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Appl Environ Microbiol. Apr 2007; 73(7): 2284–2289.
Published online Feb 9, 2007. doi:  10.1128/AEM.02223-06
PMCID: PMC1855685

Molecular Fingerprinting of the Fecal Microbiota of Children Raised According to Different Lifestyles[down-pointing small open triangle]

Abstract

In this population-based study, 90 children from three European countries were examined to determine the impact of lifestyle on the fecal microbiota. The study was designed to assess the impact of two extreme lifestyles that we hypothesized could impact the microbial composition in the gut: i.e., an anthroposophic lifestyle (restricted use of antibiotics, greater consumption of fermented vegetables, etc.) versus living on a farm (greater consumption of farm milk, contact with animals, etc.). In previous studies, these lifestyles correlated with lower prevalence of allergies. Terminal restriction fragment length polymorphism (T-RFLP) was used to assess the bacterial composition in fecal samples since recent studies have shown that the majority of this community cannot be cultivated. The T-RFLP data were used to calculate richness and evenness of the fecal microbiota. Children that were attending Steiner schools (anthroposophic children) had a significantly higher diversity of microbes in their feces than farm children, who in turn also had lower diversity than the control groups. Specific primers were also used to focus on the Lactobacillus-like community (lactic acid bacteria [LAB]). Large differences were found in the LAB subpopulations in the sampled groups. In some children, the LAB subpopulation was dominated by a species that has not yet been cultivated.

The endogenous microbiota of the human gastrointestinal tract is highly diverse and has an important function in human health and nutrition. Under normal conditions, the gut microbiota has a protective effect against potential pathogens (13). However, disruption of the ecological balance in the gut (e.g., disruption caused by antibiotic treatment) can give rise to clinical implications (17, 32). The composition of the intestinal microbial community is otherwise considered to be relatively stable over time, yet unique for each individual (35).

Certain lifestyles encompass dietary and other characteristics that may have an impact on the composition of the human gut microbiota, but these impacts are currently poorly understood. One example is living on a farm, since this lifestyle often includes consumption of unpasteurized dairy products and homegrown food products and contact with farm animals. Another example is an anthroposophic lifestyle, which includes a diet that is generally based on organically and/or biodynamically produced foods and consumption of vegetables preserved by fermentation with lactobacilli. In addition, individuals with an anthroposophic lifestyle have a restrictive use of antibiotics, antipyretics, and vaccinations (3, 10). Certain characteristics of the anthroposophic lifestyle have previously been shown to correlate with the composition of the intestinal microbiota in infants by traditional cultivation-based approaches (2). Nevertheless, it is still poorly understood how the dynamics and composition of the intestinal microbiota are affected by diet or other lifestyle factors (2), with the exception of antibiotic usage, that can have significant impacts on the intestinal microbiota (17).

Until recently, it has been difficult to characterize the composition of the human gut microbiota due to large variations between individuals and a lack of appropriate methods. For example, it has been shown that traditional cultivation-based approaches can only detect approximately 20 to 40% of the microorganisms in the gastrointestinal tract (31). However, advances in the development of molecular techniques for analysis of complex microbial communities have bypassed the necessity for cultivation and made considerable progress in characterization of intestinal bacterial communities (11, 12, 17, 35). In particular, terminal restriction fragment length polymorphism (T-RFLP) is a useful molecular tool for assessment of differences in the composition of microbial communities between individuals or over time. This method has previously been used for assessment of the microbial composition in the human gut and other ecosystems (17, 21, 23, 34).

The aim of this study was to use T-RFLP to investigate the relationship between selected lifestyle factors and intestinal microbial community profiles. We focused on a restricted age group, namely children between the ages of 5 and 13 years, to minimize the impact of wide variations in age on the results. We examined the fecal microbiota in 90 children living on farms or attending Steiner schools, who generally come from families with an anthroposophic lifestyle, and their respective reference groups, from three European countries (Germany, Sweden, and Switzerland). Our hypothesis was that these different lifestyles could have an impact on the composition of the gut microbiota.

MATERIALS AND METHODS

Subjects and study design.

