Microbiological Research 166 (2011) 99—110

0944-5013/$ - sdoi:10.1016/j.

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Microbial phylogeny and diversity: Small subunitribosomal RNA sequence analysis and beyond

J. Rajendhran, P. Gunasekaran�

Department of Genetics, Centre for Excellence in Genomic Sciences, School of Biological Sciences, Madurai KamarajUniversity, Madurai 625 021, India

Received 10 September 2009; received in revised form 9 February 2010; accepted 13 February 2010

KEYWORDS16S rRNA;Phylogeny;Diversity;Metagenomics;Multilocus sequenceanalysis

ee front matter & 2010micres.2010.02.003

ing author. Tel.: þ91 45ess: gunagenomics@gm

AbstractSmall subunit ribosomal RNA (16S rRNA) gene sequence analysis is used for theidentification and classification of prokaryotes. In addition, sequencing of 16S rRNAgenes amplified directly from the environment is used to estimate microbialdiversity. The presence of mosaicism, intra-genomic heterogeneity and the lack of auniversal threshold sequence identity value limit 16S rRNA-based phylogeneticanalysis. PCR-amplification bias and cloning bias can also result in an inaccuraterepresentation of the microbial diversity. In this review, recently reportedcomplexities of 16S rRNA gene sequence analyses and the requirement of additionaltools for microbial phylogeny and diversity analyses are discussed.& 2010 Elsevier GmbH. All rights reserved.

Contents

1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1002. Microbial phylogeny analysis. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100

2.1. Mosaicism . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1002.2. Intra-genomic heterogeneity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1012.3. rRNA operon analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1012.4. Multilocus sequence analysis (MLSA) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1032.5. Whole genome comparison. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103

3. Microbial diversity analysis. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1043.1. Estimation of bacterial diversity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1043.2. Amplification bias . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1043.3. Cloning bias . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105

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J. Rajendhran, P. Gunasekaran100

3.4. Quantitation bias. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1064. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107

Acknowledgements. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108

1. Introduction

Animals and plants possess complex morphologi-cal differences that can be used for their phylogenyand taxonomy. Similarly, morphological features,such as capsules, flagella, cell size and shape, andbiochemical properties have been used for theidentification and classification of bacterial spe-cies. However, recent understandings on thehorizontal gene transfer events among bacteriahave revealed that these characteristics are notvery useful for their phylogenetic classification.Therefore, DNA sequence analyses of evolutionarilystable marker genes are considered as a potentialstrategy to study bacterial phylogeny and diversity(Tringe and Hugenholtz, 2008).

In bacteria, the rRNA genes are transcribed fromthe ribosomal operon as 30S rRNA precursormolecules and then cleaved by RNase III into 16S,23S, and 5S rRNA molecules. The ribosomal operonsize, nucleotide sequences, and secondary struc-tures of the three rRNA genes are conserved withina bacterial species (Maidak et al., 1997). Since 16SrRNA is the most conserved of these three rRNAs, ithas been proposed as an ‘‘evolutionary clock’’,which has led to the reconstruction of the tree oflife (Woese, 1987). For the past two decades,microbiologists have primarily relied on 16S rRNAgene sequences (hereafter, 16S sequences) for theidentification and classification of bacteria. The16S sequence analysis is used in two majorapplications: (i) identification and classification ofisolated pure cultures and (ii) estimation ofbacterial diversity in environmental samples with-out culturing through metagenomic approaches. Inthis manuscript, we discuss the limitations of 16Ssequences in both applications and emphasize theneed for alternative or complementary strategies.

2. Microbial phylogeny analysis

New bacterial isolates are identified based on the16S sequence homology analysis with existingsequences in the databases. Researchers reportthe species identification based on the closestmatch obtained from comparative tools such asBLAST (http://www.ncbi.nlm.nih.gov) and Seq-match (http://rdp.cme.msu.edu). However, there

