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Keywords:

  • Crohn's disease;
  • inflammatory bowel disease;
  • phylogenetic custom microarray;
  • microbial diversity;
  • 16S rDNA;
  • intestinal microbiology;
  • real-time PCR

Abstract

  1. Top of page
  2. Abstract
  3. MATERIALS AND METHODS
  4. RESULTS
  5. DISCUSSION
  6. REFERENCES
  7. Supporting Information

Background:

A custom phylogenetic microarray composed of small subunit ribosomal RNA probes, representing ≈500 bacterial species from the human and animal gut, was developed and evaluated for analysis of gut microbial diversity using fecal samples from healthy subjects and Crohn's disease (CD) patients.

Methods:

Oligonucleotide probes (≈40 mer) used on the microarray were selected from published articles or designed with the “GoArray” microarray probe design program using selected bacterial 16S rRNA sequences. Fecal 16S rDNA from individual samples of six healthy subjects and six CD patients were used as template to generate fluorescently labeled cRNA that was hybridized to the microarray. Differences revealed by the microarray in relative abundance of microbial populations between healthy and diseased patients were verified using quantitative real-time polymerase chain reaction (PCR) with species-specific primer sets.

Results:

The microarray analyses showed that Eubacterium rectale, Bacteroides fragilis group, B. vulgatus, Ruminococcus albus, R. callidus, R. bromii, and Faecalibacterium prausnitzii were 5–10-fold more abundant in the healthy subjects than in the CD patients, while Enterococcus sp., Clostridium difficile, Escherichia coli, Shigella flexneri, and Listeria sp. were more abundant in the CD group.

Conclusions:

The microarray detected differences in abundance of bacterial populations within the phylum Firmicutes that had been reported previously for the same samples based on phylogenetic analysis of metagenomic clone libraries. In addition, the microarray showed that Enterococcus sp. was in higher abundance in the CD patients. This microarray should be another useful tool to examine the diversity and abundance of human intestinal microbiota. (Inflamm Bowel Dis 2010)

The composition of the human intestinal microbiota is recognized to be important in maintaining an individual's health,1, 2 and the dysbiosis of the enteric microbiota in human inflammatory bowl disease (IBD) may be a result of defective regulation of mucosal immune interactions.3 The interaction between gut microbes and host immune response has been recognized as a potential factor in the development of IBDs such as Crohn's disease (CD),4 which may derive from an underlying genetic susceptibility that results in an impaired mucosal immunity, and it has been reported that 17–25% of CD patients have mutations in the NOD2/CARD15 gene, which regulates host responses to bacteria.5 However, the precise microbial species, metabolites, or functional genes involved in initiation or sustaining the disease remain unknown. Recently, Manichanh et al6 reported that the fecal microbiota of healthy subjects contained a markedly increased diversity and abundance of Firmicutes, and the Clostridium leptum phylogenetic group was in significantly higher numbers in healthy subjects compared with CD patients. Some of these bacteria produce short-chain fatty acids, including butyrate, which is a source of energy for colonic enterocytes and ameliorates inflammation.7 Therefore, shifts in the population of certain beneficial or harmful gut bacteria in patients with IBD may result in potentiation or suppression of the disease.

Oligonucleotide microarray is a powerful tool that can be used for simultaneous analysis of thousands of genes or target DNA sequences on a single slide,8 but until recently most 16S rDNA based microarrays were employed with a restrictive number of probes.8–10 The technology has now evolved to high-density probe platforms (10,000–300,000 probes per microarray) that provide quantitative information on the broader taxonomic composition of diverse microbial populations.11, 12 However, there are still opportunities to improve the specificity of detection at the species level for bacteria resident in the human gastrointestinal tract because intestinal microbes from this ecosystem are extremely diverse at low taxonomic ranks. In this study we adopted a unique approach to develop and validate a new phylogenetic custom microarray. To increase specificity and sensitivity, we employed the GoArray program to design the majority of oligonucleotide probes used on the microarray.13 In addition, 40 mer oligonucleotide probes which were reported in the literature as species-specific were also used.12, 14 To validate the microarray we used pooled DNA from a group of six healthy subjects and six CD patients (ileum) whose microbial diversity and abundance had been characterized previously using 16S rDNA recovered from metagenomic clones of the DNA pools as well as fluorescent in situ hybridization (FISH) analysis of the respective fecal samples.6 Furthermore, major differences revealed by the microarray in bacterial species between the healthy and CD subjects were verified by quantitative real-time polymerase chain reaction (PCR) analysis using specific primers.

