Uncovering hidden phylo- and ecogenomic diversity of the widespread methanotrophic genus Methylobacter

Uncovering hidden phylo- and ecogenomic diversity of the widespread methanotrophic genus Methylobacter | bioRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (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];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-M677548'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search New Results Uncovering hidden phylo- and ecogenomic diversity of the widespread methanotrophic genus Methylobacter View ORCID Profile Magdalena Wutkowska , Justus A. Nweze , View ORCID Profile Vojtěch Tláskal , View ORCID Profile Julius E. Nweze , View ORCID Profile Anne Daebeler doi: https://doi.org/10.1101/2025.09.26.678804 Magdalena Wutkowska 1 Institute of Soil Biology and Biogeochemistry, Biology Centre CAS , Na Sádkách 702/7, 370 05 České Budějovice, Czech Republic Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Magdalena Wutkowska For correspondence: magda.wutkowska{at}gmail.com anne.daebeler{at}bc.cas.cz Justus A. Nweze 1 Institute of Soil Biology and Biogeochemistry, Biology Centre CAS , Na Sádkách 702/7, 370 05 České Budějovice, Czech Republic 2 Department of Ecosystem Biology, Faculty of Science, University of South Bohemia , Branišovská 31a, 370 05 České Budějovice, Czech Republic 3 Department of Science Laboratory Technology, Faculty of Physical Sciences, University of Nigeria , Owerre Eze Rd, Ihe Nsukka, Nsukka, Nigeria Find this author on Google Scholar Find this author on PubMed Search for this author on this site Vojtěch Tláskal 1 Institute of Soil Biology and Biogeochemistry, Biology Centre CAS , Na Sádkách 702/7, 370 05 České Budějovice, Czech Republic Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Vojtěch Tláskal Julius E. Nweze 1 Institute of Soil Biology and Biogeochemistry, Biology Centre CAS , Na Sádkách 702/7, 370 05 České Budějovice, Czech Republic 4 Institut National de Recherche Scientifique, Centre Armand Frappier Santé et Biotechnologie , Laval, QC, Canada Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Julius E. Nweze Anne Daebeler 1 Institute of Soil Biology and Biogeochemistry, Biology Centre CAS , Na Sádkách 702/7, 370 05 České Budějovice, Czech Republic Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Anne Daebeler For correspondence: magda.wutkowska{at}gmail.com anne.daebeler{at}bc.cas.cz Abstract Full Text Info/History Metrics Data/Code Preview PDF ABSTRACT The globally distributed genus Methylobacter plays a crucial role in mitigating methane emissions from diverse ecosystems, including freshwater and marine habitats, wetlands, soils, sediments, groundwater, and landfills. Despite their frequent presence and abundance in these systems, we still know little about the genomic adaptations that they exhibit. Here, we used a collection of 97 genomes and metagenome-assembled genomes to ecogenomically characterise the genus. Our analyses suggest that the genus Methylobacter may contain more species than previously thought, with >30 putative species clusters. Some species clusters shared >98.65% sequence identity of the full-length 16S rRNA gene, demonstrating the need for genome-resolved species delineation. The ecogenomic differences between Methylobacter spp. include various combinations of methane monooxygenases, multigene loci for alternative dissimilatory metabolisms related to hydrogen, sulphur cycling, and denitrification, as well as other lifestyle-associated functions. Additionally, we describe and tentatively name the two new Methylobacter species, which we recently cultured from sediment of a temperate eutrophic fishpond, as Methylobacter methanoversatilis , sp. nov. and Methylobacter spei , sp. nov. Overall, our study highlights previously unrecognised species diversity within the Methylobacter genus, their diverse metabolic potential, versatility, as well as the presence of distinct genomic adaptations for thriving in various environments. INTRODUCTION Methane (CH 4 ) is a potent greenhouse gas with an 84-fold greater warming potential than carbon dioxide (CO 2 ) over the first 20 years after release ( Myhre et al . 2013 ). However, the overall radiative forcing caused by CH 4 is likely heavily underestimated by approx. 25% ( Etminan et al . 2016 ). The CH 4 level in Earth’s atmosphere has been steadily increasing since the Industrial Revolution. In 2025, the global monthly mean abundance of atmospheric CH 4 concentration surpassed 1.93 ppm ( Lan et al . 2022 , version 2025-09), nearly tripling the preindustrial 0.7 ppm ( Sapart et al . 2012 ). The largest natural sources of CH 4 are global freshwater systems, including wetlands, which contribute over 50% of total emissions ( Jackson et al . 2020 ). Methanotrophs, microorganisms that can use CH 4 as their only carbon and energy source, can consume >90% of in situ -produced CH 4 before it reaches the atmosphere ( King 1990 ; King, Roslev and Skovgaard 1990 ; Oremland and Culbertson 1992 ; Michaud et al . 2017 ). However, it is uncertain whether the efficiency of CH 4 removal can be sustained in the face of the rapid progression of climate change. In many ecosystems that produce large amounts of CH 4 , gammaproteobacterial methanotrophs from the genus Methylobacter can dominate the active methanotrophic community ( Nercessian et al . 2005 ; Tveit et al . 2013 ; Rissanen et al . 2018 ; Smith et al . 2018; Savvichev et al . 2021 ; Deng et al . 2024 ; Li et al . 2025 ). Members of the Methylobacter genus are widespread, having been identified in samples collected from all continents, predominantly in diverse freshwater, wetland, and soil habitats ( Rodrigues et al . 2025 ). They are known to oxidise CH 4 with the particulate CH 4 monooxygenase (pMMO), assimilate carbon through the ribulose monophosphate pathway (RuMP), and use ubiquinone Q-8 as a major respiratory lipoquinone ( Bowman et al . 1993 ; Bowman 2006 ). The majority of Methylobacter isolates grow relatively quickly and efficiently oxidise CH 4 , even at low temperatures ( Tveit et al . 2023 ). In fully oxic conditions, CH 4 oxidation rates can reach up to 0.60 μmol CH 4 10 8 cells -1 hour -1 for M. luteus at 27°C ( Tveit et al . 2023 ). The cultured species grow optimally at temperatures between 23–30°C ( Bowman et al . 1993 ; Hanson and Hanson 1996 ; Bowman 2014 ; Bodelier et al . 2019 ), with few psychrophilic strains ( Omelchenko, Vasilyeva and Zavarzin 1993 ; Khanongnuch et al . 2022 ; Patil et al . 2024 ), widespread psychrotolerance ( Wartiainen et al . 2006 ; Roldán and Menes 2023 ), and only some thermotolerant species with optima >35°C ( Lidstrom 1988 ). Methylobacter spp., although geographically widely distributed, are predominantly found in non-acidic pH ranges ( Seppey et al . 2023 ), with a few exceptions ( Nguyen et al . 2018 ; Hogendoorn et al . 2021 ; Nweze et al . 2024 ). The genus Methylobacter was initially named in 1970 ( Whittenbury, Davies and Davey 1970 ; Whittenbury, Phillips and Wilkinson 1970 ). However, the name was only officially (taxonomically) introduced two decades later, reclassifying known methanotrophs based on the DNA-DNA hybridisation values and phospholipid fatty acid compositions ( Bowman et al . 1993 ). A genome-based reclassification of methanotrophs, including the genus Methylobacter , corrected some misclassifications, which had previously contributed to a polyphyletic character of the genus, and identified 10 species-level clusters in the genus Methylobacter ( Orata et al . 