This study included children whose samples were obtained within the EU-funded project PARSIFAL (Prevention of Allergy—Risk Factors for Sensitization in Children Related to Farming and Anthroposophic Lifestyle), as described in detail by Alfvén and coworkers (1). Fecal samples were collected between September 2001 and June 2002 from children who answered a questionnaire eliciting information on illness and use of antibiotics during the last 3 months as well as diet and different lifestyle factors (see the supplemental material). The parents collected the fecal samples and stored them at −20°C. At the time of sampling, the parents completed the questionnaire. All samples were transported on ice to Sweden and stored at −80°C until processing. Children who had not used antibiotics within the last 3 months preceding fecal sampling and who were not ill at the time of sampling were included in the present analyses. The final sample set included 90 children in total: 28 from Germany, 36 from Sweden, and 26 from Switzerland. Anthroposophic children (n = 23; referred to as Steiner schoolchildren) were identified by the Steiner schools they attended, with the corresponding reference group (n = 19) living in the same area as the Steiner schoolchildren but attending conventional public schools. In addition, children living on a farm (n = 26) and corresponding reference children (n = 22) living in the same area, but not on a farm, were analyzed.

The study was approved by local research ethics committees in each country, and informed consent was obtained from the parents of each child.

DNA isolation and PCR conditions.

The MoBio Ultraclean soil DNA kit (MoBio, Solana Beach, CA) was optimized to produce efficient and reproducible amounts of DNA from the fecal samples. DNA was extracted from 250 mg of fecal samples according to the manufacturer's instructions, after initial bead beating three times for 45 s at a setting of 5.0 using a FastPrep Instrument (Qbiogene, Carlsbad, CA). The DNA extractions were highly reproducible, as confirmed by analysis of T-RFLP patterns from duplicate subsamples from 10 different fecal samples (data not shown). Subsequently, duplicate T-RFLP analyses were conducted for each DNA extraction from each fecal sample. 16S rRNA genes were amplified from the isolated DNA with broad-range bacterial primers Bact-8F (5′-AGAGTTTGATCCTGGCTCAG-3′) (8), 5′ end labeled with 6-carboxyfluorescein, and 926r (5′-CCGTCAATTCCTTTRAGTTT-3′) (29). A more specific reverse primer, S-G-Lab-0677-A-a-17 (5′-CACCGCTACACATGGAG-3′) (14), was used to increase the resolution of the T-RFLP to monitor lactic acid bacteria (LAB) with focus on the Lactobacillus-like microbiota. These primers also detect related species such as eubacteria, some of which are members of the LAB (27). For ease of presentation, we refer to these as LAB group-specific primers here. Amplification was carried out in 50-μl reaction mixtures containing 2.5 U Taq DNA polymerase (Amersham Biosciences, Uppsala, Sweden), 1× PCR buffer, 200 μM of each deoxyribonucleoside triphosphate, 20 pmol of each primer, 0.04% bovine serum albumin, approximately 25 ng of template DNA, and sterile distilled water to a final volume of 50 μl. Thermocycling was conducted with a model 9700 Gene Amp PCR system (Applied Biosystems [ABI], Foster City, CA) starting with an initial denaturing step at 94°C for 5 min. A total of 30 cycles consisting of 40 s at 94°C, 40 s at 55°C, and 60 s at 72°C was followed by a final primer extension step at 72°C for 7 min. For the more specific approach with primer S-G-Lab-0677-A-a-17, the thermal cycling was increased to 35 cycles. The detection limit was found to be 106 cells/g feces by spiking fecal samples with known amounts of Lactobacillus reuteri cells. PCR-amplified DNA product amounts and sizes were confirmed by agarose gel electrophoresis with the GeneRuler 100-bp DNA ladder Plus (Fermentas Life Sciences, Burlington, Canada) as a size marker.

T-RFLP.

