SEARCH

SEARCH BY CITATION

Keywords:

  • gut microbiota;
  • clostridia;
  • Actinobacteria;
  • DNA-extraction;
  • storage

Abstract

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
  9. Supporting Information

Recently several human health-related microbiota studies have had partly contradictory results. As some differences may be explained by methodologies applied, we evaluated how different storage conditions and commonly used DNA-extraction kits affect bacterial composition, diversity, and numbers of human fecal microbiota. According to our results, the DNA-extraction did not affect the diversity, composition, or quantity of Bacteroides spp., whereas after a week's storage at −20 °C, the numbers of Bacteroides spp. were 1.6–2.5 log units lower (P < 0.05). Furthermore, the numbers of predominant bacteria, Eubacterium rectale (Erec)-group, Clostridium leptum group, bifidobacteria, and Atopobium group were 0.5–4 log units higher (P < 0.05) after mechanical DNA-extraction as detected with qPCR, regardless of storage. Furthermore, the bacterial composition of Erec-group differed significantly after different DNA-extractions; after enzymatic DNA-extraction, the most prevalent genera detected were Roseburia (39% of clones) and Coprococcus (10%), whereas after mechanical DNA-extraction, the most prevalent genera were Blautia (30%), Coprococcus (13%), and Dorea (10%). According to our results, rigorous mechanical lysis enables detection of higher bacterial numbers and diversity from human fecal samples. As it was shown that the results of clostridial and actinobacterial populations are highly dependent on the DNA-extraction methods applied, the use of different DNA-extraction protocols may explain the contradictory results previously obtained.


Introduction

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
  9. Supporting Information

In the past 10 years, there has been a wealth of studies in which the relationship between the human gut microbiota and human health has been investigated. After the findings of Turnbaugh et al. (2006) and Ley et al. (2006) that the relative proportion of Bacteroidetes decreased and the relative proportion of Firmicutes increased in obese mice (Ley et al., 2005; Turnbaugh et al., 2006) and men (Ley et al., 2006) as compared to their lean counterparts, obesity-related gut microbiota studies have drawn a lot of attention. After the initial findings, there have been several related studies in which the findings have been similar (Armougom et al., 2009; Santacruz et al., 2009; Turnbaugh et al., 2009; Balamurugan et al., 2010; Santacruz et al., 2010) to those of Ley et al. (2005, 2006) and Turnbaugh et al. (2006). Moreover, there have also been studies in which the findings have been contradictory or there has not been any statistically significant differences between Firmicutes and Bacteroidetes in obese and normal weight people (Duncan & Flint, 2008; Duncan et al., 2008; Zhang et al., 2009; Santacruz et al., 2010; Schwiertz et al., 2010). Some differences may be explained by the different detection methods applied [i.e. clone libraries (Ley et al., 2005, 2006; Turnbaugh et al., 2009; Zhang et al., 2009) vs. quantitative PCR (Armougom et al., 2009; Santacruz et al., 2009; Zhang et al., 2009; Balamurugan et al., 2010; Santacruz et al., 2010; Schwiertz et al., 2010) vs. FISH (Duncan et al., 2008)] and different targets (Phylum Bacteroidetes vs. genus Bacteroides and Phylum Firmicutes vs. Families Ruminococcaceae and Lachnospiraceae + Incertae Sedis XIV (Clostridial clusters IV and XIV, respectively (Collins et al., 1994) in addition to lactobacilli) and by different study populations, because for example in Europe differences in microbiota can be seen in people living in different areas (Mueller et al., 2006).

As Bacteroidetes as Gram-negative and Firmicutes as Gram-positive bacteria have different cell wall structures and compositions, the optimal DNA-extraction method for the two groups is different. Gram-negative bacteria are more easily lyzed and if too rigorous DNA-extraction method is used, it may result in detecting lower numbers and diversity of Bacteroidetes species. With Firmicutes, instead, more rigorous DNA-extraction methods are needed, especially when the matrix is as complex as a human fecal sample. The most commonly used commercial fecal DNA-extraction kit (QIAamp DNA Stool Mini kit; Qiagen, Hilden, Germany, based on heat lysis and enzymatic digestion), which is nowadays considered as the ‘golden standard’ (Dridi et al., 2009), has been originally validated using universal-denaturing gradient gel electrophoresis (DGGEs; Li et al., 2003), and/or spiking experiments (McOrist et al., 2002). However, more recent studies have been shown that mechanical cell disruption results in the detection of the highest bacterial diversity. Furthermore, as compared to enzymatic DNA-extraction, significantly improved DNA-extraction efficiency of Clostridium leptum group (Salonen et al., 2010) [clostridial cluster IV (Collins et al., 1994)] and methanogens (Dridi et al., 2009) is obtained.

The effect of various storage temperatures on the fecal microbiota quantity and composition has also been studied. However, the results are partly contradictory. In a few recent studies, it was shown that storage for a short period of time at different temperatures does not significantly influence the bacterial community structure as detected with pyrosequencing (Roesch et al., 2009; Lauber et al., 2010). In contrast, in several older studies, the storage temperature has been shown to influence the results derived from gut microbiota as detected with terminal-restriction fragment length polymorphism, single-stranded conformation polymorphism analysis, and real-time PCR (Ott et al., 2004; Molbak et al., 2006; Roesch et al., 2009).

The aim of this study was to evaluate whether different storage conditions and the DNA-extraction methods affect the detection of Firmicutes [Eubacterium rectaleBlautia coccoides group (Lachnospiraceae), C. leptum group (Ruminococcaceae), and lactobacilli], Bacteroides, bifidobacteria, and Atopobium group in human fecal samples. Furthermore, two new PCR-DGGE methods – for Bacteroides spp. and C. leptum –group – were optimized and validated in this study.

Materials and methods

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
  9. Supporting Information

Bacterial strains

The 83 bacterial reference strains used for the optimization and validation of PCR and PCR-DGGE for Bacteroides spp. and C. leptum group and quantitative PCR (qPCR) of predominant bacteria, Bacteroides spp., C. leptum group, and E. rectaleB. coccoides group, bifidobacteria and Atopobium group are listed in Supporting Information, Table S1. After optimization, negative and positive controls were included in the experiments with samples as well.