2018 ). In this study, we used 97 genomes and MAGs classified as Methylobacter , retrieved in May 2024 from the NCBI Genome portal, to comprehensively characterise them regarding their potential metabolic capabilities in the context of phylogenomics. A special focus was directed towards the presence and genomic organisation of different CH 4 monooxygenase forms, dissimilatory metabolisms other than CH 4 oxidation, and genomic adaptations to O 2 -depleted conditions. Additionally, we characterise two recently obtained novel Methylobacter spp. ( Wutkowska and Daebeler 2024 ), which we tentatively name Methylobacter spei , sp. nov., and Methylobacter methanoversatilis , sp. nov. MATERIALS AND METHODS Methylobacter genomes and MAGs On May 21, 2024, we retrieved all 97 available genomes and MAGs classified as Methylobacter from the NCBI Genome database (Table S1, Fig. 1 ). Genomes of isolates included: M. luteus 98 or IMV-B-3098 previously M. bovis ( Whittenbury, Davies and Davey 1970 ; Whittenbury, Phillips and Wilkinson 1970 ) ; M. whittenburyi (formerly M. capsulatus UCM-B-3033) ( Whittenbury, Phillips and Wilkinson 1970 ; Hamilton et al . 2015 ); M. marinus A45 (formerly Methylomonas methanica A4) ( Lidstrom 1988 ; Flynn et al . 2016 ); M. tundripaludum SV96 ( Wartiainen et al . 2006 ; Svenning et al . 2011 ); M. psychrophilus Z-0021 ( Omelchenko, Vasilyeva and Zavarzin 1993 ; Omelchenko et al . 1996b ; Rissanen et al . 2022 ); M. sp. strain BBA5.1 ( Smith, Costello and Lidstrom 1997 ; Flynn et al . 2016 ); M. sp. 21/22 and 31/32 ( Beck et al . 2013 ; Kalyuzhnaya et al . 2015 ; Oshkin et al . 2015 ); ‘ Ca. M. oryzae’ KRF1 ( Khatri et al . 2020 ); ‘ Ca . M. coli’ BlB1 ( Khatri et al . 2021 ); M . sp. YRD-M1 ( Hao et al . 2022 ), M . sp. Wu8 ( Wutkowska and Daebeler 2024 ), and M. svalbardiensis LS7-T4A ( Patil et al . 2024 ). A recent reclassification proposed M. whittenbury and M. marinus to be the heterotypic synonyms, as their genomes share a very high degree of similarity (89% dDDH and 99% ANI) ( Orata et al . 2018 ), with M. marinus being the recommended valid name, therefore we did not include a M. whittenbury genome in our analyses. Download figure Open in new tab Figure 1 Phylogeny of the genus Methylobacter based on concatenated amino acid sequences of 71 single-copy genes from 44 high-quality non-redundant Methylobacter spp. genomes and MAGs. Genomes from axenic cultures are indicated by asterisks (*), and genomes from our recently cultured strains are shown in bold . Coloured squares indicate the origin habitat type, GC content, and species included and delineated in Orata et al . (2018) (each colour represents a distinct species). Numbers and vertical lines indicate species and subspecies inferred by TYGS. The genome of Methylobacterium lacus LW14 ( Kalyuzhnaya et al . 2015 ) was used as an outgroup. Branch bootstrap support is indicated on the nodes as black (≥95%), grey (≥80%), or white (>50%) circles. The scale bar indicates 0.1 amino acid substitutions per site. Methylobacter spp. that have been sequenced from enrichment cultures belong to: M . sp. KS41 ( Nguyen et al . 2018 ); ‘ Ca. M. titanis’ sp. nov. strains K-2018 and D1-2020, as well as M . sp. U-2020 MAG003 ( Roldán and Menes 2023 ), and M . sp. strains Wu1 ( Wutkowska and Daebeler 2024 ). The remaining MAGs originated from environmental samples ( Parks et al . 2017 ; Woodcroft et al . 2018 ; Pedron et al . 2019 ; Zheng et al . 2020 ; Buck et al . 2021 ; Hogendoorn et al . 2021 ; Rissanen et al . 2021 ; Magnuson et al . 2022 ; Cabello-Yeves et al . 2023 ; Grégoire, George and Hug 2023 ; Jaffe et al . 2023 ; Ma et al . 2023 ; Ruff et al . 2023 ; Slobodkin et al . 2023 ). The gene coding sequences were predicted and translated to amino acid sequences using Prodigal v.2.6.3 ( Hyatt et al . 2010 ). To assess similarity among the 97 genomes, we calculated average nucleotide identity inferred with blastp (ANIb) using pyANI ( Pritchard et al . 2016 ) in-built in a pangenome workflow in anvio v.7.1 ‘Hope’ ( Eren et al . 2021 ) with third party software DIAMOND ( Buchfink, Reuter and Drost 2021 ) and MUSCLE ( Edgar 2004 ). To reduce redundancy in our dataset, genomes and MAGs with >99% ANIb similarity were grouped, and a representative genome for each group was chosen based on the highest completeness and lowest contamination calculated with CheckM2 v.1.0.1 (detailed summary available in Table S1) ( Chklovski et al . 2023 ). Those genomes with >90% completeness and <5% contamination was categorised as ‘high-quality’, although they sometimes consist of many contigs. Subsequently, only representative high-quality genomes and MAGs (n = 44) were retained for all downstream analyses (Table S1, Fig. 1 ). Phylogenomic analysis, genus and species delineation The phylogenomic analysis was performed using Anvi’o in the Anvi’o development environment (anvio-dev) ( Eren et al . 2021 ). The FASTA files containing genome sequences were reformatted to meet Anvi’o’s contigs database requirements using the anvi-script-reformat-fasta command. For each reformatted FASTA file, an Anvi’o contigs database was generated using the anvi-gen-contigs-database command, which used Prodigal (v2.6.3) ( Hyatt et al . 2010 ) to identify open reading frames in the DNA sequences and predict protein-coding genes. Conserved genes, including single-copy core genes (SCGs), were annotated and identified using hidden Markov models (HMMs) with the anvi-run-hmms command and the Bacteria_71 HMM profile. The sequences of the best hit for each SCG were extracted and concatenated into a single FASTA file using the anvi-get-sequences-for-hmm-hits command. The concatenated protein sequences were aligned using MAFFT (v7.520) ( Katoh and Standley 2013 ) with the FFT-NS-2 strategy, which was automatically selected using the --auto option. The aligned sequences were then used to construct a maximum-likelihood phylogenetic tree using IQ-TREE (v2.1.1) ( Nguyen et al . 2015 ; Minh et al . 2020 ). The IQ-TREE used ModelFinder Plus ( Kalyaanamoorthy et al . 2017 ) to automatically select the best-fit substitution model JJT+F+R5, and 1,000 ultrafast bootstrap replicates with UFBoot2 ( Hoang et al . 2018 ) to assess branch support. To determine whether the investigated Methylobacter genomes belong to the same genus, we calculated the average amino acid identity (AAI) using the EzAAI toolkit v1.2.2 ( Kim, Park and Chun 2021 ), which extracts and estimates the pairwise similarity using Prodigal ( Hyatt et al . 2010 ) and MMseqs2 ( Steinegger and Söding 2017 ). Methylobacter species were delineated using the Type-Strain-Genome-Server (TYGS) ( Meier-Kolthoff and Göker 2019 ; Meier-Kolthoff et al . 2022 ). This tool combines several indices and other types of evidence commonly used to distinguish species, including a phylogenomic tree calculated using genome BLAST distance phylogeny (GBDP), digital DNA–DNA hybridisation (dDDH), average nucleotide identity, and differences in genomic GC content. The dDDH scores were obtained using the Genome-to-Genome Distance Calculator 3.0, based on BLAST+ ( Camacho et al . 2009 ) with the recommended formula 2 (d 4 ) ( Meier-Kolthoff et al . 2013 , 2022 ), as well as the differences between genomic GC content ( Meier-Kolthoff, Klenk and Göker 2014 ). Species were ultimately delineated with the agreement of all the used metrics, with dDDH being the conclusive one. Two genomes were clustered into the same species when the dDDH value >70% ( Wayne et al . 1987 ; Goris et al . 2007 ), and into the same subspecies with >79% ( Meier-Kolthoff et al . 2014 ; Meier-Kolthoff and Göker 2019 ). We constructed phylogenetic trees with the 16S rRNA gene using nucleotide sequences and with protein-coding genes of interest using amino acid sequences (i.e., CH 4 monooxygenases, ATP synthase subunit c, ATP synthases). In each case, sequences were aligned using MAFFT v7.526 with the --auto option to optimise the alignment process ( Katoh and Standley 2013 ). The branch support in protein phylogenetic trees was obtained with the ultrafast bootstrap method with 1,000 replicates ( Hoang et al . 