Single-cell genomics shedding light on marine Thaumarchaeota diversification

SHORT COMMUNICATION

Single-cell genomics shedding light on marineThaumarchaeota diversification

Haiwei Luo1, Bradley B Tolar1, Brandon K Swan2, Chuanlun L Zhang1,3,Ramunas Stepanauskas2, Mary Ann Moran1 and James T Hollibaugh1

1Department of Marine Sciences, University of Georgia, Athens, GA, USA; 2Bigelow Laboratory for OceanSciences, East Boothbay, ME, USA and 3State Key Laboratory of Marine Geology, Tongji University,Shanghai, China

Previous studies based on analysis of amoA, 16S ribosomal RNA or accA gene sequenceshave established that marine Thaumarchaeota fall into two phylogenetically distinct groupscorresponding to shallow- and deep-water clades, but it is not clear how water depth interactswith other environmental factors, including light, temperature and location, to affect this patternof diversification. Earlier studies focused on single-gene distributions were not able to linkphylogenetic structure to other aspects of functional adaptation. Here, we analyzed the genomecontent of 46 uncultivated single Thaumarchaeota cells sampled from epi- and mesopelagic watersof subtropical, temperate and polar oceans. Phylogenomic analysis showed that populationsdiverged by depth, as expected, and that mesopelagic populations from different locations were wellmixed. Functional analysis showed that some traits, including putative DNA photolyase and catalasegenes that may be related to adaptive mechanisms to reduce light-induced damage, were foundexclusively in members of the epipelagic clade. Our analysis of partial genomes has thus confirmedthe depth differentiation of Thaumarchaeota populations observed previously, consistent with thedistribution of putative mechanisms to reduce light-induced damage in shallow- and deep-waterpopulations.The ISME Journal (2014) 8, 732–736; doi:10.1038/ismej.2013.202; published online 7 November 2013Subject Category: Integrated genomics and post-genomics approaches in microbial ecologyKeywords: marine group I Archaea; Thaumarchaeota; single-cell genomics; nitrification; ammoniaoxidation

Thaumarchaeota are abundant in both marine andterrestrial environments and have a significant rolein the global nitrogen cycle (Leininger et al., 2006;Wuchter et al., 2006). Oceanic Thaumarchaeota aredistributed throughout the water column, with theirrelative abundance increasing with depth to up to50% of mesopelagic prokaryotic cells (Karner et al.,2001). Cultivation of marine Thaumarchaeota hasproven to be difficult. Only one strain, Nitrosopumilusmaritimus SCM1 (Konneke et al., 2005), has beensuccessfully brought into pure culture, while severalothers have been cultivated as enrichment cultures(Blainey et al., 2011; Mosier et al., 2012a,b; Park et al.,2012a,b). Therefore, our understanding of theirdiversity and ecological function has been gainedlargely through culture-independent approaches.Using a few marker genes (16S ribosomal RNA,amoA, accA), many studies have shown that marine

Thaumarchaeota fall into shallow- and deep-waterphylogenetic clades (Francis et al., 2005; Hallam et al.,2006; Nicol et al., 2011). In addition to depth, light,temperature, latitude and dissolved oxygen have beenidentified as important correlates of Thaumarchaeotadiversity and distributions in the ocean (Prosser andNicol, 2008; Biller et al., 2012). These single-gene-based analyses have outlined the phylogenetic dis-tribution and diversity of marine Thaumarchaeota, butnot provided insights into the adaptive mechanismsgiving rise to specific ecotypes.

We turned to single amplified genomes (SAGs)in order to link additional metabolic capabilitiesto taxonomy and to reconstruct phylogeny usingcharacters sampled across the genome. Forty-sixsingle cells related to Thaumarchaeota populationsin a variety of marine environments (SupplementaryFigure S1) were obtained from epi- and mesopelagicwaters of the Southern Ocean, temperatenorth Atlantic, subtropical north Pacific and southAtlantic (Supplementary Table S1). Genomes ofthese cells were recovered with variable success,with a mean of 32% (±12%) relative tothe Nitrosopumilus maritimus SCM1 genome

Correspondence: H Luo or JT Hollibaugh, Department of MarineSciences, University of Georgia, Athens, GA 30602, USA.E-mail: [email protected] or [email protected] 15 August 2013; revised 27 September 2013; accepted 6October 2013; published online 7 November 2013

The ISME Journal (2014) 8, 732–736& 2014 International Society for Microbial Ecology All rights reserved 1751-7362/14

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http://dx.doi.org/10.1038/ismej.2013.202

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(1.65 Mbp). A maximum likelihood phylogenomictree constructed using a concatenated amino-acidsequence of 97 single-copy orthologous genes(Supplementary Table S2) with 27 041 sites stronglysupported separating the SAGs into two phylo-genetically coherent groups correspondingto epi- and mesopelagic clades (Figure 1). Thewithin-clade average nucleotide identity (ANI) is89.0% (±8.9%) and 86.8% (±5.4%) forepi- and mesopelagic clades, respectively, whereas

the between-clade ANI is 75.4% (±4.1%). When thecomposite genomes from enrichment cultures orfrom metagenomic assembly were included inthe phylogenomic analysis, the surface water SAGsappeared to be evolutionarily separated from all thecultured marine Thaumarchaeota (SupplementaryFigure S2). Among the eight Antarctic SAGssampled, three were obtained from 80 m, a depthassociated with the Winter Water (WW) water mass(Church et al., 2003), whereas the remaining fivewere sampled from 400 m in the Circumpolar DeepWater (CDW) water mass. Although separatedby only B300-m depth, the SAGs from these twowater masses were confidently assigned tothe epi- and mesopelagic clades, respectively(Figure 1). Conversely, among the mesopelagiccells collected 1000s of km apart from CDW andsubtropical waters, SAGs were intermingled withinthe mesopelagic clade (Figure 1).

