Identification and Analysis of Informative Single Nucleotide Polymorphisms in 16S rRNA Gene Sequences of the Bacillus cereus Group

Identification and Analysis of Informative Single Nucleotide

Polymorphisms in 16S rRNA Gene Sequences of the

Bacillus cereus

Group

Janetta R. Hakovirta,a_{Samantha Prezioso,}a_{*David Hodge,}b_{Segaran P. Pillai,}c _{Linda M. Weigel}a

National Center for Emerging and Zoonotic Infectious Diseases, Centers for Disease Control and Prevention, Atlanta, Georgia, USAa

; Science and Technology Directorate, U.S. Department of Homeland Security, Washington, DC, USAb

; Office of Laboratory Science and Safety, U.S. Food and Drug Administration, Silver Spring, Maryland, USAc

Analysis of 16S rRNA genes is important for phylogenetic classification of known and novel bacterial genera and species and for detection of uncultivable bacteria. PCR amplification of 16S rRNA genes with universal primers produces a mixture of ampli-cons from all rRNA operons in the genome, and the sequence data generally yield a ampli-consensus sequence. Here we describe valu-able data that are missing from consensus sequences, varivalu-able effects on sequence data generated from nonidentical 16S rRNA amplicons, and the appearance of data displayed by different software programs. These effects are illustrated by analysis of 16S rRNA genes from 50 strains of theBacillus cereusgroup, i.e.,Bacillus anthracis,Bacillus cereus,Bacillus mycoides, andBacillus thuringiensis. These species have 11 to 14 rRNA operons, and sequence variability occurs among the multiple 16S rRNA genes. A single nucleotide polymorphism (SNP) previously reported to be specific toB. anthraciswas detected in someB. cereusstrains. However, a different SNP, at position 1139, was identified as being specific toB. anthracis, which is a biothreat agent with high mortality rates. Compared with visual analysis of the electropherograms, basecaller software frequently missed gene sequence variations or could not identify variant bases due to overlapping basecalls. Accurate detection of 16S rRNA gene sequences that include intragenomic variations can improve discrimination among closely related species, improve the utility of 16S rRNA da-tabases, and facilitate rapid bacterial identification by targeted DNA sequence analysis or by whole-genome sequencing per-formed by clinical or reference laboratories.

I

sequence for phylogenetic studies and, based on that sequence, proposed a Tree of Life composed of three domains of living

or-ganisms, i.e.,Archaea,Bacteria, andEukarya(1,2). The domain of

bacteria is by far the largest and continues to expand as diverse

environments are analyzed (3). Bacterial 16S rRNA genes are

lo-cated within the rRNA operons, which also contain genes for 23S rRNA, 5S rRNA, tRNA, and associated intergenic spacer regions. Since rRNAs are essential for survival, these operons are expected to be found on the chromosome. However, a recent report by

Anda et al. (4) described a clade within the genusAureimonasfor

which the sole rRNA operon is located on a small plasmid, which suggests that there is still more to be learned about rRNA in bac-terial species. Although the DNA sequences of various rRNA genes and intergenic spacer regions have been used for identification to the genus or species level, the 16S rRNA gene is usually preferred. In addition to being universally distributed among bacteria, this gene contains both highly conserved and hypervariable regions, and there are large and constantly expanding databases of 16S rRNA gene sequences for comparison.

Widespread use of DNA sequencing technologies in clinical, public health, and research laboratories has resulted in rapid and accurate molecular diagnostic methods. A bacterial isolate can now be identified more rapidly by 16S rRNA sequence analysis than by conventional methods. In addition to novel or uncultur-able bacteria, gene sequence analysis has been employed for iden-tification of bacteria with unusual phenotypic profiles, some of which are misidentified by automated clinical identification

sys-tems (5). In the second edition of Bergey’s manual of systematic

bacteriology (6), the definitive authority on phylogenetic

classifi-cation of bacteria, phenotypic characteristics and the laborious

task of DNA-DNA hybridization procedures have been replaced

by 16S rRNA sequence analysis (6) as the basis for taxonomic

classification, and the rationale is explained in a section titled “16S rRNA: the benchmark molecule for prokaryote systematics.” In some clinical settings, however, analyses of phenotypic character-istics such as Gram staining, cell morphology, and biochemical properties are still the first steps in species identification, and 16S rRNA sequence analysis may be performed only when the pheno-typic results are not definitive.

