metatranscriptomic analysis of arctic peat soil ... - uit
TRANSCRIPT
Metatranscriptomic Analysis of Arctic Peat Soil Microbiota
Alexander T. Tveit,a Tim Urich,b,c Mette M. Svenninga
Department of Arctic and Marine Biology, UiT The Arctic University of Norway, Tromsø, Norwaya; Department of Ecogenomics and Systems Biology, University of Vienna,Vienna, Austriab; Austrian Polar Research Institute, Vienna, Austriac
Recent advances in meta-omics and particularly metatranscriptomic approaches have enabled detailed studies of the structureand function of microbial communities in many ecosystems. Molecular analyses of peat soils, ecosystems important to the globalcarbon balance, are still challenging due to the presence of coextracted substances that inhibit enzymes used in downstream ap-plications. We sampled layers at different depths from two high-Arctic peat soils in Svalbard for metatranscriptome preparation.Here we show that enzyme inhibition in the preparation of metatranscriptomic libraries can be circumvented by linear amplifi-cation of diluted template RNA. A comparative analysis of mRNA-enriched and nonenriched metatranscriptomes showed thatmRNA enrichment resulted in a 2-fold increase in the relative abundance of mRNA but biased the relative distribution of mRNA.The relative abundance of transcripts for cellulose degradation decreased with depth, while the transcripts for hemicellulosedebranching increased, indicating that the polysaccharide composition of the peat was different in the deeper and older layers.Taxonomic annotation revealed that Actinobacteria and Bacteroidetes were the dominating polysaccharide decomposers. Therelative abundances of 16S rRNA and mRNA transcripts of methanogenic Archaea increased substantially with depth. Acetoclas-tic methanogenesis was the dominating pathway, followed by methanogenesis from formate. The relative abundances of 16SrRNA and mRNA assigned to the methanotrophic Methylococcaceae, primarily Methylobacter, increased with depth. In conclu-sion, linear amplification of total RNA and deep sequencing constituted the preferred method for metatranscriptomic prepara-tion to enable high-resolution functional and taxonomic analyses of the active microbiota in Arctic peat soil.
Metatranscriptomics is the study of rRNA and mRNA of a(microbial) community in an environment. It allows the
simultaneous investigation of the gene expression (mRNA) andabundance (rRNA) of the active microorganisms. In contrast toproteins, which have a longer lifetime in the cell and more stableconcentrations in response to external influences, mRNAs pro-vide a more immediate picture of the cells responses to changingenvironmental conditions. Also, metatranscriptomics avoids thelimitations inherent to PCR primer-based methods (1, 2). Thepoly(A) tail of eukaryotic mRNAs enables the cDNA synthesisfrom these mRNA templates in total RNA pools with selectiveprimers (3). The total microbial RNA is dominated by rRNA tran-scripts, including prokaryotic 16S and 23S rRNAs and eukaryotic18S and 28S rRNAs. Only a small fraction, usually 1 to 5%, ismRNA (1, 2). Several strategies are currently applied to enrich forprokaryotic mRNA molecules. Selective nuclease degradation ofrRNA (3–5), polyadenylation of mRNA (6), and rRNA depletionby capture with commercial kits (3, 5, 7, 8) and sample-specificprobes (9) have been attempted to reduce the rRNA fraction ofmetatranscriptomes.
A second challenge, and a general problem for DNA- andRNA-based analyses of soil microbes, is the coextraction of en-zyme-inhibiting compounds such as humic and fulvic acids andphenolic compounds (1). In peat soils, the inhibition has beenshown to increase with soil depth (10). Inhibition is particularlyproblematic in the preparation of metatranscriptomic libraries, inwhich rather large quantities of RNA are needed for double-stranded cDNA synthesis prior to sequencing. Several studies haveaddressed this issue, providing strategies for the removal of humicand fulvic acids during the extraction of nucleic acids (1). Sug-gested methodology includes Sephadex spin columns and poly-ethylene glycol (PEG) precipitation of nucleic acids (11). Contin-ued inhibition after extract purification might be related to the
high concentrations of enzyme-inhibiting phenolic compounds,particularly in anoxic soils such as peat (12, 13). Metatranscrip-tomic studies have provided new and important knowledge aboutsoil ecosystems (2, 10, 14–16), but few studies have been carriedout compared to studies of marine ecosystems, in part because ofthe inhibition problems mentioned above.
Arctic peat soils store large amounts of soil organic carbon(SOC). Soil microorganisms are driving the SOC mineralizationto the greenhouse gases methane (CH4) and carbon dioxide(CO2). In anoxic peat, plant polymers are degraded through sev-eral hydrolysis and fermentation steps involving at least four func-tionally distinct types of microorganisms: primary and secondaryfermenters and two groups of methanogens (17, 18). Formate,H2/CO2, and acetate are the major substrates for methanogenesis,but in certain situations, methylamines and methanol are alsosubstrates (19).
CH4 emissions can be mitigated by microbial CH4 oxidation.In terrestrial and freshwater ecosystems, CH4 oxidation is primar-ily aerobic and performed by Proteobacteria (20) and Verrucomi-crobia (21). Proteobacterial methanotrophs closely related to the
Received 27 March 2014 Accepted 8 July 2014
Published ahead of print 11 July 2014
Editor: G. Voordouw
Address correspondence to Tim Urich, [email protected], or Mette M.Svenning, [email protected].
Supplemental material for this article may be found at http://dx.doi.org/10.1128/AEM.01030-14.
Copyright © 2014, American Society for Microbiology. All Rights Reserved.
doi:10.1128/AEM.01030-14
The authors have paid a fee to allow immediate free access to this article.
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aerobic Methylobacter are characteristic for circumarctic soils (22,23). Stable isotope signature studies indicate that a major sink forCH4 in peat soils is anaerobic CH4 oxidation (24), but the oxi-dants, enzymes, and organism(s) involved are unknown.
More knowledge is needed to understand how microbial com-munities functionally interact in the degradation of SOC and howthey will respond to environmental changes such as the predicteddrastic increase of surface temperatures in Arctic regions.
In this study, we present a method for the generation of high-quality metatranscriptomes from peat soils to circumvent inhibi-tion problems. Further, we assessed the usability of widely appliedmRNA enrichment protocols. We used the generated metatran-scriptomes for analyzing the expression of genes encoding keyfunctions in SOC degradation, such as hydrolysis of polysaccha-rides and methanogenesis, and methanotrophy in two high-Arcticpeat soils.
MATERIALS AND METHODSStudy sites and sampling. High organic Arctic peat soil samples werecollected from two sites in Svalbard, Norway, Solvatn (N78°55.550,E11°56.611) and Knudsenheia (N78°56.544, E11°49.055), in August 2009(10). Peat blocks were kept intact under transport from the field sites tothe laboratory where the first preparation was made. Processing of thedeeper layers was performed under nitrogen atmosphere to avoid oxygencontamination. Immediately after disruption of the peat blocks for sub-sampling and storage, subsamples were frozen in liquid nitrogen to avoideffects of changing conditions in the mRNA pools. The samples weretransported in a dry shipper from Svalbard to the home laboratorieswhere the further preparation for RNA isolation was done.
Sample and RNA processing. Samples from oxic and anoxic layers ofthe two sites Knudsenheia (from the top to deepest layers: Ka, Kb, and Kc)and Solvatn (Sa and Sb) were ground in liquid nitrogen using a mortarand pestle until a fine powder was obtained. The low temperature pre-vented microbial and RNase activities. From each homogenized samplesix replicates of �0.2 g of peat soil were used for nucleic acid (NA) extrac-tion using a modified version of Griffith’s protocol (2, 10). To preventRNase activity during cell lysis, bead beating was carried out in the pres-ence of the denaturant phenol. DNA was removed using RQ1 DNasetreatment (Promega, Madison, WI), followed by RNA purification using theMEGAclear kit (Ambion, Austin, TX). The two top samples, Ka and Sa, wereused for mRNA enrichment, applying the three commercially kits accordingto the manufacturers’ instructions in the following order: (i) RiboMinus kitfor bacteria (specificity: Gram-positive and -negative bacteria, human,mouse, yeast; Invitrogen, Carlsbad, CA), (ii) MICROBExpress (specificity:Gram-positive and -negative bacteria; Ambion, Austin, TX), and (iii) Ribo-Minus kit for eukaryotes (specificity: eukaryotes; Invitrogen). All three kits arebased on subtractive hybridization of rRNA with oligonucleotide probes andcapture with magnetic beads. Starting quantities for each kit are shown inTable S2 in the supplemental material. The quality of RNA was assessed usingautomated gel electrophoresis (Experion; Bio-Rad, Hercules, CA) with stan-dard-sensitivity RNA chips.
Total RNA for all samples and the mRNA-enriched sample (Km) werediluted and amplified using linear amplification with MessageAmp II-Bacteria kit (Ambion). A test with RNA extracted from Kb was performedwith concentrations of 2.5, 10, and 20 ng/�l (equivalent to 12.5, 50, and100 ng per reaction) to identify whether the concentration affected theoutput of amplified RNA. It was found that concentrations of 10 and 20ng/�l resulted in �25% of the final yield of amplified RNA. Based on this,a total of 12.5 ng of RNA at a concentration of 2.5 ng/�l was used as thetemplate for all samples. This was in accordance with the recommenda-tions of the supplier, which stated that 10 ng of RNA should be consideredthe minimum input. Double-stranded cDNA was generated from the am-plified RNA using the Superscript II double-stranded cDNA synthesis kit(Invitrogen) by following the manufacturer’s protocol, with the exception
that both the first- and second-strand syntheses were carried out for 4 heach. Library preparation, processing, and sequencing were performed atthe Norwegian High Throughput Sequencing Centre (NSC), using theIllumina HighSeq2000 (Illumina, Inc., San Diego, CA) with paired-end(PE) 101-bp sequencing of �170-bp-long templates.
Sequence analyses. Paired-end sequence reads were first assembledusing Pandaseq (25), with a minimum overlap of 10 bp and otherwisedefault settings. Preprocessing of the assembled sequences was carried outusing Prinseq (26); poly(A/T) tails longer than 15 bp (Table 1) weretrimmed away, sequences with more than 5 ambiguous bases were re-moved, and all but one sequence in pools of exactly identical sequenceswere removed (Table 1). rRNA and putative mRNAs were separated aspreviously described (2, 27). Sequences with bit scores of �50 were as-signed as putative mRNA tags. Small subunit (SSU) rRNAs (subsets of500,000 sequences) were taxonomically assigned by MEGAN analysis ofBLASTN files against the CREST SilvaMod rRNA reference database (pa-rameters: minimum bit score, 150; minimum support, 1; top percent, 2;50 best blast hits) (28). Putative mRNAs were taxonomically and func-tionally annotated by MEGAN analysis (parameters: minimum bit score,50; minimum support, 1; top percent; best blast hit only) of BLASTX filesagainst the RefSeq protein database. Analysis of methanogenic pathwayswas performed by functional annotation of mRNAs assigned to Archaeausing the KEGG and SEED classification systems available in MEGAN(29). Analysis of pathways of anaerobic respiration and fermentation wasdone the same way, using all taxonomically assigned reads. For assign-ment of transcripts to protein families (Pfams), the putative mRNAs weretranslated into all six frames, each frame into separate open readingframes (ORFs), avoiding any “*” characters marking stop codons in aresulting ORF. All ORFs corresponding to 40 amino acids or larger werescreened for assignable conserved protein domains. All ORFs were in-spected by reference hidden Markov models (HMMs) using HMMERtools (http://hmmer.janelia.org/) with the Pfam database HMMs (Pfamrelease 25; http://pfam.xfam.org/). All database hits with E values below athreshold of 10�4 were counted. Counts were used to generate Pfam pro-files for all sample metatranscriptomes. These were combined in a samplePfam profile matrix for further analysis. The matrix contains the counts oftranscripts assigned to each Pfam for each sample. The computations wereperformed on the Stallo cluster at the High Performance ComputingGroup at the University of Tromsø (https://www.notur.no/). PutativePfam sequence-containing ORFs were taxonomically annotated byMEGAN analysis (parameters: minimum bit score, 50; minimum sup-port, 1; top percent, 2) of BLASTP files against the RefSeq protein data-base.
Statistical data analyses. The R package (30) was used for subsam-pling from sample Pfam profile matrices (function: sample; replace-ment � TRUE), linear regression (function: lm), multivariate analyses,chi-squared contingency table test (function: chisq.test), and plotting.Correspondence analysis (CA) and contribution biplots were done ac-cording to Greenacre (31, 32). CA was applied because it grants a largerimpact of low-abundance variables (Pfams) in the analysis than alterna-tive methods. Also, it weights the samples based on the number of reads toensure that the ordination is not biased by the low variance that is char-acteristic for small samples.
Significant differences between the frequencies of conserved proteindomains (Pfam) in ORFs and transcripts homologous to genes encodingkey enzymes for methanogenesis and methanotrophy of different peat soildepths were evaluated statistically by using the R package (30), using thechi-squared contingency table test. The contingency table contains thefrequency counts of hits and nonhits for a certain Pfam domain categoryor methanogenesis enzyme of two different soils. The total frequencycount is given by all hits found for any domain in the Pfam database. Incases where the frequencies were too low to meet the rules of the test, theP values were calculated by Monte Carlo simulations with 2,000 replicates.
Accession numbers. The sequence data generated in this study weredeposited in the Sequence Read Archive of NCBI and are accessible
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through accession numbers SRR1509497, SRR1509498, SRR1509518,SRR1509520, SRR1509521, and SRR1509522.
RESULTSGeneration of peat soil metatranscriptomes. The nucleic acid(NA) extraction efficiency of the sample lysis procedure was sub-stantially increased by a prior grinding of the peat matrix in liquidnitrogen, from 5 to 10 �g of NA/g (dry weight) (gDW) of soil to�20 �g of NA/gDW of soil (see Materials and Methods for de-tails). The quantity and quality of extracted NAs varied betweenthe sites and the depths (see Table S1 in the supplemental mate-rial). Higher NA yields, �500 to 600 �g g of soil�1, were obtainedfrom the top layers (Ka and Sa) and the lower layer of Solvatn (Sb),while the deeper layers of Knudsenheia (Kb and Kc) yielded lowquantities (�100 �g of NA g of soil�1). The ratios of A260 to A230
obtained from spectrophotometric measurements decreased withdepth (Kb and Kc), indicating higher concentrations of coex-tracted humic substances. In line with this, cDNA synthesis effi-ciencies were high with samples from the top layers, whereas thereactions with deeper-layer samples were severely inhibited. TotalRNA was dominated by peaks corresponding to 16S/23S and 18S/28S rRNAs of pro- and eukaryotes, respectively (Fig. 1; see alsoFig. S1 in the supplemental material). rRNA removal with probe-based kits resulted in RNA profiles exhibiting a clear reduction inthe sizes of peaks corresponding to rRNA molecules (Fig. 1; seealso Fig. S1). The RiboMinus kit for bacteria (Invitrogen) used inthe first step contains probes compatible with a broad range ofbacterial taxa and correspondingly decreased the size of the 16Sand 23S rRNAs of the RNA pools. The MICROBExpress (Am-bion) kit used in the second step has a profile similar to that of theRibominus kitfor bacteria, thus removing additional 16S and 23SrRNAs from the RNA pools. The RiboMinus kit for eukaryotes(Invitrogen) used in the third step has rRNA probe compatibilityto eukaryotic rRNA and correspondingly removed 18S and 28SrRNAs from the remaining RNA pool. RNA removal after eachsubtractive hybridization step with three subsequent kits removed�70% and �77% of the rRNAs from the total RNA pools of forsamples Sa and Ka, respectively (see Table S2 in the supplementalmaterial). However, cDNA synthesis attempts with mRNA-en-riched RNA from Sa and Ka as the template failed.