is no defined ‘‘threshold value’’ above which auniversal agreement for species identification canbe obtained. In many cases, the difference be-tween the closest and next closest match to theunknown strain is o0.5%. For example, the genusBacillus is a large, heterogenous group of Gram--positive bacteria with wide phenotypic diversity.The type strains of B. globisporus and B. psychro-philus share 99.8% sequence identity with regard totheir 16S genes. However, at the genome level,they exhibit only 23–50% relatedness in reciprocalhybridization reactions (Fox et al., 1992). In thesecircumstances, such small differences cannot jus-tify selecting the closest match as a definitiveidentification. In other cases, different species ofbacteria with considerably different physiologieswere reported to contain nearly identical 16S genes(Palys et al., 1997). For example, the 16Ssequences of certain species of Rhizobium such asR. galegae were found to be more similar toAgrobacterium sequences than other Rhizobiumspecies. Based on these observations, it has beenproposed that the genus Rhizobium may be cor-rected as a species within the genus Agrobacterium(Young et al., 2001). However, this proposal was notaccepted because of the ecological and genomicdifferences that exist between these two genera(Farrand et al., 2003). In such circumstances,species identification solely based on the 16Ssequences may lead to misidentifications.

2.1. Mosaicism

The presence of mosaicism in the 16S genes isanother issue related to phylogenetic classification. Itwas generally assumed that 16S genes were unlikely tohave horizontal transfer events. However, recentstudies suggested that some bacterial species mighthave a history of horizontal transfer and recombina-tion within the 16S gene. Horizontal transfers of 16Sgene segments and the presence of mosaic-likestructures have been reported in Rhizobium,Aeromonas, Bradyrhizobium, Streptococcus and acti-nomycetes (Eardly et al., 1996; Schouls et al., 2003).Therefore, species identification using 16S gene-basedprobes or homology analysis of partial 16S sequencesmay lead to misidentification, because it may repre-sent only a part of the mosaic-like structure. Moreinterestingly, there are reports on experimental

SSU rRNA and microbial diversity 101

transfer of complete rRNA operons, both within andbetween species. Asai et al. (1999) have deleted allthe seven chromosomal rRNA operons of Escherichiacoli and introduced plasmids carrying the rRNAoperons of either Salmonella enterica or Proteusvulgaris. The resultant E. coli strains with heterologous16S genes were viable. Very little difference in eithergrowth rate or rRNA/protein ratio was observedbetween E. coli strains carrying the wild type andS. enterica 16S genes. It was also reported thattranslation efficiency and fidelity of the hybridribosomes are similar to those of E. coli ribosomes.Similarly, P. vulgaris rRNA could function effectivelywith E. coli translational components, although aslightly lowered growth rate was observed suggestingsomewhat lowered ribosome efficiency. In contrast, anrRNA operon from Pseudomonas aeruginosa, which ismore distantly related to E. coli than P. vulgaris, failedto replace the E. coli operon. These results indicatedthat variations in 16S sequence to a certain extent areallowed without affecting the fitness of the bacteria.

2.2. Intra-genomic heterogeneity

The presence of multiple copies of the rRNAoperon and intra-genomic heterogeneity of the 16Sgenes is considered as another limiting factor forthe use of this gene for species identification. Thecopy number of rRNA operons per bacterial genomevaries from 1 to 15 (Klappenbach et al., 2000). Thesequences of multicopy rRNA genes are mostlyidentical or nearly identical. However, severalrecent reports have suggested the existence ofdivergent 16S sequences within a single organism.The reported intra-genomic variability of 16S wasas high as 6.4% in the case of the Thermobisporabispora genome (Wang et al., 1997). Thus, thesequence heterogeneity within a genome limits thephylogenetic resolution of a particular speciesbased on the 16S sequences. The complexity withrespect to the 16S heterogeneity can be avoided bythe analysis of a gene that exists in a singlecopy. Therefore, several other single copy genes,which are conserved among bacteria, have beensuggested and any of them can be considered as analternative to 16S-based phylogeny. The genefor the GroEL chaperonin, RNA polymerase betasubunit (rpoB), DNA gyrase beta subunit (gyrB) andheat shock protein (dnaK) are the other routinelyused universal stable marker genes (Viale et al.,1994; Case et al., 2007; Eardly et al., 2005;Watanabe et al., 2001). These genes are proposedas complementary tools for the 16S gene to definethe evolutionary relationships among eubacterialspecies (Viale et al., 1994). In certain groups of

organisms, one of these alternative marker geneswas found to be more suitable for phylogeneticanalysis. For instance, Claesson et al. (2008)compared the Lactobacillus phylogenomics withsingle gene phylogeny. In this study, 12 genomes ofLactobacillus strains were subjected to whole-genome and single-gene-based phylogeneticapproaches and it has been concluded thatGroEL is a more robust single-gene phylogeneticmarker for the genus Lactobacillus than the 16Sgene.