Human fecal samples

The fecal samples for sample storage and DNA-extraction analyses were obtained fresh from a healthy female subject (subject 1) (44 years old) and a healthy male subject (subject 2) (51 years old). The main recruiting criterion was a good (normal) intestinal balance (absence of repeating and/or persisting gastrointestinal symptoms). The exclusion criteria were regular GI-tract symptoms, lactose-intolerance, celiac disease, and antimicrobial therapy during the last 2 months prior to the sampling point. The subjects defecated into a plastic container, which was made anaerobic with gas-generators (Anaerocult A mini, Merck, Darmstadt, Germany) placed on the lid of the container. The samples were transported to the laboratory, homogenized, and divided into subsamples in an anaerobic workstation (Don Whitley Scientific Ltd, Shipley, UK) within 0–4 h from the defecation. Part of the sample was further processed fresh, second subsample was stored at 4 °C for 2 days after which it was transferred to −70 °C, third subsample was stored at −20 °C for a week and thereafter at −70 °C, and fourth subsample was transferred directly to −70 °C for storage. The study plan is presented in Fig. 1.

image

Figure 1. The study plan. Univ, predominant bacteria; Erec, Eubacterium rectaleBlautia coccoides group; Clept, Clostridium leptum group; Bfra, Bacteroides spp. ; Lab, Lactobacillus group (comprises of genera Lactobacillus, Leuconostoc, Pediococcus, and Weissella); Ato, Atopobium group (comprises e.g. of genera Atopobium, Eggerthella, and Collinsella).

Download figure to PowerPoint

The fecal samples for optimization and validation of Bacteroides spp. and C. leptum (Clept) group protocols were obtained from two healthy females (34 and 39 years old). The larger study group for diversity and stability studies of Bacteroides spp. and Clept-group consisted of 10 subjects that were 34–62 years of age (three males and seven females). Fecal samples were obtained on three occasions 3 months apart (0, 3, and 6 months). The main recruiting and exclusion criteria were as above. The samples were collected as earlier and maintained at −70 °C until analyzed. Human studies were approved by the ethical committee of VTT Technical Research Centre of Finland, Espoo, Finland.

DNA-extraction

For DNA-extraction experiments six different protocols (FastPrep lysis) (1) 60 s 4.5 m s−1; (2) 60 s 6.5 m s−1; (3) 60 s + 30 s 6.5 m s−1; (4) 60 s + 60 s 6.5 m s−1; (5) 60 s + 60 s + 30 s 6.5 m s−1; (6) 60 s + 60 s + 60 s 6.5 m s−1) with FastDNA Spin kit for Soil (MP Biomedicals, Solon, OH; from hereon referred to as ‘mechanical DNA-extraction’) and two different protocols (1) Gram-negative bacteria and (2) Gram-positive bacteria with QIAamp DNA Stool Mini kit (Qiagen, from hereon referred to as ‘enzymatic DNA-extraction’) were evaluated from different storage conditions (Fig. 1). All the DNA-extraction experiments were performed in duplicate. DNA was extracted as previously described (Maukonen et al., 2006b) from the 0.2 g of samples that were used for studying of the diversity and stability of the Clept-group and Bacteroides spp.

PCR of C. leptum group (Clept-group)

Six different primer combinations, four MgCl2-concentrations, 11 different annealing temperatures, and three different cycle numbers were tested in preliminary PCR-DGGE experiments. Partial 16S rRNA gene of Clept-group was PCR-amplified for DGGE using primer pairs Clept-933 f and Clept-1240-r+GC (Table S2) in addition to Clept-933 f+GC and Clept-1240-r. Optimized PCR amplifications were performed in a total volume of 30 μL containing 1 μL of appropriately diluted template DNA, 0.4 μM of both primers, 0.2 mM dNTP, 1.25 units of Taq polymerase (Invitrogen, Carlsbad, CA) in a reaction buffer with 20 mM Tris–HCl (pH 8.4), 50 mM KCl, and 2.5 mM MgCl2. The PCR program consisted of initial denaturing at 94 °C for 5 min, followed by 30 cycles of denaturing at 94 °C for 45 s, primer annealing at 60 °C for 30 s and elongation at 72 °C for 60 s, and a final extension for 30 min at 72 °C.

PCR of Bacteroides spp.

Ten different primer combinations, six MgCl2-concentrations, 12 different annealing temperatures, and two different cycle numbers were tested in preliminary PCR-DGGE experiments. Partial 16S rRNA gene of Bacteroides spp. was PCR-amplified for DGGE using primer pairs Bact596f and Bacto1080r+GC in addition to Bact596f+GC and Bacto1080r (Table S2). Optimized PCR amplifications were performed in a total volume of 30 μL containing 1 μL of appropriately diluted template DNA, 0.4 μM of both primers, 0.2 mM dNTP, 1.25 units of Taq polymerase (Invitrogen) in a reaction buffer with 20 mM Tris–HCl (pH 8.4), 50 mM KCl, and 1.5 mM MgCl2. The PCR program consisted of initial denaturing at 94 °C for 5 min, followed by 30 cycles of denaturing at 94 °C for 45 s, primer annealing at 58 °C for 30 s and elongation at 72 °C for 60 s, and a final extension for 30 min at 72 °C.

PCR of predominant microbiota, E. rectale -- B. coccoides clostridial group (Erec-group), bifidobacteria, and Lactobacillus group

Partial 16S rRNA gene for the analysis of predominant bacteria was amplified using primers U968-f+GC and U1401-r (Table S2) as described previously (Mättö et al., 2005) and primers 358f+GC and 534r (Table S2) as previously described (Maukonen et al.,2006a) Erec-group was amplified using primers Ccoc-f and Ccoc-r+GC, bifidobacteria with primers Bif164-f and Bif662-GC-r, and Lactobacillus group with primers Lac1 and Lac2GC (Table S1) as previously described (Maukonen et al.,2006b; Maukonen et al., 2008),

Cloning of the PCR-amplified products

PCR amplicons of Clept-group and Bacteroides spp. for DGGE-method validation and PCR amplicons of Clept-group and Erec-group for DNA-extraction method validation were purified using a Qiaquick PCR purification kit (Qiagen) according to the manufacturer's instructions. Thereafter the cloning and analysis of the clones were performed as previously described (Maukonen et al.,2006b). Altogether 144 clones were collected from each specific PCR and sequenced. The good quality sequences (c. 110–140 clones / PCR) were subjected to ClustalW analysis (http://www.ebi.ac.uk/Tools/clustalw2/index.html?)for checking of the sequence similarities. All unequal sequences were thereafter identified through the GenBank database (http://blast.ncbi.nlm.nih.gov/Blast.cgi) using the blast algorithm (Altschul et al., 1990) or using the ‘Classifier’ tool of the Ribosomal Database Project (RDP) II (Wang et al., 2007). Library compare of RDPII (Cole et al., 2009) was used for the classification of sequences derived from DNA-extraction optimization into the phylogenetically consistent higher-order bacterial taxonomy. Each different clone was deposited in the GenBank database and the sequences are available under the accession numbers JN206701JN207127. Phylogenetic analyses were performed using the Kodon software (Applied Mathematics, Sint-Martens-Latem, Belgium).