2018 ) implemented in the IQ-TREE 2 ( Minh et al . 2020 ). Trees were visualised in iTOL v7 ( Letunic and Bork 2024 ). Visualisation of the genomic region of soluble CH 4 monooxygenase was performed using the gggenomes package ( Hackl et al . 2024 ) in R v4.0 ( R Core Team 2024 ). Annotations of open reading frames We screened all representative high-quality Methylobacter genomes and MAGs for the presence of genes encoding three CH 4 monooxygenases and a gas vesicle protein with in-house Hidden Markov Models (HMMs) with the hmmscan/hmmsearch function in HMMER 3.4 ( Eddy 2011 ). The HMM for the major gas vesicle structural protein (encoded by the gvpA gene) was constructed from 32 reviewed and aligned amino acid sequences found in the UniProt Knowledgebase (The UniProt Consortium et al . 2025 ). All HMMER hits with a score <120 and an e-value <1e -30 were considered as gvpA . Additionally, we screened the genomes and MAGs for metabolic genes (i.e., [NiFe] hydrogenases, soxB , narGHI ) using DIAMOND v2.1.9.163 ( Buchfink, Xie and Huson 2015 ) against the collections of reference amino acid sequences ( Leung and Greening 2021 ). Sequences identified as [NiFe]-hydrogenases were subsequently classified into specific groups using HydDB ( Søndergaard, Pedersen and Greening 2016 ). All MAGs were also annotated with the automatic pipeline DRAM ( Shaffer et al . 2020 ). To contextualise the distribution of soluble methane monooxygenase (i.e., mmoX) and gas protein vesicles (i.e., gvpA) among gammaproteobacterial methanotrophs, we downloaded 1,067 available genomes and MAGs listed as belonging to the order Methylococcales from NCBI on August 12, 2024, and used in-house HMMs to identify the presence of these genes. Biosynthetic gene clusters were identified in the high-quality non-redundant genomes and MAGs with the Minimum Information about a Biosynthetic Gene cluster database v4.0 ( Zdouc et al . 2025 ) using blastp in DIAMOND v2.1.9.163 ( Buchfink, Xie and Huson 2015 ), and antiSMASH v7.1.0 ( Blin et al . 2023 ), both with stringent parameters. Predicting optimal growth conditions Since the majority of Methylobacter species have not been cultured yet, little is known about their ecological preferences and optimal growth conditions. Therefore, we identified putative oxygen preferences, optimal temperature, salinity, and pH levels based on genomic amino acid compositions of all high-quality genomes and MAGs using GenomeSPOT with default statistical models ( Barnum et al . 2024 ). The obtained predictions were compared with available data from cultured Methylobacter spp. RESULTS AND DISCUSSION Phylogenomic analysis and genome characteristics of Methylobacter The Methylobacter genomes and MAGs differed substantially in general characteristics, such as size (3,452,370–5,467,791 bp), number of predicted proteins (3,206–5,043), and GC content (44–55%) (Table S1, Fig. 1 ). The highest GC content occurred in the marine cluster and an adjacent cluster of Methylobacter spp. associated with eutrophic habitats and (putative) animal hosts (Table S1, Fig. 1 ). Accordingly, genomes that belong to psychrophilic strains and species from potentially nutrient-depleted habitats had the lowest GC content among all Methylobacter spp., reflecting adaptation to temperature and resource availability as described previously ( Foerstner et al . 2005 ; Hu et al . 2022 ; Chuckran et al . 2023 ). The Methylobacter genus contains two recognised, phylogenetic lineages, termed clade I, encompassing species such as M. luteus and M. marinus , and clade II, which includes the majority of Methylobacter spp. including M. tundripaludum ( Smith et al . 2018a ). General AAI similarity recommended for clustering genomes/MAGs in one genus ranges between 65–95% ( Konstantinidis, Rosselló-Móra and Amann 2017 ); however, a Methylobacter -specific threshold has been set at 74% ( Orata et al . 2018 ). The MAG M. tundripaludum Bin_16_1, which was the earliest diverging taxon, branched out most deeply in the phylogenomic tree ( Fig. 1 ), shared only 72% AAI with all the genomes/MAGs in clade I, and 73–74% AAI with the remaining genomes/MAGs, including its closest related Methylobacter genome, ‘ Ca. Methylobacter favarea’ ( Fig. S1 ). Moreover, M. tundripaludum Bin_16_1 has been classified as belonging to the genus Crenothrix in the GTDB Release R226 ( Parks et al . 2022 ) (Table S1). According to another measure of genome similarity allowing for genus delineation—a fixed percentage of conserved proteins (POCP) set to 50%, clade II may actually consist of at least five separate genera ( Orata et al . 2018 ), whereas the GTDB classification divides clade II into three genera: Methylobacter_A, Methylobacter_B, and Methylobacter_C in the GTDB Release 226 ( Parks et al . 2022 ). However, ANIb values suggest that all the analysed genomes and MAGs, including M. tundripaludum Bin_16_1, belong to the genus Methylobacter based on the >73% ANI similarity threshold ( Barco et al . 2020 ). Hence, our analyses suggest that the taxonomy of Methylobacter , and possibly that of Crenothrix , may need re-evaluation, which is, however, beyond the scope of this study. Our phylogenomic analysis confirmed the separation of the two Methylobacter clades ( Fig. 1 ). Additionally, we identified 33 unique species-level clusters based on several methods of species delineation, such as the TYGS clustering ( Meier-Kolthoff and Göker 2019 ; Meier-Kolthoff et al . 2022 ), <95% of ANIb ( Jain et al . 2018 ), and dDDH scores of <70 ( Goris et al . 2007 ). Our analyses suggest that the Methylobacter genus may be composed of more species than previously recognised, with even more species to be identified in the near future through continued sequencing and culturing efforts. We recently cultured two new Methylobacter spp., M. sp. Wu1 and M. sp. Wu8, from the sediment of a temperate eutrophic fishpond ( Wutkowska and Daebeler 2024 ). The Wu1 strain shared >95% ANIb identity with ‘ Ca. M. coli BlB1’ ( Fig. S4 ), which was backed up by close clustering in the phylogenomic tree ( Fig. 1 ). However, further evidence, i.e., a dDDH score of 64.3 in a pairwise genome comparison, suggested that they nevertheless likely belong to different species. Interestingly, ‘ Ca. M. coli BlB1’ has been isolated from the faeces of a herbivore ( Khatri et al . 2021 ), and it therefore seems plausible that M. sp. Wu1 may in fact originate from cattle manure, which is routinely used for fertilising fishponds in the area ( Potužák, Hůda and Pechar 2007 ) and not from the sediment. Here, we tentatively propose the name Methylobacter spei , sp. nov. The genome of the Wu8 strain phylogenomically clustered most closely with three other MAGs of uncultured Methylobacter ( Fig. 1 ). These were obtained from various aquatic environments in the northern hemisphere, and one of them, termed M. tundripaludum OWC-G53F, has previously been proposed to belong to a new species within the genus ( Orata et al . 2018 ). Our ANIb and TYGS analyses corroborated the phylogenetic affiliation of the Wu8 strain with the uncharacterised species-level cluster distantly related to the next cultured relative ( Fig. 1 ) and further suggested that it represents the first cultured member of a novel species for which we propose the name Methylobacter methanoversatilis , sp. nov. ( Fig. 1 ; Fig. S4 ). The phylogeny of the 18 available full-length 16S rRNA gene sequences from the analysed Methylobacter genomes was in congruence with the phylogenomic topology, preserving the major division into two clades ( Fig. S2a ). However, several distinct Methylobacter spp. displayed >99% nucleotide identity for the entire 16S rRNA gene ( Khanongnuch et al . 