Differentiation between closely related species using 16S rRNA gene sequences can be difficult. For example, the 16S rRNA gene

sequences ofBurkholderia malleiandBurkholderia pseudomallei

differ by a single nucleotide, which is not located in one of the nine

hypervariable regions (7). For some species, the sequences may

appear to be identical, as has been reported for strains ofBacillus

anthracis,Bacillus cereus, andBacillus thuringiensis(8) (i.e.,

mem-bers of theB. cereusgroup). Indeed, it has been proposed that the

Received10 June 2016 Returned for modification1 July 2016

Accepted17 August 2016

Accepted manuscript posted online31 August 2016

CitationHakovirta JR, Prezioso S, Hodge D, Pillai SP, Weigel LM. 2016.

Identification and analysis of informative single nucleotide polymorphisms in 16S rRNA gene sequences of theBacillus cereusgroup. J Clin Microbiol 54:2749 –2756.

doi:10.1128/JCM.01267-16.

Editor:E. Munson, Wheaton Franciscan Laboratory

Address correspondence to Linda M. Weigel, [email protected].

*Present address: Samantha Prezioso, Department of Microbiology and Immunology, Emory University School of Medicine, Atlanta, Georgia, USA. Copyright © 2016, American Society for Microbiology. All Rights Reserved.

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B. cereusgroup should be considered one species (9). However, variations in 16S rRNA gene sequences often can be found within the genome of a single strain, due to the presence of single nucle-otide polymorphisms (SNPs) or small insertions or deletions (in-dels) among the multiple 16S rRNA copies. Such polymorphisms

have been documented in various genera and species (10–16).

Since universal PCR primers have been designed to target con-served sites, PCR amplification of all 16S rRNA genes found on the chromosome occurs simultaneously, resulting in a product that is a mixture of the amplicons. Consequently, references to the 16S rRNA gene sequence for a species or a strain actually refer to a consensus sequence representing all 16S rRNA genes encoded in that specific genome. A consensus sequence may mask useful dif-ferences between the multiple 16S rRNA genes.

Through DNA sequence analysis and comparison of the

mul-tiple 16S rRNA genes inB. anthracis,B. cereus, andB. thuringiensis,

we show that SNPs and other ambiguities among the multiple 16S rRNA genes in each species can be detected in the mixtures of PCR amplicons that are generated with universal 16S rRNA gene prim-ers. Visual inspection of basecaller data, such as the electrophero-grams generated by Sanger sequencing instruments, is necessary, however, because analyses that rely on the sequences generated by the software may miss key differences. Careful analysis of SNPs and indels is important for detection and correction of errors be-fore they are entered into reference databases.

MATERIALS AND METHODS

The strains used in this study are listed inTable 1. All procedures involving virulent strains ofB. anthraciswere performed in a class II type A2 bio-logical safety cabinet located in a select agent-registered, biosafety level 3 (BSL3) laboratory. Additional BSL-3 precautions included the use of powered air-purifying respirators and other personal protective labora-tory clothing. PCR amplicons of 16S rRNA gene sequences were generated from either purified genomic DNA or DNA in whole-cell lysates. Heat

lysis was performed as described by Hoffmaster et al. (17), with the fol-lowing modification: the cell lysates were cleared of cellular debris, possi-ble spores, and any remaining intact cells by centrifugation through a 0.1-␮m Durapore filter (EMD Millipore, Billerica, MA). Genomic DNA was isolated using the Qiagen DNeasy Blood and Tissue kit (Qiagen, Va-lencia, CA), following the manufacturer’s recommended protocol.

