Discovery of extremely halophilic, methyl-reducing euryarchaea provides insights into the evolutionary origin of methanogenesis

Abstract

Methanogenic archaea are major players in the global carbon cycle and in the biotechnology of anaerobic digestion. The phylum Euryarchaeota includes diverse groups of methanogens that are interspersed with non-methanogenic lineages. So far, methanogens inhabiting hypersaline environments have been identified only within the order Methanosarcinales. We report the discovery of a deep phylogenetic lineage of extremophilic methanogens in hypersaline lakes and present analysis of two nearly complete genomes from this group. Within the phylum Euryarchaeota, these isolates form a separate, class-level lineage ‘Methanonatronarchaeia’ that is most closely related to the class Halobacteria. Similar to the Halobacteria, ‘Methanonatronarchaeia’ are extremely halophilic and do not accumulate organic osmoprotectants. The high intracellular concentration of potassium implies that ‘Methanonatronarchaeia’ employ the ‘salt-in’ osmoprotection strategy. These methanogens are heterotrophic methyl-reducers that use C1-methylated compounds as electron acceptors and formate or hydrogen as electron donors. The genomes contain an incomplete and apparently inactivated set of genes encoding the upper branch of methyl group oxidation to CO2 as well as membrane-bound heterodisulfide reductase and cytochromes. These features differentiate ‘Methanonatronarchaeia’ from all known methyl-reducing methanogens. The discovery of extremely halophilic, methyl-reducing methanogens related to haloarchaea provides insights into the origin of methanogenesis and shows that the strategies employed by methanogens to thrive in salt-saturating conditions are not limited to the classical methylotrophic pathway.

Methanogenesis is one of the key terminal anaerobic processes of the biogeochemical carbon cycle, both in natural ecosystems and in industrial biogas production plants1,2. Biomethane is a major contributor to global warming3. Methanogens comprise four classes—‘Methanomicrobia’, Methanobacteria, Methanopyri and Methanococci—and part of the class Thermoplasmata, within the archaeal phylum Euryarchaeota47. The recent metagenomic discovery of putative methyl-reducing methanogens in the candidate phyla ‘Bathyarchaeota’8 and ‘Verstaraetearchaeota’9 indicates that methanogenesis might not be limited to Euryarchaeota.

Three major pathways of methanogenesis are known1,2: hydrogenotrophic (H2, formate and CO2/bicarbonate as electron acceptor), methylotrophic (dismutation of C1-methylated compounds to methane and CO2) and acetoclastic (dismutation of acetate into methane and CO2). In the hydrogenotrophic pathway, methane is produced by a sequential six-step reduction of CO2. In the methylotrophic pathway, methylated C1 compounds, including methanol, methylamines and methylsulfides, are first activated by specific methyltransferases. Next, one of four methyl groups is oxidized through the same reactions as in the hydrogenotrophic pathway occurring in reverse, and the remaining three groups are reduced to methane. In the acetoclastic pathway, methane is produced from the methyl group after activation of acetate. The only enzyme that is uniquely present in all three types of methanogen is methyl-CoM reductase, a Ni-corrinoid protein catalysing the last step of methyl group reduction to methane1012.

The recent discovery of methanogens among Thermoplasmata5,1315 drew attention to the fourth, methyl-reducing pathway, previously characterized in Methanosphaera stadtmanae (Methanobacteria) and Methanomicrococcus blatticola (‘Methanomicrobia’)1620. In this pathway, C1-methylated compounds are used only as electron acceptors, and H2 serves as an electron donor. In the few known examples, the genes for methyl group oxidation to CO2 are either present but inactive (Methanosphaera)16 or completely lost (Thermoplasmata methanogens)6,7. Recent metagenomic studies have uncovered three additional deep lineages of potential methyl-reducing methanogens, namely, candidate class ‘Methanofastidiosa’ within Euryarchaeota21 and candidate phyla ‘Bathyarchaeota’ and ‘Verstraetearchaeota’8,9, supporting the earlier hypothesis that this is an independently evolved, ancient pathway22.

The classical methylotrophic pathway of methanogenesis that has been characterized in moderately halophilic members of Methanosarcinales23 apparently dominates in hypersaline conditions2325. In contrast to the extremely halophilic haloarchaea, these microbes only tolerate saturated salt conditions and grow optimally at moderate salinity (below 2–3 M Na+) using organic compounds for osmotic balance (the ‘salt-out’ strategy)26,27.

Our recent study of methanogenesis in hypersaline soda lakes identified methylotrophic methanogenesis as the most active pathway. In addition, culture-independent analysis of the mcrA gene, a unique marker of methanogens, identified a deep lineage that is only distantly related to other methanogens28. We observed no growth of these organisms upon addition of substrates for the classical methanogenic pathways, and concluded that they required distinct growth conditions. Here, we identify these conditions and describe the discovery and physiological, genomic and phylogenetic features of a previously overlooked group of extremely halophilic, methyl-reducing methanogens.

Discovery of extremely halophilic methyl-reducers

Sediment stimulation experiments

Two deep-branching mcrA sequences have been detected previously in sediments from hypersaline soda lakes in south-eastern Siberia28. Attempts to stimulate the activity of these uncharacterized, dormant methanogens by a variety of conditions (temperature, pH and salinity) and substrates elicited a positive response at extreme salinity (4 M Na+), a pH of 9.5–10, elevated temperature (above 48–55 °C) and in the presence of methylotrophic substrates, together with formate or H2 (the combination used in the methyl-reducing pathway). The typical response involved a pronounced increase in methane production upon combining methyl compounds with formate or H2 (less active) compared to single substrates (Supplementary Fig. 1a). The mcrA profiling of such incubations revealed two distinct clusters closely related to the previously detected deep methanogenic lineage28 (Supplementary Fig. 2).

The same approach was used with sediment slurries from hypersaline lakes with neutral pH (with no previous evidence of the presence of methyl-reducing methanogens). In this case, enhanced methane production under methyl-reducing conditions (MeOH/trimethylamine+formate) was also observed at elevated temperatures (Supplementary Fig. 1b,c). The mcrA profiles indicated that typical halophilic methylotrophic methanogens (Methanohalophilus and Methanohalobium) were outcompeted at high temperatures (50–60 °C) by unknown, extremely halophilic methyl-reducers, which formed a sister clade to the sequences from methyl-reducing incubations of soda lake sediments in the mcrA tree (Supplementary Fig. 2).

Cultivation of the extremely halophilic methyl-reducing methanogens

The active sediment incubations from hypersaline lakes (Supplementary Table 1) were used as an enriched source to obtain the methyl-reducing methanogens in laboratory culture using synthetic media with 2–4 M Na+, pH 7 (for salt lakes) or 9.5–10 (for soda lakes), supplemented with MeOH/formate or trimethylamine (TMA)/formate and incubated at 48–60 °C. Methane formation was observed only at extreme salinity, close to saturation (4 M total Na+), but ceased after the original sediment inoculum was diluted by two or three consecutive 1:100 transfers. Addition of colloidal FeS × nH2O (soda lakes) or sterilized sediments (salt lakes), combined with filtration through 0.45 µm filters and antibiotic treatment, yielded a pure culture from Siberian soda lakes (strain AMET1, alkaliphilic methylotrophic thermophilic) and ten additional pure AMET cultures from hypersaline alkaline lakes in various geographic locations. A similar approach resulted in three highly enriched cultures at neutral pH from salt lakes (HMET, halophilic methylotrophic thermophilic, cultures) (Supplementary Table 2). Phylogenetic analysis of the marker genes showed that AMET and HMET formed two genus-level groups that shared 90% 16S rRNA gene sequence identity.

