Keywords

1 Introduction

Environmental soil degradation with long-term continuous cropping involving utilization of chemical fertilizers leads to the imbalance or reduction in nutrient availability and fertility of soil (Dong et al. 2012). The management of soil to ensure its long-term productivity, stability, and fertility is of paramount importance for plant growth. The maintenance of the physical and chemical soil fertility is driven by the metabolic repertoire of the soil microorganisms (Sabale et al. 2019). The soil biological fertility relies on the microbial community, which is termed as the indicators of soil health that directly impacts the functioning of soil ecosystem. Thus, the soil health comprises biological, chemical, and physical properties of soil but is mostly dependent on the activity of microorganisms. The biological measurement of soil health can be inferred from certain robust indicators (e.g. microbial diversity, enzyme activity, and soil organic matter content) that can provide instant information about the current status of soil (Rincon-Florez et al. 2013). Among different soil health indicators, there is increased concern in studying soil microorganisms in their specific environments, as microbial diversity is closely linked to soil structure and function. Moreover, soil microorganisms rapidly respond to any kind perturbations (Jacoby et al. 2017).

2 Soil Microbiome

The soil microbiome is indispensable as it performs key soil services including organic matter decomposition, biogeochemical cycling, aggregates formation, gaseous exchange, and plant growth promotion (Christopher 2017; Naylor et al. 2020). The soil represents the most diverse habitats consisting complex assemblages of bacteria, archaea, viruses, fungi, and other microbial eukaryotes which are collectively referred as the “soil microbiome” (Fierer 2017; Jansson and Hofmockel 2020). The estimate suggests 1000–10,000 bacterial species in per gram of agricultural soils as inferred from the 16S rRNA gene phylotypes (Attwood et al. 2019). The reservoir of microbial communities in soil improves plant growth by affecting nutrients availability, aids in crop residue recycling along with determination of agroecosystems productivity (Van-Der Heijden et al. 2008). The sustainable agriculture depends on the diversity of soil microbes that influences soil fertility. Therefore, the present day research focuses more in managing soil microbiome (Dubey et al. 2019).

The characterization and classification of soil microbiome by typical cultivation approaches (plate count and most probable number) have underestimated the microbial diversity as largest proportion of soil bacteria still remain uncharacterized (Dupont et al. 2016). The majority of the soil isolated microbes belonged to the phyla, namely Proteobacteria, Actinobacteria, Firmicutes, and Bacteroidetes, as these are cultivated easily under laboratory conditions (Hirsch et al. 2010). Due to severe constraints in isolation methods, there is need for switching to the molecular and genetic level approaches that will unearth more comprehensive picture of soil microbiome by discovering new microbial players through in-depth characterization (Agrawal et al. 2015; Sabale et al. 2019). During the last few years, significant improvement has been seen in the development of certain biomarkers and macromolecular probes, rapid and reliable measurements of soil microbial communities (Arias et al. 2005). The measurement of microbial diversity can be classified into phenotypic and molecular based approaches. The determination of true microbial diversity using phenotypic techniques is difficult due to lesser accuracy of the extraction or detection methods (Agrawal et al. 2011). Thus, soil microbiologists have attempted to ameliorate molecular methods. This book chapter emphasized on the recent methods adopted for evaluating soil fertility with focus on strategies for identifying microbial communities via metagenomics.

3 Molecular Approaches for Measuring Soil Microbiome

The molecular approaches to analyze soil microbiome are DNA-based methods, microscopic observation of root colonizing labeled microbes, and labeled nutrient substrates. These new molecular, enzymatic, and organism-based methods have complimented the existing physico-chemical properties and possess ability to evaluate the soil diversity and composition. All the techniques are properly evaluated for their potential to differentiate among various types of soils and their significance in the ecosystem. The current molecular strategies have led to the discovery of unusual microbial diversity, majority of which was uncharacterized so far because of non-culturable nature (Agrawal et al. 2015). Molecular techniques to determine microbial diversity in soil can be categorized into PCR-dependent and PCR-independent techniques. Nucleic acid re-association/hybridization, carbon source utilization profile/community level, physiological profile (CLPP)/BIOLOG, fatty acid methyl ester (FAME) analysis, phospholipids fatty acid (PLFA) analysis are PCR-independent approaches used for measuring microbial communities. Some of the limitations of the aforementioned techniques include dominance of culturable community and preferring microbes that can utilize the available carbon sources. These methods mainly signify metabolic diversity rather than microbial diversity (Fakruddin and Mannan 2013).

