ecology, evolution, and rrna

8/14/2019 Ecology, Evolution, And Rrna

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Ann. Rev. Microbiol. 1986. 40:337~5Copyright 1986by AnnualReviews nc. All rights reserved

MICROBIAL ECOLOGY ANDEVOLUTION: A RIBOSOMALAPPROACH

RNA

Gary J. Olsen, David J. Lane, Stephen J. Giovannoni, andNorman R. PaceDepartmentf Biology nd nstitute for MolecularndCellularBiology,University fIndiana,Bh~,omington,ndiana 7405

David A. StahlDepartmentf Veterinary athobiology, niversity f Illinois, Urbana,llinois 61801

CONTENTSINTRODUCIION.................................................................................... 338MOLECULARHYLOGENYNDMICROBES............................................ 338Ribosomal NAs s Indicators of Phylogeny............................................... 338Analysis of Population Contents by RibosomalRNA equences ........................ 341INFERRINGRELATIONSHIPS ROMMOLECULAREQUENCES.................. 342Methods f Phylogenetic ree nference .................................................... 342Distance~4atrix Method f PhylogeneticTree Inference ................................. 344Alternatives o Phylogenetic rees ........................................................... 348RIBOSOMAl_,NA EQUENCEATA ASE.............................................. 348CurrentlyAvailableDataCollections ........................................................ 348RapidDetermination f Additional 16SrRNA equences ................................ 349NATURALOPULATIONNALYSIS........................................................ 351Populationsnspected........................................................................... 3515SrRNA nalysis................................................................................ 353PopulationAnalysis by 16S Ribosomal NAGenes ...................................... 357IN SITU HYBRIDIZATION FOR COUNTINGAND IDENTIFYINGORGANISMS........................................................................... 359SUMMARYND UTUREROSPECTS..................................................... 361

0066-4227/86/1001-0337502.00 337

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338 OLSEN, LANE, GIOVANNONI, ACE & STAHLINTRODUCTIONNucleic acid sequencing technology is bringing a much-needed hylogeneticperspective into microbiology. There is no more undamentaland straightfor-ward way to classify and relate organisms than by appropriate nucleic acidsequence comparisons. The simple morphology of most microbes providesfew clues for their identification; physiological traits are often ambiguous.The microbial ecologist is particularly impeded y these constraints, since somany organisms resist cultivation, which is an essential prelude tocharacterization in the laboratory.In this article we describe the application of rapid nucleic acid sequencingand recombinant DNAmethodologies to the analysis of phylogenetic andquantitative aspects of mixedmicrobial populations. The analysis is based onnucleotide sequence comparison of ribosomal RNAs rRNAs)or their genes,extracted from naturally occurring biomass. Because the analysis isphylogenetic, population members re related to known rganisms in terms oftheir fundamentalbiochemical properties and potentials. Also, because mole-cules rather than organismsare isolated, the method s not limited to speciesthat are amenable o laboratory cultivation.Characterization of unknownorganisms by rRNA equences requires areference collection of sequences from knownorganisms. Although sub-stantial, the available reference collection is far from comprehensive,argelybecause nucleotide sequence determinations have been the province of spe-cialized laboratories. However, ecently developed sequencing methods aresufficiently simple that the routine incorporation of rRNA equence informa-tion into the systematic description of microorganisms s nowfeasible. Arapid expansionof the reference sequence collection can therefore be antici-pated. We discuss these developments and review approaches for relatingorganisms by macromolecular sequences.MOLECULAR PHYLOGENY AND MICROBESRibosomal RNAs as Indicators of PhylogenyThe use of macromolecularcomparisons to infer phylogenetic relationships(101) is now well established. Comparisons may be based either on ex-perimental measurementsof "molecular similarity" (e.g. antibody cross-reactivity, DNA-DNAybridization, and ribosomal RNA-DNAybridiza-tion) or on mathematical analyses of molecular sequence data. The formermethods require the pairwise experimental comparisonof most, or preferablyall, organismsconsidered. In contrast, sequencedata are readily accumulated,creating a "data base" that can be referred to for phylogeneticanalysis of newsequence data as they becomeavailable.


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ECOLOGY, VOLUTION,AND RNA 339What molecule is to be used for mapping phylogenetic relationships?Studies colnparing c-type cytochromes, globins, and other commonroteinshave been :rewarding, especially amonghe "higher" eukaryotes (35). On theother hand,, both prokaryotic and eukaryotic microbes are so phylogeneticallyand biochemicallydiverse that even the identification of homologousroteinsis not straightforward (20). For the analysis of natural microbial populations,in whichunknown iversity must be anticipated, there are several reasons tofocus on the rRNAs 88).

1. The rRNAs,as key elements of the protein-synthesizing machinery, arefunctionally and evolutionarily homologousn all organisms.2. The rRNAs re ancient molecules and are extremely conserved in overallstructure. Thus, the homologousRNAs re readily identifiable, by theirsizes.3. Nucleotide sequences are also conserved. Some equence stretches areinvariant across the primary kingdoms,while others vary. The conservedsequences and secondary structure elements allow the alignment of vari-able sequences so that only homologous ucleotides are employed n any

phyloge, netic analysis. The highly conserved regions also provide con-venient hybridization targets for cloning the rRNA enes and for primer-directed sequencing techniques (see below).4. The rRNAs onstitute a significant component f the cellular mass, andthey are, readily recovered rom all types of organisms or accumulation fa data base of reference sequences (see below).5. The rRNAs rovide sufficient sequence nformation to permit statisticallysignificant comparisons.6. The rRNA enes seem to lack artifacts of lateral transfer betweencon-

temporaneousorganisms. Thus, relationships between rRNAs eflect evo-lutionary relationships of the organisms.There are three rRNAs n bacteria, 5S (-120 nucleotides), 16S (-1600nucleotides), and 23S (~3000 nucleotides). Eukaryotes commonly ontain

fourth rRNA,5.8S (-160 nucleotides), which is homologouso the 5 endthe bacterial 23S. The size of rRNAs aries somewhat mong he organismsinspected, but we use the above values as generic designations.The 5S and 16S rRNAshave been used most for rRNA-basedphylogeneticcharacterizations, largely for historical and technical reasons. The 5S rRNA,because it is relatively small, was amenable o sequenceanalysis by the late1960s. However, ts paucity of independently varying nucleotide positionslimits its ultimate phylogenetic usefulness (see below). The 16S rRNAsappropriate size for broad phylogenetic analyses, but it was too large forcomplete sequence determinations until the developmentof DNAloning andsequencing: protocols. Instead, the 16S rRNAwas subjected to partial se-


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340 OLSEN, LANE, GIOVANNONI, ACE & STAHLquence analysis, so-called "oligonucleotide cataloging," which characterizes~25%of the sequence as unique RNase T1 oligonucleotides.The first comprehensivephylogenyof prokaryotes (indeed of life on theearth) emerged rom studies of 16S rRNA artial sequences (oligonucleotidecatalogs) by Carl Woese nd his colleagues (33). Earlier, the eukaryotes werethought to be relative latecomers generated by the fusion of bacterial cosym-bionts. Although the rRNA-based tudies confirmed that the mitochondriaand the chloroplasts are related to contemporary rokaryotes, the eukaryoticline of descent, i.e. the nuclear genotype, was found to be as ancient as thebacterial genotype see Figure 1). More trikingly, the Woese tudies (33,revealed that extant life on earth consists of not two, but three primary ines ofevolutionary descent; there are two phylogenetically distinct groups of pro-karyotes, termed by Woese eubacteria" and "archaebacteria." It was es-tablished, initially from rRNA equencesand later from many dditional linesof evidence, that the archaebacteria are as evolutionarily distinct from theeubacteria as either is from the eukaryotes (reviewed in 98).Figure 1 shows a quantitative phylogenetic "network" (an unrootedphylogenetic tree) of somefamiliar organisms n the three primary kingdoms,based on 16S rRNA equences. The lengths of the line segments are pro-

ARCHAEBACTERIAi Halococcu~orrhua

S~lfolob...