Microbiology of the methyl-reducing methanogens

Cell morphology and composition

Both AMET and HMET possess small coccoid cells that are motile, in the case of AMET, and lack the F420 autofluorescence that is typical of most methanogens. A thin, single-layer cell wall was present in both groups (Fig. 1 and Supplementary Fig. 3). At a salt concentration below 1.5 M total Na+, the cells lost integrity.

Figure 1: Cell morphology of methyl-reducing methanogens from hypersaline soda and salt lakes.
figure1

af, Phase contrast image (a), total electron microscopy images (b,e) and electron microscopy images (c,d,f) of thin sectioned cells of isolates from hypersaline soda (strain AMET1, ad) and salt (strain HMET1, e,f) lakes. N, nucleoid; PHA?, a possible PHA storage granule; CPM, cytoplasmic membrane; ICPM, cell membrane invaginations; CW, cell wall. Light microscopy images are typical of five to seven samples from two replicate cultures. Electron microscopy images are typical of technical replicates from the same culture (n = 5–10).

The extreme halophily of the discovered methanogens is unprecedented. The salt-tolerant methylotrophs isolated so far from hypersaline habitats, such as Methanohalobium, Methanohalophilus and Methanosalsum, all accumulate organic osmolytes (‘salt-out’ osmoprotection). In contrast, no recognizable organic osmolytes were detected in AMET1 cells; instead, they accumulated high intracellular concentrations of potassium (5.5 µmol per g protein or 2.2 M, assuming a cell density of 1.2 mg ml–1 for haloarchaea29 and a measured protein content of 30%). This concentration is twofold lower than that normally observed inside the cells of haloarchaea (12–13 µmol per g protein) but close to that of Halanaerobium (6.3 µmol per g protein), both of which have been shown to employ the ‘salt-in’ osmoprotection strategy30,31. Furthermore, half of the sodium in the medium is present in the form of carbonates, which possess exactly twofold less osmotic activity than NaCl, resulting in decreased total osmotic pressure and, accordingly, a lower intracellular concentration of osmolytes in extreme natronophiles32. This finding suggests that the extremely halophilic methyl-reducing methanogens rely on potassium as their major osmolyte.

The AMET cell pellets were pinkish in colour, suggestive of the presence of cytochromes, which was confirmed by difference spectra of a cell-free extract from AMET1 that showed peaks characteristic of b-type cytochromes (Supplementary Fig. 4a). Given that the cytochrome-containing methanogens of the order Methanosarcinales also synthesize the electron-transferring quinone analogue methanophenazine33, we attempted to detect this compound in AMET1. Indeed, two yellow-coloured autofluorescent hydrophobic fractions were recovered from the AMET1 cells, with main masses of 562 and 580 Da, which behaved similarly to methanophenazine from Methanosarcina (mass of 532 Da) upon chemical ionization (sequential cleavage of the 68 Da isoprene unit) (Supplementary Fig. 4b).

Growth physiology

Both AMET and HMET are methyl-reducing heterotrophic methanogens using C1-methyl compounds as electron acceptor, formate or H2 as electron donor, and yeast extract or acetate as the carbon source. Growth of both groups of organisms was stimulated by the addition of external CoM (up to 0.1 mM). Despite the general metabolic similarity, the AMET cultures grew and survived long storage much better than the HMET cultures. The AMET cultures grew best with MeOH as acceptor and formate as donor (Fig. 2a). Apart from MeOH, slower growth was also observed with methylamines and dimethylsulfide (Fig. 2b). In sharp contrast to the known methyl-reducing methanogens, H2 was less effective as the electron donor.

Figure 2: Growth and activity of methyl-reducing methanogens from hypersaline soda lakes.
figure2

a, Growth dynamics of strain AMET1 with MeOH+formate at 4 M total Na+, pH 9.5 and 50 °C (maximum specific growth yield Ymax = 1.5 mg protein per mM MeOH; maximum specific growth rate μmax = 0.012–0.015 h−1). b, Methanogenic activity of washed cells of strain AMET1 grown with MeOH+formate (at 4 M total Na+, pH 9.5 and 48 °C) with various methylated electron acceptors (MA, DMA and TMA: mono-, di- and trimethylamines; DMS: dimethylsulfide). c, Influence of temperature on growth and activity of washed cells of various AMET strains at 4 M total Na+, pH 9.5 with MeOH+formate as substrate. d, Influence of pH at 4 M Na+ on growth and activity of washed cells of strain AMET1 at 48 °C with MeOH+formate as substrate. e, Influence of salinity at pH 9.5 on growth and activity of washed cells of strain AMET1 with MeOH+formate as substrate. In all experiments, 100 µM hydrotroilite (FeS × nH2O) was added. Neither growth not activity was observed with a single substrate (that is, methylated compounds, H2 or formate alone). VCH4: rate of methane formation, normalized either per culture volume in growth experiments or per biomass in cell suspension experiments. Error bars in a, b and d are standard deviations (s.d.) of biological replicates (n = 2). Plots in c and e show results of a single sample analysis.

Both groups grew optimally at around 50 °C, with an upper limit at 60 °C (Fig. 2c and Supplementary Fig. 5). The AMET isolates were obligate alkaliphiles, with optimum growth at pH 9.5–9.8 (Fig. 2d), whereas the HMET cultures had an optimum at pH 6.8–7. The organisms of both groups showed the fastest growth and the highest activity under salt-saturating conditions and thus qualified as extreme halo(natrono)philes (Fig. 2e).

Effect of iron sulfides on growth and activity of AMET1

As well as with hydrotroilite (FeS × nH2O), AMET1 also grew, albeit less actively, in the presence of cristalline FeS and yet less actively with pyrite (FeS2). No other forms of reduced iron minerals tested (olivine, FeCO3, magnetite, ferrotine (FeSn) or various iron(ii) silicates) could replace FeS. Furthermore, the methanogenic activity of resting cells depleted for FeS showed a dependence on FeS addition (Fig. 3). No methane was formed in the absence of either methyl acceptors or formate/H2, suggesting that Fe2+ probably serves as a catalyst or regulator rather than a direct electron donor. The specific cause(s) of the dependence of AMET growth on iron(ii) sulfides remains to be identified.

Figure 3: Effect of hydrotroilite (FeS × nH2O) on growth and methanogenic activity of washed and exhausted cells of the AMET1 strain.
figure3

Growth and incubation conditions: 4 M total Na+, pH 9.8, 48 °C. Substrate: 50 mM CH3OH + 50 mM formate. Culture was grown in the presence of sterile sand. FeS-exhausted washed cells were obtained by prolonged incubation with substrates without addition of FeS followed by washing and resuspension in fresh buffer. Error bars indicate s.d. of biological replicates (n = 3).