2.3. rRNA operon analysis

In addition to sequence analysis, DNA fingerprint-ing analysis based on 16S genes such as denaturing-or temperature gradient gel electrophoresis(DGGE, TGGE), single-stranded conformationpolymorphism (SSCP), amplified ribosomal DNArestriction analysis (ARDRA) and terminal restric-tion fragment polymorphism analysis (T-RFLP)are widely used to differentiate microorganisms(Bouchet et al., 2008). All these methods are basedon sequence variation in the 16S genes (Table 1).The 16S–23S internally transcribed spacer (ITS)regions of the rRNA operon might be underminimal selective pressure during evolution andtherefore have more variation in the sequencesthan that of the coding regions of 16S and 23SrRNAs. The size of the ITS varies considerably fordifferent species, and even among differentoperons within a single cell having multiple rRNAoperons. The variation in length of an amplified ITSregion is mainly due to the presence of variousnumbers and types of tRNA genes. For example,there are two kinds of ITS in E. coli. Among theseven rRNA operons, four of them contain a singletRNAGlu gene and other three have two tRNA genes(tRNAIle and tRNAAla) in the ITS region. Thus, thelength and sequence polymorphisms in the ITS canbe used to distinguish different species ofprokaryotes (Janssen et al., 2002). Polymorphismamong the length of the PCR amplified productitself can be used for the recognition of genera andspecies. Additional information about the amplifiedproduct is accessible by its RFLP pattern. If the PCRproduct contains restriction endonucleaserecognition sequences at unique locations, thenthe resultant fragment size pattern can beindicative of a particular species. Finally, theamplified ITS regions can be sequenced and usedfor species identification and subtyping. Similarly,to enhance the differentiating capacity of 16S geneanalysis, a long PCR-RFLP has been reported. Thistechnique is based on the PCR amplification of the

Table 1. Microbial typing methods based on rRNA sequences.

Method Principle Reference

Ribotyping Polymorphism in the hybridization with 16S gene-based probes in total genomic DNA digested withrestriction enzymes. Ribotyping represents position ofRNA operons(s) in the whole genome. However, it isbased on the variations associated with restrictionsites only

Grimont and Grimont(1986)

Amplified ribosomal DNArestriction analysis(ARDRA)

Restriction fragment length polymorphism (RFLP) ofthe amplified or cloned 16S genes. It is faster and itrepresents variations associated with restrictionsites only

Gurtler et al. (1991)

Terminal restrictionfragment lengthpolymorphism (T-RFLP)

Polymorphism in the length of fluorescently labeledterminal restriction fragment of 16S gene. It is a high-throughput method useful for metagenomic studiesand the variation is based on the size of the terminalrestriction fragment only

Liu et al. (1997)

The 16S–23S internallytranscribed spacer (ITS)typing

Polymorphism in the length, RFLP pattern orsequences of the ITS region. It exhibits greatervariation than 16S gene sequence and thus, useful forthe typing of closely related organisms

Jensen et al. (1993)

Automated ribosomalintergenic spacer analysis(ARISA)

Polymorphism in the length of fluorescently labeledITS regions. It is a high throughput method useful formetagenomic studies and the variation is based on thesize of the amplified fragments

Fisher and Triplett (1999)

Long PCR-RFLP Restriction fragment length polymorphism of theentire rRNA operon. It has more discrimination powerthan the analysis of 16S sequence alone. However, itis based on the variations associated with restrictionsites only

Smith-Vaughan et al.(1995)

DGGE Polymorphism based on the separation of partiallymelted 16S rDNA in a linear denaturing gradient gel. Itrepresents the sequence variations other than therestriction sites also. However, it covers only less than400 bp of 16S gene

Muyzer et al. (1993)

TGGE Polymorphism based on the separation of partiallymelted 16S rDNA a linear temperature gradient. Itrepresents the sequence variations other than therestriction sites also. However, it covers only less thanless than 400 bp of 16S gene

