Use of 16S rRNA Gene-Targeted Group-Specific Primers for Real-Time PCR Analysis of Predominant Bacteria in Mouse Feces

Yun-Wen Yang, Mang-Kun Chen, Bing-Ya Yang, Xian-Jie Huang, Xue-Rui Zhang, Liang-Qiang He, Jing Zhang, Zi-Chun Hua

The State Key Laboratory of Pharmaceutical Biotechnology, College of Life Science, Nanjing University, Nanjing, China

Mouse models are widely used for studying gastrointestinal (GI) tract-related diseases. It is necessary and important to develop anew set of primers to monitor the mouse gut microbiota. In this study, 16S rRNA gene-targeted group-specific primers for Fir-micutes, Actinobacteria, Bacteroidetes, Deferribacteres, “Candidatus Saccharibacteria,” Verrucomicrobia, Tenericutes, and Pro-teobacteria were designed and validated for quantification of the predominant bacterial species in mouse feces by real-time PCR.After confirmation of their accuracy and specificity by high-throughput sequencing technologies, these primers were applied toquantify the changes in the fecal samples from a trinitrobenzene sulfonic acid-induced colitis mouse model. Our results showedthat this approach efficiently predicted the occurrence of colitis, such as spontaneous chronic inflammatory bowel disease intransgenic mice. The set of primers developed in this study provides a simple and affordable method to monitor changes in theintestinal microbiota at the phylum level.

More than 90% of the 100 trillion cells in the human body aremicrobes, most of which reside in intestines and are collec-

tively known as the intestinal microbiota (1). The intestinal mi-crobiota plays a key role in immune system maturation, food di-gestion, drug metabolism, detoxification, and prevention ofpathogenic bacterial adhesion (2). Disruption of the intestinal mi-crobiota has been demonstrated in patients suffering from inflam-matory bowel disease (IBD), asthma, obesity, liver disease, anddiabetes (1, 3). Increasing evidence suggests that the intestinalmicrobiota play a role in initiating, maintaining, and determiningthe phenotype of IBD (3). To understand the relationship betweenthe intestinal microbiota and IBD, it is important to develop asensitive and accurate molecular detection method to analyze var-ious microbial populations.

A variety of methods are available to quantify microbiota of thegut. Traditionally, culture-based techniques were used to deter-mine the composition of the gut microbiota, but only 10 to 50% ofgut bacteria are culturable (4). 16S rRNA gene-targeted oligonu-cleotide probes have been used with fluorescent in situ hybridiza-tion as a culture-independent method (5, 6). The 16S rRNA geneis a suitable marker gene for quantification of microbiota on tax-onomic and phylogenetic levels. Quantitative real-time PCR with16S rRNA gene-based specific primers has been utilized as a sen-sitive and rapid method to quantify intestinal microbiota (7–10).Now, high-throughput sequencing (HTS) technologies have beenused widely to examine the complexity of the gut microbiota dueto the speed, scale, and precise information it can provide (11).However, quantitative PCR (qPCR) can also be a good choice foranalyzing microbial communities due to the relatively small amountof template required, high sensitivity, high-throughput processing,and affordable cost (12).

Real-time PCR has been used successfully to quantify specificbacterial groups and species from intestinal mucosa and stools. Todate, phylum- and group-specific primers for Firmicutes, Actino-bacteria, Bacteroidetes, and Proteobacteria have been developedand applied to the analysis of the intestinal microbiota (7–9, 13).In the present study, we developed a new set of primers to detectthe Actinobacteria, Bacteroidetes, Deferribacteres, “CandidatusSaccharibacteria,” Verrucomicrobia, Tenericutes, and Proteobacte-

ria (including beta, epsilon, and gamma classes) in the gut. Themicrobiota from fecal samples of C57BL/6 mice were analyzed byqPCR with these primers. The accuracy of qPCR assay was vali-dated by HTS. In order to demonstrate its applicability and effec-tiveness, changes in the intestinal microbiota in mouse fecalsamples from mice with trinitrobenzene sulfonic acid (TNBS)-induced colitis and spontaneous colitis were also examined.