Ifataricus~/Me~latbo~ot~filmvilfili]"0.1

FUKN~YOTE$ FUB~CTFRI,~Figure 1 An unrooted phylogenetic tree, based on 16S rRNAsequences (10, 15, 23, 36, 37, 47,60, 67,69, 71,76, 78,82, 92,93, 99), llustratinghe threeprimaryines of descentnd omeftheir majorubgroups.he ree was nferred ya distancematrixmethod23, 31,67); he lengthsof the line segmentseflect the estimated umberf fixed mutationalvents.The calebarcorrespondso an evolutionaryeparationf 0.1 acceptedointmutationser sequenceosition.


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ECOLOGY,EVOLUTION, AND rRNA 341portional to evolutionary distances (evolutionary distance increases with time,but not necessarily linearly; see below). All of the --400 organisms inspected(mostly bacteria) are clearly affiliated with one of the three primary groups.Some of the major subgroups in each of the primary kingdoms have also beendelineated (71, 97). About ten-subgroups in the eubacteria and two in thearchaebacte, ria have been defined. So few eukaryotic rRNAs have beensequenced that major phylogenetic units remain to be defined; there is, inparticular, tremendous uncharacterized diversity among the eukaryotic mi-crobes (85). More detailed evolutionary relationships have been establishedwithin some of the familiar subgroups of each primary kingdom, but the taskof inferring the phylogeny of life on earth has hardly begun.

Analysis of Population Contents by Ribosomal RNASequencesTwo general methods have been used for exploring natural microbial pop-ulations (outlined in Figure 2). In one approach (Figure 2a), suitablepopulations of limited complexity, 5S rRNA s isolated from naturally occur-ring biomass and the various species-specific molecules are sorted by high-resolution ~gel electrophoresis. Unique 5S rRNA ypes are then sequenced,and with reference to other 5S rRNA equences, the phylogenetic affinities ofthe contributing organisms are defined.

POPULATION.NALYSISSINGS RNAsBIOMASSmixedopulation)~reakells; phenolxtractflULKRNA

I-low-resolutionolyaerylamide~gelelectrophoresisMIXI--D5 RNAs12p nd-label NAs;igh-resolutionpolyacrylamideelelectrophoresisPURIFIEDS RNAs

determineucleotideequence;I.compareith other5S RNA",~, sequencesPHYLOGENETICHARACTERIZATIONOFPOPU.ATIONEMBERS

POPULATIONNALYSISSING6S RNAGENESBIOMASSmixedopulation)~roakeils; phenolxtract

TOTAL NA

lshotgunlone"ntobacteriophageambdaRECOMBINANTNAUBRARYscreenyhybridizationith~mxed ingdom"6SRNArobe16 rRNAGENE LONESsequenceith 165 RNA-specificprimers; ompareith other~6S rRNA equencesPHYLOGENETICHARACTERIZATIONOFPOPULATIONEMBERS

Figure 2 Flow charts for analyses of (a) 5S rRNAs and (b) 16S rRNA genes from naturalpopulations.


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342 OLSEN, LANE, GIOVANNONI, ACE & STAHLIn the second approach, which seems not to be limited by population

complexity, 16S rRNA enes are "shotgun cloned" using DNA urified fromcollected biomass (Figure 2b). It does not matter if the original DNA asfrom a mixedpopulation of organisms; the individual rRNA enes are clonal-ly isolated as recombinant bacteriophage. The different types of cloned rRNAgenes are then sorted in the laboratory and are subjected to limited sequenceanalysis by a technique that affords direct access to regions of the 16S rRNAgene that are particularly useful for phylogenetic evaluations (see below).Again, by referring to existing collections of completeand partial sequences,it is possible to infer the phylogenetic affinities of the organisms n theoriginal population. The cloned rRNA enes can also be used as hybridizationprobes to quantitate the corresponding organisms in the population or toidentify similar organisms n other environments.Inference of the phylogenetic relationships of the organisms n a microbialpopulation from those in a data base is not just an exercise in genealogy. Inparticular, closely related organismsmust have similar fundamentalbiochem-ical properties. However, elatively few "fundamentalproperties" have beenidentified. Certainly the translation apparatus, components f the DNAndRNA ynthetic machineries, ion pumps, and other central functions arefundamental, i.e. conserved. On the other hand, manyovert physiologicaltraits, such as heterotrophy and autotrophy, are very scattered phylogenetical-ly (see e.g. 33). As the details of microbial phylogeny re elucidated, it willbecome ncreasingly clear which biochemical properties may be accuratelypredicted about organisms hat are well-defined phylogenetically but have notbeen cultivated or otherwise characterized in the laboratory.INFERRING RELATIONSHIPS FROM MOLECULARSEQUENCESPhylogenetic trees provide the most incisive summary f phylogenetic rela-tionships inferred from molecular sequencedata. Weoutline several differenttree inference methods and describe one of the methods n detail. This isfollowed by descriptions of two alternatives to tree analysis.Methodsof Phylogenetic Tree InferenceSeveral quite different tree inference methodsare in use (reviewed in 29);unfortunately, they do not necessarily arrive at the sameconclusions. Thus, itis important to distinguish the conceptual bases of three commonly sedinference methods and a fourth which is not in general use.CLUSTERNALYSISluster analysis (86) is a rapid method of assigningtaxa to groupson the basis of similarities. It is applicable to any formof data


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ECOLOGY, VOLUTION,AND RNA 343for which a meaningful measure of similarity can be defined, and it istherefore the methodof choice for analyzing rRNA ligonucleotide data (33).Cluster anailysis has also been applied to 5S rRNA nd 16S rRNA equencedata (e.g. 55, 63). Phylogenies nferred from cluster analysis are expectedbe valid whten he rates of sequencedivergenceare sufficiently similar in alllineages (16). When he rates of sequence divergence are unequal (seebelow), the most rapidly changing sequences tend to branch too deeply (tooearly) in the inferred phylogeny. Of the commonlysed tree-inference tech-niques, cluster analysis is the mostprone to this inaccuracy. However,t leastone variatie,n of cluster analysis, the "present-day ancestor" method 53, 61),partially avoids t.

