The Fecal Microbiota of Irritable Bowel Syndrome Patients Differs Significantly From That of Healthy Subjects

Abstract

Background & Aims: Irritable bowel syndrome (IBS) is a significant gastrointestinal disorder with unknown etiology. The symptoms can greatly weaken patients’ quality of life and account for notable economical costs for society. Contribution of the gastrointestinal microbiota in IBS has been suggested. Our objective was to characterize putative differences in gastrointestinal microbiota between patients with IBS and control subjects. These differences could potentially have a causal relationship with the syndrome. Methods: Microbial genomes from fecal samples of 24 patients with IBS and 23 controls were collected, pooled in a groupwise manner, and fractionated according to their guanine cytosine content. Selected fractions were analyzed by extensive high-throughput 16S ribosomal RNA gene cloning and sequencing of 3753 clones. Some of the revealed phylogenetic differences were further confirmed by quantitative polymerase chain reaction assays on individual samples. Results: The coverage of the clone libraries of IBS subtypes and control subjects differed significantly (P < .0253). The samples were also distinguishable by the Bayesian analysis of bacterial population structure. Moreover, significant (P < .05) differences between the clone libraries were found in several bacterial genera, which could be verified by quantitative polymerase chain reaction assays of phylotypes belonging to the genera Coprococcus, Collinsella, and Coprobacillus. Conclusions: The study showed that fecal microbiota is significantly altered in IBS. Further studies on molecular mechanisms underlying these alterations are needed to elucidate the exact role of intestinal bacteria in IBS. Background & Aims: Irritable bowel syndrome (IBS) is a significant gastrointestinal disorder with unknown etiology. The symptoms can greatly weaken patients’ quality of life and account for notable economical costs for society. Contribution of the gastrointestinal microbiota in IBS has been suggested. Our objective was to characterize putative differences in gastrointestinal microbiota between patients with IBS and control subjects. These differences could potentially have a causal relationship with the syndrome. Methods: Microbial genomes from fecal samples of 24 patients with IBS and 23 controls were collected, pooled in a groupwise manner, and fractionated according to their guanine cytosine content. Selected fractions were analyzed by extensive high-throughput 16S ribosomal RNA gene cloning and sequencing of 3753 clones. Some of the revealed phylogenetic differences were further confirmed by quantitative polymerase chain reaction assays on individual samples. Results: The coverage of the clone libraries of IBS subtypes and control subjects differed significantly (P < .0253). The samples were also distinguishable by the Bayesian analysis of bacterial population structure. Moreover, significant (P < .05) differences between the clone libraries were found in several bacterial genera, which could be verified by quantitative polymerase chain reaction assays of phylotypes belonging to the genera Coprococcus, Collinsella, and Coprobacillus. Conclusions: The study showed that fecal microbiota is significantly altered in IBS. Further studies on molecular mechanisms underlying these alterations are needed to elucidate the exact role of intestinal bacteria in IBS. See editorial on page 340. See editorial on page 340. Irritable bowel syndrome (IBS) is a common functional gastrointestinal (GI) disorder with a worldwide prevalence of 10%–20%.1Longstreth G.F. Thompson W.G. Chey W.D. Houghton L.A. Mearin F. Spiller R.C. Functional bowel disorders.Gastroenterology. 