Nubel et al. (1996)

SSCP Polymorphism based on the single-stranded 16S rDNAin polyacrylamide gel. It represents the sequencevariations other than the restriction sites also.However, it covers only less than 400 bp of 16S gene

Lee et al. (1996)

16S pyrotags Pyrosequencing of 16S rDNA. It is a high-throughputmethod useful for metagenomic studies. However, itgives only a small tag of the 16S gene, approximately200 bp

Ronaghi and Elahi (2002)

16S rDNA sequencing Sequencing of PCR amplified or cloned 16S gene.Complete 16S gene sequence (�1500 bp) has morediscriminatory power

Woese (1987)

J. Rajendhran, P. Gunasekaran102

entire 16S–23S–5S region of the ribosomal operon(�5.5 kb) followed by restriction fingerprinting(Smith-Vaughan et al., 1995). This method is morediscriminatory than 16S sequencing since it alsocovers the variability in the ITS.

Ribotyping is another widely adopted method forphylogenetic analysis. Ribotyping is based on restric-tion endonuclease cleavage of total genomic DNAfollowed by electrophoretic separation, Southernblot transfer and hybridization of transferred DNA

SSU rRNA and microbial diversity 103

fragments with a labeled rRNA probe. Followingautoradiography, those bands containing a portion ofthe ribosomal operon are visualized. The number offragments generated by ribotyping is a reflection ofthe multiplicity of rRNA operons present in a bacterialspecies. The name ‘‘ribotyping’’ is considered to be amisnomer, leading to a misconception that observedpolymorphisms arise directly from rRNA gene se-quences. However, ribotype variability is a reflectionof polymorphisms not only in the rRNA genes, butrather restriction pattern in flanking chromosomalgenes. Here, the rRNA gene sequences are exploitedas ‘‘linked tags’’ to the adjacent regions of thegenome (Bouchet et al., 2008).

2.4. Multilocus sequence analysis (MLSA)

Although different stable marker genes havebeen proposed, the discriminatory power of asingle locus sequence is considered to be of lessersignificance since it covers only a limited region ofthe genome. Therefore, sequencing of severalconserved genes (multilocus sequence analysis;MLSA) within the bacterial genome has beenproposed to improve the discriminatory power(Gevers et al., 2005). In MLSA, fragments ofapproximately 500 bp length of usually sevenmarker genes are sequenced and the combinedsequence profiles are used for the phylogenyanalysis (Jolley and Chan, 2004). MLSA is moreuseful in strain level identification within a species.For MLSA, a set of housekeeping genes areconsidered based on the sequence variabilityamong the particular species of bacteria. In certaincases, the MLSA approach also uses highly variablegenes that have direct implications in its pheno-typic characteristics. For example, the cytotoxingene vacA is used to identify the pathogenic strainsof Helicobacter pylori (Maggi-Solca et al., 2001).Therefore, the 16S sequence can be used to assignan unknown organism to a group (genus or family),which directs the set of genes to be selected forMLSA. Subsequently, the MLSA can be used to assignthe organism to a species or subspecies.

Despite the significant developments in molecu-lar tools, the identification of the fundamentalunit, the species, is questionable in bacterialtaxonomy. Bacterial taxonomists have not yetreached a consensus for defining the species(Cohan, 2002). Within a bacterial species, re-searchers have identified that there is considerablevariation in metabolic traits. Similarly, sequencingof multiple genomes within a species has shownconsiderable variation in the genes that arepresent. Therefore, it has been proposed that

bacterial species are complex, multipopulationtaxa and that the sub-populations within namedspecies can resemble ecologically adapted speciespopulations, called ecotypes (Koeppel et al., 2008).MLSA has been suggested as the first practical steptoward identifying ecotypical diversity within spe-cies. Based on the ecotypes, a Latin trinomialnomenclature, giving the genus, species, andecotype names have been proposed (Cohan, 2002).