MATERIALS AND METHODS

  1. Top of page
  2. Abstract
  3. MATERIALS AND METHODS
  4. RESULTS
  5. DISCUSSION
  6. REFERENCES
  7. Supporting Information

Samples

Forty-eight fosmid library clones and metagenomic DNA samples, described previously by Manichanh et al,6 were used for the validation and comparison with the custom microarray. Briefly, a pool of 48 fosmid library clones containing known 16S rRNA genes (Supporting Table 1) and one pool each of DNA isolated directly from the feces of six healthy subjects (referred to as the healthy pool) and the feces of six patients with CD (the CD pool) were obtained from INRA-UEPSD (France). These two pooled samples were used to optimize the specificity and sensitivity of the microarray. Finally, each of the 12 fecal DNA sample (six healthy and six CD) was used for individual analysis. Clinical information on the CD patients is provided in the article by Manichanh et al.6

Oligonucleotide Probe Design and Microarray Fabrication

A custom phylogenetic microarray consisting of eubacterial and archaeal probes was designed based on 16S rRNA gene sequences from gastrointestinal bacteria. Initially, a search for bacterial sequences previously identified as gastrointestinal microbes (human and animal) was performed in 2006 on the Entrez Nucleotide database maintained at the National Center for Biotechnology Information (NCBI) website (http://www.ncbi.nlm.nih.gov/). The resultant bacterial 16S rRNA sequences (≈400) were downloaded to the ARB phylogenetic database program.15 Individual probes were then designed using the GoArray oligonucleotide program,13 which generates probes of both enhanced specificity and sensitivity. Briefly, the GoArray probe design program employs a strategy of identifying two short oligonucleotide sequences (17 or 18 nt) that are complementary to two separate regions of the same target. A short random linker (4 or 6 nt) is inserted between the two specific sequences to form a composite probe. The resultant oligonucleotide probe sequence length is reasonably long (≈40 nt). Stable hybridization between the composite probe and its nucleic acid target induces the formation of a loop in the target sequence. In addition, 40 nt probes were also included on the microarray that were identified as specific species by way of a literature search of published articles12, 14 as well as several reference probes that served as internal standards and for normalization of the data. The reference probes targeted different sequences within the 1700–2000 bp region of the human mitochondrial gene (positive controls) and amplicons of this region were added to samples for normalization of data, while probes that targeted sequences outside this region acted as negative controls for determining the level of background signal. Furthermore, the probes designed using the GoArray program as well as those selected from the literature were also checked for specificity using the “Probe Match” function of ARB software. The oligonucleotide probes designed for the microarray represented diverse taxonomic groups, with specificities ranging from species to phylum levels, but the majority were species-specific. After validation studies using known pooled fosmid library clones and pooled fecal DNA samples, some probes were redesigned and updated using the same methods. The final oligonucleotide probes were synthesized in situ onto a 4 × 2K custom microarray by Combimatrix (Mukilteo, WA) with three replicates of each probe randomly distributed across the array to reduce biases caused by spatial variations.