PCR products were digested with the HaeIII restriction enzyme. For putative identification of terminal restriction fragments (TRFs), a subset of 48 samples was digested with HaeIII, HhaI, and MspI. (Amersham Biosciences). The efficiency of restriction digestion was confirmed by agarose gel electrophoresis, and the digested fragments were separated on an ABI 3700 capillary sequencer, as described previously (16). The sizes of the fluorescently labeled fragments were determined by comparison with the internal GS ROX-500 size standard (ABI). T-RFLP electropherograms were imaged using GeneScan software (ABI). Relative peak areas of each TRF were determined by dividing the area of the peak of interest by the total area of peaks within the following threshold values: a lower threshold at 60 bp and an upper threshold at 500 bp. A threshold for relative abundance was applied at 0.5%, and only TRFs with higher relative abundances were included in the remaining analyses. Putative identities for the most dominant peaks were predicted by in silico digestion with HaeIII, HhaI, or MspI in the Ribosomal Database Project II (http://rdp.cme.msu.edu) using the T-RFLP analysis program TAP (26).

Cloning and sequencing.

Cloning and sequencing of 16S rRNA genes from DNA extracted from the fecal samples were performed to confirm the identities of bacterial species corresponding to dominant TRFs from the LAB group data set. PCR products from seven samples (three Steiner schoolchildren, three farm children, and one farm reference child), with high abundances of specific TRFs that dominated the clustering of samples observed in PCA plots (see Results) were selected for amplification with the LAB group-specific primer S-G-Lab-0677-A-a-17 and primer Bact-8F. The PCR products were gel purified using the QIAGEN gel extraction kit (QIAGEN, Hilden, Germany) and cloned into the TOPO TA pCR 4.0 vector, followed by transformation into Escherichia coli TOP 10 competent cells (Invitrogen, Carlsbad, CA). Inserts were amplified using vector primers M13f and M13r (Invitrogen) with the same thermal cycling program mentioned previously. A total of 96 different clones were chosen for sequencing. Obtained sequences were examined using MacVector 8.1.1 (Accelrys Software, Inc., San Diego, CA), to identify their corresponding TRF size and for removal of redundant sequences. The remaining sequences were aligned against GenBank database entries using standard nucleotide BLAST at NCBI (http://www.ncbi.nlm.nih.gov) (4). Hits defined as unknown or uncultured bacteria were subsequently aligned against sequenced bacterial genomes (genomic BLAST at NCBI), as well as examined with the Ribosomal Database Project II Sequence Match, in an attempt to classify them.

Statistical analysis.

T-RFLP data from duplicate technical replicates from each individual were averaged and normalized and entered into a data matrix that consisted of the terminal restriction fragments as variables and individuals as objects. The reproducibility between duplicate samples was very high, with variation between technical replicates that was normally less than a 0.4% standard deviation of the mean. Principal component analysis (PCA) plots were generated using the Canoco multivariate statistics software (Microcomputer Power, Ithaca, NY). PCA was conducted to find clustering or trends that could be correlated to lifestyles, lifestyle characteristics, diet, sex, or geographical origin. Diversity, defined as evenness and richness of the bacterial community members detected as TRFs by T-RFLP analysis, was calculated using Simpson's index of diversity (6) and verified using Shannon's diversity and equitability index (6). Mann-Whitney's test was used for univariate significance testing of differences in diversity between groups or in relation to lifestyle factors. Chi-square tests were used for correlation between specific TRFs and lifestyles. P values of <0.05 were considered significant.

RESULTS

Characteristics of the study group.

Questionnaire data showed considerable differences in demographics and characteristics between the groups of children (see the supplemental material). Farm children more frequently reported contact with pets and farm animals, and they consumed more homegrown or farm-produced foods compared to children in other groups. Steiner schoolchildren were breastfed for a longer period and consumed organically and/or biodynamically grown food products, as well as fermented vegetables, more often. They also received antibiotics, antipyretics, and MMR (measles, mumps, rubella) vaccinations to a lesser extent than the farm and reference children but were consequently also affected by measles infections more often.

Fecal bacterial community profiles.

T-RFLP was used to obtain fingerprints of the fecal bacterial communities of all children in the groups sampled. A total of 140 different TRFs, representing different ribotypes, were found in the 90 individuals. Each child had a unique community profile, consisting of a different assemblage of TRFs. PCA confirmed that the profiles were distinctly different for the sampled individuals (Fig. (Fig.1),1), with a small number of TRFs that were common to all subjects. However, no clusters or groupings with regard to lifestyle, diet, sex, geographical origin, or any of the predefined characteristics (see the supplemental material) were observed. The ordination of the data was particularly influenced by a few dominant TRFs that were common to most individuals. For example, TRF 272 was observed in all and TRFs 223 and 318 in almost all (89/90 and 86/90, respectively) individuals analyzed. These TRFs were putatively identified as belonging to the genera Eubacterium and Clostridium and could represent core bacterial residents of the human intestinal tract.