MATERIALS AND METHODSDesign of 16S rRNA gene-targeted group-specific primers. According tothe dominating bacterial microbiota of mouse feces, listed in Table S1 inthe supplemental material (14), we designed a set of primers for testingbacteria, including Actinobacteria, Bacteroidetes, Deferribacteres, “Candi-datus Saccharibacteria,” Verrucomicrobia, Tenericutes, and the beta, epsi-lon, and gamma subdivisions of Proteobacteria. In order to develop phy-lum- and class-specific primers, between 20 and 30 16S rRNA sequencesof each dominant genus of the target taxonomic group were randomlydownloaded from the Ribosomal Database Project II (RDP-II) andgrouped into Fasta files (12, 15). Sequences from each taxon were clus-tered using ClustalX (16), and consensus sequences were obtained usingBioEdit (17). The consensus sequences were aligned with the Multalinprogram (http://bioinfo.genopole-toulouse.prd.fr/multalin/multalin.html). The alignments of these consensus sequences (see Fig. S1 in thesupplemental material) were visually inspected to design primers. It isknown that nucleotide mismatches at the primer’s 3= termini are extended

Received 9 June 2015 Accepted 14 July 2015

Accepted manuscript posted online 17 July 2015

Citation Yang Y-W, Chen M-K, Yang B-Y, Huang X-J, Zhang X-R, He L-Q, Zhang J,Hua Z-C. 2015. Use of 16S rRNA gene-targeted group-specific primers for real-time PCR analysis of predominant bacteria in mouse feces. Appl Environ Microbiol81:6749 – 6756. doi:10.1128/AEM.01906-15.

Editor: M. W. Griffiths

Address correspondence to Zi-Chun Hua, zchua@nju.edu.cn, or Jing Zhang,jzhang08@nju.edu.cn.

Supplemental material for this article may be found at http://dx.doi.org/10.1128/AEM.01906-15.

Copyright © 2015, American Society for Microbiology. All Rights Reserved.

doi:10.1128/AEM.01906-15

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by DNA polymerases with lower efficiency than correctly matched. There-fore, primers were designed to possess the taxon-specific nucleotide(s) atthe 3= end (12, 18). Primers were assessed in silico using the tool “probe-match” from the RDP-II and compared to available 16S rRNA gene se-quences by using the NCBI BLAST database search program (http://www.ncbi.nlm.nih.gov/BLAST/). Primer pairs of each target group weredeveloped based on in silico comparisons and reserved for further valida-tion (Table 1).

Primer specificity. 16S rRNA gene sequences amplified from totalfecal DNA using the primers 27F and 1525R (19) were cloned into thepMD18-T vector. After genotyping by partial 16S rRNA sequencing, 38clones (see Table S2 in the supplemental material) with 16S rRNA gene se-quences belonging to different taxa (EMBL accession numbers KP713718 toKP713755) were used as the templates to test the specificity of the grouptarget primers. The annealing temperature that maximized primer speci-ficity in vitro was determined by using the target and nontarget clonesdescribed above as the templates, with annealing temperatures rangingfrom 55 to 65°C (12). The PCR conditions were one initial denaturing stepof 3 min at 94°C, 30 cycles of 95°C for 20 s, gradient annealing for 20 s and72°C for 20 s, and a final elongation step at 72°C for 5 min. Every PCRmixture contained 0.1 U/�l of Taq polymerase (TaKaRa, Dalian, China),0.25 mM concentrations of each deoxynucleoside triphosphate, 0.5 �Mconcentrations of each primer, 1� buffer, �50 ng of DNA, and water to50 �l. A minimum of two target and the remaining nontarget clones wereused to test each primer pair, and the specificity was claimed when only

target DNA was amplified (Table 2); the PCR products were determinedby standard gel electrophoresis. Through optimization of the PCR anneal-ing temperature, it was found that 60°C maximized specificity of all pairs(Table 2). The specificity of each primer pair was also verified by qPCRand inferred from the shift of the threshold cycle (CT), obtained by am-plifying target and nontarget sequences for comparison. Real-time PCRswere carried out in 96-well optical plates on an ABI StepOne plus real-time PCR system sequence detector with 2�FastStart SYBR green mix(Vazyme, Nanjing, China). All qPCR mixtures contained 10 �l of 2�Fast-Start SYBR green with dye1, 0.5 �l of each forward and reverse primer(final concentration, 0.4 �M), and 9 �l of the DNA template (equilibratedto 10 ng).The PCR conditions were 95°C for 10 min, followed by 40 cyclesof 95°C for 15 s and 60°C for 1 min. Melting-curve analysis was performedafter amplification. The CT values and baseline settings were determinedby automatic analysis settings (9). qPCR products were selected at ran-dom and cloned into the pMD18-Tvector (TaKaRa) for sequencing con-firmation.