Sample Preparation and Labeling

Since Palmer et al12 reported that labeled single-stranded cRNA allowed for greatly enhanced specificity when compared to double-stranded DNA, we modified this protocol. The healthy and the CD pools of DNA samples were used as template for amplification of the 16S rRNA gene using the universal primer set 27F (5′-AGAGTTTGATCMTGGCTCAG-3′) and T7/1492R (5′-TCTAATACGACTCACTATAGGGGGYTACCTTGTTACGACTT-3′; the underlined region is the T7 primer).16 The PCR conditions consisted of initial denaturation for 5 minutes at 95°C, 30 cycles of amplification (30 sec at 94°C, 30 sec at 58°C, and 90 sec at 72°C), and final elongation at 72°C for 5 minutes using 100 ng template DNA per reaction. An internal DNA standard was generated to serve as a reference between samples and to determine any variation within and between individual microarrays. The DNA standard homologous to the ≈1700–2000 bp region of human mitochondrial 16S rRNA gene was amplified from human blood DNA samples using the primer set of Mito F (5′-TACTACCAGACAACCTTAGC-3′)17 and T7/Mito R (5′-TCTAATACGACTCACTATAGGGGTTTCGGGGGTCTTAGCTTT-3′, in this study). The PCR condition was the same as described above except for the elongation step which was for 30 sec at 72°C. The PCR amplicons corresponding to 1500 bp 16S rDNA and 300 bp human mitochondrial 16S rDNA were confirmed by agarose (1% w/v) gel electrophoresis. The same amount of PCR products (500 ng) generated from samples or standard DNA were purified with the MinElute PCR purification kit (Qiagen, Valencia, CA,), and then added as a template for in vitro transcription-based synthesis of single-stranded RNA (cRNA) using the MEGAScript T7 in vitro transcription kit (Ambion, Austin, TX). After purification with a MEGAclear Kit (Ambion), 1 μg of the sample cRNA and 140 ng of standard cRNA were labeled at the same time using Label IT μArray Cy5 reagent (Mirus, Madison, WI) for 1 hour at 37°C while protected from light and then 0.1 volumes of the 10× stop reagent were added (Mirus) to terminate the labeling reaction. A portion of the labeled cRNA (25 μL) was fragmented using 5× fragmentation buffer (Mirus) at 94°C for 15 minutes so that the effects of fragmentation on signal intensity could be evaluated on the microarray. Without further purification steps which compromised signal intensity, 6 μL (≈120 ng) of the labeled cRNA samples in 24 μL hybridization solution (see below) were hybridized with the microarray at 42°C overnight.

Microarray Hybridization

The microarray chip was initially incubated with nuclease free water at 65°C for 10 minutes, then incubated in “prehybridization” solution as per the Combimatrix hybridization and imaging protocol. The prehybridization solution contained 6× SSPE (1 M NaCl, 0.05 M phosphate, 5 mM EDTA pH 7.0), 0.05% Tween-20, 20 mM EDTA, 5× Denhardt's solution, denatured salmon sperm DNA (100 ng/μL), and 0.05% sodium dodecyl sulfate (SDS). The microarray chip was incubated with this solution at 42°C for 1 hour; this solution was then removed from the hybridization chamber. The chip was filled again with hybridization solution (6× SSPE, 0.05% Tween-20, 20 mM EDTA, 25% formamide, 100 ng/μL of salmon sperm DNA, 0.04% SDS, and 120 ng/μL labeled target cRNA (fragmented or nonfragmented) and incubated at 42°C overnight. After hybridization the chip was washed once in 6× SSPET solution (6× SSPE and 0.05% Tween-20) at 42°C for 5 minutes, once in 3× SSPET at room temperature for 1 minute, once in 0.5× SSPET at room temperature for 1 minute, and once in 2× phosphate buffered saline with 0.1% Tween-20 (PBST) at room temperature for 1 minute. After removing the PBST solution the microarray was scanned (see below) as per the manufacturer's recommendations. Following data acquisition, each microarray chip was stripped using a stripping kit (Combimatrix) at 65°C for 1 hour and used twice more to analyze different samples so that each sample was analyzed three times.

Signal Detection and Data Analysis

The microarray slides were scanned using an Axon Genepix 4000A microarray scanner (Axon Instruments, Union City, CA) at 100% laser power, ≈350–400 PMT photomultiplier sensitivity, and 5 μm resolution. The obtained images were analyzed directly using the GenePix Pro 6.0 program (Axon Instruments). All probes showing background (nonspecific) signal below the level of negative control probes were marked as “Flag Bad.” The resultant GenePix Results Format (GPR) files were exported to the GeneSpring 7.3 program (Agilent Technologies, Santa Clara, CA) for further analysis. The data were normalized by using the standard “one color” option of the GeneSpring program and probes that showed significant differences in signal intensity in replicates of the same sample were removed from the analysis. One-way analysis of variance (ANOVA) tests were performed to confirm significant differences between replicates of the same sample and between treatment groups. Subsequently, the filtered and normalized data were analyzed by the multivariate analysis tool, principal component analysis (PCA) using the GeneSpring program, which determined automatically the number of components in the PCA models.