4 PCR-Based Approaches

The initial molecular approach for investigating biological community depends on the cloning of target genes isolated from environmental samples (DeSantis et al. 2007). Majority of the genetic fingerprinting techniques relies on PCR amplification which provides information regarding the genetic makeup of microbes. The prokaryotic diversity, identification, and phylogenetic relationships are provided by PCR-based 16S rDNA profile. PCR-based fingerprinting methods of microbial communities involves the extraction of DNA from a culture, a bioreactor, or an environmental sample, followed by the amplification of rRNA/rDNA using the Polymerase Chain Reaction (PCR), and finally an analysis of the DNA amplification products (Ngom and Liu 2014). The PCR-based approaches are distributed into two groups depending on the differential electrophoretic migration on agarose or polyacrylamide gels: (1) size-dependent migration, viz. T-RFLP, ARISA/RISA, RAPD, SSCP, LH-PCR and (2) sequence-dependent migration which includes denaturing gradient gel electrophoresis (DGGE) and temperature gradient gel electrophoresis (TGGE). In all the above-mentioned techniques, community structure of microbial populations can be evaluated from the amplified fragments generated by selected primers. Thus, 16S rDNA-based PCR techniques such as DGGE, TGGE, single-strand conformation polymorphisms (SSCPs), amplified ribosomal DNA restriction analysis (ARDRA), terminal restriction fragment length polymorphisms (T-RFLPs), and ribosomal intergenic spacer analysis (RISA) offer comprehensive information regarding community richness, evenness, and composition present in a sample (Rawat and Johri 2014). All the PCR-based techniques are suitable for tracing the dominant members of the community in complex soil environment with selective amplification of shorter fragments comprising weaker secondary structures (Rincon-Florez et al. 2013). Moreover, these methods are time-consuming with low-throughput, PCR biased and prefer easily extractable DNA that usually leads to confusing and unsuitable results. Further, microarrays have also been developed with already known gene sequences from public databases with regular updating of new gene and genome sequences. But, the application of this technology for reviewing environmental sample still presents numerous limitations (He et al. 2012; Ngom and Liu 2014). Thus, to avoid the hindrance in evaluation of soil microbial communities, metagenomics combined with bioinformatics have been recently used and these new methods are more reliable in soil microbial diversity studies (Liu et al. 2006).

5 Concept of Metagenomics

Although several molecular approaches have been proposed but recently, the exploration of entire genomes existing in a soil sample, i.e. metagenomics, has provided a new strategy for studying microbial diversity bypassing the isolation and cultivation methods of individual species (Mocali and Benedetti 2010). The outgrowth of genomics and metagenomics demonstrated promising strategies that possess the ability to discover the hidden diversity of microbes along with their function in a well-defined manner. Further, advanced sequencing technologies recognized as the Next-Generation Sequencing (NGS) performs the analysis of soil-extracted microbial community DNA directly. The NGS resulted in the production of vast volume of data in a rapid and cost-effective manner. The ability to group the entire genome of any related organisms has permitted evolutionary and comparative studies on large scale that were impossible earlier (Weinstock 2012). The sequencing of soil by metagenomics offers understanding of microbial ecology that is beneficial or detrimental to crop production with the aim to improve agricultural sustainability (Petrosino et al. 2009). The concept of metagenomics and other associated strategies have become the prime technology in many research areas attributed to its efficiency for sequencing large volume of data. This technological advancement has generated a new direction for sequencing large-scale projects (Petrosino et al. 2009).

