Introduction

Bacterial assimilation of the one-carbon (C1) compounds methane, methanol and methylamine constitutes an important component of microbe-driven food web chains in many ecosystems. Methylotrophic bacteria, phylogenetically distributed across diverse phyla, contribute significantly towards the biogeochemical cycling of carbon by facilitating the incorporation of C1 compound-derived carbon into biomass (Anthony, 1982; Chistoserdova et al., 2009). The global cycling of methane and related C1 compounds further affects important environmental phenomena related to climate change. Methanotrophs are a specialized group of methylotrophs that use methane as the sole carbon and energy source. These are distributed among Gammaproteobacteria (type I methanotrophs), Alphaproteobacteria (type II methanotrophs) (reviewed in Trotsenko and Murrell, 2008), filamentous methane oxidizers (Stoecker et al., 2006; Vigliotta et al., 2007) and Verrucomicrobia (Dunfield et al., 2007; Pol et al., 2007; Islam et al., 2008). Methanotrophs oxidize methane to methanol by the enzyme methane monooxygenase (MMO), present either as the particulate form (pMMO) in all characterized methanotrophs (except in the genus Methylocella (Dedysh et al., 2000)) or as the soluble form (sMMO) in some methanotrophs (Trotsenko and Murrell, 2008). Methanol dehydrogenase (MDH) catalyzes the conversion of methanol to formaldehyde in methylotrophs (Trotsenko and Murrell, 2008, Chistoserdova et al., 2009). Probes targeting pmoA, mmoX and mxaF, genes that encode the 27 kDa subunit of pMMO, the active site subunits of sMMO and MDH, respectively, have been widely used for the detection of methanotrophs/methylotrophs in environmental samples (reviewed in McDonald et al., 2007). One pathway by which methylamine is used by methylotrophic bacteria contains methylamine dehydrogenase, but alternative pathways may also be present (Anthony, 1982; Latypova et al., 2009). Some of the marine methylotrophs involved in the metabolism of methylamine have been identified using PCR primers targeting the structural gene (mauA) encoding the small subunit of methylamine dehydrogenase (Neufeld et al., 2007c). Relatively few studies have focused on isolation of methylotrophs from saline and alkaline environments (Khmelenina et al., 1996; Sorokin et al., 2000; Doronina et al., 2001, 2003a, 2003b; Kaluzhnaya et al., 2001), and the active organisms (Lin et al., 2004, 2005) and enzymes involved are poorly characterized.

Lonar crater (centred at 19°59′ N and 76°31′ E) is a simple, bowl-shaped, near-circular crater formed by meteor impact (Fredriksson et al., 1973) around 52 000 years ago (Sengupta et al., 1997) in the Deccan volcanic flood basalts in Maharashtra, India. Being the only well-preserved terrestrial crater to be formed entirely on basalt, it provides an excellent analogue for studying basaltic impact crater structures that are common on the surfaces of other terrestrial planets such as Mars (Hagerty and Newsom, 2003) and the Moon (Fudali et al., 1980). The crater has an average rim diameter of 1830 m and a rim-to-floor depth of about 150 m (apparent depth) (Fredriksson et al., 1973). A saline (NaCl ∼0.9%) and alkaline lake (pH ∼10) occupies most of the crater floor (Surakasi et al., 2007). Microbiological studies using culture-dependent and -independent strategies have identified and characterized both bacterial (Kanekar et al., 1999, 2002; Nilegaonkar et al., 2002; Wani et al., 2006; Joshi et al., 2008) and archaeal (Thakker and Ranade, 2002; Surakasi, 2007; Surakasi et al., 2007) communities in the Lonar Lake water and sediments. A culture-independent study that assessed archaeal diversity in the sediments reported that most of the retrieved euryarchaeotal sequences were related to methanogens (Wani et al., 2006). Enrichment of methanogens resulted in the isolation of Methanosarcina, Methanocalculus and Methanoculleus strains (Thakker and Ranade, 2002; Surakasi et al., 2007). However, no studies have focused on the identification of active methylotrophic bacteria in sediments of the Lonar crater lake.