To circumvent inhibition of the cDNA synthesis of RNA fromthe deep peat layers (Kb, Kc, and Sb) and the mRNA-enriched toplayer (Km), the RNA was diluted to 2.5 ng/�l. Five microliters(12.5 ng) was used as the template in linear RNA amplificationprocedures using the MessageAmp II-Bacteria kit (Ambion). Atest performed prior to this experiment showed that concentra-tions of 10 and 20 ng/�l of RNA (50 and 100 ng RNA per reaction,respectively) from anoxic peat samples (Kb) resulted in approxi-mately one-fourth of the amplified RNA output compared to thatobtained in the reaction with 12.5 ng of the template. Linear am-plification with 12.5 ng of template RNA yielded large amounts ofamplified RNA for all samples (see Table S3 in the supplementalmaterial). The amplification efficiency was highest for the previ-ously uninhibited top-layer samples (Ka and Sa), while all thesamples from deep peat layers yielded smaller amounts of ampli-fied RNA, despite equal amounts of template RNA. This, togetherwith the above-described experiment, suggests a persistent inhi-bition of enzymatic steps in the RNA amplification protocol withRNA from the lower peat layers, which is concentration depen-
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14,781,0521,560,346
585,3661,665
1,360,98711,858,054
150.278,939
10,036,189(0.85)
1,812,926(0.15)
298,698(0.16)
92,630(0.051)
Km
19,899,3581,937,845
4,213,884170
2,090,26615,871,077
143.318,296
9,726,981(0.61)
6,135,800(0.39)
815,486(0.13)
256,062(0.042)
Kb
8,695,4851,011,477
545,34659
669,9907,013,959
153.207,513
5,557,385(0.79)
1,449,061(0.21)
137,257(0.09)
42,858(0.030)
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17,292,4682,006,386
850,267199
2,508,51812,777,365
151.853,129
9,198,689(0.72)
3,575,547(0.28)
272,563(0.08)
80,374(0.022)
Sa16,963,375
1,804,460514,250
5851,985,671
13,172,659150.54
7,86110,935,169
(0.83)2,229,629
(0.17)321,674
(0.14)97,115
(0.044)Sb
13,939,6901,395,723
672,145117
1,286,67211,257,178
151.764,625
9,280,077(0.82)
1,972,476(0.18)
214,568(0.11)
62,941(0.032)
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Peat Soil Metatranscriptomics
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dent. Nevertheless, the synthesis of double-stranded cDNA waspossible with the amplified RNA from all samples.
Comparison of mRNA-enriched with original samples. Se-quencing with the Illumina HighSeq2000 platform yielded 7 to 16million overlapping sequences (Table 1). The fraction of putativemRNAs was 15 to 28% in the total RNA samples, while it was 39%in the mRNA-enriched sample (Km). Between 8% and 16% ofputative mRNAs had homology to known protein-coding genes(RefSeq BLASTx bit score, 50).
To assess potential biases introduced during mRNA enrich-ment, the relative abundance of transcripts annotated to Pfamswas compared between the mRNA-enriched (Km) and nonen-riched (Ka) metatranscriptomes. Linear regressions on the dou-ble-log scatter plots of the relative abundances of Pfams in tworandom subsamples from the Pfam profile of the same metatran-scriptomic library (Ka1 and Ka2 or Km1 and Km2) gave R2 valuesof �0.96 (Fig. 2). However, the R2 was �0.75 in the comparison ofmRNA-enriched (Km) and nonenriched samples (Ka) (Fig. 2),indicating that the relative abundances of transcripts were affectedby the mRNA enrichment procedures. The Pfam profiles of non-enriched top layer samples from the two different sites (Sa and Ka)were more similar than the profiles of the mRNA-enriched andnonenriched samples from the same site (Ka and Km) (Fig. 2).This is clearly illustrated in a CA plot in which Km separated fromKa to a similar extent as the two samples from the other site,Solvatn (Sa and Sb) (Fig. 3). However, no Pfams were particularlyaffected by the mRNA enrichment, as indicated by the compari-son of differences in relative abundances of transcripts for allPfams between Ka and Km (data not shown). Also, the taxonomicdistribution of mRNA transcripts homologous to protein-codinggenes in reference sequence genomes (RefSeq protein) showedthat no microbial taxa were specifically affected by the mRNAenrichment (data not shown).
Transcripts of polysaccharide hydrolysis, fermentation,methanogenesis, and methanotrophy. The development of aprotocol for peat soil metatranscriptomes has enabled deeper in-sights into the microbiology of Arctic peat soil. We have focusedon the following key steps of SOC degradation and CH4 cycling inpeat soils: hydrolysis of polysaccharides, the initial steps of SOCdegradation, methanogenesis, and CH4 oxidation. The bacterialcommunities in the top peat layers in Solvatn and Knudsenheiawere dominated by Alpha- and Deltaproteobacteria, Acidobacteria,Planctomycetes, and Actinobacteria (see Fig. S2 in the supplemen-tal material). In Knudsenheia, the relative abundance of Actino-bacteria increased substantially with depth, while the Planctomy-cetes and Acidobacteria populations decreased. In Solvatn, therewere only minor depth-related differences between the bacterialpopulations. The Eukarya profiles were dominated by 18S rRNAfragments assigned to plants (Viridiplantae), primarily mosses.The relative abundance of plant rRNA decreased with depth inboth Knudsenheia and Solvatn. Other large taxa were the Meta-zoa, Alveolata, and Rhizaria, all of which decreased with depth,particularly in Knudsenheia.
Transcripts related to the degradation of plant polysaccha-rides (cellulose, hemicellulose, and hemicellulose branches)and aromatic compounds (e.g., phenolic compounds) were in-vestigated in detail (Fig. 4). The relative abundances of tran-scripts for cellulose and aromatic degradation decreased withdepth, while transcripts for hemicellulose debranching en-zymes showed the opposite trend. Transcripts for hemicellu-lose degradation did not have a distinct depth-related pattern.The major glycoside hydrolase families involved in hemicellu-lose degradation were GH53 (galactosidase), GH26 (man-nanase), and GH10 (xylanase), involved in the cleavage ofgalactose and mannan from, e.g., galactomannans and glu-cogalactomannans and in xylan degradation, respectively. Par-
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FIG 1 Relative abundances of different RNA fragments in the sample Ka. (A) Total RNA; (B) after treatment with the RiboMinus kit for bacteria; (C) aftertreatment with the RiboMinus kit for bacteria and the MICROBExpress kit; (D) after treatment with the RiboMinus kit for bacteria, the MICROBExpress kit, andthe RiboMinus kit for eukaryotes. Fluorescence and time are equivalent to the abundance and the size of RNA fragments, respectively. The relative abundancesof RNA fragments can be compared within and between treatments.
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ticularly abundant in the lower layers were the transcripts foralpha-L-fucosidases, rhamnosidases, and alpha-L-arabinofura-nosidases, which are involved in the degradation of highlybranched hemicelluloses of plant cell walls such as fucosidexyloglucans, rhamnogalacturonans (structural domains incomplex pectins), and arabinoxylan. The transcripts for all
four enzyme categories were taxonomically binned to a broadrange of bacterial taxa (see Fig. S3 in the supplemental mate-rial). A large fraction of the transcripts encoding oxidases wereassigned to Proteobacteria, while cellulases were predominantlyassigned to Actinobacteria. Transcripts for hemicellulases anddebranching enzymes were primarily assigned to Bacteroidetesand Actinobacteria.
Analyses of transcripts for pathways of anaerobic respirationindicated that both denitrification and sulfate reduction occurredin these soils (data not shown). However, the relative abundanceof transcripts for denitrification decreased with depth in both sites(chi-square contingency table test P value: Ka-Kb, 0.02; Ka-Kc,2e�8; Kb-Kc, 3e�13; and Sa-Sb, 4e�8), while transcripts for sul-fate reduction decreased only in Knudsenheia (chi-square contin-gency table test P value: Ka-Kb, 0.01; Ka-Kc, 7e�11; Kb-Kc, 0.003;and Sa-Sb, 0.2). The relative abundance of transcripts for fermen-tative pathways of glycolysis (KEGG pathway: glycolysis/gluco-neogenesis) (P value: Ka-Kc, 5e�7, and Kb-Kc, 3e�6), propi-onate fermentation (KEGG pathway: propanoate metabolism) (Pvalue: Ka-Kc, 4e�12, and Kb-Kc, 2e�10), and ethanol fermenta-tion (EC 1.1.1.1, EC 1.2.1.10, and EC 1.2.1.3) (P value: Ka-Kc,4e�13; Kb-Kc, 5e�7; and Sa-Sb, 0.02) was consistently lower inKc than in Ka and Kb, while there were no significant differencesbetween Ka and Kb and between Sa and Sb (with the exception ofethanol fermentation).
There was a significant increase in the relative abundance ofarchaeal 16S rRNA transcripts with depth (Fig. 5B), from �0.2%of the microbiota in the top layer to �7% in the lower layers inKnudsenheia and from �0.1% to �3% in Solvatn peat. The ma-
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FIG 2 Double-log plots showing the abundance distribution between proteinfamilies (Pfam) in subsamples of Ka (A), Km (B), Ka and Km (C), and Ka andSa (D). The Pfam profiles for all samples were randomly subsampled two timesfor 30,000 transcripts, within the number of the sample with fewest transcriptsassigned to a Pfam. Replicate number indicates the specific random subsam-pling used in the comparison. Each comparison displays the transcript abun-dances of Pfams for two subsamples plotted on a logarithmic scale in XY plots.The logarithmic scale is used to reduce the effect of outliers in the comparison.Linear regression on each XY plot gives R2 values.
14−3−3
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2−Hacid_dh_C
2OG−FeII_Oxy
2OG−FeII_Oxy_2 2OG−FeII_Oxy_3
2−oxoacid_dh
2−ph_phosp
2TM
3Beta_HSD
3D
3−dmu−9_3−mt
3−HAO 3HBOH
3HCDH
3HCDH_N
4HB_MCP_1
4HBT
4HBT_3 5_3_exonuc
5_3_exonuc_N
5_nucleotid 5_nucleotid_C
5−FTHF_cyc−lig
5−nucleotidase 60KD_IMP
6PGD
7tm_3
7TM_transglut