DGGE analysis of 16S rRNA gene fragments

DGGE analyses of predominant bacteria, Erec-group, Lactobacillus group, and bifidobacteria were performed as described previously (Maukonen et al., 2008). The primer pair Clept-933 f and Clept-1240-r+GC was chosen for the Clept-group DGGE analysis and primer pair Bact596f and Bacto1080r+GC for the DGGE analysis of Bacteroides spp. because of specificity and optimal migration. DGGE analyses of Clept-group and Bacteroides spp. were performed as previously described (Maukonen et al., 2008), with several modifications. Various denaturing gradients were tested and subsequently denaturing gradient from 30% to 60% [where 100% is 7 M urea and 40% (v/v) deionized formamide] was chosen because of optimal amplicon migration and differentiation. Similarity of the PCR-DGGE profiles of the samples obtained from a single subject at different sampling points was compared to evaluate the diversity and temporal stability of the selected bacterial populations. The comparison of the profiles was performed by calculating a similarity percentage using BioNumerics software version 5.1 (Applied Mathematics BVBA). Clustering was performed with Pearson correlation and the unweighted-pair group method (UPGMA). Amplicons with the total surface area of at least 1% were included in the similarity analysis.

After sequence analysis of the clones from the newly developed Clep-DGGE and Bacteroides DGGE, all the clones with different sequences were subjected to either Bacteroides DGGE or Clept-DGGE, after which the migration of each clone was compared to the migration of different amplicons in the original sample.

Quantitative PCR (qPCR) of ‘all’ bacteria, Clept-group, Erec-group, Bacteroides spp., bifidobacteria, and Atopobium group

The specificity of various different primer pair combinations for quantitative amplification were optimized and validated for amplification of partial 16S rRNA gene of predominant bacteria, Clept-group, Erec-group, Bacteroides spp., bifidobacteria, and Atopobium group using the bacteria listed in Table S1. Subsequently the following primer pairs were chosen; 358f and 534r for predominant bacteria, Clept-F and Clept-R3 for Clept-group, g-Ccoc-F and g-Ccoc-R for Erec-group, g-Bfra-F and g-Bfra-R for Bacteroides spp., Bifid-f and Bifid-r for bifidobacteria, and Atopo-f and Atopo-r for Atopobium group (Table S2). High Resolution Melting Master kit (Roche, Mannheim, Germany) using Sybr-Green like chemistry with ResoLight high-resolution melting dye with MgCl2 concentration of 2.5 mM (predominant bacteria and Clept-group), 1.9 mM (Erec-group), 3.1 mM (Bacteroides spp.), 2.5 mM (bifidobacteria), or 2.5 mM (Atopobium group) were used according to the manufacturer's instructions in LightCycler 480 II instrument (Roche). The initial denaturing at 95 °C for 10 min, high-resolution melting (95 °C 1 min, 40 °C 1 min, 65 °C 1 s, 95 °C), and cooling (40 °C, 30 s) steps were identical to all the protocols. The rest of the amplification protocols were as follows: predominant bacteria: initial denaturing at 45 cycles of denaturing at 95 °C for 15 s, primer annealing at 50 °C for 20 s and elongation at 72 °C for 25 s; Clept-group: 45 cycles of denaturing at 95 °C for 10 s, primer annealing at 60 °C for 15 s and elongation at 72 °C for 20 s; Erec-group: 45 cycles of denaturing at 95 °C for 15 s, primer annealing at 64 °C for 20 s and elongation at 72 °C for 25 s; Bacteroides spp.: 45 cycles of denaturing at 95 °C for 15 s, primer annealing at 58 °C for 20 s and elongation at 72 °C for 25 s; bifidobacteria: 45 cycles of denaturing at 95 °C for 10 s, primer annealing at 60 °C for 15 s and elongation at 72 °C for 20 s; and Atopobium group: 45 cycles of denaturing at 95 °C for 10 s, primer annealing at 64 °C for 15 s and elongation at 72 °C for 20 s. All the qPCRs were performed in duplicate (from duplicate DNA-extractions that is all together four replicates). Negative and positive controls were included in all the experiments. Standard curves were created with the help of DNA extracted from a known number of culturable representatives of the bacterial groups (B. coccoides for predominant bacteria and Erec-group, Anaerotruncus colihominis for Clept-group, Bacteroides fragilis for Bacteroides spp., Bifidobacterium longum for bifidobacteria and Atopobium parvulum for Atopobium group) and thereafter qPCR was performed in duplicate from serially diluted standard-DNA as described earlier.

Statistical analysis

The qPCR data were transformed to logarithmic scale to be able to use parametric statistical methods. Mean and standard deviation were calculated for each experiment. Student's t-test (two-sample assuming equal or unequal variances, depending on data-set) was used for the statistical analyses of the results.

Results

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
  9. Supporting Information

Optimization and validation of Clept-group and Bacteroides spp. specific DGGEs

When the specificity of primer pairs Clept-933 f–Clept-1240-r+GC and Bact596f–Bacto-1080r+GC was evaluated using reference strains, the primers gave positive PCR results for all the target bacteria belonging to the Clept-group or genus Bacteroides, respectively. No false-positive results were obtained. All amplicons of the reference strains for Clept-group and Bacteroides spp. migrated differently and could thus be distinguished (data not shown). Two samples were cloned after Clept PCR and Bacteroides PCR to validate the specificity. All the sequenced 204 Clept PCR clones belonged to the C. leptum clostridial cluster IV and 240 Bacteroides PCR clones to Bacteroides spp. The DGGE profiles of the cloned samples and the sequence information obtained from the cloning of Clept-group are presented in Figs S1 and S2. The same information in regard of Bacteroides spp. is presented in Figs S3 and S4.

Clept-group and Bacteroides spp. diversities were on average 20.9 and 9.2 amplicons per sample, respectively. Intraindividual similarity of the follow-up samples was higher than interindividual similarity in both groups that is in 8/10 subjects all the samples from a given subject clustered together (each individual created his/her own cluster, data not shown). The intraindividual similarities of Clept-group and Bacteroides spp. of samples taken 3 months apart were 87.5 ± 9.2% and 85.4 ± 12.1%, respectively, whereas the intraindividual similarities of Clept-group and Bacteroides spp. of samples taken half a year apart were in average 81.7 ± 11.7% and 82.0 ± 16.2%, respectively.

