences (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKelly DP, Wood AP. \u003cem\u003eThe Chemolithotrophic Prokaryotes\u003c/em\u003e. \u003cem\u003eThe Prokaryotes\u003c/em\u003e (2006). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/0-387-30742-7_15\u003c/span\u003e\u003cspan address=\"10.1007/0-387-30742-7_15\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLawton TJ, Rosenzweig AC. Biocatalysts for methane conversion: big progress on breaking a small substrate. Curr Opin Chem Biol. 2016;35:142\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKnittel K, Boetius A. Anaerobic oxidation of methane: Progress with an unknown process. Annu Rev Microbiol. 2009;63:311\u0026ndash;34.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNordi K\u0026Aacute;, Thamdrup B, Schubert CJ. Anaerobic oxidation of methane in an iron-rich Danish freshwater lake sediment. Limnol Oceanogr. 2013;58:546\u0026ndash;54.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSegarra KEA, et al. High rates of anaerobic methane oxidation in freshwater wetlands reduce potential atmospheric methane emissions. Nat Commun. 2015;6:2\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHaroon MF, et al. Anaerobic oxidation of methane coupled to nitrate reduction in a novel archaeal lineage. Nature. 2013;500:567\u0026ndash;70.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRaghoebarsing AA, et al. A microbial consortium couples anaerobic methane oxidation to denitrification. Nature. 2006;440:918\u0026ndash;21.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNordi K \u0026aacute;, Thamdrup B. Nitrate-dependent anaerobic methane oxidation in a freshwater sediment. \u003cem\u003eGeochim. Cosmochim. Acta\u003c/em\u003e 132, 141\u0026ndash;150 (2014).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEttwig KF et al. Archaea catalyze iron-dependent anaerobic oxidation of methane. \u003cem\u003eProc. Natl. Acad. Sci. U. S. A.\u003c/em\u003e 113, 12792\u0026ndash;12796 (2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLu YZ, et al. Cr(VI) reduction coupled with anaerobic oxidation of methane in a laboratory reactor. Water Res. 2016;102:445\u0026ndash;52.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBlees J, et al. Micro-aerobic bacterial methane oxidation in the chemocline and anoxic water column of deep south-Alpine Lake Lugano (Switzerland). Limnol Oceanogr. 2014;59:311\u0026ndash;24.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOswald K, et al. Aerobic gammaproteobacterial methanotrophs mitigate methane emissions from oxic and anoxic lake waters. Limnol Oceanogr. 2016;61:S101\u0026ndash;18.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBeck DAC et al. A metagenomic insight into freshwater methane-utilizing communities and evidence for cooperation between the Methylococcaceae and the Methylophilaceae. \u003cem\u003ePeerJ\u003c/em\u003e 2013, 1\u0026ndash;23 (2013).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBar-Or I, Ben-Dov E, Kushmaro A, Eckert W, Sivan O. Methane-related changes in prokaryotes along geochemical profiles in sediments of Lake Kinneret (Israel). Biogeosciences. 2015;12:2847\u0026ndash;60.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMartinez-Cruz K, et al. Anaerobic oxidation of methane by aerobic methanotrophs in sub-Arctic lake sediments. Sci Total Environ. 2017;607\u0026ndash;608:23\u0026ndash;31.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSu G, Zopfi J, Niemann H, Lehmann MF. Multiple Groups of Methanotrophic Bacteria Mediate Methane Oxidation in Anoxic Lake Sediments. Front Microbiol 13, (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEttwig KF, et al. Nitrite-driven anaerobic methane oxidation by oxygenic bacteria. Nature. 2010;464:543\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDershwitz P, et al. Oxygen generation via water splitting by a novel biogenic metal ion-binding compound. Appl Environ Microbiol. 2021;87:1\u0026ndash;14.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKits KD, Klotz MG, Stein LY. Methane oxidation coupled to nitrate reduction under hypoxia by the Gammaproteobacterium Methylomonas denitrificans, sp. nov. type strain FJG1. Environ Microbiol. 2015;17:3219\u0026ndash;32.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOrata FD, Kits KD, Stein LY. Complete genome sequence of Methylomonas denitrificans strain FJG1, an obligate aerobic methanotroph that can couple methane oxidation with denitrification. Genome Announc. 2018;6:1\u0026ndash;2.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDang CC et al. Heavy metal reduction coupled to methane oxidation: Mechanisms, recent advances and future perspectives. J Hazard Mater 405, (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZheng Y, et al. Methane-Dependent Mineral Reduction by Aerobic Methanotrophs under Hypoxia. Environ Sci Technol Lett. 2020. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1021/acs.estlett.0c00436\u003c/span\u003e\u003cspan address=\"10.1021/acs.estlett.0c00436\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTanaka K, et al. Extracellular electron transfer via outer membrane cytochromes in a methanotrophic bacterium methylococcus capsulatus (Bath). Front Microbiol. 