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1 Department of Chemical Engineering, University of Washington, Seattle, WA 98195, USA
2 Department of Microbiology, University of Washington, Seattle, WA 98195, USA
Correspondence
Ludmila Chistoserdova
milachis{at}u.washington.edu
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ABSTRACT |
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 TOP ABSTRACT INTRODUCTIONMETHODSRESULTS AND DISCUSSIONREFERENCES |
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A supplementary table is available with the online version of this paper.
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INTRODUCTION |
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TOPABSTRACTINTRODUCTIONMETHODSRESULTS AND DISCUSSIONREFERENCES |
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), thus making the overall genomic coverage of microbial diversity minuscule. The recent surge in metagenomic sequencing, and downstream applications such as metatranscriptomics and metaproteomics (Ram et al., 2005; Denef et al., 2007; Frias-Lopez et al., 2008; Gilbert et al., 2008), are transforming the field of microbiology. However, due to the novelty of these approaches and to their inherent shortcomings such as low sequence coverage and low sampling depth (Tringe et al., 2005; Rusch et al., 2007), the utility and the potential applications of these datasets remain poorly explored. Recently, applicability of high-quality genomic information in the identification of peptides between related environmental strains has been explored, using a probability-based model and a random mutation simulation model (Denef et al., 2007). Here we explore a reversed approach, using a recently generated metagenomic dataset representing multiple strains of a single bacterial species, M. mobilis (Kalyuzhnaya et al., 2008b), for protein detection in the type strain of Methylotenera mobilis, JLW8, a laboratory-cultivated strain.
M. mobilis JLW8 is the only formally described representative of Methylotenera, a new genus within the family Methylophilaceae (Kalyuzhnaya et al., 2006). All known members of this family are obligate methylotrophs, i.e. they are specialized in degradation of organic compounds containing no carbon–carbon bonds (C1 compounds), most prominently methanol and methylamine (Lidstrom, 2006). Based on culture-independent surveys, Methylotenera species appear to be ubiquitous, having been detected in marine, freshwater, acid mine drainage, polluted soil, sedimentary rock and glacier environments (according to the entries in the non-redundant database, http://www.ncbi.nlm.nih.gov/), consistent with an important role in global carbon cycling. However, the exact function of these bacteria remains unknown. M. mobilis JLW8 was isolated from a 63 m deep sampling site in Lake Washington sediment (Kalyuzhnaya et al., 2006). Samples used for stable isotope probing-based enrichment of methylotrophic communities that were also used for metagenomic sequencing were collected from the same site, but at a later date (Kalyuzhnaya et al., 2008b). From metagenomic analysis conducted on these samples, the methylamine microcosm was dominated by M. mobilis strains, which allowed for extracting a composite genome of M. mobilis and conducting metabolic reconstruction for this species (Kalyuzhnaya et al., 2008b). However, the dominant strains of M. mobilis represented in the metagenome appear to be distinct from M. mobilis JLW8, based on 16S rRNA sequence comparisons. The study undertaken here pursued three main objectives: (1) to validate the metagenome-based sequence assembly and binning of M. mobilis sequences conducted in the previous study, (2) to test whether this metagenomic sequence provides a quality protein database for peptide identification in a strain without perfect matches in the metagenome and test the limitation of such a database, and (3) to obtain new insights into the physiology of M. mobilis.
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METHODS |
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TOPABSTRACTINTRODUCTIONMETHODSRESULTS AND DISCUSSIONREFERENCES |
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). Per biological replicate, cells from a 2 l culture (OD600 0.2) were harvested by centrifugation at 4500 g for 8 min at 4 °C, briefly washed with cooled 20 mM Tris/HCl pH 8, flash-frozen in liquid nitrogen and stored at –80 °C.
Protein extraction and digestion.