7TM−7TMR_HD
7TMR−HDED A_deaminase
A2M
A2M_N
A2M_N_2
AA_kinase
AA_permease
AA_permease_2
AA_permease_C
AA_synth
Aa_trans
AAA
AAA_10
AAA_11
AAA_12
AAA_14
AAA_15
AAA_16
AAA_17 AAA_19
AAA_2
AAA_21
AAA_22 AAA_23
AAA_25 AAA_26
AAA_27
AAA_28
AAA_29
AAA_3
AAA_30
AAA_31 AAA_32
AAA_33 AAA_34
AAA_4
AAA_5
AAA_6
AAA_7
AAA_8 AAA_9
AAA_PrkA
AAA−ATPase_like
AAL_decarboxy
ABC_cobalt
ABC_membrane
ABC_membrane_2
ABC_sub_bind
ABC_tran
ABC_tran_2
ABC_transp
ABC_transp_aux
ABC1
ABC2_membrane
ABC2_membrane_2
ABC2_membrane_3
ABC2_membrane_4 ABC2_membrane_6
ABC−3
AbfB
ABG_transport
Abhydrolase_1 Abhydrolase_2
Abhydrolase_3
Abhydrolase_4 Abhydrolase_5 Abhydrolase_6
Abi
Abi_C ABM
AbrB−like
Acatn
ACBP ACCA
AceK
Acetate_kinase
AcetDehyd−dimer
AcetylCoA_hyd_C
AcetylCoA_hydro
Acetyltransf_1
Acetyltransf_10
Acetyltransf_11
Acetyltransf_2
Acetyltransf_3
Acetyltransf_4 Acetyltransf_5
Acetyltransf_6
Acetyltransf_7
Acetyltransf_9
Acid_phosphat_B
Aconitase Aconitase_2_N
Aconitase_B_N
Aconitase_C
ACOX
ACP_syn_III
ACP_syn_III_C
ACPS
ACR_tran
ACT
ACT_4
ACT_5
ACT_6
ACT_7
Acyl_CoA_thio
Acyl_transf_1
Acyl_transf_3
Acyl−ACP_TE
Acyl−CoA_dh_1 Acyl−CoA_dh_2
Acyl−CoA_dh_C
Acyl−CoA_dh_M
AcylCoA_DH_N
Acyl−CoA_dh_N
Acylphosphatase
Acyltransferase Ada_Zn_binding
Adap_comp_sub
Adaptin_N
ADC
Adenine_deam_C
Adenine_glyco
Adenosine_kin
Adenylsucc_synt ADH_N
ADH_N_assoc adh_short
adh_short_C2
ADH_zinc_N
ADH_zinc_N_2
ADK
ADK_lid
AdoHcyase
AdoHcyase_NAD
AdoMet_dc
AdoMet_Synthase
ADP_ribosyl_GH ADSL_C
AFG1_ATPase
AFOR_C
AFOR_N
AhpC−TSA
AHS1
AHS2
AHSA1
AICARFT_IMPCHas
AIG2
AIG2_2
AIM24
AIPR
AIRC
AIRS
AIRS_C
Ala_racemase_C
Ala_racemase_N
ALAD
AlaDh_PNT_C
AlaDh_PNT_N
Alba
Ald_Xan_dh_C
Ald_Xan_dh_C2
Aldedh
Aldo_ket_red
Aldolase
Aldolase_II
Aldose_epim
Alg14
Alg6_Alg8
Alginate_lyase
ALIX_LYPXL_bnd
Alk_phosphatase
AlkA_N
Allene_ox_cyc
ALO
Alph_Pro_TM
Alpha_L_fucos
Alpha−amylase
Alpha−amylase_C
Alpha−E
Alpha−L−AF_C
ALS_ss_C
Alum_res
Amidase
Amidase_2
Amidase_3
Amidase_6
Amidinotransf
Amido_AtzD_TrzD
Amidohydro_1 Amidohydro_2
Amidohydro_3
Amidohydro_4
Amidohydro_5
AMIN
Amino_oxidase
Aminotran_1_2
Aminotran_3
Aminotran_4
Aminotran_5
Aminotran_MocR AmiS_UreI
AMMECR1
Ammonium_transp
AMNp_N
AMO
AmoA
AMP_N AMP−binding
An_peroxidase
ANF_receptor
Ank
Ank_2 Ank_3 Ank_4
Ank_5
Annexin
Anoctamin
ANTAR
ANTH
Anth_synt_I_N
Antibiotic_NAT
Anticodon_1
Antitoxin−MazE
AOX AP_endonuc_2
AP_endonuc_2_N
AP2
ApbA
ApbA_C ApbE
APH
APH_6_hur
Apocytochr_F_C
APS_kinase
APS−reductase_C
ArabFuran−catal
Arabinose_bd
Arabinose_Iso_C
Arabinose_Isome AraC_binding
AraC_N
Arc
Arch_ATPase
Arch_flagellin
Archease
ARD
Arf
ArfGap
Arg_repressor
Arg_repressor_C
Arg_tRNA_synt_N Arginase
Arginosuc_synth
ArgJ
ArgK
Arm
ARPC4
Arr−ms
ArsA_ATPase
ArsC
ArsD
Arylsulfotrans
ASL_C
AsmA_2
Asn_synthase AsnA
AsnC_trans_reg
Asp
Asp_Arg_Hydrox
Asp_decarbox
Asp_Glu_race
Asp_protease_2
Asp23
Asp−Al_Ex
Asparaginase
Asparaginase_2
Asparaginase_II
ASRT
AstA
Astacin
AstB
AstE_AspA
ATC_hydrolase
ATE_C
ATE_N
ATG22
Atg8
ATP_bind_1
ATP_bind_2
ATP_bind_3
ATP_bind_4
ATP_synt_I
ATP12
ATPase_gene1
ATPase−cat_bd
ATP−cone
ATP−grasp
ATP−grasp_2
ATP−grasp_4
ATP−grasp_5
ATPgrasp_TupA
ATP−gua_Ptrans
ATP−gua_PtransN
AtpR
ATP−sulfurylase
ATP−synt
ATP−synt_A
ATP−synt_ab ATP−synt_ab_C
ATP−synt_ab_N
ATP−synt_B
ATP−synt_D
ATP−synt_DE
ATP−synt_DE_N
ATP−synt_Eps
ATP−synt_F
AurF Autoind_bind
Autoind_synth
Autotransporter Autotrns_rpt
AUX_IAA
Auxin_inducible
Auxin_repressed
Auxin_resp
AviRa
AWPM−19 AXE1
AzlC
AzlD
B_lectin
B12−binding
B12−binding_2
B12D
B3_4
B5
B56
BA14K
Bac_chlorC
Bac_DNA_binding
Bac_DnaA
Bac_DnaA_C
Bac_export_1
Bac_export_2
Bac_export_3
Bac_GDH
Bac_globin
Bac_luciferase
Bac_rhamnosid
Bac_rhamnosid_N
Bac_surface_Ag
Bac_transf
Bac_Ubq_Cox
BacA
BACON
Bact_transglu_N
Bactofilin bact−PGI_C
Band_7
Band_7_1
Barstar
BatA
BatD
BATS
Bax1−I
BaxI_1
BBE
BCA_ABC_TP_C
BCCT
BCDHK_Adom3
BCHF
BcrAD_BadFG
BcsB
Beach
BenE
Bestrophin
Beta_elim_lyase
Beta_helix
Beta_propel
Beta−Casp
Beta−lactamase
Beta−lactamase2
Bgal_small_N
Big_1 Big_2
Big_3_4
Big_5
Biopterin_H
Biotin_carb_C
Biotin_lipoyl
Biotin_lipoyl_2 BioY
BiPBP_C
BLUF
BMC
BMFP
Bmp
BNR_2
BolA
BON
BPD_transp_1
BPD_transp_2 BPL_C
BPL_LplA_LipB Branch
BRCT
Bre5
Brix
Bromodomain
BsmA
BsuBI_PstI_RE
BsuPI
BTAD
BTB
BTLCP
BtpA
BtrH
bZIP_1
C_GCAxxG_C_C
C2
C4dic_mal_tran
Caa3_CtaG Cache_2
Cadherin
CagX
Caleosin
Calmodulin_bind
Calreticulin
Calx−beta
CaMKII_AD
CAP
CAP_C
CAP_GLY
CAP_N
Caps_assemb_Wzi
Capsid_NCLDV
Capsule_synth
Carb_anhydrase
Carb_kinase Carboxyl_trans
CarboxypepD_reg
CarD_CdnL_TRCF Carn_acyltransf
Cas_Cas1
Cas_Cas2CT1978
Cas_Cas5d
CAS_CSE1
Cas_CT1975 Cas_GSU0053
CAT_RBD
Catalase
Catalase−rel
Cation_ATPase_C
Cation_ATPase_N
Cation_efflux
CbbQ_C
CBFD_NFYB_HMF
CbiA
CbiC
CbiD
CbiG_N
CbiJ CbiM
CbiQ CbiX
CBM_14
CBM_2
CBM_20
CBM_48
CBM_5_12 CBM_6
CBM_X
CBS CbtA
CbtB
CcdA
CCG
CcmB
CcmD
CcmE CcmH
CcoS
CCP_MauG
CD225
CD36
CDC48_N
CDC50
Cdd1
CdhC
CdhD
CDO_I
CDP−OH_P_tran_2
CDP−OH_P_transf
CelD_N Cellulase
Cellulase−like Cellulose_synt
CemA
Ceramidase
Ceramidase_alk
CGGC CH
ChaB
ChaC
CHAD
Chal_sti_synt_C
Chal_sti_synt_N
CHASE
CHASE2
CHASE3
CHASE4
CHAT
CHB_HEX_C
CHB_HEX_C_1
CheB_methylest
CheC
CheD
CheR
CheR_N
CheW CheX
CheZ
Chitin_bind_1
ChlI
Chlor_dismutase
Chloroa_b−bind
Choline_kinase
Choline_transpo
Chorein_N
Chorismate_bind
Chorismate_synt
CHRD
Chromate_transp
Chrome_Resist
Chromo
CHU_C
ChW
CIA30
CinA
CitF
CitG
CitMHS
Citrate_synt
CKS
Clat_adaptor_s
Clathrin
Clathrin_H_link
Clp_N
CLP_protease
ClpB_D2−small
ClpS
CM_1
CM_2
CMAS
CmcH_NodU CmcI
CMD
CN_hydrolase
Cna_B
Cna_B_2
cNMP_binding
CNP1
CO_deh_flav_C
CO_dh CoA_binding
CoA_binding_2
CoA_binding_3
CoA_trans
CoA_transf_3
CoaE
Coat_F
Coatamer_beta_C
CoatB
Coatomer_WDAD
Cob_adeno_trans
CobA_CobO_BtuR
CobD_Cbib
CobN−Mg_chel
CobS
CobS_N
CobT
CobT_C
CobU
cobW
CobW_C
COesterase
CofC
Cofilin_ADF
Cohesin
Col_cuticle_N
Colicin_V
Collagen
Collagen_bind_2
Collar
ComA
ComFB
Competence
Competence_A
Complex1_24kDa
Complex1_30kDa
Complex1_49kDa
Complex1_51K
Condensation
Cons_hypoth698 Cons_hypoth95
CopB
CopC
CopD
COPI_C
Copper−bind Coprogen_oxidas
COQ9 CorA
CorC_HlyC
Cornichon CotH
Couple_hipA
COX15−CtaA
COX2
COX2_TM
COX3
COX4_pro
COX4_pro_2
COX6B
COXG
CP_ATPgrasp_1
CP_ATPgrasp_2
CpcD
CpeT
Cpn10
Cpn60_TCP1
CPSase_L_chain
CPSase_L_D2
CPSase_L_D3
CPSase_sm_chain CPT
CpXC
CRAL_TRIO
CRCB
CreA
Creatinase_N
Creatininase
CreD
CRISPR_assoc
CRISPR_Cas2
CRM1_C
Crp
CRS1_YhbY
CrtC
Crystall
CsbD
CsgG
CsrA
CstA
CtaG_Cox11 CTI
CTP_synth_N
CTP_transf_1
CTP_transf_2 CTP_transf_3
Cu_amine_oxid
Cu2_monoox_C
Cu−binding_MopE
Cullin Cullin_Nedd8
Cu−oxidase
Cu−oxidase_2
Cu−oxidase_3
Cu−oxidase_4
Cupin_1
Cupin_2
Cupin_3 Cupin_4
Cupin_5
Cupin_6
Cupin_7
Cupin_8 Cupredoxin_1
CusF_Ec
CutA1 CutC CW_binding_2
CxxC_CxxC_SSSS
Cyanate_lyase
Cyclase Cyclase_polyket
Cyclin
Cyclin_N
Cyclophil_like
Cyc−maltodext_N Cys_Met_Meta_PP Cys_rich_CPXG
Cys_rich_CWC
CysG_dimeriser
Cystatin
Cyt_c_ox_IV
Cyt−b5
CYTH
Cytidylate_kin
Cytidylate_kin2
Cyto_ox_2
CytoC_RC
Cytochrom_B_C
Cytochrom_B_N
Cytochrom_B_N_2
Cytochrom_B559
Cytochrom_B559a
Cytochrom_B561
Cytochrom_C Cytochrom_C_2
Cytochrom_C_asm
Cytochrom_C1
Cytochrom_C552
Cytochrom_CIII
Cytochrom_D1
Cytochrom_NNT
Cytochrome_C554
Cytochrome_CBB3
Cytotoxic
Dabb
DAD
DAGK_cat
DAGK_prokar
DAHP_synth_1
DAHP_synth_2
Dak1
Dak1_2
Dak2
Dala_Dala_lig_C
Dala_Dala_lig_N
DALR_1
DALR_2
D−aminoacyl_C
DAO
DAP_epimerase
DapB_C
DapB_N
DBI_PRT
DbpA
DCD
dCMP_cyt_deam_1
dCMP_cyt_deam_2 DcpS_C
DctM
DctQ
DcuA_DcuB
DcuC
DDE_3
DDE_4
DDE_4_2
DDE_5
DDE_Tnp_1
DDE_Tnp_1_2 DDE_Tnp_1_3
DDE_Tnp_1_4
DDE_Tnp_1_5
DDE_Tnp_1_6 DDE_Tnp_1_assoc
DDE_Tnp_IS1
DDE_Tnp_IS1595
DDE_Tnp_IS240
DDE_Tnp_IS66
DDE_Tnp_IS66_C
DDE_Tnp_ISAZ013
DDE_Tnp_ISL3
DDE_Tnp_Tn3 DDOST_48kD DEAD
DEAD_assoc
DEDD_Tnp_IS110
DegT_DnrJ_EryC1
DegV
Dehalogenase
Dehydratase_LU
Dehydratase_MU
Dehydrin
DeoC
DeoRC
DER1
Desulfoferrod_N
Desulfoferrodox
DFP
DGOK
DHBP_synthase
DHC
DHC_N2
DHDPS
DHFR_1
DHFR_2
DHH
DHHA1
DHHA2
DHO_dh
DHODB_Fe−S_bind DHQ_synthase
DHQS
DHquinase_I
DHquinase_II
Dicistro_VP4 DICT
Dicty_CTDC
DIM1
Dimeth_Pyl
DinB
DinB_2
DIOX_N
Dioxygenase_C
Dioxygenase_N Diphthamide_syn
DiS_P_DiS
DisA_N
DisA−linker
Disintegrin
Disulph_isomer
DivIC
DivIVA
DJ−1_PfpI
DJ−1_PfpI_N DLH DM13
DmpG_comm
DMRL_synthase DmsC
DNA_alkylation
DNA_binding_1
DNA_gyraseA_C DNA_gyraseB
DNA_gyraseB_C
DNA_ligase_A_C
DNA_ligase_A_M
DNA_ligase_A_N
DNA_ligase_aden
DNA_ligase_OB
DNA_ligase_ZBD
DNA_methylase
DNA_mis_repair DNA_photolyase DNA_pol_A
DNA_pol_A_exo1
DNA_pol_B
DNA_pol_B_exo2
DNA_pol3_alpha
DNA_pol3_beta
DNA_pol3_beta_2 DNA_pol3_beta_3
DNA_pol3_chi
DNA_pol3_delta2
DNA_pol3_gamma3
DNA_processg_A
DNA_RNApol_7kD DNA_topoisoIV DnaA_N
DnaB
DnaB_2
DnaB_bind
DnaB_C
DnaJ
DnaJ_C
DnaJ_CXXCXGXG
DnaJ−X
DNase−RNase
DndB
dNK
Dockerin_1 Dodecin
DOMON
DOPA_dioxygen
DoxX
DoxX_2
DPBB_1
DPPIV_N
DRAT
DrsE
DrsE_2
DRTGG
DS
DSBA DsbB
DsbC
DsbC_N
DsbD
DsbD_2
dsDNA_bind
D−ser_dehydrat
DSHCT
DSPc
DsrC
DsrD
dsrm
dTDP_sugar_isom
DTW
DUF1003
DUF1006 DUF1007
DUF1009
DUF1013 DUF1015
DUF1016
DUF1021
DUF1025
DUF1028
DUF1036
DUF104
DUF1044
DUF1049
DUF1052
DUF1059
DUF106
DUF1077
DUF108
DUF1080
DUF1083 DUF1097 DUF11
DUF1109 DUF111
DUF1111
DUF1116
DUF1119
DUF1127
DUF1134
DUF1150
DUF1153
DUF1156
DUF116
DUF1161
DUF1173
DUF1175 DUF1178
DUF1186
DUF1192
DUF1194
DUF1203
DUF1206
DUF1207
DUF1211
DUF1212
DUF1214 DUF1215
DUF1223
DUF1228
DUF1230
DUF1232
DUF1236
DUF1237
DUF1244
DUF1246
DUF1249
DUF1254
DUF1255
DUF1257
DUF126
DUF1264
DUF1269
DUF1271
DUF1272
DUF1275
DUF128
DUF1285
DUF1287
DUF1289
DUF1290 DUF1294
DUF1295
DUF1297
DUF1302
DUF1304
DUF1305
DUF1311
DUF1318
DUF1326
DUF1328
DUF1329
DUF1330
DUF1338
DUF134
DUF1342
DUF1343
DUF1344
DUF1345
DUF1348
DUF1349
DUF1353
DUF1355 DUF1361 DUF1365
DUF1375
DUF1385
DUF1415
DUF1416
DUF1428
DUF1445
DUF1446
DUF1451
DUF1456
DUF1460
DUF1465
DUF1467
DUF1469
DUF1470
DUF1476
DUF1485
DUF1488
DUF1489
DUF1498
DUF1499
DUF150
DUF1504
DUF1508
DUF1512
DUF1524
DUF1529
DUF1537
DUF1540
DUF1551
DUF1565
DUF1566
DUF1569
DUF1570 DUF1571
DUF1572
DUF1573
DUF1579
DUF1583
DUF159
DUF1593
DUF1597
