- Open Access
A metagenomics roadmap to the uncultured genome diversity in hypersaline soda lake sediments
- Charlotte D. Vavourakis1View ORCID ID profile,
- Adrian-Stefan Andrei†2,
- Maliheh Mehrshad†2,
- Rohit Ghai2,
- Dimitry Y. Sorokin3, 4 and
- Gerard Muyzer1Email authorView ORCID ID profile
© The Author(s). 2018
- Received: 23 June 2018
- Accepted: 3 September 2018
- Published: 19 September 2018
Hypersaline soda lakes are characterized by extreme high soluble carbonate alkalinity. Despite the high pH and salt content, highly diverse microbial communities are known to be present in soda lake brines but the microbiome of soda lake sediments received much less attention of microbiologists. Here, we performed metagenomic sequencing on soda lake sediments to give the first extensive overview of the taxonomic diversity found in these complex, extreme environments and to gain novel physiological insights into the most abundant, uncultured prokaryote lineages.
We sequenced five metagenomes obtained from four surface sediments of Siberian soda lakes with a pH 10 and a salt content between 70 and 400 g L−1. The recovered 16S rRNA gene sequences were mostly from Bacteria, even in the salt-saturated lakes. Most OTUs were assigned to uncultured families. We reconstructed 871 metagenome-assembled genomes (MAGs) spanning more than 45 phyla and discovered the first extremophilic members of the Candidate Phyla Radiation (CPR). Five new species of CPR were among the most dominant community members. Novel dominant lineages were found within previously well-characterized functional groups involved in carbon, sulfur, and nitrogen cycling. Moreover, key enzymes of the Wood-Ljungdahl pathway were encoded within at least four bacterial phyla never previously associated with this ancient anaerobic pathway for carbon fixation and dissimilation, including the Actinobacteria.
Our first sequencing effort of hypersaline soda lake sediment metagenomes led to two important advances. First, we showed the existence and obtained the first genomes of haloalkaliphilic members of the CPR and several hundred other novel prokaryote lineages. The soda lake CPR is a functionally diverse group, but the most abundant organisms in this study are likely fermenters with a possible role in primary carbon degradation. Second, we found evidence for the presence of the Wood-Ljungdahl pathway in many more taxonomic groups than those encompassing known homo-acetogens, sulfate-reducers, and methanogens. Since only few environmental metagenomics studies have targeted sediment microbial communities and never to this extent, we expect that our findings are relevant not only for the understanding of haloalkaline environments but can also be used to set targets for future studies on marine and freshwater sediments.
- Soda lake sediments
- Haloalkaliphilic extremophiles
- Candidate Phyla Radiation
- Wood-Ljungdahl pathway
Soda lakes are evaporative, athallasic salt lakes with low calcium and magnesium concentrations and a high-alkaline pH up to 11 buffered by dissolved (bi-) carbonate ions . They are constrained to arid regions across the globe, mainly the tropical East African Rift Valley , the Libyan Desert , the deserts in California and Nevada , and the dry steppe belt of Central Asia that spans to southern Siberia, north-eastern Mongolia, and Inner Mongolia in China . On top of the extreme salinity and alkaline pH, the Eurasian soda lakes experience extreme seasonal temperature differences, causing highly unstable water regimes and fluctuating salinities . Yet, soda lakes harbor diverse communities of haloalkaliphilic microbes, mostly prokaryotes that are well adapted to survive and grow in these extreme environments and consist of similar functional groups in soda lakes around the world [1, 2, 6]. The relative abundance of different groups is typically governed by the salinity of the brine [1, 7, 8], and microbial-mediated nutrient cycles become partially hampered only at salt-saturating conditions .
So far, all characterized prokaryotic lineages cultured from soda lakes comprise over 70 different species within more than 30 genera [1, 6, 9, 10]. From these, only a limited number of genomes have been sequenced today, mostly from chemolithoautotrophic sulfur-oxidizing bacteria belonging to the genus Thioalkalivibrio (class Gammaproteobacteria) [1, 11, 12]. It is well established that metagenomics enables the recovery of genomes and the identification of novel genetic diversity where culturing efforts fail [13, 14]. In recent years, next-generation sequencing has recovered a massive number of genomes from previously unknown groups of prokaryotes [15, 16], including a strikingly large and diverse group called “Candidate Phyla Radiation” (CPR), only distantly related to other cultured bacterial lineages . Previously, we conducted a metagenomics study on soda lakes and reconstructed novel genomes from uncultured Bacteroidetes and “Candidatus Nanohaloarchaeaota” living in hypersaline Siberian soda brines . Here, we turned our attention to the far more complex prokaryotic communities living in the sediments of the hypersaline soda lakes from the same region. We give a broad overview of all the taxonomic groups sequenced and focus on the metabolic diversity found in the reconstructed genomes of the most abundant, uncultured organisms.
Overall prokaryote community structure
The varying ratio of the three dominant bacterial groups, Firmicutes, Bacteroidetes (including the newly proposed phyla Rhodothermaeota and Balneolaeota ), and Gammaproteobacteria, showed no clear trend in relation to the salinity in the lakes, but when Firmicutes were dominant, Bacteroidetes were less abundant and vice versa. Most Firmicutes belonged to the order Clostridales. Uncultured members from the family Syntrophomonadaceae had a relative abundance of more than 5% in all five metagenomes and comprised in two lakes even ~ 11–20% of the recovered amplicon sequences. The second most abundant Firmicutes order was Halanaerobiales, particularly the genus Halanaerobium (family Halanaerobiaceae) and uncultured members of the Halobacteroidaceae. The majority of Bacteroidetes-related OTUs could not be assigned down to the genus level. The uncultured ML635J-40 aquatic group (order Bacteroidales) comprised at least 5% of all five prokaryotic communities. This group has been previously found to be abundant in Mono Lake  (a soda lake) and in an anoxic bioreactor degrading cyanobacterial biomass under haloalkaline conditions . Two other highly abundant (up to ~ 8%) uncultured groups from the class Balneolia (proposed new phylum Balneolaeota ) were also detected in other soda lakes before [3, 4]. Within the Gammaproteobacteria, the genus Thioalkalivibrio was abundant (~ 3% of the total community), but also uncultured members of HOC36 were prevailing at moderate salinities.
Members of the Deltaproteobacteria, Alphaproteobacteria, and Chloroflexi comprised up to ~ 10% of the detected 16S rRNA gene in some of the metagenomes. The GIF9 family of the class Dehalococcoidia was among the top three most abundant OTUs in two lakes. The extremely salt-tolerant and alkaliphilic genera Desulfonatronobacter (order Desulfobacterales) and Desulfonatronospira (order Desulfovibrionales) were the dominant Deltaproteobacteria. Highly abundant OTUs, within the Actinobacteria belonged to the class Nitriliruptoria and within the Alphaproteobacteria to the family Rhodobacteraceae and the genus Roseibaca. The important nitrifying genus Nitrobacter (Alphaproteobacteria) was present in only one of the lakes with moderate salinity (Additional file 3).
Some bacterial top-level taxa appeared less dominant (< 5%) from the 16S rRNA genes recovered from the metagenomes but were represented mainly by a single highly abundant OTU in the amplicon sequences, including the haloalkaliphilic genus Truepera within the phylum Deinococcus-Thermus, the genus Spirochaeata within the phylum Spirochaetes, the family BSN166 within the phylum Ignavibacteriae, the BD2–11 terrestrial group within the Gemmatimonadetes, and the WCHB1–41 order within the Verrucomicrobia. All OTUs within the Thermotogae and Lentisphaerae belonged to uncultured genera from the family Kosmotogaceae and Oligosphaeraceae, respectively. All Tenericutes-related OTUs belonged to the class Mollicutes, and especially the order NB1-n was dominant. In contrast, the phylum Planctomycetes was relatively diverse, with at least 11 different genus-level OTUs spread over four class-level groups.