The effect of different DNA-extraction methods and storage conditions on DNA yield

The use of different mechanical lysis methods (DNA-extraction protocols 2–8; Fig. 1) did not affect the DNA yield from fresh samples, whereas with stored samples both the kit and different modifications to the kits caused variations on the DNA yield. The highest DNA yield was achieved with the most rigorous mechanical lysis (DNA-extraction protocol 8; 3 × 60 s; 6.5 m s−1) at all storage temperatures. The DNA yield was about 60% after gentle mechanical lysis (60 s 4.5 m s−1; P < 0.05) and 20% after enzymatic lysis (P < 0.05) as compared to the rigorous DNA-extraction. The storage temperature did not cause significant differences in the DNA yield when the same protocol was applied to the same sample stored at different temperatures.

The effect of different DNA-extraction methods and storage conditions on the diversity and quantity of predominant bacteria

The storage conditions had a significant effect on the diversity of the predominant fecal bacteria of the studied subjects. With mechanical DNA-extraction, the predominant bacterial diversity of fresh samples and samples stored at −70 °C was significantly higher (P < 0.05) than those stored initially at −20 or at 4 °C. When enzymatic DNA-extraction was applied, significant, storage temperature dependent, differences (P < 0.05) were seen only with one subject. However, the predominant bacterial diversity of both subjects was significantly higher (P < 0.005) after mechanical DNA-extraction than after enzymatic DNA-extraction at all storage temperatures tested. Furthermore, the bifidobacteria-associated bands in the predominant bacterial DGGE-profile were weaker when a gentle mechanical (60 s lysis) DNA-extraction protocol was used as compared to the more rigorous mechanical lysis (60 s + 60 s + 60 s, data not shown). After clustering of the predominant bacterial DGGE profiles of both subjects after all different storage – DNA-extraction – PCR combinations, the samples clustered primarily according to the PCR primers used (i.e. V6–V8 region vs. V3–V5 region), secondarily according to the DNA-extraction kit used (regardless of modifications) and thirdly according to the individual, and storage conditions. The similarity between the same samples obtained after DNA-extraction with different commercial kits (e.g. person A, mechanical lysis vs. enzymatic lysis) was 32–38% after amplification of V6–V8 region (Fig. 2) and 48–54% after PCR of V3–V5 region (Fig. 2), whereas the similarity of the same samples between different storage conditions was 89–98% when the similar DNA-extraction protocol was applied (data not shown). Furthermore, the storage conditions did not have a significant effect on the quantity of predominant bacteria as detected with a predominant bacterial qPCR, but the difference between quantity of predominant bacteria after DNA-extraction using different DNA-extraction kits was significant at all storage temperatures (P < 0.005; Fig. 3; results after storage at −70 °C are presented).

image

Figure 2. (a) Predominant bacterial DGGE profiles as detected with amplification of 16S rRNA gene variable region V6–V8 (lanes 1–4) or V3–V5 (lanes 5–8); (b) Eubacterium rectale – Blautia coccoides group-specific DGGE profiles; and (c) Clostridium leptum group-specific DGGE profiles. 1M, sample from subject 1 after mechanical DNA-extraction; 1E, sample from subject 1 after enzymatic DNA-extraction; 2M, sample from subject 2 after mechanical DNA-extraction; 2E, sample from subject 1 after enzymatic DNA-extraction; MM, marker, EM, Erec-group marker, CM, Clept-group marker; all these samples were stored at −70 °C.

Download figure to PowerPoint

image

Figure 3. Difference between numbers of bacterial groups obtained after different DNA-extraction protocols and qPCR from samples stored at −70 °C. The results are expressed as log-values (mechanical lysis – enzymatic lysis; i.e. positive number indicates more efficient mechanical lysis). All other differences, except Bacteroides spp., were statistically significant (P < 0.05). Univ, predominant bacteria, Bfra, Bacteroides spp., Erec, Eubacterium rectaleBlautia coccoides group, Clept, Clostridium leptum group, Bif, bifidobacteria, Ato, Atopobium group.

Download figure to PowerPoint

The effect of different DNA-extraction methods and storage conditions on the diversity, composition, and quantity of Erec-group bacteria

After clustering of the Erec-group DGGE profiles of both subjects after all different storage – DNA-extraction combinations, the samples clustered in three major cluster: cluster 1: subject 1, enzymatic DNA-extraction; cluster 2: subject 2, enzymatic DNA-extraction; and cluster 3: subjects 1 and 2, mechanical DNA-extraction (Fig. S5). Within cluster 3, there were further two sub-clusters for differentiating the two individuals. The similarity of the same samples obtained after DNA-extraction with different kits (within the same storage conditions) was 42–53% (Fig. 2), whereas the similarity of the same sample after different storage conditions was > 95% when similar DNA-extraction protocol was applied (Fig. S5). Clone library analysis confirmed the different bacterial compositions detected with DGGE (Table 1). After enzymatic DNA-extraction, the most prevalent genera detected were Roseburia (39% of clones) and Coprococcus (10%), whereas 37% of the clones belonged to unclassified Lachnospiraceae. After mechanical DNA-extraction, the most prevalent bacterial genera of Erec-group were Blautia (30% of clones), Coprococcus (13%), and Dorea (10%), whereas 27% of the clones belonged to unclassified Clostridiales; only 10% of the clones belonged to genus Roseburia and 11% to unclassified Lachnospiraceae. A few clones (1.6%) belonging to the genus Anaerostipes were detected only after enzymatic DNA-extraction. The Erec-group diversity stayed about the same regardless of the DNA-extraction protocol applied, as detected with both DGGE and clone libraries (Fig. S6). The storage conditions did not have a significant effect on the quantity of Erec-group bacteria as detected with qPCR, when similar protocols were compared. However, the numbers of Erec-group bacteria were significantly higher after mechanical DNA-extraction (P < 0.05; ~2 log unit difference; Fig. 3) than after enzymatic DNA-extraction, regardless of the protocol and storage conditions applied.