2018;9:1\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVigderovich H, et al. Aerobic methanotrophy increases the net iron reduction in methanogenic lake sediments. Front Microbiol. 2023;14:1\u0026ndash;17.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSivan O, et al. Geochemical evidence for iron-mediated anaerobic oxidation of methane. Limnol Oceanogr. 2011;56:1536\u0026ndash;44.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBar-Or I, et al. Iron-Coupled Anaerobic Oxidation of Methane Performed by a Mixed Bacterial-Archaeal Community Based on Poorly Reactive Minerals. Environ Sci Technol. 2017;51:12293\u0026ndash;301.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eElul M, et al. Metagenomic insights into the metabolism of microbial communities that mediate iron and methane cycling in Lake Kinneret sediments. Biogeosciences Discuss. 2020;1\u0026ndash;24. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.5194/bg-2020-329\u003c/span\u003e\u003cspan address=\"10.5194/bg-2020-329\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVigderovich H, et al. Long-term incubations provide insight into the mechanisms of anaerobic oxidation of methane in methanogenic lake sediments. Biogeosciences. 2022;19:2313\u0026ndash;31.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAdler M, Eckert W, Sivan O. Quantifying rates of methanogenesis and methanotrophy in Lake Kinneret sediments (Israel) using pore-water profiles. Limnol Oceanogr. 2011;56:1525\u0026ndash;35.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFarhan Ul Haque M, Crombie AT, Murrell JC. Novel facultative Methylocella strains are active methane consumers at terrestrial natural gas seeps. Microbiome. 2019;7:1\u0026ndash;17.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNeufeld JD, et al. DNA stable-isotope probing. Nat Protoc. 2007;2:860\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eParada AE, Needham DM, Fuhrman JA. Every base matters: Assessing small subunit rRNA primers for marine microbiomes with mock communities, time series and global field samples. Environ Microbiol. 2016;18:1403\u0026ndash;14.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eApprill A, Mcnally S, Parsons R, Weber L. Minor revision to V4 region SSU rRNA 806R gene primer greatly increases detection of SAR11 bacterioplankton. Aquat Microb Ecol. 2015;75:129\u0026ndash;37.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBolyen E, et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat Biotechnol. 2019;37:852\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCallahan BJ, et al. DADA2: High-resolution sample inference from Illumina amplicon data. Nat Methods. 2016;13:581\u0026ndash;3.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eQuast C, et al. The SILVA ribosomal RNA gene database project: Improved data processing and web-based tools. Nucleic Acids Res. 2013;41:590\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcMurdie PJ, Holmes S, Phyloseq. An R Package for Reproducible Interactive Analysis and Graphics of Microbiome Census Data. PLoS ONE 8, (2013).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePrjibelski A, Antipov D, Meleshko D, Lapidus A, Korobeynikov A. Using SPAdes De Novo Assembler. Curr Protoc Bioinforma. 2020;70:1\u0026ndash;29.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu YW, Simmons BA, Singer SW. MaxBin 2.0: An automated binning algorithm to recover genomes from multiple metagenomic datasets. Bioinformatics. 2016;32:605\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSieber CMK, et al. Recovery of genomes from metagenomes via a dereplication, aggregation and scoring strategy. Nat Microbiol. 2018;3:836\u0026ndash;43.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKang DD et al. MetaBAT 2: An adaptive binning algorithm for robust and efficient genome reconstruction from metagenome assemblies. \u003cem\u003ePeerJ\u003c/em\u003e 2019, 1\u0026ndash;13 (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNissen JN, et al. Improved metagenome binning and assembly using deep variational autoencoders. Nat Biotechnol. 2021;39:555\u0026ndash;60.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKieser S, Brown J, Zdobnov EM, Trajkovski M, McCue LA. ATLAS: A Snakemake workflow for assembly, annotation, and genomic binning of metagenome sequence data. BMC Bioinformatics. 2020;21:1\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChklovski A, Parks DH, Woodcroft BJ, Tyson GW. CheckM2: a rapid, scalable and accurate tool for assessing microbial genome quality using machine learning. bioRxiv. 2022. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003edoi.org/10.1101/2022.07.11.499243\u003c/span\u003e\u003cspan address=\"10.1101/2022.07.11.499243\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGurevich A, Saveliev V, Vyahhi N, Tesler G. QUAST: Quality assessment tool for genome assemblies. \u003cem\u003eBioinformatics\u003c/em\u003e 29, 1072\u0026ndash;1075 (2013).