High-throughput genome recovery
The soda lake CPR recovered from the metagenomes was restricted to a few distinct phyla within the Parcubacteria group, mostly affiliating with “Candidatus Nealsonbacteria” and “Ca. Zambryskibacteria”  (Fig. 2). The first group of MAGs encompassed four different branches in our ribosomal protein tree, suggesting a high-phylogenetic diversity, with 33 putative new species sampled here (ANI and conDNA matrices given in Additional file 6). The “Ca. Zambryskibacteria-”related MAGs consisted of at least five new species. Few MAGs were recovered from CPR groups also detected by amplicon sequencing (see Additional file 2: Figure S1), namely the “Ca. Dojkabacteria” (former WS6), “Ca. Saccharibacteria” (former TM7), CPR2, and “Ca. Katanobacteria” (former WWE3).
Most archaeal MAGs belonged to the phylum Euryarchaeota and the abundant classes Halobacteria, Methanomicrobia, and Thermoplasmata (related to OTU KTK 4A) within. In addition, three Thermoplasmata-related MAGs that encoded for the key enzyme for methanogenesis (methyl-coenzyme M reductase, mcr) affiliated with reference genomes from Methanomassilicoccales, the seventh order of methanogens have been recovered [20, 21]. Another MCR-encoding MAG was closely related to the latest discovered group of poly-extremophilic, methyl-reducing methanogens from hypersaline lakes from the class Methanonatronarchaeia  (related to OTU ST-12K10A). We recovered also one MAG from the class Methanobacteria and a high-quality MAG from the WCHA1–57 group (“Candidatus Methanofastidiosa” ) in the candidate division WSA2 (Arc I). Several MAGs were recovered from the DPANN archaeal groups “Ca. Diapherotrites,” “Ca. Aenigmarchaeota,” (see Additional file 2: Figure S3) and “Ca. Woesearchaeota” (former Deep Sea Hydrothermal Vent Group 6, DHVEG-6). Although we did not reconstruct any reasonable-sized MAGs from the TACK superphylum, we found several 16S rRNA genes on the assembled contigs that affiliated to the Thaumarchaeota (see Additional file 1: Table S2).
Nearly every known bacterial phylum had an extremophilic lineage sampled from our hypersaline soda lake sediments (Fig. 3). In most cases, the soda lake lineages clearly formed separate branches appearing as sister groups to known reference lineages. The highest genome recovery was from the same top-level taxonomic groups that were also abundant in our 16S rRNA gene analysis. From the Verrucomicrobia, most MAGs belonged to the order WCHB1-41 (16S rRNA gene identity 92–100%). However, in our ribosomal protein tree, they branched within the phylum Lentisphaerae. Sixteen Tenericutes MAGs from at least 12 different species (Additional file 6) were closely related to the NB1-n group of Mollicutes. Based on the recovered genome size and encoded metabolic potential, these organisms are free-living anaerobic fermenters of simple sugars, similar to what has recently been proposed for “Candidatus Izimaplasma” . Several MAGs belonged to the candidate phyla “Ca. Omnitrophica,” “Ca. Atribacteria,” and “Ca. Acetothermia” (former OP1), which were moderately abundant also in some sediment (see Additional file 2: Figure S1). For the latter phylum, we suspect that four MAGs were more closely related to ca. div. WS1 and “Ca. Lindowbacteria” for which only few reference genomes are currently available in NCBI (see Additional file 2: Figure S4). Due to a high-sequencing coverage, we also managed to reconstruct several MAGs from rare Bacteria (< 100 amplicon sequences detected, see Additional file 2: Figure S1), including the phyla “Ca. Hydrogenedentes,” “Ca. Cloacimonetes,” ca. div. BRC1, Elusimicrobia, Caldiserica, and “Ca. Latescibacteria.” The MAGs from the latter phylum were more closely related to the recently proposed phylum “Ca. Handelsmanbacteria” . Two additional MAGs with 16S rRNA gene fragments with 94–95% identity to the class MD2898-B26 (Nitrospinae) were more likely members of ca. div. KSB3 (proposed “Ca. Moduliflexus” , see Additional file 2: Figure S5).
Draft genomes of haloalkaliphilic CPR
It remains to be determined whether the soda lake CPR also lacks an outer membrane and perhaps anchor lipopolysaccharides, S-layer proteins, and lipoproteins to the inner cell membrane or peptidoglycan. We also found gene encoding cardiolipin and squalene synthases. Increased levels of cardiolipin and the presence of squalene make the cytoplasmic membrane less leaky for protons . In addition, cation/proton exchangers are known to play a crucial role for pH homeostasis in alkaliphilic prokaryotes as they help acidify the cytoplasm during the extrusion of cations . Putative Na+/H+ exchangers of the Nha-type and multi-subunit Mnh-type were found only within a few soda lake CPR. Secondary active transport of K+ might be mediated in most soda lake CPR by KefB (COG0475)/kch Kef-type, glutathione-dependent K+ transport systems, with or without H+ antiport (67,68).
Various soda lake CPR had an acidic proteome, with pI curves resembling those found in extremely halophilic Bacteria. Intracellular proteins enriched in acidic amino acids might be an adaptation to a “salt-in” strategy, i.e., maintaining high intracellular potassium (K+) concentrations to keep osmotic balance [7, 37] (Fig. 5b; see Additional file 2: Figure S10). Such a strategy is energetically favorable over de novo synthesis or import of osmolytes such as ectoine and glycine betaine. We did not find genes for the synthesis of organic osmolytes and homologs of ABC-type transporters for primary active uptake of proline/glycine betaine which were encoded only in one MAG (Fig. 5a). For the “Ca. Nealsonbacteria” and “Ca. Vogelbacteria,” the salt-in strategy might be a unique feature for the soda lake species explaining their high abundance in the hypersaline soda lake sediments, as we did not found an acidic proteome predicted from genomes obtained from other non-saline environments (See Additional file 2: Figure S11). The uptake of K+ ions remains enigmatic for most soda lake CPR. Low-affinity Trk-type K+ uptake transporters (generally with symport of H+) (67,68) were encoded only by a limited number of MAGs. We found three MAGs encoding for Kdp-type sensor kinases (kdpD) but no corresponding genes for the response regulator (kdpE) or for Kdp-ATPases that function as the inducible, high-affinity K+ transporters in other Bacteria (67,68). Finally, mechanosensitive ion channels (mscS, mscL) and ABC-type multidrug transport systems (AcrAB, ccmA, EmrA, MdlB, NorM) and sodium efflux permeases (NatB) were encoded in almost every MAG. The first might rapidly restore the turgor pressure under fluctuating salinity conditions by releasing cytoplasmic ions .
Novel abundant groups involved in sulfur, nitrogen, and carbon cycles
A new species of Thioalkalivibrio (family Ectothiorhodospiraceae) was by far the most abundant in the sediments of the two salt-saturated lakes (Fig. 4). In the sediment of Bitter-1, also a purple sulfur bacterium from the same family was highly abundant. It was closely related to Halorhodospira, a genus also frequently cultured from hypersaline soda lakes . None of the abundant Ectothiorhodospiraceae spp. had already a species-representative genome sequenced (Additional file 6). The potential of the Thioalkalivibrio spp. for chemolithotrophic sulfur oxidation was evident (Additional file 7; see Additional file 8: Information S1). Interestingly, the encoded nitrogen metabolisms were quite versatile. While Thioalkalivibrio sp. 1 had the potential for nitrate reduction to nitrite, Thioalkalivibrio sp. 2 might perform dissimilatory nitrite reduction to ammonia (DNRA; see Additional file 2: Figure S12).