2006; 130: 1480-1491Abstract Full Text Full Text PDF PubMed Scopus (4031) Google Scholar Although IBS does not predispose patients to severe illness, it can have significant effects on their well-being and need for medical consultation. The symptoms of IBS vary with affected individuals and include abdominal pain or discomfort, irregular bowel movements, flatulence, and constipation or diarrhea. Patients with IBS can be grouped into 3 subtypes according to their bowel habit.1Longstreth G.F. Thompson W.G. Chey W.D. Houghton L.A. Mearin F. Spiller R.C. Functional bowel disorders.Gastroenterology. 2006; 130: 1480-1491Abstract Full Text Full Text PDF PubMed Scopus (4031) Google Scholar, 2Drossman D.A. Corazziari E. Talley N.J. Thompson W.G. Whitehead W.E. Rome II: the functional gastrointestinal disorders. 2nd ed. Degnon Associates, McLean, VA2000Google Scholar These are diarrhea predominant (IBS-D), constipation predominant (IBS-C), and mixed subtype (IBS-M), which refers to patients with symptoms of both diarrhea and constipation. The etiology of IBS is poorly understood. Miscellaneous causes, including physiologic features such as altered GI motility and visceral hypersensitivity, psychological stress and disturbances, low-grade inflammation, and bacterial gastroenteritis, have been associated with IBS.3Drossman D.A. Camilleri M. Mayer E.A. Whitehead W.E. AGA technical review on irritable bowel syndrome.Gastroenterology. 2002; 123: 2108-2131Abstract Full Text Full Text PDF PubMed Scopus (1228) Google Scholar The microbiota of the human GI tract constitutes a complex ecosystem that is involved in the health and physiologic functions of the host. Alterations of the normal GI microbiota have been shown to take place as a result of antibiotic therapies4Mellon A.F. Deshpande S.A. Mathers J.C. Bartlett K. Effect of oral antibiotics on intestinal production of propionic acid.Arch Dis Child. 2000; 82: 169-172Crossref PubMed Scopus (31) Google Scholar and the use of probiotics,5Surawicz C.M. Probiotics, antibiotic-associated diarrhoea and Clostridium difficile diarrhoea in humans.Best Pract Res Clin Gastroenterol. 2003; 17: 775-783Abstract Full Text Full Text PDF PubMed Scopus (111) Google Scholar in connection with various dietary strategies,6Hayashi H. Sakamoto M. Benno Y. Fecal microbial diversity in a strict vegetarian as determined by molecular analysis and cultivation.Microbiol Immunol. 2002; 46: 819-831PubMed Google Scholar and with obesity.7Bajzer M. Seeley R.J. Obesity and gut flora.Nature. 2006; 444: 1009-1010Crossref PubMed Scopus (157) Google Scholar, 8Ley R.E. Turnbaugh P.J. Klein S. Gordon J.I. Microbial ecology: human gut microbes associated with obesity.Nature. 2006; 444: 1022-1023Crossref PubMed Scopus (6687) Google Scholar Moreover, bacteria belonging to the normal microbiota seem also to be capable of causing disease in some individuals and may have a role in certain disorders, such as inflammatory bowel disease,9Wensinck F. Proceedings: the faecal flora of patients with Crohn’s disease.Antonie van Leeuwenhoek. 1975; 41: 214-215Crossref PubMed Scopus (22) Google Scholar, 10Seksik P. Rigottier-Gois L. Gramet G. Sutren M. Pochart P. Marteau P. Jian R. Dore J. Alterations of the dominant faecal bacterial groups in patients with Crohn’s disease of the colon.Gut. 2003; 52: 237-242Crossref PubMed Scopus (588) Google Scholar, 11Ott S.J. Musfeldt M. Wenderoth D.F. Hampe J. Brant O. Folsch U.R. Timmis K.N. Schreiber S. Reduction in diversity of the colonic mucosa associated bacterial microflora in patients with active inflammatory bowel disease.Gut. 2004; 53: 685-693Crossref PubMed Scopus (955) Google Scholar allergies,12Kirjavainen P.V. Apostolou E. Arvola T. Salminen S.J. Gibson G.R. Isolauri E. Characterizing the composition of intestinal microflora as a prospective treatment target in infant allergic disease.FEMS Immunol Med Microbiol. 