We investigated the effectiveness of TRFLP as a biomarker of the human fecal microbial community. We chose TRFLP of the 16S rRNA molecule over other fingerprinting techniques because it has the advantage of being rapid and reproducible (7
). As in other molecular typing methods, there are many variables that could potentially bias the outcome of TRFLP analysis. Here, we tested various DNA extraction parameters that influenced the TRFLP peak composition in community fingerprint profiles including extraction techniques, incubation temperatures, and physical disruption for bacterial cell-lysis. We further evaluated the optimized TRFLP method to quantify methodological and inter-individual variation in microbial community profiles. We concluded that the optimum method for extracting fecal bacterial genomic DNA and subsequent TRFLP analysis was using QIAamp DNA stool minikit with 95 °C incubation and 0.5-1 min bead beating. Using this approach, we can reliably detect peaks that represent 1% of the total peak area in a tRFLP trace (). Based on these results, we are confident that this approach can be applied to fecal microbial community analysis for human intervention and observational population-based studies.
We evaluated the effect of a variety of fecal bacterial DNA extraction techniques on DNA yield, DNA quality, and TRFLP profiles. Because the TRFLP method is based upon PCR which can be easily inhibited by compounds that co-extract with environmental genomic DNA, we chose to compare two DNA extraction kits which incorporated steps that removed environmental contaminants from genomic DNA. Although processing time was slightly longer, the quantity and quality of the DNA extracted from fecal samples using the stool kit was better than the soil kit (, ). The composition of the TRFLP profiles also varied depending upon the extraction kit used (). Using different molecular techniques, others have also shown that the stool kit resulted in higher quality DNA and bacterial community profiles (18
). We also confirmed that a higher incubation temperature during the cell lysis procedure improved the DNA yield () although temperature alone had no significant effect on the relative peak area ratios (data not shown). Bead beating fecal samples prior to chemical lysis resulted in higher DNA yield () and also introduced additional peaks in the TRFLP profiles (). Others have found that bead beating affected the composition of community profiles as measured by DGGE, possibly due to more efficient lysis of gram-positive bacteria with dense, thick cell walls containing multiple layers of peptidoglycan that can only be effectively broken by mechanical action instead of sole chemical treatment (24
). However, an extended bead beating time was not an improvement because genomic DNA was sheared and the TRFLP profiles were similar, indicating that a brief bead-beating treatment was sufficient to break most bacterial cells (; ; ). Rantakokko and Jalava (27
) also found increased shearing of genomic DNA correlated with longer bead-beating times. Fecal homogenization before sample processing made the sample aliquoting easier and it reduced the variability of DNA yield and the relative peak area ratio of individual peaks in TRFLP profile (). We also show that we can reliably detect peaks that represent 1% of the total peak area (). Therefore, we concluded that extracting fecal bacterial DNA using the stool kit with 95°C incubation and moderate bead beating (0.5-1 min) on homogenized fecal sample gave representative data without significant omission of peaks from the fecal microbial fingerprinting profile.
Primer choice and nucleotide mismatch between primer and target genomic DNA may influence the resulting TRFLP fingerprint of the gut microbial communities. The universal primers (27f and 1492r) were used to amplify the 16S rRNA genes. We cannot exclude the possibility that different bacterial rRNA species, such as Lactobacillus
, were under-represented because they were not amplified with the same efficiency due to nucleotide differences in these PCR priming regions (26
). Although it was possible that distortion of community composition was introduced here, we believe that this was unlikely to affect our conclusion about the influence of extraction variables on TRFLP patterns. Even though the data might have been biased due to systematic error, the results showed that random error was minimized; thus, the differences among TRFLP profiles were reproducible. In addition, TRFLP analysis can be used to characterize these under-represented groups by using group-specific primers instead of universal primers (4
Several studies have raised concerns of the effects of partial enzyme digestion on TRFLP pattern analysis (10
). In these studies, it was concluded that inconsistent fragment patterns were due to incomplete digestion. Partial digestion could be caused by the blocking of restriction sites, and /or chimeric or 5′ overhang structures of PCR product (10
). In our study, high concentrations of endonuclease and long incubation times were used in an attempt to minimize possible incomplete digestion. Our protocol resulted in consistent relative peak area ratio data of triplicate PCR samples (Fig. , , ) and reliable detection of peaks that represent 1% or more of the total peak area (). Although incomplete digestion could still have existed when enzyme and incubation time were not limiting, there was no adequate way to adjust for this problem. Osborn et al. (2000) suggested that a parallel experiment with reduced amount of enzyme could help identify those potential pseudo terminal restriction fragments (TRFs) which would increase in relative peak area ratio with decreased enzyme concentration (25
). However, exclusion of those TRFs completely may be inadequate because real fragments of the same size would also be excluded from the analysis.
High methodological variability can greatly interfere with inter-sample comparison, particularly in human studies where within and between-individual variance is great (38
). Therefore, it is important to identify and minimize methodological variation. In our study, the level of methodological variability was lower (4.5-8.1 %) than that found in other TRFLP analyses, which ranged from 11.6 to 12.2% (25
). Inter-individual variation in peaks common to all three study participants was 50.3% and the diversity indices varied as well suggesting that composition and relative abundance of the microbial community varied substantially from individual to individual (; ). Similarly, other studies using various molecular typing methods also revealed high person-to-person variation in both gut microbial community composition and relative abundance (24
). However, they did not assess the methodological variability and inter-individual variability at the same time. Our study will be useful for estimating sample sizes required for human population-based studies of disease risk as influenced by the gut microbial community.
In conclusion, for TRFLP analysis, the most effective approach to extract fecal bacterial genomic DNA was using QIAamp DNA stool minikit with 95 °C incubation and 0.5-1 min bead beating. Homogenizing the fecal sample improved sample handling and reduced variance in DNA yield and TRFLP profiles. The TRFLP approach, when standardized, was reproducible and informative for characterizing the microbial community and inter-individual differences in human fecal samples. The advantage of this method is that the TRFLP profiles can be used as discrete units for comparative analysis without knowing their particular content, although clone libraries can be used to identify the composition of the peaks. Moreover, a combinatorial approach of nested primers that are specific for phylogenetic clusters can be used to give more targeted species resolution (4
). However, our goal was to establish overall patterns in order to elucidate similarities and differences between microbial communities among individuals rather than to identify each bacterial species in the fecal samples. With the awareness of its limitations, TRFLP can serve as a useful biomarker of microflora community structure and provide a powerful tool for population-based epidemiologic studies of gut bacteria and health outcomes.