Two deltaproteobacterial lineages of dissimilatory sulfate-reducing bacteria (SRB) were highly abundant in the soda lake sediment of Bitter-1. One MAG from the family Desulfobacteraceae (order Desulfobacterales) is the first genome from the genus Desulfonatronobacter. It encodes the genes for complete sulfate reduction to sulfide using various electron donors, as well as for the complete oxidation of volatile fatty acids and alcohols, a unique feature for the genus Desulfonatronobacter among haloalkaliphilic SRB  (see Additional file 8: Information S2). Fumarate and nitrite (DNRA, NrfAH) could be used as alternative electron acceptors. The second dominant lineage was a new species from the genus Desulfonatronospira (family Desulfohalobiaceae, order Desulfovibrionales). Like other members of this genus, it had the potential to reduce or disproportionate partially reduced sulfur compounds. In addition, it could also use nitrite as an alternative electron acceptor (NrfAH) .
A novel lineage of gammaproteobacterial SOB was highly abundant in the sediments of the moderately hypersaline Cock Soda Lake. It appeared as a sister group of the family Xanthomonadaceae in the ribosomal protein tree. This heterotrophic organism could conserve energy through aerobic respiration. It might detoxify sulfide by oxidizing it to elemental sulfur (sqr) with subsequent reduction or disproportionation of the polysulfides (psrA) chemically formed from the sulfur. It also encoded the potential for DNRA (nrfA and napC). Genes likely involved in sulfide detoxification (sqr and psrA) were found also in several other abundant MAGs of heterotrophs, including one new abundant species from the family of Nitriliruptoraceae (class Nitriliruptoria, phylum Actinobacteria). We found a wide variety of carbohydrate-active enzymes in these MAGs, such as cellulases (GH1 family) in addition to genes for glycolysis and TCA cycle and a chlorophyll/bacteriochlorophyll a/b synthase (bchG gene). The latter was also found in other Actinobacteria from the genus Rubrobacter . No evidence was found for nitrile-degrading potential.
A second novel, uncultured lineage of Gammaproteobacteria that was highly abundant at moderate salinities branched in our ribosomal protein tree as a sister group to the family Halothiobacillaceae. The MAGs encoded for a versatile metabolism typical for purple non-sulfur bacteria. The MAGs contained puf genes, bch genes, genes for carotenoid biosynthesis (not shown), and a Calvin cycle for photoautotrophic growth. Alternatively, energy may be conserved through aerobic respiration, while acetate and proprionate could be taken up via an acetate permease (actP) and further used for acetyl-CoA biosynthesis and carbon assimilation. Since the sqr gene was present, but no dsr or sox genes, the organism might oxidize sulfide only to elemental sulfur. One bin contained also nifDKH genes suggesting putative diazotrophy, as well as a coenzyme F420 hydrogenase suggesting photoproduction of hydrogen .
The abundant Euryarchaeota organism showed a clear preference for higher salinities. We obtained one highly abundant MAG from the class Thermoplasmata that encoded a full-length 16S rRNA gene only distantly related (91,2% identity, e value 0) to that of a member of the KTK 4A group found in a hypersaline endoevaporitic microbial mat . The abundant soda lake organism is likely a new genus and species. All KTK 4A-related MAGs found here encoded for similar heterotrophic, fermentative metabolisms, with the potential for anaerobic formate and CO oxidation. The KTK 4A might be also primary degraders since they encoded putative cellulases (CAZY-families GH1, GH5) and chitinases (GH18). Interestingly, half of the MAGs encoded a putative chlorophyll/bacteriochlorophyll a/b synthase (bchG), which is highly unusual for Archaea. Although little can be inferred from the presence of only one marker gene, a functional bchG was previously also found in Crenarchaeota . The remaining two highly abundant Euryarchaeota-related MAGs belonged to a new species of Halorubrum (Additional file 6).
Key genes of the Wood-Ljungdahl pathway found in novel phylogenetic groups
More than 50 MAGs were related to the family Syntrophomonadaceae (class Clostridia, phylum Firmicutes) based on ribosomal protein phylogeny. All 16S rRNA gene sequences found in the MAGS had 86–95% identity to sequences obtained from uncultured organisms related to the genus Dethiobacter. While an isolated strain of Dethiobacter alkaliphilus is a facultative autotroph that respires thiosulfate, elemental sulfur or polysulfides with hydrogen as an electron donor  or disproportionates sulfur , other haloalkaliphilic members of the Syntrophomonadaceae are reverse acetogens, oxidizing acetate in syntrophy with a hydrogenotrophic partner . Two populations (different species, Additional file 6) were especially abundant in Cock Soda Lake (Fig. 4). They encoded for a full CODH/ACS complex, the key enzyme for the reductive acetyl-CoA or Wood-Ljungdahl pathway (WL) and a complete Eastern branch for CO2 conversion to 5-methyl-tetrahydrofolate (Additional file 9) [45, 46]. Acetogens use the WL to reduce CO2 to acetyl-CoA, which can be fixed into the cell or used to conserve energy via acetogenesis. Syntrophic acetate oxidizers, some sulfate reducing bacteria and aceticlastic methanogens run the WL in reverse. Syntrophomonadaceae sp. 2 encoded for a putative thiosulfate/polysulfide reductase as well as a phosphotransacetylase (pta) and an acetate kinase (ack) for the ATP-dependent conversion of acetate to acetyl-CoA. Although alternative pathways for the latter interconversion can exist, this second species has the complete potential for (reversed) acetogenesis.
Extensive classical microbiology efforts have been already undertaken to explore the unique extremophilic microbial communities inhabiting soda lakes. These uncovered the presence of most of the functional groups participating in carbon, nitrogen, sulfur, and minor element cycling at haloalkaline conditions. The main results of this work are summarized in several recent reviews [1, 6, 47, 48]. Since most microbes, including those living in soda lakes, still evade all cultivation efforts, a very effective way to discover new microbes and assess their physiology based on their genetic repertoire is either through single cell genomics or by directly sequenced environmental DNA. This exploratory metagenomics study performed on soda lake sediments effectively overcame the existing cultivation bottleneck. First, we expanded the known diversity of CPR considerably with the first genomes of poly-extremophiles sampled from soda lake sediments. Although the presence of 16S rRNA genes from CPR in marine sediments and hypersaline microbial mats was previously shown , until now, CPR MAGs were mainly obtained from deep, subsurface environments [15, 26, 29, 32, 49–52], and human microbiota . Despite being highly abundant here, CPR went unnoticed in previous amplicon sequencing studies. This might be due to the fact that many CPR representatives have random inserts of various length in their 16S rRNA genes or due to primer mismatches [29, 34]. This illustrates also that direct metagenomics should not only be preferred over amplicon sequencing to infer functional potential, but the former is far more effective for the discovery of novel organisms. Second, we obtained many more genomes from “traditional” bacterial phyla such as the Planctomycetes and Chloroflexi, as well as candidate phyla, for which no soda lake isolates, hence no genomes were previously obtained. Third, even within the sulfur cycle, the most active and frequently studied element cycle in soda lakes , we found considerable metabolic novelty. Finally, we found the Wood-Ljungdahl pathway in several novel phyla, not closely related to any known acetogens, methanogens, or sulfate-reducing bacteria . The latter shows that our sequencing recovery effort has also significantly contributed to the discovery of metabolic novelty within various prokaryote phylogenetic groups.