Comparative genomic analysis

General genome characteristics

The general genome characteristics of AMET1 and HMET1 are provided in Table 1. Based on analysis of 218 core arCOGs34, both genomes are nearly complete, with two genes missing from this list in AMET1 and three in HMET1. Two of these genes are missing in both genomes (prefoldin paralogue GIM5 and deoxyhypusine synthase DYS1), suggesting that they were lost in the common ancestor (Supplementary Table 3). The presence of tRNAs for all amino acids is another indication of genome completeness. The high coverage of the AMET1 and HMET1 genomes by arCOGs implies that the unique phenotype of these organisms is supported largely by the already well-sampled part of the archaeal gene pool.

Table 1 Summary statistics for the AMET1 and HMET1 genomes.

Phylogenetic analysis and taxonomy

A concatenated alignment of the 56 ribosomal proteins that are universally conserved in complete archaeal genomes35, including AMET1 and HMET1, was used for maximum likelihood tree reconstruction (Fig. 4a, Supplementary Table 3 and Supplementary Data 1). Both AMET1 and HMET1 belong to a distinct clade, a sister taxon to the class Halobacteria, with 100% bootstrap support (Fig. 4a). The 16S rRNA gene tree suggests that both organisms belong to the uncultured SA1 group that was first identified in the brine–seawater interface of the Shaban Deep in the Red Sea36 and subsequently in other hypersaline habitats37 (Supplementary Fig. 6). According to the rRNA phylogeny, the group that includes AMET1 and HMET1 is well separated from the other classes in the phylum Euryarchaeota, both methanogenic and non-methanogenic. The 16S rRNA sequences of these organisms are equally distant from all classes in Euryarchaeota and fall within the range of recently recommended values (80–86%) for the class-level classification38. Together, these findings appear to justify classification of the SA1 group, including the AMET and HMET lineages, as a separate euryarchaeal class, ‘Methanonatronarchaeia’. This class would be represented by two distinct genera and species ‘Methanonatronarchaeum thermophilum’ (AMET) and ‘Candidatus Methanohalarchaeum thermophilum’ (HMET).

Figure 4: Phylogenetic analysis of ‘Methanonatronarchaeia’ (AMET1 and HMET1).
figure4

The tree represents a phylogeny of archaea based on an alignment of concatenated ribosomal proteins. Methanogen clades are shown in solid dark blue, non-methanogenic clades are in semitransparent light blue, and Halobacteriales are in orange Inferred methanogenic branches are highlighted in blue, and inferred loss of methanogenesis is indicated by dashed red branches. The arrow indicates the likely archaeal root. All branches are bootstrap-supported at the 100% level. The original tree is provided in Supplementary Data 1.

Comparative genomic analysis and reconstruction of main evolutionary events

Using arCOG assignments and the results of previous phylogenomic analysis39, we reconstructed the major evolutionary events in the history of AMET1, HMET1 and Halobacteria (Fig. 4a and Supplementary Table 4). This reconstruction indicates that evolution of the HMET1-AMET1 lineage was dominated by gene loss, whereas Halobacteria acquired most of their gene complement after divergence from ‘Methanonatronarchaeia’. As shown previously, the common ancestor of Methanomicrobia and Halobacteria was a methanogen39. The key genes coding for components of the protein complexes involved in the classical methanogenesis pathways, such as tetrahydromethanopterin S-methyltransferase (Mtr), F420-reducing hydrogenase and Ftr, appear to have been lost along the branch leading to the common ancestor of Halobacteria and ‘Methanonatronarchaeia’. After the divergence, Halobacteria continued to lose all other genes involved in methanogenesis and acquire genes for aerobic and mostly heterotrophic pathways, whereas ‘Methanonatronarchaeia’ retained most pathways for anaerobic metabolism, while rewiring the methanogenic pathways for the mixotrophic lifestyle (Fig. 5a). As in other cases, genome reduction in ‘Methanonatronarchaeia’ affected RNA modification, DNA repair and stress response systems as well as surface protein structures39. The subsequent gene loss occurred differentially in the two groups of ‘Methanonatronarchaeia’, suggesting adaptation to different ecological niches. The HMET group lost chemotaxis and motility genes and shows signs of adaptation to heterotrophy, whereas AMET retains the ability to synthesize most cellular building blocks at the expense of transporter loss. The AMET strains are motile but lost attachment pili, which are present in the vast majority of the species of the Halobacteria–Methanomicrobia clade40 and many glycosyltransferases, suggesting simplification of the surface protein structures. The presence of two complete CRISPR-Cas systems in HMET1 compared to none in AMET1, along with the large excess of genes implicated in anti-parasite defence and transposons in HMET1 (Fig. 5c and Table 1), further emphasize the lifestyle differences indicating that HMET1 is subject to a much stronger pressure from mobile elements than AMET1.

Figure 5: Comparative genomic analysis and reconstruction of gene losses and gains.
figure5

a, Reconstruction of gene loss and gain along ‘Methanonatronarchaeia’ (AMET1 and HMET1) and Halobacteriales. Light blue, arCOG complement; green, gains; dark blue, losses. b, arCOG composition of AMET1 and HMET1. c, Distribution of the differences in the arCOG composition of AMET1 and HMET1 by functional categories. For each category, the number of arCOGs unique to AMET1 and HMET1 is indicated. The functional classification of the COGs is described at ftp://ftp.ncbi.nih.gov/pub/wolf/COGs/arCOG/funclass.tab. d, Multidimensional scaling analysis of isoelectric point distributions. Orange, Halobacteria; green: halophilic archaea and bacteria, blue: other archaea and bacteria. e, Multidimensional scaling analysis of genes enriched in methanogens.

Central metabolism reconstruction

In agreement with the experimental results, genome analysis allowed us to identify the genes of AMET1 and HMET1 that are implicated in energy flow and key reactions of biomass production, which appear to be simple and straightforward (Fig. 6). The main path starts with use of C1 methyl-containing compounds for methane production by CoM methyltransferases and methyl-CoM reductase complexes, respectively. Similar to the methyl-reducing Methanomasillicoccales6, the genomes of AMET1, and especially HMET1, contain multiple operons encoding diverse methyltransferases (Supplementary Fig. 7). Methyl reduction is coupled with ATP generation and involves five membrane-associated complexes, namely formate dehydrogenase, membrane-bound heterodisulfide reductase HdrED, Ni,Fe hydrogenase I, multisubunit Na+/H+ antiporter and H+-transporting ATP synthase. The recently characterized complete biosynthetic pathway41 for coenzyme F430 is present in both genomes. In addition, membrane b-type cytochromes and methanophenazine-like compounds are implicated in electron transport.

Figure 6: Reconstruction of the central metabolic pathways shared by ‘Methanonatronarchaeia’.
figure6

The main methyl-reducing pathway is shown by thick magenta arrows. Metabolically fixed low-molecular-weight compounds are shown in green. Either the gene name or respective arCOG number is shown for each reaction and shown in red (details are provided in Supplementary Table 3). Final biosynthetic products are shown as follows: light blue, amino acids; yellow, nucleotides; brown, lipid components; pink, cofactors. MF, methanofuran; H4MPT, tetrahydromethanopterin; CoM, coenzyme M; CoB, coenzyme B; CoA, coenzyme A.