Previous studies have shown that marineThaumarchaeota are inhibited by light (Merbtet al., 2012) and suggested that sensitivity tophotoinhibition might be a key factor determiningtheir depth distribution (Mincer et al., 2007; Churchet al., 2010; Hu et al., 2011; Merbt et al., 2012). Lightwas also implicated as the factor determining theseasonal dynamics of Thaumarchaeota in the South-ern Ocean, where they are abundant during winterbut nearly absent in summer (Kalanetra et al., 2009).Yet no direct evidence from field populationshas been reported to support these hypotheses.Our analyses of genomes from uncultivatedThaumarchaeota showed that a homolog to deoxy-ribodipyrimidine photolyase, a key gene in thepathway to repair ultraviolet-induced DNA damage(Goosen and Moolenaar, 2008), was present in aSAG sampled from Gulf of Maine surface water(1-m depth), but absent from all of the 42 mesope-lagic SAGs (Supplementary Table S3). The occur-rence of putative DNA photolyases in surface waterThaumarchaeota was reinforced by conducting arigorous search for DNA photolyase genes asso-ciated with Thaumarchaeota reads in the GlobalOcean Survey (GOS) surface water metagenomes.Our identification of seven putative Thaumarch-aeota photolyase genes in the GOS data (Figure 2a) isconsistent with the hypothesis that light is animportant factor structuring marine Thaumarch-aeota populations by depth, and suggests thatsurface water members have evolved effectivemechanisms to cope with ultraviolet-induced DNAdamage. It remains unknown, however, whether theprocess of ammonia oxidation is indeed subject tophotoinhibition, as has been suggested previously(Mincer et al., 2007). DNA photolyase was not foundin the other three epipelagic clade SAGs that wereassociated with the Antarctic WW water mass andcollected from 80 m, which is inconclusive becauseof the low recovery of genome sequence from thesecells (coverage o35% relative to N. maritimusSCM1).

AAA288-N15

AAA008-O05

AAA007-N23

AAA007-E15

AAA008-N07

AAA007-O23

AAA007-N19

AAA008-E02

AAA288-J14

AAA008-P02

AAA001-A19

AAA008-E15

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AAA008-M23

AB-663-P07

AAA288-I14

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AB-663-F14

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AAA288-K02

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AB-661-M19

AAA160-J20

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770 m, South Atlantic Gyre

800 m, North Pacific Gyre

400 m, AntarcticCircumpolar Deep Water

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Surface, Gulf of Maine

Mesopelagicclade

Epipelagicclade

Figure 1 Maximum likelihood phylogenomic analysis of 47genomes of the marine Thaumarchaeota. The tree was constructedusing the RAxML v7.3.0 software (Stamatakis, 2006) using aconcatenated amino-acid sequence of 97 genes with 27 041 sites,with a data partition model determined by the PartitionFindersoftware (Lanfear et al., 2012). Values at the nodes show thenumber of times the clade defined by that node appeared in the 100bootstrapped data sets. Two Crenarchaeota outgroup species arenot shown. Details of tree construction can be found inSupplementary Material. The epi- and mesopelagic clades areindicated by shading. Single-cell genomes from different watermasses/locations/depths are marked with different colors asidentified in the legend inset.

Diversification of marine ThaumarchaeotaH Luo et al

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Members of the epi- and mesopelagic clades alsoappear to differ in their capabilities for reducingoxidative stress. Genes encoding superoxidedismutase, which catalyzes the dismutation ofsuperoxide into oxygen and hydrogen peroxide,are equally abundant in epi- and mesopelagic clades(Supplementary Table S3; w2 test; P40.05). Hydro-gen peroxide is subsequently converted to water bythe enzymes peroxiredoxin (also known as alkylhydroperoxide reductase) and catalase. Althoughperoxiredoxin gene families occur with comparablefrequency in both clades (Supplementary Table S3;w2 test; P40.05), a gene with high homology tocatalase was found exclusively in two WW SAGsof the epipelagic clade (Supplementary Table S3).A key difference between these two types ofantioxidant enzymes is that peroxiredoxin is 100-to 1000-fold less efficient than catalase; the latterbecomes crucial once the former is saturated withhydrogen peroxide (Parsonage et al., 2008). Further,there is evidence that catalase is critical in mini-mizing ultraviolet-induced oxidative damage inbacteria (Costa et al., 2010). Phylogenetic analysissuggested that this gene was acquired throughhorizontal gene transfer (Figure 2b), which is furthersubstantiated by its absence in the genomes of anyof the seven cultured marine Thaumarchaeotasequenced to date by homology search, all ofwhich are members of the epipelagic ecotype(Supplementary Figure S2). These results areconsistent with microbes in epipelagic watersexperiencing a stronger oxidative stress becauseof photochemical and photosynthetic productionof reactive oxygen species compared with those in

deep water where biological activity is the singlesource of superoxide (Diaz et al., 2013).

When gene functions were assigned more broadlyusing either COG (Tatusov et al., 1997) or arCOG(Wolf et al., 2012) categories, we found that thegenome content of the epipelagic clade wassignificantly different from the mesopelagic clade(w2 test; Po0.001), with the signal transductionfunctional category significantly enriched in theepipelagic clade (Rodriguez-Brito et al., 2006). Theability of generalist marine bacteria to respondto a changing environment has been attributedto differences in the sophistication of regulatorymachinery (Lauro et al., 2009; Luo et al., 2013),and this reasoning may apply to selectionpressures operating on epipelagic Thaumarchaeotacompared with those inhabiting the more stablemesopelagic waters. By contrast, we found a higherabundance of urease genes in SAGs from themesopelagic compared with the epipelagic clade(Supplementary Table S3), consistent with a recentreport of the depth distribution of Thaumarchaeotaurease genes in polar oceans (Alonso-Saez et al.,2012).