Salinity is often considered to be the major factor shaping prokaryote community composition in diverse habitats [53, 54]. Extreme halophilic Euryarchaeota seem to be always the dominant group in salt-saturated hypersaline brines, both those with neutral or alkaline pH [1, 7, 37]. Here, we found that although these haloarchaea are still relatively more abundant in the sediments exposed to brines with salt-saturating conditions, the clear majority of microbes in all investigated hypersaline soda lake sediments are Bacteria. It could be hypothesized that the sediment is a hide-out for the extreme alkalinity and salinity governing the water column, and that sediment stratification, especially in the anoxic part, offers plenty of opportunities for niche diversification. On the other hand, it should no longer be a surprise that soda lakes are such productive and biodiverse systems. First, it has been previously elaborated that soda lake organisms are exposed to approximately half the osmotic pressure in sodium carbonate-dominated brines compared to sodium chloride-dominated brines with the same Na+ molarity . Second, nitrogen limitation in the community can be overcome when many members contribute to the fixation of atmospheric N2, and various forms of organic nitrogen are efficiently recycled. The soda lakes examined in this study were also eutrophic, and sulfur compounds were abundant. Sulfide is also far less toxic at high pH as it mostly occurs in the form of bisulfide (HS−). Besides the evident high metabolic and taxonomic diversity of dissimilatory sulfur-cycling bacteria, a diverse heterotrophic community can be sustained comprising both generalist and very specialized carbon degraders. Less eutrophic soda lakes might not suffer from carbon limitation either, due to a presence of high-bicarbonate concentrations. These effectively eliminate the inorganic carbon limitation for primary producers who are highly active in soda lakes, especially Cyanobacteria [55, 56]. Third, light that penetrates the surface of the sediment seems to stimulate oxygenic and anoxygenic phototrophic growth. Moreover, various heterotrophs, such as the rhodopsin-containing haloarchaea and Bacteroidetes, have the option to tap into this unlimited energy source for example to help sustain the costly maintenance of osmotic balance. Unexpectedly, we even found the first rhodopsin encoded by a member of the CPR. Fourth, tight syntrophic relations, as proposed for CPR members and Syntrophomonadaceae spp., might be the solution to successful growth in an energetically challenging environment.
Since our metagenomes are snapshots in time and space, the failure to reconstruct specific MAGs gives no conclusive evidence for the absence of certain microbial-mediated element transformation in hypersaline soda lake sediments. Additionally, technical limitations of the assembly and binning of highly micro-diverse genome populations might hamper genome recovery . More importantly, the abundance of a specific microbe is not necessarily correlated to the importance of its performance in an ecosystem. Many metabolic capacities are redundant, and often key transformations are reserved for a few rare organisms that might proliferate for a short time-span when specific conditions allow for it. For example, although no MAGs were recovered from chemolithoautotrophic nitrifiers , we did detect a Nitrobacter-related OTU by amplicon sequencing and assembled 16S rRNA genes from Thaumarchaeota, suggesting bacterial and archaeal nitrifiers are present in the surface sediments of soda lakes at very low abundance. Finally, the method of DNA isolation might impact the community composition apparent in the final metagenome sequenced. Environmental samples contain complex mixtures of different organisms, and it is impossible to find a protocol where the DNA from every single organism is extracted as efficiently without compromising the final quality of the extracted DNA. However, since we find all the important taxonomic and functional groups known from previous cultivation-dependent studies back in either our amplicon sequencing datasets or our directly sequenced metagenomes, we are confident that the community composition and the MAGs presented here are representative for the microbiomes of the soda lake sediments in the Kulunda Steppe.
Years of intensive microbiological research on soda lakes seem to have paid off, since many of the described genera we could detect here have a cultured representative from soda lakes. However, as many of the abundant lineages and groups found in soda lake sediments are still uncultured, metagenomics proved to be a helpful tool to gain primary insights in the potential physiology and ecology of these poly-extremophilic prokaryotes. We reconstructed the first genomes for many of such organisms and proposed new functional roles for the most abundant ones. Future studies should provide more in depth analyses of these genomes, especially from the less abundant organisms that might perform key ecological processes, such as methanogens and nitrifiers. In addition, they should focus on gaining physiological culture-based evidence or proof for in situ activity for the abundant organisms described here. The key metabolic insights provided by this metagenomics study can lead to the design of new cultivation strategies. In general, sediment communities are far more complex than those found in the corresponding water column [53, 59] and are therefore often considered too complex for efficient metagenomic analysis. Many of the novel lineages found here may therefore have related neutrophilic lineages in marine and freshwater sediments that await discovery. We demonstrate here that, by providing sufficient sequencing depth, the “state of the art metagenomics toolbox” can effectively be used on sediments as well.
Site description and sample collection
The top 10 cm sediments from four hypersaline, eutrophic soda lakes located in the Kulunda Steppe (south-western Siberia, Altai, Russia) were sampled in July of 2010 and 2011. General features and exact location of the sampled soda lakes are summarized in Additional file 1: Table S1; a map of the area was published previously . Cock Soda Lake (a stand-alone lake, sampled both in 2010 and 2011) and Tanatar-3 (Tanatar system) were moderately hypersaline (~ 100 g L−1) with sandy sediment, while Tanatar-1 and Bitter-1 (Bitter system) were salt-saturated (400 g L−1) with sulfide-rich sapropel sediments underlined by rock trona deposits [7, 60]. Especially, Bitter-1 harbors a very active microbial community, probably due to its high-organic and -mineral content. Surface sediments were collected by a plastic corer into sterile glass containers and transported to the laboratory in a cooler.
DNA isolation, 16S rRNA amplicon, and metagenomic sequencing
The colloidal fraction of each sediment sample (~ 10% of 50 g) was separated from the course sandy fraction by several short (30–60 s) low-speed (1–2,000 rpm in 50 mL Falcon tubes) centrifugation steps and washed with 1–2 M NaCl solution. The pelleted colloidal sediment fraction was first subjected to 3 cycles of freezing in liquid nitrogen/thawing, then re-suspended in 0.1 M Tris (pH 8)/10 mM EDTA, and then subjected to harsh bead beating treatment. Next, the samples were incubated with lysozyme (15 mg/mL) for 2 h at 37 °C followed by a SDS (10% w/v) and proteinase K (10 μg/mL) treatment for 30 min. at 45 °C. High molecular weight DNA was isolated using phenol/chloroform extraction, quality-checked, and sequenced as described previously . Direct high-throughput sequencing of the DNA was performed on an Illumina HiSeq 2000 platform to generate 150 b paired-end reads. Amplification of the V4-V6 region of prokaryote 16S rRNA genes using barcoded 926F-1392R primers, amplicon purification, quantification, and Roche (454)-sequencing was performed together in a batch with brine samples from the same sampling campaigns. Barcodes and adapter sequences were removed from de-multiplexed amplicon sequence reads and analyzed with the automated NGS analysis pipeline of the SILVA rRNA gene database project  (SILVAngs 1.3, database release version 128) using default parameters. The OTUs (97% identity) assigned down to the genus level were only considered when they had a relative abundance ≥ 0.1% in at least one of the five datasets.
Processing metagenomics reads, assembly, binning, and post-binning
Metagenomic raw reads were quality trimmed using Sickle  (version 1.33), and only reads ≥ 21 b were retained. The prokaryotic community structure at taxonomic top levels was extrapolated from ten million randomly sampled singletons from each dataset. Candidate 16S rRNA fragments > 90 b were identified  and compared against the SILVA SSU database 128 (blastn, min. length 90, min. identity 80%, e value 1e-5). To verify that the microbial community composition was indeed mostly prokaryotic, we did a more general screening of the metagenomics reads that identified also candidate 18S rRNA fragments > 90 b (see Additional file 1: Tables S4-S5). The complete trimmed read sets were assembled into contigs ≥ 1 kb with MEGAHIT  (v1.0.3–6-gc3983f9) using paired-end mode, k min = 21, k max = 131, k step = 10. Genes were predicted using Prodigal  (v.2.6.2) and RNAs with rna_hmm3  and tRNAscan-SE . Assembled 16S rRNA sequences were compared to a manually curated version from the SILVA SSU database (e value ≥ 1e-5). Predicted protein sequences were annotated against KEGG with GhostKOALA (genus_prokaryotes + family_eukaryotes + viruses) . Marker genes for central metabolic pathways and key environmental element transformations were identified based on K number assignments [15, 69–71].