Table 1. Phylogenetic classification as determined with the Library compare of RDPII (Cole et al., 2009) of the Eubacterium rectale – Blautia coccoides group clone libraries derived from a single sample (library 1 = enzymatic DNA-extraction; library 2 = mechanical; and chemical DNA-extraction)
RankNameLibrary1Library2Significance
PhylumFirmicutes1261261E0
OrderClostridiales1261261E0
FamilyLachnospiraceae111546E−14
GenusDorea1121.83E−3
GenusCoprococcus13165.55E−1
GenusAnaerostipes202.5E−1
GenusRoseburia49126.68E−8
 Unclassified ‘Lachnospiraceae4614NA
FamilyIncertae Sedis XIV3385.63E−9
GenusBlautia3385.63E−9
 Unclassified Clostridiales1234NA

The effect of different DNA-extraction methods and storage conditions on the diversity, composition, and quantity of Clept-group bacteria

After clustering of the Clept-group DGGE profiles of both subjects after all different storage – DNA-extraction combinations, the samples clustered primarily according to the individual, secondarily (within the primary clusters) according to the used DNA-extraction kit and thirdly according to the storage conditions. The similarity of the same samples obtained after DNA-extraction with different kits (at the same storage condition) was 80–87% (Fig. 2), whereas the similarity of the same sample after different storage conditions was > 94% when similar DNA-extraction protocol was applied (Fig. S7), except for sub-samples stored initially at 4 °C for 2 days. The similarity of samples stored at 4 °C was 84–93% as compared to the same samples stored at −20 or at −70 °C (using identical DNA-extraction protocol). Clone library analysis confirmed the partly different bacterial composition detected with DGGE (Table 2 ). The most prevalent Clept-group genera detected after mechanical DNA-extraction were Faecalibacterium (39%) and Subdoligranulum (37%), whereas after enzymatic DNA-extraction 80% of the clones grouped to genus Subdoligranulum. In addition, the diversity of the Clept-group was higher after mechanical DNA-extraction, as detected with both DGGE and clone libraries (Fig. S6). The storage conditions did not have a significant effect on the quantity of Clept-group bacteria as detected with qPCR, when similar protocols were compared. However, the numbers of Clept-group bacteria were significantly higher after mechanical DNA-extraction (P < 0.05; ~1.5 log unit difference; Fig. 3) than after enzymatic DNA-extraction, regardless of the protocol and storage conditions applied.

Table 2. Phylogenetic classification as determined with the Library compare of RDPII (Cole et al., 2009) of the Clostiriudm leptum group clone libraries derived from a single sample (library 1 = enzymatic DNA-extraction; library 2 = mechanical and chemical DNA-extraction)
RankNameLibrary1Library2Significance
PhylumFirmicutes1121169.99E−1
ClassClostridia1111138.96E−1
OrderClostridiales1101139.5E−1
FamilyRuminococcaceae1101128.98E−1
GenusFaecalibacterium6451.98E−9
GenusSubdoligranulum90434.13E−11
GenusButyricicoccus126.52E−1
GenusAnaerotruncus104.83E−1
 Unclassified ‘Ruminococcaceae1222NA
 Unclassified Clostridiales01NA
 Unclassified ‘Clostridia10NA
 Unclassified ‘Firmicutes13NA

The effect of different DNA-extraction methods and storage conditions on the diversity and quantity of Bacteroides spp.

The storage conditions and different DNA-extraction methods did not have an effect on the diversity and composition of Bacteroides spp. All the profiles of a given person, regardless of the protocol or storage conditions applied, were similar (similarity with Pearson correlation > 90%). However, the storage conditions greatly affected the numbers of the Bacteroides spp. The highest numbers of Bacteroides spp. were obtained from fresh samples as detected with qPCR, whereas after a week's storage at −20 °C, the numbers of Bacteroides spp. were significantly lower (1.6–2.5 log reduction, depending on the DNA-extraction modification) than those in fresh samples (Fig. S8).

The effect of different DNA-extraction methods and storage conditions on the diversity and/or quantity of bifidobacteria, Lactobacillus group, and Atopobium group

The different storage conditions and DNA-extraction methods did not affect the bifidobacterial diversity or the composition as detected with bifidobacteria-specific DGGE (data not shown). The storage conditions did not have a significant effect on the numbers of bifidobacteria when similar protocols were compared. However, the numbers of bifidobacteria were significantly higher (P < 0.05; ~3 log unit difference; Fig. 3) after mechanical DNA-extraction than after enzymatic DNA-extraction as detected with qPCR.

The diversity and composition of Lactobacillus group were not significantly affected, as detected with specific DGGE, by the storage conditions when mechanical DNA-extraction was applied. After enzymatic DNA-extraction, the samples did not amplify with the Lactobacillus group-specific PCR at all.

The numbers of Atopobium group bacteria were significantly affected by both storage conditions and used DNA-extraction protocols as detected with qPCR. After mechanical DNA-extraction, the number of Atopobium group bacteria was significantly higher (P < 0.05; 2.5–4.5 log unit difference; Fig. 3) at all evaluated storage conditions. Furthermore, the numbers of Atopobium group bacteria were significantly higher (P < 0.05; > 1 log unit difference) after initial storage at −20 °C for a week or at 4 °C for 2 days than those from fresh samples or samples stored solely at −70 °C.

Discussion

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
  9. Supporting Information

Recently there have been several human health-related microbiota studies with partly contradictory results regarding obesity-related microbiota and bifidobacterial abundance of baby microbiota. As it is likely that at least some of the differences could be explained by the methodology applied, we evaluated the impact of commonly used commercial DNA-extraction kits (with several modifications) and storage temperatures on most prevalent human gut microbial groups. There are a few recent (Dridi et al., 2009; Ariefdjohan et al., 2010; Salonen et al., 2010) and older (Zoetendal et al., 2001; McOrist et al., 2002; Li et al., 2003) studies in which the effect of DNA-extraction on results derived from human fecal samples has been studied. However, in those studies where the commonly used commercial kits have been applied, only the ‘universal’ level of predominant bacteria has been studied (McOrist et al., 2002; Li et al., 2003; Ariefdjohan et al., 2010), or the study has focused on a specific microbial group, such as methanogens (Dridi et al., 2009). On the other hand, in a recent study (Salonen et al., 2010) in which the gut microbiota was studied in more detail, different DNA-extraction protocols – as compared to the ones we used – were applied. The impact of storage conditions on diversity and composition of fecal microbiota has also been studied (Molbak et al., 2006; Roesch et al., 2009; Lauber et al., 2010), but to our knowledge, this is the first study in which both the effect of DNA-extraction and storage conditions on diversity, composition, and numbers of the most prevalent fecal bacterial groups are studied from the same samples.