Pyruvate, the key entry point for biomass production, is generated through acetate incorporation by acetyl-CoA synthetase (Fig. 6). In sharp contrast to most methanogens, both genomes lack genes for the tetrahydromethanopterin S-methyltransferase Mtr complex and formylmethanofuran dehydrogenase Fwd complex, leaving all intermediate reactions, for which the genes are present, unconnected to other pathways (Fig. 6). All four recently reported deep lineages of euryarchaeal methyl-reducing methanogens (Methanomasillilicoccales and ‘Candidatus Methanofastidiosa’) and those from the TACK superphylum (‘Candidatus Bathyarchaeota’ and (‘Candidatus Verstraetearchaeota’)8,9 lack the Mtr and Fwd complexes as well, but they also lack all the genes involved in intermediate reactions. It is extremely unlikely that genes for all Mtr and Fdw complex subunits are present in both AMET1 and HMET1 but were missed by sequencing. Thus, these organisms might possess still unknown pathways to connect the intermediate reactions to the rest of the metabolic network.

In addition to the main biosynthetic pathway, AMET1 and HMET1 possess genes for three key reactions of anaplerotic CO2 fixation, namely malic enzyme, phosphoenolpyruvate carboxylase and carbamoylphosphate synthase. Furthermore, complete gene sets for the CO2 fixation pathway through archaeal RUBISCO are present in both genomes (Fig. 6)42. The great majority of the genes involved in the key biosynthetic pathways for amino acids, nucleotides, cofactors and lipids were also identified in both genomes and found to be highly expressed in proteomic analysis, as revealed by estimating the absolute protein amount based on the exponentially modified protein abundance index (emPAI) (Supplementary Tables 6 and 7). Interestingly, emPAI-based abundances follow an exponential distribution in which four proteins involved in methanogenesis are among the ten most highly expressed proteins.

HMET1 seems to be more metabolically versatile than AMET1, especially with respect to methanogenesis, as well as amino acid and sugar metabolism (Fig. 5b,c). However, unlike AMET1, HMET1 lacks several genes for cofactor biosynthesis, such as quinolinate synthase NadA and nicotinate-nucleotide pyrophosphorylase NadC, both involved in NAD biosynthesis; uroporphyrinogen-III decarboxylase HemE and protoporphyrinogen IX oxidase HemG, involved in haem biosynthesis; sulfopyruvate decarboxylase, involved in CoM biosynthesis; and cofCED genes for the coenzyme F420 biosynthesis enzyme complex. This shortage of biosynthetic enzymes is consistent with experimental observations of poorer growth and survival of HMET in culture than AMET.

Adaptation to extreme salinity

Given that acidification of proteins is a common feature of the ‘salt-in’ osmotic strategy, we estimated isoelectric points for the proteomes of a large representative set of halophilic and non-halophilic archaea and bacteria, and compared the distributions as described in the Methods (Supplementary Table 5). The distributions of isoelectric points in the AMET1 and HMET1 proteomes are similar to those of moderately halophilic archaea and bacteria, with the notable exception of their closest relatives, the extremely halophilic Halobacteria, which form a distinct cloud of extremely acidic proteomes (Fig. 5d). This separation indicates that the proteome acidity of Halobacteria dramatically changed after the divergence from ‘Methanonatronarchaeia’, which appear to be an evolutionary intermediate on the path from methanogens to extreme halophiles. In agreement with the ‘salt-in’ osmoprotection strategy, AMET1 and HMET1 encode a variety of K+ transporters (arCOG01960) but show no enrichment of transporters for known organic osmolytes, such as glycine, betaine, ectoine or glycerol, compared with other archaea (Supplementary Table 3). On more general grounds, the ‘salt-out’ strategy appears unlikely and perhaps unfeasible for extremely halophilic secondary anaerobes with relatively low energy yield. Taken together, these considerations suggest that the adaptation of ‘Methanonatronarchaeia’ to the extreme salinity relies on the ‘salt-in’ strategy. Whether these organisms possess additional mechanisms for cation-binding to compensate for the relatively low proteome acidity remains to be determined, but it is also possible that the main counter-anion, in this case, is Cl.

Analysis of the AMET1 and HMET1 protein complements revealed a major expansion of the UspA family of stress response proteins with likely chaperonin function that could contribute to the structural stability of intracellular proteins (Supplementary Fig. 8). Finally, we identified several arCOGs consisting of uncharacterized membrane proteins (for example, arCOG04755, arCOG04622 and arCOG04619) that are specifically shared by AMET1, HMET1 and the majority of Halobacteria (Supplementary Table 3). Some of these proteins contain pleckstrin homology domains, which contribute to the mechanical stability of membranes in eukaryotes43 and might play a similar role in ‘Methanonatronarchaeia’.

Notably, AMET1 protein expression analysis showed that the DNA/RNA-binding protein Alba, an archaeal histone and one of the UspA family proteins were among the ten most abundant proteins (Supplementary Table 6). These proteins contribute to RNA, DNA and protein stability, and might play important roles in supporting growth under extreme salinity conditions.

Implications for the origin of methanogenesis

In previous phylogenetic analyses of the methyl coenzyme M reductase complex (McrABCD) subunits, the topology of the tree for these proteins generally reproduced the ribosomal protein-based phylogeny13,22. In the present phylogenetic analysis that used different protein sets and methods, AMET1/HMET1, Methanomasilliicoccales13, the ANME1 group44 and ‘Candidatus Methanofastidiosa’ (WSA2 group)21 clustered together with high confidence (Supplementary Figs 9 and 10 and Supplementary Data 2, 3 and 4). This topology differs from the topology of the ribosomal protein tree (Fig. 4a). This discrepancy could result from a combination of multiple horizontal transfers of mcrABCD genes, differential gain and loss of paralogues, insufficient sampling of rare lineages, and phylogenetic artefacts caused by variation of evolutionary rates. Indeed, we observed a complex evolutionary history of McrA, including many lineage-specific duplications and losses (Supplementary Fig. 9).

Reconstruction of evolutionary events and mapping the methanogens onto the archaeal tree suggest that the origin of methanogenesis dates back to the common ancestor of archaea, with multiple, independent losses in various clades (Fig. 5a). The loss of the methanogenic pathways often proceeds through intermediate stages, as clearly observed both in ‘Methanonatronarchaeia’ and Methanomasilliicoccales (Fig. 5a). Comparison of the gene sets (arCOGs) enriched in different groups of methanogens (Supplementary Table 3) using multidimensional scaling revealed distinct patterns of gene loss in ‘Methanonatronarchaeia’, Methanomasilliicoccales, ANME1 and ‘Candidatus Bathyarchaeota’, in agreement with the independent gene loss scenario (Fig. 5e). Notwithstanding these arguments, the possibility that ‘Candidatus Bathyarchaeota’ and ANME1 acquired methanogenesis via horizontal gene transfer cannot be ruled out, relegating its origin to the common ancestor of Euryarchaeota. Further sampling of diverse archaeal genomes should resolve this issue.