2001; 32: 1-7Crossref PubMed Google Scholar, 13Watanabe S. Narisawa Y. Arase S. Okamatsu H. Ikenaga T. Tajiri Y. Kumemura M. Differences in fecal microflora between patients with atopic dermatitis and healthy control subjects.J Allergy Clin Immunol. 2003; 111: 587-591Abstract Full Text Full Text PDF PubMed Scopus (306) Google Scholar and IBS.14Balsari A. Ceccarelli A. Dubini F. Fesce E. Poli G. The fecal microbial population in the irritable bowel syndrome.Microbiologica. 1982; 5: 185-194PubMed Google Scholar, 15King T.S. Elia M. Hunter J.O. Abnormal colonic fermentation in irritable bowel syndrome.Lancet. 1998; 352: 1187-1189Abstract Full Text Full Text PDF PubMed Scopus (418) Google Scholar, 16Rodriguez L.A. Ruigomez A. Increased risk of irritable bowel syndrome after bacterial gastroenteritis: cohort study.BMJ (Clin Res Ed). 1999; 318: 565-566Crossref PubMed Google Scholar, 17Si J.M. Yu Y.C. Fan Y.J. Chen S.J. Intestinal microecology and quality of life in irritable bowel syndrome patients.World J Gastroenterol. 2004; 10: 1802-1805Crossref PubMed Scopus (189) Google Scholar Conventional culture-based methods provide an incomplete and biased picture of the biodiversity of intestinal microbiota, because the majority of the GI tract bacterial species cannot be cultivated.18Suau A. Bonnet R. Sutren M. Godon J.J. Gibson G.R. Collins M.D. Dore J. Direct analysis of genes encoding 16S rRNA from complex communities reveals many novel molecular species within the human gut.Appl Environ Microbiol. 1999; 65: 4799-4807PubMed Google Scholar To overcome these drawbacks, culture-independent molecular methods have been introduced. These methods are mainly based on ribosomal RNA (rRNA) genes that reflect the natural evolutionary relationships among different organisms. The microbial composition of human intestinal microbiota has been studied, for example, by sequencing the 16S rRNA genes of clones obtained by amplification with polymerase chain reaction (PCR) from fecal DNA preparations.19Wilson K.H. Blitchington R.B. Human colonic biota studied by ribosomal DNA sequence analysis.Appl Environ Microbiol. 1996; 62: 2273-2278PubMed Google Scholar However, multitemplate PCR amplification causes a bias in favor of dominant and low guanine-plus-cytosine (G+C) content bacteria.20Reysenbach A.L. Giver L.J. Wickham G.S. Pace N.R. Differential amplification of rRNA genes by polymerase chain reaction.Appl Environ Microbiol. 1992; 58: 3417-3418PubMed Google Scholar Fractionating the DNA preparations according to the sample G+C percentage (%G+C) has been shown to allow less abundant species as well as sequences with high G+C contents to be amplified by PCR when studying environmental bacterial samples.21Holben W.E. Harris D. DNA-based monitoring of total bacterial community structure in environmental samples.Mol Ecol. 1995; 4: 627-631Crossref PubMed Scopus (76) Google Scholar Fractionation according to the %G+C content thus enriches considerably the diversity of sequences obtained by cloning and sequencing. In this study, we applied the %G+C profiling and extensive 16S rRNA gene cloning and sequencing to compare the fecal microbiota of patients with IBS with that of control subjects using pooled samples to overcome individual variation not originating from the subjects’ IBS status. The combination of these 2 methods, applied for the first time for studying human gut microbiota, showed exceptionally high resolution and allowed a very detailed analysis of the bacterial