The 16S rRNA genes were amplified using universal oligonucleotide primers E8F and E1541R, which produced amplicons of approximately 1,500 bp (18). The amplification was performed in 50␮l (final volume) of reaction mixture containing 2.5 units of PlatinumTaqDNA polymerase, 1⫻Mg-free PCR buffer, 1.5 mM MgCl2, 200␮M each deoxynucleoside

triphosphate, 0.2␮M each primer, and 8 to 10 ng of purified genomic DNA or 10␮l of whole-cell lysate. PCR parameters were 94°C for 2 min, 30 cycles of 94°C for 30 s, 55°C for 30 s, and 72°C for 90 s, and a final extension at 72°C for 10 min.

Bidirectional 16S rRNA gene sequence data were acquired by using the published oligonucleotide primers E8F, E341F, E786F, E1115R, and E1541R (18), with one modification. The primer designated E341F was modified by substituting adenosines for inosines, and a guanosine was added to the 3=end (5=-CCTACGGGAGGAGCAG-3=). PCR primers remaining in the amplicon mixture were hydrolyzed with ExoSAP-IT reagent (USB) prior to DNA cycle sequencing reactions, which were performed with the BigDye Terminator v3.1 cycle sequencing kit (ThermoFisher, Pittsburgh PA). The manufacturer’s recommended pro-tocol was modified to use a 10-␮l reaction mixture consisting of 1.6 pmol sequencing oligonucleotide primer, 8 to 10 ng PCR-generated template DNA, and 1␮l of Terminator Ready Reaction mix. This modification decreased the final volume by one-half and was incorporated to conserve the sequencing reaction mix. No differences in sequence quality between the 10-␮l and 20-␮l reaction mixtures were detected. The cycle sequenc-ing reaction products were treated with the BigDye XTerminator purifi-cation kit, and DNA sequences were determined using an Applied Biosys-tems 3130xl genetic analyzer, with Sequencing Analysis v5.3.1 software and KB Basecaller v1.4 software.

DNA sequence analyses such as contig assembly, alignment of multi-ple contigs, and gene sequence comparisons were performed with Se-quencher v4.8 software. The 16S rRNA gene sequences of all strains in the study were compared with rRNA sequences in the Ribosomal Database Project (RDP) (http://rdp.cme.msu.edu) and NCBI GenBank databases by using the Basic Local Alignment Search Tool (BLAST) (19). The rRNA operon copy number for each species was determined from therrnDB database (https://rrndb.umms.med.umich.edu) (20,21). For sequence comparisons and analyses, the multiple 16S rRNA gene sequences within a single genome were downloaded from GenBank when the whole ge-nome sequence was available (Table 1). Following in-house DNA se-quence generation, basecaller data, in the form of an electropherogram for each 16S rRNA gene sequence, were visually inspected to identify posi-tions at which more than one peak was recorded at a single nucleotide position (indicating the presence of a SNP). Unless otherwise indicated, numbering of the nucleotide position of each SNP was assigned on the basis of the consensus sequence for the 13 known 16S rRNA genes in the genome of reference strainB. cereusATCC 14579 (GenBank accession no.

NC_004722). The presence or absence of all identified SNPs that were used to differentiateB. anthracisfromB. cereusandB. thuringiensiswas confirmed by repeat DNA sequence analysis using independent PCR products from primers E786F and E1541R.