DNA stable-isotope probing (DNA-SIP) can reveal phylogenetic identity of previously unknown and uncultivated organisms that are metabolically active in a particular ecosystem (Radajewski et al., 2000; Dumont and Murrell, 2005). DNA-SIP successfully identified active methanotrophs and methylotrophs in Transbaikal soda lake sediments (Lin et al., 2004), Washington fresh water lake sediments (Nercessian et al., 2005), Colne estuary sediments (Moussard et al., 2009) and alkaline soils (pH 9) from a Chinese coal mine (Han et al., 2009). To our knowledge, DNA-SIP experiments have not been used to characterize methylotrophs utilizing methanol and methylamine in soda lakes. In this study, we explore the diversity of active methylotrophic bacteria in saline and alkaline sediments of Lonar Lake by C1 substrates-based DNA-SIP.

Materials and methods

Sediment sampling

Surface sediment samples (top 8–12 cm) were collected in October 2008 from Lonar Lake at a depth of 6 m. Samples were stored in sterile tubes in ice and transported to the laboratory within 24 h. The surface temperature of sediment samples was determined on site to be 27 °C. The pH values measured in situ and ex situ were 10.0 and 9.5±0.2, respectively.

Analysis of sediment chemical parameters

The chemical parameters (TDS, TOC, TKN, total phosphorus as PO43−, NO3−, NaCl, CO3−, Cl−, NH3, SO42−, Ca, Co, Ni, B, Mg, K, Fe and Cu) of wet sediment samples were analysed at a certified chemical testing laboratory (Accurate Analytical Laboratory Pvt. Limited, Pune, India) using standard methods (APHA, 1998).

Stable-isotope probing

Time-course SIP incubations were carried out in triplicate microcosms (two containing 13C substrate and one containing 12C substrate). Five grams of sediment were placed in sterile 120 ml serum vials, which were then sealed with butyl rubber stoppers and injected with 13CH4 (99% 13C atom enriched; Linde gases) to yield headspace concentrations of 1% (v/v). Similarly, labelled methanol- and methylamine-based microcosms were set up with 25 mM 13CH3OH (Cambridge Isotope Laboratories, Hook, UK) and 13CH3NH2.HCl (Sigma, Poole, Dorset, UK). Microcosms set up with 12C substrates served as control for SIP incubations. Substrate uptake was not detected in methylamine SIP microcosms (data not shown). To facilitate the active utilization of the substrate, separate methylamine SIP incubations were supplemented with 10% modified nitrate mineral medium (Kaluzhnaya et al., 2001) containing (g L−1): KNO3, 0.5; NH4Cl, 0.5; KH2PO4, 0.35; Na2HPO4.12H2O, 0.65; NaCl, 7.5; MgSO4.7H2O, 0.2; CaCl2, 0.02. Added trace elements (g L−1) were: disodium EDTA, 5; NaOH, 0.1; ZnSO4. 6H2O, 0.1; CaCl2.2H2O, 0.073; MnCl2.5H2O, 0.025; CoCl2.6H2O, 0.005; FeSO4.7H2O, 0.075; CuSO4.5H2O, 0.002; and ammonium molybdate pentahydrate, 0.005. The pH was adjusted to 9.5 by the addition of 50 ml 2 M NaHCO3 and 10 ml 1 M Na2CO3 to 1 l medium. All incubations were carried out in the dark at 28 °C. Methane and methanol consumption was measured by gas chromatography (Agilent, CA, USA). Methylamine consumption was quantified on the basis of the methods of Fearon (1942) and Ormsby and Johnson (1950). A volume of 3.5 ml of the solution under test was mixed with 0.25 ml 80 mM lactose solution and 0.1 ml 5 M sodium hydroxide solution and incubated at 70 °C for 30 min. The solution was cooled to room temperature and allowed to incubate for a further 60 min. A545 was measured and the concentration of methylamine was derived on the basis of a millimolar extinction coefficient for the pigmented imine product of 1.26 mM−1 cm−1. SIP incubations were terminated after the consumption of ≈100 μmol of 13CH4 per gram sediment; ≈65 μmol of 13CH3OH per gram sediment; and ≈22 μmol of 13CH3NH2 per gram sediment.

