Predominant archaea in marine sediments degrade detrital proteins

45 Pages·2013·2.25 MB·English

by  Karen G. Lloyd

LETTER
doi:10.1038/nature12033
Predominant archaea in marine sediments degrade
detrital proteins
KarenG.Lloyd1,2*,LarsSchreiber1*,DortheG.Petersen1,KasperU.Kjeldsen1,MarkA.Lever1,AndrewD.Steen2,
RamunasStepanauskas3,MichaelRichter4,SaraKleindienst5,SabineLenk5,AndreasSchramm1&BoBarkerJørgensen1
HalfofthemicrobialcellsintheEarth’soceansarefoundinsedi- marine organic matter that can be chemically characterized7, and
ments1.ManyofthesecellsaremembersoftheArchaea2,single- theyaredegradedslowlybythus-far-unidentifiedmicrobesinanoxic
celledprokaryotesinadomainoflifeseparatefromBacteriaand sediments8. If heterotrophic archaea were able to degrade proteins,
Eukaryota.However,mostofthesearchaealackculturedrepresen- suchafindingwouldchangeourbasicconceptionofthemarinesedi-
tatives,leavingtheirphysiologiesandplacementonthetreeoflife mentarycarboncycle,asitisgenerallyassumedthatbacteriadrivethe
uncertain.Hereweshowthattheunculturedmiscellaneouscrenar- primaryremineralizationofcomplexorganicmatter.
chaeotalgroup(MCG)andmarinebenthicgroup-D(MBG-D)are Globally,marinesubsurfacearchaeaareoftendominatedbymem-
among the most numerous archaea in the marine sub-sea floor. bersoftheMCGandMBG-D9(Fig.1).Weanalysedthesegroupsof
Single-cellgenomicsequencingofonecellofMCGandthreecells archaeapresentinAarhusBay,Denmark(SupplementaryFig.1),in
of MBG-D indicated that they form new branches basal to the organic-richmarinesedimentswheremicrobialactivityishighatthe
archaeal phyla Thaumarchaeota3 and Aigarchaeota4, for MCG, surfacebutdropstolowvaluessimilartothosefoundindeepoceanic
and the order Thermoplasmatales, for MBG-D. All four cells sedimentsafewmetresbelowtheseafloor10.Here,MCGandMBG-D
encoded extracellular protein-degrading enzymes such as gingi- are abundant,basedon16SribosomalRNAgene polymerasechain
painandclostripainthatareknowntobeeffectiveinenvironments reaction (PCR) amplicon sequence libraries and quantitative PCR
chemically similar to marine sediments. Furthermore, we found (SupplementaryFig.2andSupplementaryTables1–3).Theirabun-
thesetwotypesofpeptidasetobeabundantandactiveinmarine dance is independent of the major biogeochemical zones of sul-
sediments, indicating that uncultured archaea may have a previ- phate reduction and methanogenesis, as has been noted previously
ously undiscovered role in protein remineralization in anoxic forMCG9,suggestingthattheyhavedifferentmetabolicpathwaysfrom
marinesediments. thesetypesofanaerobicrespiration.
InthecoldanoxicsedimentsunderlyingmostoftheEarth’soceans, However,thetaxonomicmarker16SrRNAgenecannotbeusedto
theonlymetabolismsknownforculturedarchaeaaremethanepro- inferphysiologiesforMCGandMBG-Dbecausenomemberorclose
ductionfromsimplecarbonsubstrates,andmethaneconsumption5. relativeofthesegroupshaseverbeengrowninlaboratoryculture.We
Recent isotopic evidence, however, has shown that sedimentary thereforeobtainedlargegenomicsequencesfromsinglecellstocouple
archaeacanbeheterotrophic6,butpotentialcarbonsubstratesremain taxonomic genes to those encoding other cellular functions11,12. We
unknown.Intriguingly,detritalproteinsarethelargestcomponentsof used density gradient centrifugation to extract intact cells from a
MCG relative to total archaea sequences MBG-D relative to total archaea sequences
60–79% 20–39% 60–79% 20–39%
80–100% 40–59% 1–19% 80–100% 40–59% 1–19%
MCG present, but clone abundance unavailable MBG-D present, but clone abundance unavailable
Figure1|Globalmarineoccurrenceofmiscellaneouscrenarchaeotalgroup andMBG-D(blue).Forsome(crosses),sequenceabundanceinformationwas
(MCG)andmarinebenthicgroupD(MBG-D). Relativeabundanceof16S unavailable.
rRNAgenesequencesinclonelibrariesfrommarinesedimentsforMCG(red)
1CenterforGeomicrobiology,DepartmentofBioscience,AarhusUniversity,Aarhus8000,Denmark.2UniversityofTennessee,Knoxville,Tennessee37996,USA.3BigelowLaboratoryforOceanSciences,
EastBoothbay,Maine04544,USA.4RiboconGmbH,Bremen28359,Germany.5MaxPlanckInstituteforMarineMicrobiology,Bremen28359,Germany.
*Theseauthorscontributedequallytothiswork.
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RESEARCH LETTER
sediment depth of 10cm, sorted individual cells with fluorescence-
Euryarchaeota
activated cell sorting (FACS), amplified their genomic DNA with
multipledisplacementamplification(MDA),andscreenedfortaxo- 36 Halobacteriales Halobacteriales
nomic identification via 16S rRNA gene sequences (Methods and 3 Methanocellales Methanocellales
SupplementaryFigs3and4).Weselectedagenomeamplifiedfrom
12 Methanosarcinales Methanosarcinales
a single MCG cell (MCG_E09, which has NCBI name Thaumar-
chaeotaarchaeonSCGCAB-539-E09)andthreegenomesfromsingle 10 Methanomicrobiales Methanomicrobiales
MBG-Dcells(MBG-D_N05,MBG-D_F20andMBG-D_C06,which 4 Archaeoglobales Archaeoglobales
haveNCBInamesThermoplasmatalesarchaeonSCGCAB-539-N05, Thermoplasma volcanium GSS1
Thermoplasma acidophilum DSM 1728
SCGCAB-539-F20andSCGCAB-539-C06intheNCBIdatabase)for Picrophilus torridus DSM 9790
whole-genomesequencing(SupplementaryTables4–6).Totaldenovo Aciduliprofundum sp. MAR08−339 Thermoplasmatales
Aciduliprofundum boonei T469
assemblysizesforthesingleamplifiedgenomes(SAGs)rangedfrom SCGC AB−540−F20, MBG-D_F20
SCGC AB−539−N05, MBG-D_N05
0.593to1.037megabases(Mb)with73–172contigs.Qualitycontrol
Uncultured marine group II euryarchaeote
showednoevidenceofcontamination(MethodsandSupplementary SCGC AB−539−C06, MBG-D_C06
Fig. 5). Estimated genome coverage was 32–70% (Table 1), due to 17 Methanococcales Methanococcales
unevenorbiasedgenomicamplificationduringMDA11. 10 Methanobacteriales Methanobacteriales
Phylogeneticanalysesbasedonaconcatenationofgenesconserved Methanopyrus kandleri AV19 Methanopyrales
in single copies in allarchaea in the Integrated Microbial Genomes 15 Thermococcales Thermococcales
(IMG)database(SupplementaryFig.6andSupplementaryTable7) Crenarchaeota
44 Crenarchaeota
placedtheMCGcellonadeepbranchwithinthephylaThaumarchaeota3
andAigarchaeota4,andtheMBG-Dcellsbasaltoorjustinsidegroup Nitrosoarchaeum limnia SFB1
Nitrosoarchaeum limnia BG20
MG-IIintheorderThermoplasmatalesinthephylumEuryarchaeota Nitrosopumilus sp. MY1 Thaumarchaeota/
Nitrosopumilus maritimus SCM1 Aigarchaeota
(Fig.2and SupplementaryFig.7).The MCG-E09placementagrees Cenarchaeum symbiosum A
withphylogeniesbasedonsingletaxonomicmarkergenesthatshow SCGC AB−539−E09, MCG_E09
Caldiarchaeum subterraneum
thatMCGaredistinctfromtheCrenarchaeota13.However,thethree OSPB−1 NAG1, Isolate draft
Korarchaeum cryptofilum OPF8 Korarchaeota
MBG-DSAGsarenotmonophyleticrelativetoMG-II,theonlyother Nanoarchaeum equitans Kin4−M Nanoarchaeota
unculturedgroupofThermoplasmataleswithgenomicinformation. 0.10
This suggestseitherthatpartial complementsofarchaealconserved Figure2|EvolutionaryplacementofSAGs. Consensusofmaximum
genescannotresolve fine-scalephylogenies orthattheevolutionary likelihood(RAxML)treesofconcatenatedcorearchaealconservedsingle-copy
historyoftheMBG-Dismorecomplexthanthatderivedfrom16S genes(individualtreesshowninSupplementaryFig.7).Phyla(bold)andorders
rRNAgenes.The16SrRNAgenesequencesfromthesinglecellsare arelabelled.Numbersofgenomesincollapsedcladesarewrittenontheboxes.
.98% similar to other environmental sequences, making the cells
representativesoftheseunculturedgroups(SupplementaryFig.8). relatedtothatofClostridiaspp.(SupplementaryFig.9),andcontains
Allfoursinglecellscontainedpredictedextracellularcysteinepep- bindingsitesforthecofactorCa21(Fig.3d)16.InMBG-D_N05,clos-
tidases found in anaerobic protein-degrading bacteria: clostripain tripainiscloselyrelatedtothatofAciduliprofundumboonei,ahyper-
(Merops family C11, 1 copy in MCG_E09 and 2 copies in MBG- thermophilicprotein-degradingmemberoftheThermoplasmatales17,
D_N05),gingipain(MeropsfamilyC25,4,12and6copiesinMBG- and is adjacent to two copies of gingipain which have the domain
D_N05, MBG-D_F20 and MBG-D_C06, respectively), papain architecture of gingipain in A. boonei18 (Fig. 3c and Supplementary
(Merops family C1A, 2 and 1 copies in MBG-D_N05 and MBG- Fig.9).However,15ofthe16gingipaincopiesfromallthreeMBG-D
D_F20, respectively) and pyroglutamyl peptidase (Merops family SAGsaremonophyleticanddistinctfromothergroups(Supplemen-
C15, 1 copy in MBG-D_F20) (Fig. 3 and Supplementary Tables 8 taryFig.9).Cysteinepeptidaseactivityrequireschemicallyreducing,
and9)14.Clostripainandgingipainaresecretedendopeptidasesthat moderate-pHenvironmentswithhighcalciumconcentrations19,which
are specific for arginine (or sometimes lysine, for gingipain) in the areconditionscommonlyfoundinmarinesediments.MCG_E09has
primaryaminoacidpositionofthesubstrate15.Papainandpyroglu- twocopiesofM19,oneofthefewpeptidasesknowntotargetD-amino
tamylpeptidaseareoftencytosolicorlysosomal;thefirstcleavesawide acids.TheDenantiomerishighlyabundantinthepeptidoglycanof
varietyofsubstratesandthesecondremovesapyroglutamateresidue bacterialcellwalls,whichcomprisethemostpersistentsedimentary
fromtheaminoterminusofapeptide15.EachSAGgenehomologue detrital matter20. MCG_E09 may therefore be specially adapted to
encodesacompleteactivesiteandsignalsequencesforextracellular degradetheserecalcitrantcomponentsofcellwalls.Acomparisonwith
transport; some may be coexpressed as they cluster on the genome all4,888genomesintheIMGdatabase21showsthatonlymesophilic
(SupplementaryTable9).TheclostripainfromMCG_E09isclosely or moderately thermophilic protein-degrading bacteria share all of
Table1|AssemblystatisticsforeachSAG
Statistic SCGCAB-539-C06 SCGCAB-539-E09 SCGCAB-539-N05 SCGCAB-540-F20
Totalgeneratedsequencedata(Mb) 491 1,437 2,666 440
Assemblysize(Mb)* 0.593 0.627 0.801 1.037
Numberofcontigs* 104 73 99 172
Maximumcontiglength(kb)* 48.2 59.3 73.3 77.0
N50value(kb)* 10.4 27.3 27.1 12.9
Numberofpredictedgenes* 792 787 879 1,272
DetectedtRNAs 9 7 22 13
Predictedgenomesize(tRNA-based){(Mb) 3.03 4.12 1.67 3.67
Achievedgenomecoverage(tRNA-based){(%) 19.6 15.2 48.0 28.3
DetectedCSCG 21 18 31 14
Predictedgenomesize(CSCG-based){(Mb) 1.24 1.53 1.14 3.26
Achievedgenomecoverage(CSCG-based){(%) 47.8 41.0 70.3 31.8
*Onlycontigslongerthan1,000basepairswereconsidered.
{Basedonexpectedaveragenumberof46transferRNAspergenome(SupplementaryFig.6).
{Basedon44determinedCSCGs(conservedsingle-copygenes)commontoallarchaealgenomes(SupplementaryFig.6).
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LETTER RESEARCH
a MCG_E09 b MBG-D_N05 and MBG-D_F20 Figure3|Proposedprotein
degradationpathwayforMCG_E09
Proteins Proteins (a)andMBG-D_N05andMBG-
Extracellular peptidases Extracellular peptidases D_F20(b),andgenearchitecture
C11, S26B, S49 C1A, C11, C15, C25, S8A, M14A forselectedextracellularpeptidases
Oligopeptides Extracellular peptidases Oligopeptides Extracellular peptidases (c,d).Substratesandproductsarein
blackitalicfont,energeticmolecules
C11, S26B, S49 C1A, C11, C15, C25, S8A, M14A
arered,enzymesareinblackbold
Pligo/dipeptides Oligo/dipeptide transporters Oligo/dipeptides Oligo/dipeptide transporters font,andbluelinesindicatethecell
and amino acids and amino acids
membrane.ACS,acetyl-CoA
synthetase;SCS,succinyl-CoA
Intracellular peptidases M19, Intracellular peptidases S15, synthetase.Otheracronymsare
M20, M24, M41, M42, M48, S16, M14A, M20, M22, M24,
definedinthetext.c,d,Gene
M79, S15, S16 M28, M29, T1A, M50
architectureforgingipainand
Amino acids Glutamate clostripaininMBG-D_N05(c),and
Amino acids dehydrogenase
Amino- 2-Oxoglutarate clostripaininMCG_E09(d).MBG-
transferases Glutamate Amino- 2-Oxoglutarate NADP+ D_C06hadapartialrepresentation
2-Keto acids POIRO/RVOR transfer2a-sKeesto acidGslutamate PORN/VAODPRH ofthepathwayspresentinb.
POR/VOR Fd(ox) POR/VOR Fd(ox) KOR IOR
IORAcyl-CoFAds(r ed)AOARldehyFdd(eosx ) 2 H+ [NiFe] TCAK OR IOARcyl-CFodA(rse d) Aldehydes
FOR Fd(red) hydrogenase cycle SCS ADP AOR Fd(ox)
Organic acids HH+ H+ ACS ATP Fd(red)
TCA cycle 2 Organic acids
c H C HC HCE
Propeptide Peptidase C25 Propeptide Peptidase C25 Peptidase C11
MBG-D_N05 C25 gingipain C25 gingipain clostripain
D
Key
d HECE Export BRleude:: aacrgtiivnein sei-tespecific residue
MCG_E09 Peptidase C11 clostripain signal sPiuterple: putative Ca2+ binding
Grey arrow: hypothetical protein
1,000 2,000 3,000 4,000 5,000
Nucleotides
theMCGandMBG-Dpeptidases(SupplementaryTable10).Thus,the andC(hdrABC),methyl-viologen-reducinghydrogenasesubunitsA,
MCGandMBG-Dsinglecellsareprobablycapableofdegradingthe G and D (mvhAGD), and N5-methyltetrahydromethanopterin-
detritalproteinsthatarepresentintheAarhusBaysediment8. coenzyme M methyltransferase subunits A and H (mtrAH) (Sup-
Allfoursinglecellscontaineddi-andtripeptidetransportersaswell plementary Table 8). In some methanogens, these enzymes couple
asgenomicpathwaysfortheintracellularbreakdownofaminoacids. hydrogenotrophic methane production to the creation of a sodium
Theseincludeaminotransferases,ATP-yieldingacetyl-CoAsynthetase motiveforce thatdrivesATPformation27, butthe enzymesare also
(inMBG-D),aswellasferredoxin-reducingoxidoreductasesspecific found in non-methanogenic microorganisms. MBG-D_N05 may
for aldehydes (AOR, absent in MBG-D_C06), formaldehyde (FOR, thereforehaveasodium-basedenergyconservationmechanism.
only in MCG_E09) and pyruvate/2-ketoisovalerate (POR/VOR, in We observed high extracellular peptidase activity consistent with
MCG_E09andMBG-D_N05),whichareintermediatesinthebreak- gingipainandclostripainat600-cmdepthinAarhusBaysediments.
down ofnon-aromaticaminoacidsin hyperthermophilicarchaea22. Gingipain substrates indicated V (velocity of enzyme-catalysed
max
MCG_E09 and MBG-D_F20 also have indolepyruvate ferredoxin reaction at saturating substrate concentrations)59.961.6mmol
oxidoreductase(IOR),whichtargetsintermediatesofaromaticamino 7-amino-4-methylcoumarin(AMC)h21g21sedimentandK (Michaelis
m
acidbreakdown22.Theseoxidoreductasesarehighlyoxygen-labileand constant)551630 mM substrate. Clostripain substrates indicated
use ferredoxin, which has the lowest redox potential of all known V 51565.8mmolAMCh21 g21 sediment,K 51886153mM
max m
electroncarriers(2500mV)23.Tungsten(theAORandFORcofactor) substrate(SupplementaryFig.10).Leucylaminopeptidase,whichto
hasthelowestredoxpotentialofanybiologicallyrelevantmetalligand, our knowledge is the only other peptidase substrate that has been
is plentiful in the Earth’s crust (but not in sea water), and confers assayed in marine sediments28, had much lower potential activity
slower kinetics than observed for the more common molybdenum- (Supplementary Fig. 10).Archaeal peptidases seem to be numerous
containingoxidoreductases24.MostgenomesintheIMGdatabasewith in marinesediments,as metagenomes fromtwogeographicallydis-
Blasthits(E,1025)toalltheferredoxin-dependentoxidoreductases paratemarinesediments(California,GoldIDGm00260,andAlaska,
present in MCG_E09 and MBG-D_N05 (the other two SAGs had Gold ID Gm00257) are replete with homologues of allextracellular
fewer oxidoreductases) were hyperthermophilic protein-degrading peptidasesfoundinthesinglecells(upto2.3peptidasehomologuesper
archaea,withhyperthermophilicprotein-degrading bacteriamaking genome;E#10210;SupplementaryTable12).Thistypeofextracel-
upmostoftherest(SupplementaryTable11).Thepresenceofoxi- lularproteindegradationwithinarchaeathereforeseemstobeactive,
doreductases normally associated with hyperthermophiles in MCG abundantandgeographicallywidespread.
and MBG-D, which inhabit permanently cold sediments (2–16uC Archaeadegradedetritalproteinsinextremeenvironments17.The
seasonally25),maybeancestralorconferenhancedmolecularstability partialgenomesobtainedforMCGandMBG-Dsuggestthatarchaea
inthisreducing,energy-limitedenvironment. haveasimilarfunctionincoldanoxicmarinesediments,whichcom-
Reducedferredoxinproducedduringproteindegradationmaybe prisethelargestorganiccarbonsinkonEarth29.Eachpartialgenome
usedtocreateaprotonmotiveforceinMCG_E09byitsmembrane- contains genes for complete degradation pathways of extracellular
bound[NiFe]-hydrogenase,analogouslytothemechanismpresentin proteins,includingenzymeswhosehomologuesoccurtogetheronly
Pyrococcussp.26.(SupplementaryTable8andFig.3a).MBG-D_N05 inculturedprotein-degradingprokaryotes.MCGmayrepresentanew
and MBG-D_F20 contain heterodisulphide reductase subunits A, B phylumintheArchaeaandMBG-Dmayrepresentaneworderinthe
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RESEARCH LETTER
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SupplementaryInformationisavailableintheonlineversionofthepaper.
andconcentration.
AcknowledgementsTheauthorsthankthecaptainandcrewoftheR/VTyrafor
FullMethodsandanyassociatedreferencesareavailableintheonlineversionof sampling;T.B.Søgaard,A.StentebjergandB.Poulsenfortechnicalwork;F.Lo¨fflerfor
thepaper. laboratoryspace;andD.KirchmanandS.Hallamforsharingtheirunpublished
metagenomicdatasets.ThisworkwasfundedbytheDanishNationalResearch
Received17October2012;accepted20February2013. Foundation,theGermanMaxPlanckSociety,NSFCenterforDarkEnergyBiosphere
InvestigationsNSF-157595(K.G.L.),TheDanishCouncilforIndependentResearch–
Publishedonline27March2013.
NaturalSciences(D.G.P.),theVillumKannRasmussenFoundation,anEUMarieCurie
fellowship(M.A.L.),theGermanResearchFoundation(S.L.)andtheUSANational
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Res.39,3204–3223(2011). M.R.gavebioinformaticsupportandaddedqualitycontroltests.A.S.andB.B.J.
5. Jarrell,K.F.etal.Majorplayersonthemicrobialstage:whyarchaeaareimportant. obtainedthemajorfundingforthiswork.Allco-authorscommentedonandprovided
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ecosystemsoffPeru.Proc.NatlAcad.Sci.USA103,3846–3851(2006). AuthorInformationThiswhole-genomeshotgunprojecthasbeendepositedatDDBJ/
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influenceonaminoacidenantiomerizationinacoastalmarinesediment.Limnol. (AOSI00000000).Reprintsandpermissionsinformationisavailableat
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4 | NATURE | VOL 000 | 00 MONTH 2013
©2013Macmillan Publishers Limited. All rights reserved
LETTER RESEARCH
METHODS (Sigma-Aldrich) was used to assay gingipain58 and Z-Phe-Val-Arg-AMC
(Bachem)wasusedtoassayclostripain59.Sedimentswerecollectedbygravity
SequencingofSAGs,sequenceassemblyandannotation.Asedimentcorewas
collectedon22March2011,inashallowgasareaatAarhusBay,Denmark(56u99 corefromAarhusBaysiteM1.Sedimentsfrom600to630cmbelowseafloor
35.889N,10u2897.893E),waterdepth16.3mand2.5uC(SupplementaryFig.1). werehomogenized,placedin5-mlserumvials(,0.5geach),mixedwith4.0ml
anoxic artificial sea water (salinity 30practical salinity units, pH7.8) and the
Cellswereextractedfrom10cmsedimentdepthbyultrasonictreatmentfollowed
by density gradient centrifugation. Single-cell sorting, whole-genome amp- precisemassofwetsedimentwasrecorded.Serumvialswerethencapped,purged
lification, and PCR screening of single cells were performed at the Bigelow withN2andvortexedtomixcompletely.Foreachsubstrateandconcentration,
LaboratorySingleCellGenomicsCenter(SCGC;http://www.bigelow.org/scgc) autoclavedsedimentwasusedasakilledcontrol.Immediatelyaftervortexing,
byFACSusingtheSYTO-9DNAstain.Thesortedcellswerelysedusingfive 2mlslurrywasremovedtomeasureinitialfluorescence.
cyclesoffreeze–thaw,followedbyfurtherlysisandDNAdenaturingwithcold To measure fluorescence, each sediment subsample was transferred into a
KOH.GenomicDNAfromthelysedcellswasamplifiedusingMDA,resultingin microcentrifugevialandcentrifugedfor10minat9,300g.Onemillilitreofsuper-
SAGs.MDAproductswerescreenedbyquantitativePCRwithprimersetstarget- natantwasremovedandtransferredtoamethacrylate1.5-mlfluorescencecuvette.
ing16SrRNAgenes.ToobtainsufficientquantityofDNAforsequencing,some Fluorescence was measured with a Promega QuantiFluor ST fluorimeter. To
SAGswerere-amplifiedbyasecondroundofMDAattheSCGC.Sequencingwith calibrate fluorescence values, 7-amino-4-methylcoumarin (AMC) was added
the454-GS-FLXTitaniumandtheIlluminaHiSeq2000wasperformedatGATC directlytosedimentslurries,mixedthoroughly,andthenprocessedinthesame
Biotech(Germany)withre-amplifiedSAGsastemplate.SequencingwiththeIon wayassamplesforenzymeassays.EnzymeactivitiesarereportedasratesofAMC
TorrentPGMplatformwasperformedatAarhusUniversityusingtheoriginal liberationperhourpergramwetsediment.
(non-re-amplified)SAGsastemplate.ThesequencedatafortheindividualSAGs Sedimentswereincubatedinthedarkinashakingincubatorforapproximately
aresummarizedinSupplementaryTable4. 8h(precisetimewasrecorded).Afterincubation,fluorescencewasmeasuredas
Readsfromthe454-GS-FLXTitaniumandtheIonTorrentPGMplatforms describedabove.Enzymeactivitywascalculatedfromthechangeinfluorescence
werequalitytrimmedusingtheprinseq-lite.plscript31.The454andIonTorrent foreachsampledividedbytheincubationtime.Kineticparameterswerecalcu-
readswerede-replicatedusingcd-hit-45432andsubsequentlyassembledusing latedusingtheRstatisticalpackage60byanonlinearleast-squaresfittotheactivity
gsAssemblerversion2.6(Roche).Inparallel,weusedtheSPAdesassemblerver- data(SupplementaryFig.10).
sion2.2.133toassembletheIlluminareadsofSCGCAB-539-E09andSCGCAB-
31. Schmieder,R.&Edwards,R.Qualitycontrolandpreprocessingofmetagenomic
539-N05,aswellastheIonTorrentreadsofSCGCAB-539-C06andSCGCAB-
datasets.Bioinformatics27,863–864(2011).
540-F20.ThegsAssemblerandSPAdesassembliesofeachSAGwerecombined
32. Niu,B.,Fu,L.,Sun,S.&Li,W.Artificialandnaturalduplicatesinpyrosequencing
using Sequencher version 5.0.1 (Genecodes). The SAG assemblies were auto- readsofmetagenomicdata.BMCBioinformatics11,187(2010).
annotatedusingtheJointGenomeInstitute(JGI)IMG-ERpipeline21.Annotations 33. Bankevich,A.etal.SPAdes:Anewgenomeassemblyalgorithmandits
weremanuallycuratedusingGenDB34supplementedbyJCoast35.Allpeptidases applicationstosingle-cellsequencing.J.Comput.Biol.19,455–477(2012).
werealignedinARB36againsttheMeropsalignment14(SupplementaryFig.9). 34. Meyer,F.etal.GenDB-anopensourcegenomeannotationsystemforprokaryote
EstimationofgenomesizeandpuritycontrolofSAGs.Thegenomesizesofthe genomes.NucleicAcidsRes.31,2187–2195(2003).
35. Richter,M.etal.JCoast—Abiologist-centricsoftwaretoolfordataminingand
SAGswereestimatedusingaconservedsingle-copygene(CSCG)analysissimilar
comparisonofprokaryotic(meta)genomes.BMCBioinformatics9,177(2008).
toref.37.CSCGspresentinallarchaeawereidentifiedusingtheJGIIMGsite38. 36. Ludwig,W.etal.ARB:asoftwareenvironmentforsequencedata.Database32,
TheCSCG-basedapproachwassupplementedbyatRNA-basedapproach39where 1363–1371(2004).
SAG tRNA numbers were compared to the numbers of tRNAs of complete 37. Woyke,T.etal.Assemblingthemarinemetagenome,onecellatatime.PLoSONE
archaealgenomes. 4,e5299(2009).
ThepurityoftheSAGswastestedbyPCR-screeningforarchaealandbacterial 38. Markowitz,V.M.etal.Theintegratedmicrobialgenomes(IMG)system.Nucleic
AcidsRes.34,D344–D348(2006).
16SrRNAgenesusingmultipleprimersets.Additionally,allpredictedORFswere
39. Lowe,T.M.&Eddy,S.R.tRNAscan-SE:AProgramforimproveddetectionof
checkedwithBlastp40againstNCBI-nrforaminoacididentityobservationsof
transferRNAgenesingenomicsequence.NucleicAcidsRes.25,0955–0964
over 96% to detect common contaminants. Finally, contigs showing a k-mer (1997).
patterndivergentfromtherestoftheSAGsequences41weremanuallychecked 40. Altschul,S.F.etal.GappedBLASTandPSI-BLAST:anewgenerationofprotein
forapossiblecontaminationbyexaminingtheirclosestBlastphits. databasesearchprograms.NucleicAcidsRes.25,3389–3402(1997).
Phylogeneticreconstructionusingconservedsingle-copygenesand16SrRNA 41. Swan,B.K.Potentialforchemolithoautotrophyamongubiquitousbacteria
lineagesinthedarkocean.Science333,1296–1300(2011).
genes.Similartothestudiesperformedbyrefs42and43,weusedarchaealCSCGs
42. Matte-Tailliez,O.,Brochier,C.,Forterre,P.&Philippe,H.Archaealphylogenybased
forinferringthephylogeneticaffiliationoftheinspectedSAGs.Theidentified
onribosomalproteins.Mol.Biol.Evol.19,631–639(2002).
CSCGaminoacidsequencesofcompletedarchaealgenomesandourSAGswere 43. Brochier,C.,Forterre,P.&Gribaldo,S.AnemergingphylogeneticcoreofArchaea:
extractedfromtheJGIIMG-ERsite.Thesequenceswereindividuallyaligned phylogeniesoftranscriptionandtranslationmachineriesconvergefollowing
using MAFFT version 6.86444. All alignments were manually curated using additionofnewgenomesequences.BMCEvol.Biol.5,36(2005).
ARB36.ThealignmentswereconcatenatedusingthePerlscriptcatfasta2phyml.pl 44. Katoh,K.&Toh,H.RecentdevelopmentsintheMAFFTmultiplesequence
(http://www.abc.se/,nylander/catfasta2phyml/).Maximumlikelihoodbootstrap alignmentprogram.Brief.Bioinform.9,286–298(2008).
45. Stamatakis,A.,Hoover,P.&Rougemont,J.Arapidbootstrapalgorithmforthe
treeswerecalculatedusingRAxML-HPC2(RAxMLversion7.2.745)asprovidedby
RAxMLwebservers.Syst.Biol.57,758–771(2008).
the CIPRES cluster (http://www.phylo.org/46). The 16S rRNA gene tree was
46. Miller,M.A.,Pfeiffer,W.&Schwartz,T.CreatingtheCIPRESScienceGatewayfor
createdusingRAxML-HPC45attheCIPREScluster46(SupplementaryFig.8). inferenceoflargephylogenetictrees.GatewayComputingEnvironments
AarhusBay16SrRNAampliconsequencingandquantitativePCR.Asediment Workshop1–8(2010).
corewastakenatstationM1,(56u07.079N,10u20.809E;seeSupplementaryFig. 47. Jørgensen,B.B.Casestudy—A˚rhusBay.EutrophicationinCoastalMarineSystems
1),awell-characterizedsitenearthesitefromwhichSAGswerederivedinAarhus 137–154(AmericanGeophysicalUnion,1996).
Bay47. DNA was extracted48 from five depths. Two different archaea-targeted 48. Kjeldsen,K.U.etal.Diversityofsulfate-reducingbacteriafromanextreme
hypersalinesediment,GreatSaltLake(Utah).FEMSMicrobiol.Ecol.60,287–298
primerpairs49–52wereusedforPCRamplificationof16SrRNAgenefragments.
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TheresultantPCRproductsweresequencedona454-GS-FLXTitaniummachine 49. DeLong,E.F.Archaeaincoastalmarineenvironments.Proc.NatlAcad.Sci.USA89,
aspreviouslydescribed53.SequenceanalysiswasperformedinMothur54.Sequence 5685–5689(1992).
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MCGsubgroups9(SupplementaryFig.2). 1991).
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©2013Macmillan Publishers Limited. All rights reserved
RESEARCH LETTER
56. Ashelford,K.E.,Weightman,A.J.&Fry,J.C.PRIMROSE:acomputerprogramfor mutantsofPorphyromonasgingivalis:Evidenceforsignificantcontribution
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©2013Macmillan Publishers Limited. All rights reserved
SUPPLEMENTARY INFORMATION
doi:10.1038/nature12033
Supplementary Materials and Methods
Detailed procedure for cell extraction, similar to [Fazi et al. 2005]
Sediment was diluted 1:5 with 1x PBS to form sediment slurry. The slurry was sonicated on ice with a
Sonoplus HD2070 ultrasonic homogenizer with an MS 72 probe placed in the ice slurry outside the 2 mL
Eppendorf tube containing the slurry (Bandelin, Germany; sonication parameters: 2x 20 s, pulsed cycles:
2 s-1, amplitude setting: 20%). The slurry was further diluted 1:8 with 1x PBS and vortexed for 10 s.
Sediment particles were allowed to settle for 10 min and the supernatant was collected. Further sediment
particles were removed by separating the supernatant into 0.75 mL aliquots in 2 mL tubes, and carefully
layering 0.75 mL of a 60% Nycodenz solution (w/v, in 1xPBS; Sigma-Aldrich) under each aliquot using a
syringe and fine needle. Samples were centrifuged at 10,500xg for 60 min at 4°C. The upper density layer
was carefully collected and supplemented with 1x TE and 5% w/v glycerol (final concentrations) for sorting
batch AB-539 or 1x TE and 60 mg/mL betaine for sorting batch AB-540. The cell extract was stored at -80°C
until sent on dry ice for single cell sorting in April 2011.
Details of cell sorting, whole-genome amplification and PCR screening
Prior to cell sorting at the Single Cell Genomics Center (SCGC), samples were diluted 1,000x with DNA-free
Sargasso Sea water and pre-screened through a 40 µm mesh-size cell strainer (BD). The diluted subsamples
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 sort gate was based on nucleic acid fluorescence after staining with SYTO-9 and side
scatter (Figure S3). The “purify 0.5 drop” mode was used for maximal sort purity. Cells were deposited into
384-well plates containing 600 nL per well of 1x TE buffer and stored at -80°C until further processing. Of
the 384 wells, 315 were dedicated for single cells, 66 were used as negative controls (no droplet
deposition) and 3 received 10 cells each (positive controls).
The sorted cells were lysed using five cycles of freeze-thaw, followed by further lysis and DNA denaturing
with cold KOH [Raghunathan et al., 2005], at 4°C for plate AB-539 (MCG_E09, MBG-D_C06, and MBG-
D_N05) and 20°C for plate AB-540 (MBG-D_F20). 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 and then inactivated by a 15 min incubation at 65°C. Such MDA-reactions typically yield 500 ng
to 5 µg of amplified genomic DNA. The DNA was stored at -80°C until further processing. We will 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 [Woyke et al., 2011]. Cell sorting and MDA setup were performed in a
HEPA-filtered environment. As a quality control, kinetics of all MDA reactions were monitored by
measuring the SYTO-9 fluorescence using a 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 et al., 2006]. Further analyses were performed
only on those MDA plates where the Cp values were significantly lower in 1-cell wells compared to 0-cell
wells (p<0.05; Wilcoxon Two Sample Test).
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. Initial 16S rRNA gene PCR amplification of all
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single cells was performed using LightCycler 480 SYBR Green I Master Mix (Roche) and the Roche
LightCycler® 480 II real-time thermal cycler. PCR amplification of 16S rRNA genes from SAGs was done using
primers Arc_344F (5’-ACGGGGYGCAGCAGGCGCGA-3’; [Casamayor et al., 2000]) and Arc_915R (5’-
GTGCTCCCCCGCCAATTCCT-3’; [Stahl and Amann, 1991]) targeting the 16S rRNA sequences of most archaea,
and primers 27F (5’-AGRGTTYGATYMTGGCTCAG-3’; [Lane, 1991]) and 907R (5’-CCGTCAATTCMTTTRAGTTT-
3’; [Lane, 1991]) targeting most bacteria. Forward (5’-GTAAAACGACGGCCAGT-3´) and reverse (5´-
CAGGAAACAGCTATGACC-3´) M13 sequencing primers were added to the 5´ ends of each primer pair to aid
direct sequencing of PCR products. All PCR reactions were run for 15 cycles at 71°C followed by 40 cycles at
the appropriate annealing temperature (Arc_344F/Arc_915R: 63°C; 27F/907R: 56°C), 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.
In total 71 high-quality 16S rRNA gene sequences were obtained from 630 sorted cells. The phylogenetic
affiliation of the obtained sequences was determined with the ARB software package [Ludwig et al., 2004]
in combination with Release 104 of the SILVA database [Pruesse et al., 2007]. The sequences could be
divided into 6 archaeal and 65 bacterial sequences (more detailed phylogenetic affiliations of the recovered
SAGs are shown in Figure S4).
Details of sequencing of SAGs
Based on their phylogenetic position, one SAG from the MCG group (SCGC AB-539-E09) and three SAGs
from the MBG-D group (SCGC AB-539-C06, SCGC AB-539-N05, and SCGC AB-540-F20) were chosen for
sequencing. In order to obtain sufficient quantity of DNA for sequencing, SAGs were re-amplified by MDA at
the SCGC: four replicate 125 μL MDA reactions were performed for each SAG and then pooled together.
This resulted in 186 µg (SCGC AB-539-E09) and 192 µg (SCGC AB-539-N05), of genomic dsDNA. This DNA is
from here on referred to as re-amplified SAG.
Sequencing of the SAGs was performed using the 454-GS-FLX Titanium, the Illumina HiSeq 2000, and the
Ion Torrent PGM platforms. Sequencing with the 454-GS-FLX Titanium (single read mode) and the Illumina
HiSeq 2000 (single read mode, 51 cycles), was carried out by a commercial sequencing company (GATC
Biotech, Konstanz, Germany) with re-amplified SAGs as template. Sequencing with the Ion Torrent PGM
platform was performed in-house using the Ion PGM™ 200 Sequencing Kit according to the manufacturer’s
instructions and using the original (non-re-amplified) SAGs as template. The sequencing libraries were
prepared using the Ion XpressTM Plus Fragment Library Kit (Life Sciences) and the Ion OneTouchTM 200
Template Kit (Life Sciences) according to the manufacturer’s instructions. In case of SCGC AB-539-E09, we
additionally normalized the re-amplified SAG and sequenced the resultant DNA with the Ion Torrent PGM
platform as specified above. Normalization of the re-amplified SAG was performed using protocols
published by the Joint Genome Institute (“JGI normalization”;
http://www.jgi.doe.gov/sequencing/protocols/20100410-Illumina-Normalized-Library-Creation-SOP.doc)
and by Rodrigue et al. [2009] (“Rodrigue normalization”). An overview of the generated sequence data for
the individual SAGs is shown in Table S4.
Quality control of Illumina and 454 pyrosequencing raw reads
Unassembled Illumina sequence reads were pre-processed (quality control and alignment) by the
bioinformatics pipeline of the SILVA project (http://www.arb-silva.de). Briefly, reads with more than 2%
ambiguities or 2% homopolymers were removed. The remaining reads were aligned against the SSU rRNA
seed of the SILVA database release 106 (http://www.arb-silva.de/documentation/background/release-106;
Pruesse et al., 2007), whereupon non-aligned reads were not considered for downstream analysis. Using
this strategy, putative partial SSU rRNA gene reads within the dataset could be extracted. Subsequently,
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remaining reads were (1) dereplicated, (2) clustered, and (3) classified. For statistical reasons, the
considerable overlapping of extracted reads in terms of gene region coverage is extremely unlikely;
therefore, steps (1) and (2) cannot lead to any meaningful information. However, from a technical
perspective, the procedures are favorable because they minimize the number of reads in the final
classification step. Dereplication (the identification of identical reads) was performed with cd-hit-est of the
cd-hit package 3.1.2 (Li and Godzik, 2006) using an identity criterion of 1.00 and a word size of 8. The
remaining sequences were clustered again with cd-hit-est using an identity criterion of 0.98 (same word
size). The longest read of each cluster was used as a reference for taxonomic classification, which was
performed by a local BLAST search against the SILVA SSURef 106 NR dataset (Pruesse et al., 2007) using
blast-2.2.22+ (Altschul et al., 1997) with standard settings. The full SILVA taxonomic path of the best blast
hit was assigned to the reads in the event that the value for (% sequence identity + % alignment
coverage)/2 was at least 93.0. In the final step, the taxonomic path of each cluster reference read was
mapped to the additional reads within the corresponding cluster and the corresponding replicates, which
were identified in the previous analysis step, to finally obtain (semi-)quantitative information concerning
the number of individual reads representing a taxonomic path.
With this procedure, up to 3 reads carrying 16S rRNA gene fragments of other, parallelly sequenced SAGs
were detected. Raw 454 pyrosequencing reads were checked similarly. Here, no foreign 16S rRNA gene
fragments were detected in the individual SAG sequence data. This pointed to a post-sequencing, mis-
assignment of Illumina reads, rather than a contamination of the starting material. In order to identify all
mis-assigned reads, we used the following strategy: Sequencing data of the SAGs was assembled using the
CLC workbench Version 4.6.1 (CLCbio, Aarhus, Denmark). Subsequently, all-against-all BLAST searches were
performed for the individual assemblies. This identified several contigs present in multiple SAGs. In order to
identify the source of the contigs and thereby the corresponding reads, contigs present in more than one
SAG assembly were manually inspected in terms of their read coverage values. All contigs present in
multiple SAG assemblies always featured a high read coverage in one SAG assembly and only low read
coverages in the other SAG assemblies. The SAG where the contigs featured the highest read coverage was
consequently considered the contig’s source. In total, we thereby identified 197 and 348 cross-
contaminating contigs in SAG SCGC AB-539-E09 and SAG SCGC AB-539-N05, respectively. Furthermore, we
also detected one contig in each SAG assembly that represented mis-assigned reads originating from the
phiX control DNA used during Illumina sequencing. In order to remove the mis-assigned Illumina reads,
reads of the individual SAGs were mapped onto the identified cross-contaminating- and PhiX-contigs with
bowtie2 [Langmead and Salzberg, 2012] using the pre-set alignment parameter very-sensitive-local. In total
0.1% (SCGC AB-539-E09; 22,726/23,203,535 reads) and 0.13% (SCGC AB-539-N05; 58,132/43,150,804
reads) of the reads were identified as mis-assigned. Subsequently, all mis-assigned reads were removed
from the SAG data sets using the .sam read-alignment file generated by bowtie2 and the samtools software
suite [Li et al., 2009].
Sequence assembly
Reads from the 454-GS-FLX Titanium and the Ion Torrent PGM platforms were quality trimmed using the
prinseq-lite.pl script [Schmieder and Edwards, 2011] with the following parameters, 454 data: -minlen 100
-trim_to_len 450 -trim_left 10 -trim_qual_right 20 -trim_qual_window 4; Ion Torrent data: -minlen 100
-trim_to_len 200 -trim_left 15 -trim_qual_right 20 -trim_qual_window 4. The 454 and Ion Torrent reads
were assembled using the GS De Novo Assembler version 2.6 (gsAssembler, Roche). Manual inspection of
preliminary assemblies with the gsAssembler showed that several loci did not appear in the assemblies
even though they were clearly present in the un-assembled raw reads. One possible reason for this was
that gsAssembler is optimized to assemble reads which represent the target genome with a relative
homogenous coverage. The MDA procedure, however, introduces a considerable bias in terms of genome
coverage, so that some genome regions have little (or no representation) while others can be over-
represented by a factor of 103 [Rodrigue et al., 2009] to up to 106 (this study, data not shown) within a
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RESEARCH SUPPLEMENTARY INFORMATION
single SAG. In order to create a more homogenous genome representation and thereby improve the
gsAssembler assembly, 454 and Ion torrent reads were de-replicated using cd-hit-454 [Niu et al., 2010] with
a 99% similarity cut-off (see Table S5). The de-replicated reads where subsequently assembled with
gsAssembler with the following parameters: -mi 98 –ml 50 (i.e. 98% minimum sequence similarity and 50bp
minimum overlap).
In parallel to gsAssembler, we used the SPAdes assembler version 2.2.1 [Bankevich et al., 2012] (used
parameters: -k 21,33,45 --sc --only-assembler) to assemble the Illumina reads of SCGC AB-539-E09 and
SCGC AB-539-N05, as well as the Ion torrent reads of SCGC AB-539-C06 and SCGC AB-540-F20. SPAdes was
developed and optimized for dealing with the biased genome representation introduced by MDA. Thus by
using this assembler, we could considerably improve the recovery of genome fragments from our single cell
genomes (a comparison for SCGC AB-539-C06 and SCGC AB-540-F20 where ion torrent reads were
assembled with both gsAssembler and SPAdes is shown in Table S6).
The individual gsAssembler and SPAdes assemblies of each SAG were combined using Sequencher version
5.0.1 (Genecodes) as follows: All contigs shorter than 500 bp (short contigs) were re-assembled with
Sequencher using a minimum similarity value of 98%, a minimum overlap value of 30 bp, and the Dirty Data
algorithm. All contigs longer than 500 bp were split into 500 bp fragments with a 200 bp overlap using the
splitter script of the EMBOSS suite [Rice et al., 2000]. The split contigs as well as the previously re-
assembled short contigs were assembled with a minimum similarity value of 98%, a minimum overlap value
of 50 bp, and again the Dirty Data algorithm. All contigs in the final assembly which were longer than
500 bp were manually curated for sequencing errors and mis-assemblies. Manual correction of sequencing
errors was based on the manufacturer’s error specifications for the used sequencing platforms, i.e. Illumina
reads are less erroneous than 454 reads, which in turn are less erroneous than Ion Torrent reads. For SCGC
AB-539-C06 and SCGC AB-540-F20 were no Illumina or 454 reads were present, consensus bases from
gsAssembler assemblies were preferred over those of SPAdes. All manually corrected contigs were
repeatedly re-assembled with Sequencher until no further contigs could be combined.
Genome annotation
With the exception of contigs carrying rRNA genes, all contigs shorter than 1,000 bp were excluded from
further analysis and removed prior to annotation. The SAG genomes were auto-annotated using the
standard operation procedure (for details see https://img.jgi.doe.gov/er/doc/MGAandDI_SOP.pdf) of the
IMG-ER pipeline version 4.0 [Markowitz et al., 2009]. In short, CRISPR regions were identified by CRT [Bland
et al., 2007] and PILERCR [Anonymous, 2009], tRNAs were identified by tRNAScan-SE-1.23 [Lowe and Eddy,
1997], rRNA was identified RNAmmer [Lagesen et al., 2007], and finally all other genes were identified by
Prodigal [Hyatt et al., 2010]. Functional annotation was based on gene comparisons with the KEGG
database (release 63.0, July 1, 2012) [Ogata et al., 1999], the PFAM database (version 25.0, March 30, 2011)
[Bateman et al., 2004], the cluster of orthologous groups (COG) [Tatusov et al., 2003] database, and the
TIGRfam database (release 11.0, August 3, 2011) [Haft et al., 2003].
The annotated data sets were exported from IMG-ER and subsequently manually curated using the GenDB
genome annotation system, version 2.2 [Meyer et al., 2003], supplemented by the tool JCoast, version 1.6
[Richter et al., 2008]. For each gene, observations were collected from similarity searches by BLAST
[Altschul et al., 1997] against the sequence databases NCBI-nr, Swiss-Prot [Magrane and Consortium, 2011],
KEGG [Ogata et al., 1999] and genomesDB, and for protein family databases from Pfam [Bateman et al.,
2004] and InterPro [Mulder et al., 2005]. SignalP [Petersen et al., 2011] was used for signal peptide
predictions and TMHMM [Krogh et al., 2001] for transmembrane helix-analysis. All annotations of proteins
highlighted within the scope of this study were subject to manual inspection. For all observations regarding
putative protein functions, an E value cutoff of 1e-5 was considered.
For metabolic pathway identification, genes were searched for similarity against the KEGG database. A
match was counted if the similarity search resulted in an expectation E value below 1e-5. All occurring KO
4 | WWW.NATUR E .COM/NATURE