DUF1598
DUF1599
DUF1602
DUF1608
DUF161
DUF1611
DUF1614
DUF1616
DUF162
DUF1622
DUF1624
DUF1625
DUF1631
DUF1632
DUF1637
DUF1638
DUF164
DUF1648
DUF165 DUF1653 DUF167
DUF1670
DUF1674 DUF1680
DUF1681 DUF1684
DUF1688 DUF169
DUF1696 DUF1697
DUF1702
DUF1704
DUF1707
DUF1713
DUF1722
DUF1730
DUF1731
DUF1732 DUF1738
DUF1761
DUF1768
DUF177
DUF1778
DUF1786
DUF179
DUF1793
DUF1794
DUF1800
DUF1801
DUF1802
DUF1805
DUF1810
DUF1814
DUF1819
DUF1820
DUF1830
DUF1839
DUF1841
DUF1844
DUF1846
DUF1847
DUF1848
DUF1849
DUF1854
DUF1858 DUF1863
DUF187 DUF1876
DUF188
DUF1899
DUF190 DUF1900
DUF1902
DUF1905
DUF1918
DUF192
DUF1924 DUF1925
DUF1926
DUF1928
DUF1929
DUF1956
DUF1957
DUF1967
DUF1969
DUF1972
DUF1974 DUF1982
DUF1987
DUF1989
DUF1990
DUF1992
DUF1993
DUF1996
DUF1998 DUF2007
DUF2009
DUF2017
DUF202
DUF2024
DUF2029
DUF204
DUF2058
DUF2059
DUF2061
DUF2062 DUF2063
DUF2064
DUF2065
DUF2071 DUF2075
DUF2076
DUF2077
DUF2079
DUF2085 DUF2086
DUF2087
DUF2088 DUF2089 DUF2090
DUF2092
DUF2093
DUF2094
DUF21
DUF211 DUF2114
DUF212 DUF2124
DUF2126
DUF2127
DUF2128 DUF2130
DUF2132
DUF2133 DUF2136
DUF2141
DUF2145
DUF2147
DUF2154
DUF2155
DUF2156
DUF2157
DUF2158
DUF2160
DUF2167
DUF2169
DUF2171
DUF2172
DUF2177
DUF2179
DUF218
DUF2180
DUF2182
DUF2183
DUF2188
DUF2189 DUF2191
DUF2193 DUF2196
DUF2200
DUF2202
DUF2203
DUF2207
DUF221
DUF2219
DUF222
DUF2220
DUF2223
DUF2229
DUF2233
DUF2235
DUF2236 DUF2237
DUF2238
DUF2239
DUF2240
DUF2244
DUF2248
DUF2249
DUF2252
DUF2254 DUF2256
DUF2260
DUF2263
DUF2264
DUF2265
DUF2267 DUF2270
DUF2271
DUF2272
DUF2277 DUF2278
DUF2281 DUF2282
DUF2283
DUF2284
DUF2288
DUF2292
DUF2294
DUF2298
DUF2306
DUF2309
DUF2312
DUF2314
DUF2317
DUF2319
DUF2320
DUF2322
DUF2324
DUF2325
DUF2326
DUF2328
DUF2330
DUF2331
DUF2332
DUF2333
DUF2334 DUF2335
DUF2336
DUF2339
DUF2341
DUF2342
DUF2344
DUF2382
DUF2383
DUF2384 DUF2385
DUF2391
DUF2400
DUF2437
DUF2442
DUF2461 DUF2469
DUF2470
DUF2480
DUF2490
DUF2497
DUF2505
DUF2510
DUF2513
DUF2516
DUF2520
DUF255
DUF2550
DUF2551
DUF2568
DUF258
DUF2581
DUF2582
DUF2585 DUF2587
DUF2600
DUF262
DUF2625
DUF2628
DUF2630
DUF2631
DUF2699
DUF2721
DUF2723
DUF2735
DUF2752
DUF2760
DUF2769 DUF2779
DUF2782
DUF2784
DUF2786
DUF2789
DUF2791
DUF2794
DUF2795
DUF2797
DUF2804
DUF2806
DUF2807
DUF2809
DUF2817
DUF2823
DUF2828
DUF2834
DUF2845
DUF2847
DUF2848
DUF2849
DUF2851
DUF2852
DUF2853
DUF2855
DUF2863
DUF2865
DUF2874
DUF2887
DUF2889
DUF2891
DUF2892
DUF29
DUF2905
DUF2909
DUF2910
DUF2911
DUF2914
DUF2917
DUF2924
DUF2927
DUF2933
DUF2934
DUF2938
DUF294
DUF294_C
DUF2944
DUF2945
DUF2948
DUF2950
DUF2959
DUF296 DUF2961 DUF297
DUF2970 DUF2983
DUF2993
DUF2997
DUF3000
DUF3006
DUF3011
DUF3014
DUF3015
DUF3016
DUF3017
DUF3018
DUF302
DUF3027
DUF3029
DUF303 DUF3034
DUF3035
DUF3036
DUF3037
DUF3039
DUF304
DUF3040
DUF3043
DUF3046
DUF3047 DUF3048
DUF305
DUF3050
DUF3052 DUF3054
DUF3066
DUF3068
DUF307
DUF3071
DUF3073
DUF308
DUF3084
DUF3088
DUF3089
DUF309
DUF3090
DUF3093
DUF3095
DUF3096
DUF3097
DUF3098 DUF3099
DUF3105
DUF3106
DUF3107
DUF3108
DUF3109
DUF3117
DUF3124
DUF3126
DUF3127
DUF3141
DUF3145
DUF3149
DUF3151
DUF3152
DUF3155
DUF3159
DUF3160
DUF3179
DUF318
DUF3180
DUF3185
DUF3186
DUF3224
DUF3225
DUF3231
DUF3237
DUF3243
DUF3257
DUF3260
DUF3263
DUF3276
DUF328
DUF329
DUF3291
DUF3297
DUF3298
DUF3299
DUF330
DUF3300
DUF3301
DUF3302 DUF3303
DUF3305
DUF3306
DUF3307
DUF3308
DUF3309
DUF3313
DUF3315
DUF3326
DUF3329
DUF3332
DUF3333
DUF3335
DUF3339
DUF3340
DUF3341 DUF3343
DUF3347
DUF3349
DUF3353
DUF3358 DUF336
DUF3362
DUF3363
DUF3365
DUF3368
DUF3369
DUF3372
DUF3373
DUF3374
DUF3375
DUF3380
DUF3387
DUF3390 DUF3391
DUF3400 DUF3412
DUF3416
DUF3417
DUF3418
DUF3419
DUF342
DUF3421
DUF3422
DUF3423
DUF3426
DUF3427
DUF3429
DUF3443
DUF3445
DUF3448
DUF3455 DUF3457 DUF3458
DUF3460
DUF3463 DUF3467
DUF347
DUF3470
DUF3471 DUF3473
DUF3474
DUF3478
DUF3479
DUF348
DUF3485
DUF3488
DUF3489
DUF349 DUF3492
DUF3494
DUF3499
DUF35
DUF35_N
DUF350
DUF3500
DUF3501 DUF3502
DUF3516
DUF3520
DUF3529
DUF3536
DUF354
DUF3551
DUF3552
DUF3556
DUF3563
DUF3566
DUF3567
DUF3570
DUF3574
DUF3576
DUF3577 DUF3579
DUF3594
DUF3597
DUF360
DUF3604
DUF3606
DUF3616 DUF3617
DUF3618
DUF3619
DUF362
DUF3620
DUF3623
DUF3626 DUF3641
DUF3646
DUF3656
DUF3662
DUF3667
DUF367 DUF3670 DUF368 DUF3680
DUF3683
DUF3686
DUF370
DUF3700
DUF3703
DUF3708 DUF3710
DUF3731
DUF3734
DUF3738
DUF374
DUF3750 DUF3766
DUF3768
DUF377
DUF3775
DUF378
DUF3780
DUF3786
DUF3798
DUF3810
DUF3814
DUF3815 DUF3817
DUF382
DUF3820 DUF3822
DUF3830
DUF3833
DUF3842
DUF385
DUF386
DUF3864
DUF3866
DUF387 DUF3883
DUF389
DUF3892
DUF3897
DUF39
DUF393
DUF3943
DUF397
DUF3987
DUF399
DUF4007
DUF4008
DUF4009
DUF4010
DUF4011 DUF4012
DUF4013
DUF4015
DUF4019
DUF402
DUF4032
DUF4037
DUF4038
DUF4040
DUF4056
DUF4058
DUF4062
DUF4065
DUF4066 DUF4070
DUF4079
DUF4081
DUF4082 DUF4089
DUF4091
DUF4095
DUF4096
DUF4098
DUF4102
DUF4104
DUF4105
DUF4109
DUF411
DUF4112
DUF4113
DUF4114
DUF4115
DUF4116
DUF4117
DUF4118
DUF4123
DUF4124
DUF4126
DUF4130
DUF4131
DUF4135
DUF4136
DUF4137
DUF4139
DUF4142
DUF4143
DUF4147
DUF4148
DUF4149
DUF4150 DUF4153
DUF4154
DUF4157
DUF4158
DUF4159
DUF4160
DUF4161
DUF4164
DUF4167
DUF4169
DUF417
DUF4170
DUF4172
DUF4173 DUF4175
DUF4177
DUF418
DUF4180 DUF4184
DUF4188
DUF419
DUF4190
DUF4191
DUF4193
DUF4197
DUF4199
DUF420
DUF4202
DUF421
DUF4212
DUF4214
DUF4215
DUF422
DUF4229
DUF423
DUF4230
DUF4234 DUF4235
DUF4236
DUF4242 DUF4245
DUF4246
DUF4249
DUF4252 DUF4254
DUF4255
DUF4256
DUF4258
DUF4260
DUF4265
DUF4266
DUF427
DUF4270
DUF4276
DUF4277
DUF4280
DUF4281
DUF4282 DUF4287
DUF4289
DUF429
DUF4290
DUF4291
DUF4293 DUF4294 DUF4295
DUF4301
DUF4308
DUF4323
DUF4324
DUF4325 DUF433
DUF4331
DUF4332
DUF4337
DUF4338
DUF4339
DUF4340
DUF4341
DUF4342
DUF4344
DUF4347
DUF4349
DUF4351
DUF4357
DUF4360
DUF4365
DUF4369
DUF4372
DUF4379
DUF4380
DUF4383
DUF4384
DUF4386
DUF4388
DUF4389
DUF4390
DUF4392
DUF4393
DUF4394
DUF4395
DUF4396
DUF4397
DUF4398
DUF4399
DUF4402
DUF4404
DUF4407
DUF4411
DUF4415
DUF4416
DUF4418
DUF4419
DUF442
DUF4420
DUF4423
DUF444
DUF445
DUF447
DUF448
DUF45
DUF455
DUF456
DUF457
DUF458
DUF461
DUF463
DUF465
DUF466
DUF475
DUF479 DUF480
DUF481
DUF482
DUF484
DUF485
DUF486
DUF488 DUF489
DUF490
DUF493
DUF494
DUF497
DUF498 DUF500
DUF501
DUF502
DUF503
DUF506
DUF507
DUF512
DUF519
DUF520
DUF521
DUF523
DUF525
DUF533
DUF538 DUF547
DUF552
DUF554
DUF556
DUF559 DUF563
DUF58
DUF59
DUF599
DUF606
DUF608
DUF615
DUF647 DUF664
DUF676
DUF692
DUF694
DUF697
DUF706
DUF711
DUF718
DUF72
DUF721
DUF742
DUF748 DUF760
DUF763
DUF77
DUF770
DUF772
DUF779
DUF790
DUF791
DUF796
DUF799 DUF805
DUF808
DUF814
DUF815
DUF817
DUF82
DUF830
DUF836
DUF839
DUF849
DUF853
DUF86
DUF862
DUF866
DUF867
DUF87
DUF872
DUF876
DUF877
DUF879
DUF881
DUF883 DUF885
DUF89
DUF892
DUF898
DUF899
DUF900
DUF91
DUF917
DUF92
DUF924
DUF928
DUF932
DUF934
DUF937
DUF938
DUF940
DUF95 DUF951
DUF952
DUF955
DUF962
DUF965
DUF970
DUF971 DUF975
DUF983
DUF989
DUF992
DUF993
DUF998
Dus
dUTPase dUTPase_2
DXP_redisom_C
DXP_reductoisom
DXP_synthase_N DXPR_C
Dynamin_M
Dynamin_N
Dynein_heavy
Dynein_light
Dyp_perox
DYW_deaminase
DZR
E1_dh
E1−E2_ATPase
E3_binding
EAL
EamA
EB_dh
EBP
ECF−ribofla_trS
ECH
ECH_C
EcoEI_R_C
EcoR124_C
EcoRI_methylase
EcoRII−C
ECR1_N EcsC
EF_hand_3
EF_hand_4
EF_hand_5
EF_hand_6
EF_TS
EF1_GNE
EF1G
EFG_C
EFG_IV
efhand
EFP
EFP_N
EGF
EGF_3
EGF_CA
EHN
EIF_2_alpha eIF−1a
eIF2_C
eIF2A
eIF−3_zeta
eIF−3c_N
eIF−5_eIF−2B
eIF−5a
eIF−6
EIIA−man
EIIC−GAT
EIID−AGA
EII−GUT
EKR ELFV_dehydrog
ELFV_dehydrog_N
ELO
Elong−fact−P_C
EMG1
EMP70
Endonuc−BglII
Endonuclease_1
Endonuclease_5 Endonuclease_7 Endonuclease_NS
Endosulfine
Enolase_C
Enolase_N
Eno−Rase_FAD_bd
Eno−Rase_NADH_b
Enoyl_reductase
Entericidin
ENTH
Epimerase
Epimerase_2 Epimerase_Csub
EPSP_synthase
ER ER_lumen_recept
eRF1_1
eRF1_2
eRF1_3
ERG2_Sigma1R
ERG4_ERG24
Erythro_esteras
Esterase
Esterase_phd
ETC_C1_NDUFA4
ETF
ETF_alpha
ETF_QO
EthD
EutA EutB EutC
EutN_CcmL
EutQ EVE
ExbD
Exo_endo_phos
Exo70
ExoD
Exonuc_V_gamma
Exonuc_VII_L Exonuc_VII_S
Exonuc_X−T_C
Exosortase_EpsH
EXS
ExsB Extensin−like_C
F_actin_cap_B
F_bP_aldolase
F420_ligase
F420_oxidored
F5_F8_type_C
FA_desaturase
FA_desaturase_2
FA_hydroxylase
FA_synthesis
FAA_hydrolase
FabA
FAD_binding_1
FAD_binding_2
FAD_binding_3 FAD_binding_4 FAD_binding_5
FAD_binding_6
FAD_binding_7
FAD_oxidored FAD_syn
FAD−oxidase_C
Fae
FAE1_CUT1_RppA
Fapy_DNA_glyco
Fasciclin
FAT
F−box
F−box−like
FbpA
FBPase FBPase_2
FBPase_3
FBPase_glpX
FCD
FCSD−flav_bind
FdhD−NarQ
FdhE
FdsD
FdtA
FDX−ACB
Fe_dep_repr_C
Fe_dep_repress
Fe_hyd_lg_C
Fe_hyd_SSU
Fe−ADH
Fe−ADH_2
FecCD
FecR
FemAB
FeoA FeoB_C
FeoB_N
Fer2
Fer2_2
Fer2_3
Fer2_4
Fer2_BFD
Fer4
Fer4_10
Fer4_11
Fer4_12
Fer4_13
Fer4_14
Fer4_15
Fer4_16
Fer4_17
Fer4_18
Fer4_2
Fer4_5
Fer4_6 Fer4_7
Fer4_8 Fer4_9
Fer4_NifH
Ferric_reduct
Ferritin
Ferritin_2 Ferritin−like
Ferrochelatase
FeS
Fe−S_assembly
Fe−S_biosyn
FeThRed_A
FeThRed_B
FGase
FGE−sulfatase
FG−GAP
FG−GAP_2
FGGY_C
FGGY_N
FH2
FHA
FHIPEP
Fib_succ_major
Fibrillarin
Fibrinogen_C
Fic
Fic_N
Filamin FIST
FIST_C
FixH
FixO
FixQ
FKBP_C
FKBP_N
FlaA
FlaE FlaF
FlaG
Flagellar_rod
Flagellin_C
Flagellin_IN Flagellin_N
Flavin_Reduct
Flavodoxin_1
Flavodoxin_2
Flavodoxin_5
Flavokinase
Flavoprotein
FlbD
FlbT
Flg_bb_rod
Flg_bbr_C
Flg_hook
Flg_new
FlgD
FlgD_ig
FlgH
FlgI
FlgM
FlgN
FlhC FlhD
FliD_C FliD_N
FliE
FliG_C FliL
FliM
FliO
FliP
FliS
FliW
FliX
FlpD
FmdA_AmdA
FmdE
FMN_bind
FMN_bind_2 FMN_dh
FMN_red
FMO−like FmrO
fn3
Fn3_assoc Fn3−like
FolB
Form−deh_trans
Formyl_trans_C
Formyl_trans_N
FPN1
FragX_IP
Frankia_peptide Frataxin_Cyay
FRG
FrhB_FdhB_C
FrhB_FdhB_N Fructosamin_kin
FTCD
FTCD_C
FTCD_N
FTHFS
FTP
FTR
FTR_C
FTR1
FtsA
FtsH_ext FtsJ
Ftsk_gamma
FtsK_SpoIIIE
FtsL
FTSW_RODA_SPOVE
FtsX
FtsZ_C
Fucose_iso_C
Fucose_iso_N1
Fucose_iso_N2
FumaraseC_C
Fumarate_red_C
Fumarate_red_D Fumble Fumerase
Fumerase_C
FUR
FxsA
FYDLN_acid
G_glu_transpept
G3P_acyltransf
G5
G6PD_C
G6PD_N
GAD
GAF
GAF_2
GAF_3
gag−asp_proteas
Gal_mutarotas_2
Gal−bind_lectin
GalKase_gal_bdg
GalP_UDP_tr_C
GalP_UDP_transf
G−alpha
Gar1
GARS_A
GARS_C
GARS_N
Gas_vesicle
GATA
GATase
GATase_2
GATase_3 GATase_4
GATase_5
GATase_6
GATase_7
GatB_N
GatB_Yqey
Gate
GBA2_N
GBP