Primer amplification efficiencies. The primer amplification efficien-cies were determined by standard procedure: making dilution series offecal total DNA, calculating a linear regression based on the CT datapoints, and inferring the efficiency from the slope of the line. Serial dilu-tions of 1, 1:4, 1:16, 1:64, and 1:256 were used (12). Each dilution pointand primer pair was tested three times. In order to confirm that the CT

value generated by the lowest concentrated DNA was not an artifact, anontemplate control was included in each assay. Lastly, CT data were

TABLE 1 16S rRNA gene-targeted group-specific primers used in this study

Target group Primera Sequence (5=–3=)b Amplicon length (bp) Source or reference

Bacteroidetes Bac960F GTTTAATTCGATGATACGCGAG 122 This studyBac1100R TTAASCCGACACCTCACGG 122 This study

Firmicutes Firm934F GGAGYATGTGGTTTAATTCGAAGCA 126 9Firm1060R AGCTGACGACAACCATGCAC 126 9

Actinobacteria Act664F TGTAGCGGTGGAATGCGC 277 This studyAct941R AATTAAGCCACATGCTCCGCT 277 This study

“Candidatus Saccharibacteria” Sac1031F AAGAGAACTGTGCCTTCGG 187 This studySac1218R GCGTAAGGGAAATACTGACC 187 This study

Deferribacteres Defer1115F CTATTTCCAGTTGCTAACGG 150 This studyDefer1265R GAGHTGCTTCCCTCTGATTATG 150 This study

Verrucomicrobia Ver1165F TCAKGTCAGTATGGCCCTTAT 97 This studyVer1263R CAGTTTTYAGGATTTCCTCCGCC 97 This study

Tenericutes Ten662F ATGTGTAGCGGTAAAATGCGTAA 200 This studyTen862R CMTACTTGCGTACGTACTACT 200 This study

Betaproteobacteria Beta979F AACGCGAAAAACCTTACCTACC 174 This studyBeta1130R TGCCCTTTCGTAGCAACTAGTG 174 This study

Epsilonproteobacteria Epsilon940F TAGGCTTGACATTGATAGAATC 189 This studyEpsilon1129R CTTACGAAGGCAGTCTCCTTA 189 This study

Delta- and Gammaproteobacteria Gamma877F GCTAACGCATTAAGTRYCCCG 189 This studyGamma1066R GCCATGCRGCACCTGTCT 189 This study

Universal 926F AAACTCAAAKGAATTGACGG 136 121062R CTCACRRCACGAGCTGAC 136 12

Bacterial 16S rRNA 27F AGAGTTTGATCCTGGCTCAG 181525R AAGGAGGTGWTCCARCC 18

a Numbers within the primer name indicate the nucleotide position based on the Escherichia coli ATCC 11775T 16S rRNA gene.b Nucleotide symbols: R � A or G; Y � C or T; N � any nucleotide; W � A or T; M � A or C; K � T or G; S � C or G; and H � A/C/T.

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uploaded to an Excel spreadsheet, and the resulting efficiency graphs areshown in Fig. S2 in the supplemental material.

Quantitative analysis of synthetic communities of 16S rRNA genes.The real-time PCR protocol was validated by constructing an artificialmixture of 16S rRNA genes in a similar way to published approaches (12,20). Briefly, equal amounts of plasmid containing the 16S rRNA genesequence for each taxon investigated were mixed to simulate the artificialcommunities of 16S rRNA genes, which were then used as the template forqPCR validation experiments. The plasmids were selected from these 38clones. The average CT value obtained from each primer pair was trans-formed into a percentage using the following formula (12):

X ��Eff.Univ�CTuniv

�Eff.Spec�CTspec� 100

where Eff.Univ is the calculated efficiency of the universal primers (2 �100% and 1 � 0%), and Eff.Spec refers to the efficiency of the taxon-specific primers. CTuniv and CTspec are the CT values registered by thethermocycler. “X” represents the percentage of 16S taxon-specific copynumber existing in a sample (12). All of the fecal samples were analyzedwith the qPCR assay, and the CT values were used to calculate the propor-tion of higher bacterial taxa in the feces.