Our findings showed that the commercial DNA-extraction kits had a significant effect on both composition and numbers of abundant human fecal microbial groups. The numbers and composition of Bacteroides spp. were not significantly affected by the various DNA-extraction protocols, which is in line with the previous findings (Salonen et al., 2010). However, the storage temperature had a significant effect on the quantity of Bacteroides spp., causing > 1 log reduction with all tested storage condition as compared to the same sample as fresh. These findings may partly explain why no Bacteroidetes were found in the study of Gill et al. (2006). The clostridial populations, namely E. rectale – B. coccoides (Erec) group and C. leptum (Clept) group, were significantly affected in both composition and numbers by the DNA-extraction protocol used. The numbers of both clostridial groups were significantly lower when enzymatic DNA-extraction kit was used (~2 log reduction) as compared to mechanical DNA-extraction kit. In addition, the composition of Erec-group was different when different commercial kits were applied to the same samples. The most prevalent genus detected after enzymatic DNA-extraction was Roseburia (39% of clones), whereas after mechanical DNA-extraction, the most prevalent genus was Blautia (30% of clones). The same phenomenon was also noticed with the Clept-group bacteria; the most prevalent Clept-group genera detected after mechanical DNA-extraction were Faecalibacterium (39%) and Subdoligranulum (37%), whereas after enzymatic DNA-extraction, 80% of the clones grouped to genus Subdoligranulum. The higher proportion of genus Faecalibacterium after rigorous DNA-extraction has also been noted in earlier studies with different protocols (Salonen et al., 2010).

Most of the obesity-related studies that have obtained similar results, that is, that the relative proportion of Bacteroidetes decreases and the relative proportion of Firmicutes increases in obese human (Ley et al., 2006), have used QIAmp DNA Stool Mini Kit (Santacruz et al., 2009; Zhang et al., 2009; Balamurugan et al., 2010; Santacruz et al., 2010), whereas in those studies, in which there has been no difference between obese and lean subjects or the results have been contradictory to the initial findings, a more rigorous DNA-extraction protocol has been applied (Mai et al., 2009; Schwiertz et al., 2010; Arumugam et al., 2011) or the samples have been studied with fluorescent in situ hybridization (Duncan et al., 2007; Collado et al., 2008; Duncan et al., 2008; Mai et al., 2009). As our results demonstrate that the DNA-extraction protocol has a major effect on the clostridial populations while having no effect on the Bacteroides population, it may be that the contradictory results are, at least partly, caused by different DNA-extraction protocols.

Another recent example of contradictory findings has involved the bifidobacterial populations of baby feces. Bifidobacterial populations have been found to constitute a dominant part of baby feces many decades ago by culture-based methods (Bullen et al., 1976; Stark & Lee, 1982). With molecular techniques the results have, however, been partly contradictory. There are numerous studies conducted with molecular techniques in which bifidobacteria have been shown to dominate baby fecal microbiota (Harmsen et al., 2000b; Favier et al., 2002; Magne et al., 2006; Fallani et al., 2010), but then there are those in which bifidobacteria have been found to constitute only a minor part of the infant microbiota (Palmer et al., 2007). Similarly to the obesity issue, also this inconsistency may result from different DNA-extraction techniques applied. In those molecular studies where bifidobacteria have been shown to predominate in the baby feces, mechanical DNA-extraction has been applied (Favier et al., 2002; Magne et al., 2006) or the samples have been studied with FISH (Harmsen et al., 2000b; Fallani et al., 2010). In those studies where the authors have concluded that bifidobacteria constitute only a minor part of the baby fecal microbiota, enzymatic DNA-extraction using the same commercial kit as in this study has been applied (Palmer et al., 2007). Because our results showed that with enzymatic DNA-extraction, the number of bifidobacteria may be even 3 log units lower than with rigorous mechanical DNA-extraction, differences in DNA-extraction likely explain these contradictory results. In addition, Nakamura et al. (2009) showed that when enzymatic DNA-extraction was applied bifidobacterial abundance was 0.1–1.7%, whereas when FISH was applied to the same samples the bifidobacterial abundance increased to 20.7–83.5% in baby feces. Furthermore, previously it has been found (Salonen et al., 2010) that with more rigorous mechanical disruption, the proportion of Actinobacteria (e.g., bifidobacteria and Atopobium group) increased. Interestingly, the protocol that yielded the lowest levels of Actinobacteria was based on the same kit that we used for enzymatic DNA-extraction (Salonen et al., 2010). However, we used the kit according to the manufacturer's instructions, whereas Salonen et al. (2010) added 3 × 30 s bead beating and extended the duration of heat lysis in their study. Even with these modifications, the proportion of Actinobacteria was low.

The highest numbers of most of the studied bacterial groups were detected from fresh samples and from samples stored at −70 °C. Unexpectedly, the highest numbers of Atopobium group were detected after an initial storage at 4 °C for 2 days. The unexpected effect of storage conditions on Atopobium group numbers was confirmed with FISH (data not shown). The numbers of Atopobium group bacteria were 5–6 log units higher in a sample that was initially stored at 4 °C for 2 days and thereafter at −70 °C and when DNA-extraction was performed mechanically (3 × 60 s) than on the same sample that was stored at −70 °C and the DNA-extraction was performed enzymatically. There was no clear explanation for this phenomenon in the literature. The only possibly relevant finding was that Collinsella spp. cell wall contains a unique A4β-type peptidoglycan (Kageyama et al., 1999). Therefore it is possible that the cell wall structure is extremely difficult to lyse without the extra stress of storage at 4 °C. However, these results may explain, why in some studies the Atopobium group bacteria are not considered to be part of the normal dominant microbiota (Eckburg et al., 2005), whereas in others, especially those conducted with FISH, Atopobium group bacteria are shown to constitute 1–8% of the total population of the human gut microbiota (Harmsen et al., 2000a; Matsuki et al., 2004; Lay et al., 2005; Mueller et al., 2006).

In conclusion, rigorous mechanical lysis enables detection of higher bacterial numbers and diversity from human fecal samples. As it was shown that the results of clostridial and actinobacterial populations are highly dependent on the DNA-extraction methods applied, the use of different DNA-extraction protocols may partly explain the contradictory results previously obtained in regard of obesity related and infant microbiota.

Acknowledgements

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
  9. Supporting Information

This study was supported by EU-funded projects TORNADO (FP7-KBBE-222720) and ETHERPATHS (FP7-KBBE-222639), TEKES funded FIBREFECTS and by the Portuguese Foundation for Science and Technology, reference SFRH/BD/40920/2007. Ms Marja-Liisa Jalovaara and Ms Tiina Hyytiäinen are greatly acknowledged for their excellent technical assistance.