MAXIMUMARSIMONYree inference methods based on the principle ofparsimony re frequently used for analyses of sequencedata (e.g. 14, 30, 54).These methods seek the tree branching order (topology) that requires thefewest mutational events. Proponents emphasize that the method in-dependently examines the evolution of every sequence position, rather thanreducing he data to averagesimilarities (as in cluster analysis) or distances(see below). Felsenstein (27) has demonstrated that the methodaccuratelyreconstructs,; phylogenies ither if the rates of evolution in the various lineagesare similar or if the sequence changes in the most rapidly evolving lineagesare not too numerous. However,many nteresting groups, such as the myco-plasmas (96) and mitochondria (e.g. 11, 55, 67, 99), exhibit unusuallyevolutionaEy rates and deep divergences. In such cases, the most rapidlyevolving lineages tend to branch oo deeply in the inferred phylogenetic ree.DISTANCE ATRIX ETHODSistance matrix methods (e.g. 31, 81) usethe differences between pairs of sequences to estimate the "evolutionarydistance" (usually expressed as the average numberof accepted point muta-tions per sequence position) separating the sequence pairs. This step caninclude a correction for the superposition of multiple mutations at a singlesequence p,asition. After the evolutionary distances separating every pair ofsequences ihave been estimated, the phylogenetic tree is found that mostfaithfully represents the pairwise distance estimates (according to any one ofseveral mathematicaldefinitions of "faithfully") (29). To the extent thatmethodcornpensates for multiple changes at sequence positions, it is morereliable than parsimony n situations that involve large amountsof sequencedivergence and lineage-to-lineage variations in evolution rate. Schwartz &Dayhoff (81) have presented evidence that distance methods might alsoless susceptible to statistical errors (see below) esulting from he finite lengthof the sequences being compared.


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344 OLSEN, LANE, GIOVANNONI, ACE & STAHLMAXIMUMIKELIHOODelsenstein (28) has pointed out that the currentmethodsof tree inference are not directly derived froma specific modelof theevolutionary process. Heproposes that tree inference should find the tree withthe maximumikelihood of explaining the data in the context of a concreteevolutionary model. He illustrates his proposal with a simple model thatassumes that all sites are equally and independently mutable and have thesame average base usage. Although maximumikelihood has been applied toproblems with few organisms (e.g. 28, 42), the method s computationallydemanding and hence has not been frequently used.A GENERALAVEATite-to-site differences in mutability present a largelyunsolved problem in tree inference. In combinationwith lineage-to-lineagevariations in mutation rate, the site-specific variations introduce systematicerrors into all of the tree inference techniques. There is abundant evidence(14, 18, 30, 34, 38, 64, 67) for sufficiently great position-to-position varia-tion in mutability to give dramatic errors in evolutionary distance estimates(34). This error places the branch points of rapidly evolving ineages too earlyin the inferred trees. The effect can be partially alleviated if there are moreorganisms represented in the tree, which increases the chance of having amoderatelyevolvingsequence hat is specifically related to the rapidly evolv-ing sequence 99). It is also possible to estimate the actual distribution of ratesover the length of the sequences and to appropriately compensate for thevariations (34, 43; G. J. Olsen, unpublished), though muchwork remainsbe done.Random rrors arise because comparisons of finite length sequences pro-vide a limited sampling of evolutionary history. That is, the number ofsequence differences observed from the finite number of nucleotides com-pared is subject to a binomial-typecounting error. The magnitude f this errorcan be minimized by examining a larger number of independently evolvingsequencepositions. This is illustrated in Figure 3.Distance Matrix Method of Phylogenetic Tree InferenceAlthought is beyond he scope of this review o detail all of the tree inferencemethods mentioned above, we discuss one for additional perspective. Themajority of the discussion is applicable to all methods.The process of phylogenetic ree inference can be divided into three distinctcomponents. First, the various sequences must be "aligned" so that theevolutionarily homologous ucleotides of each sequence are in registry withone another. Second, the data are fit into a given tree branching order, andcriteria are defined for evaluating its faithfulness in representing the data.Third, alternative branching orders are tested to seek the tree that mostfaithfully reflects the sequence ata by the criteria defined n step two. Cluster


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ECOLOGY,EVOLUTION,AND RNA 345

2.0

0ILl 0.5

Nominalalues95~Confidencemits:

I I6SrRNAISS FINA

1,0 0,2Sequence HomologyFigure 3 The relative statistical uncertainties (52) of evolutionary distance estimates (50) basedon 5S and 165. rRNAs. Because not all sequence positions are independently variable, the 5S and16S rRNAs have been treated as 80 and 1000 sequence positions, respectively. Adapted from(72).

analysis does not require this search of alternative branchingorders, so it isthe most rapid of the various methods.SEQUENCELIGNMENThe drawing of evolutionary conclusions fromnucleotide sequence data requires the assumption that each set of comparednucleotides (one from each sequence) is derived from a single ancestral nu-cleotide by a concrete (thoughgenerally unknown)eries of mutational events.Conversely,. f nucleotides do not share a commonncestry, then their compari-son provides no valid evolutionary information. The process of sequencealign-mentdefines this one-to-one correspondenceof the residues in each sequencewith their evolutionary homologsn each of the other sequences.The basis of an alignment lies in the recognition of regions that are moresimilar among he various sequences than could be expected at random.Initially, the most clearly homologous equence regions are aligned. Later,regions of less substantial homology re aligned. In addition to primarystructure (the linear sequence), conserved econdarystructures (base pairings)provide additional guidance for the alignment of transfer RNA nd rRNAsequences. The process would be simple if there were no insertions ordeletions in the mutational history of the molecules. However, significantfraction of the mutations n genes of interest lead to changes n the length of


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346 OLSEN, LANE, GIOVANNONI, ACE & STAHLthe molecule. Becauseof these internal insertions and deletions, the align-ment of the sequences is only regional and must be constantly reconsideredalong the length of the molecules. It is usually necessary to introduce somenumberof "alignment gaps" in order to align all of the homologousequenceregions in a collection of molecules. The greater the numberof homologousfeatures used to define a sequencealignment, the better-defined the locationsof the alignment gaps become.After a set of sequences has been aligned there are generally regions inwhich he alignment s ambiguous,particularly if the sequencesare diverse.Because of the importance of comparing only homologous ucleotides, it isnecessary to exclude these regions of ambiguous lignment from evolutionaryanalyses.FITTING ALIGNEDSEQUENCEDATATO A TREE BRANCHINGRDERBe-cause this step differs with each major tree inference method, the followingdiscussion will mostly be limited to the distance matrix methodused in ourlaboratory (23, 70; G. J. Olsen, unpublished). In outline, the numbernucleotide differences is used to estimate the number f mutational events thatseparate each pair of aligned sequences. The estimates of evolutionary separa-tion are fit to an assumedphylogenetic branching order; we find the optimaltree branch lengths and then calculate how well this phylogenetic treerepresents the evolutionary distance estimates (see below).Whenquantitating the differences between aligned sequences, the treat-ment of alignment gaps must also be defined. In sequence regions for whichwe are sufficiently certain of the alignment to be confident that onlyhomologous ucleotides are compared see above), there are generally so fewalignment gaps that the details of the treatment have little influence on thephylogenetic conclusions. Juxtaposition of an alignment gap in one sequencewith a nucleotide in a second sequence has been treated as 0--2 sequencedifferences in estimating evolutionary divergence.Because multiple mutations occur at single sequence positions, theobserved numberof nucleotide differences generally underestimates the num-ber of mutational events that have occurred since the separation of thecorresponding genes. A variety of formulas (e.g. 34, 43, 50, 51) have beenproposed for estimating the average numberof fixed mutational events persequence position (i.e. the "evolutionary distance") separating the two se-quences. The resulting values are properly referred to as estimates becausethey are limited by both randomand systematic errors (see above).Havingestimated the evolutionary separations of all pairs of sequences,one must then answer the question, howaccurately can a given phylogenetictree branchingorder represent the pairwise distance data? Wedefine the errorof the representation as the sumof the squares of the differences between he