Conclusions

We have discovered an unknown, deep euryarchaeal lineage of moderately thermophilic and extremely halo(natrono)philic methanogens that thrive in hypersaline lakes. This group is not monophyletic with the other methanogens, but forms a separate, class-level lineage ‘Methanonatronarchaeia’ that is most closely related to Halobacteria. The ‘Methanonatronarchaeia’ possess the methyl-reducing type of methanogenesis, where C1-methylated compounds serve as acceptor, and formate or H2 serve as external electron donor, but differ from all other methanogens with this type of metabolism in terms of the electron transport mechanism. In contrast to all previously described halophilic methanogens, ‘Methanonatronarchaeia’ grow optimally in saturated salt brines and probably employ potassium-based osmoprotection, similar to extremely halophilic archaea and Halanaerobiales. This discovery is expected to have a substantial impact on our understanding of biogeochemistry, ecology and evolution of the globally important microbial methanogenesis.

Methods

Samples

Anaerobic sediments (depth from 5 to 15 cm) and near bottom brines were obtained in hypersaline soda and salt lakes in south-western Siberia (Altai region) and the south Russia (Volgograd region and Crimea) in July of 2013 to 2015. The salt concentration varied from 100 to 400 g l–1 and the pH from 6.5–8 (salt lakes) to 9.8–10.5 (soda lakes). In addition, sediments from Wadi al Natrun alkaline hypersaline lakes in Egypt (October 2000) and the alkaline hypersaline Searles Lake in California (April 2005) were used as inocula in methanogenic enrichment cultures. Details of the lake properties are provided in Supplementary Table 1. Methanogenic potential activity measurements and mcrA analysis were performed in 1:1 sediment–brine slurries, as described previously28.

Enrichment and cultivation conditions

For soda lakes, sodium carbonate-based mineral medium containing 1–4 M total Na+ strongly buffered at pH 10 (refs 28,45) was used for enrichments. For salt lakes, we used mineral medium containing 4 M NaCl and 0.1 M KCl buffered with 50 mM K phosphates at pH 6.8. Both media, after sterilization, were supplied with 1 ml l–1 of acidic46 and alkaline W/Se (ref. 47) trace metal solutions, 1 ml l–1 of vitamin mix46, 4 mM NH4Cl, 20 mg l–1 yeast extract and 0.1 mM filter-sterilized CoM. The media were dispensed in serum bottles (with butyl rubber stoppers) of various capacity at 50% (H2)–80% (formate) filled volumes, made anoxic with five cycles of argon flushing–evacuation, and finally reduced by the addition of 1 mM Na2S and 1 drop per 100 ml of 10% dithionite in 1 M NaHCO3. H2 was added on top of an argon atmosphere at 0.5 bar overpressure, formate and methanol at 50 mM, methylamines at 10 mM, and methyl- and dimethyl sulfides at 5 mM. In the case of methylamines, ammonium was omitted from the basic medium. Incubation temperature was varied from 30 to 65 °C. Analyses of growth parameters, pH-salt profiling of growth and activity of washed cells, optical and electron microscopy and chemical analyses were performed as described previously28,45.

Biomass composition

The presence of organic compatible solutes was tested by HPLC and 1H-NMR after extraction from dry cells with EtOH, and intracellular potassium concentration was quantified by inductively coupled plasma mass spectrometry (ICP-MS). The presence of methanophenazine analogues was analysed in acetone extract from lyophilized cells, followed by thin-layer chromatography (TLC) separation, re-extraction with a MeOH–chloroform mixture and tandem mass (MS-MS) spectrometry.

Genome sequencing and assembly

Genomic DNA from pure and highly enriched cultures was obtained using an UltraClean Microbial DNA Extraction Kit (MoBio Laboratories). Genome sequencing, assembly and automatic annotation of a pure culture from soda lakes and of a metagenome from a highly enriched salt lake culture were performed using BaseClear with a combination of Illumina and PackBio platforms. Kmer tetranucleotide frequency analysis was used to identify contigs that are likely to belong to the HMET1 (meta)genome. Genome completeness was estimated as described previously48.

Genome annotation and sequence analysis

The final gene call combined results from PROKKA49 and GeneMarkS50 pipelines. All protein-coding genes were assigned to the current archaeal Clusters of Orthologous Groups (arCOGs) as described previously34. Protein annotations were obtained by a combination of arCOGs and PROKKA annotations and, where there was conflict, the respective protein was manually reanalysed using PSI-BLAST51 and HHpred results52 and the annotations modified if necessary. Other genomes for comparative genome analysis were extracted from GenBank (March 2016) and, where necessary, open reading frames were predicted using GeneMarkS50.

Protein sequences were aligned using MUSCLE53. Alignments for the tree reconstruction were filtered to obtain informative position as described previously34. Approximate maximum likelihood phylogenetic trees were reconstructed using FastTree54 and PHYML55 methods. The PHYML program was used for phylogenetic tree reconstruction from an alignment of 51 concatenated ribosomal proteins (287 species, 8,072 positions), with the following parameters: LG matrix, gamma distributed site rates and default frequencies determined by the PROTTEST program56. Support values were estimated using an approximate Bayesian method implemented in PhyML. For McrA, multiple alignment (145 sequences and 553 positions) was used for tree reconstruction using PhyML and PROTTEST, as described above.

Two sets of genes reconstructed previously using the program COUNT39, which uses a Markov chain gene birth and death model, for the ancestors of Halobacteriales Halobacteriales/Methanomicrobiales were used to infer gene gains and losses on the branches leading to Halobacteriales and the discovered clade of extremely halophilic methanogens. We considered an arCOG to be present in these two clades when the respective COUNT probability was higher than 50%. Further reconstruction was done using a straightforward parsimony approach, as explained in detail in Supplementary Table 4.

Isoelectric points (pI) of individual proteins were calculated according to ref. 57 using pK values from the EMBOSS suite58. Genome-wide distributions of protein pI were obtained as probability density estimates at 100 points in the 2.0–14.0 pH range using the Gaussian kernel method59. Kullback–Leibler divergence of the pI distributions for the pair of genomes A and B, DKL(A|B) was computed for all ordered pairs of the set. The distance between the genomes was estimated as D(A,B) = D(B,A) = (DKL(A|B) + DKL(B|A))/2 (ref. 60). The matrix of genome distances was projected into two-dimensional space using the classical multidimensional scaling method61,62, as implemented in the R package63.

Proteomics

Proteomic analyses were conducted using the soda lake pure culture AMET1 (48 °C, MeOH+formate) and the salt lake enrichment HMET1 (37 °C, TMA+H2) (Supplementary Tables 6 and 7). Cell pellets were dissolved in lysis buffer (8 M urea, 2 M thiourea, 5% 3-[(3-cholamidopropyl)dimethylammonio]-1-propanesulfonate (CHAPS), 5 mM Tris(2-carboxyethyl)phosphine (TCEP)-HCl and a protease inhibitors cocktail). Homogenization of the cells was achieved by ultrasonication for 5 min in an ultrasonic bath. After homogenization, the lysed cells were centrifuged at 20,000g for 10 min at 4 °C, and the supernatant containing the solubilized proteins was used for the liquid chromatography–tandem mass spectrometry (LC–MS/MS) experiment. All samples were precipitated by the methanol/chloroform method and resuspended in a multi-chaotropic sample solution (7 M urea, 2 M thiourea, 100 mM triethylammonium bicarbonate (TEAB), pH 7.5). Total protein concentration was determined using a Pierce 660 nm protein assay (Thermo). A total of 40 µg of protein from each sample was reduced with 2 µl of 50 mM TCEP (SCIEX), pH 8.0, at 37 °C for 60 min, followed by 1 µl of 200 mM cysteine-blocking reagent (methyl methanethiosulfonate (MMTS), Pierce) for 10 min at room temperature. Samples were diluted to 140 µl to reduce the urea concentration with 25 mM TEAB. Digestions were initiated by adding 2 µg Pierce MS-grade trypsin (Thermo Scientific) to each sample in a ratio of 1:20 (wt/wt), which were then incubated at 37 °C overnight on a shaker. Sample digestions were evaporated to dryness in a vacuum concentrator and then desalted onto StageTip C18 pipette tips (Thermo Scientific) until the mass spectrometric analysis.

