Applications of Metagenomics: Agriculture, Environment, and Health

Applications of Metagenomics: Agriculture, Environment, and Health examines current metagenomics methods and their applications in soil, polluted environment sites, agriculture production, and health care, with separate sections dedicated to each application area. Special attention is paid to the biotechnological study of novel microbial resources for social welfare. Beyond applications, the book discusses evolving next generation technology and techniques used for carrying out metagenomics studies, and in doing so highlights the latest research and advances in the field, along with ways to adapt these approaches for different study types across the biological sciences.Chapter topics range from metagenomics for studying root microbial communities to microbial diversity of the rhizosphere, fungal diversity, microbial biodiversity in forest environments, the human microbiome, and disease epidemiology, with one chapter dedicated to Covid-19 metagenomics.

• Offers tools to apply evolving next generation sequencing technologies in the detection of disease pathogens, bacteria, viruses, fungi, and parasites across various environments, as well as host response
• Includes separate sections dedicated to topics and current studies in environmental science, agriculture production and health care
• Features chapter contributions from international experts in the field

• Language: English
• Publisher: Academic Press
• Release date: Apr 23, 2024
• ISBN: 9780323984034

Book preview

Preface

Microbes are ubiquitous in nature, present everywhere in both biotic and abiotic environment. Because of their complexities, majorities of these microbes are still unexplored as well as uncharacterized and act as reservoir of genetic and metabolic diversity. The transformation from classical microbiology to modern metagenomics studies requires the development of advanced techniques, new branches of knowledge and specialization. Next-generation sequencing (NGS) techniques allow large-scale analysis of unculturable microbes present in the environment by performing comparative metagenomics, metatranscriptomics, and metaproteomics. The correlation and interpretation of the comprehensive datasets derived from these approaches with varied parameters of environment, agriculture, and health help to decipher the complex functions of microbial communities of that system. High-performance computing technologies have empowered scientists to collect, process, and extract useful novel biological information from a variety of samples and complex datasets. Metagenomics study requires the integration of advanced computational techniques and enrichment of reference databases so that comprehensive analyses of diverse metagenomic datasets are possible.

Soil microorganisms play a vital role in provoking the growth, stress, and defense responses in plants. Exploring the relationship between the huge microbial diversities available in the soil environment and plants using metagenomics techniques is helpful in designing the crop systems. Soil metagenomics study gives insights into the soil nutrient status and reduces the dependence on inorganic fertilizers. Beneficial microorganisms of agricultural importance are crucial for sustainable agricultural production. Metagenomics analysis helps in finding out the microbial community structure of those agriculturally important microorganisms.

Clinical microbiology involves diagnostic as well as identification of pathogens from the patient samples to supervise the management and treatment strategies and surveillance and monitoring of infectious disease outbreaks in the community. The clinical metagenomic mNGS analysis of microbiome and host genetic material isolated from patient samples is rapidly moving from research to clinical laboratories to understand the host−gene−microbial interactions.

The proposed book will highlight the various metagenomics methods and its application in soil, polluted environment sites, agriculture production system as well as in health care. Special attention will be given to biotechnological exploitation of novel microbial resources for social welfare. Besides various methods and applications, the book will discuss different modern bioinformatics tools and techniques used for carrying out the metgenomics study.

The proposed book intends to bring out the latest research and advancements as well as modern experimental/computational tools and techniques in the field of metagenomics in one platform covering different areas of biological sciences, providing the readers with up-to-date information on it. The book would serve as an excellent reference book for researchers and students in the field of metagenomics.

In closing, we would like to express our sincere gratitude to the esteemed authors of the chapters in this book. This book would not have been possible without their valuable efforts. We are also thankful to the members of the Elsevier editorial team for guiding us through the assembly of a useful book on metagenomics.