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ECOLOGY, VOLUTION,AND RNA 347pairwise evolutionary distance estimates and the corresponding tree recon-structions of those distances, with each difference weighted by the corre-sponding statistical uncertainty (70; for other weighting factors see 29).Kimura& Ohta (52) have calculated the variance (the square of the expectedcounting enor due to finite sequence length) of the Jukes & Cantor (50)evolutionary .distance estimates. Witha given tree branching order, it isnecessary to compute the branch lengths that minimize tree error. Thesebranch lengths define the minimum-error ree consistent with the branchingorder.When ranch lengths are assigned by such a least-squares approach, it ispossible for lengths to assume egative values (this problem oes not arise inthe other methods isted above). These negative values do not correspond tomeaningful evolutionary phenomena,but rather are the mathematical re-sponse o c,~rtain suboptimalbranchingorders. In trees that contain negative-length segments we currently change the negative values to zero (withoutmakingan3, compensatory djustments in the lengths of the other branches)and then reevaluate the overall tree error (G. J. Olsen, unpublished). Thissimple trea~Iment provides meaningful alues for relative tree errors, even inthe presence of one or more short negative-length segments. Usually, someminor rearrangement of the tree branching order yields a tree that does notcontain an3, negative-length segmentsand that also has a lower tree error.FINDING HEBESTTREEBRANCHINGRDER Even with a quantitativedefinition o,f howwell the sequence data are accomodated y a given branch-ing order (see above), as yet no process is guaranteed to find the "best"branching order of a large phylogenetic tree in a practical amountof com-putational time. In essence, methodsook instead for "better" trees by system-atically testing a tractable number f alternatives to the current "best knownbranchingorder." Whenhe process stops finding better trees, the current treeis assumed o be the "best."

To find better tree branching orders we use a computerprogram (70) thatexamines ill rearrangementsof the tree that can be achievedeither by movingany single group of organisms(subtree) to every alternative location in thetree or by interchanging any pair of subtrees. For trees of 10, 20, or 30organisms his requires testing about 250, 1600, or 4200 alternatives to thecurrent tree. The programexamines all of these tree rearrangements beforetaking the most improved tree as the starting point for another cycle ofsearching.Two riteria suggest that this method onsistently finds the best tree. First,the method oes not dependon the starting point of the search for better trees;random nitial tree configurations always converge on the same solution.

Second,extending the vicinity of the search for improved rees by examining


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348 OLSEN, LANE, GIOVANNONI, ACE & STAHLrearrangements n the vicinities of near-optimal branchingorders (as opposedto the vicinity of the presumptive est tree) does not yield a better solution.Alternatives to Phylogenetic TreesAs can be seen, the casting of phylogenetic trees from sequence data is not atrivial undertaking. What are the alternatives to phylogenetic trees? Twosimpler, particularly useful alternatives are described here.Thepossibility that a novel sequence s very closely related to a previouslyknown equence s easily investigated. Thesimilarity of the novel sequence oeach of the previously known equences is determined; if the novel sequenceis muchmore similar to one of the known equences than either are to theremainder of the sequences, then it may mmediately be concluded that aspecific relative of the novel sequence has been identified.If no individual sequence s particularly similar to the novel sequence, t isnatural to ask whether the sequence fits into any previously defined majorphylogenetic division. Frequently, the novel sequence can be examined or"signature" sequences (or signature nucleotides) that have been compiledthe basis of their ability to distinguish rRNAsromeach of the majorbacterialgroups (19, 97). By definition, signature nucleotides change sufficientlyrarely that they are generally constant within each major group, but fortuitous-ly vary between one or more groups. These nucleotides can be considered aclassification key to the phylogenetic divisions of the corresponding rRNAsequences. Thus, by determining the nucleotide identities at a limited numberof specific sequence ocations, we can fit novel sequences nto the predefinedphylogenetic groups. As the frameworkof reference sequences increases, itwill be possible to increase the resolution with which placements can bemade.RIBOSOMAL RNA SEQUENCE DATA BASECurrently Available Data CollectionsAn mportant aspect of the analyses described here is the correlation of rRNAsequences derived from natural populations with those in existing referencecollections. Three rRNA equence collections are of particular value: 5SrRNAcomplete sequences, 16S rRNAoligonucleotide catalogs, and 16SrRNA omplete and partial sequences. The following discussion outlines thevarious methodsof generating these rRNA equences, the current status of thecorrespondingdata collections, and the prospects for significant expansionofthese collections using new sequencingstrategies.5S rRNAsare -120 nucleotides in length. Complete 5S rRNA equencesare determined by enzymatic(21) and chemical (74) sequencingprotocols.brief, gel-purified, 32p-end-labeled,5S rRNAs cleaved in separate reactions


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ECOLOGY, EVOLUTION, AND rRNA 349with base-~,Ipecific enzymesor reagents such that, on average, one break perRNAmolecule is introduced. The partial digestibn products are then sepa-rated by size on high-resolution polyacrylamide gels. When denosine-,cytidine-, guanosine~, and uddine-specific reactions are electrophoresed inadjacent ge.l tracks, a "ladder" is producedon an autoradiograph of the gelfrom wliich.the nucleotide sequence can be read. We re aware of nearly 400complete5S rRNAequences; most or. all of these will eventually work heirway nto published compilations (25) and will be availabl~ for phylogeneticcomparisons.

The 16S rRNAs, which are -13 times the size of 5S rRNAs,contain morephylogenetic information, but determination of their sequenceswas a greatertechnical claallenge. Three methodswereusedin obtaining the majority of theavailable lt6S rRNAdata: oligonucleotide cataloging, gene cloning andsequencing:, and reverse transcription from 16S rRNA.Woese nd his colleagues have used oligonucleotide catalogs to determineevolutiona~3~ elationships among rganisms(32). Uniformly32P-labeled 16SrRNAsolated from cells grown in the presence of [3ZP]brthophosphate isdigested e~:haustively with ribonuclease T~I. RibonucleaseT1 cuts at the 3side of all guanylic acid residues, and so generates a set of oligonucleotides,each with a single guanylic acid residue at its 3 end. Theseare separated bytwo-dimen:sional electrophoresis according to size, base composition, andsequence 79, 95). Individual oligonucleotides are recovered and. then sequ-enced: using a variety of enzymatic and chemical techniques (87). A 16SrRNAatalog, i.e. the sequences of the ribonuclease Tl-generated 16S rRNAoligonucleotides, is characteristic of the source organismand maybe com-pared to l~e catalogs derived from other organisms. Approximately 400prokaryotic 16S rRNAshave been cataloged (97). Each catalog containsabout 400 nucleotides (25% of the 16S rRNA) n pentanucleotide or largerfragments. Becauseof its broad samplingof organisms, the catalog collectionis particul~xly useful as a source of "signature" sequences (see above).Rapid Determination of Additional 16S rRNA SequencesThe 16S rPJ~IAcatalog collection will continue to be a valuable resource forphylogenetic information; however, more expedient techniques~for 16S rRNAsequence determination are becoming vailable. One is aimed at determiningcomplete16S rRNAgene sequences, and another is designed to 0btain~sizableblocks of phylogenetically useful sequence data from the RNAtself. Bothapproaches makeuse of dideoxynucleotide sequencing protocols (80) and 16SrRNA-specific oligodeoxynucleotide primers (56).In the dideoxynucleotide chain termination sequencing protocol a DNAstrand cortkplementary to the template nucleic acid is synthesized from a"priming oligonucleotide" (primer) which has been annealed to its specific