Community DNA extraction and density gradient fractionation

After completion of SIP incubations with labelled CH4, CH3OH and CH3NH2, total community DNA was extracted from the respective sediment samples using a FastDNA SPIN Kit (Qbiogene Inc., Carlsbad, CA, USA). DNA concentrations were measured using a NanoDrop ND-1000 spectrophotometer. DNA fractionation and precipitation were subsequently carried out as described previously (Neufeld et al., 2007b). The buoyant density of each fraction was estimated by determining the refractive index (nD) of CsCl solutions with a digital refractometer (Reichert AR200, Reichert Inc., NY, USA).

PCR amplification of 16S rRNA and functional genes

Aliquots comprising ≈30 ng of 13C or 12C DNA pooled from microcosms representing each substrate were used as template in PCRs employing 16S rRNA and functional gene primers. Denaturing gradient gel electrophoresis (DGGE) and clone library analyses based on 16S rRNA genes were performed using PCR products amplified with primer sets GC341F/907R (Muyzer et al., 1998) and 27F/1492R (Weisburg et al., 1991), respectively. PCR amplifications were also carried out with primers specific for the functional genes mxaF, 1003f and 1555r (Neufeld et al., 2007c); pmoA, A189f and mb661r (Costello and Lidstrom, 1999); mmoX, 206F and 886R (Hutchens et al., 2004); and mauA, mauAf1 and mauAr1 (Neufeld et al., 2007c). All PCR reactions were carried out in a total volume of 50 μl in 0.5 ml tubes. Each PCR mix consisted of 1.5 mM MgCl2, 250 μM dNTPs, 50 pmol of each primer, 0.75 μl (3.75 U) Taq DNA polymerase (Fermentas, Burlington, Ontario, Canada), 5 μl 10 × PCR buffer, 0.07% bovine serum albumin (BSA) and ≈30 ng DNA. With the exception of the PCR for pmoA, all reactions were performed with an initial denaturation at 94 °C for 3 min, followed by 35 cycles of 94 °C for 1 min, annealing (55 °C with 27F/1492R, GC341F/907R and 1003f/1555r; 60 °C with 206F/886R; and 48 °C with mauAf1/mauAr1) for 1 min and at 72 °C for 1 min, followed by a final extension at 72 °C for 10 min. For PCR with A189f/mb661r, the following touchdown conditions were used: 94 °C for 5 min, then 11 cycles of 1 min at 94 °C, 1 min at 62 °C (−1 °C per cycle for 10 cycles), 1 min at 72 °C, followed by 25 cycles of 1 min at 94 °C, 1 min at 52 °C, 1 min at 72 °C, then a final elongation step of 10 min at 72 °C. All PCR products were checked for size and purity on 1% (w/v) agarose gels.

DGGE analysis of ‘heavy’ and ‘light’ DNA

PCR products generated from ‘heavy’ and ‘light’ DNA fractions after SIP were resolved by DGGE on an 8% acrylamide:bisacrylamide (37.5:1) gel with a denaturing gradient ranging from 30 to 70%. Denaturant of 100% is 7 M urea and 40% deionized formamide. Electrophoresis was carried out on a DCode universal mutation detection system (BioRad, Hercules, CA, USA) at 80 V for 16 h at 60 °C. The gel was run in 1 × TAE buffer and stained with Sybr Gold (Invitrogen, Paisley, UK). The most prominent bands from the DGGE gel were sequenced as previously described (Han et al., 2009).

Construction of clone libraries for 16S rRNA and functional genes

PCR products were purified using the QIAquick PCR purification kit (Qiagen, Crawley, West Sussex, UK), cloned into the pGEMT easy vector (Promega, Southampton, UK) and then transformed into E. coli JM109 (Promega) following the manufacturer's instructions. A total of 100 clones (from each 16S rRNA gene library) and 50 clones (from each functional gene library) were picked for direct colony PCR, with M13F/M13R primers targeting the flanking vector sequences. PCR products were run on agarose gels with DNA ladder to confirm the correct size of the cloned inserts, and subsequently purified by PEG-NaCl precipitation (Sambrook et al., 1989) before sequencing.