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOlson RD, et al. Introducing the Bacterial and Viral Bioinformatics Resource Center (BV-BRC): a resource combining PATRIC, IRD and ViPR. Nucleic Acids Res. 2023;51:D678\u0026ndash;89.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAltschul SF. BLAST Algorithm. \u003cem\u003eeLS\u003c/em\u003e 1\u0026ndash;4 (2005). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/npg.els.0005253\u003c/span\u003e\u003cspan address=\"10.1038/npg.els.0005253\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBoratyn GM, et al. BLAST: a more efficient report with usability improvements. Nucleic Acids Res. 2013;41:29\u0026ndash;33.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eO\u0026rsquo;Leary NA, et al. Reference sequence (RefSeq) database at NCBI: Current status, taxonomic expansion, and functional annotation. Nucleic Acids Res. 2016;44:D733\u0026ndash;45.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePritchard L, Glover RH, Humphris S, Elphinstone JG, Toth IK. Genomics and taxonomy in diagnostics for food security: Soft-rotting enterobacterial plant pathogens. Anal Methods. 2016;8:12\u0026ndash;24.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOlmsted CN, et al. Environmental predictors of electroactive bacterioplankton in small boreal lakes. Environ Microbiol. 2023;25:705\u0026ndash;20.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePaysan-Lafosse T, et al. InterPro in 2022. Nucleic Acids Res. 2023;51:D418\u0026ndash;27.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDavis JJ, et al. PATtyFams: Protein families for the microbial genomes in the PATRIC database. Front Microbiol. 2016;7:1\u0026ndash;12.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEdgar RC. Multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res 32. 2004;MUSCLE:1792\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStamatakis A, Hoover P, Rougemont J. A rapid bootstrap algorithm for the RAxML web servers. Syst Biol. 2008;57:758\u0026ndash;71.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChistoserdova L. Methylotrophs in natural habitats: current insights through metagenomics. Appl Microbiol Biotechnol. 2015;99:5763\u0026ndash;79.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDumont MG, Pommerenke B, Casper P. Using stable isotope probing to obtain a targeted metatranscriptome of aerobic methanotrophs in lake sediment. Environ Microbiol Rep. 2013;5:757\u0026ndash;64.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHe R, et al. Metabolic flexibility of aerobic methanotrophs under anoxic conditions in Arctic lake sediments. ISME J. 2021;1\u0026ndash;13. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41396-021-01049-y\u003c/span\u003e\u003cspan address=\"10.1038/s41396-021-01049-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRissanen AJ, et al. Vertical stratification patterns of methanotrophs and their genetic controllers in water columns of oxygen-stratified boreal lakes. FEMS Microbiol Ecol. 2021;97:1\u0026ndash;16.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSmith GJ, et al. Members of the genus methylobacter are inferred to account for the majority of aerobic methane oxidation in oxic soils from a freshwater wetland. MBio. 2018;9:1\u0026ndash;17.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIslam MM, Le T, Daggumati SR, Saha R. Investigation of microbial community interactions between Lake Washington methanotrophs using genome-scale metabolic modeling. \u003cem\u003ePeerJ\u003c/em\u003e 2020, 1\u0026ndash;28 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGuo W, et al. Genome-scale revealing the central metabolic network of the fast growing methanotroph Methylomonas sp. ZR1. World J Microbiol Biotechnol. 2021;37:1\u0026ndash;17.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGeymonat E, Ferrando L, Tarlera SE. Methylogaea oryzae gen. nov., sp. nov., a mesophilic methanotroph isolated from a rice paddy field. Int J Syst Evol Microbiol. 2011;61:2568\u0026ndash;72.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang Y, Kitajima M, Whittle AJ, Liu WT. Benefits of genomic insights and CRISPR-Cas signatures to monitor potential pathogens across drinking water production and distribution systems. Front Microbiol. 2017;8:1\u0026ndash;15.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTavormina PL, et al. Abundance and distribution of diverse membrane-bound monooxygenase (Cu-MMO) genes within the Costa Rica oxygen minimum zone. Environ Microbiol Rep. 2013;5:414\u0026ndash;23.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVekeman B, et al. Genome Characteristics of Two Novel Type I Methanotrophs Enriched from North Sea Sediments Containing Exclusively a Lanthanide-Dependent XoxF5-Type Methanol Dehydrogenase. Microb Ecol. 2016;72:503\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKeltjens JT, Pol A, Reimann J. Op Den Camp, H. J. M. PQQ-dependent methanol dehydrogenases: Rare-earth elements make a difference. Appl Microbiol Biotechnol. 2014;98:6163\u0026ndash;83.