FIG. 1.
PCA plot of the T-RFLP data for the fecal bacterial community compositions of 90 individuals. Each symbol contains the average of duplicate T-RFLP profiles based on TRF size and relative abundance data for a specific individual. Blue, farm children; green, ...

Diversity of bacteria based on T-RFLP data.

The T-RFLP data also provide a measure of the diversity of the bacterial community as described by the total number of TRFs and their relative abundances. It is important to keep in mind that only dominant populations are detected using this PCR-based approach, so this is not a measure of the total diversity of the samples, but instead a measure of relative diversity for comparison between samples. When the different groups of children were compared with regard to the diversity of their fecal bacteria, some highly significant differences were observed. Foremost, Steiner schoolchildren had a higher diversity of fecal bacteria compared to farm children (P = 0.0001; Fig. Fig.2),2), as exhibited by a larger number of total TRFs and higher evenness than those of the farm children, (average TRF numbers are 28.3 among Steiner schoolchildren and 23.4 among farm children). Farm children had higher relative abundances of some TRFs (such as 223 and 272; putatively corresponding to Clostridium or Eubacterium species) that contributed to the lower diversity values of this group. Both of the reference groups had intermediate diversity levels, with significant differences between the Steiner schoolchildren and their reference group (P = 0.026; Fig. Fig.2),2), but not between farm children and their reference group (P = 0.065; Fig. Fig.2).2). Differences in diversity could also be correlated to several other factors from the questionnaire. Mainly, consumption of biodynamically and organically produced food (nonconventional) had a strong correlation to a high diversity (P = 0.0009; Fig. Fig.2),2), but high consumption of farm milk correlated to a lower diversity compared with that in children who never consumed farm milk (P = 0.007; Fig. Fig.2).2). In addition, children who had never used antibiotics or antipyretics had a significantly higher diversity of bacteria than those who had used antibiotics (P = 0.024; Fig. Fig.2)2) or antipyretics (P = 0.031; Fig. Fig.2)2) during the first year of life. Children who had received MMR vaccinations showed lower diversity than those who had not received MMR vaccination (P = 0.035; Fig. Fig.2).2). No significant differences in diversity were found with regard to sex, age, or geographical origin.

FIG. 2.
Diversity of the fecal microbiota presented for groups of individuals according to the questionnaire data (see Table S1 in the supplemental material). Data represent the median values with the interquartile range. Simpson's index was calculated based ...

Profiling of LAB and related groups.

We were interested in further definition of key members of the intestinal microbiota that could differ between the sample groups. Therefore, we focused on the LAB and related groups, as these are often considered to be beneficial members of the commensal gut microbiota (18, 27, 33). In addition, LAB are prevalent in fermented foods, which are more commonly consumed among children from families with an anthroposophic lifestyle.

Eighty-two samples were analyzed using LAB group-specific primers, as 8 did not yield PCR products, presumably as the LAB were under the detection limit in those samples. The complexity of the community profiles using the specific primers was lower than that obtained using the general bacterial primers described above. Usually one TRF, but not always the same one, was highly dominant in each sample.

Two distinct clusters were found when T-RFLP sample data (including relative abundance values of each TRF) were analyzed by PCA (Fig. (Fig.3).3). These two clusters had different dominant TRFs with negligible overlap. One cluster had TRFs 213 and 214 as the most dominant TRFs (25/82 individuals; Fig. Fig.3A)3A) and was mainly represented by samples from farm children, whereas the other cluster was dominated by TRF 250 (57/82 individuals; Fig. Fig.3B)3B) and was more prevalent in Steiner schoolchildren and both reference groups. However, differences were only significant between the farm children and the other sample groups according to a chi-square test at the 95% confidence level. Individuals with representative TRFs from both clusters were only found in 6 out of 82 individuals. Therefore, it is clear that most of the individuals from whom samples were obtained had either one or the other dominant LAB type.