2022 ; Rissanen et al . 2022 ), which is above the recommended 98.65% threshold to delineate bacterial species for this marker ( Kim et al . 2014 ). For instance, the identity of the 16S rRNA genes of M. psychrophilus Z-0021 and M. tundripaludum SV96 is above this threshold, but their genome comparison yielded values below the 95% ANIb and 70% dDDH scores, which are recommended for species delineation ( Goris et al . 2007 ; Jain et al . 2018 ), suggesting they are two distinct species. These discrepancies between the 16S rRNA gene-based and the genome-based species delineation can lead to erroneous conclusions when (often <250 bp) sequences of 16S rRNA generated in amplicon studies are used to study Methylobacter sp. community composition at the species level. Upon closer inspection, we also noticed that the most commonly targeted region of the 16S rRNA gene, the V4 region, which is also included in the Earth Microbiome Project ( Caporaso et al . 2018 ), is highly conserved among several Methylobacter species. This was, for example, true for M. tundripaludum SV96 and M. methanoversatilis Wu8, which differed by only 1 nucleotide in the V4, but by 14 nucleotides in the V7–V8 region ( Fig. S3 ). We therefore conclude that attempting to distinguish between Methylobacter species by comparing 16S rRNA gene sequences will often lead to an underestimation of diversity, as has been recently shown for other genera ( Alleman et al . 2025 ). The PmoA-based trees ( Fig. S2b , Fig. S2c ) present a less congruent picture than the 16S rRNA and phylogenomic tree ( Fig. S2a and Fig. 1 , respectively), which is expected and has been previously shown to yield Methylobacter spp. as a polyphyletic genus ( Knief 2015 ; Orata et al . 2018 ). A varied repertoire of CH 4 and methanol oxidation Nearly all the investigated Methylobacter genomes (91%) contained at least one gene cluster coding for particulate CH 4 monooxygenase, either in its canonical pmoCAB (pMMO) or in the sequence-divergent pxmABC (pXMO) form ( Tavormina et al . 2011 ) ( Fig. 2 ). Almost half of the genomes (45%) encoded for both forms. Additionally, eight genomes (18%) contained the complete inventory for the cytoplasmic, soluble CH 4 monooxygenase (sMMO) with structural ( mmoXYBZ(D)C ) and regulatory genes, such as mmoG ( Fig. 2 , Fig. 3 ). A phylogenetic analysis of the protein sequence of the hydrolase alpha subunit of sMMO, MmoX, showed a clear separation of Methylobacter MmoX into two clusters. MmoX sequences from M. psychrophilus Z-0021 and M. sp. S3L5C are affiliated closely with sequences from Crenothrix polyspora and Methylovulum miyakonense HT12, whereas the other cluster with the remaining MmoX, including sequences from M. methanoversatilis , formed a deeper branching, isolated group ( Fig. 3B ). The latter cluster lost the additional gene coding for an unknown hypothetical protein between mmoC and mmoG , similarly to Methylovulum miyakonense HT12 ( Iguchi, Yurimoto and Sakai 2010 ) ( Fig. 3A , Fig. 3B ). Download figure Open in new tab Figure 2 Distribution of distinct metabolic traits in Methylobacter . Protein homology was inferred based on the HMM models, alignment to collections of reference amino acid sequences, and automatic annotation using DRAM. Additionally, in some instances, we used phylogenetic trees to confirm functions and identification of motifs determining specific functions (see Materials and methods for details). Solid, partial, and open squares indicate the presence, incompleteness, and absence of gene clusters, respectively. Numbers inside the squares indicate the number of identified amino acid sequences. Abbreviations: PmoCAB: particulate methane monooxygenase (EC: 1.14.18.3); PxmABC: sequence-divergent particulate methane monooxygenase (EC: 1.14.18.3); MmoXYBZ(D)CG: soluble methane monooxygenase (EC: 1.14.13.25); MxaF: calcium-dependent methanol dehydrogenase (subunit 1; EC: 1.1.2.7); XoxF: lanthanide-dependent methanol dehydrogenase (EC: 1.1.2.10); NqrA-F: Na + -transporting NADH:ubiquinone oxidoreductase (EC: 7.2.1.1); NuoA-N: NADH:quinone dehydrogenase (EC: 7.1.1.2); CydAB(X): cytochrome bd(-I) (EC: 7.1.1.7); aa3: aa3-type cytochrome c oxidase with the adjacent hemerythrin (EC: 7.1.1.9); caa3: cytochrome c oxidase fused subunit I+III (characteristic for caa3-type cytochrome c oxidase) (EC: 7.1.1.9); Nif and Rsx: molybdenum-dependent nitrogenase complex (>10 genes found, EC: 1.18.6.1) and H + /Na + -translocating ferredoxin:NAD + oxidoreductase (RsxABCDGE, EC: 7.1.1.11, 7.2.1.2); NarGHI: nitrate reductase (EC: 1.7.5.1, 1.7.99.-); CynS: cyanate lyase (EC: 4.2.1.104); UrtABC: urea transporter; UreABC: urease (EC: 3.5.1.5); SoxB: thiosulphohydrolase (EC: 3.12.1.1); SoxCD: S-disulphanyl-L-cysteine oxidoreductase (EC: 1.8.2.6); Sqr: sulphide:quinone oxidoreductase (EC: 1.8.5.4); BGC: biosynthetic gene cluster. Genomes from our recently cultured strains are shown in bold . Download figure Open in new tab Figure 3 Genomic organisation of the soluble CH 4 monooxygenase operon and phylogeny of the MmoX subunit among methanotrophs. (A) Gene map of the sMMO operon across representative genomes, illustrating gene order and conservation. Gene lengths are calculated as the raw difference between end and start coordinates (in base pairs, bp), with small gaps added for visualisation; the x-axis is therefore shown in bp. (B) Phylogenetic tree of the MmoX subunit from investigated Methylobacter spp. in the context of other methanotrophs from diverse taxonomic groups, showing taxonomic clustering patterns. Branch bootstrap support is indicated by numbers next to the nodes. The scale bar indicated 0.1 amino acid substitutions per site. Although the presence of sMMO has been reported for the genomes of psychrophilic M. psychrophilus Z-0021 ( Rissanen et al . 2022 ) and in M . sp. S3L5C ( Khanongnuch et al . 2022 ), earlier works had failed to identify sMMO in M. psychrophilus (Omelchenko et al ., 1996; Trotsenko and Khmelenina, 2005 ). The presence of sMMO is rarely associated with Methylobacter spp. ( Bowman et al . 1993 ; Smith, Costello and Lidstrom 1997 ; Bowman 2014 ), or in fact, with the majority of the Methylococcales ( Semrau 2011 ; Dedysh and Knief 2018 ). However, in our survey of >1,000 genomes of Methylococcales , MmoX was found in ∼15%, belonging to Methylococcus , Crenothrix , Methylomonas, Methyloprofundus, Methylovulum, Methylomagnum, ‘ Ca . Methylocalor cossyra’, ‘ Ca. Methylospira mobilis’, and many uncultured Methylococcales (Table S2). Interestingly, all four strains belonging to the cluster with M. methanoversatilis encoded all three CH 4 monooxygenase gene clusters: pMMO, pXMO, and sMMO ( Fig. 2 ). Although uncommon, the occurrence of all three MMOs has been reported for other methanotrophs, including Methylocystis hirsuta CSC1, Methylocystis bryophila S285, and Methylosinus sp. R-45379 ( Han, Dedysh and Liesack 2018 ; Oshkin et al . 