Contigs ≥ 2.5 kb were binned with METABAT  (superspecific mode) based on differential coverage values obtained by mapping all five trimmed readsets to all five contig sets with Bowtie2 . The bins were subjected to post-binning (an overview of the workflow is given in Additional file 2: Figure S13). Bins were assessed with lineage-specific single copy genes using CheckM  and further processed with the metagenomics workflow in Anvi’o  (v2.3.2). Since Candidate Phyla Radiant (CPR) is not included in the CheckM reference trees and are likely to have low-genome completeness, we used an existing training file of 797 CPR genomes to identify putative CPR bins . Bins with CheckM-completeness ≥ 50% (884 out of 1778) and an additional four CPR bins were further processed. Coding sequences were annotated for taxonomy against NCBI-nr (July, 2017) with USEARCH  (5.2.32) to verify that most hits in each bin were to prokaryotic references. Phage or viral contigs were manually removed. Genome contamination (redundancy) was estimated based on marker sets of universal single copy genes identified for Bacteria  and Archaea  as implemented in Anvi’o. Genome coverage was obtained by mapping trimmed reads with BBMap  v36.x (kfilter 31, subfilter 15, maxindel 80). Bins with ≥ 5% redundancy were further refined with Anvi’o using circle phylograms (guide trees tnf-cov: euclidian ward) and scanned again for CPR. Post-binning resulted in a total of 2499 metagenome-assembled genomes (MAGs), of which 871 were either medium-quality genome drafts (CheckM estimated completeness ≥ 50% and contamination ≤ 10% , Additional file 4) or lower quality draft genomes from CPR.
Phylogeny of the MAGs was assessed based on 16 single-copy ribosomal proteins and representative reference genomes of major prokaryote lineages across the tree of life . Individual ribosomal proteins in our MAGs were identified by K number assignments. Only ribosomal proteins ≥ 80 aa were considered. Initial maximum-likelihood (ML) trees were constructed to determine which organisms belonged to the Archaea, Bacteria, or CPR with FastTree 2  (WAG + CAT). Final separate trees for the three distant evolutionary groups were constructed in the same manner. Each ribosomal protein set was aligned separately with MAFFT  (v7.055b, − auto) and concatenated only if a MAG encoded at least 8 out of 16 proteins. For all trees, a 100× posterior bootstraps analysis was performed. Phylogenetic trees were visualized together with genome statistics and abundance information using iTOL . We cross-checked the taxonomic assignments based on the phylogeny of the ribosomal protein cassette with the top hit contig annotations against NCBI-nr and with the reference lineage obtained with CheckM. Lastly, we manually corrected the MAGs for misplaced 16S rRNA genes. The final trees presented in the manuscript were redrawn using FigTree v1.4.3 .
Detailed genome analyses
CPR MAGs were re-annotated more thoroughly: genes were predicted with Prokka , and functional predictions were performed by running InterProScan 5 locally on the supplied COG, CDD, TIGRFAMs, HAMAP, Pfam, and SMART databases . BLAST Koala was used for KEGG pathway predictions . To find putative carbohydrate-active enzymes in all final MAGs, we used the web-resource dbCAN  to annotate all predicted proteins ≥ 80 aa against CAZy .
To identify the top ten abundant MAGs from each respective dataset, ten million randomly sampled singletons were mapped onto each MAG with a cut-off of 95% identity in minimum of 50 bases. Coverage values were additionally normalized for genome size and expressed as reads per kilobase of sequence per gigabase of mapped reads (RPKG) . A positive score (from 871 to 1) was assigned to each MAG according to the ranking of the summed RPKG of MAGs in the high-salinity datasets (B1Sed10 and T1Sed) and a negative score according to the ranking of the summed RPKGs in the moderate salinity datasets (CSSed10, CSSed11, T3Sed10). Both scores were summed to get a “salinity preference score” with MAGs recruiting preferably from high salinity datasets on the positive end, moderate salinity datasets in the negative end, and those without preference in the middle.
We determined species delineation for the most abundant MAGs and their closest reference genomes (NCBI-nr) by Average Nucleotide Identity (ANI) and conserved DNA-matrices, as follows : ANI ≥ 95%, conDNA ≥ 69% = same species, ANI ≥ 95%, condDNA < 69% = might be same species, ANI < 95%, condDNA < 69% = different species. Single gene trees based on maximum likelihood were constructed with untrimmed alignments (MAFFT, L-INS-i model) and FastTree 2 (WAG + CAT, increased accuracy, -spr4 -mlacc 2 -slownni) using 100× bootstraps. References were pulled from eggNOG (v4.5.1)  and supplemented with sequences from NCBI-nr or refined according to [7, 33, 46, 92–94]. The curated MAGs were scanned for the presence of rhodopsin sequences with the hmmsearch software  and a profile hidden Markov model (HMM) of the bacteriorhodopsin-like protein family (Pfam accession number PF01036). The identified sequences with significant similarity were aligned together with a curated database composed of a collection of type-1 rhodopsins, using MAFFT (L-INS-i accuracy model) . This protein alignment was further utilized to construct a maximum likelihood tree with 100× bootstrap with FastTree 2 . All other genes were identified using the KEGG annotation.
We thank Dr. Nikolai Chernych for his technical assistance during the isolation and purification of metagenomics DNA. We also thank the Department of Energy Joint Genome Institute for sequencing the metagenomes.
CDV and GM were supported by the ERC Advanced Grant PARASOL (no. 322551). A-SA and RG were supported by the research grant 17-04828S from the Grant Agency of the Czech Republic. MM was supported by the Czech Academy of Sciences (Postdoc program PPPLZ application number L200961651). DYS was supported by the SIAM/Gravitation Program (Dutch Ministry of Education and Science, grant 24002002) and by the Russian Science Foundation (grant 16–14-00121). Sequencing was performed by the U.S. Department of Energy Joint Genome Institute, a DOE Office of Science User Facility, as part of the Community Sequencing Program (contract no. DE-AC02- 05CH11231).
Availability of data and materials
The raw sequence reads of the five metagenomes have been deposited to the NCBI Sequence Read Archive (see Additional file 1: Table S6 for accession numbers and read and contig statistics). The final 871 MAGs described in this paper have been deposited as Whole Genome Shotgun projects at DDBJ/EMBL/GenBank, and accession numbers are listed in Additional file 4 (BioProject ID PRJNA434545). All versions described in this paper are version XXXX01000000. The cleaned and dereplicated amplicon sequence datasets are available in FigShare (https://figshare.com/s/7684627445e3621aba24). Maximum likelihood trees based on the concatenated alignment of 16 ribosomal proteins, basis for Figs. 2 and 3, in newick format (.tre file) and complementary datasets (used to plot completeness, contamination, genome recovery size, G + C mol% and RPKG in iTOL), as well as K number assignments for the predicted proteins of all MAGs (KEGG-orthologues, Ghost Koala) and the fully annotated CPR MAGs supporting the conclusions of this article are also available in FigShare (https://figshare.com/s/7684627445e3621aba24).
GM and DYS initiated this study and were responsible for the fieldwork, sample preparation, and sequencing effort. CDV conceptualized the research goals under supervision of DYS and GM, and performed the bioinformatics analysis under close guidance of A-SA and RG. CDV is the primary author of this manuscript. MM, RG, and CDV prepared the main figures. All authors read and approved the final manuscript.