In conclusion, differentiation of epi- and mesope-lagic Thaumarchaeota populations first detected byanalysis of single genes (Francis et al., 2005; Hallamet al., 2006) is supported by phylogenomic analysisof partial genomes retrieved from uncultivated cells.The exclusive presence of putative DNA photolyaseand catalase in SAGs from the epipelagic is strongevidence that light or light-driven photochemistry isa major factor structuring marine Thaumarchaeotaby depth.

Figure 2 Bayesian phylogenetic tree of (a) photolyase and (b) catalase amino-acid sequences. The trees were constructed using theMrBayes v3.1.2 software (Ronquist and Huelsenbeck, 2003) using the WAG þG4 model. Values at the nodes are posterior probabilities ofthe internal branches. Details of tree construction can be found in Supplementary Material. The distinct phylogenetic groups(Tharmarchaeota, Euryarchaeota, Crenarchaeota, Bacteria) are indicated by shading. The trees consist of sequences from single cells(filled star), reference taxa with sequence id (NCBI gi/accession/locus tag) given in parenthesis, and homologs in GOS metagenomes withsequence id in the format of ‘JCVI_READ_XXXX’.


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Conflict of Interest

The authors declare no conflict of interest.

Acknowledgements

We thank the Georgia Advanced Computing ResourceCenter at the University of Georgia for providing computa-tional resources. This research was funded by grantsfrom NSF (OPP 08-38996; OCE-1232982, EF-826924 andOCE-821374) and the Gordon and Betty Moore Founda-tion, with sequencing support from the US Department ofEnergy Joint Genome Institute Community SupportedProgram grants 2010-77 and 2011-387.

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Supplementary Information accompanies this paper on The ISME Journal website (http://www.nature.com/ismej)


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http://www.nature.com/ismej

Supplemental Material:

Single Cell Genomes of Marine Thaumarcheota Reveal Insights into Population

Differentiation by Depth

Haiwei Luo, Bradley B. Tolar, Brandon K. Swan, Chuanlun L. Zhang, Ramunas Stepanauskas,

Mary Ann Moran, James T. Hollibaugh

Supplemental Methods

Single cell sample collection and construction of single amplified genome (SAG) libraries

Water samples for single cell analyses were collected and replicate, 1 mL subsamples

were cryopreserved with 6% glycine betaine (Sigma) and stored at –80 ºC (Cleland et al., 2004).

Prior to cell sorting, samples with prokaryote cell abundances above 5x105 mL

-1 were diluted

10x with filter-sterilized field samples and screened through a 70 µm mesh-size cell strainer

(BD). For heterotrophic prokaryote detection, diluted subsamples (1-3 mL) were incubated for

10-120 min with SYTO-9 DNA stain (5 µM; Invitrogen). Cell sorting was performed with a

MoFlo™ (Beckman Coulter) flow cytometer using a 488 nm argon laser for excitation, a 70 µm

nozzle orifice and a CyClone™ robotic arm for droplet deposition into microplates. The

cytometer was triggered on side scatter. The “single 1 drop” mode was used for maximal sort

purity. Prokaryote cells were separated from eukaryotes, viruses, and detritus based on SYTO-9

fluorescence (proxy to nucleic acid content) and light side scatter (proxy to particle size) (del

Giorgio et al., 1996). Synechococcus cells were excluded, based on their autofluorescence

signal. Target cells were deposited into 384-well plates containing 600 nL per well of either a)

1x TE buffer or b) prepGEM™ Bacteria (Zygem) reaction mix and stored at –80 ºC until further

processing. Of the 384 wells, 315 were dedicated for single cells, 66 were used as negative

controls (no droplet deposited) and 3 received 10 cells each (positive controls).

The accuracy of droplet deposition was determined by depositing 10 mm fluorescent

beads into 384-well plates then the results were checked by microscopically verifying the

presence of beads in the plate wells. Of the 2-3 plates examined each sort day, with one bead

deposited per well, fewer than 2% of wells were found to contain no bead and 0.4% to contain

more than one bead. The latter is most likely caused by co-deposition of two beads attached to

each other, which at certain orientation may have similar optical properties to single beads.

Cells were sorted into TE buffer were lysed and their DNA was denatured using cold

KOH (Raghunathan et al., 2005). Genomic DNA from the lysed cells was amplified using

multiple displacement amplification (MDA) (Dean et al., 2002; Raghunathan et al., 2005) in 10

µL final volume. The MDA reactions contained 2 U/µL Repliphi polymerase (Epicentre), 1x

reaction buffer (Epicentre), 0.4 mM each dNTP (Epicentre), 2 mM DTT (Epicentre), 50 mM

phosphorylated random hexamers (IDT) and 1 µM SYTO-9 (Invitrogen) (all final

concentration). The MDA reactions were run at 30 °C for 12-16 h, then inactivated by a 15 min

incubation at 65 °C. Amplified genomic DNA was stored at -80 °C until further processing. We

refer to the MDA products originating from individual cells as single amplified genomes

(SAGs).

Prior to cell sorting, the instrument and the workspace were decontaminated for DNA as

previously described (Stepanauskas and Sieracki, 2007). High molecular weight DNA

contaminants were removed from all MDA reagents by a UV treatment in Stratalinker

(Stratagene) (Woyke et al., 2011). During UV treatment, reagents were placed on ice to avoid

overheating. An empirical optimization of the UV exposure was performed to remove all

detectable contaminants without inactivating the reaction. Cell sorting and MDA setup were

performed in a HEPA-filtered environment. As a quality control, the kinetics of all MDA

reactions was monitored by measuring the SYTO-9 fluorescence using either LightCycler 480

(Roche) or FLUOstar Omega (BMG). The critical point (Cp) was determined for each MDA

reaction as the time required to produce half of the maximal fluorescence. The Cp is inversely

correlated to the amount of DNA template (Zhang and Fang, 2006).