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKrause SMB et al. Lanthanide-dependent cross-feeding of methane-derived carbon is linked by microbial community interactions. \u003cem\u003eProc. Natl. Acad. Sci. U. S. A.\u003c/em\u003e 114, 358\u0026ndash;363 (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKalyuzhnaya MG, Gomez OA, Murrell JC. \u003cem\u003eThe Methane-Oxidizing Bacteria (Methanotrophs)\u003c/em\u003e. \u003cem\u003eTaxonomy, Genomics and Ecophysiology of Hydrocarbon-Degrading Microbes\u003c/em\u003e (2019). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/978-3-030-14796-9_10\u003c/span\u003e\u003cspan address=\"10.1007/978-3-030-14796-9_10\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKalyuzhnaya MG, et al. Highly efficient methane biocatalysis revealed in a methanotrophic bacterium. Nat Commun. 2013;4:1\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStolpera DA, Revsbech NP, Canfield DE. Aerobic growth at nanomolar oxygen concentrations. \u003cem\u003eProc. Natl. Acad. Sci. U. S. A.\u003c/em\u003e 107, 18755\u0026ndash;18760 (2010).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen KHC, et al. Bacteriohemerythrin bolsters the activity of the particulate methane monooxygenase (pMMO) in Methylococcus capsulatus (Bath). J Inorg Biochem. 2012;111:10\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOshkin IY et al. Thriving in Wetlands: Ecophysiology of the Spiral-Shaped Methanotroph Methylospira mobilis as Revealed by the Complete Genome Sequence. (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVon Canstein H, Ogawa J, Shimizu S, Lloyd JR. Secretion of flavins by Shewanella species and their role in extracellular electron transfer. Appl Environ Microbiol. 2008;74:615\u0026ndash;23.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCoursolle D, Baron DB, Bond DR, Gralnick JA. The Mtr respiratory pathway is essential for reducing flavins and electrodes in Shewanella oneidensis. J Bacteriol. 2010;192:467\u0026ndash;74.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFuller SJ, et al. Extracellular electron transport-mediated fe(iii) reduction by a community of alkaliphilic bacteria that use flavins as electron shuttles. Appl Environ Microbiol. 2014;80:128\u0026ndash;37.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWatanabe K, Manefield M, Lee M, Kouzuma A. Electron shuttles in biotechnology. Curr Opin Biotechnol. 2009;20:633\u0026ndash;41.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eScholtysik G, et al. Geochemical focusing and burial of sedimentary iron, manganese, and phosphorus during lake eutrophication. Limnol Oceanogr. 2022;67:768\u0026ndash;83.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStrong PJ, Kalyuzhnaya M, Silverman J, Clarke WP. A methanotroph-based biorefinery: Potential scenarios for generating multiple products from a single fermentation. Bioresour Technol. 2016;215:314\u0026ndash;23.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu LY, et al. Biological conversion of methane to polyhydroxyalkanoates: Current advances, challenges, and perspectives. Environ Sci Ecotechnology. 2020;2:100029.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGilman A et al. Oxygen-limited metabolism in the methanotroph Methylomicrobium buryatense 5GB1C. \u003cem\u003ePeerJ\u003c/em\u003e 2017, (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDimitri Kits K, Campbell DJ, Rosana AR, Stein L. Y. Diverse electron sources support denitrification under hypoxia in the obligate methanotroph Methylomicrobium album strain BG8. Front Microbiol. 2015;6:1\u0026ndash;11.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKits KD, Klotz MG, Stein LY. Methane oxidation coupled to nitrate reduction under hypoxia by the Gammaproteobacterium Methylomonas denitrificans, sp. nov. type strain FJG1. Environ Microbiol. 2015;17:3219\u0026ndash;32.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi B, et al. Iron oxides act as an alternative electron acceptor for aerobic methanotrophs in anoxic lake sediments. Water Res. 2023;234:119833.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHolmes DE, Dang Y, Walker DJF, Lovley DR. The electrically conductive pili of Geobacter s pecies are a recently evolved feature for extracellular electron transfer. (2016). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1099/mgen.0.000072\u003c/span\u003e\u003cspan address=\"10.1099/mgen.0.000072\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Unsectioned Paragraphs","content":"\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"downldfle_cls\" name=\"MediaObject\"\u003e \u003ca href=\"only Table1.xlsx\"\u003e\u003cdiv fileref=\"only Table1.xlsx\" class=\"DataObject\"\u003eonly Table1.xlsx\u003c/div\u003e\u003c/a\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"downldfle_cls\" name=\"MediaObject\"\u003e \u003ca href=\"only Table2.xlsx\"\u003e\u003cdiv fileref=\"only Table2.xlsx\" class=\"DataObject\"\u003eonly Table2.xlsx\u003c/div\u003e\u003c/a\u003e\u003c/div\u003e\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"environmental-microbiome","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"sigs","sideBox":"Learn more about [Environmental