GCC2_GCC3
GCD14
GCHY−1
GcpE GcrA
GCS2
GCV_H
GCV_T
GCV_T_C
Gcw_chp
GD_AH_C
GDC−P
GDE_C
GDE_N GDI
GDPD
GDPD_2 GDYXXLXY
Gelsolin
GerE
Germane
GFA
GFO_IDH_MocA
GFO_IDH_MocA_C
GGDEF
GH3
GHMP_kinases_C
GHMP_kinases_N
GIDA
GIDA_assoc_3
GidB
GIDE
GIIM GILT
GIY−YIG GlcNAc GlcNAc_2−epim
GldH_lipo
GldM_C
GldM_N
GLF
GlnD_UR_UTase
GlnE
Gln−synt_C
Gln−synt_N
Glt_symporter Glu_cyclase_2
Glu_cys_ligase
Glu_syn_central
Glu_synthase
Glucan_synthase
Glucodextran_N
Glucokinase
Gluconate_2−dh3 Glucosamine_iso Glucosaminidase
Glug
Glutaminase
Glutaredoxin
GlutR_dimer
GlutR_N
Glu−tRNAGln
Gly_kinase
Gly_radical
Gly_rich
Glyco_hydro_1
Glyco_hydro_10
Glyco_hydro_100
Glyco_hydro_108 Glyco_hydro_12
Glyco_hydro_14
Glyco_hydro_15
Glyco_hydro_16
Glyco_hydro_17
Glyco_hydro_18
Glyco_hydro_19
Glyco_hydro_2
Glyco_hydro_2_C
Glyco_hydro_2_N
Glyco_hydro_20
Glyco_hydro_25
Glyco_hydro_26 Glyco_hydro_28 Glyco_hydro_3
Glyco_hydro_3_C
Glyco_hydro_30
Glyco_hydro_31
Glyco_hydro_32N
Glyco_hydro_35 Glyco_hydro_38
Glyco_hydro_38C
Glyco_hydro_39
Glyco_hydro_4
Glyco_hydro_42 Glyco_hydro_42C
Glyco_hydro_42M
Glyco_hydro_43
Glyco_hydro_44
Glyco_hydro_45 Glyco_hydro_47
Glyco_hydro_48
Glyco_hydro_4C
Glyco_hydro_52
Glyco_hydro_53
Glyco_hydro_57 Glyco_hydro_59
Glyco_hydro_6
Glyco_hydro_62
Glyco_hydro_63
Glyco_hydro_65C
Glyco_hydro_65m
Glyco_hydro_65N
Glyco_hydro_67C
Glyco_hydro_67M
Glyco_hydro_7
Glyco_hydro_75
Glyco_hydro_77
Glyco_hydro_8 Glyco_hydro_81
Glyco_hydro_88
Glyco_hydro_9
Glyco_hydro_92
Glyco_hydro_97
Glyco_tran_28_C
Glyco_tran_WbsX
Glyco_tran_WecB
Glyco_tranf_2_2
Glyco_tranf_2_3
Glyco_trans_1_2
Glyco_trans_1_3
Glyco_trans_1_4
Glyco_trans_2_3
Glyco_trans_4_2 Glyco_trans_4_3 Glyco_trans_4_4
Glyco_transf_10
Glyco_transf_11 Glyco_transf_20
Glyco_transf_21
Glyco_transf_28
Glyco_transf_34
Glyco_transf_36
Glyco_transf_4
Glyco_transf_41
Glyco_transf_5
Glyco_transf_7C
Glyco_transf_8
Glyco_transf_9
Glycoamylase Glycogen_syn
Glycolipid_bind
Glycolytic
Glycos_trans_3N
Glycos_transf_1
Glycos_transf_2
Glycos_transf_3
Glycos_transf_4
Glycos_transf_N
Glyoxal_oxid_N Glyoxalase
Glyoxalase_2
Glyoxalase_3
Glyoxalase_4
Glyphos_transf
Gly−zipper_Omp
Gly−zipper_OmpA
Gly−zipper_YMGG
Gmad2
GMC_oxred_C GMC_oxred_N
GMP_synt_C
GNAT_acetyltr_2
GntP_permease
GntR
GNVR
Got1
Gp_dh_C
Gp_dh_N
Gp23
Gp37_Gp68
Gp49
GPI
GPI−anchored
GPP34
GPW_gp25 Granulin
GRAS
GRDA
GreA_GreB GreA_GreB_N
Grp1_Fun34_YaaH
GrpE
GSCFA
GSDH
GshA
GSHPx
GSH−S_ATP
GSH−S_N
GSIII_N
GSP_synth GspH
GST_C
GST_C_2
GST_N
GST_N_2
GST_N_3
GT36_AF
GTP_CH_N
GTP_cyclohydro2 GTP_cyclohydroI
GTP_EFTU
GTP_EFTU_D2
GTP_EFTU_D3
GTP1_OBG
GTPase_Cys_C
GTP−bdg_N
Gtr1_RagA
GtrA
Guanylate_cyc
Guanylate_kin
GvpK
GvpL_GvpF GVQW
GXGXG
GYD
GyrI−like
H_kinase_N
H_lectin
H_PPase
H2TH
HA
HA2
HAD_2 Haem_bd
Haemagg_act
Haemolytic
Ham1p_like
HAMP
HATPase_c
HATPase_c_2 HATPase_c_3
HATPase_c_4
HD
HD_3
HD_4
HD_5
HD_assoc
HDOD He_PIG Head−tail_con
HEAT
HEAT_2
HEAT_PBS
HECT
Helicase_C
Helicase_C_2
Helicase_C_3
HEM4
Heme_oxygenase
Hemerythrin
HemN_C
HemolysinCabind
Hemopexin hemP
HemY_N
Hen1_L
Hep_Hag
Hepar_II_III
HEPN
Hexapep
Hexokinase_1
Hexokinase_2 HflK_N
HGD−D
HgmA
HGTP_anticodon
HHH
HHH_2 HHH_3
HhH−GPD
HI0933_like
HicB HIG_1_N
Hint
Hint_2 HipA_C
HipA_N
HIPIP
His_biosynth His_kinase
His_Phos_1
HisG
HisG_C
HisKA
HisKA_2
HisKA_3
Hist_deacetyl
Histidinol_dh
Histone
Histone_HNS
HIT
HK
H−kinase_dim
HlyD
HlyD_2
HlyD_3
HlyIII
HlyU
HMA
HMG_box
HMG−CoA_red
HMGL−like
HNH
HNH_2 HNH_3
Homoserine_dh
Host_attach
HpaB HpaB_N
HpcH_HpaI
HPP
HPPK
Hpr_kinase_C
Hpr_kinase_N
Hpt
HR_lesion
HrcA
HRDC
HrpB_C
HSCB_C
HsdM_N HSDR_N
HSDR_N_2
HSP20
HSP33
HSP70
HSP90
HTH_1
HTH_11
HTH_17
HTH_18
HTH_19
HTH_20
HTH_21
HTH_23
HTH_24
HTH_25
HTH_26 HTH_27
HTH_28
HTH_29
HTH_3
HTH_30
HTH_31
HTH_32
HTH_33 HTH_34 HTH_37
HTH_38 HTH_5
HTH_6
HTH_7
HTH_8
HTH_AraC
HTH_AsnC−type
HTH_Crp_2
HTH_DeoR
HTH_IclR
HTH_OrfB_IS605
HTH_Tnp_1
HTH_Tnp_IS66
HTH_Tnp_ISL3
HTH_WhiA
HTS
HupE_UreJ
HupE_UreJ_2
HupF_HypC
HWE_HK
HxlR HxxPF_rpt
HXXSHH
HycI
Hydant_A_N
Hydantoinase_A
Hydantoinase_B
Hydrolase
Hydrolase_2
Hydrolase_3
Hydrolase_4
Hydrolase_6
Hydrolase_like
Hydrolase_like2
HypA
HypD
Hypoth_Ymh
HYR
I_LWEQ
IalB
IBR
ICL
IclR
IcmF−related
ICMT
IDH
IDO
IF−2
IF2_assoc
IF2_N
IF−2B
IF3_C
IF3_N
IF4E
Ig_3
IGPD
IGPS IlvC
ILVD_EDD
IlvN
ImcF−related_N
ImpA−rel_N
IMPDH
ImpE
IMS IMS_C
IMS_HHH
Indigoidine_A
Inhibitor_I29
Inhibitor_I42 Inhibitor_I9
Inos−1−P_synth
Inositol_P
Ins134_P3_kin
Intg_mem_TP0381
Intron_maturas2 Ion_trans_2
iPGM_N
IPPT
Iron_permease
Iron_traffic
I−set
Iso_dh
Isochorismatase
IspA
IspD
IstB_IS21
IstB_IS21_ATP IU_nuc_hydro
JAB
K_trans
KaiB
KaiC KAP_NTPase
Kazal_1
Kazal_2
Kdo_hydroxy
KdpA
KdpC KdpD
KduI Kelch_1
Kelch_3
Kelch_4
Kelch_5
Kelch_6
Keratin_assoc
ketoacyl−synt
Ketoacyl−synt_2
Ketoacyl−synt_C
KfrA_N KGG
KH_1
KH_2
KH_3
KH_4
KH_5
Kinase−PPPase
Kinesin
KorB KorB_C
KOW
KR
KTSC
Ku
Kua−UEV1_localn
KWG La
LAB_N
LacAB_rpiB LacI
Lact_bio_phlase
Lactamase_B Lactamase_B_2
Lactamase_B_3
Lactate_perm
Lactonase
LAGLIDADG_1
LAGLIDADG_2
LAM_C
LamB
LamB_YcsF Laminin_EGF
Laminin_G_3
Lant_dehyd_N
LCCL
LCM Ldh_1_C
Ldh_1_N
Ldh_2
Ldl_recept_a
LEA_2
LEA_5
Lectin_C
Lectin_legB Legionella_OMP
LEH
LemA
LepA_C
Leu_Phe_trans
LeuA_dimer
Leuk−A4−hydro_C
LexA_DNA_bind
LGFP
LGT
LHC
LigA
Ligase_CoA
LigB
LigD_N LigT_PEase LIM
Lin0512_fam
Linker_histone Linocin_M18
LIP Lip_A_acyltrans
Lipase_3
Lipase_GDSL_2
Lipid_DES
Lipl32
Lipocalin_2
Lipocalin_5 Lipoprotein_15
Lipoprotein_19
Lipoprotein_9
Lipoprotein_Ltp
Lipoxygenase
LMF1
LMWPc
LolA
LON Lon_2
Lon_C Longin
LPAM_2 LptC
LpxB
LpxC
LpxD LpxK
LrgA
LRR_1
LRR_4
LRR_5
LRR_6
LRR_7
LRR_8
LRRNT_2
LSM
Lsr2 LssY_C
LTD LtrA
LTXXQ
LUC7 Lum_binding
LVIVD
Lyase_1
Lyase_aromatic
Lycopene_cycl
Lys−AminoMut_A
LysE
Lysine_decarbox
LysM
LysR_substrate
Lysyl_oxidase
LYTB
LytR_C LytR_cpsA_psr
LytTR
LZ_Tnp_IS481
LZ_Tnp_IS66
M16C_assoc
M20_dimer
MA3
Mac
MacB_PCD
Macro
Maf
MAF_flag10
Mago_nashi
Malate_DH
Malate_synthase Malectin
malic
Malic_M
Mannitol_dh
Mannitol_dh_C
MannoseP_isomer
MaoC_dehydratas MAPEG
MarC
MarR
MarR_2
MASE1
MatC_N MatE
MazG
MBF1
MBOAT
MbtH
MCD MCE
MCH
MCM
MCPsignal
MCPsignal_assoc
MCR_beta_N
MCR_C
MCR_D
MCR_gamma
MDMPI_N
MdoG
MEDS
Melibiase Mem_trans
Memo
MerR
MerR_1
MerR_2
MerR−DNA−bind
Met_10
Met_asp_mut_E
Met_synt_B12
META
Metal_hydrol Metalloenzyme
Metallophos
Metallophos_2
Metallophos_C
Metallothio_Pro
Meth_synt_1
Meth_synt_2
Methylase_S
Methyltrans_RNA
Methyltrans_SAM
Methyltransf_10
Methyltransf_11
Methyltransf_12
Methyltransf_13
Methyltransf_14
Methyltransf_18
Methyltransf_19
Methyltransf_2
Methyltransf_20
Methyltransf_21
Methyltransf_23
Methyltransf_24
Methyltransf_26
Methyltransf_28 Methyltransf_29
Methyltransf_3
Methyltransf_30
Methyltransf_31
Methyltransf_32
Methyltransf_4
Methyltransf_5
Methyltransf_6
Methyltransf_9
MethyltransfD12
Methyltrn_RNA_3
Methyltrn_RNA_4
MethyTransf_Reg
MetW
MFS_1
MFS_1_like
MFS_2
MFS_3 Mg_chelatase
Mg_chelatase_2
Mg_trans_NIPA
MG1
MGDG_synth
Mg−por_mtran_C
MGS
MgsA_C
MgtC
MgtE
MgtE_N
MHYT
MiaE
MiaE_2
MIF MIF4G
MinC_C
MinC_N
MinE
MIP
MipA
MipZ MitMem_reg
Mito_carr
Mito_fiss_Elm1
Mitovir_RNA_pol
Mlo
MlrC_C
MltA
MM_CoA_mutase MmgE_PrpD
MMPL
MMR_HSR1
MMR_HSR1_C
Mn_catalase
MnhB
MNHE
Mo25
MoaC
MoaE
Mob_synth_C
Mob1_phocein
MobA_MobL
MobB
MoCF_biosynth
MoCo_carrier
Mo−co_dimer
MoeA_C
MoeA_N
MoeZ_MoeB
MOFRL
Molybdop_Fe4S4
Molybdopterin
Molydop_binding
Mo−nitro_C
MORN
MORN_2
MOSC
MOSC_N
MotA_ExbB
MotB_plug
Motile_Sperm
Mpt_N
Mpv17_PMP22
Mqo
MR_MLE MR_MLE_C
MR_MLE_N
MraZ
MreB_Mbl
MreC
Mrr_cat
Mrr_N
MS_channel
MscL
MSP
MT0933_antitox
MtaB
MTD
MtfA
MTH865 MTHFR
MTHFR_C
MtmB
MtrA
MtrC
MtrD
MtrF
MtrH
MTS
MttA_Hcf106
MTTB
MucB_RseB Multi_Drug_Res
Multi−haem_cyto
Mur_ligase
Mur_ligase_C
Mur_ligase_M
MurB_C
MutL
MutL_C
Mu−transpos_C
MutS_I
MutS_II
MutS_III
MutS_IV
MutS_V
MVIN
MVP_shoulder
Myb_DNA−bind_6
Myb_DNA−binding
Myosin_head
Myosin_tail_1
N_methyl
N_methyl_2
N6_Mtase
N6_N4_Mtase
Na_Ala_symp
Na_Ca_ex
Na_H_antiport_1 Na_H_Exchanger
Na_Pi_cotrans
Na_sulph_symp
NAC
NAD_binding_1
NAD_binding_10 NAD_binding_2
NAD_binding_3
NAD_binding_4
NAD_binding_5
NAD_binding_7
NAD_binding_8 NAD_binding_9
NAD_Gly3P_dh_C
NAD_Gly3P_dh_N
NAD_kinase
NAD_synthase
NadA
NAD−GH NADH_4Fe−4S
NADHdh
NADH−G_4Fe−4S_3
NAF
NAGidase
NAGLU
NAM
NAP
NapB
NapE
NAPRTase
NdhL
NDK
NDUFA12
NERD
NeuB
Neur_chan_LBD NfeD
Nfu_N
NgoMIV_restric
NhaB
NHase_alpha
NHase_beta
NHL
Ni_hydr_CYTB
NicO
NIF
Nif11
NIF3
NiFeSe_Hases
NifU
NifU_N
NikR_C
NIL
NIPSNAP
NIR_SIR
NIR_SIR_ferr
Nit_Regul_Hom
Nitrate_red_del
Nitrate_red_gam Nitro_FeMo−Co
Nitroreductase
NLPC_P60
NMN_transporter
NMO NmrA
NMT_C
NMT1
NMT1_2
NnrS
NnrU NO_synthase
NOG1
NOGCT Nol1_Nop2_Fmu
Nop
Nop10p
NOP5NT
NosD
Notch
NotI
NPCBM
NQR2_RnfD_RnfE
NQRA
NQRA_SLBB
Nramp
NRDD
NRDE NrfD
NTase_sub_bind
Nterm_IS4
NTF2
NTP_transf_2
NTP_transf_3
NTP_transf_4
NTP_transferase
NTPase_1
NTPase_I−T
Nuc_deoxyrib_tr
Nuc_H_symport
Nuc_sug_transp
Nucleos_tra2_C
Nucleos_tra2_N
Nuc−transf
NUDIX
Nudix_N
NUMOD4
NUP
NusA_N
NusB
NusG NYN NYN_YacP
OAD_beta
OAD_gamma
O−antigen_lig
OB_NTP_bind
OB_RNB
OCD_Mu_crystall
Octopine_DH
OEP OHCU_decarbox
OKR_DC_1 OKR_DC_1_C
OKR_DC_1_N
Oleosin oligo_HPY
Oligomerisation
OmdA
OMP_b−brl
OMP_b−brl_2
OmpA
OMPdecase
OmpH
OmpW
OpcA_G6PD_assem
OpgC_C
OppC_N
OprB OPT
OpuAC
ORF6N OrfB_IS605
OrfB_Zn_ribbon Orn_Arg_deC_N
Orn_DAP_Arg_deC
OSCP
OsmC
OstA
OstA_2
OstA_C
OST−HTH
OTCace
OTCace_N Oxidored_FMN
Oxidored_molyb
Oxidored_nitro
Oxidored_q1
Oxidored_q1_N
Oxidored_q2
Oxidored_q3
Oxidored_q4
Oxidored_q5_N Oxidored_q6
OxoDH_E1alpha_N
Oxysterol_BP
P_proprotein
P21−Arc
P34−Arc p450
P63C
PA
PA14
PaaA_PaaC
PaaB
PAAR_motif
PaaX
PaaX_C
PABP
PAC2
PAD_porph
PadR PAF−AH_p_II
Paired_CXXCH_1
PALP
PAN_4
Pan_kinase
Pantoate_ligase
Pantoate_transf
PAP_assoc
PAP2
PAP2_3
PAPS_reduct
ParA
ParBc ParBc_2
ParcG
PARP
PAS
PAS_10
PAS_2
PAS_3
PAS_4
PAS_5
PAS_7
PAS_8
PAS_9
PASTA
Patatin
PB1
PbH1
PBP
PBP_dimer
PBP_like
PBP_like_2
PBP5_C
PBS_linker_poly
PCI PCMT
PCNA_N
PCP_red
PcrB
PCRF
PCYCGC
PD40
PDDEXK_1
PDDEXK_2
PDDEXK_3
PDGLE
PDH
PDT
PduL PdxA PdxJ
PDZ
PDZ_2
Pec_lyase