populations. With this extensive molecular characterization, significant differences between the GI microbiota of symptomatically categorized patients with IBS and age- and sex-matched volunteers devoid of GI disturbances were revealed both in the microbial community structures (%G+C profiling) as well as on genus-level bacterial composition (cloning and sequencing). The detected divergences in the bacterial phylotypes could be verified from fecal DNA preparations of individual subjects already with a small primary set of quantitative real-time PCR (qPCR) assays using primers designed specifically for the phylogenetic differences found. In this study, we characterized the GI microbiota in the fecal samples of 24 patients with IBS and 23 sex- and age-matched control individuals. These samples were previously analyzed by qPCR with a set of primers targeting selected known GI bacterial groups.22Malinen E. Rinttilä T. Kajander K. Mättö J. Kassinen A. Krogius L. Saarela M. Korpela R. Palva A. Analysis of the fecal microbiota of irritable bowel syndrome patients and healthy controls with real-time PCR.Am J Gastroenterol. 2005; 100: 373-382Crossref PubMed Scopus (608) Google Scholar The patients with IBS were recruited by experienced physicians (Table 1). The patients fulfilled the Rome II criteria for IBS,23Thompson W.G. Longstreth G.F. Drossman D.A. Heaton K.W. Irvine E.J. Muller-Lissner S.A. Functional bowel disorders and functional abdominal pain.Gut. 1999; 45: II43-II47Crossref PubMed Scopus (2043) Google Scholar except for 3 subjects who reported slightly less than 12 weeks of abdominal pain during the preceding year. All patients with IBS had undergone clinical investigation and endoscopy or barium enema of the GI tract less than 1 year before being recruited. The patients were classified as having IBS-C, IBS-D, or IBS-M by a questionnaire following the Rome II subgrouping criteria,2Drossman D.A. Corazziari E. Talley N.J. Thompson W.G. Whitehead W.E. Rome II: the functional gastrointestinal disorders. 2nd ed. Degnon Associates, McLean, VA2000Google Scholar which have been recognized as valid also in the Rome III criteria.1Longstreth G.F. Thompson W.G. Chey W.D. Houghton L.A. Mearin F. Spiller R.C. Functional bowel disorders.Gastroenterology. 2006; 130: 1480-1491Abstract Full Text Full Text PDF PubMed Scopus (4031) Google Scholar The participants gave their written informed consent and were permitted to withdraw from the study at any time. For the IBS patients, the study protocol had the approval of the Human Ethics Committee at the Joint Authority for the Hospital District of Helsinki and Uusimaa. Healthy control subjects devoid of GI symptoms were recruited to form an age- and sex-matched control group for the patients with IBS (Table 1). Intestinal disturbances (including lactose intolerance and celiac disease) and ongoing antibiotic treatments were considered exclusion criteria for the control group.Table 1Characteristics of Patients With IBS and ControlsVariablePatients With IBSControlsAge (y), mean (range)47.3 (21–65)45.2 (26–64)Sex (F/M)19/516/7Predominant bowel habit, n (%) Diarrhea10 (42) Constipation8 (33) Mixed type6 (25)Exclusion criteriaPregnancyLactationOrganic GI diseaseSevere systematic diseaseMajor or complicated abdominal surgerySevere endometriosisDementiaGI symptomsOngoing antibiotic treatment+ All exclusion criteria of the patients with IBS Open table in a new tab A total of 24 patients with IBS classified as IBS-D (n = 10), IBS-C (n = 8), or IBS-M (n = 6) and 23 volunteer control subjects donated fecal samples. Bacterial genomic DNA was extracted as described by Apajalahti et al.24Apajalahti J.H. Särkilahti L.K. Mäki B.R. Heikkinen J.P. Nurminen P.H. Holben W.E. Effective recovery of bacterial DNA and percent-guanine-plus-cytosine-based analysis of community structure in the gastrointestinal tract of broiler chickens.Appl Environ Microbiol. 