2.5. Whole genome comparison

Whole genomic DNA relatedness is considered toprovide the absolute resolution in bacterial taxonomy.It is generally accepted that all taxonomic informa-tion about a bacterium is incorporated in thecomplete nucleotide sequence of its genome (Goriset al., 2007). The MLSA approach represents thebacterial genome to some extent and has higherdiscriminatory power than single locus analysis.Similarly, genomic fingerprinting methods such asRep-PCR (ERIC-, REP-, BOX-PCR), RAPD and AFLPprovide more discriminatory power. However,whole genomic DNA–DNA hybridization (DDH) isconsidered as the ‘‘gold standard’’ for bacterialspecies identification. Organisms that show morethan 70% DDH values and less than 5% difference intheir melting temperature (DTm) are considered tobelong to the same species (Wayne et al., 1987;Gevers et al., 2005). The rationale for using DDH asthe gold standard for species delineation originatesfrom the results of numerous studies, in which ahigh degree of correlation was found between DDHand chemotaxonomic, serological, and numericalphenetic similarity. In general, species having 70%or greater DNA similarity usually have more than97% 16S gene sequence identity but not vice versa.Several groups of organisms have been identifiedthat share almost identical 16S rRNA sequences butin which DNA hybridization is significantly lowerthan 70%, thus indicating that they representdifferent species (Stackebrandt and Goebel,1994). However, those organisms having less than97% 16S sequence homology will not reassociate tomore than 60%. Therefore, 16S sequence analysis isextremely helpful in deciding whether the labor-ious DDH process needs to be performed or not.New bacterial species are reported based on thepolyphasic approach, in which DDH plays a domi-nant role. The polyphasic approach utilizes pheno-typic, genotypic and phylogenetic data of themicroorganisms (Colwell, 1970; Vandammeet al., 1996; Ghosh et al., 2007). Whole genomesequencing using second-generation methodologies(Shendure and Ji, 2008) and genome–genome

J. Rajendhran, P. Gunasekaran104

hybridization using microarrays (Gresham et al.,2006) can also be used for the identification ofmicroorganisms and for subtyping.

Few studies have evaluated the consistency ofthe existing taxonomic ranks of bacterial strainsbased on their whole genome sequences. Theaverage nucleotide identities (ANI) and averageamino acid identities (AAI) were used to measurethe genetic and evolutionary relatedness amongstrains (Konstantinidis and Tiedje, 2005a, b). TheANI and AAI showed better resolution than the 16Ssequences between closely related species. It wasfound that the 70% DDH values, which is used forthe species delineation, corresponds to 495% ofAAI. However, the AAI-based relatedness among thestrains was often inconsistent with their taxonomicrelatedness based on 16S sequences. Konstantinidisand Tiedje (2005b) have evaluated the correlationbetween various stable marker genes and the AAI of175 bacterial strains. The reported R2 values wereas follows: 0.84 (16S), 0.84 (23S), 0.78 (rpoB), 0.77(GyrB), 0.68 (RecA). Therefore, as far as a singlelocus is considered, 16S gene is the optimum choicefor microbial phylogeny analysis. The MLSA andwhole genome comparison methods provide morediscriminatory power.

3. Microbial diversity analysis

Estimating the microbial diversity of environ-mental samples is the second major application of16S sequence analysis. Ever since the discovery ofbacterial pure culture techniques by Robert Koch,microbiological culture techniques have beensignificantly improved. However, a majority ofbacterial species in any environment are stilluncultivatable in the laboratory, due to the lackof knowledge of the real conditions under whichthese bacteria are growing in their natural envir-onment. The analyses of 16S genes from the DNAdirectly extracted from environment, metagenomicDNA, are being used to study the diversity ofmicroorganisms without culturing (Gill et al., 2006;Gray and Herwig, 1996; Lane et al., 1985;Rajendhran and Gunasekaran, 2008).

Over the last two decades, phylogenetic diversityof environments such as ocean surfaces, deep seavents, hot springs, soil, animal rumen and gut,human skin, oral cavity and intestine have beendescribed and many new lineages were identified.The cloning and sequencing of 16S genes amplifieddirectly from these environments through metage-nomic approaches demonstrated that microbialdiversity is far more extensive than we ever

imagined from culture-based studies (Handelsman,2004). Comparisons between classical culture-dependent and metagenomic methods haverevealed that only about 1% of the total micro-organisms are amenable to culture. In a few cases,understanding the phylogenetic diversity has led tothe development of improved culturing methods(Janssen et al., 2002). However, estimating thetotal microbial diversity in any environment is apersisting challenge. Despite the many thousandsof 16S sequences that are accumulating indatabases through culture-based and metagenomicapproaches, it is not even predictable howmany prokaryotic species are there in the world(Lopez-Garcia and Moreira, 2008).