Pec_lyase_C
Pectate_lyase
Pectate_lyase_3
Pectinesterase
PEGA
PemK
PEMT
Pencillinase_R
Penicil_amidase
Pentapeptide
Pentapeptide_3 Pentapeptide_4
Pep_deformylase
PEP_mutase
PEPcase
PEPcase_2
PEPCK
PEPCK_ATP
PepSY PepSY_2
PepSY_TM_1
PepSY_TM_3
Pept_tRNA_hydro
Peptidase_A22B
Peptidase_A24
Peptidase_A6
Peptidase_A8
Peptidase_C1
Peptidase_C1_2 Peptidase_C10
Peptidase_C11
Peptidase_C13 Peptidase_C14
Peptidase_C15 Peptidase_C2
Peptidase_C25
Peptidase_C26
Peptidase_C39 Peptidase_C39_2 Peptidase_C69
Peptidase_M1
Peptidase_M10
Peptidase_M10_C
Peptidase_M13
Peptidase_M13_N
Peptidase_M14
Peptidase_M15
Peptidase_M15_2 Peptidase_M15_3
Peptidase_M15_4
Peptidase_M16 Peptidase_M16_C
Peptidase_M17
Peptidase_M17_N
Peptidase_M18
Peptidase_M19
Peptidase_M2
Peptidase_M20
Peptidase_M22
Peptidase_M23
Peptidase_M24
Peptidase_M28
Peptidase_M29
Peptidase_M3
Peptidase_M3_N
Peptidase_M32
Peptidase_M36
Peptidase_M4
Peptidase_M4_C
Peptidase_M41
Peptidase_M42
Peptidase_M43
Peptidase_M48
Peptidase_M49
Peptidase_M50
Peptidase_M50B
Peptidase_M54
Peptidase_M55
Peptidase_M56
Peptidase_M57
Peptidase_M6
Peptidase_M61
Peptidase_M64
Peptidase_M74
Peptidase_M75
Peptidase_MA_2
Peptidase_S10
Peptidase_S11
Peptidase_S13
Peptidase_S15
Peptidase_S24
Peptidase_S26
Peptidase_S37 Peptidase_S41
Peptidase_S46
Peptidase_S49 Peptidase_S51
Peptidase_S55
Peptidase_S58
Peptidase_S66
Peptidase_S74
Peptidase_S8
Peptidase_S9
Peptidase_S9_N
Peptidase_U32
PEP−utilisers_N
PEP−utilizers
PEP−utilizers_C
PepX_C
Peripla_BP_1
Peripla_BP_2 Peripla_BP_3
Peripla_BP_4
Peripla_BP_5
Peripla_BP_6
Permease
peroxidase
PFK
PfkB
PFL
PFO_beta_C
PG_binding_1
PGA_cap PGAP1
PGI
PGK PGM_PMM_I
PGM_PMM_II
PGM_PMM_III
PGM_PMM_IV
PgpA PGPGW
PHA_gran_rgn
PhaC_N
PhaG_MnhG_YufB
Phasin
Phasin_2
PHB_acc
PHB_acc_N
PHB_depo_C
PHBC_N
PHD
PhdYeFM_antitox
Phe_tRNA−synt_N
Phenol_Hydrox
Phenol_MetA_deg
PHF5 PhnA
PhnA_Zn_Ribbon
PhnG
PhnH
PhnI
PhnJ
PHO4 PhoD
PhoH
PhoPQ_related
Phos_pyr_kin
PhosphMutase
Phosphodiest
Phosphoesterase
Phospholip_B
Phosphonate−bd
Phosphorylase
PhoU
PhoU_div
PHP
PHP_C
PHY
Phycobilisome
PhyH
Phytase
Phytase−like
Phytochelatin
PhzC−PhzF PI3_PI4_kinase
PI3Ka PIF1
PIG−L
PIG−U
P−II
Pilin
Pilin_PilA
PilN
PilO
PilP Pilus_CpaD
Pilus_PilP
PilX_N
PilZ PIN
PIN_2
PIN_3
PIN_4
PIP5K
PI−PLC−X
PI−PLC−Y
Pirin
Pirin_C
PITH Piwi
PK
PK_C
PKD
Pkinase
Pkinase_Tyr
PLA1
PLAC8
Plasmid_killer
Plasmid_stabil Plasmid_Txe
PLAT
PLATZ PLDc
PLDc_2
PLDc_N
Plug
Plug_translocon
PmbA_TldD
PMI_typeI
PMM
PmoA
Pmp3
PMSR
PMT_2
PNP_UDP_1
PNPase
PNPOx_C
PNTB
PocR
PolC_DP2
Poly_export
PolyA_pol PolyA_pol_arg_C PolyA_pol_RNAbd
Polyketide_cyc
Polyketide_cyc2
polyprenyl_synt
Polysacc_deac_1
Polysacc_deac_2
Polysacc_lyase
Polysacc_syn_2C
Polysacc_synt
Polysacc_synt_2
POR
POR_N
Porin_1
Porin_2
Porin_3
Porin_4
Porin_O_P
Porphobil_deam
Porphobil_deamC
Potass_KdpF
POTRA_1
POTRA_2
PP_kinase
PP_kinase_C PP_kinase_N
PP2C
PP2C_2
PP−binding
PPC
PPDK_N
PPK2
PPR_2 Ppx−GppA
PqiA
PQ−loop
PQQ
PQQ_2
PqqA
PqqD
PRA1
PRA−CH
PRAI
PRA−PH
PRC
PrcB_C PRCH
Prefoldin
Prefoldin_2 Prenyltrans
Prenyltrans_1
Prenyltrans_2
Prenyltransf
PRiA4_ORF3
Pribosyltran
Pribosyltran_N
Prim−Pol
Prismane
PRK
PrkA
PrmA
PRMT5
Pro_CA
Pro_dh
Pro_isomerase
Pro_racemase
PROCN
PROCT
Profilin
Prok−E2_E
Prok−JAB Pro−kuma_activ Propeptide_C1
ProQ
ProRS−C_1
Proteasome
Proteasome_A_N
Protein_K
PRP38
PRP8_domainIV
PrpF PrsW−protease
PS_Dcarbxylase
PS_pyruv_trans
PsaA_PsaB
PsaD
PsaL
PsaM
PsbH
PsbI
PsbK
PsbL
PsbP
PsbR
PsbW PSCyt1
PSCyt2
PSCyt3
PSD1
PSD2
PSD3
PSD4
PSD5
PseudoU_synth_1
PseudoU_synth_2
PSI_PsaE
PSI_PsaF
PSI_PsaJ
PsiE
PsiF_repeat
PSII
PSK_trans_fac
PSP1
PspA_IM30
PspC
PSS
PT
PTA_PTB
PTE
Pterin_4a
Pterin_bind
PTH2
PT−HINT
PTPA PTPLA
PTPS
PTR2
PTS_2−RNA
PTS_EIIA_1
PTS_EIIA_2
PTS_EIIB
PTS_EIIC
PTS_IIB
PTS−HPr
PTSIIB_sorb
PUA
PUA_2
PUCC
PucR
PUF Pup
Pup_ligase
Pur_DNA_glyco
PurA
PurS
Put_DNA−bind_N
PvlArgDC
PX
PYC_OADA
PYNP_C
Pyr_redox
Pyr_redox_2
Pyr_redox_3
Pyr_redox_dim PyrI
PyrI_C
Pyridox_ox_2
Pyridox_oxidase
Pyridoxal_deC
Pyrophosphatase
QRPTase_C
QRPTase_N
Queuosine_synth
R_equi_Vir
R3H
RabGAP−TBC
Rad51
Rad60−SLD
RadC Radical_SAM
Radical_SAM_N
Raffinose_syn
RAMP4
RAMPs
Ran_BP1
Rap_GAP
RapA_C
Ras
RasGAP
RasGEF
RBFA
RbsD_FucU
RCC1
RCC1_2
Rcd1
RDD
RdgC
RdRP
RdRP_1
RdRP_3
RdRP_4 Rdx
RE_ApaLI RE_Eco29kI
RecA RecO_C
RecO_N
Recombinase
RecR
RecT
RecX
Redoxin
REF Reg_prop
RelA_SpoT
RelB RelB_N
Rep_3
RepA_C
Repair_PSII
Reprolysin_2
Reprolysin_3
Rer1
RES
ResB
ResIII
Resolvase
Response_reg
Reticulon
RF−1
Rft−1
RGP
RgpF
RhaA
Rhamno_transf
RhaT
RHH_1 RHH_2
RHH_3
RHH_4
Rho_GDI
Rho_N
Rho_RNA_bind
Rhodanese
RhoGAP
RhoGEF
Rhomboid
RHS_repeat
Rib_5−P_isom_A
RibD_C
Ribonuc_L−PSP
Ribonuc_red_2_N
Ribonuc_red_lgC Ribonuc_red_lgN
Ribonuc_red_sm
Ribonuclease
Ribonuclease_3
Ribonuclease_P
Ribonuclease_T2
Ribophorin_I
Ribophorin_II
Ribos_L4_asso_C
Ribosomal_60s
Ribosomal_L1
Ribosomal_L10
Ribosomal_L11 Ribosomal_L11_N
Ribosomal_L12
Ribosomal_L13
Ribosomal_L13e
Ribosomal_L14
Ribosomal_L14e
Ribosomal_L15e
Ribosomal_L16
Ribosomal_L17
Ribosomal_L18_c
Ribosomal_L18ae
Ribosomal_L18e Ribosomal_L18p
Ribosomal_L19
Ribosomal_L19e
Ribosomal_L2 Ribosomal_L2_C
Ribosomal_L20
Ribosomal_L21e
Ribosomal_L21p
Ribosomal_L22
Ribosomal_L22e
Ribosomal_L23
Ribosomal_L23eN Ribosomal_L24e
Ribosomal_L25p
Ribosomal_L27
Ribosomal_L27e
Ribosomal_L28
Ribosomal_L28e
Ribosomal_L29
Ribosomal_L29e
Ribosomal_L3
Ribosomal_L30
Ribosomal_L30_N
Ribosomal_L31
Ribosomal_L31e Ribosomal_L32e
Ribosomal_L32p
Ribosomal_L33
Ribosomal_L34
Ribosomal_L34e
Ribosomal_L35Ae
Ribosomal_L35p
Ribosomal_L36
Ribosomal_L36e
Ribosomal_L37ae
Ribosomal_L37e Ribosomal_L38e
Ribosomal_L39
Ribosomal_L4
Ribosomal_L40e
Ribosomal_L44
Ribosomal_L5 Ribosomal_L5_C
Ribosomal_L6
Ribosomal_L6e
Ribosomal_L6e_N
Ribosomal_L7Ae
Ribosomal_L9_C
Ribosomal_L9_N
Ribosomal_S10
Ribosomal_S11
Ribosomal_S12
Ribosomal_S13
Ribosomal_S13_N
Ribosomal_S14 Ribosomal_S15
Ribosomal_S16
Ribosomal_S17
Ribosomal_S17e
Ribosomal_S18
Ribosomal_S19
Ribosomal_S19e
Ribosomal_S2
Ribosomal_S20p
Ribosomal_S21
Ribosomal_S21e
Ribosomal_S23p
Ribosomal_S24e
Ribosomal_S25
Ribosomal_S26e Ribosomal_S27
Ribosomal_S27e
Ribosomal_S28e
Ribosomal_S3_C
Ribosomal_S30
Ribosomal_S30AE
Ribosomal_S3Ae
Ribosomal_S4
Ribosomal_S4e
Ribosomal_S5
Ribosomal_S5_C
Ribosomal_S6
Ribosomal_S6e
Ribosomal_S7
Ribosomal_S7e
Ribosomal_S8
Ribosomal_S8e
Ribosomal_S9
Ribul_P_3_epim
Ricin_B_lectin
RicinB_lectin_2
Rick_17kDa_Anti
Rieske
Rieske_2 RimK
RimM
Ring_hydroxyl_A
Ring_hydroxyl_B
RINGv
RIO1
RLAN
RloB
RmlD_sub_bind
RMMBL
RmuC RNA_GG_bind
RNA_helicase
RNA_pol RNA_pol_A_bac
RNA_pol_A_CTD RNA_pol_L
RNA_pol_L_2 RNA_pol_N RNA_pol_Rpb1_1
RNA_pol_Rpb1_2
RNA_pol_Rpb1_3
RNA_pol_Rpb1_4
RNA_pol_Rpb1_5
RNA_pol_Rpb2_1
RNA_pol_Rpb2_2
RNA_pol_Rpb2_3
RNA_pol_Rpb2_4
RNA_pol_Rpb2_45
RNA_pol_Rpb2_6
RNA_pol_Rpb2_7
RNA_pol_Rpb4
RNA_pol_Rpb5_C
RNA_pol_Rpb6
RNase_E_G
RNase_H
RNase_H_2
RNase_HII
RNase_PH
RNase_PH_C
RNase_T
RNB RnfC_N
Rnf−Nqr
Robl_LC7
Rod−binding
ROK
ROS_MUCR
Rossmann−like
Rotamase
Rotamase_2
Rotamase_3
RPA
RPE65
RQC
RRF
Rrf2
RRM_1
RRM_5
RRM_6
RrnaAD
RRXRR
RS4NT
RsbRD_N
Rsd_AlgQ
RseA_N
RseC_MucC
RskA
RTC
RuBisCO_large
RuBisCO_large_N RuBisCO_small
Rubredoxin
Rubrerythrin
RuvA_C
RuvA_N
RuvB_C
RuvB_N
RuvC rve
rve_2
rve_3
RVT_1
RVT_3
RVT_N
RyR
S1
S1_2
S10_plectin
S1−P1_nuclease
S4
S4_2 S6PP
Saccharop_dh
Saccharop_dh_N
S−AdoMet_synt_C
S−AdoMet_synt_M
S−AdoMet_synt_N
SAF
SAF_2
SAICAR_synt
SAM_1
SAM_adeno_trans
SAM_decarbox
SAM_MT
SAP
SapB_1
SapC
SASP_gamma
SATase_N
SBBP
SbcCD_C
SBDS
SBF
SbmA_BacA SBP
SBP_bac_1
SBP_bac_10
SBP_bac_11
SBP_bac_3
SBP_bac_5
SBP_bac_6
SBP_bac_7
SBP_bac_8
SBP_bac_9
SBP56
SCAMP
ScdA_N
SCFA_trans
SCO1−SenC
SCP2 SCP2_2
ScpA_ScpB
SCPU
Scramblase
SDF
SDH_alpha
SDH_beta
Sdh_cyt
SDH_sah
Sdh5
SdpI
SE
Sec_GG Sec61_beta
Sec63
Sec7 SecA_DEAD
SecA_PP_bind
SecA_SW
SecB
SEC−C
SecD_SecF
SecD−TM1
SecE
SecG
Secretin
Secretin_N
SecY
Se−cys_synth_N
Sedlin_N
SEFIR
Sel1
SelA
SelB−wing_2
SelB−wing_3
SelR
Semialdhyde_dh
Semialdhyde_dhC
Senescence
Sensor_TM1
Ser_hydrolase
Serinc
Serpin Seryl_tRNA_N
SET
SF3b10
SfsA
SGL SH2
SH3_1
SH3_3
SH3_4
SHD1
Shikimate_DH
Shikimate_dh_N
SHMT
SHOCT
SHS2_FTSA
Sigma54_activat
Sigma54_AID
Sigma54_CBD
Sigma54_DBD
Sigma70_ECF
Sigma70_ner
Sigma70_r1_1
Sigma70_r1_2
Sigma70_r2
Sigma70_r3
Sigma70_r4
Sigma70_r4_2 SIMPL
SIP
SIR2
SIR2_2
SirA
SirB
SIS
SIS_2
SKI
Skp1
Skp1_POZ
SLBB
SLH
Slp
SLT
SLT_2
SlyX
SMC_hinge SMC_N
S−methyl_trans
SmpA_OmlA
SmpB
Smr
SNARE_assoc
SNase
SNF
SNF2_N
Snf7
SNO
SnoaL
SnoaL_2
SnoaL_3
SnoaL_4
Sod_Cu
Sod_Fe_C
Sod_Fe_N
Sod_Ni
Solute_trans_a SOR_SNZ
Sortase
SOUL
SoxD
SoxY SoxZ
SpecificRecomb
Spermine_synth
Spherulin4
SpoA
SpoIID
SpoIIE
Spond_N
SPOR
Spore_YtfJ
SpoU_methylase SpoU_sub_bind
SPOUT_MTase
SpoVAD
SpoVG
SpoVR
SpoVS SprA_N
SprA−related
Spt4 Spt5−NGN
SpvB
SPW
SQS_PSY
SrfB
SRP_SPB
SRP54
SRP54_N
SRP−alpha_N
SSB
SSF
SsgA
SspB
STAS
STAS_2
Steroid_dh
Sterol_MT_C
Sterol−sensing
Stig1 Stimulus_sens_1
STN
Strep_67kDa_ant
STT3
SUA5
Sua5_yciO_yrdC Suc_Fer−like
Succ_CoA_lig
Succ_DH_flav_C
Sucrose_synth
SufE
SUFU Sugar_tr
Sugar−bind
SUI1
Sulf_transp
Sulfatase
Sulfate_tra_GLY
Sulfate_transp Sulfotransfer_3
Sulphotransf
SurA_N
SurA_N_3
SurE
Surf_Ag_VNR
SURF1
SURNod19
SusD
SusD−like
SusD−like_2
SusD−like_3
SWIB SWM_repeat Syja_N
Synaptobrevin
T_hemolysin
T2SE
T2SE_Nter
T2SF
T2SG
T2SK
T2SM_b
T2SS−T3SS_pil_N
T4SS−DNA_transf
T6SS−SciN
Tad
TadE
Tagatose_6_P_K
Tannase
TAT_signal
TatC
TatD_DNase
TauD
TauE
Tautomerase
TB2_DP1_HVA22
TBP
TcdB_toxin_midC
TcdB_toxin_midN
TctA
TctB
TctC
TCTP
TehB TelA
TENA_THI−4
TerB
TerC
TerD
Terminase_2
Terminase_GpA
TetR
TetR_C_4
TetR_C_6
TetR_N
Tetraspannin
Tex_N TF_Zn_Ribbon
TFIIB
TfoX_C
TfoX_N
TfuA
TGS
TGT
THF_DHG_CYH
THF_DHG_CYH_C Thg1
Thg1C Thi4
ThiC
ThiC−associated
ThiF ThiG
ThiI
Thioesterase
Thiolase_C
Thiolase_N
Thiol−ester_cl
Thioredoxin
Thioredoxin_2
Thioredoxin_3
Thioredoxin_4
Thioredoxin_7
Thioredoxin_8
ThiS
ThiW
Thr_dehydrat_C ThuA
Thy1
ThylakoidFormat
Thymidylat_synt
Thymidylate_kin
Thymosin
Thyroglobulin_1
Tic20
tify
TIG
TIL
TilS_C
TIM
Tim44
TIM−br_sig_trns
TIP49