Frozen cell pellets were resuspended in 500 µl hot resuspension buffer (20 mM Tris/HCl pH 8.0, 5 mM DTT) and lysed by boiling for 2 min in a water bath, followed by cooling on ice for 10 min. For nucleic acid digestion, 10 units benzonase nuclease (Roche) was added after adjusting the suspension to 2 mM MgCl2 (final concentration). After 15 min incubation at room temperature, 200 µl of ethanol- and buffer-washed glass beads (150 µm) was added, the sample volume was adjusted to 1.5 ml with resuspension buffer, and bead beating was performed for 4 min at 48 r.p.m. in a MiniBeadBeater (BioSpec Products). Total protein concentration was determined by Bradford protein assay (Bio-Rad); this was approximately 1 mg ml–1. The homogenate was lyophilized to dryness. After resuspension in 500 µl digestion buffer (2 M urea, 5 % acetonitrile, 5 mM dithiothreitol, 0.1 % Rapigest), the sample was digested as previously described (Bosch et al., 2008).
HPLC pre-fractionation and linear ion trap mass spectrometry.
The soluble fraction after digestion was lyophilized to a volume of approximately 150 µl and centrifuged for 10 min at 14 000 r.p.m. in a tabletop centrifuge (Eppendorf). A 10–20 µl sample of the supernatant was applied to a PLRP-S reversed-phase column (2.1 mm i.d.x150 mm, 300 Å, 5 µm; Polymer Laboratories) with mobile phases of 0.1 % trifluoroacetic acid in H2O and acetonitrile. Peptides were eluted at 0.2 ml min–1 with a gradient of 2–60 % acetonitrile in 60 min and 60–90 % acetonitrile in 20 min and collected as five separate fractions. The fractions were each lyophilized to 20 µl, and after reconstitution to volumes of 120 µl with acetic acid and acetonitrile at final concentrations of 0.5 % and 5 % (v/v), respectively, subjected to LC/LC-MS/MS (two-dimensional capillary HPLC/linear ion trap tandem mass spectrometry) analysis as previously described (Bosch et al., 2008).
SEQUEST database searching, DTAselect filtering and estimation of random false positive identification rate.
Using the SEQUEST algorithm, raw data were searched against a concatenated FASTA database that consisted of polypeptide sequences derived from the M. mobilis composite genome (Kalyuzhnaya et al., 2008b), the human subset of the non-redundant database (nrdb) depleted of all virus sequences, and reversed sequence (decoy) versions of both databases appended. The concatenated database comprised a total of 214 148 protein sequences, 12 951 of which represented the M. mobilis specific meta-database. The search results were filtered in DTAselect applying the parameters described by Bosch et al. (2008). Sequest 
Cn/Xcorr values of 0.08/1.9, 0.08/2.2 and 0.08/3.3 were used for singly, doubly and triply charged peptides, respectively. Three peptides unique to a particular ORF were required for positive identification. Random false positive identifications were determined at the peptide level as a ratio of two times the number of reversed peptide identifications in the concatenated database to the total number of identified peptides (Elias & Gygi, 2007). By this means, the qualitative random false positive rate was estimated to be 1.5 % for the first biological replicate and 2.1 % for the second biological replicate.
Data processing and normalization.
The in-house methods developed for estimating protein abundances from spectral counting values have been described by Xia et al. (2007a, b). In order to normalize the two biological replicates, the total sum of the average spectral counts for the second biological replicate was divided by the total sum of the average spectral counts for the first biological replicate, resulting in a normalization factor of 1.077. Each average spectral count value in the first biological replicate was multiplied by this normalization factor.
Reproducibility of biological replicates.
Linear regression analysis demonstrated good data reproducibility for the two biological replicates (R=0.86 for the 1924 proteins common to both replicates; data not shown).
DNA amplification, sequencing and analysis.
DNA of M. mobilis JLW8 was isolated using a QIAamp DNA minikit (Qiagen). Semi-randomly selected fragments of DNA, as shown in Table 1, were amplified by PCR, cloned and sequenced essentially as described by Kalyuzhnaya et al. (2008a). Oligonucleotide primers for amplification were designed based on the metagenomic sequence, as follows. First, a gene to be amplified was chosen (genes involved in methylotrophy, central pathways or hypothetical genes). All the homologues representing different M. mobilis strains in the metagenome were then aligned using tools available as part of the Joint Genome Institute IMG/M package (http://img.jgi.doe.gov/cgi-bin/m/main.cgi), and consensus sequences were chosen with no or few mismatches.
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Enzyme assays.