Real-time PCR

The primers used for the real-time PCR are described in Table 1. The primers for quantifying R. albus, Enterococcus faecium-1 and E. faecium-2 were selected based on ARB 16S ribosomal sequence multiple alignments downloaded from the ribosomal database project (RDP II) and validated against isolated reference strains. To ascertain primer specificity the designed primers were then compared with sequences available at NCBI using BLAST search18 and against the RDP II and ARB databases using the probe match analysis function.15, 19 Real-time PCR analysis was performed on extracted DNA pools from healthy and CD fecal contents. Total microbial fecal DNAs (≈100 ng) were diluted 1:10 prior to use in real-time PCR assays to reduce possible inhibition. The amplification was performed using an iCycler Real-Time PCR Detection System (BioRad, Hercules, CA). The PCR reaction was performed in a final volume of 25 μL that contained 12.5 μL of 2× iQ SYBR Green Supermix (BioRad), 300 nM each of forward and reverse primer, ≈10 ng template DNA, and sterile water. The reaction conditions for amplification of DNA were 95°C for 10 minutes, followed by 40 cycles of 95°C for 15 seconds, 56°C for 30 seconds, and 72°C for 30 seconds. The fold ratio of a target organism relative to the total eubacterial population was calculated according to the equation of 2-ΔΔCt (ΔCt = specific bacteria mean Ct − total bacteria mean Ct; ΔΔCt = specific bacteria ΔCt mean of the healthy pool − specific bacteria ΔCt mean of the CD pool).

Table 1. Real-time PCR Primers Used in the Present Study
BacteriaForward Primer (5′-3′)Reverse Primer (3′-5′)Ref.Length (bp)
UniversalCGGCAACGAGCGCAACCC(1114f)CCATTGTAGCACGTGTGTAGCC(1275r)42130
Ruminococcus albusCAAAACCCTAAAAGCAGTCTTAGTTCG (1277f)GACGGGCGGTGTGTACAAG (1331r)This study131
Bacteroides fragilis groupATAGCCTTTCGAAAGRAAGAT (148f)CCAGTATCAACTGCAATTTTA (625r)43495
Clostridium difficileATTAGGAGGAACACCAGTTG (706f)AGGAGATGTCATTGGGATGT (994r)44307
Faecalibacterium prausnitziiGATGGCCTCGCGTCCGATTAG (223f)CCGAAGACCTTCTTCCTCC (420r)This study197
Enterococcus faecium-1CCACCGGAGCTTGCTCCACCGGAAA (75f)CCGTCAAGGGATGAACAGTTACTCTCA (446r)This study371
Enterococcus faecium-2TGGTTTTGATTTGAAAGGCGCTTTC (189f)CCGTCAAGGGATGAACAGTTACTCTCA (446r)This study257

RESULTS

  1. Top of page
  2. Abstract
  3. MATERIALS AND METHODS
  4. RESULTS
  5. DISCUSSION
  6. REFERENCES
  7. Supporting Information

Development and Optimization of Microarray

First, probes chosen for use on the microarray were comprised of 593 sequences from published literature and 163 designed using the GoArray program. These probes target species and higher taxonomic ranks of Firmicutes, Proteobacteria, Actinobacteria, and Bacteroidetes (34.7%, 29.6%, 11.1%, and 7.8%, respectively). Six positive control probes targeting human mitochondrial 16S rDNA were also included (see Supporting Data 1).

In order to optimize the specificity and sensitivity of the microarray we hybridized Cy5-dye-labeled cRNA synthesized from pools of 48 fosmid library clones containing known 16S rRNA genes at different temperatures (30°C, 37°C, 42°C, and 50°C). After comparing both sensitivity and specificity of the probes at different hybridization temperatures, we found the optimum temperature to be 42°C (data not shown) but some of the probes showed nonspecific hybridization and were thus removed from the microarray (see Supporting Data 2). To estimate relative abundance of microbial populations from microarray signal intensity, different amounts of the labeled cRNA synthesized from the two pools of fecal DNA were hybridized to the microarray and the curve representing relative signal intensity ratio was constructed. A strong correlation between sample concentration and signal intensity was achieved (R2 = 0.9985) (Supporting Fig. 1) but the larger error bars at the lowest sample concentration indicate that the relationship was not as strong for some probes but they were in the minority. A comparison of hybridization signal intensities between fragmented and unfragmented cRNA did not show any significant differences (Supporting Fig. 2). After removing the nonspecific probes identified in three validation studies and adding new probes, a new microarray with specific probes targeting members of Firmicutes, Proteobacteria, Actinobacteria, and Bacteroidetes (272, 229, 79, and 68 probes, respectively) was finally developed (see Supporting Data 3 for each probe target). This microarray was used in analyzing individual samples from the six healthy subjects and six CD patients.

