Denaturing gradient electrophoresis (DGE) and single‐strand conformation polymorphism (SSCP) molecular fingerprintings revisited by simulation and used as a tool to measure microbial diversity

Abstract

The exact extent of microbial diversity remains unknowable. Nevertheless, fingerprinting patterns [denaturing gradient electrophoresis (DGE), single-strand conformation polymorphism (SSCP)] provide an image of a microbial ecosystem and contain diversity data. We generated numerical simulation fingerprinting patterns based on three types of distribution (uniform, geometric and lognormal) with a range of units from 10 to 500,000. First, simulated patterns containing a diversity of around 1000 units or more gave patterns similar to those obtained in experiments. Second, the number of bands or peaks saturated quickly to about 35 and were unrelated to the degree of diversity. Finally, assuming lognormal distribution, we used an estimator of diversity on in silico and experimental fingerprinting patterns. Results on in silico patterns corresponded to the simulation inputs. Diversity results in experimental patterns were in the same range as those obtained from the same DNA sample in molecular inventories. Thus, fingerprinting patterns contain extractable data about diversity although not on the basis of a number of bands or peaks, as is generally assumed to be the case.

Host species as a strong determinant of the intestinal microbiota of fish larvae

“…2) also revealed significant differences (P<0.05) among the intestinal bacterial structures of the four species, suggesting that each had different relative abundances of dominant bacteria. Although the DGGE technique showed biases in the detection of bacterial taxa (Woodcock et al, 2007), band abundances as reflected by bacterial rank-abundance plots can indicate ecological shifts in the characteristics of bacterial communities (Loisel et al, 2006;van der Gast et al, 2006;Woodcock et al, 2007). Diet and the environment affect the intestinal microflora of fish and mammals (Holben et al, 2002;Savas et al, 2005;Ley et al, 2008), but the same environment and same natural food in the present study did not result in similar intestinal bacteria.…”

Freshwater bacterioplankton richness in oligotrophic lakes depends on nutrient availability rather than on species–area relationships

“…Yet, it has to be assumed that this discrepancy in results between these and our study is very likely methodological in nature. Microbial richness estimates in the above-mentioned studies derived either from communityprofiling techniques (Green et al, 2004;Bell et al, 2005;Reche et al, 2005), an approach heavily criticised for yielding results that have very little to do with the actual richness (Loisel et al, 2006;Blackwood et al, 2007), or from 16S rRNA clone libraries (Horner-Devine et al, 2004). Both, traditional cloning and sequencing and community profiling methodologies, however, markedly undersample communities (Curtis and Sloan, 2005;Curtis et al, 2006;Woodcock et al, 2006) and, thus, most likely provide less reliable richness estimates compared with the 454-pyrosequencing approach employed in our study.…”

PCR-DGGE analysis reveals a distinct diversity in the bacterial population attached to the rumen epithelium

“…This heterogeneous environment could promote bacterial diversity as shown in other ecosystems (Horner-Devine et al, 2004). The biodiversity measured with DGGE should be interpreted with caution though, as there is a limit in the number of OTU (bands) that can be visualised in a gel (Loisel et al, 2006). In conclusion, using PCR-DGGE, we have shown that BEC was different from the communities present in rumen contents of lambs.…”