DNA sequencing and phylogenetic analysis

Sequencing was performed on a 3730 DNA analyzer (Applied Biosystems, Foster City, CA, USA) using the ABI Big-Dye version 3.1 sequencing kit as per the manufacturer's instructions, with both M13F and M13R primers for all functional gene library-based PCR products and with only M13F for 16S rRNA gene library-based products (partial sequencing). The generated sequences were analysed using ChromasPro software (http://www.technelysium.com.au/ChromasPro.html) and compared with the current database of nucleotide sequences at GenBank and Ribosomal Database Project (RDP). Reference sequences were chosen on the basis of BLASTn similarities. All 16S rRNA gene sequences were checked for possible chimeric artefacts using the Pintail program (Ashelford et al., 2006) in conjunction with Bellerophon (Huber et al., 2004). Functional gene sequences were inspected for chimeras by BLASTn analysis. Multiple sequence alignments of 16S rRNA gene sequences were performed with Clustal W, Version 1.8 (Thompson et al., 1994) and were edited manually using DAMBE (Xia and Xie, 2001) to obtain an unambiguous sequence alignment. Nucleotide distance matrices were constructed with DNADIST from PHYLIP version 3.61 (Felsenstein, 1989) using the Kimura two-parameter model (Kimura, 1980). OTUs were generated using the DOTUR program (Schloss and Handelsman, 2005) at 97% sequence similarity cutoff (for 16S rRNA gene sequences) and 94% sequence similarity cutoff (for functional gene sequences) with the furthest neighbour algorithm. A Bayesian method was used for the construction of phylogenetic tree. Before Bayesian inference analysis, a DNA substitution model for the complete data set was selected using MrModeltest2 (Posada and Crandall, 1998) and the Akaike information criterion (AIC). The model selected for the Bayesian approach for the phylogenetic tree was GTR+G with a log likelihood ratio (-lnL)=2567.2607 and Akaike information criterion (AIC)=5152.5215. The Markov chain Monte Carlo chains were started from a random tree and run for three million generations (MrBayes version 3.0b4 (Ronquist and Huelsenbeck, 2003)). Trees were sampled every 100 generations and a consensus tree was built over all trees with the exclusion of the first 1200 trees (burn-in). Posterior probabilities were determined by constructing a 50% majority-rule tree of all trees sampled. Three separate runs were performed using the above parameters because the Bayesian approach is known to result in inflated levels of nodal support. 16S rRNA, pmoA, mmoX and mxaF gene sequences obtained in this study were deposited in GenBank under accession numbers GU363876–GU363923.

Results

The chemical properties of Lonar Lake sediment samples used in SIP incubations are presented in Supplementary Table S1 (See Supplementary Information). Methane, methanol and methylamine uptake rates of the sediments were calculated to be 3.3 μmol CH4 day−1 g−1 wet sediment, 8.3 μmol CH3OH day−1 g−1 wet sediment and 3 μmol CH3NH2 day−1 ml−1 sediment enrichment medium, respectively (Figure 1). Fractionation of sediment community DNA from labelled methane-, methanol- and methylamine-based SIP incubations yielded ‘heavy’ or 13C fraction and ‘light’ or 12C fraction with buoyant densities of 1.725 and 1.707 g ml−1, respectively. The DGGE analyses of bacterial 16S rRNA gene PCR products (∼606 bp) amplified from heavy and light DNA fractions were used to confirm the success of SIP incubations. Banding patterns associated with all of the heavy fractions were distinct from those of light fractions (Figure 2), implying assimilation of each of the labelled C1 substrates by active methylotrophic populations in the sediment samples. Analysis of unlabelled (12C) substrate controls further confirmed the enrichment of specific organisms in the 13C-exposed samples (data not shown).

Figure 1
figure 1

C1 substrate utilization by Lonar Lake sediment samples. The values shown are the mean of triplicate microcosm experiments (two 13C and one 12C). Standard error bars are indicated. Methane microcosms were injected with 1.2 ml of CH4 (10 080 ppmv); methanol microcosms were set up with 25 mM CH3OH and methylamine microcosms were set up with 25 mM CH3NH2.HCl, in addition to 10% nutrient medium. All incubations were carried out in the dark at 28 °C. Substrate utilization rates were calculated by measuring the disappearance of each substrate during incubation.

