*5.3.6 Temperature/denaturing gradient gel electrophoresis (TGGE/DGGE)*

Muyzer with his teammates expanded the use of DGGE to study microbial genetic diversity [40]. In DGGE, DNA is extracted from soil samples and amplified using PCR with universal primers targeting part of the 16S or 18S rRNA sequences. The 5′-end of the forward primer contains a 40 base pair (16S rRNA) or 50 base pair (18S rRNA) GC clamp to ensure that at least part of the DNA remains intact or to avoid the complete dissociation of the amplified products into single strands which might flow away from the gel. This is necessary as the separation of amplified DNA on a polyacrylamide gel with a gradient of increasing concentration of denaturants (formamide and urea) will occur based on melting behavior of the double-stranded DNA (**Figure 3**). TGGE uses the same principle as DGGE except the gradient is temperature rather than chemical denaturants. Polymorphism based on the separation of partially melted 16S rDNA a linear temperature gradient. It represents the sequence variations other than the restriction sites also. Sequence variation among different PCR amplicons determines the melting behavior, and therefore amplicons with different sequences stop migrating at different positions in the gel. However, it covers only less than less than 400 bp of 16S gene. Conservative fragments of available 16S rDNA sequences were mined and searched for candidate primers within the fragments by measuring the coverage rate defined as the percentage of bacterial sequences containing the target. Thirty predicted primers with a high coverage rate (>90%) were identified [41] and can be successfully used for generating DGGE fingerprints. Abundance of denitrifying genes and microbial community structure in volcanic soils [42], assessment of silver nanoparticles on soil bacterial diversity [43],

**Figure 3.** *DGGE profile of the 16S rDNA (V5 region) of soil rhizosphere samples.*

effect of long term fertilization on bacterial and fungal diversity in brown soils [44], Changes of Soil Bacterial Diversity in a Semi-Arid Ecosystem [45] has been successfully studied by using DGGE profiles.

How to get more out of molecular fingerprints remains the question, because estimating the species diversity is the most important factor towards better understanding of the microbial load in the given soil environment. Various investigations indicate that the species richness is the simplest measure of diversity. Any characterized soil environment shows few common species but in greater abundance as compared to more uncommon species harboring in the same environment but in less number therefore, one should consider the species evenness also. A common point of agreement on the diversity is that, species richness and evenness aggregately estimates the diversity and these components should be defined so that they are independent of each other [46]. One of the most commonly used evenness measure is Pielou's evenness index, evenness expresses how evenly the individuals in the community are distributed over the different species [47].

Claude Shannon originally proposed this measure which has been useful in comparing diversity between the different habitats [48]. Shannon index is easy for calculation and interpretation, the Shannon index generally ranges between 1.5 and 3.5 for many ecological habitats. Simpson diversity is less sensitive to richness and more sensitive to evenness; whereas Shannon diversity is more sensitive to evenness. For comparing the similarity between the samples, one can apply Jaccard index or Sorensen index. They are most widely used and are based on the presence/absence of species in paired assemblages and are simple to compute [49]. A modified version of Jaccards has been proposed by Chao and his colleagues to include the effect of unseen shared species, based on either (replicated) incidence or abundance based sample data [50].

As the soils have a dynamic nature, there is always a shift in microbial population during the different time or during different seasons. At a given time, particular number of species significantly dominates over other and vice versa. Therefore, such shift in microbial load can be estimated using different comparison tools. Some of them are cluster analysis [51], moving window analysis [52] or by visual inspection [53] or the Dice index [54] which can be applied for estimation of microbial shift during a defined period of time. The percent change in microbial composition between the two sampling interval can be calculated by subtracting percent similarity (calculated by any of the similarity indices) from 100. This can be done for consecutive sampling points over a period of experimentation. Using that % change values, moving window analysis is plotted between consecutive sampling

### *Soil Metagenomics: Concepts and Applications DOI: http://dx.doi.org/10.5772/intechopen.88958*

points. The rate of change (Δt) value is calculated as the average of the respective moving window curve data points [43]. Higher Δt values represent higher shifts between two successive sampling points.

The above diversity analysis parameters are based on the relative abundance of the species in the given sample and extensively used to analyze denaturing gradient gel electrophoresis profiles.