FIG. 3.
PCA plots of T-RFLP profiles using LAB group-specific primers for all individuals from whom samples were obtained. Farm children are shown in blue, and Steiner schoolchildren are shown in red. Both reference groups are shown in gray. Panels A and B are ...

Correlation of the T-RFLP data to sequence data obtained from the clone libraries showed that clones with TRF sizes of 213 and 214 bp were most likely Eubacterium species—similar to Eubacterium biforme and Eubacterium cylindroides. These results were not surprising as the LAB group primers also detect members of the Eubacterium genus, some of which are also LAB (27). All of the six clones with a TRF size of 250 bp could only be matched to previously uncultured bacterial clones: e.g., entry AY984785 derived from a human stool sample. When attempting to classify the cloned sequences of TRF 250, they were tentatively placed in the Erysipelothrix genus, belonging to the phylum Firmicutes, with the closest match to Clostridium ramosum (88% identity). This similarity is too low for classification. Thus, the 16S clones corresponding to TRF 250 should still be considered unclassified below the phylum level. As this phylotype was very dominant using LAB group primers in many individuals, it would be highly interesting to determine its phylogeny and function in the gut microbiota.

DISCUSSION

Molecular profiling of the fecal bacterial composition in children showed that factors associated with different lifestyles have an impact on the intestinal microbiota. More specifically, there was a significant difference in diversity of bacteria, measured as richness and evenness of TRFs, in Steiner schoolchildren compared to farm children (Fig. (Fig.22).

Recently, two studies used a different molecular approach, fluorescent in situ hybridization, to analyze the fecal microbiota in healthy individuals from several European countries (20, 28). In the first study, Lay and coworkers found no significant correlation between the microbial compositions with regard to age, geographical origin, or gender; although they observed a trend that the geographical region could have an impact on the microbial composition, with differences between Dutch and French study populations (20). A similar observation was made by Mueller et al. (28), who found higher levels of bifidobacteria in Italians compared to French, Germans, and Swedes. In addition, this study reported an age and gender effect with respect to enterobacteria and Bacteroides-Prevotella, respectively, using fluorescent in situ hybridization probes (28). In our study, however, when comparing children from Germany, Switzerland, and Sweden, we observed no significant differences correlating to geographical origin. In addition, there was no correlation between microbial composition and age or gender. Since we analyzed individuals with a narrow age span, and the impact of age on the microbial composition has only been reported in infants or old persons (9), we did not expect a difference correlated to age in our study. Instead, we observed that significant differences in diversity of the microbiota correlated to lifestyle factors.

It is generally considered that a high diversity of microbes is desirable in nature as it allows more resilience in response to disturbances (5). The relevance of the diversity of intestinal microbes in relation to health and nutrition is, however, poorly understood. A recent study by Manichanh and coworkers reported that the fecal microbiota in patients with Crohn's disease had a reduced diversity of Firmicutes compared to healthy individuals (25), suggesting that diversity of bacteria in the intestine could have an impact on human health. Interestingly, in our study, the lowest diversity of bacteria was found in farm children, yet this group has previously been reported to have a reduced risk of developing immunoglobulin E-mediated allergies (19, 30). These findings support the hypothesis that “keystone species” may obviate the need for higher diversity (22).

In our study, it was not possible to specifically determine which lifestyle factors resulted in a difference in fecal bacterial diversity. However, several factors associated with the anthroposophic lifestyle correlated with a higher diversity, whereas several other factors associated with a farm lifestyle correlated with a lower diversity. In particular, we found that consumption of biodynamically or organically produced food items was significantly correlated with a higher diversity, as compared to that in children who mainly consumed a conventional diet. Some earlier publications, using conventional microbiological plating techniques (24), have also indicated that diet can have a general influence on the cultural fecal microbiota (15, 27), which warrants more detailed exploration using molecular approaches such as those described here.