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350 OLSEN, LANE, GIOVANNONI, ACE & STAHL"priming site" on the template (80). The use of DNA olymerase or reversetranscriptase permits the sequencing of DNA r RNAemplates, respectively.Chain elongation from the primer is randomly erminated in separate reactionsat either adenosine, cytidine, guanosine,or thymidine esidues by inclusion oflow levels of the respective 2~,3~-dideoxynucleoside triphosphate in thereaction mixtures. Reaction products are detected by inclusion of a radioac-tively labeled nucleoside triphosphate (e.g. [a-35S]dATP)n the reaction (5)or by initiating synthesis with a radioactively labeled primer. Theproducts arethen resolved on a polyacrylamide gel, and the nucleotide sequence is readfrom an autoradiograph of the gel.For the determination of complete 16S rRNA equences, DNAestrictionfragments containing all or part of the 16S rRNA ene are typically clonedinto one of the single-stranded bacteriophage M13vectors. In the standardM13 equencing system, the priming site is a unique stretch of DNAdjacentto the M13 loning site (68). Chain elongation thus proceeds from thisvector-specific site into the cloned segment of DNA.Since only about300-500nucleotides of sequence can be read from a given primer, a variety ofsubcloning and "trimming" strategies have been devised to bring differentparts of larger cloned DNAsnto juxtaposition with the priming site (68).Considerablemanipulationof the original clone is required for a gene the sizeof the 16S rRNAgene.Analternative strategy, which is specific for the determination of 16SrRNA ene sequences, takes advantage of the fact that 16S rRNAsare notuniformly variable in sequence (38). Several 15-20-nucleotide regionssequence with little or no variation are found within every 16S rRNA x-amined. Because of their length and their universality, a synthetic DNAoligonucleotide that is complementaryo one of these sequenceswill specifi-cally anneal to the corresponding site in the 16S rRNA ene from any sourceorganism, thereby providing a specific priming site for dideoxynucleotide-terminated sequencing (56). Because these priming sites are in the genesequence tsel,f,, there is no need o manipulate he clonedgene so that it abutson the vector-specific priming site mentionedabove. Additional oligonucleo-tides that are homologouswith (rather than complementaryo) the universalsequences anneal to the opposite DNAtrand of a cloned gene and permitsequence determination in the opposite direction. Thus, cloned 16S rRNAgenes maybe rapidly sequenced by working outward in both directions fromthe universal 16S rRNA equences (23, 99).About 80 complete 16S rRNAgene sequences representing the threeprimary kingdoms ave been determined; a steady expansion of this collectionis anticipated.

Phylogenetic characterization of most organisms will not require completesequences. A protocol for rapidly generating large blocks of 16S rRNA


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ECOLOGY,EVOLUTION,AND RNA 351sequence data, which requires neither isolation of purified 16S rRNA or thecloning of its gene, has been described recently (56). The 16S rRNAn bulk,cellular RNA reparations is selectively targeted for dideoxynucleotide-terminated sequencing with reverse transcriptase and the 16S rRNA-specificprimers mentionedabove. Three primers, complementaryo three strategical-ly positioned regions of conservation in 16S rRNA,have been found particu-larly useful[ for phylogenetic valuations becauseof their general applicabifity(56). Figure 4 shows he location of these three primingsites in representativeeubacterial, archaebacterial, and eukaryotic small-subunit rRNAs, nd in-dicates the extent of nucleotide sequence routinely accessed from each. Theseprimers routinely yield 800-1000 nucleotides of sequence from each 16SrRNAmolecule, i.e. three 250-350-nucleotide "blocks" (Figure 4).Phylogenel:ic rees inferred from hese limited regions of the 16S rRNAeithersingly or together) have topologies identical to those obtained from completesequences 56, 67, 70). At present our collection contains ~75 such "partial"16S rRNA equences, --50 from eubacteria and --25 from eukaryotes. Thephylogeneticusefulness of the data obtained and the relative simplicity of theapproachnaake his technique attractive for classifying microbesof uncertainaffiliation. It requires only about four days of effort to evaluate the phyloge-netic statu:~ of a cultured organism. Weanticipate, therefore, that the 16SrRNA artial sequence data base will expand rapidly.NATURAL POPULATION ANALYSISPopulations InspectedSo far, three natural populations have been characterized by 5S rRNAnaly-sis: (a) the bacteria inhabiting OctopusSpring in YellowstoneNational Park;(b) the eukaryotic and bacterial components f the unusual symbioses nvolv-ing the "gutless" marine invertebrates Riftia pachyptila Jones, Calyptogenamagnifica Boss & Turner, and Solemya velum Say; and (c) the bacteriainhabiting a leaching pondatop a copper recovery dump t the Chinomine in

A B C~ ~ E.COilA B C~ ~ ~ H.VO/CatE/A B C O. discoideumFigure Hybridizationites (A, B, andC)andapproximatemountsf sequenceccessible

(arrows)rom hree small-subunitRNArimers, hownn linear representationsf the 16SrRNAsrom scherichiaoli (a eubacterium),alobacteriumolcaniian archaebacterium),ndDictyosteliurniscoideuma eukaryote).olidboxeslong he sequenceines markegionswithsufficient ntrakingdomomologyo be generallyseful n the inference f phylogenies.epro-duced rom 56).


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352 OLSEN, LANE, GIOVANNONI,PACE & STAHLsouthern NewMexico (57, 58, 89, 90). Octopus Spring, in addition, hasserved as a test environment for the 16S rRNA ene-cloning approach :de-scribed below.THE OCTOPUSPRING COMMUNITYne of the better studied thermalsprings in YellowstoneNational Park is OctopusSpring (9). Abundant,.growthof microorganisms is evidenced by fibrous pink tufts on the wails of theslightly alkaline, 9 IC source pool and the immediate un-off channel, and bymicrobial mats in the cooler, peripheral waters. There has been no reportedcultivation of microorganisms esembling those observed in the source pool(9). Appreciable nucleic acid could not be extracted from pink tuft material,probably because most of it is dead and leached by the ~hot water. How.ever,since contact (microscope) slides immersed in the 91C source pool--arerapidly colonized (9), an alternative strategy was to-introduce growthsurfaresfor collection. Cotton and fiberglass battings sewn into nylon screens werevisibly colonized after 7-10-day immersion, and provided ample biomass foranalysis.THE SULFUR-OXIDIZINGYMBIONTShe submarine hydrothermal ventsystems:are associated with crustal spreading centers at the Mid-Ocean idge,which extends over about 70,000 km of the earths surface (22). Itestimated that a volumeof water equivalent to the worlds oceans percolatesthrough these systems about once every eight million years. The chemistry ofthe ocean water is markedlyaltered during convective passage; it is depletedof somecompounds e.g. Ca2, Mg, SO42-) and charged with others (e.g.metallic sulfides, H2S, CH4,and CO). Nourished by the reduced compoundsin the exudate, dense populations of chemolithotrophic bacteria develop inand around manyof 1the vents (45). These, in turn, support rich animalcommunities that graze and filter-feed upon them. In manycases, sulfur-oxidizing bacteria have formed symbiotic associations with macrobiota toproduce, in effect, chemoautotrophic nimals. The giant vestimentiferan tubeworm, Riftia pachyptila Jones, and the giant clam, CalyptogenamagnificaBoss & Turner, are two such hydrothermal vent-associated invertebrates (13,26). SolemyavelumSay is a mussel that inhabits sulfide-laden mudsof tidalmarshes 12). All three share the anatomical peculiarity of partially or com-pletely lacking mouths and digestive systems. They are nourished by theirchemoautotrophic endosymbionts, which colonize a specialized organ inRiftia (the trophosome) and the gill tissues of the bivalves. Attemptscultivate these bacterial symbionts have so far been unsuccessful or haveyielded equivocal results (H. Jannasch & C. Cavanaugh, personal com-munication). Small amounts of homogenized issue (< 1 g from each organ-ism) served as starting material for the rRNA equence analyses of thesetwo-component populations.