1998; 64: 4084-4088PubMed Google Scholar The DNA preparations were subsequently pooled (IBS-D, IBS-C, IBS-M, and control), centrifuged in a cesium chloride/bisbenzimidazole gradient, and divided into fractions within 5% intervals according to their G+C content.25Holben W.E. Jansson J.K. Chelm B.K. Tiedje J.M. DNA probe method for the detection of specific microorganisms in the soil bacterial community.Appl Environ Microbiol. 1988; 54: 703-711PubMed Google Scholar The %G+C fractions displaying the most variation between pooled DNA specimens were selected for further analysis (fraction 7 [%G+C, 25–30], fraction 10 [%G+C, 40–45], and fraction 13 [%G+C, 55–60]) (Figure 1). To study whether the differences observed in the %G+C profiles were significant, the divergent fractions were subjected to cloning and sequencing of the 16S rRNA gene. The selected fractions of 4 pooled samples were cloned and sequenced with a high-throughput protocol. The cloning was performed with the Qiagen PCR Cloning plus Kit (Qiagen, Hilden, Germany) by using independently 2 universal 16S rRNA gene PCR primer pairs. The first primer pair corresponded to the Escherichia coli 16S rRNA gene in the positions 8–27 base pairs (bp) and 1492–1512 bp,18Suau A. Bonnet R. Sutren M. Godon J.J. Gibson G.R. Collins M.D. Dore J. Direct analysis of genes encoding 16S rRNA from complex communities reveals many novel molecular species within the human gut.Appl Environ Microbiol. 1999; 65: 4799-4807PubMed Google Scholar, 26Hicks R.E. Amann R.I. Stahl D.A. Dual staining of natural bacterioplankton with 4′,6-diamidino-2-phenylindole and fluorescent oligonucleotide probes targeting kingdom-level 16S rRNA sequences.Appl Environ Microbiol. 1992; 58: 2158-2163PubMed Google Scholar and the second primer pair corresponded to the Escherichia coli 16S rRNA gene in the positions 7–27 bp and 1522–1541 bp.27Wang R.F. Kim S.J. Robertson L.H. Cerniglia C.E. Development of a membrane-array method for the detection of human intestinal bacteria in fecal samples.Mol Cell Probes. 2002; 16: 341-350Crossref PubMed Scopus (47) Google Scholar A PCR protocol of minimum amount of cycles (20 cycles for fraction 7 and 27 cycles for fractions 10 and 13) was applied for each fraction to avoid biases in favor of abundant templates. The 2 amplicons were mixed in a 1:1 molecular ratio before cloning, and a library of nearly 14,000 clones was constructed. From each fraction, the 5′ end of the 16S rRNA gene was sequenced from 384 clones with the sequencing primer corresponding to the E coli 16S rRNA gene position 536–518 bp.28Edwards U. Rogall T. Blocker H. Emde M. Bottger E.C. Isolation and direct complete nucleotide determination of entire genes Characterization of a gene coding for 16S ribosomal RNA.Nucleic Acids Res. 1989; 17: 7843-7853Crossref PubMed Scopus (2289) Google Scholar The BigDye terminator cycle sequencing kit (Applied Biosystems, Foster City, CA) and an ABI 3700 Capillary DNA Sequencer (GMI, Ramsey, MN) were used to analyze the products. All sequences were checked manually with the Staden program package.29Staden R. Beal K.F. Bonfield J.K. The Staden package, 1998 Methods in molecular biology. The Humana Press Inc., Totowa, Clifton, NJ2000: 115-130Google Scholar Putative chimeras were eliminated from sequences aligned with ClustalW.30Chenna R. Sugawara H. Koike T. Lopez R. Gibson T.J. Higgins D.G. Thompson J.D. Multiple sequence alignment with the Clustal series of programs.Nucleic Acids Res. 2003; 31: 3497-3500Crossref PubMed Scopus (4130) Google Scholar The number of operational taxonomic units (OTUs) was determined with DOTUR.31Schloss P.D. Handelsman J. Introducing DOTUR, a computer program for defining operational taxonomic units and estimating species richness.Appl Environ Microbiol. 