PCR screening of SAG libraries

MDA products were diluted 50-fold in TE buffer and 500 nL aliquots of diluted MDA

product served as the template DNA in 5 µL final volume real-time PCR screens. All PCR

reactions were performed using LightCycler 480 SYBR Green I Master Mix (Roche) and the

Roche LightCycler® 480 II real-time thermal cycler. PCR amplification of Archaeal SSU rRNA

from SAGs was done using primers Arch_344F (ACG GGG YGC AGC AGG CGC GA) and

Arch_915R (GTG CTC CCC CGC CAA TTC CT) (Lane et al. 1991). Forward (5´–

GTAAAACGACGGCCAGT–3´) and reverse (5´–CAGGAAACAGCTATGACC–3´) M13

sequencing primers were added to the 5´ ends of each target primer pair to aid direct sequencing

of PCR products. All PCR reactions were run for 40 cycles at the appropriate annealing

temperature, followed by melting curve analysis performed as follows: 95°C for 5 s, 52°C for 1

min, and a continuous temperature ramp (0.11°C/s) from 52 to 97°C. Real-time PCR kinetics and

amplicon melting curves served as proxies for detecting SAGs positive for target genes. New,

20 µL PCR reactions were set up for all PCR-positive SAGs and amplicons were sequenced

from both ends using Sanger technology by Beckman Coulter Genomics.

Single cell sorting, whole genome amplification, real-time PCR screens and PCR product

sequence analyses were performed at the Bigelow Laboratory Single Cell Genomics Center

following protocols described on their web site (www.bigelow.org/scgc). Antarctic SAGS were

also screened at the University of Georgia for the presence of Archaeal amoA genes using

primers and qPCR conditions described in Francis et al. (2005) and Wuchter et al. (2006). PCR

products were sequenced at the Georgia Genomics Facility to verify amplification of the target

gene.

SAG sequencing and analysis

A total of 46 Thaumarchaeota SAGs were chosen for whole genome sequencing based on

multiple displacement amplification (MDA) kinetics, presence of metabolic genes from PCR

screening and geographic location of the sampling site. Three approaches were used for

sequencing marine Thaumarchaeota SAGs: 1) A combination of Illumina and 454 shotgun

sequencing (AAA007-O23), or Illumina only (AB-661-I02, AB-661-L21, AB-661-M19, AB-

663-F14, AB-663-G14, AB-663-N18, AB-663-O07, AB-663-P07, AAA160-J20, AAA001-

A19), as described in Swan et al. (2011); 2) a combination of Illumina and PacBio long read

sequence data (AAA007-N19, AAA288-I14, and AAA288-J14) as described in Martinez-Garcia

et al. (2012) and assembled using Velvet-SC (Chitsaz et al., 2011) and PBcR (Koren et al.,

2012) and; 3) 454 shotgun sequencing of Nextera-prepared libraries followed by dual assembly

with Newbler v2.4 and Geneious Pro v.5.5.6 (Drummond et al., 2011) (all remaining SAGs; total

http://www.bigelow.org/scgc

of 32). For each of these 32 SAGs, raw 454 sequences were trimmed in Geneious Pro v5.5.6 and

any remaining transposons were removed using TagCleaner v0.11 (Schmieder et al., 2010).

Sequences were then assembled separately in Newbler v.2.4 (Roche) using default settings and

Geneious using the high-sensitivity setting. The Newbler-assembled sequences were imported

into Geneious and co-assembled with both the Geneious-assembled contigs and the unused

reads. The dual assembled contigs and all other contigs longer than 300 bp were pooled and

annotated. Nextera-prepared sequencing libraries were generated using the Roche Titanium-

Compatible kit with MDA product as the input DNA, following the manufacturer’s instructions

(Adey et al., 2010). A total of 32 Nextera sequencing libraries constructed from SAGs were

barcoded and sequenced (454 FLX Titanium chemistry) on 1/2 microtiter plate. Whole-genome

sequence data for all Thaumarchaeota SAGs are available in IMG under accession numbers

listed in Supplementary Table S1.

SAG whole genome sequence quality control

Each raw sequence data set was screened against all finished bacterial and archaeal

genome sequences (downloaded from NCBI) and the human genome to identify potential

contamination in the sample. Reads were mapped against reference genomes with bwa version

0.5.9 (Li and Durbin, 2009) using default parameters (96% identity threshold). None of the

libraries showed significant contamination. Additionally, gene sequences of the final assemblies

(see below) were compared against the GenBank nr database by BLASTX and taxonomically

classified using MEGAN (57).

To further verify the absence of contaminating sequences in the assemblies, tetramer

frequencies were extracted from all scaffolds using two alternative settings: 1) sliding window of

1000 bp and 100 bp step size and 2) sliding window of 5000 bp and 500 bp step size. Reverse-

complementary tetramers were combined and the frequencies represented as a N×136 feature

matrix, where N is the number of windows and each column of the matrix corresponds to the

frequency of one of the 136 possible tetramers. Principal component analysis (PCA) was then

used to extract the most important components of this high dimensional feature matrix. The

analysis produced unimodal distribution along the first four PCs for the majority of SAGs,

suggesting homogenous DNA sources. Scaffolds representing extremes on the first four PCs

were identified and manually examined for their closest TBLASTX hits against the NCBI nt

database.