The data available to date, including this study, suggest that each individual harbors his or her own unique microbiota (17, 35). However, there is some overlap between individuals based on common TRFs. For example, TRFs putatively identified as belonging to the genera Eubacterium and Clostridium were found in most individuals. This observation was supported by recent reports based on cloning and sequencing of the gut microbiota, which demonstrate that Clostridium, Eubacterium, and Bacteroides are the most predominant representatives in fecal samples (7, 22). In addition, we found that farm children generally had higher relative abundances of these dominant populations, as compared to the Steiner schoolchildren, and the prevalence of the corresponding bacterial populations was largely responsible for the decrease in evenness of the fecal community in those individuals.

Our hypothesis was that Steiner schoolchildren would have a different Lactobacillus biota as the anthroposophic lifestyle encompasses more consumption of fermented foods that are often high in lactobacilli. Although, we did not find any significant differences in diversity in the LAB community in the individuals sampled, some significant differences in dominant TRFs were observed between farm children and the other sample groups (Fig. (Fig.3),3), suggesting that lifestyle can influence specific subpopulations of the gut microbial community, although we do not know the physiological relevance of these differences.

The TRFs that were important for structuring of the LAB group data set have been observed in other recent studies of the human gut microbiota (7). However, these particular bacterial populations have not previously been examined in any detail. Our results suggest that the LAB community can be divided into two classes based on the presence of the bacterial populations represented by these TRFs. In particular, it would be of interest to determine the role in the human gut of the species represented by TRF 250 since it has not yet been isolated or adequately classified.

In conclusion, the observed differences in compositions of the gut microbiota could be correlated to certain lifestyle features. However, it is not possible to pinpoint single factors that are responsible. The physiological influence of composition and diversity of the human gut microbiota on the human host requires more investigation to increase understanding of its functional role in normal and disordered states.

Supplementary Material

[Supplemental material]

Acknowledgments

We thank all fieldworkers and other PARSIFAL team members, especially Stina Gustafsson, Eva Hallner, André Lauber, Wiveka Lundberg, Helena Svensson, Anki Wigh, Annika Zettergren, and Anne-Charlotte Öhman-Johansson from Sweden; Susanne Löhliger, Remo Frey, Marianne Rutschi, Stefan Worminghaus, and Michaela Glöckler from Switzerland; and Markus Benz and Jörg Budde from Germany. We also thank all school doctors and teachers and all children and parents who contributed to this study.

This work was supported by a research grant from the European Union, QLRT 1999-01391; by funding from the Swedish Foundation for Health Care Science and Allergy Research; and by the Swedish Foundation for Strategic Research, Microbes and Man program.

Footnotes

[down-pointing small open triangle]Published ahead of print on 9 February 2007.

Supplemental material for this article may be found at http://aem.asm.org/.