Fecal sample collection and DNA extraction. All animal care proce-dures were approved by the Institutional Animal Care and Use Commit-tee of Nanjing University prior to initiation of the experiment. C57BL/6mice aged 8 to 10 weeks were housed in a standard animal laboratory witha 12-h light-dark cycle and were fed a standard diet. The feces sampleswere collected within 2 h when mice were transferred to fresh sterilizedcages. After the fecal samples were thoroughly frozen in liquid nitrogen,

they were then stored at �80°C until DNA extraction (14). The totalgenomic DNA from each fecal sample (100 mg) was extracted (14) usingthe QIAamp DNA stool minikit (Qiagen, Germany) according to themanufacturer’s instructions. Absorbance ratios at 260/280 nm and at 260/230 nm were determined to quantify and assess the purity of DNA sam-ples.

Quantitative analysis of natural bacterial communities. Six wild-type C57BL/6 mice were housed separately for feces collection. The totalgenomic DNA from each fecal sample (100 mg) was extracted using aQIAamp DNA stool minikit according to the manufacturer’s instructions,resuspended in 100 �l of TE buffer (pH 8.0), and quantified with anEppendorf biophotometer. These samples were analyzed with the qPCRassay, and the CTs were used to calculate the proportion of higher bacterialtaxa in the feces, as described above.

Deep sequencing of 16S rRNA genes. Total genomic DNA from fecalsamples (100 mg) was extracted using the QIAamp DNA stool minikitaccording to the manufacturer’s instructions. The forward primer (5=-CCTACGGGNGGCWGCAG-3=) and the reverse primer (5=-TACNVGGGTATCTAATCC-3=) containing the A and B sequencing adaptors (454 LifeSciences) were used to amplify a region covering the V3-V4 region of the16S rRNA gene (14). PCRs were carried out in triplicate using 25-�l re-action mixtures with 0.5 �M concentrations of each primer, 20 to 50 ng oftemplate DNA, 5 �l of the PCR buffer, and 2.5 U of DNA polymerase. Theamplification program consisted of an initial denaturation step at 94°C for5 min, followed by 25 cycles of 94°C for 10 s (denaturation), 55°C for 10 s(annealing), and 72°C for 15 s (extension), and then a final extension of72°C for 10 min (14). Replicated PCR products of the same sample weremixed and purified with a DNA gel extraction kit. Prior to sequencing, the

TABLE 2 Testing the specificity of the group target primers and optimization of PCR annealing temperature

Target group Primer Tm (°C) Template(s) PCR resulta

Bacteroidetes Bac960F 60 Clones 29, 33, 34, and 35 �Bac1100R The remaining clones –

Firmicutes Firm934F 60 Clones 1, 2, 3, 23, 28, 31, and 32 �Firm1060R The remaining clones –

Actinobacteria Act664F 60 Clones 26 and 27 �Act941R The remaining clones –

“Candidatus Saccharibacteria” Sac1031F 60 Clones 24 and 25 �Sac1218R The remaining clones –

Deferribacteres Defer1115F 60 Clones 7, 8, and 9 �Defer1265R The remaining clones –

Verrucomicrobia Ver1165F 60 Clones 21 and 22 �Ver1263R The remaining clones –

Tenericutes Ten662F 60 Clones 16, 17, and 36 �Ten862R The remaining clones –

Betaproteobacteria Beta979F 60 Clones 4, 5, 6, 18, 19, and 20 �Beta1130R The remaining clones –

Epsilonproteobacteria Epsilon940F 60 Clones 13, 14, 15, 30, 37, and 38 �Epsilon1129R The remaining clones –

Delta- and Gammaproteobacteria Gamma877F 60 Clones 10, 11, and 12 �Gamma1066R The remaining clones –

Universal 926F 60 All of the clones �1062R

a �, positive; –, negative.

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DNA concentration of each PCR product was determined using a Quant-iTPicoGreen double-stranded DNA assay (Invitrogen, USA) and wasquality controlled on an Agilent 2100 bioanalyzer (Agilent, USA). A mas-ter DNA pool was generated from the purified products in equimolarratios. The pooled products were sequenced using a Roche 454 Titaniumpyrosequencer at BGI Shenzhen Science and Technology Co., Ltd., Shen-zhen, China.