SAG annotation

The gene modeling program Prodigal (http://prodigal.ornl.gov/) was run on the draft

single cell genomes, using default settings that permit overlapping genes and using ATG, GTG,

and TTG as potential starts. The resulting protein translations were compared to the GenBank

non-redundant database (NR), the Swiss-Prot/TrEMBL, Pfam, TIGRFam, Interpro, KEGG, and

COGs databases using BLASTP or HMMER. From these results, product assignments were

made. Initial criteria for automated functional assignment set priority based on TIGRFam, Pfam,

COG, Interpro profiles, pairwise BLAST versus Swiss-Prot/TrEMBL, and KO groups. The

annotation was imported into the Joint Genome Institute Integrated Microbial Genomes (IMG;

http://img.jgi.doe.gov/cgi-bin/pub/main.cgi) (Markowitz et al., 2010).

Phylogenomic tree construction

We compiled two data sets for phylogenomic analyses of marine Thaumarchaeota. The

first data set used sequence data from 46 single cell genomes and the single cultured isolate

Nitrosopumilus maritimus SCM1 genome. The second set included all 8 published composite

Thaumarchaeota genomes in addition to N. maritimus and the 46 single cell genomes used in the

first compilation. These composite genomes are Candidatus Nitrosoarchaeum koreensis MY1,

Candidatus Nitrosoarchaeum limnia SFB1, Candidatus Cenarchaeum symbiosum A, Candidatus

Nitrosoarchaeum limnia BG20, Candidatus Nitrosopumilus salaria BD31, Candidatus

Nitrosopumilus koreensis AR1, Candidatus Nitrosopumilus sediminis AR2, and Candidatus

Nitrososphaera gargensis Ga9.2. Genome sequences from 2 Crenarchaeota, Pyrobaculum

islandicum DSM 4184 and Sulfolobus acidocaldarius DSM 639, were included as outgroups.

These two data sets were analyzed separately, because it is not clear how composite genomes

may affect the phylogenomic reconstruction.

The two data sets were processed in an identical way based on the following procedure.

Orthologous gene families were identified using the OrthoMCL software (Li et al., 2003).

Inparalog copies in a gene family were discarded, and gene members assigned to different COGs

were also discarded. For the remaining single copy orthologous families, only those found in

genomes from at least 25 (in the first data set) or 33 (in the second data set) Thaumarchaeota and

1 outgroup member were retained. This resulted in retention of 97 (in the first data set) or 83 (in

the second data set) gene families. Members in each gene family were aligned at the amino acid

level using MAFFT (Katoh et al., 2005) and the alignments were trimmed using TrimAl

(Capella-Gutiérrez et al., 2009) with the criteria of “-automated1 -resoverlap 0.55 -seqoverlap

60”. Then the trimmed alignments were concatenated, with missing sequences treated as gaps.

To account for heterogeneity in the evolutionary processes among different genes, we applied a

data partition model during phylogenetic construction using the RAxML v7.3.0 software

(Stamatakis, 2006). The PartitionFinder software (Lanfear et al., 2012) grouped the 97 proteins

into 16 partitions and grouped the 83 proteins into 14 partitions, respectively, and estimated the

best-fit substitution matrix for each partition using a maximum likelihood framework. Gamma

distribution of rate variation was also applied in RAxML analysis. Genomes obtained from

single cells have many missing genes and taxa with insufficient phylogenetic signal may become

rogues that take uncertain positions in a phylogenetic tree. We applied the RogueNaRok software

(Aberer et al., 2013) and identified one rogue, SCGC AAA008-M23. Another RAxML

phylogenomic tree was constructed with this genome excluded, but the bootstrap support for

unresolved branches was only slightly improved compared to the original tree. Therefore, only

the original RAxML tree containing sequences from all SAGs is presented. Orthologous protein

sequences are available upon request.

Comparative analysis of genome content

All of the predicted amino acid sequences from the 46 SAGs and Nitrosopumilus

maritimus SCM1 were clustered into orthologous gene families using the OrthoMCL software

(Li et al. 2003). Then the occurrence rate of each family in the 4 epipelagic clade SAGs and the

42 mesopelagic clade SAGs was calculated, respectively. The most interesting ecologically

relevant gene families, that had a higher occurrence rate in one clade compared to the other, were

identified and are listed in Table S3.

Analysis of photolyase and catalase

Inferred amino acid sequences closely related to homologs of photolyase and catalase

were identified in the Global Ocean Survey (GOS) metagenomic database using a three-step

procedure. Firstly, the GOS DNA read sequences were translated to amino acid sequences using

all 6 reading frames. Peptide fragments with at least 60 amino acids were retained. Next, the

photolyase and catalase amino acid sequences identified in the SAGs were used as query

sequences to search against GOS using the BLASTp program. The criteria to retain GOS hits for

further analyses were similarity scores ≥60, alignment lengths ≥100, and bit scores ≥100 for

photolyase, and similarity scores ≥75, alignment lengths ≥310, and bit scores ≥500 for

catalase. These parameter values were estimated based on preliminary phylogenetic analyses

showing that sequences recovered using more relaxed criteria were not related to

Thaumarchaeota. Finally, GOS DNA sequences identified as photolyase and catalase peptide

fragments, were extracted and searched against the NCBI non-redundant database using the

BLASTx program to guarantee that these GOS reads encoded photolyase or catalase.