Metagenomics is basically community genomics which provides access to the genetic makeup of whole communities of organisms present in different ecosystems. It involves the isolation of soil DNA, fragmentation, and insertion of DNA into appropriate vectors followed by DNA cloning and transformation of suitable host cells and then delivering a metagenomic library and further screening of the clone library (Mocali and Benedetti 2010). In metagenomics, the combined genome is randomly sampled from simultaneously existing microbial communities and then sequenced (Ghazanfar et al. 2010). Through the direct assessment of collective genome, metagenomics possesses the potential to provide detailed insight about genetic diversity, species composition, development, and interactions with the microbial communities prevailing naturally in the environment (Fakruddin and Mannan 2013). Mass genome sequencing based shotgun analysis, genomic activity-driven studies aimed to find exact microbial functions, phylogenetic or functional gene expression analysis of genomic sequences, and next-generation sequencing strategies for evaluating entire gene content in environmental samples are the four sub-categories of unselective/untargeted and targeted metagenomics based on the various screening methods. The unselective/untargeted metagenomics involves shotgun analysis and next-generation sequencing, whereas targeted metagenomics includes activity and sequence-driven studies. Due to the cost-effectiveness and ease in DNA sequencing techniques, unselective metagenomic approach has been preferred widely (Neelakanta and Sultana 2013). Targeted metagenomics commonly sequences in parallel and extremely target genes, serving ribosomal RNA (rRNA) as evolutionary clocks. This biomarker relied on the massive databank of rRNA gene sequences (more than 200,000) collected for the reconstruction of the universal Tree of Life which increased exponentially due to targeted and untargeted sequencing. The rRNAs of all the organisms are sufficiently related to each other that they can be recognized as the same molecule but different enough that the differences are a good measure of evolutionary distance (Perito and Cavalieri 2018).

In sequence-based metagenomics, the researcher’s emphasis on finding the complete genetic sequence, i.e., the arrangement of all the nucleotide bases (A, C, G, and T) found in the DNA strands of a sample. The sequence obtained can then be analyzed in several ways which includes utilization of community’s sequence in determination of entire genome of a specific organism or this sequence can also be used to analyze the genome of the community as a whole that offers insight about evolution and population ecology. Further, the function-based metagenomics involves screening of metagenomic libraries for several functions/products, such as genes involved in nutrients cycling and metabolic pathways, vitamins or antibiotics produced by microbes in a community. Scientists can recognize various functions through this method that was known in microbes. Recently through advances in function-based metagenomics technology, researchers can also directly extract novel proteins from a microbial community and identify their metabolites involved in cellular processes. Therefore, the study of soil fertility indicators through metagenomic approach will enhance the soil biological system, which in turn promotes soil fertility and improved productivity.

5.1 Metagenomic-Based Studies on Soil Microbiome

The advancement in high-throughput molecular biology methods over the last decades has resulted in significant increase in the understanding of the soil microbiome (Nannipieri et al. 2019). The metagenomic approach showed enormous potential in unlocking myriad of functions which include identification of uncultivated or new phyla possessing novel traits, understanding metabolic and biochemical activity of microbial players, functional diversity of microbes, finding shifts in microbial diversity associated with stress and disease tolerant plants (Köhl et al. 2014; Dubey et al. 2019). The additional target of metagenomic-based studies is to gain insights into biochemical cycling of nutrients (C, N, P, S, and other elements) summarized in Fig. 14.1 (Myrold et al. 2013). One international effort focusing on sequencing and interpreting the soil metagenome was proposed by combining the abilities of the global scientific community (Vogel et al. 2009) and named the project as the Terra Genome. This international sequencing consortium possesses primary objective of complete sequencing of a reference soil metagenome. The soil system selected for research is Park Grass, an internationally recognized agroecology field experiment that has been running for more than 150 years at the UK agricultural sciences institute, Rothamsted Research (Fujii et al. 2009).

Fig. 14.1
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Summary of the various soil aspects studied by metagenomics

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The rhizospheric and phyllospheric bacterial population of Basmati rice in Pakistan were studied using metagenomic approach by Rasul et al. (2020). The results described the dominance of phylum Proteobacteria, Chloroflexi, Actinobacteria, and Firmicutes at different sites in the rhizosphere than phyllosphere. The plant growth promoting genera, Azospirillum, Bacillus, Brevibacillus, Mesorhizobium, Paenibacillus, Streptomyces, and Sphingomonas were also abundant in rhizosphere. Woźniak et al. (2019) compared rhizospheric and endophytic microbiome of Paulownia trees by Illumina MiSeq sequencing and described higher bacteria and fungi in endosphere samples. The abundant bacterial phyla reported were Actinobacteria and Proteobacteria. The rhizospheric fungal diversity includes Ascomycota, Mortierellomycota, and Basidiomycota, whereas the endophytic diversity involves Olpidiomycota, Oomycota, Ascomycota, and Basidiomycota. Hara et al. (2019) identified functional N2-fixing bacteria associated with sorghum through omics approaches. Here, the roots extracted bacterial cells were studied by metagenome and proteome. Majority of the sequences were assigned to nif HDK of Bradyrhizobium species.