REFERENCES

1. Alfvén, T., C. Braun-Fahrlander, B. Brunekreef, E. von Mutius, J. Riedler, A. Scheynius, M. van Hage, M. Wickman, M. R. Benz, D. Schram, E. Ublagger, M. Waser, G. Pershagen, et al. 2006. Allergic diseases and atopic sensitization in children related to farming and anthroposophic lifestyle—the PARSIFAL study. Allergy 61:414-421. [PubMed]
2. Alm, J. S., J. Swartz, B. Bjorksten, L. Engstrand, J. Engstrom, I. Kuhn, G. Lilja, R. Mollby, E. Norin, G. Pershagen, C. Reinders, K. Wreiber, and A. Scheynius. 2002. An anthroposophic lifestyle and intestinal microflora in infancy. Pediatr. Allergy Immunol. 13:402-411. [PubMed]
3. Alm, J. S., J. Swartz, G. Lilja, A. Scheynius, and G. Pershagen. 1999. Atopy in children of families with an anthroposophic lifestyle. Lancet 353:1485-1488. [PubMed]
4. Altschul, S. F., W. Gish, W. Miller, E. W. Myers, and D. J. Lipman. 1990. Basic local alignment search tool. J. Mol. Biol. 215:403-410. [PubMed]
5. Backhed, F., R. E. Ley, J. L. Sonnenburg, D. A. Peterson, and J. I. Gordon. 2005. Host-bacterial mutualism in the human intestine. Science 307:1915-1920. [PubMed]
6. Begon, M., J. L. Harper, and C. R. Townsend. 2006. Ecology: from individuals to ecosystems, 4th ed., p. 471-472. Blackwell, Oxford, United Kingdom.
7. Eckburg, P. B., E. M. Bik, C. N. Bernstein, E. Purdom, L. Dethlefsen, M. Sargent, S. R. Gill, K. E. Nelson, and D. A. Relman. 2005. Diversity of the human intestinal microbial flora. Science 308:1635-1638. [PMC free article] [PubMed]
8. Edwards, U., T. Rogall, H. Blocker, M. Emde, and E. C. Bottger. 1989. Isolation and direct complete nucleotide determination of entire genes—characterization of a gene coding for 16S ribosomal RNA. Nucleic Acids Res. 17:7843-7853. [PMC free article] [PubMed]
9. Egert, M., A. A. de Graaf, H. Smidt, W. M. de Vos, and K. Venema. 2006. Beyond diversity: functional microbiomics of the human colon. Trends Microbiol. 14:86-91. [PubMed]
10. Floistrup, H., J. Swartz, A. Bergstrom, J. S. Alm, A. Scheynius, M. van Hage, M. Waser, C. Braun-Fahrlander, D. Schram-Bijkerk, M. Huber, A. Zutavern, E. von Mutius, E. Ublagger, J. Riedler, K. B. Michaels, G. Pershagen, et al. 2006. Allergic disease and sensitization in Steiner school children. J. Allergy Clin. Immunol. 117:59-66. [PubMed]
11. Franks, A. H., H. J. M. Harmsen, G. C. Raangs, G. J. Jansen, F. Schut, and G. W. Welling. 1998. Variations of bacterial populations in human feces measured by fluorescent in situ hybridization with group-specific 16S rRNA-targeted oligonucleotide probes. Appl. Environ. Microbiol. 64:3336-3345. [PMC free article] [PubMed]
12. Furrie, E. 2006. A molecular revolution in the study of intestinal microflora. Gut 55:141-143. [PMC free article] [PubMed]
13. Guarner, F., and J. R. Malagelada. 2003. Gut flora in health and disease. Lancet 361:512-519. [PubMed]
14. Heilig, H. G. H. J., E. G. Zoetendal, E. E. Vaughan, P. Marteau, A. D. L. Akkermans, and W. M. de Vos. 2002. Molecular diversity of Lactobacillus spp. and other lactic acid bacteria in the human intestine as determined by specific amplification of 16S ribosomal DNA. Appl. Environ. Microbiol. 68:114-123. [PMC free article] [PubMed]
15. Hill, M. J. 1998. Composition and control of ileal contents. Eur. J. Cancer Prev. 7(Suppl. 2):S75-S78. [PubMed]
16. Hjort, K., A. Lembke, A. Speksnijder, K. Smalla, and J. K. Jansson. 31 August 2006. Community structure of actively growing bacterial populations in plant pathogen suppressive soil. Microb. Ecol.[Epub ahead of print.] doi:.10.1007/s00248-006-9120-2 [PubMed] [Cross Ref]
17. Jernberg, C., Å. Sullivan, C. Edlund, and J. K. Jansson. 2005. Monitoring of antibiotic-induced alterations in the human intestinal microflora and detection of probiotic strains by use of terminal restriction fragment length polymorphism. Appl. Environ. Microbiol. 71:501-506. [PMC free article] [PubMed]
18. Kalliomaki, M., S. Salminen, H. Arvilommi, P. Kero, P. Koskinen, and E. Isolauri. 2001. Probiotics in primary prevention of atopic disease: a randomised placebo-controlled trial. Lancet 357:1076-1079. [PubMed]