RESULTS

Sequence variations among multiple rRNA operons.The 16S

rRNA gene sequences of 50 strains ofBacillusspp., includingB.

anthracis(n⫽14),B. cereus(n⫽18),B. thuringiensis(n⫽16),

Bacillus mycoides(n⫽1), andBacillus licheniformis(n⫽1), were determined. The ability of basecaller software to detect sequence variations was investigated by comparing computer-generated se-quences with visually inspected electropherograms. Visual

exam-TABLE 1B. cereusgroup species and strains of each species from which 16S rRNA gene sequences were analyzed and compared in this study

B. anthracis B. cereus B. thuringiensis Other

Pasteur 03BB102 97-27a _{B. mycoides}

ATCC 6462 Sternea _{ATCC 4342 Al Hakam}a _{B. licheniformis}

ATCC 14580a

A0102 D17 HD571

A0149 E33La _HD682

A0188 FM1 HD1011

A0248 G9241 HD1002

A0264 S2-8 HD1

A0293 3A HD600

A0465 ATCC 43881 ATCC 33679 A0488 (Vollum) ATCC AH 1134 HD453 ASC159 (Ames)a _{R3090-UK-03 HD538}

UT223 172560W-UK-04 HD44

240 FRI-3 HD974

SK-57 03BB108 HD848 ATCC 11950 HD868 ATCC 11778 ATCC 10792 FRI-43

ATCC 14579a

a_{16S rRNA gene sequences from each rRNA operon within the genome were}

downloaded from GenBank database.

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ination of electropherograms generated by sequencing of 16S rRNA gene PCR products that were a mixture of amplicons from 16S rRNA genes encoded in a single genome revealed multiple peaks at single nucleotide positions, indicating that one or more of

the 16S rRNA genes contained a SNP (Fig. 1AandB). Based on a

comparison of individual 16S rRNA gene sequences within a whole genome sequence, the height of each of the multiple peaks at one nucleotide position was proportional to the number of gene copies that contained the SNP. As an example, in a strain that contained thirteen 16S rRNA genes and only one copy with a SNP at a specific nucleotide position, the minor peak generated by that base was proportionately smaller than the peak representing the base present at that position in the other 12 copies. In some cases, a minor peak was difficult to differentiate from background noise,

as observed forB. cereusstrain E33L at nucleotide position 1143, at

which only one of the 16S rRNA gene copies had C instead of T (Fig. 1A). When more than one copy of the multiple genes con-tained the SNP, however, the resulting peak was more prominent

and was easier to detect. At nucleotide position 1148 ofB. cereus

strain E33L, A was present in four copies of the gene and T was

present in the other nine copies of the gene (Fig. 1A). Also, we

found that a single nucleotide position might have more than two peaks present; however, the maximum number of peaks at a single position is, of course, limited to four (A, C, T, and G). An example of more than two peaks at one nucleotide position was seen for

position 204 ofBacillus mycoides ATCC 6462; the various 16S

rRNA gene copies contained G, A, or T (Fig. 1B), producing three

overlapping peaks. Since the multiple 16S rRNA gene sequences of this strain have not been individually analyzed, however, the exact nucleotide ratios cannot be determined, although the electro-pherogram indicates that G is the prominent nucleotide at this position.

In addition to SNPs, indels were identified among the multiple

16S rRNA gene copies in a genome (Fig. 2). When an indel occurs

in one or more of the 16S rRNA genes in a genome, the DNA sequences of the 16S rRNA genes are no longer synchronized

downstream from that position in the electropherogram. The sequence reported by the basecaller software then appears as a mixed or “dirty” sequence, indicated by N throughout the quence. In comparison with the individual rRNA gene

se-quences from the completed genome ofB. licheniformisATCC

14580, the start of the shift was located at position 194 (indicated

by the arrow inFig. 2), with respect to the consensus sequence of

the 16S rRNA genes for the same strain (GenBank accession no.

CP000002). Indels were also observed in electropherograms

gen-erated with the E1541R primer for 13 strains ofB. anthracis, 8

strains ofB. cereus, and 9 strains ofB. thuringiensisin this study

(data not shown).