Sequence processing and bioinformatics analysis. Pyrosequencingreads with more than one ambiguous nucleotide within correct barcodesor the length of the reads outside the range of 200 bp and 1,000 bp wereremoved and excluded from further analysis. Sequences that passed qual-ity filtering using the default parameters in QIIME were checked for chi-meras (21) and assigned to operational taxonomic units (OTU) usingmothur (22). Sets of sequences with �97% identity were defined as anOTU. OTU were assigned to a taxonomy using an RDP naïve Bayesianclassifier (23). Species analysis of every OTU was based on the reads clas-sified by the RDP. If �51% of the reads in an OTU belonged to the sametaxon (such as a genus), then that OTU was considered to belong to thattaxon. The number of effective reads in every OTU was counted, and theeffective reads of every species were generalized. These analyses were usedto reveal the changes of bacterial communities in fecal samples from micewith IBD compared to those from wild-type mice. Most of the sequenceanalysis was completed by BGI.

Colitis model in mice. TNBS-induced colitis is a classical modelwidely used in the research of IBD (24). A total of 24 male mice aged 8weeks were divided into four groups and fasted but were allowed freeaccess to water overnight prior to the induction of colitis. The animalswere anesthetized with a 50% (vol/vol) mixture of ketamine and xylazine(100 mg/ml) diluted in saline (0.9% [wt/vol] NaCl) at the dose of 1 ml/kg.TNBS (Sigma-Aldrich, USA) diluted in 50% (vol/vol) ethanol was in-jected via a polyethylene catheter inserted at 4 cm from the anus at a doseof 100 mg/kg in 25 �l to induce an experimental colitis (13). When thecolitis was the most serious, at day 3, the feces were collected according tothe method described above. The mice were then sacrificed, and theircolons were prepared for hematoxylin and eosin (H&E) staining. Spon-taneous chronic colitis that occurred in FADD (Fas-associated death do-main-containing protein)-deficient mice was described by Welz et al.(25). We constructed a series of FADD mutant mice. The feces were col-lected from these transgenic mice at about 8 weeks of age. By using a qPCRassay to screen their feces samples, it was determined that one of them,designated TgD, showed a significant alteration in intestinal microbiota.To verify the presence of chronic IBD, the mouse was sacrificed, and itscolon was assessed for microscopic examination and H&E staining (26).

Microarray data accession numbers. Newly determined sequencedata were deposited in the Sequence Read Archive (SRA) under accessionnumbers SRX862855 and SRX863943.

RESULTSNewly designed qPCR assay targeting bacterial taxa. To analyzethe predominant bacteria in mouse feces, we designed a set ofphylum- and class-specific primers for testing Actinobacteria, Bac-teroidetes, Deferribacteres, “Candidatus Saccharibacteria,” Verru-comicrobia, Tenericutes, and the beta, epsilon, and gamma subdi-visions of Proteobacteria. The primers were optimized by in silicoanalyses in the RDP database to maximize the theoretical specific-ity and universality of the newly designed primers. The primerswere modified to function at the same annealing temperatures(60°C), allowing us to perform all PCRs simultaneously in thesame thermocycler. The specificity of each primer set was vali-dated with plasmids containing target or nontarget 16S rRNA se-quences as the template, and the primer pairs were specific fortheir target taxa at the appropriate annealing temperatures andyielded the PCR products of the expected size only from theirtargets (Table 2). In real-time PCR analyses, all of the assays were

specific for the target group, and the CT values obtained with non-target 16S rRNA sequences were close to the CT values of theno-template control (see Fig. S3 in the supplemental material).Further testing was carried out using total stool DNA as the tem-plate, and qPCR products of each primer pairs were determinedby gel electrophoresis (Fig. 1). Each specific primer yielded theexpected PCR product for the corresponding target bacteria 16SrRNA without any unexpected results, and sequencing of the PCRproducts confirmed the identity of the corresponding target bac-teria. qPCR products were cloned into pMD18-T vector, and 20clones were picked randomly for sequencing. Amplification prod-ucts belonging to the target group defined as targeted rate arelisted in Table 3. Most of the primer pairs had a very high targetingefficiency; the lower targeted rate with the primer pairs Act664F/Act941R and Gamma877F/Gamma1066R also reached 90%. Afterwe confirmed the specificity, we validated the qPCR assay in arti-ficial mixes of 16S rRNA sequences (data not shown). The ampli-fication efficiency of each primer pair was determined by using theCT method (Table 3), which provided an estimate of the propor-tion of 16S rRNA copies belonging to each phylum, as describedabove. After optimization and evaluation, a real-time PCR wasused to detect the microbiota composition in mouse feces.