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ECOLOGY,EVOLUTION,AND rRNA 353THE CHINOCOPPER-LEACHINGONDAbout 10% of total commercialcopper recovery in the United States relies, uponmicrobial leaching of low-grade ore (7). At the Chino.mine,a microbially conditioned, low-pH eachingfluid is pumpednto ponds atop large dumps f coarsely crushed; sulfide-richcopper ore. Sulfuric acid produced by microbial chemolithotrophic metabo-lism in the pondspercolates.through he ore stack, solubilizing the copperas asulfate sail.. Thiobacillusspp. are the most abundant solates from these sitesand; so generally are considered to be the primary effectors of leaching.However,given the traditional difficulty in cultivating chemoautotrophicbacteria and the known resence of physiologically distinct bacteria in theseenvironments (7), the ecology of such leaching communities remains in-completely described. Samplesof pond water and surface mudunderlying onepH2.5, iron-rich leaching pond at the Chino mine were anal~czed. Between109 and1010 cells, as estimated, by microscopi~nspection, were recoveredbycentrifugally washing --1 kg of mud.5S rRNA AnalysisNUCLEIC .ACID EXTRACTION AND SEQUENCING The 5S rRNA analysisrequires relatively few cells. From.108-109 eils of each species present willusually suffice for the complete5S analysis outlined in Figure 2a. Collectionand extraction are complicated by other factors, including the presence ofmaterials, that interfere with. nucleic acid recovery or, manipulation nd thepossibility of bias introduced at either the collection or extraction steps. Inmost cases, standard methods for the isolation of nucleic acids (73) willsuffice. Cells maybe. lysed, by any of a variety of enzymatic nd/or mechani~cal techniques, including lysozyme and/or protease treatment; passagethrough a French pressure cell; or, most simply, direct extraction with de-tergent and phenol. In our experience, a good yield of low-molecularweightRNAs an be recovered from most bacteria (both gram-positive and gram-negative) without cell breakageby subjecting the bacteria to several freeze-thaw cycles with dry ice, followed by extraction at 60C against buffer-saturated phenol (90).The 5S :rRNAs re i~olated from total nucl~i~ acid~ by polyacrylamide el.electropho:resis. Theyare then radioactively labeled at their 3 termini using[5-32p]cyfidine bisphosphate and RNAigase, or at their 5 termini (follow-ing removal of preexisting-terminal phosphates with alkaline phosphatase)using [T-3~:P]ATPnd polynucleotide kinase (89). Species-specific 5S rRNAsare separated on high-resolution (sequencing-type) polyacrylamide gels.only very small amountsof RNA re available, the total RNA opulation canbe 3-end-labeled before~ gel electrophoresis, and the preliminary fraction-ation of unlabeled rRNAs an be omitted. Following heir recovery from gels,each of the purified 5S rRNAs re sequenced as described above.


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354 OLSEN, LANE, GIOVANNONI, ACE & STAHLRESULTS OF 5S rRNA ANALYSES The organisms comprising the naturalpopulations discussed here span the three phylogenetic kingdoms Figure 1).

The Octopus Spring source-pool community contains three dominantmembers, wo eubacteria (comprising -50%of the total 5S rRNA ecovered)and one archaebacterium (90). The two eubacterial 5S rRNAsmost closelyresemble those of the twoThermus pecies (T. aquaticus and T. thermophilus)in our reference collection. The archaebacterial 5S rRNAs distantly relatedto those from the "sulfur-metabolizing"branch of the archaebacteria, a physi-ologically diverse group which includes Sulfolobus and Pyrodyctiutn (91).Some pecies grow heterotrophically or as sulfur-oxidizing autotrophs, whileother members f this branch grow by sulfur-dependent respiration of hydro-gen or organic compounds. t has recently been demonstrated that certainSulfolobus spp. grow anaerobically by the sulfur-dependent respiration ofhydrogen 83, 100). Since OctopusSpring is a low-sulfide environment, thisarchaebacteriumand the Thermus-likecohabitants are likely to grow hetero-trophically (90).The Chino mine leaching pond yielded two distinct 5S rRNAs 57). Themore abundant species is identical in sequence to that of the Thiobacillusferrooxidans strain in our reference collection (ATCC9859). This resultconsistent with previous observations that T. ferrooxidans is the predominantbacterial species cultivated from this environment,followed by Thiobacillusthiooxidansand the moderately hermophilic, iron-oxidizing THstrains (6, 8:J. A. Brierley, personal communication). The second 5S rRNAChino 2)not specifically related to any organismn our reference collection, but like T.ferrooxidansandT. thiooxidans, it is affiliated with the fl subdivision of thepurple bacteria (see below). Lacking reference sequences from TH trains,cannot assess its relationship to these bacteria.The invertebrate symbionts each contained two distinct 5S rRNAs:oneeukaryotic, i.e. the host, and one eubacterial (58, 89). In each case, theeukaryotic 5S rRNAs very similar to those of mollusks. Although this wasexpected of the bivalve sequences, the tube worm iftia had been classified asa member f a different phylum 48). Becauseof the generally small amountof sequence variation observed among he invertebrate 5S rRNAs, it isdifficult to assess the significance of this result. Indeed, previous 5S rRNA-based analyses (54) had grouped members of three additional phyla(Brachiopoda, Coelenterata, and Porifera) among he mollusks.The bacterial endosymbionts re all affiliated with the "purple bacteria"group (phylum) of the eubacteria. Oligonucleotide catalog comparisonshavedelineated three main subdivisions of the purple bacteria group: 0, repre-sented in Figure 1 by Agrobacterium umefaciens; ~, represented by Pseudo-monas estosteroni; and y, represented by Escherichia coli (97). The bacterial


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ECOLOGY, EVOLUTION, AND rRNA 355symbionts all fall into the 7 subdivision, as do several strains (A7, G3, HSP,and E7) obtained as end-point dilution isolates from Riftia trophosome(46, 58). Figure 5 presents a 5S rRNA-based phylogeny of the 7 subdivisionof the purple bacteria, illustrating the inferred affiliations of all these or-ganisms.