Microbiome](https://environmentalmicrobiome.biomedcentral.com)","snPcode":"40793","submissionUrl":"https://submission.nature.com/new-submission/40793/3","title":"Environmental Microbiome","twitterHandle":"@bmc","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"lake sediment, aerobic methanotrophy, methanogenic zone, hypoxia, Methylomonas, Methylobacter","lastPublishedDoi":"10.21203/rs.3.rs-3790875/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3790875/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eMicrobial methane oxidation, methanotrophy, plays a crucial role in mitigating the release of the potent greenhouse gas methane from aquatic systems. While aerobic methanotrophy is a well-established process in oxygen-rich environments, emerging evidence suggests their activity in hypoxic conditions. However, the adaptability of these methanotrophs to such environments has remained poorly understood. Here, we explored the genetic adaptability of aerobic methanotrophs to hypoxia in the methanogenic sediments of Lake Kinneret (LK). These LK methanogenic sediments, situated below the oxidic and sulfidic zones, were previously characterized by methane oxidation coupled with iron reduction via the involvement of aerobic methanotrophs.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eIn order to explore the adaptation of the methanotrophs to hypoxia, we conducted two experiments using LK sediments as inoculum: i) an aerobic \"classical\" methanotrophic enrichment with ambient air employing DNA stable isotope probing (DNA-SIP) and ii) hypoxic methanotrophic enrichment with repeated spiking of 1% oxygen. Analysis of 16S rRNA gene amplicons revealed the enrichment of Methylococcales methanotrophs, being up to a third of the enriched community. \u003cem\u003eMethylobacter\u003c/em\u003e, \u003cem\u003eMethylogaea\u003c/em\u003e, and \u003cem\u003eMethylomonas\u003c/em\u003e were prominent in the aerobic experiment, while hypoxic conditions enriched primarily \u003cem\u003eMethylomonas\u003c/em\u003e. Using metagenomics sequencing of DNA extracted from these experiments, we curated five Methylococcales metagenome-assembled genomes (MAGs) and evaluated the genetic basis for their survival in hypoxic environments. A comparative analysis with an additional 62 Methylococcales genomes from various environments highlighted several core genetic adaptations to hypoxia found in most examined Methylococcales genomes, including high-affinity cytochrome oxidases, oxygen-binding proteins, fermentation-based methane oxidation, motility, and glycogen use. We also found that some Methylococcales, including LK Methylococcales, may denitrify, while metals and humic substances may also serve as electron acceptors alternative to oxygen. Outer membrane multi-heme cytochromes and riboflavin were identified as potential mediators for the utilization of metals and humic material. These diverse mechanisms suggest the ability of methanotrophs to thrive in ecological niches previously thought inhospitable for their growth.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eOur study sheds light on the ability of enriched Methylococcales methanotrophs from methanogenic LK sediments to survive under hypoxia. Genomic analysis revealed a spectrum of genetic capabilities, potentially enabling these methanotrophs to function. The identified mechanisms, such as those enabling the use of alternative electron acceptors, expand our understanding of methanotroph resilience in diverse ecological settings. These findings contribute to the broader knowledge of microbial methane oxidation and have implications for understanding and potential contribution methanotrophs may have in mitigating methane emissions in various environmental conditions.\u003c/p\u003e","manuscriptTitle":"Survival strategies of aerobic methanotrophs under hypoxia in methanogenic lake sediments","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-18 18:48:53","doi":"10.21203/rs.3.rs-3790875/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-04-24T16:56:22+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-03-28T17:25:23+00:00","index":"","fulltext":""},{"type":"submitted","content":"Environmental Microbiome","date":"2024-03-26T13:02:30+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"environmental-microbiome","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"sigs","sideBox":"Learn more about [Environmental Microbiome](https://environmentalmicrobiome.biomedcentral.com)","snPcode":"40793","submissionUrl":"https://submission.nature.com/new-submission/40793/3","title":"Environmental Microbiome","twitterHandle":"@bmc","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"17dbd55d-feb4-4ab4-93ed-b7046d0a16a7","owner":[],"postedDate":"April 18th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2024-06-23T20:38:19+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-18 18:48:53","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3790875","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3790875","identity":"rs-3790875","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}