Comparison of Bacterial Populations in the Healthy Subjects and CD Patients

After normalization and statistical modification of the data using GeneSpring, we recorded data for probes that showed greater than a 3-fold change in signal intensity between healthy and CD samples. In both of the pooled and the majority of individual subject samples, the microarray analysis showed that known butyrate-producing bacteria of the clostridial clusters XIVa including Eubacterium rectale and clostridial cluster IV (C. leptum subgroup, Faecalibacterium prausnitzii) were 5–10-fold higher in the healthy group than in the CD patients (Figs. 1, 2). Similarly, low G+C content Gram positives R. albus, R. callidus, and R. bromii within Firmicutes and Bacteroides fragilis and B. vulgatus within Bacteroidetes were also in higher abundance in the healthy group. These results matched well with the results of the published clone library study using the same pooled samples, which reported significantly lower abundance of C. leptum subgroup and related microbes in CD patients than in healthy subjects.6 On the other hand, Enterococcus sp., Lactobacillus fermentum, Clostridium difficile (Firmicutes), Shigella flexneri, and Listeria sp. (Proteobacteria) were more than 5-fold higher in abundance in the CD group (Figs. 1, 2). The PCA analysis showed no significant correlation between the healthy subjects and CD patients, but greater individual variations were observed among the CD patients than among the healthy subjects, with CD1 and CD5 being outliers (Fig. 3). In the case of CD5, C. difficile and some of Enterococcus sp. (i.e., E. faecium, E. faecalis, E. gallinarum, and E. solitarius) showed very high abundance, and F. prausnitzii, Dorea longicatena (clostridial cluster IV and XIVa, respectively), Prevotella ruminicola (Bateroidetes), and Bifidobacterium sp. (Actinobacteria) were relatively very low in both CD5 and CD1 (Fig. 2).

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Figure 1. Relative changes in bacterial species from pooled fecal samples of healthy subjects versus CD patients on the microarray. Bacterial species showing greater than 5-fold differences in probe signal-intensity between healthy subjects and CD patients are presented. Black and white bars indicate probes with higher signal intensity in the healthy subjects and CD patients, respectively. The bacterial names against probe identities are to the left of zero on the x axis.

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Figure 2. Differential signal intensity of probes on the custom microarray in individual CD patients versus healthy subjects. The selected probes represent those bacterial species showing large differences in relative abundance between six CD patients and six healthy subjects. Black and blue bars represent CD patients and healthy subjects, respectively. The gradient arrows represent relative signal intensity between CD patients and healthy subjects sorted by the fold change option of GeneSpring program. Faecalibacterium prausnitzii is represented by several probes with different “signature” sequences for this species. *Butyrate-producing bacteria includes clostridium clusters IV and XIVa. [Color figure can be viewed in the online issue, which is available at www.interscience.wiley.com.]

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Figure 3. Principal component analysis (PCA) of the microarray data from six healthy subjects (H) and six CD patients. [Color figure can be viewed in the online issue, which is available at www.interscience.wiley.com.]

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Comparison Between Microarray and Real-time PCR Analyses

In order to validate the microarray data, we performed real-time PCR which can provide both confirmation of the microbial detection and quantification from the environmental samples.20 Quantification of four species (i.e., R. albus, F. prausnitzii, C. difficile, and E. faecium) and the B. fragilis related group that exhibited significant difference in microarray signal intensities between the healthy subjects and the CD patients showed identical trends using real-time PCR (Fig. 4). Both the real-time PCR and microarray showed that E. faecium was ≈15–20-fold more abundant in the CD patients than in the healthy subjects, exhibiting the greatest difference in abundance between the pools of these two groups. This result suggests that the microarray analysis as employed in this study might be quantitative in examining bacterial populations among samples and reveal particular microbial species which change in CD but might not be detected using other molecular approaches.

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Figure 4. A comparison of real-time PCR and microarray analysis of the fold difference in related bacterial species in pooled healthy subjects and CD patients. White (real-time PCR) and black (microarray) bars represent the method of data generation. PCR data for E. faecium was generated using two different primer sets. Bar above (healthy subjects) and below (CD) on the x axis represents bacterial species in higher abundance in the respective group.