Comparison of DNA sequence basecaller software.The 16S rRNA gene sequences generated with the ABI 3130xl genetic ana-lyzer and viewed with Applied Biosystems DNA Sequence Analy-sis Software v5.3.1 were compared with those viewed with Chro-mas Lite v2.01, DNASTAR Lasergene 8, and Sequencher v4.8

software (Fig. 3). Electropherograms displayed using Chromas

Lite (Fig. 3A), ABI Sequencing Analysis (Fig. 3B), and DNASTAR

Lasergene 8 (Fig. 3C) software were similar in appearance. SNPs

represented by multiple peaks at a single nucleotide position were usually pronounced and not difficult to detect visually. However, small multiple peaks were sometimes difficult to dis-tinguish from background noise. Sequencher provided more clearly defined peaks, with almost no background noise, but the software also reduced the peak heights of the bases called, and multiple peaks at one base position were more difficult to

de-tect by visual examination (Fig. 3D).

Species discrimination by visual analysis of electrophero-grams for SNPs.BLAST and RDP databases were used to analyze

the consensus sequences of 16S rRNA genes from 14B. anthracis

strains, 18B. cereusstrains, and 16B. thuringiensisstrains. These

databases were capable of confirming the sequences to the genus level but not to the species level; this was because the sequences

wereⱖ98.7% identical, which is considered the species

identifica-tion standard (22). However, when the electropherograms were

FIG 1Representative electropherograms of 16S rRNA gene sequences with multiple peaks at a single nucleotide position due to SNPs in one or more of the multiple 16S rRNA genes within the genome. Arrows, positions of double peaks inB. cereusE33L (A) and triple peaks inB. mycoidesATCC 6462 (B). The size of each peak at these positions is dependent on the number of operons with the SNP. Nucleotide position numbers are relative to those in theB. cereusATCC 14579 gene sequence.

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visually inspected for SNPs, allB. anthracisstrains had a double peak (G and A) at nucleotide position 1139 (based on sequences

generated with both the E786F and E1541R primers), while theB.

cereusandB. thuringiensisstrains had a single peak (G) at this

position (Table 2). In addition, sequence data from the automated

basecaller analysis indicated that 4 of the 14 strains ofB. anthracis

were assigned G as the only peak at this position (Table 2). As a

consequence, this nucleotide position would not have been

iden-tified as a SNP that could be used to differentiateB. anthracisfrom

B. cereusandB. thuringiensiswithout visual inspection of the se-quence data. Additional data from bioinformatic analyses of 54 publicly available, assembled, closed circular genome sequences

revealed that all 54 strains ofB. anthracishad 16S rRNA genes with

the mixed G/A variation at position 1139 and none of the available

B. cereusorB. thuringiensisgenomes had this combination of nu-cleotides at that position (data not shown).

Although a previous publication reported that dual peaks (A

and T) at nucleotide position 1148 were unique toB. anthracis

(16), 6 of 18 strains ofB. cereusand 4 of 16 strains ofB.

thurin-giensisin the current study also had dual peaks (A and T) at this

position (Table 2), based on our visual inspection of the

electro-pherograms. Automated basecaller software data indicated only

one strain ofB. cereusand one strain ofB. thuringiensiswith an

ambiguity at position 1148; for all other strains, a single peak (either A or T) was detected.

DISCUSSION

For decades, 16S rRNA gene sequencing has been a widely used method for the accurate identification of bacterial isolates in

clin-ical microbiology laboratories (5). In some instances, 16S rRNA

gene sequencing continues to outperform traditional culture-based methods or non-16S molecular methods for identification

of clinically relevant bacterial pathogens (23). Recently, a clinically

validated next-generation sequencing-based approach that uses targeted 16S rRNA gene sequencing for the diagnostic identifica-tion of bacterial species directly from clinical samples was

de-scribed (24).

