**5. Culture independent approaches**

The viable source of information regarding the microbial players in the soil can be discovered through the biomolecules such as lipids, DNA, RNA and proteins. The extraction procedures of the biomolecules from the soil are another challenge as the content of the soil, structure and humic acids varies from place to place, time to time. Over the years, several procedures has been devised for extracting the biomolecules such as nucleic acids but still there is the compromise in concentration or quality of the biomolecules. However, in recent years there has been huge progress in developing the new techniques for studying the microbes in soil and other environments.

#### **5.1 Microbial lipid based techniques**

As we know fatty acids are the components of the cellular membrane of all living cells, and their composition can reveal the types of organisms present without actual culturing the micro-organisms. The constant proportion of fatty acids microbial cell biomass as well as the presence of signature fatty acid and their profile helps to differentiate major taxonomic group within the community.

### *5.1.1 Phospholipid fatty acid analysis (PLFA)*

Phospholipid fatty acid analysis (PLFA) quantifies a set of biomarkers that track primarily viable biomass, avoids culturing of micro-organisms, and represents insitu conditions. PLFA provides a community measurement that is phenotypic rather than genotypic in nature. It does not give information on species composition but rather is analogous to the ecological concept of functional groups [17]. In a comparison study regarding the PLFA analysis and 16S rRNA gene metabarcoding of bacterial communities across the biomes, the PLFA profiling has been found better in distinguishing bacterial community [18]. It was also noticed that the PLFA profiling was better at detecting community responses to heavy metal pollution [19]. The other method which is also based on fatty acid extraction and profiling is fatty acid methyl ester and was used for estimating microbial biomass and characterizing microbial community composition in soil [20].

#### *5.1.2 Fatty acid methyl ester (FAME)*

Fatty acid methyl ester (FAME) provides information on the microbial community composition based on groupings of fatty acids. The fatty acids are extracted by saponification followed by derivatization to give the respective FAMEs, which are then analyzed by gas chromatography. The pattern thus obtained is compared to a

reference FAME database to identify the fatty acids and their corresponding microbial signatures by multivariate statistical analyses [21]. Bacterial fatty acids are highly conserved due to their role in cell structure and function and are the major constituents of the lipid bilayer of bacterial membranes and lipopolysaccharides. They have been used extensively for taxonomic and identification purposes. Whole cellular FAME content is a bacterial profile and is a direct and stable expression of the cellular genome. The cellular fatty acid pattern is a phenotypic character that is not affected by mutations, acquisition or loss of plasmids. The use of fatty acid analysis by gas chromatography for the identification of bacteria is rapid, efficient, reproducible and used for the identification of both clinical and environmental isolates. It has been used to study microbial community composition and population changes due to agricultural practices. Miura and her associates reported that EL- FAME method was simple and would produce similar results to PLFA method for bacteria in both quantitative and qualitative assessments when comparing different soils across ecosystems [20].

The importance of FAME analysis for the identification of bacteria is based on the large structural differences within these molecules viz., (i) variation in length (8–20 C-atoms), (ii) presence of saturated and monounsaturated fatty acids, (iii) occurrence of branched fatty acids (iso and anteiso fatty acids or methylated within the molecule), (iv) occurrence of cyclopropane fatty acids (17:0c, 19:0c), (v) occurrence of hydroxy-fatty acids with an OH-group at position two or three of the molecule. For classification or identification of bacteria the presence of distinct fatty acids and their relative amount is analyzed and compared with the fatty acid profiles of reference strains [22].

The microbial community characterization using nucleic acids has been further discussed as follows:

#### **5.2 Non-PCR based techniques**

#### *5.2.1 DNA re-association*

DNA re-association kinetics measures the genetic complexity of the microbial community and has been used to estimate microbial diversity. Total DNA is extracted from environmental samples, purified, denatured and allowed to re-anneal. The rate of hybridization or re-association will depend on the similarity of sequences present. As the complexity or diversity of DNA sequences increases, the rate of re-association of DNA will decrease. Under specific conditions, the time needed for half of the DNA to re-associate (the half association value C0t1/2) can be used as a diversity index, as it takes into account both the amount and distribution of DNA re-association. The parameter controlling the re-association reaction is concentration of DNA product (C0) and time of incubation (t), usually described as the half association value, C0t1/2 (the time needed for half of the DNA to reassociate). Under specific conditions, C0t1/2 can be used as a diversity index, as it takes into account both the amount and distribution of DNA re-association [23]. Alternatively, the similarity between communities of two different samples can be studied by measuring the degree of similarity of DNA through hybridization kinetics [24].

#### *5.2.2 Guanine plus cytosine (G+C) content of DNA*

Differences in the guanine plus cytosine (G+C) content of DNA can be used to study the bacterial diversity of soil communities [25]. It is based on the knowledge that microorganisms differ in their G+C content and that taxonomically related

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

groups only differ between 3 and 5%. Even though GC fractionation provides coarse level of resolution as different taxonomic groups may share the same G+C range, it is probably the only technique that is completely independent of any previous knowledge regarding which bacterial populations comprise the community or their genomic content [26]. However, this method can be used with other PCR based methods like DGGE or TGGE for better accessibility of the microbial picture.

### *5.2.3 Reverse sample genome probing (RSGP)*

This method utilizes genome microarrays to analyze microbial community composition. RSGP has four steps: (1) isolation of genomic DNA from pure cultures; (2) cross-hybridization testing to define species with less cross hybridization (<70%). DNA fragments with greater than 70% cross-hybridization are considered to be the same species; (3) preparation of genome arrays by spotting known amounts of denatured genomic DNAs from all identified standards onto a solid support; and (4) random labeling of a defined mixture of total community DNA and internal standard, hybridization of the labeled probe with the genome array and detection and analysis of the individual dot hybridization data [27]. Due to low level of hybridization, low levels of gene expression cannot be quantitated which limits the use of this technique. However, this technique allows the coverage of the uncultured component of environmental microbial communities. Although possible in principle, the problem of linking a specific DNA fragment to a particular strain is formidable and requires extensive characterization of any metagenome through cloning and sequencing.

#### **5.3 PCR based methods**

Targeting the 16S rDNA is used extensively to study prokaryote diversity and allows identification of prokaryotes. The prediction of phylogenetic 18S rDNA and internal transcribed spacer (ITS) regions are increasingly used to study fungal communities. Soil community DNA is extracted, purified and amplified using either specific or universal primers, the resultant products are then separated by various ways and analyzed accordingly.

#### *5.3.1 Highly repeated sequence characterization or microsatellite regions*

During the process of evolution both in prokaryotic and eukaryotic organisms, there is the accumulation of highly repetitive short DNA sequences (1–10 bp) throughout their genomes, which can be used in differentiation between the organisms at species or strain level. Highly repeated sequences are also termed as microsatellite regions and have been used for identification of mycorrhiza. Fingerprints of the PCR-amplified microsatellites can be compared using similarity indices to investigate differences between or among the species. The design of primers is solemnly dependent on the sequence information of microsatellite regions. The use of this method to study microbial diversity may be limited depending on the complexity of the community [12].

## *5.3.2 Random amplified polymorphic DNA*

In 1990, William and his team developed a method which includes amplification of DNA fragments by using short arbitrary primer targeting multiple loci in genomic DNA, generating unique profile (amplicons of various lengths [28]). Both genomic variations between bacterial species and genetic polymorphism between

bacterial strains could be identified based on the differences in the molecular size and the number of DNA fragments obtained (**Figure 2**). RAPD analysis was used to study metagenome diversity in soil microbial community of arid zone plants [29] and in soil affected by industrial pollutants [30].