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DISCUSSION

  1. Top of page
  2. Abstract
  3. MATERIALS AND METHODS
  4. RESULTS
  5. DISCUSSION
  6. REFERENCES
  7. Supporting Information

In this study we developed a phylogenetic microarray for analysis of human intestinal microbiota. The microarray was validated by hybridization with 48 sequenced fosmid clones (Supporting Tables 1, 2) and comparison analysis with quantitative real-time PCR (Fig. 4). The microarray analysis was consistent with the published findings from the fosmid libraries of the same pooled community DNA samples, which showed that Firmicutes was higher in abundance in the healthy subjects than in the CD patients.6 Since Guschin et al21 described the use of phylogenetic microarray to detect and quantify microbes in environmental samples, the technique has gained recognition and acceptance as an additional tool for high-throughput microbial diversity analysis. Palmer et al12 developed a DNA oligonucleotide microarray composed of 10,462 probes to target 1590 bacterial and 39 archaeal species for analysis of commensals and medically relevant microbes from the human gastrointestinal tract and they provided quantitative information on the taxonomic composition of diverse microbial populations. More recently, a phylogenetic microarray comprising 4800 oligonucleotide “HITChip” has been developed which provides broad coverage of the human gastrointestinal microbiota.22 DeSantis et al11 reported a novel microarray containing 297,851 probes that target the 16S rDNA of 842 prokaryotic subfamilies and demonstrated its utility on 16S rRNA gene amplicons generated from aerosol, soil, and water. However, it is still challenging to develop robust microarrays that have species-level specificity. Such microarrays are especially useful for analysis of bacteria resident in the human gastrointestinal tract, which contains great species richness within several predominant phyla. The microarray described in this study does not have the depth of taxonomic coverage described for the “HITChip”22 because there is a physical limitation of 2240 probes on the Combimatrix 4× 2K microarray chip. Because of this limitation we chose to include a higher proportion of Proteobacteria probes on the microarray relative to gut diversity due to the potential role of this phylum in IBD. Furthermore, several species including Anaerostipes, Anaerotruncus, Subdoligranulum were not included because sequence information unavailable in public databases when the probe design was being performed. Although the GoArray probes comprised 21% of the total probes on the microarray, this approach facilitated the design of species-specific probes which could not be achieved for some target species through the conventional design of a continuous 40 nt probe. This microarray does not generate truly quantitative data on bacterial population size, limiting the comparisons which may be made with data generated using other molecular approaches.

Since Palmer et al12 found specificity increased greatly when they hybridized labeled cRNA instead of DNA, we used labeled single-strand cRNA rather than double-strand DNA for hybridization on the microarray. In addition, we examined whether fragmentation of labeled cRNA improved sensitivity but there was no significant difference from unfragmented material, which was in accord with the findings of Hoen et al.23 They reported that there was no significant difference between average signals from fragmented cRNA (40–200 nt) and signals from intact cRNA (500–2000 nt) on their oligonucleotide microarray. However, in the case of native rRNA, it has been reported that direct labeling and fragmentation of native rRNA (20–100 nt) by using NaOH and ZnCl2 catalyzed methods not only enhanced hybridization signal intensities but also reduced false-negative signals.24 Based on our studies and these published results we used fragmented single-stranded cRNA to hybridize samples on the microarray. It is possible that bias could have occurred in the PCR amplification of different bacterial species but comparisons of differences in relative population abundance between CD and healthy samples were not invalidated by any PCR bias.

Manichanh et al6 showed that the fecal microbiota of patients suffering from CD contained a markedly reduced abundance and diversity of Firmicutes, and in particular, the C. leptum group was significantly less abundant in CD patients than in healthy subjects. Using the microarray, we found that some bacteria of Firmicutes were 5–10-fold more abundant in the healthy group than in the CD patients. These included Eubacterium rectale of the Lachnospiraceae, Ruminococcus albus, R. callidus, R. bromii, and F. prausnitzii of the Ruminococcaceae. The metagenomic data of Manichanh et al also showed that the C. coccoides group (includes E. rectale) was significantly higher in the healthy group. Based on a report by Duncan et al,25 these bacteria were included in the clostridial clusters IV and XIV, many of which can utilize and metabolize a wide range of carbohydrate substrates including starch and/or produce butyrate in the colon. This finding is interesting because butyrate is considered beneficial to colonic health as the main energy source for colonic epithelial cells26 and it inhibits proinflammatory cytokine mRNA expression in the mucosa.27 Also recently, Sokol et al28 reported that the species F. pausnitzii from the C. leptum group was significantly underrepresented in colitis compared with the fecal microbiota composition of patients with colitis to that of healthy subjects. This result should be another crucial evidence that lower counts of F. prausnitzii are consistently associated with a reduced protection of the gut mucosa.