Full size image
Figure 2
figure 2

DGGE fingerprint profiles for 12C (fraction 11) and 13C DNA (fraction 7) from the 13C-methane, methanol and methylamine SIP incubations. Bands that were successfully sequenced are indicated and those that failed are assigned a star. L indicates DGGE ladder.

Full size image

Characterization of active methane utilizers

PCR product of the expected size (∼1.4 kb) was obtained from the methane SIP heavy DNA fraction using universal bacterial 16S rRNA gene-specific primer set 27F/1492R. Cloning of PCR product and subsequent partial sequencing (∼700 bp) of inserts generated 78 good-quality sequences. DOTUR analysis of the clone sequences identified 10 operational taxonomic units (OTUs) phylogenetically affiliated with Gammaproteobacteria (4 OTUs), Betaproteobacteria (1 OTU), Deltaproteobacteria (2 OTUs), Firmicutes (2 OTUs) and Verrucomicrobia (1 OTU) (Figure 3). BLASTn analysis showed OTUs CH4_A9, CH4_A7 and CH4_A8 (representing around 57% of the library) to be most closely related (98% identity) to the 16S rRNA gene sequences of ‘Methylomicrobium buryatense’ (AF096093), Methylomicrobium japanense (D89279) and an unpublished soda lake isolate Methylomicrobium sp. ML1 (DQ496231), respectively. The sequences of CH4_A10 and CH4_A6 (representing around 13% of the library) were related to the 16S rRNA genes of methylotrophs Methylophaga sp. AM3Q (EU001739; 96% identity) and Methylophilus leisingeri (AB193725; 92% identity), respectively. For CH4_A4, CH4_A3 and CH4_A1 sequences, closest cultivated neighbours were Paenibacillus sp. xw-6-66 (FJ862051; 93% identity), Symbiobacterium thermophilum (AB004913; 90% identity) and Kofleria flava (AJ233944; 93% identity), respectively. The rest of the OTU sequences, CH4_A5 and CH4_A2, showed maximum affiliation with 16S rRNA genes of uncultured representatives of Verrucomicrobia (AF454310; 93% identity) and Deltaproteobacteria (EU283460; 95% identity), respectively. From the ‘heavy’ fraction DGGE profile of methane SIP incubations, seven prominent bands (CH1–CH7), representing partial bacterial 16S rRNA gene products (∼560 bp), were excised and sequenced (Figure 2). Sequencing DGGE bands yielded sequences that were also well represented in the 16S rRNA gene clone library (Figure 3).

Figure 3
figure 3

Bayesian phylogenetic tree showing the relationship between 16S rRNA gene sequences recovered from clone libraries constructed with the ‘heavy’ DNA from 13C-methane, methanol and methylamine SIP incubations and reference sequences obtained from the NCBI database. 16S rRNA gene sequences obtained from DGGE fingerprint profiles (indicated on Figure 2) are also included. One sequence per OTU is shown and GenBank accession numbers of reference sequences are given in brackets. Bayesian posterior probabilities (based on the mean of three separate analyses) are shown. The scale bar represents 2% substitution per site. The percentage values indicate the relative abundance of each OTU in the respective clone libraries.

Full size image

Primer set A189f/mb661r amplified a 472 bp fragment of pmoA gene and primer set 206F/886R amplified a 719 bp fragment of mmoX from the ‘heavy’ DNA fraction of methane SIP experiment. The pmoA and mmoX gene-based clone libraries (∼45 sequences from each) generated two OTUs (PM1 and PM2) and a singleton OTU (MM1), respectively. PM1 and PM2 sequences were most closely related to the pmoA genes of Methylomicrobium japanense (AB253367; 95% identity) and ‘Methylomicrobium buryatense’ (AF307139; 91% identity), respectively. The MM1 sequence was most closely related to the mmoX gene of Methylomicrobium japanense (AB253366; 96% identity).