Phylogenetic analysis of the photolyase and catalase sequences we retrieved followed an

identical procedure. Since the homologous sequences are very divergent, 7 alignment methods

were used and compared to better account for alignment uncertainty. These methods include

(Larkin et al., 2007), MAFFT (Katoh et al., 2005), MUSCLE (Edgar, 2004), T-coffee

(Notredame et al., 2000), DIALIGN (Morgenstern, 2004), Kalign (Lassmann and Sonnhammer,

2005), and OPAL (Wheeler and Kececioglu, 2007). The qualities of the alignments were

compared using the TrimAl software (Capella-Gutiérrez et al., 2009); the best alignment was

selected according to the consistency score calculated by TrimAl. Next, the amino acid

substitution model was determined using the ProtTest v3 software (Darriba et al., 2011). A

phylogenetic tree was constructed using the MrBayes v3.1.2 software (Ronquist and

Huelsenbeck, 2003). One cold and three heated Markov chain Monte Carlo (MCMC) chains

were run for 1,000,000 generations with trees sampled every 100 generations. Two independent

runs of MCMC were performed. The first 25% of all runs were discarded as ‘burn-in’. A 50%

majority-rule consensus tree was constructed from the post-burn-in trees. The average standard

deviation of split frequencies reached <0.01, indicative of convergence.

Supplemental Figure Legends

Figure S1. Maximum likelihood phylogenetic tree of marine Thaumarchaeota 16S rRNA

genes. The tree was constructed using the RAxML v7.3.0 software using the GTR substitution

model with Gamma distributed rate heterogeneity among sites. Values at the nodes show the

number of times the clade defined by that node appeared in the 100 bootstrapped datasets.

Bootstrap values below 50 are not shown. The taxa included in the tree are the Thaumarchaeota

SAGs, cultures, and a few environmental sequences, with 5 soil and hot spring sequences as

outgroups. The epi- and mesopelagic clades are indicated by shading.

Figure S2. Maximum likelihood phylogenomic analysis of 55 Thaumarchaeota genomes.

The tree was constructed using the RAxML v7.3.0 software using a concatenated amino acid

sequence of 83 genes with 24,061 sites, with a data partition model determined by the

PartitionFinder software. Values at the nodes show the number of times the clade defined by that

node appeared in the 100 bootstrapped datasets. Two Crenarchaeota outgroup species are not

shown. Details of tree construction can be found in Supplemental Material. The epi- and

mesopelagic clades are indicated by shading. Single cell genomes from different water

masses/locations/depths are marked with different colors as identified in the legend inset.

Supplemental Table Legends

Table S1. Accession numbers and environmental characteristics of the 46 marine

Thaumarchaeota single-cell amplified genomes used in this study.

Table S2. COG annotations of the 97 proteins used for phylogenomic analysis.

Table S3. Examples of gene families distributed in the epi- and mesopelagic clades of

marine Thaumarchaeota.

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0.04

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Uncultured_Arctic_crenarchaeote_clone_SCICEX1231236E12_EU199667

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AAA001-A19 AAA288-J14 AAA008-P02 AAA008-E02

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Cand. Cenarchaeum symbiosum A Cand. Nitrososphaera gargensis Ga9.2