2005; 71: 1501-1506Crossref PubMed Scopus (2127) Google Scholar Each sequence was checked against the EMBL Nucleotide Sequence Database with Fasta homology search program at http://www.ebi.ac.uk/fasta33/nucleotide.html.32Pearson W.R. Lipman D.J. Improved tools for biological sequence comparison.Proc Natl Acad Sci U S A. 1988; 85: 2444-2448Crossref PubMed Scopus (10130) Google Scholar Sequences with <95% similarity to existing entries in the EMBL Nucleotide Sequence Databases of prokaryotes and environmental samples were considered novel. These were further sequenced to nearly full-length 16S rRNA gene using the vector primers T7 and SP6 (Qiagen PCR Cloning plus Kit) and primers corresponding to the E coli 16S rRNA gene in the positions 908–928 bp and 1073–1053 bp.28Edwards U. Rogall T. Blocker H. Emde M. Bottger E.C. Isolation and direct complete nucleotide determination of entire genes Characterization of a gene coding for 16S ribosomal RNA.Nucleic Acids Res. 1989; 17: 7843-7853Crossref PubMed Scopus (2289) Google Scholar After the supplementary sequencing, they were reanalyzed with the Fasta and Ribosomal Database Project II (RDP II) Classifier Tool (http://rdp.cme.msu.edu/classifier/classifier.jsp)33Cole J.R. Chai B. Farris R.J. Wang Q. Kulam S.A. McGarrell D.M. Garrity G.M. Tiedje J.M. The Ribosomal Database Project (RDP-II): sequences and tools for high-throughput rRNA analysis.Nucleic Acids Res. 2005; 33: D294-D296Crossref PubMed Scopus (1259) Google Scholar to assign them into phyla. The statistical comparisons of the individual clone libraries representing the 3 different IBS symptom subtypes and the control were performed with 3 individual methods revealing different aspects: the ∫-LIBSHUFF program,34Schloss P.D. Larget B.R. Handelsman J. Integration of microbial ecology and statistics: a test to compare gene libraries.Appl Environ Microbiol. 2004; 70: 5485-5492Crossref PubMed Scopus (296) Google Scholar BAPS 4.1 program for Bayesian analysis of genetic population structure,35Corander J. Tang J. Bayesian analysis of population structure based on linked molecular information.Math Biosci. 2007; 205: 19-31Crossref PubMed Scopus (197) Google Scholar and the RDP II Library Compare Tool (http://rdp.cme.msu.edu/comparison/comp.jsp).33Cole J.R. Chai B. Farris R.J. Wang Q. Kulam S.A. McGarrell D.M. Garrity G.M. Tiedje J.M. The Ribosomal Database Project (RDP-II): sequences and tools for high-throughput rRNA analysis.Nucleic Acids Res. 2005; 33: D294-D296Crossref PubMed Scopus (1259) Google Scholar The ∫-LIBSHUFF analysis generates the homologous and heterologous coverage curves from the 16S rRNA clone libraries and thus analyzes the differences in the libraries based on Good’s formula of coverage.36Good I.J. The population frequencies of species and the estimation of population parameters.Biometrika. 1953; : 237-264Google Scholar In the Bayesian analysis, we used the unsupervised sequence classification option of BAPS 4.1 to discover homogeneous subgroups in the investigated microbiota. The resulting groups within each fraction were further analyzed using multidimensional scaling based on the average relative sequence dissimilarity to discover whether there are systematic differences in the sample composition between the control and the symptom subtypes.37Seber G.A.F. Multivariate observations. Wiley, Mississauga, Canada1984Crossref Google Scholar Furthermore, the RDP II Library Compare Tool33Cole J.R. Chai B. Farris R.J. Wang Q. Kulam