Cells were grown on methylamine as above, to OD600 0.8, pelleted by centrifugation, as above, resuspended in Tris/HCl pH 8.0 buffer and disrupted by passage through a French pressure cell at 1.2x108 Pa. Cell extracts were centrifuged at 14 000 r.p.m. for 25 min at 4 °C to remove cell debris. The activity of glutamine synthetase was assayed as described by Murrell & Dalton (1983), the activity of glutamate synthase was assayed as described by Bravo & Mora (1988), and the activity of glutamate dehydrogenase was assayed as described by Yarrison et al. (1972).
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RESULTS AND DISCUSSION |
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TOPABSTRACTINTRODUCTIONMETHODSRESULTS AND DISCUSSIONREFERENCES |
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), there are three matches to the 16S rRNA gene of M. mobilis JLW8. One of these is a small fragment (291 nt) that seems to represent a very conserved part of the gene, with 100 % homology to the M. mobilis JLW8 16S rRNA gene, at low sequence coverage (1.8x; Kalyuzhnaya et al., 2008b). Two others are nearly complete genes with 97 and 98 % homology, respectively, present at much higher coverage (13.2x and 20.4x, respectively). Typically, the level of divergence for protein-coding genes between different strains is higher than for 16S rRNA genes (Konstantinidis & Tiedje, 2005). To obtain an estimate of divergence between proteins of M. mobilis JLW8 and proteins of M. mobilis represented in the metagenome, we amplified 15 DNA fragments, as described in Methods, translated polypeptide sequences from these, and compared them to the metagenome-based M. mobilis protein database. Each query peptide had a few hits in the database, in accordance with the composite nature of this database. Few 100 % matches were observed. While most environmental sequences were highly similar (93–98 %) to the M. mobilis JLW8 sequences, some sequences diverged as much as 21 % (Table 1
). The divergence from the sequences present in the metagenome at the highest coverage (i.e. the most well-assembled sequences) ranged from 0 to 21 %. Complete sequences for two proteins, methylene tetrahydromethanopterin dehydrogenase (MtdB) and formaldehyde-activating enzyme (Fae), both involved in formaldehyde metabolism (Chistoserdova et al., 2003), were obtained, and these were compared to the homologues translated from the composite genome in terms of predicted tryptic peptide masses. Only two full-sequence homologues were found for MtdB, both at 93 % amino acid identity (Table 1). Out of the 20 predicted tryptic peptides, 8 (40 %) had perfect matches in the database. Four full-sequence homologues were detected for Fae, with amino acid identities ranging from 94 to 98 %. Out of the 12 predicted peptides, 10 (83 %) had perfect matches in the database. These results suggested that for most proteins, a large portion of the peptides of strain JLW8 obtained by tryptic digestion were expected to have identical matches in the protein database translated from the metagenome.
Qualitative proteome coverage
Spectral counts as used here are summations of the number of redundant peptides (i.e. including repeated measurements of the same peptide at different stages of the HPLC separation) observed that map uniquely to a particular protein or protein grouping as defined by the user (Bosch et al., 2008). While the precise relationship between observed spectral counts and abundance within a given proteome is complex, for microbial systems high counts usually correlate with high abundance, as validated by global transcription measurements and other means (Bosch et al., 2008). However, low counts do not necessarily imply low abundance, due to detectability differences among peptides. Data for two biological replicates were collected and analysed. The M. mobilis specific protein database utilized for peptide identification was represented by polypeptide sequences translated from the 12 951 gene sequences that constituted the M. mobilis composite genome (covering approximately five closely related M. mobilis genomes; Kalyuzhnaya et al., 2008b), downloaded from the IMG/M database, along with their automated annotations. These annotations were manually curated for proteins with high spectral counts and thus inferred high relative abundance (see Supplementary Table S1, available with the online version of this paper). A total of 2353 and 2336 polypeptides in this database were matched with the peptides identified in the first and the second biological replicate samples, respectively (Table S1), with a positive detection cutoff of at least three peptide matches per protein (at a cutoff of two peptide matches, 2767 and 2787 peptides were identified, respectively; data not shown). This identification rate (approx. 20 % of total proteins in the database) is, as expected, in a lower range compared to the identification rate typical of proteins matched to a database specific for a given organism (up to 60 %, for example, in M. extorquens AM1; Bosch et al., 2008), but within the range of the previously conducted metagenome-based proteomic analyses (Ram et al., 2005) and in agreement with the recent models for protein detection efficiency as a function of sequence divergence (Denef et al., 2007). An additional factor affecting the peptide detection was the partial nature of many of the polypeptides translated from the M. mobilis metagenome, due to low sequence assembly (Kalyuzhnaya et al., 2008b).