3.1. Estimation of bacterial diversity

Microbial diversity is considered as a function of thenumber of different classes (richness) and the relativedistribution of individual elements among these classes(evenness) (Nubel et al., 1999). The diversity assess-ment is generally based on (i) PCR-amplification of 16Sgenes from metagenomic DNA; (ii) making 16S genelibraries; (iii) sequencing of randomly selected clones;and (iv) phylogenetic analysis. The richness andevenness of a community are qualitatively estimatedbased on the number of unique clones and theirrelative frequencies (Rani et al., 2008). However, thevalidity of metagenomics-based microbial diversityanalysis depends on obtaining representative nucleicacids from entire microbial community. The qualityand quantity of the metagenomic DNA influences themicrobial community structure (Chandler et al.,1998). Incomplete cell lysis, DNA sorption to inertmatrices, coextraction of enzymatic inhibitors, anddegradation of DNA at various steps of extractionprocedures may influence the microbial diversitypattern (Stach et al., 2001). In addition, biases maybe introduced during PCR amplification and cloningsteps.

3.2. Amplification bias

Primer selection for 16S gene amplification itselfis an important source of bias. In several publica-tions, PCR primers have been mentioned as ‘‘uni-versal 16S gene primers’’. Universal primers, bydefinition, should be complementary to all 16Sgene sequences. However, it is obvious that manysequences in the databases deviate from theconserved regions targeted by the universalprimers. The standard primers used to amplify16S genes fail to capture 16S sequences obtainedthrough PCR-independent routes (Schloss and

SSU rRNA and microbial diversity 105

Handelsman, 2004). To accommodate the devia-tions from the conserved sequences, the commonlyused primers have been modified with degeneratepositions to enable the primers to target a widerrange of 16S sequences (Baker and Cowan, 2004).However, the degeneracy at selected positions isnot sufficient to cover all 16S genes. Increasing thenumber of degenerate positions may resultin spurious amplifications in PCR. In addition, theprimers currently used to obtain 16S genesequences are all based on the sequences fromcultured organisms or the uncultured organismsthat retained the conserved regions of culturedorganisms. Therefore, the amplified 16S genes maybe biased toward the sequences that are similar toknown species and the divergent 16S genes fromthe environment may not be amplified.

To overcome the complexities with selectinguniversal primers to some extent, Isenbargeret al. (2008) have recently proposed that 10-ntminiprimers can be used to identify more divergent16S gene sequences from environmental samples.In this study, an engineered polymerase S-Tbr hasbeen used instead of Taq DNA polymerase. In S-Tbr,the N-terminal 50–30 exonuclease domain of Ther-mus brockianus DNA polymerase I is removed andreplaced with a 7-kDa double-stranded DNA-bindingprotein Sso7d from Sulfolobus solfataricus (Wanget al., 2004). The S-Tbr exhibited a higherspecificity than Taq DNA polymerase and no non-specific amplification was observed though the 10-nt miniprimers were used. Because miniprimers areshorter, they can target more divergent 16S genesand hence, amplified a greater proportion of novelsequences that were poorly matched with thedatabases of previously known 16S genes.

Another dilemma in the use of PCR to amplify 16Sgenes from environmental DNAs is the formation ofchimeric molecules (Payne et al., 2005). Computeralgorithms, such as the CHECK_CHIMERA of RDP arebeing used to predict possible chimeric moleculesformed during PCR. However, when complex com-munity DNA is used as the template, one cannotpredict whether the chimera is formed during PCRor whether the organisms possess mosaic-like16S genes. Thus, the naturally occurring mosaic-like structures of 16S genes may be misidentifiedas experimental chimera, which eventually affectsthe overall estimation of community structure.