S.A. McGarrell D.M. Garrity G.M. Tiedje J.M. The Ribosomal Database Project (RDP-II): sequences and tools for high-throughput rRNA analysis.Nucleic Acids Res. 2005; 33: D294-D296Crossref PubMed Scopus (1259) Google Scholar was used to test whether the corresponding sequence libraries diverged from each other at the genus level. The comparisons were run with a bootstrapping confidence threshold of 95%. The Library Compare Tool uses naive Bayesian rRNA classifier to assign sequences in the 16S rRNA gene libraries under comparison to taxa and subsequently estimates the significance of the observed differences.33Cole J.R. Chai B. Farris R.J. Wang Q. Kulam S.A. McGarrell D.M. Garrity G.M. Tiedje J.M. The Ribosomal Database Project (RDP-II): sequences and tools for high-throughput rRNA analysis.Nucleic Acids Res. 2005; 33: D294-D296Crossref PubMed Scopus (1259) Google Scholar From the output, the genus-level results were selected. For phylogenetic comparison of gene libraries, all sequences were aligned using the ARB program designed for 16S rRNA gene sequence database handling and data analysis.38Ludwig W. Strunk O. Westram R. Richter L. Meier H. Yadhukumar Buchner A. Lai T. Steppi S. Jobb G. Forster W. Brettske I. Gerber S. Ginhart A.W. Gross O. Grumann S. Hermann S. Jost R. Konig A. Liss T. Lussmann R. May M. Nonhoff B. Reichel B. Strehlow R. Stamatakis A. Stuckmann N. Vilbig A. Lenke M. Ludwig T. Bode A. Schleifer K.H. ARB: a software environment for sequence data.Nucleic Acids Res. 2004; 32: 1363-1371Crossref PubMed Scopus (5381) Google Scholar For some of the sections of the tree, where putative deviation could be seen between the pooled samples, qPCR primers were designed. The primers and their optimal annealing temperatures are shown in Table 2. The qPCR primers corresponded to phylotypes belonging to a particular species or resembling the given species by a denoted percentage: Ruminococcus torques, 91%; Clostridium cocleatum, 88%; Coprococcus eutactus, 97%; Ruminococcus torques, 94%; Collinsella aerofaciens; Bifidobacterium catenulatum; Lactobacillus farciminis; Lactobacillus gasseri; Streptococcus bovis; and to all eubacteria with universal primers.39Nadkarni M.A. Martin F.E. Jacques N.A. Hunter N. Determination of bacterial load by real-time PCR using a broad-range (universal) probe and primers set.Microbiology. 2002; 148: 257-266Crossref PubMed Scopus (1512) Google Scholar Quantitative PCR was performed with an iCycler iQ apparatus (Bio-Rad, Hercules, CA) using SYBR Green I detection chemistry according to Malinen et al.22Malinen E. Rinttilä T. Kajander K. Mättö J. Kassinen A. Krogius L. Saarela M. Korpela R. Palva A. Analysis of the fecal microbiota of irritable bowel syndrome patients and healthy controls with real-time PCR.Am J Gastroenterol. 2005; 100: 373-382Crossref PubMed Scopus (608) Google Scholar A set of 22 control samples and 24 IBS patient samples consisting of 10 IBS-D, 8 IBS-C, and 6 IBS-M subtypes were analyzed. All qPCR reactions were performed using triplicate parallel samples. For statistical analysis of the results, the control samples were compared with the IBS patient samples using the Mann–Whitney test.Table 2Real-Time PCR Primers for Selected Sequence GroupsTarget species/groupControl sequenceSequence lengthTmForward primerReverse primerB catenulatum/B pseudocatenulatumAM277149275685′-ACTCCTCGCATGGGGTGTC-3′5′-CCGAAGGCTTGCTCCCGAT-3′C cocleatum 88%AM276544104605′-AATACATAAGTAACCTGGCRTC-3′5′-CGTAGCACTTTTCATATAGAGTT-3′C aerofaciensAM276107260675′-CCCGACGGGAGGGGAT-3′5′-CTTCTGCAGGTACAGTCTTGA-3′C eutactus 97%AM27582597635′-AGCTTGCTCCGGCYGATTTA-3′5′-CGGTTTTACCAGTCGTTTCCAA-3′L farciminisAM275648127635′-ATGATTCAGAYCTTGGTGAG-3′5′-AAGCTACGATCATGTGAAAGTA-3′L