*5.3.10 Real time PCR (qPCR)*

Real time PCR (qPCR) helps in real time quantification of the active population present in the environmental sample by targeting ribosomal genes specific to different species of the microbial community. It gives an idea of phylogenetic community composition by assessing the range of phyla or classes [63]. Quantitative PCR (qPCR), or real-time PCR, has been used in microbial investigations to measure the abundance and expression of taxonomic and functional gene markers. Unlike traditional PCR, which relies on end-point detection of amplified genes, qPCR uses either intercalating fluorescent dyes such as SYBR Green or fluorescent probes (TaqMan) to measure the accumulation of amplicons in real time during each cycle of the PCR. Software records the increase in amplicon concentration during the early exponential phase of amplification which enables the quantification of genes (or transcripts) when they are proportional to the starting template concentration. When qPCR is coupled with a preceding reverse transcription (RT) reaction, it can be used to quantify gene expression (RT-qPCR). qPCR is highly sensitive to starting template concentration and measures template abundance in a large dynamic range of around six orders of magnitude. Several sets of 16S and 5.8S rRNA gene primers have been designed for rapid qPCR based quantification of soil bacterial and fungal microbial communities [64]. Aparna and his co-workers investigated the quantification of 16S rDNA, *amoA* and *nifH* genes in organic and inorganic cropping soil using qPCR, which shown a clear 1.8 fold increase in both organic cropping and organic orchard soils whereas the abundance of *amoA* gene decreased by 22- and 11-folds in organic cropping and orchards [65].

### **5.4 Sequencing based methods**

#### *5.4.1 Clone library sequencing*

Clone library sequencing involves extraction of environmental DNA followed by amplification of partial or full length of 16S rRNA (27f (5′-AGRGTTTGAT CMTGGCTCAG)) and 1492R (5′-GGTTACCTTGTTACGACTT). The amplified sequences are then ligated and cloned in a suitable vector. Further the clones containing organism-specific 16S rRNA gene fragments are purified and sequenced from each terminus. Sequences are then assembled and checked for QC. Phylum, class, order, family, subfamily, or OTU placement are determined when a clone surpasses similarity thresholds of 80, 85, 90, 92, 94, or 97%, respectively. When similarity to nearest database sequence falls below 94%, the clone is considered to represent a novel subfamily and a novel class was denoted when similarity is less than 85% [66]. A comprehensive study was done to characterize the relative abundance, diversity and composition of acidobacterial communities across a range of soil types using clone library analyses [67]. Vázquez and co-workers collected the soil and sediment samples from the coastal region in response to diesel spill, based on DGGE analysis they selected six soils and two sediment samples for identification of bacterial community structure using clone library analyses [68].

#### *5.4.2 Amplicon sequencing*

Soil DNA is extracted and then 16S/18S rDNA genes are amplified by using specific set of specific primers targeting variable regions of 16S/18S rDNA, followed by purification of fragments using magnetic beads, then adapters are ligated and the library of fragments (clones) is amplified and the samples are sequenced using NGS platform. The dataset obtained after sequencing can be

compared with Ribosomal Database Project (RDP) for identification of microbial community harboring in environmental sites [69]. Using NGS, it is possible to resolve highly complex microbiota compositions with greater accuracy, as well as to link microbial community diversity with niche function. The soil DNA was extracted and the two step PCR amplification was done using domain-specific primer, 515F/806R (for prokaryotes), F1427/R1616 (for eukaryotes) or ITSF1/ ITSF2 (for fungi) followed by purification and amplicon sequencing using illumina platform for identification of soil bacterial and fungal community. [70]. Schöler and colleagues has stated brief highlights regarding the crucial steps that should be considered for accurate analysis and data interpretation while opting for amplicon sequencing using marker genes [71].

#### *5.4.3 Shotgun metagenome sequencing*

Exploration of microbial universe in environmental systems through shotgun metagenome sequencing allows us to investigate deeper strata of the microbial communities and provides an unbiased view on the phylogentic and functional composition of the environmental microbial communities [21]. For sequencing of the soil or environmental DNA the steps involved are, extraction of high quality total community DNA followed by fragmentation to obtain desired length of fragments, which are further purified followed by amplification and sequencing using desired or available sequencing platform. The data set thus obtained is analyzed with the offline (MEGAN) or web- based software (MG-RAST) for visualization or comparison of the pictures of microbial world. Although shotgun metagenomic sequencing does not involve the biased amplification of 16S rRNA genes, the relative organism abundances inferred from metagenomic sequences vary significantly depending on the DNA extraction and sequencing protocol utilized [72]. Utilizing the Illumina sequencing platform, the impact of N fertilization on soil microbial communities was studied, where the field was cultivated under soybean and corn [73]. Luo and is team reported whole-genome shotgun metagenomic analysis of microbial communities of temperate grassland soils that experienced 2°C infrared heating for 10 years and observed that the heated communities showed significant shifts in composition and predicted metabolism, and these shifts were community wide as opposed to being attributable to a few taxa. Key metabolic pathways such as cellulose degradation (~13%), CO2 production (~10%), and to nitrogen cycling (~12%), were enriched under heating, which was consistent with independent physicochemical measurements. [74].