Protein-based reconstruction of methylotrophy-specific metabolic pathways
In most cases, peptides of M. mobilis JLW8 were matched to a number of homologous proteins in the database, representing protein clusters, similarly to the results represented in Table 1. Those homologues with positive peptide matches were grouped manually into clusters, and peptide spectral counts within each cluster were summed. Some of the inferred proteins with the highest summed spectral counts observed in both biological replicates were the ones usually found highly expressed in bacteria, such as the translation elongation factor, chaperonins GroEL and DnaK, subunits of ATP synthase, DNA-directed RNA polymerase, ribosomal proteins, etc. (Table S1) that can be defined as core proteins (Callister et al., 2008). However, of most interest to us were the peptides representing major enzymes and pathways specifically related to methylamine metabolism in M. mobilis. One of the most highly expressed metabolic enzymes was hexulosephosphate synthase (Hps, phylotype 1; Table 2, Table S1 and Fig. 1), the first enzyme in the ribulose monophosphate (RMP) cycle operating in formaldehyde assimilation/oxidation in Methylophilaceae (Lidstrom, 2006). The gene for this enzyme is part of a methylotrophy gene island in M. mobilis (Kalyuzhnaya et al., 2008b) that is very similar to an island also found in Methylobacillus flagellatus (Chistoserdova et al., 2007b). Interestingly, both Methylotenera mobilis and Methylobacillus flagellatus encode a second copy of Hps (phylotype 2, approximately 89 % identity with phylotype 1 in M. mobilis). This enzyme is also detectable in the M. mobilis proteome, but at a much lower level of total counts (Table 1). Other enzymes that are parts of the RMP cycle, both assimilatory and dissimilatory branches, were also identified at relatively high spectral counts, the most abundant being transaldolase and transketolase, which are involved in RMP regeneration (Fig. 1). Peptides for the catalytic subunits of methylamine dehydrogenase (MauAB) were found at high counts, along with the proposed novel electron-accepting cytochrome (MauO; Kalyuzhnaya et al., 2008b), suggesting that this is the main primary oxidation system. Interestingly, MauD, whose function has been proposed as being in small-subunit maturation (van der Palen et al., 1997), was found at relatively high counts, unlike the proteins involved in cofactor biosynthesis (for example, MauG; Li et al., 2008), suggesting a possible role as a chaperone. Polypeptides were detected at high counts forming the glutamine synthetase/glutamate synthase (GS/GOGAT) pathway. While not typically considered as part of the methylamine utilization pathway, the presence of these enzymes suggests a mechanism for utilizing the ammonia that results from methylamine oxidation. In this respect, the incomplete tricarboxylic acid cycle (Kalyuzhnaya et al., 2008b), in addition to its anaplerotic function in providing central metabolism intermediates (Lidstrom, 2006), must play another important role, in providing the 
-ketoglutarate that is an essential intermediate in the GS/GOGAT cycle. The presence of the activities of methylamine dehydrogenase and the hexulosephosphate synthase/isomerase pair in M. mobilis JLW8 has been previously reported, supporting the analysis conducted here (Kalyuzhnaya et al., 2006). To confirm identification of the GS/GOGAT cycle, we measured the activities of the respective enzymes and found them at high levels (95±3 and 174±5 mU, respectively). Glutamate dehydrogenase (GDH), another enzyme known to assimilate ammonia, was undetectable in cell extracts, in agreement with the lack of a gene homologue encoding GDH in the M. mobilis genomic database.