Section I

Agriculture

Outline

Chapter 1 Meta-omics approaches for understanding and exploring soil microbial communities for sustainable agriculture

Chapter 2 Impact of long-term agricultural management practices on rhizospheric microbiome vis-à-vis soil and plant health

Chapter 3 Plant microbiome: trends and prospects for sustainable agriculture management

Chapter 4 Long-term application of compost influences soil and root microbial communities under diverse rice-based cropping systems

Chapter 1

Meta-omics approaches for understanding and exploring soil microbial communities for sustainable agriculture

Jyotsana Tilgam¹, ², Deepanshu Jayaswal², ³, Mushineni Ashajyothi², ⁴, Jyoti Prakash Singh¹, Adarsh Kumar¹ and Hillol Chakdar¹,    ¹ICAR-National Bureau of Agriculturally Important Microorganisms, Mau, Uttar Pradesh, India,    ²ICAR-Indian Agricultural Research Institute, New Delhi, India,    ³ICAR-Indian Institute of Seed Science, Mau, Uttar Pradesh, India,    ⁴ICAR-Central Agroforestry Research Institute, Jhansi, Uttar Pradesh, India

Abstract

The global population is projected to rise to about 10 billion by the year 2050, hiking the demand for food and energy resources. Moreover, climate change and land degradation are continuously intimidating food security and agriculture sustainability. Soil health maintenance is one of the factors to sustain agroecosystem productivity. Soil microbiota, as a biological component, influences soil properties and health greatly. Along with different hosts and environments, microorganisms interact intimately with one another shaping an interconnecting complex system or community. In the recent past, meta-omics approaches have increased our understanding of soil microbial communities considerably. Meta-omics approaches, namely meta-genomics, meta-transcriptomics, meta-proteomics, and stable isotope probing–omics, offer enormous potential for understanding uncultivated microbial diversity. Overall, meta-omics approaches are utilized to explore microbial community structure, potential, function, activity, and interactions, community shift to climate changes. This chapter is presenting an overview of meta-omics approaches to study microbial communities for sustainable agriculture.

Keywords

Meta-omics; meta-genomics; meta-transcriptomics; meta-proteomics; SIP-omics; microbial community; sustainable agriculture

1.1 Introduction

Food and Agricultural Organization has predicted that world food production must be boosted by 60%–100% by 2050 to suffice the increasing demand of 10 billion escalated population. Meanwhile, climate change, the frequency, duration, and severity of various abiotic and biotic stresses have distressed the agro-ecosystems (Markova et al., 2018). Salinity and drought like abiotic stresses have decreased the arable acreages in both arid and semiarid areas of the globe (Cheeseman, 2016). Subsequent loss in the crop yield is ingrained by pests and plant pathogens (Savary et al., 2019). An intensive agricultural production has extensive use of chemical fertilizers and pesticides; however, their use has severely affected the soil properties and altered biogeochemical cycles in the terrestrial ecosystem. The manufacture of artificial chemical fertilizers and pesticides is not sustainable as well. Synthesis of nitrogenous fertilizer is energy-consuming, for instance, the energy spent to produce 7 kg of nitrogen is equal to energy in 8 L of diesel fuel (Boswell et al., 1985). Phosphorous (P) and potassium (K) fertilizers are obtained from limited mined resources, which are going to exhaust by next century (Ke et al., 2021). Additionally, in many extensive farming areas, the use of inorganic inputs (fertilizers and pesticides) does not enhance productivity due to declined soil fertility (Trivedi et al., 2017). Hence, there is an immediacy to find more economical and sustainable strategies to realize ever-higher crop production in the scenario of climate change and land degradation. In view of this, harnessing the agroecosystem services of soil microbial communities has emerged as a potential option.