Since the introduction of high-throughput DNA sequencing technologies and their applications to metagenomics, the impor-tance of 16S rRNA sequence analysis for bacterial identification has not diminished but increased. Next-generation sequencing is used in large-scale metagenomic studies of microbial

communi-ties for culture-independent taxonomic classification (25) and for

whole-genome sequence characterization of isolates. Metag-enomic sequence analysis relies on operational taxonomic units (OTUs), i.e., the clustering of similar 16S rRNA gene sequences of closely related organisms, to characterize microbial communities. These OTUs are employed to determine microbial diversity, which was defined by the Human Microbiome Project Consor-tium as the number and abundance distribution of bacteria within

a microbiome (26). For pure cultures of a single species,

whole-FIG 2Electropherogram illustrating the loss of synchronicity that occurs when there is a nucleotide deletion or insertion within one or more of the 16S rRNA genes among the multiple rRNA operons in the genome.B. licheniformisATCC 14580 has seven rRNA operons, and the individual gene sequences are available from GenBank (GenBank accession no.CP000002). These sequence data were generated from a DNA template amplified with 16S rRNA universal primers. Arrow, position of the indel.

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genome sequences generated by platforms that produce short

reads (⬍250 bp) are insufficient for assembly of the multiple

in-dividual copies of the 16S rRNA genes, which are about 1,500 bp in length. A combination of the short reads and longer reads from other platforms is needed to assemble the individual genes and to identify any SNPs that may be present in the various copies. Sanger sequencing of the 16S rRNA gene is more commonly used

for rapid identification of unknown isolates from clinical speci-mens and environmental samples and for confirmation of variant sequence data that might have been generated by the higher-throughput methods. Although interpretative criteria for bacte-rial identification by 16S rRNA gene sequence analysis are avail-able, they do not provide sufficient detail regarding the intragenomic complexities of the rRNA operons of a species, and

FIG 3Relative peak sizes on a computer screen when a sequence data file is displayed by four different software programs, i.e., Chromas Lite v2.01 (A), ABI Sequencing Analysis software v5.3.1 (B), DNASTAR Lasergene 8 (C), and Sequencher v4.8 (D). The sequence presented is the 16S rRNA gene sequence fromB. mycoidesATCC 6462 generated with the universal oligonucleotide primer E8F.

TABLE 2Results from basecaller software and from base identification by visual inspection of electropherograms

Species

No. of strains

No. of strains with resulta

At nucleotide position 1139 At nucleotide position 1148

Basecaller

Visual

inspection Basecaller Visual inspection

G N G R T A N T A W

B. anthracis 14 4 10 0 14 0 0 14 0 0 14

B. cereus 18 18 0 18 0 6 11 1 3 9 6

B. thuringiensis 16 16 0 16 0 7 8 1 5 7 4

a

Results represent data from two independent sequencing reactions using primer E786F. N, any nucleotide (basecaller assignment was not consistent from run to run for independent sequencing reactions); R, IUPAC code for A or G; W, IUPAC code for T or A.

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there is no emphasis on the importance of visual evaluation and interpretation of gene sequence data presented by basecaller

soft-ware (27). Also, many of the 16S rRNA gene sequences in the

databases are incomplete, covering only areas of the genes that are designated hypervariable regions. Partial 16S rRNA gene se-quences are limited in their utility for genus or species

identifica-tion (28).

Reference sequences for 16S rRNA genes are available in pub-licly accessible databases such as the International Nucleotide Se-quence Database Collaboration (INSDC) (i.e., DDBJ, EMBL, and GenBank) and RDP databases. Between 1992 and 2015, the num-ber of 16S rRNA gene sequences in the RDP database increased from approximately 500 to more than 3.2 million. However, the quality of the sequences submitted to these databases is highly variable, due to the lack of universal DNA sequence quality

stan-dards (29–31). In 2008, according to the RDP, approximately 10%

of the archeal and bacterial sequences in the database were

sus-pected to be of poor quality (32). Since then, all sequences pass

through a quality control program prior to addition to the RDP database, to minimize the impact of low-quality data. Even though some quality controls are now in place, many DNA se-quences in public databases still contain ambiguities (N) and un-corrected sequencing errors.