TipAS
TIR_2
TIR−like
TK TLC
TM_helix
TM2
TMA7
Tmemb_14
TMP−TENI
TnpB_IS66 TOBE
TOBE_2
TOBE_3
Tocopherol_cycl
Tol_Tol_Ttg2
TolB_N
Toluene_X
TonB
TonB_2
TonB_dep_Rec
Topo_C_assoc
Topoisom_bac
Topoisom_I
Topo−VIb_trans
Toprim
Toprim_4
Toprim_C_rpt
Toprim_Crpt
Toprim_N
Toxin_YhaV
TP_methylase
TPK_catalytic
TPMT
TPP_enzyme_C
TPP_enzyme_M
TPP_enzyme_N
TPPK_C
TPR_1
TPR_10 TPR_11
TPR_12
TPR_14
TPR_16
TPR_17
TPR_18 TPR_2
TPR_6
TPR_7
TPR_8
TPR_9
TPT
TraB
TraG−D_C
TRAM
TRAM_LAG1_CLN8
Trans_reg_C
Transaldolase
Transcrip_reg
Transglut_core
Transglut_core2 Transglut_core3
Transgly
Transgly_assoc
Transglycosylas
Transket_pyr Transketolase_C
Transketolase_N
Translin
Transp_cyt_pur
Transpeptidase
Transposase_20
Transposase_31
Transposase_mut
Transthyretin
TRAP−gamma
TrbC
TrbI
TRCF Trehalase
Trehalose_PPase
Trigger_C
Trigger_N
TrkA_C TrkA_N
TrkH
TRL Trm112p
TrmB
TrmE_N
tRNA_anti
tRNA_anti_2
tRNA_anti−like
tRNA_bind
tRNA_m1G_MT
tRNA_Me_trans
tRNA_NucTran2_2
tRNA_SAD
tRNA_synt_2f
tRNA_U5−meth_tr
tRNA−synt_1
tRNA−synt_1_2
tRNA−synt_1b
tRNA−synt_1c
tRNA−synt_1c_C tRNA−synt_1d
tRNA−synt_1e
tRNA−synt_1f
tRNA−synt_1g tRNA−synt_2
tRNA−synt_2b
tRNA−synt_2c
tRNA−synt_2d
tRNA−synt_2e
tRNA−synt_His
tRNA−Thr_ED
Trns_repr_metal
Tropomyosin
TROVE
Trp_dioxygenase
Trp_halogenase
Trp_repressor
Trp_syntA
TruB_N
TruB−C_2
TruD
TrwB_AAD_bind
TrwC
Trypsin
Trypsin_2
TSCPD
TSP_1
TSP_3
TspO_MBR
Tub
Tub_2
Tubulin
Tubulin_2
Tubulin_C
TUDOR
TylF
Tyr_Deacylase
Tyrosinase
UBA
UBA_e1_thiolCys
UBACT
UbiA
UbiD
Ubie_methyltran
Ubiq_cyt_C_chap
ubiquitin
U−box
Ub−RnfH UCH
UCR_Fe−S_N
UCR_UQCRX_QCR9
UDG
UDPG_MGDP_dh
UDPG_MGDP_dh_C
UDPG_MGDP_dh_N
UDPGP
UDPGT
UFD1
Ufm1
Uma2
UnbV_ASPIC Unstab_antitox
UPF0004
UPF0014
UPF0016
UPF0020
UPF0027
UPF0029
UPF0041
UPF0047
UPF0051
UPF0052 UPF0054
UPF0058
UPF0060
UPF0061
UPF0066
UPF0075
UPF0079
UPF0081 UPF0093
UPF0102
UPF0104
UPF0114
UPF0118
UPF0126
UPF0147
UPF0149
UPF0150
UPF0157
UPF0160
UPF0167 UPF0175
UPF0182
UPF0197
UPF0227
UPF0233
UPF0261
UPF0262 UPF0278
UPF0547
UQ_con
Urease_alpha
Urease_beta
Urease_gamma
UreD
UreE_C
UreE_N
UreF
Ureidogly_hydro
Uricase
Urocanase
URO−D
Usg
Usher
Usp
UT
UTRA UvdE
UVR
UvrB
UvrC_HhH_N
UvrD_C
UvrD_C_2
UvrD−helicase
UxaC
UxuA
V_ATPase_I V4R
VacJ
Val_tRNA−synt_C
VanW
VanY
VanZ vATP−synt_AC39
vATP−synt_E
Vault
VCBS
VHS
Viral_coat
VirJ
Virul_Fac
Virul_fac_BrkB
Virulence_fact
Virulence_RhuM
VIT
VIT_2 VIT1
Vitellogenin_N
VitK2_biosynth
VKG_Carbox
VKOR
Voltage_CLC
VPEP
Vps35
Vps55
Vsr
VTC VWA
VWA_2
VWA_3
VWA_CoxE
vWF_A
W2
WbqC
WCOR413
WD40
WES_acyltransf
WGR
WHG
WhiA_N Whib
WRKY
WW
WXG100
WYL Wyosine_form
Wzy_C
Wzz
Xan_ur_permease
XdhC_C
XdhC_CoxI
XFP
XFP_C
XFP_N
XhoI
XisH
XisI
XPG_I
XPG_N
Y_phosphatase Y_phosphatase3
Y_phosphatase3C
Y_Y_Y
Y1_Tnp
Y2_Tnp
Yae1_N
YaeQ
YajC
YARHG
YbaB_DNA_bd YbaK
YbjN
YbjQ_1 YcaO
YccV−like
YceG
YceI
Ycf4
Ycf66_N
Ycf9
YcfA
YchF−GTPase_C YCII
YcxB YdfA_immunity
YdjC YfhO
YfiO
YgbA_NO
YgbB
YGGT
YhhN
YHS
YHYH
YiaAB
YibE_F
YicC_N
YiiD_Cterm
Yippee
YjeF_N
YjfB_motility
YjgP_YjgQ
YkuD
YkuD_2
YmdB
YMF19
YpzG YqcI_YcgG
YqeY
YqjK
YscJ_FliF
YscJ_FliF_C
YtkA
YTV
YtxH
YukD
YWTD
Z1
ZapA
zf−A20
zf−AN1
zf−B_box
zf−C2H2
zf−C3HC4_3
zf−C4_ClpX
zf−C4_Topoisom
zf−CCCH zf−CCHC
zf−CDGSH
zf−CGNR zf−CHC2
zf−CHCC
zf−CHY
zf−dskA_traR
zf−FPG_IleRS
zf−H2C2_2
zf−HC2 zf−HYPF
zf−LITAF−like
zf−MaoC
zf−MYND
zf−NADH−PPase
zf−PARP
zf−RanBP
zf−rbx1
zf−ribbon_3
zf−RING_2
zf−Sec23_Sec24
zf−TFIIB
zf−trcl
zf−UBP
zf−ZPR1
zinc_ribbon_2
zinc_ribbon_4
zinc_ribbon_5
Zip
ZipA_C
Zn_clus Zn_peptidase
Zn_peptidase_2
Zn_ribbon_2
Zn_Tnp_IS1595
Zn_Tnp_IS91
ZZ
KbKc
Km
Sa
Sb
MCR_alpha MCR_beta
Actin
DUF1501
gpD
RNA_replicase_B
SASP
ATP−synt_C
COX1
Flp_Fap Monooxygenase_B
Photo_RC
AmoC CSD
Ka
−0.2 0.0 0.2 0.4
−0.3
−0.2
−0.1
0.0
0.1
0.2
0.3
0.6
22.6
%
46.3%
FIG 3 Biplot of correspondence analysis of sample Pfam profiles, illustratingthe difference in gene expression. Pfam profiles corresponding to the differentdepths of Knudsenheia (Ka, Kb, and Kc) and Solvatn (Sa and Sb) are shown inblue. Individual Pfams responsible for the majority of the differences betweensamples are shown in red. The x axis represents the first dimension, whichexplained 46.3% of the inertia (a measure of the total difference between sam-ple profiles); the y axis represents the second dimension, which explained22.6% of the inertia. The distance between the sample Pfam profiles indicatesthe difference in gene expression between samples. The length of the lineconnecting Pfams to the center of the plot is equivalent to the weight of par-ticular Pfams in the final solution, given the inertia of the dimensions it crosses(i.e., the longer the line, the larger part of the inertia it explains). The directionof the line indicates the sample orientation of its weight (e.g., if a line pointstoward deeper-layer samples, the relative abundance of transcripts for thatPfam is highest in the deeper layers).
Peat Soil Metatranscriptomics
September 2014 Volume 80 Number 18 aem.asm.org 5765
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nloaded from
jority of the archaeal 16S rRNAs were assigned to known metha-nogenic taxa, with the family Methanosaetaceae being the overallmost abundant (�10% of Archaea in the top layer and �40% inlower layers), followed by Methanosarcinaceae and the ordersMethanomicrobiales and Methanobacteriales (50% in the top layerof Knudsenheia) (Fig. 5B). Similarly, at Knudsenheia, the relativeabundance of mRNAs assigned to Archaea was low in the toplayers and increased with depth, from �0.5% (�1,500 reads) to�8% (�25,000 reads) of all taxonomically assigned mRNAs (Fig.5A). For the Solvatn site, archaeal mRNA transcripts increasedfrom �0.5% (�1,500 reads) to �1.5% (�3,000 reads). MostmRNAs stemmed from the Methanosaetacaea, followed byMethanosarcinaceae and Methanomicrobiales. A detailed analysisof the transcripts encoding enzymes of methanogenesis pathwaysshowed that all major pathways were present (Fig. 6). In general,the relative abundance of transcripts for all pathways increasedwith depth. Among these, the transcripts encoding subunits ofmethyl coenzyme M reductase (methyl-CoM reductase [MCR];EC 2.8.4.1) were most abundant. Corresponding to the highabundance of Methanosaetaceae identified by 16S rRNA andmRNA annotation, the transcripts for AMP-forming acetate-co-
enzyme A ligase (EC 6.2.1.1), a key enzyme for acetoclastic metha-nogenesis by Methanosaetaceae, was the most abundant. In linewith this, there was also a high relative abundance of transcriptsfor the carbon monoxide dehydrogenase, catalyzing the reductionof acetyl-coenzyme A to 5-methyl tetrahydromethanopterin inacetoclastic methanogens. The transcripts encoding enzymes foracetoclastic methanogenesis typical for the Methanosarcinaceaewere of low relative abundance (acetate kinase, EC 2.7.2.1, andphosphate acetyltransferase, EC 2.3.1.8). Among the hydrog-enotrophic branches of methanogenic pathways, the transcriptsfor the pathway for methanogenesis from formate were present atthe highest relative abundance (key enzyme, formate dehydroge-nase, EC 1.2.1.2), while those specific for the reduction of CO2
with reducing power from H2 (ferredoxin hydrogenase, EC1.12.7.2) were present at a low relative abundance. The transcriptsfor the remaining steps of hydrogenotrophic methanogenesis,which are common to both H2 and formate metabolism, werepresent at relative abundances similar to that of formate dehydro-genase. Transcripts related to methylotrophic methanogenesiswere at lower relative abundances, with methanol-related tran-scripts (EC 2.1.1.246) being highest, followed by trimethylamine-
0
Ka Kb Kc Sa Sb
Hemicellulases
GH8 GH11 GH10
GH12 GH26 GlH28 GH53
0Ka Kb Kc Sa Sb
Cellulases
GH6 GH9 GH48
GH44 GH45 Cellulase Cellulase-like
0
2e-4
4e-4
6e-4
8e-4
1e-3
1.2e.-3
Ka Kb Kc Sa Sb
Oxidases active on lignin and phenolic compounds
Dioxygenase C Cu-oxidase Cu-oxidase 2Cu-oxidase 3 Cu-oxidase 4 peroxidase
0
Ka Kb Kc Sa Sb
Debranching enzymes
GH62 GH67C GH67MAlpha-L-AF C ArabFuran-catal Bac rhamnosid
Bac rhamnosid N Alpha L fucos
Frac
tion
of a
ll id
entifi
ed P
fam
dom
ains
2e-4
4e-4
6e-4
8e-4
1e-3
2e-4
4e-4
6e-4
8e-4
4e-4
8e-4
1.2e.-3
1.6e.-3
2e.-3
(A) (B)
(C) (D)
*** (*)
***
*
***
*
***
(*)(**)
FIG 4 Relative abundances of transcripts encoding enzymes involved in plant polymer decomposition at different depths in Knudsenheia (Ka, Kb, and Kc) andSolvatn (Sa and Sb). (A) Oxidases that catalyze the degradation of lignin and other phenolic compounds; (B) cellulases that hydrolyze cellulose; (C) endohemi-cellulases that hydrolyze the core chain of hemicelluloses; (D) debranching enzymes that hydrolyze the side chains of hemicelluloses. Protein family domains wereidentified by searches in the protein family database Pfam. Each name given for the color code represents a Pfam domain. The whole range of enzymatic functionsassociated with glycoside hydrolases (GH) and other Pfam domains can be found in the Pfam (http://pfam.sanger.ac.uk/) and CAZy (carbohydrate-activeenzymes) (http://www.cazy.org/) databases. Note that some Pfams have overlapping activities and that the grouping of these into categories is based on their mainor one of their main functions. Significant differences in the number of transcripts for the categories of polymer decomposition between peat soil depths areindicated as follows: “*” over Kb means that there is a difference between Ka and Kb, “*” over Kc means that there is a difference between Ka and Kc, “(*)” overKc means that there is a difference between Kb and Kc, and “*” over Sb means that there is a difference between Sa and Sb. P value confidence levels: *, �0.05;**, �0.01; and ***, �0.001.
Tveit et al.
5766 aem.asm.org Applied and Environmental Microbiology
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nloaded from
related transcripts (EC 2.1.1.250), with transcripts for dimethyl-amine (EC 2.1.1.249) and monomethylamine (EC 2.1.1.248) atthe lowest abundances.