gasseriAM275470160635′-ATTTGGTGCTTGCACCAGA-3′5′-CAGAACCATCTTTTAAACTCTAGA-3′R torques 91%AM276558119625′-TGCTTAACTGATCTTCTTCGGA-3′5′-CGGTATTAGCAGTCATTTCTG-3′R torques 94%AM275522137655′-AATCTTCGGAGGAAGAGGACA-3′5′-ACACTACACCATGCGGTCCT-3′S bovisAM276559150605′-TTAGCTTGCTAAAGTTGGAA-3′5′-ATCTACTAGTGAAGCAATTGCT-3′UniversalB longum DSM 20219466505′-TCCTACGGGAGGCAGCAGT-3′5′-GGACTACCAGGGTATCTAATCCTGTT-3′NOTE. Percentages in the names of target species refer to the sequence homology with nearest cultured bacterial species. Tm values refer to optimized annealing temperatures used in each assay. Open table in a new tab NOTE. Percentages in the names of target species refer to the sequence homology with nearest cultured bacterial species. Tm values refer to optimized annealing temperatures used in each assay. The obtained sequences were deposited to the EMBL Nucleotide Sequence Database under the accession numbers AM275396–AM279148. Genomic bacterial DNA from fecal samples was subjected to a %G+C profile analysis in which the genomes become differentiated according to their %G+C content. In this community-level analysis, considerable differences were detected among the 4 pooled samples of IBS-D, IBS-C, IBS-M, and healthy control subjects in 3 distinct %G+C fractions (fraction 7 [%G+C, 25–30], fraction 10 [%G+C, 40–45], and fraction 13 [%G+C, 55–60]) (Figure 1). The %G+C profile is a culture-independent but rather robust and coarse characterization of the total bacterial community structure; thus, observed differences may be caused by number of bacterial species of %G+C content corresponding to the fraction in question. Therefore, these 3 fraction groups were directed to high-throughput DNA cloning and sequencing analyses. From 14,000 clones in the 16S rRNA gene library constructed, 4,608 sequences, encompassing approximately 450 bp from the 5′ end of the 16S rRNA gene, were gathered. After excluding sequences of weak quality or chimeric structure, the success rate of sequencing was approximately 81%. According to Good’s formula,36Good I.J. The population frequencies of species and the estimation of population parameters.Biometrika. 1953; : 237-264Google Scholar the coverage of clone libraries ranged from 80% to 93%. Table 3 illustrates the number of sequences acquired and the number of OTUs these sequences represent when a similarity criterion of 98% was used. A total of 79 novel phylotypes were detected, after which the corresponding clones were further sequenced to obtain the almost complete 16S rRNA gene sequence. After a subsequent Fasta search, 53 phylotypes, representing altogether 98 sequences, remained previously uncharacterized novel ones (Table 4).Table 3Number of Sequences and OTUs in Each Clone LibrarySampleSequencesOTUsFraction 7 Control31991 IBS-M324108 IBS-C29163 IBS-D34270Fraction 10 Control346119 IBS-M327100 IBS-C32390 IBS-D31878Fraction 13 Control31145 IBS-M28961 IBS-C29170 IBS-D27250NOTE. DOTUR was used for the calculation of OTUs with 98% similarity criteria. Open table in a new tab Table 4Novel Near Full-Length 16S rRNA Sequences in All Samples With <95% Similarity to Any EMBL SequencePhylumAccession no.SampleSimilarity (%)Closest neighborDescriptionSourceActinobacteriaAM275775Control88.554AY959023Uncultured bacteriaHuman vaginaFirmicutesAM275415Control89.624DQ394685Uncultured bacteriaCow rumenActinobacteriaAM275737Control90.283DQ353921Uncultured bacteriaWild gorilla fecesFirmicutesAM275764Control90.606DQ394628Uncultured bacteriaReindeer rumenBacteroidetesAM275765Control90.817AJ400236Uncultured bacteriaMouse fecesFirmicutesAM275604Control91.764AF129867Uncultured bacteriaAnaerobic digesterFirmicutesAM275757Control92.158AB185775Uncultured bacteriaCow rumenAc