Characterization of active methanol utilizers

A total of 79 sequences were obtained with the 16S rRNA gene-based clone library and three prominent bands were sequenced after DGGE fingerprint analysis of the methanol SIP ‘heavy’ DNA fraction. One chimeric OTU was detected and removed from the clone library sequences. Of the 10 OTUs identified, five OTUs affiliated with Gammaproteobacteria and the rest affiliated with Alphaproteobacteria, Deltaproteobacteria, Spirochaetes, Bacteroidetes and Actinobacteria (Figure 3). The sequences of OTUs CH3OH_B9 and CH3OH_B10 (representing 81% of the library) and DGGE band OH1 showed maximum identity to the 16S rRNA genes of Methylophaga spp. (EU001739; NR_026313; 94% identity). DGGE band OH2 sequence and CH3OH_B8 sequence from the library showed maximum identity of 98% to the 16S rRNA gene of Methylomicrobium sp. 4G (AF194539). The CH3OH_B6 sequence shared 97% identity with the 16S rRNA gene of Rhodobacter sp. EL-50 (AJ605746). The CH3OH_B1 sequence lacked cultivated affiliates in the database and was most closely related to the 16S rRNA gene of uncultured Myxococcales bacterium (AB265925; 93% identity).

Primer set 1003f/1555r targeting mxaF yielded a PCR product of 552 bp when ‘heavy’ DNA from methanol SIP was used as template. The subsequent clone library constructed generated 45 good-quality sequences that grouped into two OTUs (MX1 and MX2). The MX1 and MX2 sequences were most closely related to the mxaF genes of Methylomicrobium japanense (AB432885; 92% identity) and Methylophaga alcalica (EU001862; 83% identity), respectively.

Characterization of active methylamine utilizers

No methylamine uptake was detected in the methylamine SIP incubations without added nutrients (data not shown). Consumption of labelled methylamine was initiated when the microcosm sediments were supplemented with 10% nitrate mineral salts medium modified on the basis of sediment chemical properties (Supplementary Table S1). Four DGGE band sequences (Figures 2 and 3) and 76 clone library sequences were obtained from the ‘heavy’ DNA fraction of methylamine SIP. The library sequences grouped into a total of eight OTUs, out of which six were associated with Firmicutes and two were associated with Acidobacteria and Gammaproteobacteria (Figure 3). The majority of the DGGE band sequences (NH1, NH2, NH3 and NH5) and OTU sequences (CH3NH2_C1, CH3NH2_C2, CH3NH2_C3, CH3NH2_C4 and CH3NH2_C7) representing over 84% of the library showed maximum identity of 96–98% to the 16S rRNA genes of extant Bacillus spp. (DQ188945, DQ416793, DQ079010, DQ079009 and AM950294) (Figure 3). Ten sequences representing OTU CH3NH2_C6 along with DGGE band NH4 sequence shared 100% identity with the 16S rRNA gene of Halomonas sp. (GU113002).

The primer set mauAf1/mauAr1 targeting mauA did not yield amplicons from the ‘heavy’ DNA fraction of methylamine SIP, despite the use of PCR additives such as BSA and successful amplification of appropriate positive controls (data not shown).

Discussion

Lonar Lake represents an extreme environment with high pH and moderate salinity. Iron and magnesium concentrations were particularly high (21.9 g kg−1 sediment and 10.9 g kg−1 sediment, respectively) (Supplementary Table S1). This may be due to the Fe- and Mg-rich composition of the basalt bed rock and to meteorite iron (Schoonen et al., 2004). High total organic carbon (TOC) levels in Lonar Lake sediments could be attributed to the high primary productivity rates (up to 10 g m−1 per day) in soda lakes that often exceed other aquatic ecosystems (Jones et al., 1998). Exceptionally high total P levels may be explained by the basaltic origin of the sediments and fertilizer runoff from agricultural fields close to the crater. The Lonar crater is the only known depression in the region and hence may serve as a drain for excess runoff from anthropogenically influenced surrounding areas. However, the contribution of such natural or anthropogenic factors towards elevated phosphate and nitrate levels in the lake sediments warrants further investigation. The detected level of carbonates was relatively low but analysing the extent of contribution of other natural components towards alkalinity was beyond the scope of this study. Lonar Lake water is green throughout the year because of dense cyanobacterial blooms dominated by Arthrospira (Surakasi et al., unpublished). Decomposition of cyanobacterial biomass in soda lakes is likely to produce high quantities of methane, methanol, methylamine and dimethylsulfide (Jones et al., 1998). Organisms in soda lakes intracellularly accumulate osmolytes such as betaine and dimethylsulfoniopropionate, and their degradation by methanogens (Zavarzin et al., 1999) is likely to enrich the pool of methylated compounds. Methanotrophs and methylotrophs in such environments oxidize the C1 compounds produced, returning carbon to the food web. Methane oxidation rates are at least two-fold higher than the rates of methane formation in some soda lakes of the southern Transbaikal region (Doronina et al., 2003a). C1 intermediates (methanol, formaldehyde and formate) excreted by methanotrophs might also drive alkaline methylotrophy (Trotsenko and Khelenina, 2002).