Quantification of bacterial groups in intestinal microbiota.To evaluate the ability of the qPCR assay to resolve the composi-tion of complex bacterial communities, fecal samples fromhealthy mice were collected to detect the distribution of the pre-dominant bacterial groups in fecal microbiota. The same samplewas divided into two parts: one for the qPCR assay described hereand another for HTS (Sequence Read Archive accession numberSRX862855). Upon comparison of the qPCR assay to 16S rRNAgene HTS in detection of fecal samples, the relative proportions ofbacteria were found to be largely similar, as shown in Table 4.Since there was a certain deviation between the qPCR assay andHTS, we included data from an earlier study (27). The proportionof Bacteroidetes was slightly lower with HTS, whereas the percent-age of Deferribacteres was higher with the qPCR method. Overall,this qPCR assay can be successfully used to determine the relativeabundance of fecal microbiota at the phylum level.

Microbiota composition analysis of the TNBS model in mice.Disruption of the intestinal microbiota has been demonstrated tobe associated with the occurrence of enteritis. To prove that thisqPCR method can be effectively applied to analysis of changes ofmicrobial populations under pathological conditions, we estab-

FIG 1 Specificity of qPCR amplification with new sets of primer pairs. QPCRproducts amplified from the mouse fecal DNA with group-target primers asfollows: universal (lane 1), Bacteroidetes (lane 2), Firmicutes (lane 3), Verruco-microbia (lane 4), “Candidatus Saccharibacteria” (lane 5), Betaproteobacteria(lane 6), Deltaproteobacteria and Gammaproteobacteria (lane 7), Epsilonproteo-bacteria (lane 8), Deferribacteres (lane 9), Tenericutes (lane 10), Actinobacteria(lane 11); DNA markers indicated in base pairs (lane M).

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lished a classical model of TNBS-induced colitis in mice. Modelmice were examined for various inflammatory indices, such as thetypical features of acute enteritis, including a shortened but thick-ened colon (Fig. 2A). Figure 2B shows a representative H&E-stained colon with mucosal ulceration, submucosal edema, andinflammatory cell infiltration. Fecal samples from TNBS modelmice or control mice were collected, and the relative abundance ofbacteria was calculated from the CT values as described above. Thecomposition ratios of the target bacteria are reported as means the standard deviations (SD) (Fig. 2C). To more clearly and di-rectly reflect the change of predominate bacteria, the characteriza-tion of communities was also shown in a pie chart (Fig. 2C). Com-pared to that in control mice, the proportion of Bacteroidetes wasstable, whereas the subdivisions of Proteobacteria in the TNBSmodel increased significantly from 2.78% up to 9.46% (P � 0.05).This increase in the relative abundance of proteobacteria was inagreement with previous findings that the presence of Enterobac-teriaceae correlates with disease activity in mice (27, 28). The re-sults suggest that the qPCR assay can be used to analyze changes ofgut microbiota.

Application on the screening of chronic enteritis. Sincechanges in microbe populations are closely related to IBD, theqPCR assay was a simple and quick method to assess the enteritis

sample by analyzing the shifts in the predominant microbiotacommunity. We applied our qPCR to predict chronic inflamma-tion in transgenic mice. Fecal samples were collected from differ-ent transgenic mice prepared by our lab. Compared to samplesfrom healthy mice, the fecal samples from TgD mice exhibited adistinct shift in microbiota composition, as presented in Fig. 3.The proportion of Bacteroidetes was significantly decreased (P �0.05), whereas the proportions of Deferribacteres and the subdivi-sions of Proteobacteria were significantly increased (P � 0.05)(Fig. 3A) in TgD mice compared to those in control mice. Wefurther performed HTS and obtained results similar to those ob-tained by using qPCR (SRA accession number SRX863943) withregards to the proportions of Bacteroidetes, Deferribacteres, andProteobacteria (Fig. 3A). This distribution of intestinal microbiotawas observed in IL-10�/� or TLR-5�/� mice (27, 29, 30), and acomparison of data is presented in Fig. 3B, implying the existenceof spontaneous enteritis in TgD mice. We next determinedwhether TgD mice develop colitis by assessing several well-definedindicators of this disease state. Compared to healthy C57/BL6mice, TgD mice displayed gross features of severe colitis, includingcontracted ceca, swelling, and the absence of well-formed feces(Fig. 3C). Moreover, mouse colonic specimens assessed by H&Estaining demonstrated moderate mucosal and submucosal in-