3.3. Cloning bias

After PCR amplification, there is a possibility forthe bias in cloning step also. Though the genes fromseveral groups of organisms with atypical GþC

content could not be cloned in E. coli, it is thestandard host used to clone the amplified 16Sgenes. As an alternative, different host cells suchas Bacillus and Streptomyces may be used to avoidthe cloning bias. However, transformations of thesecells are tedious and less efficient. Similarly,extraction of plasmids from recombinant cells forsequencing is also cumbersome, and hence, tilldate, no paper has been published on microbialdiversity analysis using different host cells forcloning and sequencing.

Recently, cell-free cloning strategies have beendeveloped. The multiple displacement amplifica-tion (MDA) strategy using F29 DNA polymerase canbe used for cell-free cloning (Hutchison et al.,2005). Similarly, the in vitro polony (PCR ampliconsderived from a single DNA molecule amplifiedin situ) method has been introduced to replacebiological cloning (Mitra and Church, 1999). Thepolony method has been applied in various second-generation sequencing methodologies such as 454(Roche, Branford, CT, USA), Solexa (Illimina, SanDiego, CA, USA) and SOLiD (ABI, Foster City, CA,USA) (Shendure and Ji, 2008). Among these, the454 pyrosequencing method was first introduced inthe market and widely used next to the Sanger’smethod (Rothberg and Leamon, 2008). In the 454method, construction of clone libraries is notneeded (Figure 1). Due to the avoidance ofamplification and cloning biases and thegeneration of large amounts of sequence in asingle run, this method is claimed as an efficientstrategy for metagenomics studies. However, themajor limitation of the 454 and other second-generation technologies is the shorter read length,and hence the assembly of these sequences andhomology searches are challenging. The recentlydeveloped Titanium kit from Roche can providemore than 400-base reads, which has dramaticallyimproved the efficiency of sequence assembly. Inaddition, the possibility of developing instrumentsproviding more than 1000 base single-moleculereads has been reported by Pacific Biosciences,Menlo Park, CA (Korlach et al., 2008). Thesedevelopments will improve the accuracy ofexisting methods for building phylogenetic treesand classifying novel organisms in metagenomicstudies. Similarly, the availability of manyimproved computational methods for comparinglarge numbers of microbial communities includingUniFrac (Lozupone et al., 2006), SONS (Schloss andHandelsman, 2006) and SCARF (Barker et al., 2009)improved the de nova clustering and sequenceassembly. Thus, with the advent of the second-generation sequencing technologies, rapididentification and classification of microorganisms

Figure 1. Conventional (A) and 454 pyrosequencing (B) methods for metagenome sequencing. In the conventional shot-gun method, the metagenomic DNA is sheared by sonication and cloned in a plasmid vector. The cloned plasmid libraryis transformed into E. coli. Recombinant plasmids from individual colonies are isolated and sequenced by Sanger’smethod. In 454 pyrosequencing technology, smaller fragments of metagenomic DNA is obtained by nebulization andligated with adapters. One of the adapters carries a 50 biotinylated strand. The ligation mixture is denatured andadhered to sepharose beads containing streptavidin. DNA and beads are taken in ratio, where each bead gets a singleDNA molecule. The single DNA molecule is amplified in situ by emulsion-PCR and the amplified strands are tethered tothe same bead within the emulsion. Each bead is considered a PCR-colony (polony) containing about 1000 copiesderived from the single DNA molecule. The polonies are then sequenced in picolitre-scale wells by pyrosequencingchemistry.

J. Rajendhran, P. Gunasekaran106

is possible, which has the potential to replace theexisting classical ‘‘clone and sequence’’approaches. Advancements in second-generationsequencing methods with respect to improving theread lengths and the developments incomputational tools will strengthen theirapplication in microbial and functional diversityanalyses.

3.4. Quantitation bias

16S-gene-based real time PCR has been proposedfor the quantification of bacteria in the environ-

ment (Nadkarni et al., 2002). Broad range primersand probes have been designed based on theconserved regions and demonstrated to quantify16S genes from most bacterial species. However, asdiscussed earlier, amplification bias may result inimprecise quantification. The broad range primersmay not capture all 16S sequences from themetagenomic DNA, which underestimate the realbacterial load. In addition, the presence of multi-ple 16S gene copies in many organisms limits theaccurate quantification of organisms. Therefore,real time PCR using other single copy marker genessuch as rpoB may provide more reliable data.However, the amplification bias associated with

SSU rRNA and microbial diversity 107

PCR cannot be overcome by this approach as therpoB sequences are more divergent than the 16Ssequences.