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- Sorokin DY, Berben T, Melton ED, Overmars L, Vavourakis CD, Muyzer G. Microbial diversity and biogeochemical cycling in soda lakes. Extremophiles. 2014;18:791–809.View ArticleGoogle Scholar
- Oduor SO, Kotut K. Soda lakes of the East African Rift System: the past, the present and the future. In: Schagerl M, editor. Soda lakes of East Africa. Berlin: Springer; 2016. p. 365–74.Google Scholar
- Mesbah NM, Abou-El-Ela SH, Wiegel J. Novel and unexpected prokaryotic diversity in water and sediments of the alkaline, hypersaline lakes of the Wadi An Natrun, Egypt. Microb Ecol. 2007;54:598–617.View ArticleGoogle Scholar
- Humayoun SB, Bano N, James T, Hollibaugh JT. Depth distribution of microbial diversity in Mono Lake, a meromictic soda lake in California. Appl Environ Microbiol. 2003;69:1030–42.View ArticleGoogle Scholar
- Foti MJ, Sorokin DY, Zacharova EE, Pimenov NV, Kuenen JG, Muyzer G. Bacterial diversity and activity along a salinity gradient in soda lakes of the Kulunda Steppe (Altai, Russia). Extremophiles. 2008;12:133–45.View ArticleGoogle Scholar
- Sorokin DY. Anaerobic haloalkaliphiles. eLS. 2017; https://doi.org/10.1002/9780470015902.a0027654.
- Vavourakis CD, Ghai R, Rodriguez-Valera F, Sorokin DY, Tringe SG, Hugenholtz P, et al. Metagenomic insights into the uncultured diversity and physiology of microbes in four hypersaline soda lake brines. Front Microbiol. 2016;7:211.View ArticleGoogle Scholar
- Sørensen KB, Canfield DE, Oren A. Salinity responses of benthic microbial communities in a solar saltern (Eilat, Israel). Appl Environ Microbiol. 2004;70:1608–16.View ArticleGoogle Scholar
- Sorokin DY, Makarova KS, Abbas B, Ferrer M, Golyshin PN, Galinski EA, et al. Discovery of extremely halophilic, methyl-reducing euryarchaea provides insights into the evolutionary origin of methanogenesis. Nat Microbiol. 2017;2:17081.View ArticleGoogle Scholar
- Sorokin DY, Chernyh NA, Poroshina MN. Desulfonatronobacter acetoxydans sp. nov.,: a first acetate-oxidizing, extremely salt-tolerant alkaliphilic SRB from a hypersaline soda lake. Extremophiles. 2015;19:899–907.View ArticleGoogle Scholar
- Ahn A-C, Meier-Kolthoff JP, Overmars L, Richter M, Woyke T, Sorokin DY, et al. Genomic diversity within the haloalkaliphilic genus Thioalkalivibrio. PLoS One. 2017;12:e0173517.View ArticleGoogle Scholar
- Sorokin DY, Kuenen JG. Haloalkaliphilic sulfur-oxidizing bacteria in soda lakes. FEMS Microbiol Rev. 2005;29:685–702.View ArticleGoogle Scholar
- Albertsen M, Hugenholtz P, Skarshewski A, Nielsen KL, Tyson GW, Nielsen PH. Genome sequences of rare, uncultured bacteria obtained by differential coverage binning of multiple metagenomes. Nat Biotechnol. 2013;31:533–8.View ArticleGoogle Scholar
- Qin J, Li R, Raes J, Arumugam M, Burgdorf KS, Manichanh C, et al. A human gut microbial gene catalogue established by metagenomic sequencing. Nature. 2010;464:59–65.View ArticleGoogle Scholar
- Anantharaman K, Brown CT, Hug LA, Sharon I, Castelle CJ, Probst AJ, et al. Thousands of microbial genomes shed light on interconnected biogeochemical processes in an aquifer system. Nat Commun. 2016;7:13219.View ArticleGoogle Scholar
- Parks DH, Rinke C, Chuvochina M, Chaumeil P-A, Woodcroft BJ, Evans PN, et al. Recovery of nearly 8,000 metagenome-assembled genomes substantially expands the tree of life. Nat Microbiol. 2017;2:1533–42.View ArticleGoogle Scholar
- Hug LA, Baker BJ, Anantharaman K, Brown CT, Probst AJ, Castelle CJ, et al. A new view of the tree of life. Nat Microbiol. 2016;1:16048.View ArticleGoogle Scholar
- Hahnke RL, Meier-Kolthoff JP, García-López M, Mukherjee S, Huntemann M, Ivanova NN, et al. Genome-based taxonomic classification of Bacteroidetes. Front Microbiol. 2016;7:2003.View ArticleGoogle Scholar
- Nolla-Ardevol V, Strous M, Tegetmeyer HE. Anaerobic digestion of the microalga Spirulina at extreme alkaline conditions: biogas production, metagenome and metatranscriptome. Front Microbiol. 2015;6:597.View ArticleGoogle Scholar
- Borrel G, Parisot N, Harris HM, Peyretaillade E, Gaci N, Tottey W, et al. Comparative genomics highlights the unique biology of Methanomassiliicoccales, a Thermoplasmatales-related seventh order of methanogenic archaea that encodes pyrrolysine. BMC Genomics. 2014;15:679.View ArticleGoogle Scholar
- Sorokin DY, Abbas B, Geleijnse M, Pimenov NV, Sukhacheva MV, van Loosdrecht MCM. Methanogenesis at extremely haloalkaline conditions in the soda lakes of Kulunda Steppe (Altai, Russia). FEMS Microbiol Ecol. 2015;91:4.View ArticleGoogle Scholar
- Nobu MK, Narihiro T, Kuroda K, Mei R, Liu WT. Chasing the elusive Euryarchaeota class WSA2: genomes reveal a uniquely fastidious methyl-reducing methanogen. ISME J. 2016;10:2478–87.View ArticleGoogle Scholar
- Skennerton CT, Haroon MF, Briegel A, Shi J, Jensen GJ, Tyson GW, et al. Phylogenomic analysis of Candidatus “Izimaplasma” species: free-living representatives from a Tenericutes clade found in methane seeps. ISME J. 2016;10:2679–92.View ArticleGoogle Scholar
- Sekiguchi Y, Ohashi A, Parks DH, Yamauchi T, Tyson GW, Hugenholtz P. First genomic insights into members of a candidate bacterial phylum responsible for wastewater bulking. PeerJ. 2015;3:e740.View ArticleGoogle Scholar
- Wrighton KC, Thomas BC, Sharon I, Miller CS, Castelle CJ, VerBerkmoes NC, et al. Fermentation, hydrogen, and sulfur metabolism in multiple uncultivated bacterial phyla. Science. 2012;337:1661–5.View ArticleGoogle Scholar
- León-Zayas R, Peoples L, Biddle JF, Podell S, Novotny M, Cameron J, et al. The metabolic potential of the single cell genomes obtained from the Challenger Deep, Mariana Trench within the candidate superphylum Parcubacteria (OD1). Environ Microbiol. 2017;19:2769–84.View ArticleGoogle Scholar
- Castelle CJ, Brown CT, Thomas BC, Williams KH, Banfield JF. Unusual respiratory capacity and nitrogen metabolism in a Parcubacterium (OD1) of the Candidate Phyla Radiation. Sci Rep. 2017;7:40101.View ArticleGoogle Scholar
- Anantharaman K, Brown CT, Burstein D, Castelle CJ, Probst AJ, Thomas BC, et al. Analysis of five complete genome sequences for members of the class Peribacteria in the recently recognized Peregrinibacteria bacterial phylum. PeerJ. 2016;4:e1607.View ArticleGoogle Scholar
- Brown CT, Hug LA, Thomas BC, Sharon I, Castelle CJ, Singh A, et al. Unusual biology across a group comprising more than 15% of domain Bacteria. Nature. 2015;523:208–11.View ArticleGoogle Scholar