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; Kalyuzhnaya et al., 2005). In some of them, such as Methylobacterium extorquens, this is an essential pathway in both formaldehyde detoxification and energy metabolism (Chistoserdova et al., 1998, 2003), while in others, such as Methylobacillus flagellatus, this pathway has been demonstrated to be non-essential, the cyclic RMP pathway playing the major role (Chistoserdova et al., 2000). Low abundances of most of the H4MPT-linked enzymes and of the formate dehydrogenases suggest a secondary role for this linear oxidative pathway in Methylotenera mobilis grown on methylamine. However, Fae (formaldehyde-activating enzyme; phylotype 1), the first enzyme in this pathway, was found to be one of the most abundant proteins. The demonstrated function of this enzyme is in directly binding formaldehyde (Vorholt et al., 2000). However, the unusual location of the fae gene in the M. mobilis genome has prompted a hypothesis of its having a secondary role, as a sensor or signal transducer (Kalyuzhnaya et al., 2008b). A second phylotype of Fae (56 % amino acid identity with phylotype 1 Fae) was also detected in the proteome, at a much lower level. Interestingly, a Fae homologue named Fae2, of unknown function (Chistoserdova et al., 2003; Kalyuzhnaya et al., 2008a), was also observed at high counts, suggesting an important if not yet demonstrated role in C1 metabolism.
One metabolic feature that distinguishes Methylotenera mobilis from Methylobacillus flagellatus, as predicted from genome–genome comparisons, is the presence of the genes for a complete methylcitric acid (MCA) cycle. All the proteins constituting this pathway were positively identified in this study (Table 2, Supplementary Table S1 and Fig. 1). The role of this pathway in M. mobilis remains unknown. Its involvement in metabolism of methylamine is not likely as all the essential metabolites are produced in the RMP cycle in combination with the enzymes interconverting C3 metabolites and the partial tricarboxylic acid cycle (Fig. 1). More likely, the presence of the specific MCA cycle proteins during growth on methylamine is due to the peculiarities of regulation of this metabolic pathway. It has been previously suggested that this pathway may play a role in utilization of methylated compounds whose degradation results in propionate as a product (Kalyuzhnaya et al., 2008b). While this hypothesis is a subject for experimental testing, the present study demonstrates expression of the entire set of the genes involved, thus supporting functionality of this pathway.
Curiously, XoxF and XoxG, homologues of, respectively, the large subunit of methanol dehydrogenase and an associated cytochrome, were found at high abundance. The potential role of the Xox system has been attracting attention recently, implicating it in C1 metabolism, but so far without a well-defined function (Wilson et al., 2008; Kalyuzhnaya et al., 2008a). Another highly abundant enzyme was a putative aldehyde dehydrogenase (Adh) whose function also remains unknown.
Conclusions
We utilized a metagenomic sequence of M. mobilis for a strain-specific proteomic analysis of the closely related M. mobilis JLW8 grown on methylamine. To address the first specific objective (see Introduction), we provided an experimental validation of sequence assembly and M. mobilis-specific sequence binning conducted in our previous study (Kalyuzhnaya et al., 2008b), by detecting proteins involved in major metabolic pathways predicted to operate in metabolism of methylamine. To address the second specific objective, we demonstrated that a metagenomic dataset of inherently lower quality compared to completed genomic sequences is usable for cross-strain protein detection, by identifying a large portion of polypeptides predicted to be encoded in the genome of M. mobilis JLW8, with high confidence. To address the third specific objective, we obtained new insights into the metabolism of methylamine by M. mobilis, including expression of multiple pathways for formaldehyde oxidation (linear versus cyclic) and multiple enzyme paralogues (Hps, Fae), suggesting redundancy of key methylotrophy functions previously noted for other organisms (Chistoserdova et al., 2000, 2007a). In addition, we established a potential link between methylamine oxidation and ammonia utilization via the GS/GOGAT pathway. This pathway is likely to play a role in the removal of ammonia produced by the methylamine dehydrogenase reaction to prevent its accumulation to toxic levels. We also demonstrated that all the proteins involved in the proposed MCA cycle were expressed, suggesting a central role for this cycle in M. mobilis metabolism. This work is a prelude to future applications of this metagenomic dataset for protein identification in other M. mobilis strains as well as in natural microbial populations (metagenome-based metaproteomics).
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ACKNOWLEDGEMENTS |
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Edited by: J. A. Vorholt
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Received 9 October 2008;
revised 2 January 2009;
accepted 5 January 2009.
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