A 1 µg aliquot of each sample was subjected to 1D-nano liquid chromatography–electrospray ionization tandem mass spectrometry (LC ESI-MSMS) analysis using a nano liquid chromatography system (Eksigent Technologies nanoLC Ultra 1D plus, AB SCIEX) coupled to a high-speed Triple TOF 5600 mass spectrometer (SCIEX) with a Nanospray III source. The analytical column used was a silica-based reversed-phase Acquity UPLC M-Class Peptide BEH C18 column (75 µm × 150 mm, 1.7 µm particle size and 130 Å pore size, Waters). The trap column was a C18 Acclaim PepMap 100 (Thermo Scientific, 100 µm × 2 cm, 5 µm particle diameter, 100 Å pore size), switched online with the analytical column. The loading pump delivered a solution of 0.1% formic acid in water at 2 µl min–1. The nanopump provided a flow rate of 250 nl min–1 and was operated under gradient elution conditions. Peptides were separated using a 250 min gradient ranging from 2 to 90% of mobile phase B (mobile phase A: 2% acetonitrile, 0.1% formic acid; mobile phase B: 100% acetonitrile, 0.1% formic acid). The injection volume was 5 µl.

Data acquisition was performed with a TripleTOF 5600 System (SCIEX). Data were acquired using an ionspray voltage floating (ISVF) of 2,300 V, curtain gas (CUR) of 35, interface heater temperature (IHT) of 150, ion source gas 1 (GS1) 25 and a declustering potential (DP) of 100 V. All data were acquired using the information-dependent acquisition (IDA) mode with Analyst TF 1.7 software (SCIEX). For IDA parameters, a 0.25 s MS survey scan in the mass range of 350–1,250 Da was followed by 35 MS–MS scans of 100 ms in the mass range of 100–1,800 (total cycle time of 4 s). Switching criteria were set to ions with a mass to charge ratio (m/z) greater than 350 and smaller than 1,250, with a charge state of 2–5 and an abundance threshold of more than 90 counts (c.p.s.). Former target ions were excluded for 15 s. The IDA rolling collision energy (CE) parameters script was used to automatically control the CE.

MS and MS–MS data obtained for individual samples were processed using Analyst TF 1.7 Software (SCIEX). The reconstituted AMET1 and HMET1 chromosome sequence was used to generate the database for protein identification using Mascot Server v. 2.5.1 (Matrix Science). Search parameters were set as follows: carbamidomethyl (C) as fixed modification and acetyl (Protein N-term), Gln to pyro-Glu (N-term Q), Glu to pyro-Glu (N-term E) and methionine oxidation as variable modifications. Peptide mass tolerance was set to 25 ppm and 0.05 Da for fragment masses, and two missed cleavages were allowed. The confidence interval for protein identification was set to ≥95% (P < 0.05), and only peptides with an individual ion score above the 1% false discovery rates (FDR) at the PSM level were considered correctly identified. FDRs were calculated manually. The threshold of only one identified peptide per protein identification was used because FDR controlled experiments counter-intuitively suffer from the two-peptide rule64. To rank the protein abundance in each sample, the Exponentially Modified Protein Abundance Index (emPAI) was used in the present study as a relative quantitation score of the proteins in a complex mixture based on protein coverage by the peptide matches in a database search result62. Although emPAI is not as accurate as quantification using synthesized peptide standards, it is quite useful for obtaining a broad overview of proteome profiles.

Data availability

The final assembled and annotated genomic sequences of the two isolates of ‘Methanonatronarchaeia’ have been deposited in GenBank under accession nos PRJNA356895 (AMET1) and PRJNA357090 (HMET1). Other data supporting the findings reported in this article are available in the Supplementary tables or from the corresponding authors upon request.

Additional information

How to cite this article: Sorokin, D. Y. et al. Discovery of extremely halophilic, methyl-reducing euryarchaea provides insights into the evolutionary origin of methanogenesis. Nat. Microbiol. 2, 17081 (2017).

Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

References

  1. 1

    Ferry, J. G. & Kastead, K. A. in Archaea: Molecular and Cellular Biology (ed. Cavicchioli, R. ) 288–214 (ASM, 2007).

    Google Scholar 

  2. 2

    Conrad, R. The global methane cycle: recent advances in understanding the microbial processes involved. Environ. Microbiol. Rep. 1, 285–292 (2009).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  3. 3

    Inventory of US Greenhouse Gas Emissions and Sinks: 1990–2014. (US EPA, 2016).

  4. 4

    Garrity, G. M. & Holt, J. G. in Bergey's Manual of Systematics of Archaea and Bacteria Vol. 1 http://doi.org/10.1002/9781118960608.pbm00014 (Wiley, 2015).

    Google Scholar 

  5. 5

    Iino, T. et al. Candidatus Methanogranum caenicola: a novel methanogen from the anaerobic digested sludge, and proposal of Methanomassiliicoccaceae fam. nov. and Methanomassiliicoccales ord. nov., for a methanogenic lineage of the class Thermoplasmata. Microbes Environ. 28, 244–250 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  6. 6

    Borrel, G. et al. Comparative genomics highlights the unique biology of Methanomassiliicoccales, a Thermoplasmatales-related seventh order of methanogenic archaea that encodes pyrrolysine. BMC Genomics 15, 679 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  7. 7

    Lang, K. et al. New mode of energy metabolism in the seventh order of methanogens as revealed by comparative genome analysis of ‘Candidatus Methanoplasma termitum’. Appl. Environ. Microbiol. 81, 1338–1352 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  8. 8

    Evans, P. N. et al. Methane metabolism in the archaeal phylum Bathyarchaeota revealed by genome-centric metagenomics. Science 350, 434–438 (2015).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  9. 9

    Vanwonterghem, I. et al. Methylotrophic methanogenesis discovered in the archaeal phylum Verstraetearchaeota. Nat. Microbiol. 1, 16170 (2016).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  10. 10

    Hedderich, R. & Whitman, W. B. in The Prokaryotes—Prokaryotic Physiology and Biochemistry (ed. Rosenberg, E. ) 636–663 (Springer, 2013).