Group-specific probes are often used in qualita-tive and quantitative analysis of microbial popula-tions (Brunk et al., 1996). The major developmentin 16S-gene-based diversity analysis is the applica-tion of high density microarrays of phylogeneticallyspecific probes called the PhyloChip (Loy et al.,2005). However, their application depends on theavailability of suitable nucleic acid probes, whichrequire prior knowledge of the sequences to bedetected. Since the available 16S gene sequencesmay not represent the complete bacterial diversity,the microarray-based hybridization analysis canquantify only the 16S genes with known sequences.In addition, microbial populations that are notnumerically dominant are not represented,because the template DNAs from these populationsrepresents a small fraction of the total communityDNA. Consequently, the species diversity of themicrobial community is underestimated. Thedifferences in gene copy number between speciesalso influence the estimation of apparent abun-dance of different populations. The presence ofmultiple rRNA operons with considerable sequenceheterogeneity in certain bacteria might lead to anoverestimation of the bacterial diversity.

4. Conclusions

The 16S genes are regarded as the molecularclocks and they have been widely used in phyloge-netic, evolutionary and taxonomic studies. Thepresence of mosaicism, intra-genomic heterogene-ity and lack of a universal threshold sequenceidentity value are considered as the limiting factorsfor 16S sequence-based microbial identification.Similarly, the presence of abundant partial se-quences of 16S genes in the databases results inambiguous classification. In fact, the whole genomesequences of bacterial strains have also beenambiguously annotated. One of the major functionsof 16S rRNA is binding with the Shine–Dalgarnosequences of mRNA during translation, and thus theanti-SD sequence is one of the most significant andconserved features in 16S rRNA genes. Recently, Linet al. (2008) have examined the 16S gene annota-tions in completed whole genome sequences for thepresence of anti-SD sequences. It was reported thatthe 16S genes of 67 out of 252 different specieswere inaccurately annotated without covering theanti-SD sequence. The inaccuracy was primarilydue to the prevalence of partial 16S sequences in

the databases. Therefore, as far as possible, fulllength 16S gene sequences of about 1500 bp shouldbe used for the homology searches and the %coverage in the best hit should also be considered.In addition to the 16S sequence homology analysis,MLSA, DDH and genomic fingerprinting patterns arerecommended for the species identification ofprokaryotes.

Direct amplification and sequencing of 16S genesprovides a more representative view of a microbialcommunity structure than classical pure culturetechniques. However, estimating the microbial di-versity is still a persistent challenge and no reportsare available on the estimation of complete micro-bial biodiversity. The universal 16S primers, whichare currently being used, are designed based on theexisting 16S sequences in the databases and theseprimers may target only a portion of the total speciesdiversity. In addition, the preferential amplificationof some DNA templates together with the cloningbias may lead to a biased view of the microbialcommunity structure. The MLSA has revealed multi-ple ecotypes within the species of cultivatedbacteria. Therefore, the 16S sequence analysis maynot reveal the ecotypes among the uncultivatedorganisms since the ecotypes are typically identicalor nearly identical in their 16S sequences.

In conclusion, in addition to the 16S sequenceanalysis, a sufficient number of alternativeapproaches have been developed and are currentlyin practice for microbial phylogenetic analysis.However, innovative strategies are yet to bedeveloped for total microbial diversity analysis.The 10-nt miniprimer PCR strategy is a recentadvancement, which has revealed more divergent16S genes than does normal PCR. Recently, the 454pyrosequencing method has been proposed forhigh-throughput environmental genome sequencingwithout amplification and cloning biases. There-fore, advancements in cell-free cloning, second-generation sequencing technologies with improvedread lengths and massive environmental DNAsequencing may ultimately reveal the total diver-sity of the microbial world.

Acknowledgements

JR acknowledges the Department of Science andTechnology, New Delhi for providing financialsupport (No. 0070/2008). Authors gratefullyacknowledge the University Grants Commission forthe support through Centre for Excellence inGenomic Sciences and Networking Resource Centrein Biological Sciences.

J. Rajendhran, P. Gunasekaran108

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