2020 ). Despite its rare occurrence, the presence of all three MMO forms might be an ancestral trait, as all three MMOs have been inferred to be present in extant ancestral proteobacterial methanotrophs ( Osborne and Haritos 2018 ). Likely, this repertoire confers broader metabolic flexibility for energy conservation from CH 4 oxidation by providing different substrate affinities and specificities (Sullivan, Dickinson, and Chase 1998; Baani and Liesack 2008 ). The metabolic flexibility of Methylobacter spp. is also evident in their genomic repertoire for the second step of CH 4 oxidation— the oxidation of methanol to formaldehyde. The majority of analysed genomes encoded for calcium-dependent and lanthanide-dependent methanol dehydrogenase, mxaFI-MDH (EC: 1.1.2.7) and xoxF-MDH (EC: 1.1.2.10), respectively. The latter contains only one subunit, which was found in almost all analysed Methylobacter genomes as a single-copy gene (84% of genomes) or with two copies (11% of genomes) ( Fig. 2 , Table S3). Xox-MDH can act as a primary methanol dehydrogenase ( Chu and Lidstrom 2016 ) and has been speculated to be more efficient and potentially more metabolically versatile than the calcium-dependent form, for instance, by not only being able to oxidise methanol to formaldehyde but also to formate ( Keltjens et al . 2014 ). Versatile electron transport chain Our comparative genomics analyses revealed several variations of electron transport chain components in Methylobacter , which are likely adaptations to diverse habitats and provide the capacity to withstand fluctuating environmental conditions. Firstly, we identified redox-driven Na + -transporting NADH:ubiquinone oxidoreductase (EC: 7.2.1.1; NQR encoded by nqrA-F ), which acts as a complex I, in almost all analysed Methylobacter spp. ( Fig. 2 , Table S3). Several genomes in clade I, including those of marine species, encoded two NQRs. Another type of complex I, NADH:quinone dehydrogenase (EC: 7.1.1.2, NUO encoded by nuoA-N ) was present in the majority of the genomes, but lacking in many Methylobacter genomes and MAGs, including the four strains of M. methanoversatilis ( Fig. 2 , Table S3). Both forms of complex I transfer an electron from NADH to quinone, but they pump different cargo to the periplasm: either two H + protons (NUO) or one Na + ion (NQR); however, it has been shown that NQR can also translocate H + protons ( Raba et al . 2018 ). Moreover, these complexes differ in their energy conservation capacity, with NQR being likely more energy-efficient compared to NUO ( Hreha et al . 2021 ). A fine-tuned differential expression of these two forms has been observed in M. tundripaludum SV96, with the NQR dominating expression at temperatures below 15°C ( Tveit et al . 2023 ). Additionally, it is likely that the expression of NQR/NUO may be regulated by other factors, such as stoichiometry of Na + /e − and H + /e − , redox, and low O 2 levels and substrate availability, as indicated for other bacteria ( Bogachev, Murtazina and Skulachev 1997 ; Spero et al . 2015 ; Ito et al . 2020 ; Kaila and Wikström 2021 ). Certainly, the possibility to choose between them allows those Methylobacter spp. that possess both forms to make optimal use of resources for maintaining and building membrane potential. Sole reliance on NQR, on the other hand, may reflect adaptation to elevated salt concentration and/or be necessary for anaerobic metabolism ( Buckel et al . 2025 ). Secondly, we detected up to two high-affinity terminal oxidases of the bd(-I) -type (EC: 7.1.1.7; Fig. 2 ) in the vast majority of investigated Methylobacter genomes. These cytochrome bd quinol oxidases consist either of two or three subunits (genes cydAB(X) ). They have been found to enable aerobic respiration under hypoxia (<50 nM O 2 ) and withstand nitrosative and oxidative stress better than other types of terminal oxidases ( Giuffrè et al . 2014 ). Next to these bd -type oxidases, most analysed Methylobacter spp. contained other terminal oxidases of the heme-copper A and C types in their genomes ( Fig. 2 , Table S3). In all genomes containing the aa3 -type terminal oxidase, which has a low affinity for O 2 ( Berg et al . 2022 ), we found a gene coding for hemerythrin adjacently located, as seen in other gammaproteobacterial methanotrophs ( Rahalkar and Bahulikar 2018 ; Nariya and Kalyuzhnaya 2020 ). Hemerythrins act as high-affinity O 2 -sensors and carriers whose expression in methanotrophs has been linked to hypoxia ( Kalyuzhnaya et al . 2013 ; Rahalkar and Bahulikar 2018 ; Nariya and Kalyuzhnaya 2020 ; Weiblen, Sauvageau and Stein 2024 ) and to high copper concentrations with a pMMO-enhancing function ( Kao et al . 2008 ; Chen et al . 2012 ). Since the transcription of these proximally located genes has been proven to be connected to enhanced O 2 -dependent respiration in Methylomicrobium alcaliphilum in O 2 -limited conditions ( Nariya and Kalyuzhnaya 2020 ), it is possible that the exact mechanism can occur in other Methylobacter spp. Finally, we identified the presence of Na⁺-translocating ATP synthases next to the H + -translocating ones in most analysed genomes ( Fig. 2 , Table S3). By comparing the amino acid sequences of the subunit c, encoded by atpE , from the Na⁺-translocating ATP synthases, we found subunits with one- and two-carboxylate ion coupling motifs ( Fig. S5B ). The two-carboxylate form has been shown to enable the enzyme to couple Na + transport to ATP synthesis only when Na + is in excess over H + in the environment ( Schulz et al . 2013 ; Leone et al . 2015 ). This diversity in components of the respiratory chain, together with the presence of diverse dissimilatory protein complexes (see below), most likely allows Methylobacter spp. to employ flexible mechanisms suited for thriving in different, often fluctuating, environmental conditions. Potential for using other sources of energy than CH 4 We identified genes involved in partial denitrification in the majority of the analysed clade II genomes and MAGs. Many of the investigated Methylobacter spp. have the potential to reduce nitrate to nitrite (e.g., with the nitrate reductase (EC: 1.7.5.1, 1.7.99.-)), nitrite to nitric oxide (e.g., with the nitrite reductase (NO-forming) (EC: 1.7.2.1)), and nitric oxide to nitrous oxide (e.g., cytochrome c coupled nitric oxide reductase (EC:1.7.2.5)) ( Fig. 2 , Table S3). However, we did not detect nitrous oxide reductase encoded by nosZ reported for some acidophilic methanotrophs belonging to Alphaproteobacteria and Verrucomicrobiia ( Awala et al . 2024 ). Indeed, axenic cultures of Methylobacter sp. YRD-M1 growing in O 2 -limiting conditions produced N 2 O or NO ( Hao et al . 2022 ). According to our analyses, all Methylobacter genomes contained [NiFe]-hydrogenases of the 1d, 1e, 2c, and 3d groups in various combinations ( Fig. 2 , Table S3). The groups 1d and 1e are known to be involved in respiratory hydrogen uptake liberating electrons for aerobic and anaerobic respiration (1d) or sulphur respiration (1e), whereas hydrogenases of the group 2c and 3d on the other hand are cytosolic and not known to be coupled to energy conservation ( Vignais, Billoud and Meyer 2001 ; Greening et al . 2016 ; Søndergaard, Pedersen and Greening 2016 ). Only the hydrogenase of the 3d group was present in all analysed Methylobacter genomes and MAGs. They most likely act as redox valves, either providing NADH as a reductant for carbon fixation or catalysing the fermentative production of hydrogen ( Vignais, Billoud and Meyer 2001 ; Søndergaard, Pedersen and Greening 2016 ). Despite this apparent diversity of hydrogenases present in Methylobacter genomes and MAGs, to our knowledge, there is no evidence on the metabolic use of hydrogen from axenically cultured Methylobacter spp. This contrasts with the proven hydrogen utilisation by alphaproteobacterial ( Hakobyan and Liesack 2020 ; Hakobyan et al . 2020 ), verrucomicrobial ( Carere et al . 2019 ), and gammaproteobacterial methanotrophs with the Calvin-Benson-Bassham cycle ( Stanley and Dalton 1982 ; Liu et al . 