    Google Scholar 

  11. 11

    Liu, Y. & Whitman, W. B. Metabolic, phylogenetic, and ecological diversity of the methanogenic archaea. Ann. NY Acad. Sci. 1125, 171–189 (2008).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  12. 12

    Thauer, R. K., Kaster, A. K., Seedorf, H., Buckel, W. & Hedderich, R. Methanogenic archaea: ecologically relevant differences in energy conservation. Nat. Rev. Microbiol. 6, 579–591 (2008).

    CAS  Article  PubMed  Google Scholar 

  13. 13

    Borrel, G. et al. Phylogenomic data support a seventh order of methylotrophic methanogens and provide insights into the evolution of methanogenesis. Genome Biol. Evol. 5, 1769–1780 (2013).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  14. 14

    Dridi, B., Fardeau, M. L., Ollivier, B., Raoult, D. & Drancourt, M. Methanomassiliicoccus luminyensis gen. nov., sp. nov., a methanogenic archaeon isolated from human faeces. Int. J. Syst. Evol. Microbiol. 62, 1902–1907 (2012).

    CAS  Article  PubMed  Google Scholar 

  15. 15

    Paul, K., Nonoh, J. O., Mikulski, L. & Brune, A. ‘Methanoplasmatales,’ Thermoplasmatales-related archaea in termite guts and other environments, are the seventh order of methanogens. Appl. Environ. Microbiol. 78, 8245–8253 (2012).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  16. 16

    Fricke, W. F. et al. The genome sequence of Methanosphaera stadtmanae reveals why this human intestinal archaeon is restricted to methanol and H2 for methane formation and ATP synthesis. J. Bacteriol. 188, 642–658 (2006).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  17. 17

    Miller, T. L. & Wolin, M. J. Methanosphaera stadtmaniae gen. nov., sp. nov.: a species that forms methane by reducing methanol with hydrogen. Arch. Microbiol. 141, 116–122 (1985).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  18. 18

    Sprenger, W. W., Hackstein, J. H. & Keltjens, J. T. The energy metabolism of Methanomicrococcus blatticola: physiological and biochemical aspects. Antonie van Leeuwenhoek 87, 289–299 (2005).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  19. 19

    Sprenger, W. W., Hackstein, J. H. & Keltjens, J. T. The competitive success of Methanomicrococcus blatticola, a dominant methylotrophic methanogen in the cockroach hindgut, is supported by high substrate affinities and favorable thermodynamics. FEMS Microbiol. Ecol. 60, 266–275 (2007).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  20. 20

    Sprenger, W. W., van Belzen, M. C., Rosenberg, J., Hackstein, J. H. & Keltjens, J. T. Methanomicrococcus blatticola gen. nov., sp. nov., a methanol- and methylamine-reducing methanogen from the hindgut of the cockroach Periplaneta americana. Int. J. Syst. Evol. Microbiol. 50, 1989–1999 (2000).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  21. 21

    Nobu, M. K., Narihiro, T., Kuroda, K., Mei, R. & Liu, W. T. Chasing the elusive Euryarchaeota class WSA2: genomes reveal a uniquely fastidious methyl-reducing methanogen. ISME J. 10, 2478–2487 (2016).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  22. 22

    Borrel, G., Adam, P. S. & Gribaldo, S. Methanogenesis and the Wood–Ljungdahl pathway: an ancient, versatile, and fragile association. Genome Biol. Evol. 8, 1706–1711 (2016).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  23. 23

    McGenity, T. J. in Handbook of Hydrocarbon and Lipid Microbiology (ed. Timmis, K. N. ) 665–679 (Springer, 2010).

    Google Scholar 

  24. 24

    Kelley, C. A., Poole, J. A., Tazaz, A. M., Chanton, J. P. & Bebout, B. M. Substrate limitation for methanogenesis in hypersaline environments. Astrobiology 12, 89–97 (2012).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  25. 25

    Oremland, R. S. & King, G. M. in Microbial Mats. Physiological Ecology of Benthic Microbial Communities (eds Cohen, Y. & Rosenberg, E. ) 180–190 (American Society for Microbiology, 1989).

    Google Scholar 

  26. 26

    Martin, D. D., Ciulla, R. A. & Roberts, M. F. Osmoadaptation in archaea. Appl. Environ. Microbiol. 65, 1815–1825 (1999).

    CAS  PubMed  PubMed Central  Google Scholar 

  27. 27

    Menaia, J. A. G. F. Osmotics of Halophilic Methanogenic Archaeobacteria. PhD thesis, Oregon Health Sci Univ. (1992).

  28. 28

    Sorokin, D. Y . et al. Methanogenesis at extremely haloalkaline conditions in the soda lakes of Kulunda Steppe (Altai, Russia). FEMS Microbiol. Ecol. 91, pii: fiv016 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  29. 29

    Ginzburg, M., Sachs, L. & Ginzburg, B. Z. Ion metabolism in a Halobacterium. I. Influence of age of culture on intracellular concentrations. J. Gen. Physiol. 55, 187–207 (1970).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  30. 30

    Elevi Bardavid, R. & Oren, A. The amino acid composition of proteins from anaerobic halophilic bacteria of the order Halanaerobiales. Extremophiles 16, 567–572 (2012).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  31. 31

    Oren, A. Life at high salt concentrations, intracellular KCl concentrations, and acidic proteomes. Front. Microbiol. 4, 315 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  32. 32

    Sorokin, D. Y., Banciu, H. L. & Muyzer, G. Functional microbiology of soda lakes. Curr. Opin. Microbiol. 25, 88–96 (2015).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  33. 33

    Abken, H. J. et al. Isolation and characterization of methanophenazine and function of phenazines in membrane-bound electron transport of Methanosarcina mazei Go1. J. Bacteriol. 180, 2027–2032 (1998).

    CAS  PubMed  PubMed Central  Google Scholar 

  34. 34

    Makarova, K. S., Wolf, Y. I. & Koonin, E. V. Archaeal clusters of orthologous genes (arCOGs): an update and application for analysis of shared features between Thermococcales, Methanococcales, and Methanobacteriales. Life (Basel) 5, 818–840 (2015).

    CAS  Google Scholar 

  35. 35

    Yutin, N., Puigbo, P., Koonin, E. V. & Wolf, Y. I. Phylogenomics of prokaryotic ribosomal proteins. PLoS ONE 7, e36972 (2012).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  36. 36

    Eder, W., Schmidt, M., Koch, M., Garbe-Schonberg, D. & Huber, R. Prokaryotic phylogenetic diversity and corresponding geochemical data of the brine–seawater interface of the Shaban Deep, Red Sea. Environ. Microbiol. 4, 758–763 (2002).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  37. 37

    Jiang, H. et al. Microbial response to salinity change in Lake Chaka, a hypersaline lake on Tibetan plateau. Environ. Microbiol. 9, 2603–2621 (2007).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  38. 38

    Yarza, P. et al. Uniting the classification of cultured and uncultured bacteria and archaea using 16S rRNA gene sequences. Nat. Rev. Microbiol. 12, 635–645 (2014).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  39. 39

    Wolf, Y. I., Makarova, K. S., Yutin, N. & Koonin, E. V. Updated clusters of orthologous genes for Archaea: a complex ancestor of the Archaea and the byways of horizontal gene transfer. Biol. Direct 7, 46 (2012).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  40. 40