The Riftia symbiont is placed, by 5S rRNAsequence comparisons, verynear the root of the tree shown n Figure 5. Its closest known elative (89%5SrRNA sequence similarity) is "Thiobacillus ferrooxidans" strain ml, aniron-oxidizing, Thiobacillus-like bacterium that does not appear to oxidizereduced sulfur compounds (41). None of the bacteria isolated by end-pointdilution of trophosome tissue has a 5S rRNAsequence identical to thatextracted from the sample ofRiftia trophosome material (58). Isolates G3 andHSP have nearly identical 5S rRNA equences and are affiliated with thewell-defined Vibrio cluster, represented in Figure 5 by Photobacterium phos-phoreum and Vibrio harveyi. Isolate E7 and Pseudomonas fluorescens have

Figure5 A phylogeny f the gammaubdivisionof the purple bacteria (see text) basedonrRNAequences.The ree was nferred as in Figure 1. Theanalysis also included severalorganismsullsideof the group not shown),which ontributedo the placementf the root of thistree in the vicinity of the open ircle. Reproducedrom 58).


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356 OLSEN, LANE, GIOVANNONI, ACE & STAHLidentical 5S rRNA equences. The 5S rRNAsmost Closely related to that Ofisolate A7 are from other hydrothermal vent-associated prokaryotes, the-Calyptogenasymbiont 88%similarity)and Thiomicrospira strain ~L-12 (seebelow). Recentlyan even closer relative of isolate,A7 (at least 97% imilarity)has been identified, Acinetobacter calcoaceticus (58).The Solemyagill ~prokaryote is affiliated, as a deep branching, with thefluorescent pseudomonad luster. Its closest known elative (87%5S rRNAsequence imilarity) is Thiothrix nivea, ,a sheathed, filamentousorganism hataccumulates sulfur whengrown n the presence of sulfide or thiosulfate.RELATIVE ABUNDANCEOF UNIQUE 5S rRNAs The relative abundance ofeach unique 5S rRNA xtracted from an environment offers some ndicationof the relative amountof the corresponding microorganism n the population(89, 90). Because 5S rRNAsaccept an end label (see above) with varyingefficiency (primarily owing o secondarystructure differences), the amountradioisotope incorporated into the various intact 5S rRNAss an unreliablemeasure Of their relative abundance. However, his bias maybe avoided byfirst digesting the mixture of 5S RNAso completion with RNaseT1, andthen labeling the resultant oligonucleotides at their 5 termini with [3/-32p]ATP nd polynucleotide kinase. The oligonucleotides are fractionated bytwo-dimensionalhigh-voltage paper electrophoresis (79) and the radioactivecontent of each is determined. Since the oligonucleotides-are efficiently anduniformly labeled, the relative amounts of label incorporated into thoseoligonucleotides that are unique to a specific 5S rRNA.providen estimate ofits relative abundance n the original population.LIMITATIONS OF THE 5S rRNA-BASED ANALYSES .PQpulation analysis by5S rRNA equences has two major limitations: (a) Complex mixturessimilarly sized molecules must be separated, and (b) relatively littlephylogenetic information is available in the molecule (--120 nucleotides).Current fractionation techniques rely upon high-resolution gel elec-trophoresis. Although the fractionation of --10 5S rRNAss feasible, morecomplexmixtures are problematic. The contribution by individual organismsof 5S rRNAswith terminal or internal length heterogenei.ty may also con-found fractionation and analysis of somepopulations.Although mproved echniques of nucleic acid fractionation can be antici-pated, the precision Of 5S rRNA-based hylogenetic analysis will remainlimited by the small size of the molecule. One exampleof the problems thatcan arise from.this is the placementof Thiomicrospira train E-12, an isolatefrom a sulfide diffusion-gradient enrichment inoculated with water and par-,ticulate,matter.from,the GalhpagosRift (77). In previous 5S rRNAharacter-izations (without the Riftia trophosome nd-p0int dilution.isolates), .T.hiomi-crospira strain L-12 had been placed, along with Thiomicrospira pelophila,


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358 OLSEN, LANE, GIOVANNONI, ACE & STAHLThe sample DNAs "shotgun cloned" into a phage lambda cloning vector,producing a recombinant"library" whichcan be amplified and probed for 16SrDNA--containinglones (Figure 2b; for a general discussion of gene cloningsee Reference 66). The Octopus Spring DNA as partially digested with therestriction endonuclease Sau3A, and 10-15-kb fragments were selected byagarose gel electrophoresis. Since Sau3Arecognition sites are located, onaverage, at 256-bp intervals, the products of the partial digestion were anearly random collection of overlapping DNAragments. These fragmentswere ligated into the BamHIloning site of the phage lambdavector L47.1(62), and the resulting recombinant phage DNAwas "packaged" into in-fectious lambdaphage particles in vitro (24, 75).A numberof hybridization probes have been tested for detection of rDNA

clones on membraneilter replicas of phageplaques (4). In current use (72)a "mixedkingdom"probe, i.e. a mixture of 16S rRNAs,one from each of theprimary kingdoms, which have been alkali-fragmented and radioactivelylabeled (65). Becausesubstantial portions of 16S rRNAequences are similaramong all known organisms, low-stringency hybridization permits theidentification of rRNA enes from unknown rganisms. Initial characteriza-tions have used a mixture of E. coli (eubacterial), Sulfolobus solfataricus(archaebacterial), and Dictyostelium discoideum (eukaryotic) 16S rRNAs.Hybridization conditions that minimizebackgroundhybridization to nonribo-somal DNA, et that are of sufficiently low stringency that even cross-kingdom 16S rRNA/rDNAybridization takes place, have been empiricallydetermined (72). Of the recombinant phage derived from the Octopus Springsource-pool DNA, .2-0.3% contained 16S rDNAnserts. This is about thepercentage of rDNAn the typical bacterium, which suggests that rDNAwasuniformly recovered from the mixed population.Subsequent to the identification of the 16S rDNA-containing lones, re-dundant genes are identified, and the sequences are determined for eachunique gene (presumablyone per source organism). Several approaches to thesorting and sequencing steps are being explored. The methods n commonse(sorting by a combinationof restriction enzyme nalysis and hybridization,followed by subcloning into single-stranded bacteriophage M13 or sequenc-ing) are cumbersome or screening large numbers of clones (66). Shotguncloning directly into M13 would xpedite these steps of the analysis, since the16S rDNA equences could immediately be determined using the universalrRNA rimers discussed above. However, arge DNAnserts are unstable inthis vector (68; D. J. Lane, unpublished).Hong (44) has reported a procedure for direct dideoxynucleotide-terminated sequencing using double-stranded phage lambda DNAemplates,DNA olymerase (Klenow fragment), and 20-nucleotide synthetic oligode-oxynucleotide primers. Wehave so far been unable to reproduce this resultwith rDNA-containing lambda DNAemplates and the 16S rRNA-specific