References

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
  9. Supporting Information
  • Altschul SF, Gish W, Miller W, Myers EW & Lipman DJ (1990) Basic local alignment search tool. J Mol Biol 215: 403410.
  • Ariefdjohan MW, Savaiano DA & Nakatsu CH (2010) Comparison of DNA extraction kits for PCR-DGGE analysis of human intestinal microbial communities from fecal specimens. Nutr J 9: 23.
  • Armougom F, Henry M, Vialettes B, Raccah D & Raoult D (2009) Monitoring bacterial community of human gut microbiota reveals an increase in Lactobacillus in obese patients and methanogens in anorexic patients. PLoS ONE 4: e7125.
  • Arumugam M, Raes J, Pelletier E, et al. (2011) Enterotypes of the human gut microbiome. Nature 473: 174180.
  • Balamurugan R, George G, Kabeerdoss J, Hepsiba J, Chandragunasekaran AMS & Ramakrishna BS (2010) Quantitative differences in intestinal Faecalibacterium prausnitzii in obese Indian children. Br J Nutr 103: 335338.
  • Bullen CL, Tearle PV & Willis AT (1976) Bifidobacteria in the intestinal tract of infants: an in vivo study. J Med Microbiol 9: 325333.
  • Cole JR, Wang Q, Cardenas E, et al. (2009) The Ribosomal Database Project: improved alignments and new tools for rRNA analysis. Nucleic Acids Res 37: D141D145.
  • Collado MC, Isolauri E, Laitinen K & Salminen S (2008) Distinct composition of gut microbiota during pregnancy in overweight and normal-weight women. Am J Clin Nutr 88: 894899.
  • Collins MD, Lawson PA, Willems A, Cordoba JJ, Fernandez-Garayzabal J, Garcia P, Cai J, Hippe H & Farrow JAE (1994) The phylogeny of the genus Clostridium: proposal of five new genera and eleven new species combinations. Int J Syst Bacteriol 44: 812826.
  • Dridi B, Henry M, El Khéchine A, Raoult D & Drancourt M (2009) High prevalence of Methanobrevibacter smithii and Methanosphaera stadtmanae detected in the human gut using an improved DNA detection protocol. PLoS ONE 4: e7063.
  • Duncan SH & Flint HJ (2008) Proposal of a neotype strain (A1-86) for Eubacterium rectale. Request for an Opinion. Int J Syst Evol Microbiol 58: 17351736.
  • Duncan SH, Belenguer A, Holtrop G, Johnstone AM, Flint HJ & Lobley GE (2007) Reduced dietary intake of carbohydrates by obese subjects results in decreased concentrations of butyrate and butyrate-producing bacteria in feces. Appl Environ Microbiol 73: 10731078.
  • Duncan SH, Lobley GE, Holtrop G, Ince J, Johnstone AM, Louis P & Flint HJ (2008) Human colonic microbiota associated with diet, obesity and weight loss. Int J Obes 32: 17201724.
  • Eckburg PB, Bik EM, Bernstein CN, Purdom E, Dethlefsen L, Sargent M, Gill SR, Nelson KE & Relman DA (2005) Microbiology: diversity of the human intestinal microbial flora. Science 308: 16351638.
  • Fallani M, Young D, Scott J, et al. (2010) Intestinal microbiota of 6-week-old infants across Europe: geographic influence beyond delivery mode, breast-feeding, and antibiotics. J Pediatr Gastroenterol Nutr 51: 7784.
  • Favier CF, Vaughan EE, De Vos WM & Akkermans ADL (2002) Molecular monitoring of succession of bacterial communities in human neonates. Appl Environ Microbiol 68: 219226.
  • Gill SR, Pop M, DeBoy RT, Eckburg PB, Turnbaugh PJ, Samuel BS, Gordon JI, Relman DA, Fraser-Liggett CM & Nelson KE (2006) Metagenomic analysis of the human distal gut microbiome. Science 312: 13551359.
  • Harmsen HJM, Wildeboer-Veloo ACM, Grijpstra J, Knol J, Degener JE & Welling GW (2000a) Development of 16S rRNA-based probes for the Coriobacterium group and the Atopobium cluster and their application for enumeration of Coriobacteriaceae in human feces from volunteers of different age groups. Appl Environ Microbiol 66: 45234527.
  • Harmsen HJM, Wildeboer-Veloo ACM, Raangs GC, Wagendorp AA, Klijn N, Bindels JG & Welling GW (2000b) Analysis of intestinal flora development in breast-fed and formula-fed infants by using molecular identification and detection methods. J Pediatr Gastroenterol Nutr 30: 6167.
  • Kageyama A, Benno Y & Nakase T (1999) Phylogenetic and phenotypic evidence for the transfer of Eubacterium aerofaciens to the genus Collinsella as Collinsella aerofaciens gen. nov., comb. nov. Int J Syst Bacteriol 49: 557565.
  • Lauber CL, Zhou N, Gordon JI, Knight R & Fierer N (2010) Effect of storage conditions on the assessment of bacterial community structure in soil and human-associated samples. FEMS Microbiol Lett 307: 8086.
  • Lay C, Rigottier-Gois L, Holmstrøm K, et al. (2005) Colonic microbiota signatures across five northern European countries. Appl Environ Microbiol 71: 41534155.
  • Ley RE, Bäckhed F, Turnbaugh P, Lozupone CA, Knight RD & Gordon JI (2005) Obesity alters gut microbial ecology. P Natl Acad Sci USA 102: 1107011075.
  • Ley RE, Turnbaugh PJ, Klein S & Gordon JI (2006) Microbial ecology: human gut microbes associated with obesity. Nature 444: 10221023.
  • Li M, Gong J, Cottrill M, Yu H, De Lange C, Burton J & Topp E (2003) Evaluation of QIAamp® DNA Stool Mini Kit for ecological studies of gut microbiota. J Microbiol Methods 54: 1320.
  • Magne F, Hachelaf W, Suau A, Boudraa G, Mangin I, Touhami M, Bouziane-Nedjadi K & Pochart P (2006) A longitudinal study of infant faecal microbiota during weaning. FEMS Microbiol Ecol 58: 563571.
  • Mai V, McCrary QM, Sinha R & Glei M (2009) Associations between dietary habits and body mass index with gut microbiota composition and fecal water genotoxicity: an observational study in African American and Caucasian American volunteers. Nutr J 8: 49.
  • Matsuki T, Watanabe K, Fujimoto J, Takada T & Tanaka R (2004) Use of 16S rRNA gene-targeted group-specific primers for real-time PCR analysis of predominant bacteria in human feces. Appl Environ Microbiol 70: 72207228.
  • Mättö J, Maunuksela L, Kajander K, Palva A, Korpela R, Kassinen A & Saarela M (2005) Composition and temporal stability of gastrointestinal microbiota in irritable bowel syndrome – A longitudinal study in IBS and control subjects. FEMS Immunol Med Microbiol 43: 213222.
  • Maukonen J, Saarela M & Raaska L (2006a) Desulfovibrionales-related bacteria in a paper mill environment as detected with molecular techniques and culture. J Ind Microbiol Biotechnol 33: 4554.
  • Maukonen J, Mättö J, Satokari R, Söderlund H, Mattila-Sandholm T & Saarela M (2006b) PCR DGGE and RT-PCR DGGE show diversity and short-term temporal stability in the Clostridium coccoides-Eubacterium rectale group in the human intestinal microbiota. FEMS Microbiol Ecol 58: 517528.
  • Maukonen J, Mättö J, Suihko M & Saarela M (2008) Intra-individual diversity and similarity of salivary and faecal microbiota. J Med Microbiol 57: 15601568.
  • McOrist AL, Jackson M & Bird AR (2002) A comparison of five methods for extraction of bacterial DNA from human faecal samples. J Microbiol Methods 50: 131139.
  • Molbak L, Sommer HM, Johnsen K, Boye M, Johansen M, Møller K & Leser TD (2006) Freezing at −80°C distorts the DNA composition of bacterial communities in intestinal samples. Curr Issues Intest Microbiol 7: 2934.
  • Mueller S, Saunier K, Hanisch C, et al. (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: 10271033.
  • Nakamura N, Gaskins HR, Collier CT, et al. (2009) Molecular ecological analysis of fecal bacterial populations from term infants fed formula supplemented with selected blends of prebiotics. Appl Environ Microbiol 75: 11211128.
  • Ott SJ, Musfeldt M, Timmis KN, Hampe J, Wenderoth DF & Schreiber S (2004) In vitro alterations of intestinal bacterial microbiota in fecal samples during storage. Diagn Microbiol Infect Dis 50: 237245.
  • Palmer C, Bik EM, DiGiulio DB, Relman DA & Brown PO (2007) Development of the human infant intestinal microbiota. PLoS Biol 5: 15561573.
  • Roesch LF, Casella G, Simell O, Krischer J, Wasserfall CH, Schatz D, Atkinson MA, Neu J & Triplett EW (2009) Influence of fecal sample storage on bacterial community diversity. Open Microbiol J 3: 4046.
  • Salonen A, Nikkilä J, Jalanka-Tuovinen J, Immonen O, Rajilić-Stojanović M, Kekkonen RA, Palva A & de Vos WM (2010) Comparative analysis of fecal DNA extraction methods with phylogenetic microarray: effective recovery of bacterial and archaeal DNA using mechanical cell lysis. J Microbiol Methods 81: 127134.
  • Santacruz A, Marcos A, Wärnberg J, et al. (2009) Interplay between weight loss and gut microbiota composition in overweight adolescents. Obesity 17: 19061915.
  • Santacruz A, Collado MC, García-Valdés L, et al. (2010) Gut microbiota composition is associated with body weight, weight gain and biochemical parameters in pregnant women. Br J Nutr 104: 8392.
  • Schwiertz A, Taras D, Schäfer K, Beijer S, Bos NA, Donus C & Hardt PD (2010) Microbiota and SCFA in lean and overweight healthy subjects. Obesity 18: 190195.
  • Stark PL & Lee A (1982) The microbial ecology of the large bowel of breast-fed and formula-fed infants during the first year of life. J Med Microbiol 15: 189203.
  • Turnbaugh PJ, Ley RE, Mahowald MA, Magrini V, Mardis ER & Gordon JI (2006) An obesity-associated gut microbiome with increased capacity for energy harvest. Nature 444: 10271031.
  • Turnbaugh PJ, Hamady M, Yatsunenko T, et al. (2009) A core gut microbiome in obese and lean twins. Nature 457: 480484.
  • Wang Q, Garrity GM, Tiedje JM & Cole JR (2007) Naïve Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl Environ Microbiol 73: 52615267.
  • Zhang H, DiBaise JK, Zuccolo A, et al. (2009) Human gut microbiota in obesity and after gastric bypass. P Natl Acad Sci USA 106: 23652370.
  • Zoetendal EG, Ben-Amor K, Akkermans ADL, Abee T & De Vos WM (2001) DNA isolation protocols affect the detection limit of PCR approaches of bacteria in samples from the human gastrointestinal tract. Syst Appl Microbiol 24: 405410.