TABLE 3 Specificity test of phylum- and class-specific primers by qPCR

Target group Primer pairSpecificitya

(%)Efficiencyb

(%)Taxon-specific nucleotideposition(s)c

Amplicon sequences belongingto the target groupd (%)

Bacteroidetes Bac960F/Bac1100R 99.2 98.5 0 and 8/* 100Firmicutes Firm934F/Firm1060R 99.6 94.6 0/* 100Actinobacteria Act664F/Act941R 99.4 92.7 0 and 7/0 and 2 90“Candidatus Saccharibacteria” Sac1031F/Sac1218R 99.1 94.6 0–3/* 100Deferribacteres Defer1115F/Defer1265R 99.4 92.6 */* 100Verrucomicrobia Ver1165F/Ver1263R 98.7 93.2 */* 100Tenericutes Ten662F/Ten862R 95.0 96.5 0 and 10/0 and 2 100Betaproteobacteria Beta979F/Beta1130R 98.4 94.2 0–2/* 95Epsilonproteobacteria Epslion940F/Epsloin1129R 99.7 92.1 */* 100Delta- and Gammaproteobacteria Gamma877F/Gamma1066R 76.4 96.1 0 and 3/* 90Universal 926F/1062R 99.1a The percentage matches within target groups obtained by using the online tool Probe Match within the RDP-II database.b The amplification efficiency of the primer uses the fecal DNA as the template.c The distance of a specific nucleotide(s) from the 3= end of the primer is reported, with 0 being the nucleotide at the 3= termini. *, the specificity is given by a combination ratherthan a single nucleotide.d That is, the percentage of qPCR products belonging to the target group based on sequencing and classified by using the RDP-II database.

TABLE 4 Comparison of bacterial populations in fecal samples from C57BL/6 mice as determined by qPCR and HTSa

Target group

Mean SD Pd

qPCRa (%) HTSb (%) HTSc (%) qPCR vs HTS HTS vs HTS

Bacteroidetes 77.26 6.64 81.50 2.7 62.92 2.85 0.33 *Actinobacteria 0.13 0.11 0.20 0.20 0.11 0.03 0.51 0.28Firmicutes 13.95 6.02 13.97 2.8 20.73 1.21 0.99 *Deferribacteres 0.44 0.54 0.01 0.01 0.48 0.20 0.22 0.06“Candidatus Saccharibacteria” 0.42 0.24 0.27 0.11 0.13 0.06 0.35 *Tenericutes 0.78 0.44 0.87 0.15 6.77 2.45 0.74 *Verrucomicrobia 0.37 0.34 0.03 0.05 0.04 0.01 0.14 0.63Proteobacteria 2.78 0.41 2.43 0.06 2.83 0.60 0.19 0.14Other 3.63 2.16 0.97 0.40 6.00 0.61 0.13 *a Wild-type C57BL/6 mouse fecal samples were collected from 8-week-old mice in our study (n � 6).b Wild-type C57BL/6 mouse fecal samples were collected from 8-week-old mice in our study (n � 3).c These results were obtained from Carvalho et al. (27). The mice were 12 weeks old (n � 6).d P value analysis was done using ANOVA. Statistical significance (P � 0.05) is denoted by an asterisk (*).

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flammation (Fig. 3D). Therefore, real-time PCR detection withour designed primer set can be applied successfully on thescreening, tracking, and evaluation for IBD and related dis-eases.

DISCUSSION

Gut microbiota plays an important role in the pathogenesis of IBD(31–33). Mice are the most commonly used animals for studyinggut microbiota-related disease. Here, we developed phylum- andclass-specific primers to assess the population structure of the pre-dominant bacteria in mouse intestinal microbiota. In comparisonto previously published taxon-specific quantitative analysis, thesenew primers possess similar specificity but largely increase thecoverage of the taxon they target. Furthermore, an important ad-vantage of the assay presented here lies in the ability to use allprimer pairs with the same highly specific annealing temperature,which significantly facilitates data acquisition. However, due tothe variability in rRNA operon copy number in different bacteria,the proportion of 16S rRNA gene copies that the qPCR assay pro-vides cannot be directly transformed into the number of cells (34).Nevertheless, qPCR is an accurate method to determine the abun-dance of DNA sequences that can be used to estimate the distri-bution of bacteria (12). The real-time PCR system with universalprimers and specific probes presented here provides an accurateand stable method to assess bacterial concentrations in mousefeces. We want to emphasize that the efficiencies of the primerspresented here need to be recalculated in individual laboratories,since their values are known to be influenced by the presence ofPCR inhibitors or instrumental factors.