In this study, experiments with 13CH4 identified phylotypes closely related to the type I methanotroph Methylomicrobium (Fuse et al., 1998; Kaluzhnaya et al., 2001; Eshinimaev et al., unpublished). Methylomicrobium spp. have been isolated from several terrestrial and marine samples (Bowman et al., 1993, 1995; Sieburth et al., 1987; Fuse et al., 1998) and soda lake sediments (Kalyuzhnaya et al., 1999, 2008; Khmelenina et al., 1997, 2000; Sorokin et al., 2000; Kaluzhnaya et al., 2001). Interestingly, the haloalkaliphilic/-tolerant Methylomicrobium isolates were resistant to heat and desiccation despite the absence of cysts (Kaluzhnaya et al., 2001). Protection from such extreme conditions is mediated by the intracellular accumulation of the compatible solute ectoine (Khmelenina et al., 1997, 2000; Trotsenko et al., 2005). Methane-based DNA-SIP experiments with Transbaikal soda lake sediments identified the dominant methanotrophs as Methylomicrobium spp. (Lin et al., 2004). Methylomicrobium spp. have also been detected in the sediments of a fresh water lake (Lake Washington) through reverse-transcription-PCR amplification of pmoA and fae transcripts (Nercessian et al., 2005).

Methylotroph sequences related to Methylophaga thalassica and Methylophilus sp. were also retrieved in methane DNA-SIP experiments. These organisms are known to use methanol in saline and alkaline environments (reviewed in Trotsenko et al., 2007) and may have cross-fed on methanol produced by 13C-labelled methanotrophs. Some Methylophaga strains exhibit high growth rates on methanol (De Zwart et al., 1996) and this might have led to the rapid assimilation of labelled methanol by phylotypes related to Methylophaga spp. in the sediment microcosms. Surprisingly, ∼29% of 16S rRNA gene clone library sequences and a number of DGGE band sequences had no phylogenetic affiliation with extant methanotrophs or methylotrophs. One explanation here would be the potential cross-feeding by these organisms on some labelled component from active methylotrophs. A relatively long incubation period (12 days) was necessary to permit sufficient incorporation of 13C-methane and this may have led to enrichment of ‘cross-feeders’ (reviewed in Neufeld et al., 2007a). The 16S rRNA gene sequences discussed above shared low phylogenetic identities with that of nearest cultivated neighbours in the database (90–93%). Therefore, it is difficult to determine whether these phylotypes have been labelled by cross-feeding or these sequences represent uncharacterized methanotrophs. One of them showed maximum identity to the 16S rRNA gene of an uncultured Verrucomicrobium clone obtained from alkaline Mono Lake (Humayoun et al., unpublished). This sequence did not, however, cluster with that of thermo-acidophilic Verrucomicrobia methanotrophs (Dunfield et al., 2007; Pol et al., 2007; Islam et al., 2008) (Figure 3).