Ahmed et al. (2018) assessed the microbial diversity in the two rhizospheric saline soil samples through metagenomic approach and observed the dominance of halophilic/halotolerant phylotypes affiliated to Proteobacteria, Actinobacteria, Gemmatimonadetes, Bacteroidetes, Firmicutes, and Acidobacteria. Identification of osmotolerant clones SSR1, SSR4, SSR6, SSR2 harboring BCAA_ABCtp, GSDH, STK_Pknb, and duf3445 genes confirmed their function in osmotolerance. The soil metagenomic libraries also reported the abundance and diversity of phosphatase genes using functional metagenome analysis. Similarly, Molina-Montenegro et al. (2018) compared the rhizospheric microbiome using shotgun metagenomic technology and found abundance of bacterial species (98%) followed by eukaryota (1.77%) and archaea (0.22%). The major genera reported in the rhizospheric soil were Proteobacteria, Actinobacteria, Bacteroidetes, Acidobacteria, and Firmicutes. Metagenomic shotgun sequencing and functional annotation by means of eggNOG functional categories showed that metabolism was the highest represented category, followed by cellular process and signaling, and information storage and processing. In the category metabolism, the highest characterized terms were amino acid transport and metabolism, energy production and conversion, carbohydrate transport and metabolism, and inorganic ion transport and metabolism. Baeza et al. (2017) evaluated fungal sequences from Antarctica by amplicon metagenomic analysis and found 87 genera and 123 species, from which 37 genera were not reported previously. Lecanoromycetes and Eurotiomycetes were dominant the fungal classes.

The metagenomic DNA from bulk soil of tomato, vegetables, and native forest extracted by Val-Moraes et al. (2013) represented uncultured fungi. The individual amplified sequences matched with Glomeromycota, Fungi incertae sedis, and Neocallimastigomycota. The tropical mangrove soil microbial diversity was characterized by Ismail et al. (2012) through the metagenome of a Malaysian mangrove soil sample and its microbial ecological roles via next-generation sequencing (NGS). Shotgun NGS data analysis revealed high diversity of ecologically essential microbes from bacteria and archaea domains. Also, an unusually high number of archaea was observed along with abundance of Deltaproteobacteria.

5.2 Metagenomic Insight of Soil Management Practices

Agricultural intensification for increased production resulted in severe food security and adverse impacts on soil fertility, nutrient leaching, and increased greenhouse gas emissions (Hartman et al. 2018; Souza et al. 2018). These conventional agriculture based strategies have affected the biodiversity and functionality of soil microbiome through curtailment of functions performed by microbes and reduction in their species (Souza et al. 2018). Moreover, these practices alter soil physico-chemical as well as biological properties which act as valuable indicators of soil quality and health (Carbonetto et al. 2014). Thus, the adoption of soil conservation practices is required to prevent soil degradation and to maintain active soil biota (Souza et al. 2013). Numerous conservation practices such as tillage, organic fertilization, crop rotations/successions and crop residues retention have resulted in improved sustainability and have promoted beneficial ecosystem services (Srour et al. 2020). Soil metagenomics also unravels the understanding of different soil management approaches (such as tillage, organic fertilizer amendments) aimed for enhancing plant productivity and nutrient acquisition (Attwood et al. 2019). Furthermore, the understanding of aforementioned functions associated with soil microbiome will play an essential role in management of soil fertility. Some of the recent studies highlighting diversity analysis among various soil management practices through metagenomics are summarized in Table 14.1.