2024 ). However, anaerobic laboratory incubations of complex microbial communities from Arctic lake sediments revealed the expression of Methylobacter group 1d hydrogenases, suggesting that hydrogen may provide energy for CH 4 oxidation by Methylobacter spp. under O 2 -depleted conditions (He et al., 2022). Our analyses revealed that sulphide:quinone oxidoreductase ( sqr ; EC: 1.8.5.4) was universally present in all 44 Methylobacter genomes and MAGs in one or more copies ( Fig. 2 , Table S3). A recent study showed that at low CH 4 concentrations, Methylobacter sp. S3L5C used dissolved organic matter and reduced sulphur compounds, such as sulphide, acting as electron donors, while maintaining elevated expression of sqr ( Rissanen et al . 2025 ). In seven Methylobacter spp. from clade I, we additionally detected thiosulphohydrolase (SoxB; EC: 3.12.1.1) along with S-disulphanyl-L-cysteine oxidoreductase (SoxCD; EC: 1.8.2.6), which are components of the periplasmic thiosulphate-oxidising complex catalysing thiosulphate oxidation to sulphate ( Fig. 2 , Table S3). Therefore, it seems likely that Methylobacter spp. are capable of using reduced sulphur compounds as electron donors for growth. These different types of genes may play important roles in dissimilatory metabolisms; however, especially the hydrogen- and sulphur-related genes are understudied in Methylobacter spp. ( Fig. 4 ), and largely in methanotrophs in general. Therefore, it is unclear how much they contribute to energy generation relative to CH 4 oxidation, and under what conditions these metabolisms could be relevant to the cells. Download figure Open in new tab Figure 4 Visual depiction of some of the proteins and protein complexes in three Methylobacter spp.: M. tundripaludum SV96, M. methanoversatilis Wu8 and M. spei Wu1. Abbreviations: PmoCAB: particulate methane monooxygenase (EC: 1.14.18.3); PxmABC: sequence-divergent particulate methane monooxygenase (EC: 1.14.18.3); MmoXYBZ(D)CG: soluble methane monooxygenase (EC: 1.14.13.25); MxaF: calcium-dependent methanol dehydrogenase (subunit 1; EC: 1.1.2.7); XoxF: lanthanide-dependent methanol dehydrogenase (EC: 1.1.2.10); NqrA-F: Na + -transporting NADH:ubiquinone oxidoreductase (EC: 7.2.1.1); NuoA-N: NADH:quinone dehydrogenase (EC: 7.1.1.2); CydAB(X): cytochrome bd(-I) (EC: 7.1.1.7); aa3: aa3-type cytochrome c oxidase (EC: 7.1.1.9); caa3: cytochrome c oxidase fused subunit I+III (characteristic for caa3-type cytochrome c oxidase) (EC: 7.1.1.9); Nif: molybdenum-dependent nitrogenase complex (>10 genes found, EC: 1.18.6.1); NarGHI: nitrate reductase (EC: 1.7.5.1, 1.7.99.-); CynS: cyanate lyase (EC: 4.2.1.104); UrtABC: urea transporter; UreABC: urease (EC: 3.5.1.5); Sox: potential for oxidising sulphur compounds, such as thiosulphate. Diverse genomic potential for nitrogen fixation and assimilation Methylobacter spp. are known for their diverse roles in the nitrogen cycle ( Bowman et al . 1993 ; Bowman 2014 ; Khatri et al . 2020 ). We identified at least 10 genes for molybdenum-dependent nitrogenase (Nif; Fig. 2 , Table S3). The presence of Nif coincided with the presence of a gene cluster coding for H + /Na + -translocating ferredoxin:NAD + oxidoreductase ( rsxABCDGE , EC: 7.1.1.11, 7.2.1.2), which is architecturally similar to NQR, or Rnf gene cluster found in anaerobic organisms ( Koo et al . 2003 ). Rsx transfers electrons to a transcriptional regulator involved in responding to oxidative and nitrosative stress—SoxR, which is reduced during aerobic growth ( Ding and Demple 1997 ; Koo et al . 2003 ). Aerobic dinitrogen fixation has been demonstrated for the Nif and Rsx-containing ‘ Ca. M. svalbardensis’ ( Patil et al . 2024 ), and it is therefore probable that the other Methylobacter spp. with the same gene clusters are capable of this metabolism. Methylobacter spp. isolated from various environments can assimilate both nitrate (e.g., with nasAB ) and ammonia (e.g., with glnA , gdhA ) as they can grow in both nitrate- and ammonia-amended salt media ( Bowman et al . 1993 ; Bowman 2014 ). Additionally, we found that more than half of the analysed species additionally held the potential for assimilation of (reduced) forms of dissolved organic nitrogen, such as cyanate and/or urea ( Fig. 2 , Table S3). Urea has been identified as a source of nitrogen for several methanotrophs ( de la Torre et al . 2015 ; Nguyen et al . 2017 ; Wang et al . 2024 ), and it stimulates the growth of Methylobacter -like species ( Zheng et al . 2014 ). ‘ Ca . Methylobacter coli’ strain BlB1 has been shown to grow on urea as a source of nitrogen ( Khatri et al . 2021 ); however, in our analyses, we have identified neither urease nor urea-transporting genes ( Fig. 2 ). Moreover, urease may play additional roles, for instance, in the adjustment of internal or external pH ( Scott et al . 1998 ; Stingl, Altendorf and Bakker 2002 ). For other organisms, it has been demonstrated that cyanate serves as a nitrogen source, facilitating growth ( Guilloton and Karst 1987 ; Wood et al . 1998 ). Clearly, however, organic nitrogen uptake by Methylobacter spp., and perhaps by all methanotrophs, remains functionally unexplored and understudied. Unusual genomic adaptations Unexpectedly, we found a multigene cluster encoding gas vesicle proteins in all four members of M. methanoversatilis , as well as the two psychrophilic species M. psychrophilus Z-0021 and M. sp. S3L5C, and a groundwater MAG that belonged to M. sp. C13 ( Fig. 2 , Table S3). Typically, the gas protein vesicles gene clusters contain 8–14 genes, and produce a vesicle protein monolayer formed by the gas vesicle protein A (encoded by gvpA ). The presence of this gene cluster suggests a planktonic lifestyle, as gas vesicles are typically known for enabling organisms to control their buoyancy in the water column ( Walsby 1994 ; Pfeifer 2012 ). The gas protein vesicle gene cluster has only been reported for two methanotrophs so far—‘ Ca . Methylomirabilis limnetica’ known for its planktonic lifestyle ( Graf et al . 2018 ), and M. psychrophilus Z-0021 isolated from tundra soil ( Omelchenko, Vasilyeva and Zavarzin 1993 ). To place our findings in a wider context for gammaproteobacterial methanotrophs, we surveyed >1,000 genomes and MAGs of Methylococcales and detected at least one gvpA gene copy in ∼12.5% of them (Table S2). Interestingly, they were present in up to five copies of gvpA , primarily in the MAGs of uncultured Methylococcaceae (NCBI-sourced taxonomy) from O 2 -stratified freshwater bodies in the Northern Hemisphere sequenced in Buck et al. (2021) . As some methanotrophs are known to grow preferentially in defined CH 4 -O 2 gradients ( Bussmann, Rahalkar and Schink 2006 ; Reim et al . 2012 ; Danilova et al . 2016 ; Beals and Puri 2024 ), perhaps the ability to regulate buoyancy enables them to position themselves at the water column depths with optimal CH 4 :O 2 stoichiometry. However, five of the seven Methylobacter strains in which we detected gvpA originate from terrestrial habitats ( Fig. 1 , Table S1). Other terrestrial bacteria have been reported to contain gvp gene clusters ( Van Keulen et al . 