- Campbell JH, O ‘donoghue P, Campbell AG, Schwientek P, Sczyrba A, Woyke T, et al. UGA is an additional glycine codon in uncultured SR1 bacteria from the human microbiota. 2013; doi:https://doi.org/10.1073/pnas.1303090110.View ArticleGoogle Scholar
- Hanke A, Hamann E, Sharma R, Geelhoed JS, Hargesheimer T, Kraft B, et al. Recoding of the stop codon UGA to glycine by a BD1-5/SN-2 bacterium and niche partitioning between Alpha- and Gammaproteobacteria in a tidal sediment microbial community naturally selected in a laboratory chemostat. Front Microbiol. 2014;5:231.View ArticleGoogle Scholar
- Kantor RS, Wrighton KC, Handley KM, Sharon I, Hug LA, Castelle CJ, et al. Small genomes and sparse metabolisms of sediment-associated bacteria from four candidate phyla. MBio. 2013;4:1–11.View ArticleGoogle Scholar
- Wrighton KC, Castelle CJ, Varaljay VA, Satagopan S, Brown CT, Wilkins MJ, et al. RubisCO of a nucleoside pathway known from Archaea is found in diverse uncultivated phyla in bacteria. ISME J. 2016;10:2702–14.View ArticleGoogle Scholar
- Luef B, Frischkorn KR, Wrighton KC, Holman HYN, Birarda G, Thomas BC, et al. Diverse uncultivated ultra-small bacterial cells in groundwater. Nat Commun. 2015;6:1–8.View ArticleGoogle Scholar
- Krulwich TA, Sachs G, Padan E. Molecular aspects of bacterial pH sensing and homeostasis. Nat Rev Microbiol. 2011;9:330–43.View ArticleGoogle Scholar
- Hauß T, Dante S, Dencher NA, Haines TH. Squalane is in the midplane of the lipid bilayer: implications for its function as a proton permeability barrier. Biochim Biophys Acta Bioenerg. 2002;1556:149–54.View ArticleGoogle Scholar
- Oren A. Life at high salt concentrations, intracellular KCl concentrations, and acidic proteomes. Front Microbiol. 2013;4:315.View ArticleGoogle Scholar
- Levina N. Protection of Escherichia coli cells against extreme turgor by activation of MscS and MscL mechanosensitive channels: identification of genes required for MscS activity. EMBO J. 1999;18:1730–7.View ArticleGoogle Scholar
- Gupta RS, Khadka B. Evidence for the presence of key chlorophyll-biosynthesis-related proteins in the genus Rubrobacter (phylum Actinobacteria) and its implications for the evolution and origin of photosynthesis. Photosynth Res. 2016;127:201–18.View ArticleGoogle Scholar
- Basak N, Das D. The prospect of purple non-sulfur (PNS) photosynthetic bacteria for hydrogen production:the present state of the art. World J Microbiol Biotechnol. 2007;23:31–42.View ArticleGoogle Scholar
- Meng J, Wang F, Wang F, Zheng Y, Peng X, Zhou H, et al. An uncultivated crenarchaeota contains functional bacteriochlorophyll a synthase. ISME J. 2009;3:106–16.View ArticleGoogle Scholar
- Sorokin DY, Tourova TP, Mußmann M, Muyzer G. Dethiobacter alkaliphilus gen. nov. sp. nov., and Desulfurivibrio alkaliphilus gen. nov. sp. nov.: two novel representatives of reductive sulfur cycle from soda lakes. Extremophiles. 2008;12:431–9.View ArticleGoogle Scholar
- Poser A, Lohmayer R, Vogt C. Extremophiles KK-, 2013 U. Disproportionation of elemental sulfur by haloalkaliphilic bacteria from soda lakes. Extremophiles. 2013;17:1003–12.View ArticleGoogle Scholar
- Sorokin DY, Abbas B, Tourova TP, Bumazhkin BK, Kolganova TV, Muyzer G. Sulfate-dependent acetate oxidation under extremely natron-alkaline conditions by syntrophic associations from hypersaline soda lakes. Microbiology. 2014;160(Pt_4):723–32.View ArticleGoogle Scholar
- Ragsdale SW. Enzymology of the Wood-Ljungdahl pathway of acetogenesis. Ann N Y Acad Sci. 2008;1125:129–36.View ArticleGoogle Scholar
- Adam PS, Borrel G, Gribaldo S. Evolutionary history of carbon monoxide dehydrogenase/acetyl-CoA synthase, one of the oldest enzymatic complexes. Proc Natl Acad Sci. 2018;115:E1166–73.View ArticleGoogle Scholar
- Sorokin DY, Banciu HL, Muyzer G. Functional microbiology of soda lakes. Curr Opin Microbiol. 2015;25:88–96.View ArticleGoogle Scholar
- Grant WD, Jones BE. Bacteria, Archaea and viruses of soda lakes. In: Schagerl M, editor. Soda lakes of East Africa. Berlin: Springer; 2016. p. 97–147.Google Scholar
- Bruno A, Sandionigi A, Rizzi E, Bernasconi M, Vicario S, Galimberti A, et al. Exploring the under-investigated “microbial dark matter” of drinking water treatment plants. Sci Rep. 2017;7:1–7.View ArticleGoogle Scholar
- Danczak RE, Johnston MD, Kenah C, Slattery M, Wrighton KC, Wilkins MJ. Members of the Candidate Phyla Radiation are functionally differentiated by carbon- and nitrogen-cycling capabilities. Microbiome. 2017;5:112.View ArticleGoogle Scholar
- Hu P, Tom L, Singh A, Thomas BC, Baker BJ, Piceno YM, et al. Genome-resolved metagenomic analysis reveals roles for candidate phyla and other microbial community members in biogeochemical transformations in oil reservoirs. MBio. 2016;7:e01669–15.PubMedPubMed CentralGoogle Scholar
- Probst AJ, Castelle CJ, Singh A, Brown CT, Anantharaman K, Sharon I, et al. Genomic resolution of a cold subsurface aquifer community provides metabolic insights for novel microbes adapted to high CO2 concentrations. Environ Microbiol. 2017;19:459–74.View ArticleGoogle Scholar
- Lozupone CA, Knight R. Global patterns in bacterial diversity. Proc Natl Acad Sci. 2007;104:11436–40.View ArticleGoogle Scholar
- Thompson LR, Sanders JG, McDonald D, Amir A, Ladau J, Locey KJ, et al. A communal catalogue reveals Earth’s multiscale microbial diversity. Nature. 2017;551:457.View ArticleGoogle Scholar
- Samylina OS, Sapozhnikov FV, Gainanova OY, Ryabova AV, Nikitin MA, Sorokin DY. Algo-bacterial communities of the Kulunda steppe (Altai region, Russia) Soda Lakes. Microbiology. 2014;83:849–60.View ArticleGoogle Scholar
- Krienitz L, Schagerl M. Tiny and tough: microphytes of east African soda lakes. In: Schagerl M, editor. Soda lakes of East Africa. Berlin: Springer; 2016. p. 149–77.Google Scholar
- Nelson WC, Maezato Y, Wu Y-W, Romine MF, Lindemann SR. Identification and resolution of microdiversity through metagenomic sequencing of parallel consortia. Appl Environ Microbiol. 2015;82:255–67.View ArticleGoogle Scholar
- Hansel C. Small but mighty: how minor components drive major biogeochemical cycles. Environ Microbiol Rep. 2017;9:8–10.View ArticleGoogle Scholar
- Zinger L, Amaral-Zettler LA, Fuhrman JA, Horner-Devine MC, Huse SM, Welch DBM, et al. Global patterns of bacterial beta-diversity in seafloor and seawater ecosystems. PLoS One. 2011;6:e24570.View ArticleGoogle Scholar
- Isachenko BL. Chloride sulfate and soda lakes of Kulunda steppe and its biogenic processes. In: Selected works, vol. 2. Leningrad: Academy of Sciences USSR; 1951. p. 143–62.Google Scholar
- Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 2012;41:D590–6.View ArticleGoogle Scholar
- Joshi NA, Fass JN. Sickle: a sliding-window, adaptive, quality-based trimming tool for FastQ files (Version 1.33). 2011.Google Scholar
- Ghai R, Pašić L, Fernández AB, Martin-Cuadrado A-B, Mizuno CM, McMahon KD, et al. New abundant microbial groups in aquatic hypersaline environments. Sci Rep. 2011;1:135.View ArticleGoogle Scholar
- Li D, Liu CM, Luo R, Sadakane K, Lam TW. MEGAHIT: An ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph. Bioinformatics. 2015;31:1674–6.View ArticleGoogle Scholar
- Hyatt D, Chen G-L, LoCascio PF, Land ML, Larimer FW, Hauser LJ. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinformatics. 2010;11:119.View ArticleGoogle Scholar
- Huang Y, Li W, Finn PW, Perkins DL. Ribosomal RNA identification in metagenomic and metatranscriptomic datasets. In: De Bruijn FJ, editor. Handbook of Molecular Microbial Ecology I. Hoboken: Wiley; 2011. p. 387–91.View ArticleGoogle Scholar
- Lowe TM, Eddy SR. tRNAscan-SE: a program for improved detection of transfer RNA genes in genomic sequence. Nucleic Acids Res. 1997;25:955–64.View ArticleGoogle Scholar
- Kanehisa M, Sato Y, Morishima K. BlastKOALA and GhostKOALA: KEGG tools for functional characterization of genome and metagenome sequences. J Mol Biol. 2016;428:726–31.View ArticleGoogle Scholar
- Lauro FM, Demaere MZ, Yau S, Brown MV, Ng C, Wilkins D, et al. An integrative study of a meromictic lake ecosystem in Antarctica. ISME J. 2010;5:879–95.View ArticleGoogle Scholar
- Hernsdorf AW, Amano Y, Miyakawa K, Ise K, Suzuki Y, Anantharaman K, et al. Potential for microbial H2 and metal transformations associated with novel bacteria and archaea in deep terrestrial subsurface sediments. Nat Publ Gr. 2017;11:1915–29.Google Scholar
- Llorens-Marès T, Yooseph S, Goll J, Hoffman J, Vila-Costa M, Borrego CM, et al. Connecting biodiversity and potential functional role in modern euxinic environments by microbial metagenomics. ISME J. 2015;9:1648–61.View ArticleGoogle Scholar
- Kang DD, Froula J, Egan R, Wang Z. MetaBAT, an efficient tool for accurately reconstructing single genomes from complex microbial communities. PeerJ. 2015;3:e1165.View ArticleGoogle Scholar
- Langmead B, Salzberg SL. Fast gapped-read alignment with bowtie 2. Nat Methods. 2012;9:357–9.View ArticleGoogle Scholar
- Parks DH, Imelfort M, Skennerton CT, Hugenholtz P, Tyson GW. CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Res. 2015;25:1043–55.View ArticleGoogle Scholar
- Eren AM, Esen ÖC, Quince C, Vineis JH, Morrison HG, Sogin ML, et al. Anvi’o: an advanced analysis and visualization platform for ‘omics data. PeerJ. 2015;3:e1319.View ArticleGoogle Scholar
- Eren AM, Delmot TO. Predicting CPR genomes in metagenomic bins. http://merenlab.org/2016/04/17/predicting-CPR-Genomes/.
- Edgar RC. Search and clustering orders of magnitude faster than BLAST. Bioinformatics. 2010;26:2460–1.View ArticleGoogle Scholar
- Rinke C, Schwientek P, Sczyrba A, Ivanova NN, Anderson IJ, Cheng JF, et al. Insights into the phylogeny and coding potential of microbial dark matter. Nature. 2013;499:431–7.View ArticleGoogle Scholar
- Bushnell B. BBMap short read aligner. 2016.Google Scholar
- Bowers RM, Kyrpides NC, Stepanauskas R, Harmon-Smith M, Doud D, Reddy TBK, et al. Minimum information about a single amplified genome (MISAG) and a metagenome-assembled genome (MIMAG) of bacteria and archaea. Nat Biotechnol. 2017;35:725–31.View ArticleGoogle Scholar
- Price MN, Dehal PS, Arkin AP. FastTree 2--approximately maximum-likelihood trees for large alignments. PLoS One. 2010;5:e9490.View ArticleGoogle Scholar
- Katoh K, Standley DM. MAFFT multiple sequence alignment software version 7: improvements in performance and usability. Mol Biol Evol. 2013;30:772–80.View ArticleGoogle Scholar
- Letunic I, Bork P. Interactive tree of life (iTOL) v3: an online tool for the display and annotation of phylogenetic and other trees. Nucleic Acids Res. 2016;44:W242–5.View ArticleGoogle Scholar
- FigTree. http://tree.bio.ed.ac.uk/software/figtree/.
- Seemann T. Prokka: rapid prokaryotic genome annotation. Bioinformatics. 2014;30:2068–9.View ArticleGoogle Scholar
- Jones P, Binns D, Chang H-Y, Fraser M, Li W, McAnulla C, et al. InterProScan 5: genome-scale protein function classification. Bioinformatics. 2014;30:1236–40.View ArticleGoogle Scholar
- Yin Y, Mao X, Yang J, Chen X, Mao F, Xu Y. dbCAN: a web resource for automated carbohydrate-active enzyme annotation. Nucleic Acids Res. 2012;40:W445–51.View ArticleGoogle Scholar
- Cantarel BL, Coutinho PM, Rancurel C, Bernard T, Lombard V, Henrissat B. The Carbohydrate-Active EnZymes database (CAZy): an expert resource for glycogenomics. Nucleic Acids Res. 2009;37:D233–8.View ArticleGoogle Scholar
- Nayfach S, Pollard KS. Average genome size estimation improves comparative metagenomics and sheds light on the functional ecology of the human microbiome. Genome Biol. 2015;16:1–18.View ArticleGoogle Scholar
- Goris J, Konstantinidis KT, Klappenbach JA, Coenye T, Vandamme P, Tiedje JM. DNA-DNA hybridization values and their relationship to whole-genome sequence similarities. Int J Syst Evol Microbiol. 2007;57:81–91.View ArticleGoogle Scholar
- Huerta-Cepas J, Szklarczyk D, Forslund K, Cook H, Heller D, Walter MC, et al. eggNOG 4.5: a hierarchical orthology framework with improved functional annotations for eukaryotic, prokaryotic and viral sequences. Nucleic Acids Res. 2016;44:D286–93.View ArticleGoogle Scholar
- Tikhonova TV, Slutsky A, Antipov AN, Boyko KM, Polyakov KM, Sorokin DY, et al. Molecular and catalytic properties of a novel cytochrome c nitrite reductase from nitrate-reducing haloalkaliphilic sulfur-oxidizing bacterium Thioalkalivibrio nitratireducens. Biochim Biophys Acta - Proteins Proteomics. 2006;1764:715–23.View ArticleGoogle Scholar
- Tikhonova T, Tikhonov A, Trofimov A, Polyakov K, Boyko K, Cherkashin E, et al. Comparative structural and functional analysis of two octaheme nitrite reductases from closely related Thioalkalivibrio species. FEBS J. 2012;279:4052–61.View ArticleGoogle Scholar
- Tabita FR, Hanson TE, Li H, Satagopan S, Singh J, Chan S. Function, structure, and evolution of the RuBisCO-like proteins and their RuBisCO homologs. Microbiol Mol Biol Rev. 2007;71:576–99.View ArticleGoogle Scholar
- Eddy SR. Accelerated profile HMM searches. PLoS Comput Biol. 2011;7:e1002195.View ArticleGoogle Scholar