In contrast, the CD patients carried Enterococcus sp., Clostridium difficile, Lactobacillus fermentum, E. coli, Shigella flexneri, and Listeria sp. (Proteobacteria) in greater abundance (more than 5-fold) than the healthy subjects. It is interesting that L. fermentum and bifidobacteria appeared to be in high abundance in some CD patients, as these bacteria are generally regarded as beneficial and used as probiotics. L. fermentum has some peculiar properties compared to other probiotics bacteria, such as the ability to produce antioxidants (glutathione) and induce the growth of other lactobacilli species and prevent colitis.29 However, this should be interpreted with caution because the probiotic strains may possess beneficial attributes that differ from the bacteria detected in this study. Also Enterococcus sp., particularly E. faecium, which belongs to the same cluster as L. fermentum, has not been identified previously as being in high abundance in CD patients. Over the last two decades E. faecalis has emerged as a pathogen, especially of nosocomial infections.30 Although E. faecalis can suppress the proliferation of Listeria sp.31, 32 and infant intestinal E. faecalis led to downregulation of inflammatory responses in human intestinal cell lines,33 many reports have shown that E. faecalis can induce IBD in interleukin-10 knockout mice34 and activate macrophages by releasing proinflammatory cytokines (TNF-α, IL-6, and IL-12 p70).35 Moreover, Huycke and Wang36, 37 reported that the intestinal commensal E. faecalis is unique in producing extracellular superoxide (Omath image), and the superoxide can induce macrophage cyclooxygenase-2 (COX-2). Eventually, chromosomal instability (CIN) in mammalian cells was promoted by COX-2, leading to colorectal cancer. Indeed, Balamurugan et al38 reported that E. faecalis increased in the feces of colon-cancer patients and butyrate producers were decreased, these shifts in colonic bacterial population could be associated with or potentially lead to epithelial cell damage. In our microarray analysis, we found both E. faecalis and E. faecium were in higher abundance in CD patients. Interestingly, these two species are phylogenetically close based on 16S rRNA gene sequences (96% similarity) but E. faecium was present in higher numbers than E. faecalis using both microarray analysis and species-specific real-time PCR (data not shown). Although E. faecium has not been implicated in any intestinal immune response, this species also has been shown to account for the majority of infections caused by Enterococcus spp.39 and to produce extracellular superoxide.40 In our real-time PCR analysis of several bacterial species, E. faecium and C. difficile showed very high abundance in one of the six CD patients (CD5). C. difficile has been reported previously as being present in recurrent CD patients.41 In the PCA plot (Fig. 3), the CD5 sample was also significantly different than the other five CD samples. This may suggest that bacterial populations in CD patients could be different based on the stage and severity of the disease and give crucial information about the higher variability in microbial composition between CD patients and healthy individuals (Fig. 3). The microarray and qPCR analysis indicated that the B. fragilis group was higher in the healthy subjects, which is similar to the metagenomic data from Manichanh et al,6 although their FISH analysis of the total Bacteroides showed no difference between the groups. Nevertheless, future studies are required to determine the implication of E. faecium in CD. Our preliminary results suggest that F. prausnitzii and E. faecium might serve as “indicator” bacteria of a dysbiosis in patients. It should also be noted that the microarray approach does not generate truly quantitative data on bacterial population size but can be effectively used to focus the attention on discrete populations for quantitative analysis such as real-time PCR.

REFERENCES

  1. Top of page
  2. Abstract
  3. MATERIALS AND METHODS
  4. RESULTS
  5. DISCUSSION
  6. REFERENCES
  7. Supporting Information

Supporting Information

  1. Top of page
  2. Abstract
  3. MATERIALS AND METHODS
  4. RESULTS
  5. DISCUSSION
  6. REFERENCES
  7. Supporting Information

Additional supporting information may be found in the online version of this article.

FilenameFormatSizeDescription
IBD_21319_sm_suppdata1.xls192KSupporting Information 1
IBD_21319_sm_suppdata2.xls30KSupporting Information 2
IBD_21319_sm_suppdata3.xls183KSupporting Information 3
IBD_21319_sm_suppfig1.doc21KSupporting Figure 1
IBD_21319_sm_suppfig2.doc49KSupporting Figure 2

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