2005 ), and the function of these organelles remains little understood. As gas vesicles increase the cell surface-to-volume ratio and could therefore improve gas diffusion and aid in survival under stressful conditions ( Walsby 1972 , 1994 ; Pfeifer 2012 ). Indeed, M. psychrophilus Z-0021 has been shown to produce gas vesicles increasingly with temperatures ranging from 7 to 20°C, but no gas vesicles were detected when the temperature dropped below 7°C ( Omelchenko, Vasilyeva, and Zavarzin, 1993 ). These findings imply that increased gas vesicle production helps to overcome substrate limitation due to lower CH 4 and O 2 solubility in the medium with increasing temperature. Protein vesicles are permeable to many gases, including CH 4 and O 2 ( Walsby 1969 , 1971 , 1982 ), but it is unclear which gas(es) methanotrophs contain in the vesicles. It is tempting to speculate that the accumulated gas(es) could serve as a temporal reservoir of metabolically important gases, such as O 2 or CH 4 . However, these speculations require appropriate experimental verification. On average, we identified nearly eight biosynthetic gene clusters (BGCs) per analysed Methylobacter genome/MAG, ranging from three in the genomes of organisms from nutrient-poor, pelagic environments to 15 in a MAG from freshwater sediment ( Fig. 2 ). The most attention among Methylobacter spp. BGCs was given to tundrenone, a quorum-sensing molecule likely involved in hypoxia stress response ( Puri et al., 2018 ; Yu et al., 2020 ), and to ectoine, an organic osmoprotectant that, among other functions, increases halotolerance ( Reshetnikov et al., 2011 ). Nevertheless, we could only confirm the complete tundrenone gene cluster (encoded by tunA-P and several regulatory genes on both sides of the gene cluster) for M. tundripaludum SV96, 21/22, and 31/32 ( Fig. 2 ). Similarly, a complete ectoine BGC was only detected in three genomes of the clade I: M. marinus A45, M . sp. BBA5.1, and M . sp. B4-02 ( Fig. 2 ). Moreover, most genomes/MAGs contained more than one BGC encoding terpenes, a metabolite group involved in communication or interaction between species, that has been shown to be produced by M. luteus in the presence of Pseudomonas mandellii ( Veraart et al . 2018 ). Furthermore, all analysed Methylobacter contained at least one BGC for redox-cofactors and aryl polyenes. The latter have recently been reported as common in gammaproteobacterial methanotrophs ( Krause et al . 2025 ) and may encode antioxidative pigments ( Schöner et al . 2016 ). Finally, we detected an abundance of diverse, yet unexplored, BGC gene clusters encoding for polyketides, which are known to often carry antibiotic and pharmacological properties. Impressively, two BGCs contained polyketide synthetases that were among the longest genes in M. methanoversatilis , spanning over 18.5k (NCBI Reference Sequence: WP_331306173.1) and 26k bp (NCBI Reference Sequence: WP_331307029.1). Despite the availability of genomic information and the presence of gene clusters with high interest for applied science, the biotechnological potential of Methylobacter spp. has not been fully explored. Genomic data can be used for metabolic modelling (Islam et al., 2020; Wutkowska et al., 2024) to describe the functioning of Methylobacter spp. alone and within microbial communities, which may direct future design of biotechnological applications. Genome-inferred optimal growth conditions may guide future cultivation efforts A large diversity of methanotrophs, including most analysed Methylobacter spp., remains uncultured, which hampers the investigation of genome-derived hypotheses regarding their metabolism and ecological niche. To assist and possibly direct cultivation efforts, we predicted optimal conditions for growth regarding temperature, salinity, and pH, as well as tolerance to oxygen from the 44 high-quality genomes and MAGs ( Fig. S6 ). Comparing the predictions with experimentally verified optima for those species that are cultured showed that the genome-wide amino acid composition predictions do not identify the exact optima ( Fig. S6 ). However, general temperature and salinity preferences were likely accurately predicted; for example, the predicted temperature optima for known psychrophiles were among the lowest ( Fig. S6a ), and known halophilic species and species from oligotrophic environments were predicted to fall at opposite ends of the salinity range ( Fig. S6c ). Contrastingly, the predictions of optimal pH failed to confirm known pH preferences ( Fig. S6b ) and were associated with large uncertainties. Without further studies, it is challenging to identify the cause of these discrepancies. Although all Methylobacter genomes and MAGs in this study were expectedly tagged as oxygen-tolerant and are classified as aerobic CH 4 oxidisers, an increasing number of studies reported Methylobacter spp. found in O 2 -limited or O 2 -depleted environments ( Nercessian et al . 2005 ; Biderre-Petit et al . 2011 ; Graef et al . 2011 ; Reim et al . 2012 ; Hernandez et al . 2015 ; Oshkin et al . 2015 ; Martinez-Cruz et al . 2017 ; Rissanen et al . 2018 ; Singleton et al . 2018 ; Cabrol et al . 2020 ; Mayr et al . 2020 ; van Grinsven et al . 2020b , 2020a; Hao et al . 2022 ; He et al . 2022 ; Grégoire, George and Hug 2023 ; Li et al . 2023 , 2024 , 2025 ; Gafni et al . 2024 ; Kallistova et al . 2024 ; Reis et al . 2024 ; Schorn et al . 2024 ), and sometimes able to outcompete other methanotrophs ( Beck et al . 2013 ; Hernandez et al . 2015 ; Islam et al . 2020 ; Li et al . 2025 ). To date their persistence or continuous activity under O 2 -limited and O 2 -depleted conditions is far from being fully understood ( Hernandez et al . 2015 ; Gafni et al . 2024 ; Li et al . 2024 ; Reis et al . 2024 ; Ruff et al . 2024 ), but in the analysed Methylobacter genomes and MAGs we found many genes that provide insights into their functioning under O 2 -depleted conditions, such as nitrate reductases, high-affinity terminal oxidases, hemerythrins, hydrogen uptake [NiFe]-hydrogenases, and the inventory for sulphide and thiosulphate oxidation ( Fig. 2 . Table S3). Evidence for additional genes responding to O 2 -depleted conditions is being continuously reported. For instance, a recent transcriptomics study found that some proteins within the type VI secretion system operon, which are present only in genomes of the clade I, including M. luteus , were upregulated in low O 2 conditions ( Beals and Puri, 2024 ). Attempting to culture and isolate new Methylobacter spp. in carefully designed O 2 -depleted conditions, therefore, represents a promising avenue to a better understanding of their spread in the environment. Conclusions Despite apparent environmental importance and widespread occurrence, Methylobacter species are still poorly understood in many aspects. Our study shows that the genus harbours more species than currently described, and some of them cannot be distinguished based on 16S rRNA gene sequence comparison. Methylobacter species display diverse adaptations to their environments, i.e. diverse combinations of the three different CH 4 monooxygenases and the potential for several dissimilatory metabolisms. We would also like to encourage testing some of the metagenomic-based hypotheses that we outlined here to move to the post-genomic era with the Methylobacter spp. Taxonomic considerations Description of Methylobacter spei , sp. nov Etymology: L. fem. n. spes , hope; genitive singular spei , meaning “of hope”, since the organism was enriched from a fishpond in SE Czech Republic called Hope (Naděje). A freshwater aerobic chemoorganotroph that oxidises CH 4 was obtained from organic-rich sediment in an eutrophic fishpond near Hluboká nad Vltavou, called Hope (Naděje). Phylogenetically affiliated with the genus Methylobacter , family Methylomonadaceae , order Methylococcales , phylum Pseudomonadota . The genome is 100% complete with 0.36% contamination (CheckM2). It consists of 85 contigs of a total length of 4.4 Mbp and 52% GC content. The coding density is 86.5% with 4,078 total predicted protein sequences and an average gene length of 311.5 bp. The genome Wu1 Ts represented by a MAG (GenBank accession: GCA_036553575.1, GCF_036553575.1), is designated the nomenclatural type for the species, and was recovered from a low-complexity enrichment containing three species. Description of Methylobacter methanoversatilis, sp. nov Etymology: methyloversatilis N.L. neut. n. methanum , methane; L. adj. versatilis , versatile, adaptable; N.L. adj. methanoversatilis , referring to the potential metabolic versatility of the organism in utilizing CH 4 with three diverse CH 4 monooxygenases, i.e. pMMO, pXMO and sMMO, encoded in the genomes. A freshwater aerobic chemoorganotroph that oxidises CH 4 was obtained from organic-rich sediment of a eutrophic fishpond in the vicinity of Hluboká nad Vltavou called Hope (Naděje). Phylogenetically affiliated with the genus Methylobacter , family Methylomonadaceae , order Methylococcales , phylum Pseudomonadota . The proposed species is represented by 4 MAGs: Methylobacter tundripaludum OWC-G53F (GenBank accession: GCA_002934365.1 and GCF_002934365.1), Methylobacter tundripaludum Tobar13m-G39 (GenBank accession: GCA_021736725.1), Methylobacter tundripaludum SO_2017_LW4 bin 69 (GenBank accession: GCA_023227865.1), Methylobacter sp. Wu8 (GenBank accession: GCA_036440755.1 and GCF_036440755.1), collected in 4 different geographic localities. The latter was obtained from an axenic culture that is no longer available. None of the genomes are complete/circular, however, they are of high quality and low contamination (Table below). The genome size is 4.03–4.18Mbp, with the G+C content 51–52%, coding density 88.1–88.3%, total predicted coding sequences 3,715–3,788, and average gene length 319.5–327.7bp. The metabolic predictions indicated that the organism encodes for three different CH 4 monooxygenases, i.e. two particulate (pMMO and pXMO) and soluble (sMMO). View this table: View inline View popup Download powerpoint The genome Wu8 Ts represented by a MAG available under the GenBank accession: GCA_036440755.1, is designated nomenclatural type for the species, and was recovered from temperate eutrophic fishpond sediments. Data availability The data were derived from sources in the public domain. 97 Methylobacter spp. genomes and MAGs were downloaded from NCBI Genome portal on May 21, 2024, from https://www.ncbi.nlm.nih.gov/datasets/genome/?taxon=429.1067 Methylococcaceae genomes and MAGs were downloaded from NCBI Genome portal on August 12, 2024, from https://www.ncbi.nlm.nih.gov/datasets/genome/?taxon=403 . Code generated to analyse the data is deposited at https://github.com/magdawutkowska/methylobacter_comparative_genomics/ . The names of the novel Methylobacter species Methylobacter methanoversatilis , sp. Nov. and Methylobacter spei , sp. nov. have been registered under the SeqCode: https://seqco.de/i:52928 and https://seqco.de/i:52927 , respectively. CRediT author statement MW: conceptualization, formal analysis, visualisation, writing - original draft, writing - review & editing; JAN: formal analysis, writing - review & editing; VT: methodology, formal analysis, writing - review & editing; JEN: formal analysis, writing - review & editing; AD: conceptualization, funding acquisition, formal analysis, writing - review & editing. Funding This work was supported by a Junior Star grant (21-17322 M) awarded to AD by the Czech Science Foundation (CSF). Supplementary figures Download figure Open in new tab Supplementary Figure S1 Average amino acid identity (AAI) clustering of the investigated Methylobacter spp. genomes and MAGs. The genome AAI similarity matrix was visualised using the Morpheus web-based tool ( https://software.broadinstitute.org/morpheus/ ). Hierarchical clustering was performed using one minus Pearson correlation as the distance metric and average linkage. Both rows and columns were clustered to identify coherent genome groups. Download figure Open in new tab Download figure Open in new tab Download figure Open in new tab Supplementary Figure S2 Phylogenetic trees of genes extracted from the investigated Methylobacter spp. Panel A shows the full-length 16S rRNA gene (>1,500 bp); panel B : subunit A of the particulate methane monooxygenases (pMMO), and panel C : subunit A of the sequence-divergent particulate methane monooxygenase (pXMO). The 16S rRNA tree was rooted with Methylomicrobium lacus LW14 full 16S rRNA nucleotide sequence (NCBI accession number: NR_042712.1 ). The methane monooxygenase tree was made with all the identified particulate methane monooxygenases, except partial sequences, and was rooted with the AmoA amino acid sequence from Nitrosomonas europaea strain ATCC 19718 (UniProtKB/Swiss-Prot: Q04507.2). Bootstrap values are represented by circles in three colours: black (>= 95%), grey (80-95%) and white (50-80%). Download figure Open in new tab Download figure Open in new tab Download figure Open in new tab Download figure Open in new tab Download figure Open in new tab Supplementary Figure S3 Comparison of 16S rRNA genes in Methylobacter tundripaludum SV96 and Methylobacter methanoversatilis Wu8. Panel A shows multiple sequence alignment of these two sequences with 16S rRNA of Escherichia coli JO1859.1 ( Brosius et al . 1978 ), highlighting the hypervariable regions V1-V9 in bold ( Chakravorty et al . 2007 ). Panel B shows the differences between the two methanotrophic 16S rRNA sequences in red. A) Multiple sequence alignment of full-length 16S rRNA genes of Methylobacter tundripaludum SV96, Methylobacter methanoversatilis Wu8, with Escherichia coli JO1859.1 ( Brosius et al ., 1978 ) highlights the hypervariable regions V1-V9 in bold ( Chakravorty et al ., 2007 ) Download figure Open in new tab Supplementary Figure S4 Average nucleotide identity (ANIb) clustering of the investigated Methylobacter spp. genomes and MAGs. Download figure Open in new tab Download figure Open in new tab Supplementary Figure S5 Unrooted phylogenetic trees of ATP synthase subunit 3 (panel A: identified H + -ATP synthase AtpE; panel B: identified Na + -ATP synthase AtpE). Collapsed clade on panel B contains AtpE sequences that are known for Na + -ATP synthase activity. The scale bar indicated 0.1 amino acid substitutions per site. Branch bootstrap support is shown on the nodes as black (≥95%), grey (≥80%), or white (>50%) circles. The scale bar indicates 0.1 amino acid Download figure Open in new tab Download figure Open in new tab Download figure Open in new tab Supplementary Figure S6 Genome-based predictions of putative optimal temperature (A), pH (B) and salinity (C) values for culturing the investigated Methylobacter spp. Green lines indicate verified growth ranges of cultured species. Closed and open circles (with attached thin black lines) indicate predicted optima and experimentally verified values, respectively. Supplementary tables https://github.com/magdawutkowska/methylobacter_comparative_genomics/blob/main/Supplementary_tables.ods Supplementary Table S1 The collection of 97 Methylobacter spp. genomes and MAGs deposited in the NCBI Genome database before May 21, 2024. The table contains database identifiers, metadata, and descriptors, such as GenBank assembly accession, accession version, taxonomic assignment from NCBI and GTDB, origin of geographic localities and habitats, completeness and contamination (inferred from CheckM2 analysis), genome/MAG characteristics, and inclusion criteria (columns W and X). Columns W indicate whether the genome passed the high-quality mark (>90% completeness and 99% ANIb similarity were labelled as redundant and not included in the dataset (see Fig. S2 ). Supplementary Table S2. Copy number of genes encoding for GvpA (the major gas protein vesicle protein) and MmoX (a subunit of soluble methane monooxygenase) in 1,067 genomes and MAGs from NCBI-sourced Methylococcales . Supplementary Table S3. Automatic gene annotations from DRAM. Funder Information Declared Czech Science Foundation, https://ror.org/01pv73b02 , 21-17322 M REFERENCES ↵ Alleman AB , Stolyar S , Marx CJ et al. 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