The number of rRNA operons, and thus the number of 16S rRNA genes, varies across species and even among different strains within a given species. The rRNA operon copy number is generally correlated with the growth characteristics of a microbe, based on various environmental conditions. When exposed to a favorable environment and plentiful nutrients, microbial species with nu-merous rRNA operons grow more rapidly than species with few

rRNA operons (20). TherrnDB database indicates that 16S rRNA

gene copy numbers within a genome vary from 1 to 15 copies

across species of bacteria (20,21).Borrelia burgdorferi, Coxiella

burnetii, and various species ofMycobacteriaandRickettsiaare known to have a single chromosomal rRNA operon. Examples of species known to contain 10 or more 16S rRNA operons include

Bacillusspp. such asB. anthracis(11 copies),B. cereus(11 to 14

copies),Bacillus subtilis(10 copies),B. thuringiensis(13 or 14

cop-ies), andBacillus weihenstephanensis(14 copies) andClostridium

spp. such asClostridium paradoxum(15 copies),Clostridium

bei-jerinckii(14 copies),Clostridium perfringens(13 copies), and Clos-tridium botulinum(11 copies). The number of 16S rRNA operons

may also vary within a species, as indicated above forB. cereusand

B. thuringiensis.

When 16S rRNA gene variations such as SNPs or indels are present in a genome, multiple peaks (two or more) are displayed at a single nucleotide position in the electropherogram or the se-quence loses synchronicity. As a result, the DNA sese-quence makes an abrupt change following an indel (from “clean” to “dirty”), as

we have shown. Pettersson et al. (12,33) and Reischl et al. (11)

observed in the 1990s that such data suggested intragenomic

vari-ations among the 16S rRNA gene sequences of theMycoplasma

mycoidescluster andMycobacterium celatum, respectively. Since that time, reliance on automated analysis by basecaller software has become routine. It is understandable that automated methods are necessary with the rapid proliferation of benchtop sequencing platforms, but it should be recognized that there is the possibility that potentially important information available in a DNA se-quence electropherogram may be lost with reliance solely on data from automated basecaller software. As our findings show,

base-caller software does not consistently detect and report all of the SNPs present in a sequence and does not have the ability to rec-ognize when an insertion or deletion is responsible for what ap-pears to be mixed or overlapping DNA sequences. Visual inspec-tion of the electropherogram can locate the posiinspec-tion of a sequence shift that results when a deletion or insertion among the multiple 16S rRNA genes within a genome interrupts the previously syn-chronized sequence data.

There are numerous commercial software programs available for automated Sanger DNA sequence analysis. All of the software programs present the sequence data in an electropherogram, with software-specific characteristics that may assist or impair the abil-ity to visually detect SNPs. Chromas Lite v2.01 and DNASTAR Lasergene 8 were comparable to the ABI Sequencing Analysis v5.3.1 software of the ABI 3130xl genetic analyzer. However, elec-tropherograms observed with Sequencher had relatively lower peak heights overall, which resulted in some loss of detail. Some characteristics can be adjusted by the operator when setting pref-erences for the software. Sequencher presents an electrophero-gram as multiple rows on the computer screen, which compresses the peak height, while the other software programs open an elec-tropherogram as a single row on the screen. It is important to be aware of the possible impact of different software programs on the appearance of electropherograms when performing visual analy-ses to detect SNPs and indels.