Table 14.1 Metagenomic-based studies on diversity and function analysis of soil in different management practices
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5.3 Functional Metagenomic-Based Insight of Soil Enzymes

The soil enzyme activities (β-glucosidase, cellulose, protease, urease, and phosphatase) are directly involved in the nutrient cycling (such as carbon, nitrogen, and phosphorus) and reflect the metabolic requirements of soil microorganisms, which are important in the processing and recovery of key nutrients from detrital inputs and accumulated soil organic matter (Burns et al. 2013; Yang et al. 2017). Soil enzymes are crucial for the functioning of soil because of their role in decomposition and transformation processes (Jesus et al. 2016). The activity of soil enzymes is directly related to the metabolic requirements of the soil community and the available nutrients present in soil. The soil enzymes are categorized into hydrolases and oxidases that decompose substrates and release nutrients to the soil. Another enzyme, urease is associated with microbial N acquisition, as it catalysis the urea decomposition. Microbially produced hydrolytic enzyme, β-1,4-glucosidase decomposes polysaccharides whereas acidic and alkaline phosphatase are associated with P-acquisition (Hai-Ming et al. 2014). The most studied enzymes from the soil metagenome are esterase and lipase attributed to wide potential in industry (Lee and Lee 2013). The molecular methods deliver valuable information on expression and potential of enzymes targeting the abundance of enzyme-encoding genes or transcribed sequences (Baldrian 2009). The soil microbiome harbors numerous novel enzymes which are identified by various metagenosmic studies and summarized in Table 14.2.

Table 14.2 Soil enzymes studies performed by metagenomic strategies
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The application of metagenomic approaches for evaluating soil microbiomes and related functions has facilitated the better understanding of taxonomic, genetic, and functional characteristics of soil microbial community (Fierer et al. 2012). Still there are challenges that need to overcome by combining application of metatranscriptomics, metaproteomics, and metabolomics that are helpful to fill knowledge gaps about genes/protein expression and metabolic interactions (Jansson and Hofmockel 2018). Metatranscriptomics involves study of microbial RNA transcripts produced in a particular ecological sample (Baldrian et al. 2012). Metatranscriptomics approach immediately deciphers gene regulatory response as majority of bacteria exhibit transcriptional gene control that permits quick adaption to change altered environmental conditions at the sampling time (Moran 2009). The steps performed in this technique comprise extraction followed by reverse transcription, amplification, and lastly sequencing of transcripts. The transcript obtained is highly unstable and has shorter life span which is a major bottleneck to this technology (Cabellos-Ruiz et al. 2010). Meta-transcriptomic approach is widely preferred for unfolding microbial nutrient cycling (Barua et al. 2017).

Next, metaproteomics is the characterization of the microbial proteins (Ngom and Liu 2014) extracted from a sample, followed by fractionation, separation using liquid chromatography or two dimension polyacrylamide gel and then detection with tandem mass spectrometry (Zhang et al. 2010). Lin et al. (2013) conducted the metaproteomic profile of rhizospheric soil for elucidation of mechanism involved in yield decline of ratoon sugarcane. The results revealed 143 protein spots with high resolution and repeatability including 38 differentially expressed proteins involved in carbohydrate/energy, amino acid, protein, nucleotide, auxin and secondary metabolisms, membrane transport, signal transduction and resistance, etc. Thus, metagenomics is the predictive of community potential and combining it with the metaphenome will reveal the functional potential of soil communities and links between community genes and functions.

6 Conclusion

Soil microbiome plays an imperious role in cycling of nutrients, mineralization, enzymes production, and improvement of indispensable soil processes that impacts soil fertility. Owing to drawbacks of traditional plate count techniques, molecular methods have offered a desired alternative for exploring soil microbiome. The taxonomists have developed various molecular techniques that permit rapid analysis of desired traits within microbial communities. Several PCR and non-PCR based techniques have been developed to explain functional profiling of natural microbial communities. The advancement in sequencing tools has resulted in the advent of novel and rapid molecular method known as integrated omic approaches that constitutes metagenomics, transcriptomics, metaproteomics, and metabolomics. Metagenomics techniques are based on the direct analysis of DNA extracted from environmental samples and have circumvent the steps of isolation and culturing of microbes. No single technique till date can measure the whole microbial diversity. Biases are introduced at each treatment step as all of these techniques present advantages as well as drawbacks. Advanced screening approaches involving function-driven and sequence-dependent metagenomics will provide deeper insights of soil metagenome that will aid in sustaining crop management and soil fertility.