19. Klintberg, B., N. Berglund, G. Lilja, M. Wickman, and M. van Hage-Hamsten. 2001. Fewer allergic respiratory disorders among farmers' children in a closed birth cohort from Sweden. Eur. Respir. J. 17:1151-1157. [PubMed]
20. Lay, C., L. Rigottier-Gois, K. Holmstrøm, M. Rajilic, E. E. Vaughan, W. M. de Vos, M. D. Collins, R. Thiel, P. Namsolleck, M. Blaut, and J. Doré. 2005. Colonic microbiota signatures across five northern European countries. Appl. Environ. Microbiol. 71:4153-4155. [PMC free article] [PubMed]
21. Leser, T. D., R. H. Lindecrona, T. K. Jensen, B. B. Jensen, and K. Møller. 2000. Changes in bacterial community structure in the colon of pigs fed different experimental diets and after infection with Brachyspira hyodysenteriae. Appl. Environ. Microbiol. 66:3290-3296. [PMC free article] [PubMed]
22. Ley, R. E., D. A. Peterson, and J. I. Gordon. 2006. Ecological and evolutionary forces shaping microbial diversity in the human intestine. Cell 124:837-848. [PubMed]
23. Liu, W. T., T. L. Marsh, H. Cheng, and L. J. Forney. 1997. Characterization of microbial diversity by determining terminal restriction fragment length polymorphisms of genes encoding 16S rRNA. Appl. Environ. Microbiol. 63:4516-4522. [PMC free article] [PubMed]
24. Mai, V. 2004. Dietary modification of the intestinal microbiota. Nutr. Rev. 62:235-242. [PubMed]
25. Manichanh, C., L. Rigottier-Gois, E. Bonnaud, K. Gloux, E. Pelletier, L. Frangeul, R. Nalin, C. Jarrin, P. Chardon, P. Marteau, J. Roca, and J. Dore. 2005. Reduced diversity of faecal microbiota in Crohn's disease revealed by a metagenomic approach. Gut 55:205-211. [PMC free article] [PubMed]
26. Marsh, T. L. 1999. Terminal restriction fragment length polymorphism (T-RFLP): an emerging method for characterizing diversity among homologous populations of amplification products. Curr. Opin. Microbiol. 2:323-327. [PubMed]
27. Moore, W. E. C., and L. H. Moore. 1995. Intestinal floras of populations that have a high risk of colon cancer Appl. Environ. Microbiol. 61:3202-3207. [PMC free article] [PubMed]
28. Mueller, S., K. Saunier, C. Hanisch, E. Norin, L. Alm, T. Midtvedt, A. Cresci, S. Silvi, C. Orpianesi, M. C. Verdenelli, T. Clavel, C. Koebnick, H.-J. F. Zunft, J. Doré, and M. Blaut. 2006. Differences in fecal microbiota in different European study populations in relation to age, gender, and country: a cross-sectional study. Appl. Environ. Microbiol. 72:1027-1033. [PMC free article] [PubMed]
29. Muyzer, G., E. C. De Waal, and A. G. Uitterlinden. 1993. Profiling of complex microbial populations by denaturing gradient gel electrophoresis analysis of polymerase chain reaction-amplified genes coding for 16S rRNA. Appl. Environ. Microbiol. 59:695-700. [PMC free article] [PubMed]
30. Riedler, J., C. Braun-Fahrlander, W. Eder, M. Schreuer, M. Waser, S. Maisch, D. Carr, R. Schierl, D. Nowak, and E. von Mutius. 2001. Exposure to farming in early life and development of asthma and allergy: a cross-sectional survey. Lancet 358:1129-1133. [PubMed]
31. Suau, A., R. Bonnet, M. Sutren, J.-J. Godon, G. R. Gibson, M. D. Collins, and J. Doré. 1999. Direct analysis of genes encoding 16S rRNA from complex communities reveals many novel molecular species within the human gut. Appl. Environ. Microbiol. 65:4799-4807. [PMC free article] [PubMed]
32. Tannock, G. W. 1999. Analysis of the intestinal microflora: a renaissance. Antonie Leeuwenhoek 76:265-278. [PubMed]
33. Tannock, G. W. 2004. A special fondness for lactobacilli. Appl. Environ. Microbiol. 70:3189-3194. [PMC free article] [PubMed]
34. Wang, M., S. Ahrne, M. Antonsson, and G. Molin. 2004. T-RFLP combined with principal component analysis and 16S rRNA gene sequencing: an effective strategy for comparison of fecal microbiota in infants of different ages. J. Microbiol. Methods 59:53-69. [PubMed]
35. Zoetendal, E. G., A. D. L. Akkermans, and W. M. De Vos. 1998. Temperature gradient gel electrophoresis analysis of 16S rRNA from human fecal samples reveals stable and host-specific communities of active bacteria. Appl. Environ. Microbiol. 64:3854-3859. [PMC free article] [PubMed]

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