16S rRNA gene sequences from methanol SIP experiments were dominated by sequences related to Methylophaga sp. retrieved from a marine methanol SIP study (Neufeld et al., 2007c). Another possibly methylotrophic OTU (CH3OH_B10) was affiliated with the 16S rRNA gene sequence of Methylophaga sulfidovorans, a methylotroph isolated from a microbial mat using dimethylsulfide as substrate (De Zwart et al., 1996). Sequences related to Methylophaga alcalica, a haloalkaliphilic methylotroph isolated from sediments of an East Mongolian soda lake (Doronina et al., 2003b), were recovered from the mxaF clone library. Methylophaga are aerobic, moderately halophilic, non-methane using methylotrophs, mostly isolated from marine (Janvier et al., 1985; Doronina et al., 1997; Kim et al., 2007) and soda lake ecosystems (Doronina et al., 2003a, 2003b). 16S rRNA and mxaF gene sequences related to the alkaline environment isolate Methylomicrobium sp. 4G (Kaluzhnaya et al., 2001) and Methylomicrobium japanense, respectively, were recovered from 13C DNA. High methanol concentrations ranging from 5 to 7% v/v are known to support the growth of soda lake Methylomicrobium isolates (Kaluzhnaya et al., 2001). The presence of Rhodobacter-related clone sequences is not surprising, as Rhodobacter spp. are capable of growth on methanol (Wilson et al., 2008). The detection of a singleton OTU (CH3OH_B7) clustering closely with Halomonas sp. (Figure 3) may be a result of cross-feeding, as some Halomonas strains are known to metabolize C1 intermediates such as formaldehyde and formate (Azachi et al., 1995). The rest of the OTUs related to Aquiflexum sp., Spirochaeta sp. and uncultured representatives of Actinobacterium and Myxococcales again did not affiliate with known methylotrophs.

Absence of PCR amplicons for mauA confirmed the findings of DGGE fingerprinting and clone library analysis, as none of the bacteria represented by the OTUs identified (Figure 3) are known to assimilate methylamine by the methylamine dehydrogenase pathway. Methylamine can be metabolized by other pathways containing methylamine–oxidase or methylamine–glutamate N-methyl-transferase (Anthony, 1982; Chistoserdova et al., 2009; Latypova et al., 2009). The majority of sequences in the methylamine SIP 16S rRNA gene library and DGGE fingerprint profile were related to Bacillus spp. (Figure 3). Bacillus strains growing on methylamine, methanol and dimethylsulfide have been characterized (Dijkhuizen et al., 1988; Arfman et al., 1989; Anesti et al., 2005). Though the methylamine degradation pathway in the genus Bacillus is poorly characterized, all Gram-positive methylotrophs studied to date use the methylamine oxidase pathway (Chistoserdova et al., 2009). Methylamine SIP sequences from our study clustered with the 16S rRNA gene sequences of both methylotrophic Bacillus strains (Arfman et al., 1992; Anesti et al., 2005) and strains isolated from contaminated soils (Desai et al., 2009; Stobdan et al., unpublished) and marine sediments (Dick et al., 2006; Sass et al., 2008) (Figure 3). This is the first SIP study to identify methylamine-utilizing Bacillus spp. directly from environmental samples. However, it may be noted here that these results may not entirely represent active participants in methylamine metabolism in situ. Methylophylaceae were implicated as active consumers of labelled methylamine in Lake Washington sediment microcosms (Nercessian et al., 2005). Although Methylophilus-related 16S rRNA gene sequences were retrieved from our methane SIP heavy fraction, no such phylotypes were detected in the methylamine SIP microcosms. Methylamine SIP studies carried out with sea water (Neufeld et al., 2007c) and estuarine sediments (Moussard et al., 2009) identified Methylophaga spp. as the dominant methylamine utilizers. Clone library and DGGE band sequences closely related to Methylophaga spp. were recovered from our methane and methanol SIP heavy fractions, but were not detected in the methylamine SIP heavy fraction. This may be due to the lack of suitable microcosm conditions for Methylophaga spp. to utilize methylamine or due to distinct substrate preferences developed as a result of competition for C1 substrates among bacterial communities of the extreme Lonar Lake environment.

In conclusion, SIP enabled the identification of Methylomicrobium, Methylophaga and Bacillus spp. as the predominant utilizers of methane, methanol and methylamine, respectively, in Lonar Lake sediments. We also detected a number of uncultured organisms associated with C1 metabolism and these data will assist the design of future culture-based studies to isolate novel methylotrophs from Lonar Lake.