TheBacillus cereusgroup poses a serious challenge to the use of 16S rRNA gene sequence data for discrimination between the

highly related species. Previously, Sacchi et al. reported thatB.

anthraciscan be rapidly differentiated fromB. cereusandB. thu-ringiensisby 16S rRNA gene sequencing, through analysis of

se-quence differences among the 16S rRNA genes (16). According to

their basecaller software, nucleotide position 1146 (correspond-ing to our position 1148) was always reported as a mixture of A

and T for 32B. anthracisstrains, while this was not the case for 10

B. cereusstrains and 11B. thuringiensisstrains. Our results concur

with their results forB. anthracisstrains; the basecaller software

indicated an ambiguity character (N) at this position, and visual inspection of the electropherograms confirmed dual peaks (A and

T) at this position. ForB. cereusE33L andB. thuringiensis97-27,

however, the basecaller software reported possible multiple peaks at this position and, when electropherograms for all strains were

visually inspected, fiveB. cereusstrains and threeB. thuringiensis

strains also had A and T peaks. Therefore, dual peaks of A and T at nucleotide position 1148 are not necessarily reliable for identify-ingB. anthracis. In contrast, in visual analyses of the

electrophero-grams for the numerous strains in this study,B. anthracishad dual

G and A peaks at position 1139 whileB. cereusandB. thuringiensis

had only G, and this position appears to be more reliable for

iden-tifyingB. anthracis. This result was supported by bioinformatic

analysis of the publicly available genome sequences for these spe-cies.

The 16S rRNA consensus gene sequence cannot always defin-itively classify an organism to the species level or discriminate between two closely related species. As demonstrated in this study, 16S rRNA gene SNPs within a genome can facilitate this discrim-ination. High-quality sequences that include SNPs would improve the existing public databases if SNP locations were included and the appropriate bases were designated by using IUPAC nucleotide notations (e.g., W, R, and Y). Unfortunately, many sequences in the databases still contain N characters, which suggests that many

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investigators have not visually examined the electropherograms. It is difficult to know exactly why this is not done, but some of the reasons might include the additional time required, the percep-tion that it is too laborious a task, a lack of proficiency in analytical

skills required for data analysis (30), and a lack of guidelines and

quality standards for DNA sequence data (29). With the fast pace

at which sequence data are currently being generated, data analy-sis has become the rate-limiting step in DNA sequence analyanaly-sis. It has been proposed that automatic basecalling analysis is needed to eliminate human error or subjectivity, with the assumption that

computer software would provide more reliable sequences (29–

31). It is understood that an automated analysis approach may be

sufficient when multiple gene sequences are analyzed for species identification. However, if the 16S rRNA gene sequence alone is being used for identification or if the 16S rRNA gene sequence data are to be submitted to a public database, N characters should be resolved and any ambiguous bases should be confirmed with additional data from the complementary strand. In the case of

closely related species such as theBacillus cereusgroup, the

auto-mated basecalling that results in a consensus sequence for the 16S rRNA genes may lack potentially important information that is available in the electropherograms. High-quality DNA sequences with SNP analysis could increase the power of databases to facili-tate bacterial identification and discrimination between closely related species.

ACKNOWLEDGMENTS

We thank James Gathany for assisting with the electropherogram figures, David Sue and Andrew Conley for bioinformatics support, and Stephan A. Morse for critical review of the manuscript.

An appointment to the Research Participation Program at the Centers for Disease Control and Prevention, administered by the Oak Ridge In-stitute for Science and Education, for J.R.H., was provided through an interagency agreement between the U.S. Department of Energy and the Centers for Disease Control and Prevention.

We declare no competing financial interests.

J.R.H. and S.P. performed sequence determinations and analysis, D.H. and S.P.P. contributed to study design and the acquisition of DNA sam-ples, L.M.W. contributed to sequence analysis, and J.R.H. and L.M.W. wrote the manuscript.

The findings and conclusions in this report are those of the authors and do not necessarily represent the views of the Centers for Disease Control and Prevention or the U.S. Department of Homeland Security. The use of trade names and commercial sources is for identification pur-poses only and does not imply endorsement by the U.S. Public Health Service, the U.S. Department of Health and Human Services, or the U.S. Department of Homeland Security.

FUNDING INFORMATION

This work was funded by Department of Homeland Security USA (HSHQDC-09-X-00240).

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