High-throughput 16S rRNA gene sequencing is one of themost effective means widely used to examine the complexity of thegut microbiota. It is important to compare and contrast qPCRwith HTS to validate this method for genetic fecal screening. It isclear that there is a striking degree of similarity among the Actino-bacteria, Firmicutes, Tenericutes, and Proteobacteria proportions

identified by qPCR and HTS, as shown in Table 4. Although somedeviation in the proportions of Bacteroidetes, Deferribacteres, andVerrucomicrobia was detected in the two analytical methods, thechanges in intestinal microbiota in the inflammatory state relativeto the normal state remained highly consistent (Fig. 3) betweenthese two detection methods. We found the targeted qPCR assayto have greater values in low-abundance bacterial groups than didthe HTS method, such as 0.42% versus 0.27% (“Candidatus Sac-charibacteria”) or 0.37% versus 0.03% (Verrucomicrobia), respec-tively, in the qPCR-versus-HTS detection rates. Taken together,the results of the qPCR and HTS approaches tested here demon-strate that the two methods are capable of generating high-fidelitydata sets with no statistical difference between them. AlthoughHTS technologies might give a more precise and a deeper scale atthe genus level, our qPCR assay provides a relatively inexpensiveplatform for high-throughput detection and analysis.

Although the DNA stool kit for stool DNA isolation used in ourstudy may give an under-representation of Gram-positive bacte-ria, the overall taxonomic groups represented within the mousefeces were similar to previous findings. The bacterial phyla Bacte-roidetes and Proteobacteria could be specifically detected by ourdesigned primers in both physiological and pathological states inthe gut. It should be remembered that the microbiota shifts aresubtle and generally only visible at the genus level in some cases,such as in dietary interventions; the primers cannot detect changesin microbiota at the genus level. However, microbiota differencescan be observed at the phylum level in many intestinal diseases,such as IBD. The qPCR method developed in our study effectivelydetected the dynamic changes of gut microbiota at the phylumlevel. We used this method for rapid screening of the fecal samplefrom different transgenic mice. It successfully predicted the sam-ple with potential IBD. Next, we plan to track the changes of in-testinal microbiota using qPCR detection in the process of growthin TgD mice in order to investigate the relationship between themicrobiota and the immune system in IBD. Of course, qPCR can

FIG 2 Microbiota composition analysis of a TNBS model in mice. (A) Gross picture of colons from two groups of model mice. (B) Histopathologic changes inindividual colons (magnification, �40) were shown in representative H&E-stained sections. Epithelial damage and leukocyte infiltration was observed in theTNBS model group. (C) Fecal microbiota detected by qPCR. The proportions of predominant bacterial groups are presented as means SDs (n � 6), analysisof variance (ANOVA) was performed, and statistical significance is denoted by an asterisk (*, P � 0.05). The relative abundances of phyla in each of the samplegroups are indicated in the upper panel.

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be a powerful tool used in the research of gut-related diseases.Many environmental factors, including diet and drugs, may influ-ence the microbiota and shape gut physiology and disease patho-genesis. qPCR would be important for assessing changes in the gutmicrobiota and evaluating its impact on IBD susceptibility at agreatly reduced cost compared to that of HTS. Further studies arealso needed to determine whether changes in particular microbi-ota species induced by inflammation may impact progression tocolorectal cancer. Perhaps the qPCR assay can be applied to theearly detection of colorectal cancer.

ACKNOWLEDGMENTS

This study was supported by the Doctoral Station Science Foundationfrom the Chinese Ministry of Education (grant 20130091130003), theChinese National Nature Sciences Foundation (grant 81421091), the Ji-angsu Provincial Nature Science Foundation (grant BE2013630), and theBureau of Science and Technology of Changzhou, Jiangsu, China (grantsCZ20130011, CE20135013, CZ20120004, CM20122003, and WF201207).

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