The relative abundance of SSU rRNA assigned to the methane-oxidizing bacteria (MOB) within Methylococcaceae increased withdepth (Fig. 7). The majority of these sequences were assigned toMethylobacter, being most similar to the 16S rRNA of Methylobac-ter tundripaludum (33). Correspondingly, there was also an in-crease in the relative abundance of mRNA assigned to Methylococ-caceae (Fig. 7). The majority of the mRNA transcripts were mostsimilar to homologues encoded in the genome of M. tundripalu-dum SV96 (34) (see Table S4 in the supplemental material). Thetranscripts encoding the subunits of the particulate methanemonooxygenase (pMMO) were the most abundant, being �5-fold higher than transcripts encoding enzymes for the down-stream steps of methane oxidation: methanol, formaldehyde, andformate oxidation (see Table S4). Other methanotrophic taxa,such as the candidate division NC10 (relative abundance � 2e�3)and the Methylocystaceae (�1.5e�4), were observed at lower rel-ative abundances.
DISCUSSION
A major limitation for the utilization of metatranscriptomics inthe study of active microorganisms in the environment is the shorthalf-lives of mRNA molecules (35) and the variations in half-livesbetween species and different genes (1). Thus, changes in the soilconditions upon sample retrieval might cause a change in thetranscript patterns. RNA isolation from soils is particularly chal-lenging due to the inaccessibility of cells located on and within soilparticles, inefficient cell lysis, the adsorption of RNA to soil parti-cles, and the presence of RNases (1). In our study, changes intranscript patterns were counteracted by rapid flash-freezing ofsamples in liquid nitrogen immediately after sampling, whilehigh-efficiency extraction was achieved by grinding the peat soil inliquid nitrogen before NA extraction.
Due to the absence of poly(A) tails in prokaryotic mRNAs,common enrichment strategies mostly rely on the removal ofrRNAs (3, 4). Soil contains organisms from all the three domains,Bacteria, Archaea, and Eukarya, making it difficult to synthesize acompatible combination of probes for subtractive hybridization.
Frac
tion
of m
RNA
(A)
Thermoplasmatales Halobacteriales MethanobacterialesMethanosarcinales Methanosarcinaceae MethanosaetaceaeMethanomicrobiales Other Euryarchaeota ThaumarchaeotaCrenarchaeota Other Archaea
0
2e-2
4e-2
6e-2
Ka Sa
1e-2
3e-2
5e-2
0
2e-2
4e-2
6e-2
8e-2
1e-1
Kb Kc Sb
***
***
***
(***)
0
6e-4
9e-4
1.2e-3
1.5e-3
3e-4
1.8e-3
Ka Sa Kb Kc Sb
1e-2
2e-2
3e-2
4e-2
5e-2
0
6e-2
7e-2 *** ***
***
Frac
tion
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FIG 5 Archaeal community profiles based on mRNA (A) and SSU rRNA (B) at the different peat depths of Knudsenheia (Ka, Kb, and Kc) and Solvatn (Sa andSb). Column sizes represent the fraction of sequences assigned to taxa within Archaea at order-level taxonomy or higher. Methanosarcinales was split into thefamilies Methanosarcinaceae and Methanosaetaceae. Those assigned as Methanosarcinales could not be further assigned to family level with the thresholds applied(see Materials and Methods). Other Euryarchaeota, Crenarchaeota, and Thaumarchaeota include orders that made up very small fractions of the total; thus, thesewere pooled. Other Archaea include sequences belonging to other phyla or could not be assigned to phylum with the thresholds applied. Significant differencesin the number of transcripts between peat soil depths are indicated as explained in the Fig. 4 legend.
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Eukaryotes have been shown to comprise 10% or more of therRNA in soil metatranscriptomes (2, 10), stemming from fungi,protists, and metazoa. Therefore, a removal of the eukaryotes isessential to achieve a high fraction of mRNA. Subtractive hybrid-ization using custom-generated probes, constructed only from thebacterial rRNA, was shown to reduce the rRNA fraction to 50 to60%, leaving 40 to 50% of putative mRNA in marine metatran-scriptomes (for an example, see reference 9). This is similar to thekit-based rRNA removal from the high-Arctic peat soils per-formed in this study, where the rRNA fraction was reduced to60%, leaving 40% putative mRNA (Km) (Table 1). Thus, the com-mercial kit-based method was as efficient as the custom probe-based method. This might in part be due to the efficient utilizationof two kits with different probe compatibilities with bacterialrRNA and one kit compatible with eukaryotic rRNA, removingthe 16S and 23S and the 18S and 28S fractions of the rRNA, re-
spectively (Fig. 1; see also Fig. S1 in the supplemental material). Ina polycyclic aromatic carbon-contaminated soil microcosm,mRNA enrichment with MICROBExpress (Ambion) gave a finalfraction of putative mRNA at �18% (16), less than half that ob-tained in our mRNA enrichment. MICROBExpress was one of thethree kits used with the peat microbiota, and the higher fraction ofputative mRNA obtained might be explained by the utilization oftwo additional kits.
Organic molecules such as humic and tannic acids have beenshown to inhibit PCR by affecting the polymerase activity or bind-ing the template, rendering it inaccessible to the enzyme (11, 36).The activities of hydrolytic extracellular enzymes as well as generalbacterial activity are inhibited by accumulated phenolic com-pounds in the anoxic layers of peat soils (13). The cDNA synthesisfrom the deeper layers in Arctic peat soils was severely inhibiteddespite the extraction of high-quality RNA and purification of the
0
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Methylamines and Methanol
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Acetyl-CoA
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(B)
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* (*
)
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FIG 6 Relative abundances of transcripts for key enzymes in methanogenesis (A) and the specific catalytic steps for the enzymes in the different pathways ofmethanogenesis (B). Red arrows indicate omitted pathway steps. Abbreviations: THMPT, tetrahydromethanopterin (THSPT [S for Sarcina] within Methano-sarcinales); CoA, coenzyme A; CoM, coenzyme M. Enzyme categories (EC): 6.2.1.1, acetate-CoA ligase (AMP forming); 2.7.2.1, acetate kinase; 2.3.1.8, phosphateacetyltransferase; 1.2.99.2, carbon monoxide dehydrogenase; 1.2.1.2, formate dehydrogenase; 1.12.7.2, ferredoxin hydrogenase; 1.5.99.9, 5,10-methylene-THMPT dehydrogenase; 1.12.98.1, coenzyme F420 hydrogenase; 1.5.99.11, 5,10-methylene-THMPT reductase; 2.1.1.250, trimethylamine methyltransferase;2.1.1.249, dimethylamine methyltransferase; 2.1.1.248, monomethylamine methyltransferase; 2.1.1.246, methanol methyltransferase; 2.1.1.86, tetrahydro-methanopterin S-methyltransferase; 2.8.4.1, methyl-CoM reductase (CoB-CoM heterodisulfide reductase). Significant differences in the number of transcriptsbetween peat soil depths are indicated as explained in the Fig. 4 legend.
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extracts. To circumvent the inhibition problem, the RNA sampleswere diluted and linearly amplified after poly(A) tailing. Thisproved to be an efficient method for generating high RNA yields.Linear amplification is not prone to skewing the relative abun-dances of different mRNAs, since the templates for the RNA syn-thesis are cDNA fragments that after initial reverse transcriptionare present at the same concentration throughout the amplifica-tion step. However, the amplification of RNA has been shown toincrease the mRNA fraction of the metatranscriptomes (6, 9). Ithas been suggested that this is due to a preferential polyadenyla-tion of mRNA and an inefficient amplification of molecules with ahigh degree of secondary structure (6). This was also observed inour study, with the fraction of putative mRNA ranging from 15 to28%, 2- to 4-fold higher than that observed in previously obtainedmetatranscriptomes processed from the same soil samples with-out amplification (putative mRNA, �5 to 7%) (10). Compared tothe moderate mRNA enrichment during the rRNA removal pro-cedures, the RNA amplification step was particularly powerfuland in parallel circumvented the inhibition problems.
Comparison between the gene expression profiles of themRNA-enriched library and the mRNA fraction of the total RNAin human stool samples has shown that there was little effect onthe relative abundance of mRNAs (double-log scatterplot R2 val-ues, 0.91 and 0.94) (8). Also, subtractive hybridization with sam-ple-specific probes had a low impact on the relative abundances ofmRNAs (9). In contrast to this, our study showed that the mRNAenrichment had substantial effects on the relative abundances oftranscripts within many different protein families. Ideally, thetranscripts in an mRNA-enriched library should be a larger sub-sample of the nonenriched library; however, the differences ob-served were larger than that obtained between two random sam-plings from the same set of annotated mRNA sequence tags (e.g.,Ka replicate 1 and Ka replicate 2) (Fig. 2A). The difference was alsoshown to be larger than that observed between the nonenrichedlibraries from two different peat microbiotas (Ka and Sa). It wasnot possible to determine the exact cause for the observed skew inrelative abundances of transcripts for different Pfams. It wasshown that the relative abundances of transcripts for some Pfams
were particularly high in Ka relative to Km, while other Pfamswere higher in Km. Also, the taxonomic annotation of mRNAindicated that no specific taxonomic lineages were affected by themRNA enrichment. Rather, the effect was broad as shown by thelow R2 value in the double-log scatter plots (Fig. 2A). Thus, it canbe speculated that the skew was due to unspecific mRNA degra-dation during the prolonged sample processing. Consideringthese limitations and the high-throughput sequencing technolo-gies available, we recommend performing deep sequencing onlinearly amplified or nonamplified peat soil metatranscriptomes,leaving out the time-consuming subtractive hybridization steps.
Metatranscriptomics allows for a holistic study of microbialprocesses in response to environmental changes. Particularly, itenables the study of microbial transcription of genes that are tax-onomically diverse and those that encode enzymes involved incomplex pathways, including many enzymes with different rolesin different organisms. In anoxic peat soils, extracellular degrada-tion of plant polymers and methanogenesis are the initial and laststeps in the mineralization of SOC, and both are difficult to studyusing targeted primer PCR-based methods due to the diversity oforganisms and metabolic pathways involved. The partly degradedpeat soil matrix contains a mixture of polysaccharides, proteins,and aromatic (e.g., lignin and other phenolic compounds) andaliphatic compounds which differs depending on the vegetationinput to the soil (37). The Arctic peat soils studied in this work arecharacterized by a cover of moss species interspersed by grasses(10). Mosses and grasses contain many similar cell wall polysac-charides, but some are unique to each of them and many arepresent at different proportions (38). Also, while grasses are richin lignin, mosses are not, but they contain other types of phenoliccompounds (38). The degradation of these compounds is initiallycatalyzed by extracellular hydrolytic and oxidative enzymes. Thevegetation input in these soils is reflected in the relative abundanceof transcripts for polymer-degrading enzymes. In particular, thehigh relative abundance of transcripts for cellulases in the toplayers of both Knudsenheia and Solvatn correspond to the highcellulose content in both moss and grass cell walls (38). Also, con-sidering hemicelluloses, the high relative abundance of galactosi-
0
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Methylosoma Methylocaldum
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FIG 7 Relative abundances of 16S rRNA sequences (A) and mRNA assigned to methanotrophic taxa (relative to total assigned sequences) (B) at the differentdepths of Knudsenheia (Ka, Kb, and Kc) and Solvatn (Sa and Sb). Due to the low number of genomes, particularly from Arctic regions, within the Methylococ-caceae, mRNA sequence tags were not assigned further to genus level taxonomy. The majority of mRNA was assigned to the genome of Methylobactertundripaludum SV96 (see Table S4 in the supplemental material for details). Significant differences in the number of transcripts between peat soil depths areindicated as explained in the Fig. 4 legend.
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dases (GH53) and mannanases (GH26) reflects the high fractionof mosses, which are known to contain larger proportions ofgalactomannans and glucogalactomannans than grasses (38). In-terestingly, the decreased relative abundance of cellulase tran-scripts and increase in transcripts for the degradation of highlybranched hemicelluloses indicate that the SOC in deeper peat lay-ers contained less cellulose. Thus, it appears that the microbiotametabolism had shifted toward degradation of highly branchedhemicelluloses containing fucose, arabinose, and rhamnose.These are known as fucoside xyloglucans, heteroxylans, and rh-amnogalacturonans and are more common in grasses than mosses(38), indicating that these parts of the grass cell walls are particu-larly recalcitrant to decomposition and/or that the contribution ofgrasses to peat accumulation has been higher in the past. Extracel-lular hydrolytic degradation of polysaccharides has been consid-ered a potential rate-limiting step in SOC mineralization (13, 39).Therefore, the characterization of changes in microbial transcrip-tion patterns might help to identify the specific compounds thatmake up the substrates for limiting steps in decomposition indifferent peat deposits and the dynamics of their decomposition.Genes encoding plant polymer-degrading enzymes are found inthe genomes of members of a broad range of bacterial phyla (40,41). Thus, it has been difficult to investigate which members of agiven bacterial community are involved in the decomposition ofplant polymers and the dynamics of these particular communities.Taxonomic annotation of transcripts encoding extracellular en-zymes has allowed us to investigate these microbial taxa, showingthat many different phyla are involved in polymer decomposition.However, Bacteroidetes seemed to be particularly important in thedecomposition of branched hemicelluloses, Actinobacteria in cel-lulose and hemicellulose degradation, and Proteobacteria in thedegradation of phenolic compounds.
Most major groups of methanogens in the Svalbard peat soilshave been observed earlier, including Methanomicrobiales, Metha-nobacteriales, Methanosaetaceae, and Methanosarcinales (10, 42).Correspondingly, all these groups were identified in this study(Fig. 5). Several studies have shown that methanogenic commu-nities switch from hydrogenotrophic to acetoclastic methanogen-esis with peat depth (43–45), while others have shown that theproportion of hydrogenotrophic methanogenesis increased withdepth (46). Our results indicate that acetoclastic methanogenswere both more abundant and more active in the deeper layers,while hydrogenotrophs were more abundant in the top layers.However, mRNA analysis showed that genes for a broad range ofmethanogenic pathways were transcribed, including methano-genesis from formate, methanol, and methylamines in addition toacetate. Particularly, transcripts for methanogenesis from acetateand formate were abundant in the deeper layers. It has been shownthat at low temperature, acetoclastic methanogenesis predomi-nates, possibly due to the activity of homoacetogenic bacteria (39,47–49). This might also be the case for these peats, as the relativeabundance of transcripts for methanogenesis from H2/CO2 werelow compared to those for methanogenesis from acetate and for-mate. However, whether this is due to homoacetogenic competi-tion for H2 or to formate and acetate being major fermentationproducts while H2 is not was not clear from our results. Whilepathways of methanogenesis from methanol and methylamineshave received little attention in earlier studies, our results indi-cated that these pathways might be important for methanogenesisin the Arctic peat soils.
Bacteria closely related to M. tundripaludum were the mostabundant MOB at both Knudsenheia and Solvatn at all peatdepths, similar to previous studies (10). The increase in relativeabundances of 16S rRNA and mRNA of M. tundripaludum withdepth indicated that this species has means of coping with anoxicconditions, enabling the cells to sustain a high number of ribo-somes and mRNA. However, whether this is due to an anaerobicenergy metabolism involving methane oxidation utilizing pMMOand intracellularly produced O2 (50), alternative energy metabo-lism (51–53), or an ability to survive longer periods of starvation isnot evident from the current data set.
Conclusion. The utilization of metatranscriptomics has beenlimited in part due to the difficulties associated with generatinghigh-quality cDNA from the total RNA extracted from the peatsoils. In this paper, we have described the generation of metatran-scriptomes from soil samples with inhibitory components. Wehave tested the application of kit-based mRNA enrichment oncomplex peat soil ecosystems, finding that the introduced biasoutweighs the advantages of this approach. Particularly in poorlystudied ecosystems, it can be of advantage to simultaneously ob-tain broad information about present and active organisms (de-rived from rRNA) in addition to the transcript profile (derivedfrom mRNA). We have demonstrated the analysis of such data setsand how these holistic community system analyses can providenew knowledge about microbial activities in peat soils, ecosystemsimportant to the global carbon cycle. In particular, the transcriptpatterns reveal the gradual shift from aerobic to anaerobic micro-biotas and metabolisms in the deeper peat layers, correspondingto a transition from CO2- to CH4-dominated greenhouse gasemissions with depth.
ACKNOWLEDGMENTS
We thank Peter Frenzel, Vigdis Torsvik, and Ricardo Alves for importantcontributions during the fieldwork in Ny-Ålesund, Svalbard. The se-quencing service was provided by the Norwegian High-Throughput Se-quencing Centre (http://www.sequencing.uio.no).
This work was funded through The Research Council of Norway,grant 191696/V49.
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