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ECOLOGY, EVOLUTION, AND rRNA 359primers. However, daptation of the protocol for use with reverse transcrip-tase did permit 100-150 nucleotides of the sequence to be read from manyrDNA-containing lambda clones, although the legibility of the sequenceinformation varied amongclones (D. J. Lane, unpublished). While thisamountof the sequence is suboptimal for phylogenetic purposes, it doesprovide a relatively rapid meansof sorting the lambdaclones into uniquesetsrepresenting each of the different members f the sampledpopulation. Theseare then subcloned and sequenced in phage M13 or more detailed analysis,again using the 16S rRNA-specific primers, As with the 5S rRNA nalysis,the derive,d 16S rRNA equences are compared o others in the referencecollection of complete and partial 16S rRNA equences (see above), and theorganisms hat contributed these sequences are phylogenetically classified.Data so f~r available from the Octopus Spring rDNA lones are consistentwith the results of the 5S rRNA nalysis, but promise to yield moredetail.Many spects of the cloning approach o natural population analysis are stillbeing dew:loped and evaluated. Assumptions bout the uniform "clonability"of heterogeneous RNAistrons are still largely untested, and certain of therequired technical manipulations remain cumbersomeor routine use. Histor-ically, rec.overy of recombinantrRNA istrons has not proven particularlydifficult, although cloning of exceptionally active promoters or highly mod-ified DNA equences can be troublesome. The restriction enzymeSau3A,which is iinsensitive to DNAmethylation and which generates relativelyrandom fragments of the sample DNA, s used to avoid these potentialpitfalls. Tlae cumbersome spects of the sorting and sequencing steps aretechnical and there seems ittle doubt that future improvementsn bifunction-al, cloning;/sequencingvectors will further streamline these operations.IN SITU HYBRIDIZATION FOR COUNTING ANDIDENTIFYING ORGANISMSThe analysis of cloned rRNA enes from a mixed population of microorgan-isms offer:s phylogenetic characterization of the resident organisms, but itdoes not provide a goodestimate of their abundance.This information can bederived by quantitating the hybridization between each of the rDNA lonesand the DNAxtracted from the population of organisms. A novel alternativeapproachntowbeing developeduses in situ hybridization techniques to detectorganismospecific or group-specific sequences in the rRNA. This shouldpermit the direct visual identification and counting of the correspondingorganisms. The methodwouldbe somewhat nalogous to the use of fluores-cently labeled antibodies against cell surface antigens, but without the prereq-uisite for organismcultivation prior to raising antibodies. In addition, thephylogenetic breadth of the probe can be chosen (see below).In situ hybridization (reviewed in 1) uses nucleic acid probes to detect


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360 OLSEN, LANE; GIOVANNONI, ACE & STAHLcomplementary equences inside fixed cells or in fixed, thin sections oftissues. It has been widely used for detecting mRNAs1) and viral nucleicacids (39) in cells, anff for localizing genes in chromosomes40). Weapply the methods o natural microbial populations using contact slides .orenvironmental samples. Microscopic autoradiography has frequently beenused to detect the binding of radioactively labeled (3H or 35S) nucleic acidprobes. This method s sensitive, and bound probes can be quantitated , bycountingsilver grains in a photographic mulsionoverlaying l~e fixed cells ortissue. Nucleic acid probes can also be labeled with fluorescent compoundsand subsequently detected by fluorescence microscopy 2, 3, 59, 84).. Somemethods or detecting nucleic acid probes by fluorescence, have relied uponthe binding of fluor-coupled avidin to biotinated nucleotides (e.g. 59). Sincefixed cells maynot be freely permeable to large molecules, methods usingavidin (MW --68,000) may not be generally useful for the analysisnatural microbial populations, in whichmuch ariability in cell wall structuremust be expected. However,fluorescent compounds oupled directly to thenucleic acid probes should be usable.The rRNAs re uniquely attractive targets for in situ hybridization probesbecause of their abundance; each cell contains --10,000 ribosomes. While itis possible to detect --50 copies of a nucleotide sequence n a cell (17), thepresence of many opies increases the signal obtained, reducing problemsdueto nonspecific, background inding. Abundant arget sequences also allow theuse of fluorescently labeled probes, which are not as efficiently detected asradioactive probes.Two eneral approaches toward the identification of microorganismsby insitu hybridization are being developed. Oneuses organism-specific hybridiza-tion probes, whichcan be produced, in several, ways. Restriction. fragments.containing all or part of the rRNA enes can be cloned and labeled by nicktranslation, generating a duplex, or "symmetric,"probe (1). Alternatively, therestriction fragments can be cloned adjacent to the bacteriophage promoter na "transcription vector" and used to produce ranscripts complementaryo therRNAan "asymmetric" probe) (17). An asymmetric probe specific forrRNA f an organism in culture can be efficiently produced by reverse-transcribing cellular RNA rom one of the "universal" 16S rRNA-specificprimers (see above). With any of the above probes, high-stringencyhybridization is used to identify organisms hat specifically bind them.The second approach uses synthetic oligodeoxynucleotides hat are specificfor restricted phylogenetic groups of organisms. As discussed above in thecontext of "signature" sequences, some hort sequences are diagnostic of 16SrRNAs rom particular phylogenetic groups, so oligonucleotides com-plementary to those sequences should help identify organisms from thosegroups in the context of mixedpopulations. Current experience is limited tooligonucleotide probes that are specifiC at. the. kingdom, evel. Based on


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ECOLOGY, EVOLUTION, AND rRNA 361complete 16S rRNAsequences, we have synthesized probes complementaryto sequences diagnostic of the eukaryotic, eubacterial, or archaebacterialrRNAs. These indeed allow the determination of the kingdom affiliation ofunknown cells from natural populations fixed to microscope slides. Futuredirections include the development of more selective probes for more limitedphylogenefic groups.SUMMARYAND FUTURE PROSPECTSThe techniques of molecular biology are constantly opening new domains tomicrobiology. Molecular shylogeny and macromolecular sequencing havemade possible the natural classification of microorganisms, allowing us tosubstitute a molecular record for the fossil record used to characterize meta-zoan evolution. The ribosomal RNAs provide a measurable connectionamong all organisms, permitting the development of a universal phylogeny.By analyziing mixtures of rRNAs (or rRNAgenes), it is now possiblephylogenetically identify the component organisms of mixed populations.The phylogenetic characterization of microorganisms that have not beengrown in the laboratory provides an opportunity to explore beyond the culturecollections in assessing the diversity of terrestrial life.

The techniques discussed above will facilitate other studies as well. Forexample, tlae 5S rRNA ligonucleotide analysis of population composition iswell-suited to study of the stability of populations through time, or of thechanges following perturbation. Also, the rapid sequencing techniques dis-cussed in n~lation to 16S rRNA re equally applicable to the 23S rRNA 74a).Finally, the data gathered for phylogenetic studies is fodder for comparativeanalyses ol." rRNA econdary and tertiary structure (38, 70).ACKNOWLEDGMENTSThis work was supported by National Institutes of Health Grant GM34527oNRP.Literature Cited

1. Angerer, R. C., Cox, K. H., Angerer,L. M. 1985. In situ hybridization tocellular RNAs.n GeneticEngineering,ed. J. K. Setlow, R. Hollaender,7:43-65. NewYork/London:Plenum2. Banman, . G. J., Weigant, J., vanDuijn, P. 1981. Cytochemicalybridi-zation with fluorochrome-labeled NA.I. Developmentf a method sing nucle-ic acids bound o agarose beads as amodel. J. Histochem. Cytochem.29:227-373. Bauman, . G. J., Weigant, J., vanDuijn, P. 1983.Thedevelopment,sing.

poly(Hg-U)n a model ystem,of a newmethod to visualize cytochemicalhybridization in fluorescencemicros-copy.J. Histochetn.Cytochem.4:571-78Benton, W. D., Davis, R. W. 1977.Screening ambda-gt ecombinantlonesby hybridization to single plaques insitu. Science196;180-82Biggin, M.D., Gibson,T. J., Hong,G.F. 1983. Buffer gradient gels and 3sSlabel as an aid to rapid DNAequencedetermination.Proc. Natl. Acad. Sci.USA80:3963-65


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