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31

07

SA

12°2

9′4

1.4

″S

4°5

9′5

5.2

″W

01D

ec2007

80

0

4.8

3

4.5

0

.45

24

4

70

4

AA

A007

-G1

7

25

24

02

31

08

SA

12°2

9′4

1.4

″S

4°5

9′5

5.2

″W

01D

ec2007

80

0

4.8

3

4.5

0

.34

18

9

58

1

AA

A007

-M2

0

25

24

02

31

09

SA

12°2

9′4

1.4

″S

4°5

9′5

5.2

″W

01D

ec2007

80

0

4.8

3

4.5

0

.30

25

3

50

3

AA

A007

-N2

3

25

24

02

31

10

SA

12°2

9′4

1.4

″S

4°5

9′5

5.2

″W

01D

ec2007

80

0

4.8

3

4.5

0

.17

12

3

29

8

AA

A008

-E0

2

25

24

02

31

11

SA

12°2

9′4

1.4

″S

4°5

9′5

5.2

″W

01D

ec2007

80

0

4.8

3

4.5

0

.53

28

3

83

8

AA

A008

-E1

5

25

24

02

31

12

SA

12°2

9′4

1.4

″S

4°5

9′5

5.2

″W

01D

ec2007

80

0

4.8

3

4.5

0

.46

23

6

70

6

AA

A008

-E1

7

25

24

02

31

13

SA

12°2

9′4

1.4

″S

4°5

9′5

5.2

″W

01D

ec2007

80

0

4.8

3

4.5

0

.43

25

7

73

0

AA

A008

-G0

3

25

24

02

31

14

SA

12°2

9′4

1.4

″S

4°5

9′5

5.2

″W

01D

ec2007

80

0

4.8

3

4.5

0

.53

23

6

85

2

AA

A008

-M2

1

25

24

02

31

15

SA

12°2

9′4

1.4

″S

4°5

9′5

5.2

″W

01D

ec2007

80

0

4.8

3

4.5

0

.36

19

7

60

6

AA

A008

-M2

3

25

24

02

31

16

SA

12°2

9′4

1.4

″S

4°5

9′5

5.2

″W

01D

ec2007

80

0

4.8

3

4.5

0

.35

17

1

55

8

AA

A008

-N0

7

25

24

02

31

17

SA

12°2

9′4

1.4

″S

4°5

9′5

5.2

″W

01D

ec2007

80

0

4.8

3

4.5

0

.28

16

3

48

9

AA

A008

-O0

5

25

24

02

31

18

SA

12°2

9′4

1.4

″S

4°5

9′5

5.2

″W

01D

ec2007

80

0

4.8

3

4.5

0

.35

15

0

55

7

AA

A008

-O1

8

25

24

02

31

19

SA

12°2

9′4

1.4

″S

4°5

9′5

5.2

″W

01D

ec2007

80

0

4.8

3

4.5

0

.39

23

4

65

3

AA

A008

-P0

2

25

24

02

31

20

SA

12°2

9′4

1.4

″S

4°5

9′5

5.2

″W

01D

ec2007

80

0

4.8

3

4.5

0

.44

19

5

73

5

AA

A008

-P2

3

25

24

02

31

21

SA

12°2

9′4

1.4

″S

4°5

9′5

5.2

″W

01D

ec2007

80

0

4.8

3

4.5

0

.50

23

0

80

1

AA

A288

-I1

4

25

13

23

70

66

NP

22°4

5′ N

158°0

0′ W

09S

ep2009

77

0

4.7

3

4.3

1

.06

15

3

15

34

AA

A288

-J1

4

25

13

23

70

65

NP

22°4

5′ N

158°0

0′ W

09S

ep2009

77

0

4.7

3

4.3

0

.68

25

3

10

73

tel:2524023096

tel:2524023084

tel:2524023095

tel:2523533633

tel:2524023085

tel:2524023093

tel:2524023092

tel:2524023094

tel:2529292698

AA

A288

-C1

7

25

24

02

31

22

NP

22°4

5′ N

158°0

0′ W

09S

ep2009

77

0

4.7

3

4.3

0

.59

26

9

95

3

AA

A288

-D0

3

25

24

02

31

23

NP

22°4

5′ N

158°0

0′ W

09S

ep2009

77

0

4.7

3

4.3

0

.53

33

5

88

0

AA

A288

-D2

2

25

24

02

31

24

NP

22°4

5′ N

158°0

0′ W

09S

ep2009

77

0

4.7

3

4.3

0

.55

23

4

85

9

AA

A288

-E0

9

25

24

02

31

25

NP

22°4

5′ N

158°0

0′ W

09S

ep2009

77

0

4.7

3

4.3

0

.55

30

0

88

7

AA

A288

-G0

5

25

24

02

31

26

NP

22°4

5′ N

158°0

0′ W

09S

ep2009

77

0

4.7

3

4.3

0

.71

24

2

10

63

AA

A288

-K0

2

25

24

02

31

27

NP

22°4

5′ N

158°0

0′ W

09S

ep2009

77

0

4.7

3

4.3

0

.66

31

9

10

53

AA

A288

-K0

5

25

24

02

31

28

NP

22°4

5′ N

158°0

0′ W

09S

ep2009

77

0

4.7

3

4.3

0

.49

27

9

80

3

AA

A288

-K2

0

25

24

02

30

97

NP

22°4

5′ N

158°0

0′ W

09S

ep2009

77

0

4.7

3

4.3

0

.57

28

8

88

8

AA

A288

-M0

4

25

24

02

30

98

NP

22°4

5′ N

158°0

0′ W

09S

ep2009

77

0

4.7

3

4.3

0

.56

32

7

91

7

AA

A288

-M2

3

25

24

02

30

99

NP

22°4

5′ N

158°0

0′ W

09S

ep2009

77

0

4.7

3

4.3

0

.51

32

0

84

7

AA

A288

-N1

5

25

24

02

31

00

NP

22°4

5′ N

158°0

0′ W

09S

ep2009

77

0

4.7

3

4.3

0

.27

17

0

50

0

AA

A288

-N2

3

25

24

02

31

01

NP

22°4

5′ N

158°0

0′ W

09S

ep2009

77

0

4.7

3

4.3

0

.49

24

8

76

8

AA

A288

-O1

7

25

24

02

31

02

NP

22°4

5′ N

158°0

0′ W

09S

ep2009

77

0

4.7

3

4.3

0

.13

12

7

24

9

AA

A288

-O2

2

25

24

02

31

03

NP

22°4

5′ N

158°0

0′ W

09S

ep2009

77

0

4.7

3

4.3

0

.37

17

8

62

1

AA

A288

-P0

2

25

24

02

31

04

NP

22°4

5′ N

158°0

0′ W

09S

ep2009

77

0

4.7

3

4.3

0

.86

30

7

12

74

AA

A288

-P1

8

25

24

02

31

05

NP

22°4

5′ N

158°0

0′ W

09S

ep2009

77

0

4.7

3

4.3

0

.68

33

5

10

91

1W

W (

Anta

rcti

c W

inte

r W

ater

), C

DW

(A

nta

rtic

Cir

cum

pola

r D

eep W

ater

), G

OM

(G

ulf

of

Mai

ne)

, S

A (

South

Atl

anti

c g

yre

), N

P

(Nort

h P

acif

ic g

yre

).

2T

he

cult

ure

d r

efer

ence

str

ain

Nit

roso

pum

ilus

mari

tim

us

scm

1 h

as a

gen

om

e si

ze o

f 1.6

5 M

bp.

Table S2. COG annotation of the 97 proteins used for phylogenomic analysis.

COG id Biological function

COG0541 Signal recognition particle GTPase

COG2511 Archaeal Glu-tRNAGln amidotransferase subunit E (contains GAD domain)

COG0252 L-asparaginase/archaeal Glu-tRNAGln amidotransferase subunit D

COG5257 Translation initiation factor 2, gamma subunit (eIF-2gamma; GTPase)

COG0152 Phosphoribosylaminoimidazolesuccinocarboxamide (SAICAR) synthase

COG1797 Cobyrinic acid a,c-diamide synthase

COG2138 Uncharacterized conserved protein

COG2082 Precorrin isomerase

COG0096 Ribosomal protein S8

COG0113 Delta-aminolevulinic acid dehydratase

COG1881 Phospholipid-binding protein

COG2109 ATP:corrinoid adenosyltransferase

COG0468 RecA/RadA recombinase

COG0126 3-phosphoglycerate kinase

COG0520 Selenocysteine lyase

COG1093 Translation initiation factor 2, alpha subunit (eIF-2alpha)

COG0615 Cytidylyltransferase

COG0097 Ribosomal protein L6P/L9E

COG0128 5-enolpyruvylshikimate-3-phosphate synthase

COG2260 Predicted Zn-ribbon RNA-binding protein

COG0093 Ribosomal protein L14

COG0396 ABC-type transport system involved in Fe-S cluster assembly, ATPase component



COG1471 Ribosomal protein S4E

COG0034 Glutamine phosphoribosylpyrophosphate amidotransferase

COG1339 Transcriptional regulator of a riboflavin/FAD biosynthetic operon

COG2090 Uncharacterized protein conserved in archaea

COG1675 Transcription initiation factor IIE, alpha subunit


COG2125 Ribosomal protein S6E (S10)

COG0225 Peptide methionine sulfoxide reductase

COG0504 CTP synthase (UTP-ammonia lyase)

COG0169 Shikimate 5-dehydrogenase


COG2139 Ribosomal protein L21E

COG1324 Uncharacterized protein involved in tolerance to divalent cations

COG2262 GTPases



COG0010 Arginase/agmatinase/formimionoglutamate hydrolase, arginase family

COG1958 Small nuclear ribonucleoprotein (snRNP) homolog

COG1646 Predicted phosphate-binding enzymes, TIM-barrel fold

COG0189 Glutathione synthase/Ribosomal protein S6 modification enzyme (glutaminyl transferase)



COG1258 Predicted pseudouridylate synthase

COG0265 Trypsin-like serine proteases, typically periplasmic, contain C-terminal PDZ domain

COG1412 Uncharacterized proteins of PilT N-term./Vapc superfamily


COG0287 Prephenate dehydrogenase

COG1798 Diphthamide biosynthesis methyltransferase


COG1903 Cobalamin biosynthesis protein CbiD

COG2890 Methylase of polypeptide chain release factors

COG2073 Cobalamin biosynthesis protein CbiG

COG1867 N2,N2-dimethylguanosine tRNA methyltransferase

COG2875 Precorrin-4 methylase

COG0667 Predicted oxidoreductases (related to aryl-alcohol dehydrogenases)

COG1491 Predicted RNA-binding protein


COG1985 Pyrimidine reductase, riboflavin biosynthesis

COG0358 DNA primase (bacterial type)

COG0057 Glyceraldehyde-3-phosphate dehydrogenase/erythrose-4-phosphate dehydrogenase

COG1024 Enoyl-CoA hydratase/carnithine racemase

COG1537 Predicted RNA-binding proteins

COG0671 Membrane-associated phospholipid phosphatase

COG4221 Short-chain alcohol dehydrogenase of unknown specificity





COG0644 Dehydrogenases (flavoproteins)

COG1587 Uroporphyrinogen-III synthase


COG0030 Dimethyladenosine transferase (rRNA methylation)

COG2241 Precorrin-6B methylase 1


COG0863 DNA modification methylase

COG1180 Pyruvate-formate lyase-activating enzyme

COG0195 Transcription elongation factor


COG1254 Acylphosphatases

COG1378 Predicted transcriptional regulators

COG1439 Predicted nucleic acid-binding protein, consists of a PIN domain and a Zn-ribbon module

COG1522 Transcriptional regulators

COG1964 Predicted Fe-S oxidoreductases

COG1599 Single-stranded DNA-binding replication protein A (RPA), large (70 kD) subunit and

related ssDNA-binding proteins

COG1940 Transcriptional regulator/sugar kinase

COG0054 Riboflavin synthase beta-chain


COG2242 Precorrin-6B methylase 2

COG1409 Predicted phosphohydrolases

COG1382 Prefoldin, chaperonin cofactor

COG1703 Putative periplasmic protein kinase ArgK and related GTPases of G3E family

COG3185 4-hydroxyphenylpyruvate dioxygenase and related hemolysins

COG0805 Sec-independent protein secretion pathway component TatC

Table S3. Examples of gene families distributed in the epi- and mesopelagic clades of marine

Thaumarchaeota.

Family

id1

Gene Epipelagic SAGs

(N=4)

Nmar2

(N=1)

Mesopelagic

SAGs (N=42)

OR3550 urease subunit alpha 0 0 2

OR3005 urease subunit beta 0 0 3

OR3010 urease accessory protein UreD 0 0 3

OR2375 urease accessory protein 0 0 7


OR2326 urea amidohydrolase subunit gamma 0 0 8


OR2270 urease accessory protein UreD 0 0 9

OR1967 urea active transporter 0 0 10

OR2098 urease subunit alpha 0 0 10

OR3671 Deoxyribodipyrimidine photolyase 1 0 0

OR3847 twin-arginine translocation pathway

signal 2 0 0

OR2460 universal stress protein 3 1 0

OR3848 catalase/peroxidase HPI 2 0 0

OR2257 ammonia monooxygenase, subunit A 2 1 6

OR2022 ammonium transporter 2 1 8

OR1861 superoxide dismutase 3 1 11

OR2115 ammonia monooxygenase operon-

associated hypothetical protein 2 1 9

OR2077 ammonia monooxygenase subunit B 2 1 10

OR1607 blue (type1) copper domain-

containing protein 2 1 17

OR1653 multicopper oxidase type 3 2 1 17

OR1554 ammonia monooxygenase/methane

monooxygenase subunit C 2 1 19

OR1042 DSBA oxidoreductase 3 1 27

OR2116 peroxiredoxin 2 1 9











1These are orthologous gene families identified by the OrthoMCL software; the family id is

arbitrary.

2Nmar: Nitrosopumilus maritimus scm1, the only marine Thaumarchaeota strain in pure culture.

Single-cell genomics shedding light on marine Thaumarchaeota diversification

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