    Makarova, K. S., Koonin, E. V. & Albers, S. V. Diversity and evolution of type IV pili systems in archaea. Front. Microbiol. 7, 667 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  41. 41

    Zheng, K., Ngo, P. D., Owens, V. L., Yang, X. P. & Mansoorabadi, S. O. The biosynthetic pathway of coenzyme F430 in methanogenic and methanotrophic archaea. Science 354, 339–342 (2016).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  42. 42

    Aono, R. et al. Enzymatic characterization of AMP phosphorylase and ribose-1,5-bisphosphate isomerase functioning in an archaeal AMP metabolic pathway. J. Bacteriol. 194, 6847–6855 (2012).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  43. 43

    Baines, A. J. Evolution of spectrin function in cytoskeletal and membrane networks. Biochem. Soc. Trans. 37, 796–803 (2009).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  44. 44

    Hallam, S. J., Girguis, P. R., Preston, C. M., Richardson, P. M. & DeLong, E. F. Identification of methyl coenzyme M reductase A (mcrA) genes associated with methane-oxidizing archaea. Appl. Environ. Microbiol. 69, 5483–5491 (2003).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  45. 45

    Sorokin, D. Y. et al. Methanosalsum natronophilum sp. nov., and Methanocalculus alkaliphilus sp. nov., haloalkaliphilic methanogens from hypersaline soda lakes. Int. J. Syst. Evol. Microbiol. 65, 3739–3745 (2015).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  46. 46

    Pfennig, N. & Lippert, K. D. Über das vitamin B12-Bedürfnis phototropher schwefelbakterien. Arch. Mikrobiol. 55, 245–256 (1966).

    CAS  Article  Google Scholar 

  47. 47

    Plugge, C. M. Anoxic media design, preparation, and considerations. Meth. Enzymol. 397, 3–16 (2005).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  48. 48

    Podar, M. et al. Insights into archaeal evolution and symbiosis from the genomes of a nanoarchaeon and its inferred crenarchaeal host from obsidian pool, Yellowstone national park. Biol. Direct 8, 9 (2013).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  49. 49

    Seemann, T. Prokka: rapid prokaryotic genome annotation. Bioinformatics 30, 2068–2069 (2014).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  50. 50

    Besemer, J., Lomsadze, A. & Borodovsky, M. GeneMarkS: a self-training method for prediction of gene starts in microbial genomes. Implications for finding sequence motifs in regulatory regions. Nucleic Acids Res. 29, 2607–2618 (2001).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  51. 51

    Altschul, S. F. et al. Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res. 25, 3389–3402 (1997).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  52. 52

    Soding, J., Biegert, A. & Lupas, A. N. The HHpred interactive server for protein homology detection and structure prediction. Nucleic Acids Res. 33, W244–W248 (2005).

    Article  PubMed  PubMed Central  Google Scholar 

  53. 53

    Edgar, R. C. MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 32, 1792–1797 (2004).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  54. 54

    Price, M. N., Dehal, P. S. & Arkin, A. P. Fasttree 2—approximately maximum-likelihood trees for large alignments. PLoS ONE 5, e9490 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  55. 55

    Guindon, S. et al. New algorithms and methods to estimate maximum-likelihood phylogenies: assessing the performance of PhyML 3.0. Syst. Biol. 59, 307–321 (2010).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  56. 56

    Darriba, D., Taboada, G. L., Doallo, R. & Posada, D. Prottest 3: fast selection of best-fit models of protein evolution. Bioinformatics 27, 1164–1165 (2011).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  57. 57

    Bjellqvist, B. et al. The focusing positions of polypeptides in immobilized pH gradients can be predicted from their amino acid sequences. Electrophoresis 14, 1023–1031 (1993).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  58. 58

    Rice, P., Longden, I. & Bleasby, A. EMBOSS: the European Molecular Biology Open Software Suite. Trends Genet. 16, 276–277 (2000).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  59. 59

    Parzen, E. On estimation of a probability density function and mode. Ann. Math. Statist. 33, 1065–1076 (1962).

    Article  Google Scholar 

  60. 60

    Kullback, S. & Leibler, R. A. On information and sufficiency. Ann. Math. Stat. 22, 79–86 (1951).

    Article  Google Scholar 

  61. 61

    Gower, J. C. Some distance properties of latent root and vector methods used in multivariate analysis. Biometrika 53, 325–338 (1966).

    Article  Google Scholar 

  62. 62

    Torgeson, W. S. Theory and Methods of Scaling (Wiley, 1958).

    Google Scholar 

  63. 63

    R (R Foundation for Statistical Computing, 2013).

  64. 64

    Gupta, N. & Pevzner, P. A. False discovery rates of protein identifications: a strike against the two-peptide rule. J. Proteome Res. 8, 4173–4181 (2009).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

D.Y.S. was supported by STW (project no. 12226), the Gravitation-SIAM Program (grant no. 24002002 from the Dutch Ministry of Education and Science) and by RFBR (grant no. 16-04-00035). K.S.M., Y.I.W. and E.V.K. are supported by the intramural programme of the US Department of Health and Human Services (to the National Library of Medicine). The proteomic analysis was performed in the Proteomics Facility of The Spanish National Center for Biotechnology (CNB-CSIC), which belongs to ProteoRed (PRB2-ISCIII), supported by grant no. PT13/0001. This project received funding from the European Union's Horizon 2020 research and innovation programme (Blue Growth: Unlocking the potential of Seas and Oceans) under grant agreement no. 634486. This work was further funded by grant no. BIO2014-54494-R from the Spanish Ministry of Economy, Industry and Competitiveness.

Author information

Affiliations

Authors

Contributions

D.Y.S. performed the fieldwork, the sediment activity incubations, enrichment and isolation of pure cultures and microbiological investigation of enriched and pure cultures. B.A. and A.Y.M. analysed the mcrA and 16S rRNA genes in sediments and methanogenic cultures. M.F., P.N.G., S.C. and M.C.M.v.L. were responsible for the proteomic analysis. E.A.G. analysed compatible solutes. K.S.M., Y.I.W. and E.V.K. performed genomic analysis and evolutionary reconstructions. D.Y.S., K.S.M. and E.V.K. wrote the paper. M.C.M.v.L. oversaw the project and participated in the data interpretation and discussion.

Corresponding authors

Correspondence to Dimitry Y. Sorokin or Eugene V. Koonin.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary information

Supplementary Figures 1–10; Supplementary Tables 1 and 2; Supplementary Data 1–4. (PDF 17605 kb)

Supplementary Table 3

Comparative genomic analysis based on arCOG assignments. (XLSX 6009 kb)

Supplementary Table 4

Reconstruction of gene gain and loss. (XLSX 434 kb)

Supplementary Table 5

Isoelectric point calculation data. (XLSX 25 kb)

Supplementary Table 6

Proteomic analysis for AMET1. (XLSX 121 kb)

Supplementary Table 7

Proteomic analysis for HMET1. (XLSX 179 kb)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Sorokin, D., Makarova, K., Abbas, B. et al. Discovery of extremely halophilic, methyl-reducing euryarchaea provides insights into the evolutionary origin of methanogenesis. Nat Microbiol 2, 17081 (2017). https://doi.org/10.1038/nmicrobiol.2017.81

Download citation

Further reading

Search

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing