From Genome Sequence to Taxonomy

20 Pages·2013·0.91 MB·English

8 Comparative Genomics
AsliIsmihanOzen.TammiVesth.DavidW.Ussery
DepartmentofSystemsBiology,CenterforBiologicalSequenceAnalysis,Kemitorvet,TheTechnical
UniversityofDenmark,Lyngby,Denmark
Prokaryotic Classification .................................209 dependentonthedevelopmentofmicrobialtechniquessuchas
isolatingandgrowingmicroorganismsinpurecultures,staining
CurrentTaxonomyofProkaryotes.........................210 andmicroscopeobservations.Intheirearlyobservations,lackof
guidelinesonnaminginevitablyledtoavastnumberofinvalid
TheExplosionofSequencedBacterialGenomes ..........210 namesandsynonyms.FerdinandCohnmadethefirstclassifica-
tion system of bacteria in 1872; six genera of bacteria were
StatisticsonProkaryoticGenomes ........................211 classifiedbasedontheirshape,cellularstructures,pigmentation,
DataGrowthovertheYears .............................211 andmetabolicactivities(Cohn1872).
TaxonomyAnalysis,MostSequencedPhyla Atthebeginningofthetwentiethcentury,besidesmorphol-
andGenera ..............................................212 ogy,theuseofphysiologicalandbiochemicalinformationcould
BasicGenomeStatistics .................................213 beincorporated.Europeanscientistsalsoproposedphysiology,
ThousandsofGenomeSequences .......................216 metabolism, pigments, and pathogenicity as new systems for
Whole-Genome-BasedToolsforTaxonomy ............216 classification.However,someofthesemethodswerethencriti-
rRNAPhylogeneticTrees ................................216 cized for being not important for assessing taxonomic ranks.
AverageNucleotideIdentities(ANI)andTetra Later,advancesinbiochemistryandmolecularbiologyfromthe
NucleotideFrequencyCalculations .....................218 isolation of nucleic acids to elucidation of macromolecular
BLASTMatrixUsingReciprocalBestHits ...............220 structureofproteinsandnucleicacidsledtothefoundationof
CompositionVectorTrees(CVTree) ....................222 genomicsciences.Developmentofcomputersinthe1950swas
Pan-genomeTrees .......................................224 anotherimportantstepinbacterialtaxonomy,wheretheywere
first used for analysis of phenotypic and molecular data.
Summary ..................................................225 Betweentheyears1960–1980,numericaltaxonomyandchemo-
taxonomywereontherise(Stackebrandt2006;Schleifer2009).
In late 1950s, scientists were able to identify the molecules
Prokaryotic Classification conservedthroughouthistoryoflife,suchasproteins,DNA,or
RNAmolecules.Theideaofusingthesemoleculesasblueprints
Classification covers the theory and practice of how to order of the evolutionary history of organisms emerged in the 1960s
characterized organisms into different groups based on their (Zuckerkandletal.1962).Tertiarystructureandsequenceanal-
degreeofrelatedness.Togetherwithidentificationandnomen- ysisofmolecules,suchascytochromeC,ferrodoxins,andfibri-
clature,classificationisapartoftaxonomy,asciencethatdeals nopeptides,andalsoimmunologicalapproacheswerebeingused
withtherelatednessoforganisms.Thegoalofmanytaxonomists afterward. However, theinterestin these methods decreased as
istohaveaclassificationsystemthatreflectsthenaturalrelation- rapidsequencingtechniquesforDNAbecamemoresignificant.
shipsamongorganisms.Thisnaturalsystemhasbeendepicted Thefirstgenotypicapproachthatallowedbacteriologiststo
mostly as phylogeny (Doolittle 1999)—or an evolutionary classify prokaryotes on the basis of their phylogenetic related-
tree—whichisadiagramthatshowsancestor-descendantrela- nesswasDNA-DNAhybridization(DDH)(Wayneetal.1987).
tionships of organisms based on their evolutionary history. Inthefollowingyears,moregenotypicstudies,includingcom-
However,inferringatruephylogenyforprokaryoticorganisms parative analysis of Ribosomal RNA (ribonucleic acid) genes
isverychallengingduetothediversityoftheseorganisms,aswell andprotein-codinggenesequence,allowedmoreinsighttothe
asfrequenthorizontaltransferofgenes. relationshipsofprokaryotes(Schleifer2009).Thesmallsubunit
Prokaryotes, known as unicellular organisms with no rRNA (16S rRNA in prokaryotes) was shown to be one of the
nuclear membrane structure, have a history of more than 3.5 universally conserved molecules became the primary molecule
billionyearsonearth,yethumanshavebeenawareofthemfor of interest. Being ubiquitous, having functional consistency,
onlythepastfewcenturies,afterfirstbeingdescribedbyRobert genetic stability, appropriate size, and independently evolving
Hooke in the seventeenth century. Louis Pasteur and other domains caused this molecule to be chosen for phylogenetic
scientists of the nineteenth century described microorganisms analysisandthisapproachbecameaclassicaltoolfortaxonomy
indetail,andbegantocategorizethem.Theirclassificationwas (HarayamaandKasai2006).AnimportantstudybyCarlWoese
E.Rosenbergetal.(eds.),TheProkaryotes–ProkaryoticBiologyandSymbioticAssociations,DOI10.1007/978-3-642-30194-0_11,
#Springer-VerlagBerlinHeidelberg2013
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210 ComparativeGenomics
revolutionizedbacterialtaxonomy,proposingthenewkingdom classificationsystembasedonevolutionaryhistory,clearclus-
ofArchaebacteria(WoeseandFox1977).Hislaterstudiescon- tersoftaxonomicunitsinaphylogenetictreeareseensuchthat
cludedinaphylogeneticschemeofthreemainbranchesoflife species that share a common ancestor would form the genus
(Bacteria,Archaea,andEukarya)thathecalledDomains(Woese andgenerathatshareacommonancestorformafamilyandso
etal.1990). forth.Majorsourcesforbacterialnamesandtaxonomicalorder
In other genotypic classifications, many protein-coding areBergey’sManualofSystematicBacteriology(Brenneretal.
genes were used for phylogenetic relatedness, some of which 2005a), Bergey’s Taxonomic Outlines (http://www.bergeys.org/
are recA, gyrB, genes of some chaperonins, RNA polymerase outlines.html),andthecomprehensivelistavailableatTheTax-
subunits(i.e.,rpoB)orsigmafactors (rpoD),elongation factor onomic Outline of Bacteria and Archaea (TOBA) journal
G(fus).Themostacceptedcriteriaforselectionoftheseproteins (Garrityetal.2007).
is such that, they should not be subjected to horizontal gene Taxonomy tools historically have been mainly based on
transfer(HGT),shouldbepresentinallbacteria,preferablyin laboriouslaboratoryexperimentstryingtocharacterizebacteria
single copies and at least two highly conserved regions for the based on their phenotypic and biochemical properties until
designofPCRprimers(YamamotoandHarayama1996).These molecularapproachesandsequencingtechnologiesweredevel-
propertiesgivethemanadvantageofbeingmoreappropriatefor oped. Today, such research can be handled using robotic and
phylogeneticanalysisofcloselyrelatedbacteriathan16SrRNA computationaltechniques,wheremostoftheknowledgegained
analysis. fromresultsrelyonthedatathatisbeinghandled.
In addition to thesinglegene basedmethods, MultiLocus
Sequence Typing (MLST) has been widely used for genotypic
characterizationandclassificationofprokaryotesbycomparing The Explosion of Sequenced Bacterial
multiple housekeeping gene sequences (Maiden et al. 1998). Genomes
However, usually a different set of genes is useful for different
setoforganisms,andsomedifficultiesoccurinprimerdesignfor Biological data generated by researchers worldwide has been
amplification of genes in all strains if the analysis is not growing with a tremendous rate, especially with the advances
conducted all in silico. A widely used website and database in molecular biology techniques in the past 50 years. Much of
currentlyismlst.net(AanensenandSpratt2005). this vast information can now be accessed through biological
databases that hold records for experimental data, sequence
data, classification schemes, literature, and some also provide
Current Taxonomy of Prokaryotes computationalanalysistools.
A part of this huge biological information is the genomic
Classificationisdonebycomparinganewlyidentifiedorganism sequences. In modern molecular biology and genetics,
withthecollection ofpreviouslyclassified organisms andthen a‘‘genome’’istheentiretyofanorganism’shereditaryinforma-
assigning it with a previously described or new species. If tion. Therefore, genomics can be referred to as the science of
a bacterial species is considered novel, the proper naming for genome analysis. As such the field of comparative microbial
theneworexistingtaxaaremadebynomenclaturethatisbased genomics(CMG)workwithcomparingtheentireDNAmaterial
ontheInternationalCodeofNomenclatureofBacteria(Lapage ofamicrobialorganismtootherorganisms.Thefirsttwocom-
etal.1992),alsonamedastheBacteriologicalCode.Nomencla- pletebacterialgenomesequenceswerepublishedin1995.Asthe
ture is, however, subject to changes because classification is technologies advanced and the sequencing cost went down,
adynamicprocess.Thepublicationofnamesfornovelprokary- manymoresequenceswerebeingpublishedandmoredatabases
otictaxaismadeintheInternationalJournalofSystematicand were established to handle this information. One of the most
EvolutionaryMicrobiology(IJSEM),whichistheofficialjournal useddatabasesisbasedonGenBank,nowlocatedaspartofthe
forthispurpose.IJSEMalsopublishes‘‘ValidationLists’’which NationalCenterforBiotechnology Information,NCBI(http://
contain new names published in other journals (Tindall et al. www.ncbi.nlm.nih.gov/genome/browse/). The NCBI GenBank
2006).Anupdatedlistofapprovednamesformicroorganisms holds the nucleotide sequence data from expression sequence
based on the international rules can also be found at The tag (EST), genome survey sequences, other high-throughput
DSMZ—Deutsche Sammlung von Mikroorganismen und sequencessuchaswhole-genomesequencesandgenomeanno-
Zellkulturen GmbH (German Collection of Microorganisms tations of thousands of organisms. Both prokaryotic and
andCellCultures)depository(http://www.dsmz.de). eukaryotic data is available (Benson et al. 2008). GenBank is
In taxonomy, groups of organisms that are brought a part of an international collaboration called International
together based on shared properties are called ‘‘taxa’’ or SequenceDatabase Collaboration, which also consists of DNA
‘‘ranks,’’ and prokaryotic taxonomy makes use of several DataBaseofJapan(DDBJ)andtheEuropeanMolecularBiology
ranks or levels. The current classification scheme has laboratory(EMBL).AnotherpartofNCBIthatishighlyrelated
a hierarchical structure, where the higher taxonomic ranks tothischapterisNCBITaxonomy.Althoughclaimingnottobe
consist of the lower ranked groups. In other words, higher aprimarysource,NCBIprovidestaxonomicalinformationthat
taxa(e.g.,genus)containlowertaxa(e.g.,species).Inanideal isgatheredfromvarioussources.
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ComparativeGenomics 211
400
300
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of 200
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1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
Year
.Fig.8.1
GenomespublishedanddepositedtopublicNCBIGenBanksince1995(DatagatheredfromNCBI(http://www.ncbi.nlm.nih.gov/
genomes/lproks.cgi,Jan.2012))
In May 2011, NCBI GenBank contained around 1,500 thanothers,aclearstandardforgenomepublicationhasyetto
genome sequences labeled as ‘‘finished.’’ Six months later, at beestablished(Me´digueandMoszer2007).
the time of writing (November 2011), this number has gone
upto1,790.Currently(Feb.2012),theNCBI‘‘GenomeProjects’’
is changing to ‘‘BioProjects,’’ in order to relate genomic infor- Statistics on Prokaryotic Genomes
mation to other data types, such as the transcriptome, prote-
ome,andmetagenome. With such a large amount of data, it is interesting to
In addition to NCBI (GenBank) and EMBL (Nucleotide see the trends in the basic statistics of the genomic data and
Sequence Database), a source for genomic information is the comparisons on different taxonomic levels and years of
GenomesOnlinedatabase(GOLD).GOLDaimstoprovidean sequence publications. The data presented in this section is
accurate and complete set of finished and ongoing genome taken from the NCBIcompletegenomes list inJan. 2012 and
projects with a broad range of information on each project. GenBankfilesfor1,500sequencedgenomesweredownloaded
Thesequencedataitselfisnotstoredinthedatabase,however, (November2011).
externallinkstowherethedatacanbefoundisgiven,mostof
whicharetotheNCBIGenomeProjectpages.GOLDalsopro-
videstaxonomicalinformation,thoughnottheprimarysource DataGrowthovertheYears
(Bernaletal.2001;Lioliosetal.2010).
Onepartofcomparativemicrobialgenomicsistomonitor >Figure8.1illustrateshowmanygenomeswerepublishedeach
the available microbial genomic data. Even though the year, since 1995. The two first complete genomes to be
sequences available may only be a small fraction of the real sequenced and deposited was Mycoplasma genitalium G37
world,theinformationgatheredisgrowingeveryday.Ittook (Fraser et al. 1995) and Haemophilus influenzae Rd KW20
14 years to sequence the first thousand bacterial genomes (Fleischmann et al. 1995). From 1995 until 1999, only 25
(1995–2009), and already in 2012, less than 3 years later, the genomes were published as complete and they covered 14
two thousandth genome sequence has been deposited to phyla.OfthesetheArchaealgenomesconstitutealargeportion
GenBank.Notonlyhasthecostofgenomesequencingdecrease comparedtothefractiontoday(around31%ofthe25compared
dramaticallybutalsothetimeandeffortputintothetaskhas to99outof1,500(6.6%)).Thegenomesfromthisfirstperiodof
alsogonedown.Alsothecomputationalpowerandsoftwareto genome sequencing cover a large span of the microbial land-
handlesequencingdataisbeingrevolutionizedandfastassem- scape with no obvious medical bias. From 2000 to 2010 the
bly and interpretation is increasing the number of published number steadily increased from 26 to 1,423 with the major
genomes(Ansorge2009).Theincreaseingenomedatahasgiven phylacoveredbeingFirmicutes,Gamma,andAlphasubdivisions
risetoawholenewareaofproblemswhenitcomestopublica- ofProteobacteria.Itispossiblethatproducingacompletegenome
tion and sharing of data. Databases usually have their own sequence is becoming less popular due to the improvement
formatting of the raw data and though some are more used insoftwarethatcanworkondraftgenomes(Chainetal.2009).
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212 ComparativeGenomics
a
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s 300
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pro 200
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es i 100
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ge 0
e,
nc R)
a E
Count of occur Nanoarchaeota (NAN) Fusobacteria (FUS) Planctomycetes (PLA) Acidobacteria (ACI) Aquificae (AQU) Thermotogae (THE) Chloroflexi (CHLFX) Deinococcus−Thermus (DEITH) Chlamydiae/Verrucomicrobia (CHL−V Crenarchaeota (CRE) Spirochaetes (SPI) Cyanobacteria (CYA) Deltaproteobacteria (DEL−PRO) Other Epsilonproteobacteria (EP−PRO) Euryarchaeota (EUR) Bacteroidetes/Chlorobi (BAC−CHLO) Betaproteobacteria (BET−PRO) Alphaproteobacteria (ALP−PRO) Actinobacteria (ACT) Gammaproteobacteria (GAM−PRO) Firmicutes (FIR)
b
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gr
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Count of ACT) Streptomyces CYA) Synechococcus DEL−PRO) Desulfovibrio GAM−PRO) Xanthomonas CYA) Prochlorococcus GAM−PRO) Acinetobacter CRE) Sulfolobus GAM−PRO) Buchnera GAM−PRO) Francisella SPI) Borrelia EP−PRO) Campylobacter GAM−PRO) Haemophilus CHL−VER) Chlamydophila FIR) Listeria ALP−PRO) Brucella ALP−PRO) Rickettsia FIR) Mycoplasma BET−PRO) Neisseria CHL−VER) Chlamydia GAM−PRO) Vibrio GAM−PRO) Yersinia ACT) Bifidobacterium GAM−PRO) Salmonella GAM−PRO) Shewanella GAM−PRO) Pseudomonas BET−PRO) Burkholderia ACT) Mycobacterium ACT) Corynebacterium EP−PRO) Helicobacter FIR) Staphylococcus FIR) Lactobacillus FIR) Clostridium FIR) Bacillus GAM−PRO) Escherichia FIR) Streptococcus
( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( (
.Fig.8.2
Numberofgenomessequencesfromeachphylaandgenera.Onlygenerawithmorethan10representativegenomesareshown
The cost of sequencing and the development of cheaper the GOLD database (http://genomesonline.org, March 2011)
sequencingmethodshavemostdefinitelyhadanimpactonthe over 73% of the listed Streptococcus and more than 64% of
rateofsequencing(Sboneretal.2011). Escherichiaarelabeledaspathogens.
It is likely that some organisms are sequenced because of
theirmedicalrelevance.OrganismsbelongingtotheEscherichia
TaxonomyAnalysis,MostSequencedPhylaand genus have a considerable role within the medical world, with
Genera Escherichiacolibeingthecauseofserious food poisoning. The
tendencyofpathogenstobemoreoftensequencedis,however,
>Figure8.2ashowsthenumberofgenomeswithineachphyla; not as strong overall. The fraction of pathogens within each
Firmicutes and Gamma Proteobacteria are the most highly genusvariesfrom7%(Lactobacillus)to98%(Listeria),forthe
represented. The plot in >Fig. 8.2b shows genera with more six different genera belonging to the Firmicutes. For all the
than10sequencedgenomes.ThegenusofStreptococcusishighly genera listed in >Fig. 8.2b the range covers everything from
overrepresented (63 genomes) while the closest other group is 3% (Synechococcus) and 100% (Borrelia and Rickettsia,
Escherichia (45 genomes). According to supporting data from supporting data from GOLD). It should be noted that the
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annotation of ‘‘pathogen’’ is not always accurate; for example, Chlamydiae/Verrucomicrobia,andNanoarchaeota.Ofthelargest
thefirstsequencedgenome,Haemophilusinfluenzaeislistedas genomes (more than 8,000 kb, 32 genomes), members of
a ‘‘pathogen,’’ although the strain sequenced (Rd KW20) is Actinobacteriaareprevalent(14genomes)rangingfrom925kb
anonpathogenicstrain. to 11,937 kb. The largest genome, as of May 2011, at
Inanyevent,itisclearthatthereisastrongsequencingbias, 13,033.779kb,wasSorangiumcellulosumSoce56(soil-dwelling
makingtheavailabledataforcertainphylaandgeneraconsid- bacteria)(Schneikeretal.2007).
erablymorethanothers.Itcouldbeexpectedthatmorepatho- Aninterestingperspectiveongenomesizeisthefocusonthe
gensthannonpathogenswouldbesequenced,astheimmediate minimalgenomeforafree-livingorganism.Definingtheminimal
interest in these organisms is larger. However, the bias is not genomeisascienceinitselfandhasbeenheavilydiscussedinthe
directly linked to pathogenicity, as some genera aresequenced scientificcommunity (Galperin 2006). In 1995, the genome of
more often though not being serious pathogens. For example, Mycoplasma genitalium (a parasite) was published, and at that
species like Escherichia coli (urinary tract infections, simple time this was thought to be the smallest genome of any free-
diarrhea, dysentery-likeconditions) include pathogens but are livingorganism(Fraseretal.1995).Ofthe1,500genomesinthis
not as severe as other species like Borrelia (Lyme disease) or study, M. genitalium isthe18th smallestgenome. Upon closer
Listeria (Listeriosis in newborn infants, elderly patients, and inspection,theeightsmallest‘‘genomes’’aredescribedasphage,
immunocompromised). Escherichia coli is, however, Integrating and conjugative elements (ICEs), pathogenicity
asignificantplayerinthefinancialaspectofmedicalrelevance. island, or genomic island, so not free-living organisms. These
These organisms, though rarely lethal, can occur frequently in nongenomic sequences have been reported to GenBank, and
thepopulation,andstillrequiretreatment;thisisaburdenon since have been deleted from the list of genomes during this
any healthcare system. Another factor could be the economic work. Other genomes smaller than M. genitalium consist of
cost,assomeorganismsgrowlesseasilyorreplicateveryslowly Buchnera (an endosymbiant (Pe´rez-Brocal et al. 2006) and
making experiments long and expensive. Some pathogens Nanoarchaeum) another symbiont (Waters et al. 2003).
requireextremesafetyprocedureswhenculturedandthiscon- The remaining seven genomes are Candidatus species, from
sumestime,space,andmoney.Thehistoricfactorcouldalsobe proposed genera, and all of these are described as symbionts
partlyresponsibleforsequencingbias.Someorganismsbecame (McCutcheon et al. 2009). It is worth mentioning that the
model organisms from the early stage of microbiology and as smallest genome of a ‘‘true’’ free-living organism (as opposed
such, many procedures are optimized for these organisms. toparasiteslikeM.genitalium)isconsiderablylarger,containing
Unfortunately, due to the large variety within the microbial more than a thousand protein-encoding genes. Two proposed
world, many organisms will not respond well to procedures ‘‘minimal free-living organisms’’ are Pelagibacter ubique (het-
developed with Escherichia coli as the template. Taxonomical erotroph,133thsmallestinthisstudy;DeWallandCheng2011)
biasinsequencingdatais,asstated,acomplexandmultifaceted andProchlorococcusmarinus(autotroph,209thsmallestinthis
discussionthatwillprobablyneverend.However,thesetenden- study; Moya et al. 2009). Note that these genomes are still
ciesshouldbekeptinmindwhenaccessingthedataavailablefor smallerthanthelargestviralgenomes(Arslanetal.2011).
analysis. Anothergenomestatisticscommonlyusedisthepercentage
ofAT(>Fig.8.3b),whichiscalculatedastheaverageATcontent
ofalltheDNAsequence.GenomeswithhighATcontentinclude
BasicGenomeStatistics Candidatus Zinderia insecticola CARI (86%, Beta
Proteobacteria), Candidatus Carsonella ruddii PV DNA (83%,
Thesequencesof1,500genomeshavebeenobtainedfromNCBI GammaProteobacteria),andBuchneraaphidicolastr.Cc(Cinara
GenBankandanalyzedaccordingtobasicstatisticalparameters. cedri; 80%, Gamma Proteobacteria). These are all extremely
Here,basicgenomestatisticsreferstocertainDNApropertiesof small genomes. They are also all symbiotic organisms living
thegenome,suchasgenomesize,frequenciesofAandTbasesin inside insects, the spittlebug Clastoptera arizonana, jumping
the DNA, and bias on the third codon positions for the open plant lice and plant lice, respectively. Genomes with low AT
readingframes. (high GC) content include Anaeromyxobacter dehalogenans
>Figure8.3aisaboxandwhiskerplotshowingthevariation (Delta Proteobacteria; Sanford et al. 2002) and Cellulomonas
ofgenomesizeswithineachphylum.Asseen,severalphylahave flavigena (Actinobacteria; Abt et al. 2010), both with around
awidedistributionofsizes.Phylacontainingonlyafewgenomes 25% AT. These genomes consist of an anaerobic and aerobic
(less than five sequences) show very little size variation that soil-bacteria, respectively. The AT content within each phyla
couldbetheresultofsequencingseveralcloselyrelatedstrains. shows some specificity with phyla like Acidobacteria,
Formostphylasizeisnotakeyfeature,althoughChlamydiaand Actinobacteria, and Deinococcus/Thermus having a significant
Nanoarchaeotaareexpectedtobesmallgenomes.Thegenomes skew toward low AT and Fusobacteria, Epsilon Proteobacteria,
within the Firmicutes are distributed over a broad spectrum andAquificaehavingaskewtowardhighAT(>Fig.8.3b).
(580–8,300kb),whileGammaProteobacteriasizevariesbetween CantheATcontentofanorganismberelatedtoitssize?The
32(cid:1)7,215 kb. Large genomes are often seen within answer can be both ‘‘yes’’ and ‘‘no.’’ The numbers from 1,500
Planctomycetes, Beta Proteobacteria, and Actinobacteria while genomesshowthatthesetwopropertiesarenotalwayspropor-
small genomes are found within Epsilon Proteobacteria, tionaltoeachother.However,forverylargeandsmallgenomes,
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a
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12000 G
10000 G G
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k G
e ( 8000
siz GG
me 6000 G GG G
o G
n G
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G 4000 G
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2000 GG GGGGGG
a
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Acidobacteria Actinobacteria Aquificae Archaea Crenarchaeota Archaea Euryarchaeota Archaea Nanoarchaeota Archaea Other Bacteroidetes/Chlorobi Chlamydiae/Verrucomicr Chloroflexi Cyanobacteria Deinococcus-Thermus Firmicutes Fusobacteria Other Bacteria Planctomycetes Proteobacteria Alpha Proteobacteria Beta Proteobacteria Delta Proteobacteria Epsilon Proteobacteria Gamma Spirochaetes Thermotogae
b
G
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70
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A 60 G
ent GG G
c
er 50
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40 GGGGGGGGGGG G G
30 G GG
G
a
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Acidobacteria Actinobacteria Aquificae Archaea Crenarchaeota Archaea Euryarchaeota Archaea Nanoarchaeota Archaea Other Bacteroidetes/Chlorobi Chlamydiae/Verrucomicr Chloroflexi Cyanobacteria Deinococcus-Thermus Firmicutes Fusobacteria Other Bacteria Planctomycetes Proteobacteria Alpha Proteobacteria Beta Proteobacteria Delta Proteobacteria Epsilon Proteobacteria Gamma Spirochaetes Thermotogae
.Fig.8.3
Boxplotsshowingthedistributionofgenomesize(inkilobase-pairs,panela)andATcontent(inpercentage,panelb)foreachphyla
(asdescribedbyNCBITaxonomy).Themiddlebaristhe50%percentile,thebottomandtopoftheboxarethe25%and75%percentiles
(Q1andQ3,respectively).Whiskerbarsextendtothemostextremedatapointwhichisnomorethan+/(cid:1)1.58IQR/sqrt(n),whereIQRis
theinterquartilerange(IQR=Q3(cid:1)Q1).Anydatapointthatexceedsthislimitisplottedasanindividualdatapoint(outlier).Thegenome
sizewascalculatedasthesumoflengthsofallcontigs
the answer can be ‘‘yes.’’ >Figure 8.4 shows a scatterplot of astrongcorrelationbetweenthesetwopropertiesofagenome
genomesizeandATcontent(Pearsoncorrelationcoefficientof (Pearson correlation coefficient of (cid:1)0.94). The third codon
(cid:1)0.48),showingthatsmallgenomeshavehighATcontentand positionisthemostvariablepositionforthecodonandthisis
large genomes have low AT content. The analysis also shows wherethelargestvariationinbaseusewouldbeexpected.The
a cloud around the middle values, indicating that average size correlation was therefore expected and shows that high AT
correspondstoanATcontentwithhighfluctuations. content in a genome correlates with a bias close to (cid:1)1 (which
Ontheotherhand,aninterestingrelationisseenbetweenAT is100%ATinthethirdcodon)andlowATcontentcorrelates
contentandbiasinthirdcodonposition.>Figure8.5illustrates withabiascloseto+1(whichis100%GCinthethirdcodon).
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80 factor(Group)
Acidbacteria
Actinobacteria
Aquificae
70
Archaea Crenarchaeota
Archaea Euryarchaeota
Archaea Nanoarchaeota
60 Archaea Other
Bacteroidetes.Chlorobi
Chlamydiae.Verrucomicrobia
T
A Chloroflexi
nt 50 Cyanobacteria
e
c Deinococcus-Thermus
Per Firmicutes
Fusobacteria
40 Other Bacteria
Planctomycetes
Proteobacteria alpha
Proteobacteria beta
30
Proteobacteria delta
Proteobacteria epsilon
Proteobacteria gamma
20 Spirochaetes
Thermotogae
2000 4000 6000 8000 10000 12000
Genome size (kb)
.Fig.8.4
ScatterplotshowingpercentageATcomparedtototalgenomesize(kb)for1,500genomesequences.ThePearsonCorrelation
Coefficient(PCC)forthisdatais(cid:1)0.48,whichshowsamediumcorrelation.PCCisoftenusedtomeasurethelineardependencebetween
twovariables,andtakesavaluebetween+1and(cid:1)1,where0reflectsnolinearcorrelation
80 Phyla
Acidobacteria
Actinobacteria
Alphaproteobacteria
Aquificae
70 Bacteroidetes/Chlorobi
Betaproteobacteria
Chlamydiae/Verrucomicrobia
Chloroflexi
ge 60 Crenarchaeota
a Cyanobacteria
ent Deinococcus-Thermus
erc Deltaprotebacteria
p Epsilonproteobacteria
AT 50 Euryarchaeota
Firmicutes
Fusobacteria
Gammaprotebacteria
40 Nanoarchaeota
Other Archaea
Other Bacteria
Planctomycetes
Spirochaetes
30 Thermotogae
–0.5 0.0 0.5
Bias in third position
.Fig.8.5
ScatterplotshowingpercentageATcomparedtobasebiasinthirdcodonpositionfor1,500genomesequences.Biasiscalculatedsothat
100%AorTinthirdcodonpositiongivesascoreof(cid:1)1,100%GorCinthirdpositiongivesascoreof+1.ThePearsonCorrelation
Coefficientforthisdatais(cid:1)0.94,whichshowsastrongcorrelation.PCCisoftenusedtomeasurethelineardependencebetweentwo
variables,andtakesavaluebetween+1and(cid:1)1,where0reflectsnolinearcorrelation
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ThousandsofGenomeSequences theAverageNucleotideIdentity(ANI)betweenpairsofgenomes
or the Average Amino acid Identity (AAI) of the shared genes
Availabilityofthousandsofgenomesmakesitpossibletoinves- between two genomes. The study of Goris et al. on pairwise
tigatephylogeniesbasedongenomicinformationandseehow comparisonofcompletesequencedgenomesshowedtheANIof
current taxonomy is affected. The development of manycom- the core genes show results similar to analysis of 16S rRNA
putational tools and increasing computational power makes it sequence identity and DDH similarity values, concluding that
possible to compare whole genomes in a reasonable time, yet a 70% DDH value corresponds to 95% ANI. Hence ANI has
comparison of thousands of whole genomes is still a tedious been shown to be an alternative to the tedious DDH method
process. Therefore, three data sets were selected that represent (Goris et al. 2007). Another genome-based method, AAI, has
different taxonomic levels of prokaryotes. The first data set is been shown to result in strong correlation between 16S rRNA
chosentocoverawidecoverageofalltheprokaryoticorganisms geneidentityandAAI-basedphylogenetictreescongruentwith
(126 genomes and 23 phyla). The second data set is coregenome-basedtrees(KonstantinidisandTiedje2005).
a representative of a well-defined prokaryotic family There are many methods and types of data used to build
(Enterobactericiaefamily,50genomes).Thethirdoneischosen evolutionarytrees.Theresultsofthewhole-genome-basedtools
as an example of a prokaryotic species and close relatives are usually values representing similarity between organisms,
(Escherichiacoli,Escherichiafergusonii,Shigella).Differentcom- which can then be converted to distance-based phylogenies.
putationalmethodsthatwehaveencounteredtobefittinginthe Distancemethodsconstituteamajorpartofphylogeneticanal-
currenttaxonomyofprokaryoteswereshownforeachdatasetin ysis.‘‘Leastsquares’’isoneofthesemethodswherethesumof
thefollowingsections. squares of difference between the observed and the predicted
distancesofa treeshouldbeminimized. Unweighted (Cavalli-
SforzaandEdwards1967)andweighted(FitchandMargoliash
Whole-Genome-BasedToolsforTaxonomy 1967; Beyer et al. 1974) algorithms are suggested for least
squares. Minimum Evolution, Neighbor joining, and UPGMA
The previous section showed the growth in available sequence arealldistance-basedmethods.Therearealsomethodsthatrely
data as well as the bias and diversity in this data. This large onprobabilitiesofevolutionarychange.Maximumlikelihoodis
diversity and coverage opens the doors to large-scale phyloge- oneofthem,wheredifferentevolutionaryratescanbetakeninto
neticanalysisofgenomesequences.Asaresult,greatinsightinto account and several models can be implemented (Felsenstein
bacterialevolutionanddiversityhascomefromcomparisonof 2004). The evolutionary models and the distance methods
many microbialgenomesequencesinthelastdecade.The dif- should be chosen carefully when phylogenies aregenerated, as
ferences, even between strains of a distinct taxonomic cluster, theymightresultindifferentresultsevenforasmallset.
show that bacteria representa greatdiversity, which led to the
formation of the hypothetical concepts of ‘‘pan-genomes’’ and
‘‘core-genomes.’’Thepan-genomecontainsthetotalnumberof rRNAPhylogeneticTrees
genesfoundinthegenepoolofasetofgenomes(Usseryetal.
2009)andcanbeviewedinthreeseparateparts.Thepartthat Inthissectionthe16SrRNAand23SrRNAcomparisonof126
consists of conserved essential genes common to all genomes various organisms from all bacterial and archaeal phyla is
compared(core-genome).Ithasbeenseenthatcore-genomesof presented (>Fig. 8.6). This data set represents a collection of
phylogenetically coherent groups contain genes that are less distantlyrelatedprokaryoticorganisms.Thefirstcriteriaforthe
prone to horizontal gene transfer and are more stable such as selectionoforganismsforthisdataset,wastogetthelargestand
housekeeping genes. The genes essential for colonization, sur- smallest genomes from each phyla (taxonomy reference is
vival, or adaptation to a specific environment are thought to Genome metadata from NCBI and GOLD). More organisms
formthelifestylegenes,whichcanalsobenamedasthe‘‘shell’’ from each phylawere selected from different environments or
for frequently occurring genes. The thirdpart is called ‘‘acces- hostassociations,inordertogetalessbiaseddataintotal.
sory’’ or ‘‘cloud’’ genes, as these are rarely found, often strain Ribosomal RNA sequences of all 126 genomes were
specificandnonessential(LapierreandGogarten2009).Though predicted using RNAmmer program (Lagesen et al. 2007).
hypothetical,thesetermscanserveusefordefiningandclassi- Foreach genome one 16S and 23S rRNA sequence was selected
fyingbacteria.Thesedifferent‘‘genomes’’canbeusedtoexplain based on the highest RNAmmer score and appropriate length
the differences and similarities between species or genera, and (Lagesen et al. 2010). The length requirements were between
visualized by pan-genome trees (Snipen and Ussery 2010). An 1,400 and 1,700 bp for 16s rRNA sequences, 2,500 and
elaborate work on the comparisons of genomic DNA using 3,800 bp for 23S rRNA sequences. Once the RNA sequences
oligonucleotide-based methods and proteomes with a pan- weregatheredtheywerealignedusingCLUSTALWwithdefault
genome approach was presented in a study by Bohlin et al., parameters (10 for gap opening penalty, 0.20 gap extension
whereBrucellaspecieswereclassifiedusing32genomes(Bohlin penalty,30%Delaydivergentsequences,0.5forDNAtransitions
etal.2010). weight, IUB for DNA weight matrix) (Larkin et al. 2007).
Othergenome-basedmethodsincludemeasuresforreplace- After obtaining the alignments, the phylogenetic trees were
mentoftheDDH(DNA-DNA-Hybridization)analysis,suchas constructed using MEGA5 (Tamura et al. 2011) and
8
ComparativeGenomics 217
.Fig.8.6
(a)16SrRNAand(b)23SrRNAtreewithNJmethodand1,000bootstrapresamplingsfromClustalWalignment.Thetreesareviewedand
coloredwithMEGA5.Branchlengthsaremeasuredinthenumberofsubstitutionspersite.Eachphylumiscollapsedwhenpossible,
exceptclassesofProteobacteriawerecollapsedinsteadofphyla
8
218 ComparativeGenomics
Neighbor-Joining (NJ) with 1,000 bootstrap resamplings. The method, to make it more similar to DDH, was made by ran-
bootstrapvaluesinthesephylogeniesweretransformedtoper- domlychoppingupthegenomesequencesin1,020nucleotide
centages. They give a statistical measure for how reliable fragmentsregardlessofwhetherornottheycorrespondtoany
a branch separation is. Therefore, higher percentages support ORFs. The fragments from two genomes are aligned using
a stronger evidence of grouping, meaning a more prominent a BLAST (Altschul et al. 1990) algorithm or a fast alignment
commonancestor,whereaslowerpercentagesmeanthesepara- toolsuchasMUMmerwithoutfragmentation.
tiononthatbranchisstatisticallyinsignificant. In this section, 50 genomes from different genera of the
RibosomalRNAphylogeniesareusuallyabletodistinguish Enterobacteriaceaefamily(datagatheredfromNCBIGenBank)
thedomains,phyla,andgenerainagivenset.Thedistanceson arecomparedbasedontheirANIvalues.ANIcalculationswere
this type of phylogeny show the divergence in the rRNA performed as explained in the paper by Richter and Rosello-
sequences. According to the bootstrap values in the 16S rRNA Mora’s using Jspecies (Richter and Rossello´-Mo´ra 2009). The
phylogenetic tree (>Fig. 8.6a), the phyla level clusters aresig- genomesequencecomparisonwasbasedonnucleotideMUM-
nificantwithhigherthan80%bootstrapvalueontheirroots.To mer (NUCmer) which is a fast DNA alignment tool for large-
better illustrate this, the two different clades were left scale comparisons (Delcher et al. 1999). MUMmer aligns two
uncollapsed while the remaining phyla clades were collapsed. given genome sequences based on maximal unique matches
The correspondence of the significant clades to phyla in pro- (MUMs) between the sequences. A ‘‘MUM’’ is an exact string
karyotesis,however,anexpectedresult.Phylum,asataxonomic match that occurs once in each genome. Once the MUMs are
unit is not defined by the official nomenclature (International identified,theyaresortedinascendingorderaccordingtotheir
Code of Nomenclature of Bacteria (Lapage et al. 1992)). The positionsinthegenomes.AftertheglobalMUM-alignment,the
highest rank according to the official nomenclature is a class; gaps between them are closed based on the properties of the
however, the rank phylum is also being used in prokaryotic gaps.Agapcanbeasinglenucleotidepolymorphism,aninser-
taxonomy quite often and seems to serve practical use for the tionordeletionwherealargesequenceisfoundinonebutnot
taxonomists. Historically, phyla were referred as divisions the other genome, tandem repeats, or polymorphic regions. If
andProkaryotes,asoneofthesuperkingdomsproposedbyWhit- gaps are found, they are aligned using the Smith-Waterman
takerandMargulis,weredividedintothreedivisionsbasedoncell algorithm(SmithandWaterman1981).
wallstructureorabsence(WhittakerandMargulis1978;Gibbons Comparisonsbasedonthetetranucleotidefrequencieswere
and Murray 1978). Although the classification largely changed calculatedusingJspeciesandthealgorithmsfromTeelingetal.
sincethedivisionofarchaealphyla(Murray1989)werediscov- (2004). In this method, all possible combinations of
ered, most of these names are still in use today. In the 2nd tetranucleotidefrequencies(256frequencies)foreachsequence
edition of Bergey’s Manual, phylum was defined as the major is calculated and their z-scores are computed based on the
prokaryoticlineages,basedonthe16SrDNAsequencedataand difference between the observed and the expected frequencies
usedasmainorganizationalunit(Brenneretal.2005b). for a genomic fragment. The similarity between the two
RibosomalRNAbasedphylogeniesusuallyinvolvesthe16S sequences (or genomic fragments) in terms of having similar
rRNAsubunitcomparisons.Hereweshowthat23SrRNAphy- patternsoftetranucleotidesisaddressedbycalculatingthePear-
logeny can also be useful. When the same dataset is analyzed soncorrelationcoefficientfortheirz-scores.Similarpatternsare
using23SrRNAgenes,thebootstrapvaluesaregenerallyhigher expectedtocorrelateandthereforehavehighercorrelationcoef-
than16SrRNAphylogenies(>Fig.8.6b).Thereareexceptions ficients,whereasthedistantpatternswouldhavelowercorrela-
to this, for example, the bootstrap value on the roots of the tion coefficients. Oligonucleotide frequencies are thought to
GammaProteobacteriacladethatishigheronthe16SrRNAtree. carry species-specific signals, where longer signatures carry
Thegenerallyhighervaluesforthe23SrRNAanalysismightbe more signals. Thus, closely related organisms are expected to
due to the size or information content of the sequences and showsimilardistributionoftheusageofthesesignatures.
maybe due to the different mutation rates of the genes. The >Figure 8.7 shows a pairwise genome comparison of ANI
separation of phyla on >Fig. 8.6b is significant, but the order value(heatmap).Thegenomesaremanuallyorderedbasedon
is different, which might lead to the idea of having different 16S rRNA similarities. It is seen that DNA similarity within
relationships among different phyla. However, since the boot- agenusishighercomparedtothesimilaritybetweengenera.It
strap values are very lowat that level, it is still not relevant to isthereforepossibletodistinguishgroupsofgenusandspecies
conclude how close Firmicutes is to Proteobacteria based on basedontheir DNA similarity. Forcomparison, TetraNucleo-
rRNAphylogeny. tidefrequencieswerecalculatedforthesamedataandordered
basedon16SrRNAsimilarity.Thetwoheatmapsareexpectedto
showsimilarresults,withANIvaluesabove96%identitywould
AverageNucleotideIdentities(ANI)andTetra correspondtoveryhighTetraNucleotidefrequenciescorrelation
NucleotideFrequencyCalculations coefficients of (cid:3)0.99 (Richter and Rossello´-Mo´ra 2009). It is
seenthatwithingenera,sequencesarehighlycorrelatedbasedon
Average nucleotide identity was developed as an alternative to tetranucleotide signature usage. Changing the order of the
the DDH values, and was initially based on comparison of all matrixbasedonhierarchicalclusteringmightgiveabetterres-
shared genes among two genomes. Lateron an advance in the olutionusingTetraNucleotidefrequencies.