Supporting Information

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
  9. Supporting Information
FilenameFormatSizeDescription
fem1257-sup-0001-FigS1.tifimage/tif179KFig. S1. The cloned and sequenced bands from the Clostridium leptum group-specific PCR-DGGE of the subject B1 (n.d. = the clone is not visible in the community profile).
fem1257-sup-0002-FigS2.tifimage/tif142KFig. S2. The cloned and sequenced bands from the Clostridium leptum group-specific PCR-DGGE of the subject B2 (n.d. = the clone is not visible in the community profile).
fem1257-sup-0003-FigS3.tifimage/tif94KFig. S3. The cloned and sequenced bands from the genus Bacteroides-specific PCR-DGGE of the subject B1 (n.d. = the clone is not visible in the community profile).
fem1257-sup-0004-FigS4.tifimage/tif111KFig. S4. The cloned and sequenced bands from the genus Bacteroides-specific PCR-DGGE of the subject B2 (n.d. = the clone is not visible in the community profile).
fem1257-sup-0005-FigS5.docWord document576KFig. S5. Clustering of Erec-group DGGE profiles of both subjects after different storage – DNA-extraction combinations.
fem1257-sup-0006-FigS6.tifimage/tif56KFig. S6. Rarefaction curves of different clones per sample.
fem1257-sup-0007-FigS7.docWord document1068KFig. S7. Clustering of Clept-group DGGE profiles of both subjects after different storage – DNA-extraction combinations.
fem1257-sup-0008-FigS8.tifimage/tif79KFig. S8. Difference in numbers of Bacteroides spp. after all different storage combinations and mechanical DNA-extraction.
fem1257-sup-0009-TableS1.docWord document123KTable S1. Bacterial pure cultures used in this study for optimization of group-specific PCR-DGGEs and real-time PCR methods.
fem1257-sup-0010-TableS2.docWord document90KTable S2. Primers used in the present study.

Please note: Wiley Blackwell is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.