Microbial communities are found in almost all ecosystems including ocean, soil, and inside host organisms (animals, plants, and lower eukaryotes) (Aguiar-Pulido et al., 2016). They are pivotal for functioning and health of the ecosystem in which they dwell, and any disparity is perhaps harmful. Microbial communities serve most of the ecosystem services including provisioning services (e.g., food, fuel, freshwater, genetic resources, pharmaceuticals), regulating services (e.g., plant−microbial symbiosis, decontamination, climate and water regulation), and supporting services (e.g., organic matter transformation, plant growth, nutrient cycling, soil fertility, diversity) (Saccá et al., 2017; Marco and Abram, 2019). The soil ecosystem inhabits a complex and extremely diverse biological community possibly due to its vast chemical and physical heterogeneity at microscale that facilitates the formation and maintenance of numerous niches (Barrios, 2007). Soil microbiota (mainly composed of bacteria, archaea, and microfungi) are indispensable as they contribute to biogeochemical cycling (Falkowski et al., 2008), organic matter degradation (Schimel and Schaeffer, 2012) besides affecting biodiversity and productivity of aboveground ecosystems. Plants and other organisms are benefitted by microorganisms in terms essential nutrients, vitamins, and growth factors, protection from biotic and abiotic stress, etc. Microorganisms play key role in affecting plants’ response to altered climate. Numerous studies indicated that plant growth−promoting microbiota positively influenced plants subjected to biotic and abiotic stress. Lau and Lennon (2012) explained that prompt reactions of the surrounding soil microbial communities can buffer plants to drought stress. Phyllobacterium brassicacearum strain STM196 improved drought tolerance and water use efficiency in Arabidopsis thaliana (Bresson et al., 2013). It has been found that Piriformospora indica inoculation in barley plants has both enhanced resistance to Fusarium and Blumeria infections and increased salt tolerance (Waller et al., 2005).

Microbial communities are the main ones responsible for soil homeostatic capabilities as well as soil ecosystem functioning. They are in large number with huge cumulative mass and activity. Fierer et al. (2007) have estimated that a single gram of soil contains more than 10⁶ unique species-level operational taxonomic units. These vast majority of organisms cannot be studied by traditional techniques that have been designed over the last three centuries. Cultivation of microorganisms is highly influenced by the type and concentration of the nutrients used in growth medium, duration, and conditions of incubation. The intricate metabolism, adaptive systems apart from their diverse community, with their spatial allocation, are tough to imitate in the laboratory (Sánchez-Otero et al., 2019). Consequently, it has been extremely difficult to cultivate the plethora of microorganisms actually residing in complex niche such as soil. These drawbacks have been moderately addressed by breakthroughs of meta-genomics and other omic technologies, which have allowed us to understand microbial communities and their relations within, and with, the environment. Meta-omics techniques, namely meta-genomics, meta-transcriptomics, meta-proteomics, and stable isotope probing (SIP)-omics, are now providing new insights into the dynamics of microbiome (Aguiar-Pulido et al., 2016). Meta-genomics provides information about microbial diversity, composition, relative abundance, and genetic potential. Microbial community function, intra- and/or intercommunication with macroorganisms are provided by transcriptomics, proteomics metabolomics, and SIP-omics. Overall, meta-omics promises the entire depiction of microbiota, including DNA, RNA, protein, and metabolites (Liu et al., 2021). The integrative investigation that entails all the meta-omics tools can certainly enable sophisticated comprehensive analysis of microbiomes on earth. The meta-omics approach unveils the huge unculturable microbial communities residing in the environment for novel biotechnological, therapeutic, industrial, and sustainable agricultural applications. This chapter gives insight into different meta-omics tools and their application in exploring microbial communities for sustainable agriculture.

1.2 Effect of climate change on soil microbial communities

Soil microbe−plant interactions are altered by various ecological factors, agriculture practices, and environmental stress factors. Climate change brings variation in community structure, distribution, composition, abundance, and function of soil microbiota. Modification in microbial community structure and composition influence ecosystem functioning, additionally, the modification in their relative abundance controlling the prime processes that have an immediate effect on the rate of that very process (Schimel and Schaeffer, 2012). The altered climate renders direct and indirect consequences on microbial members by influencing plant physiology and plant community composition (Bardgett et al., 2013). However, so far we cannot reliably predict how microbial communities respond to individual or interactive climate change factors. Meta-omics tools offer the reliable basis of characterization of the microbiome in the scenario of climate change.

Environmental changes possibly induce alteration in the plant physiology and rhizodeposition. Under elevated CO2, plant growth and carbon allocation in root increase in many cases, leading to the varying composition of root exudates. This might result in altered microbial biomass as well as respiration rate (de Vries and Griffiths, 2018). Though changes in microbial diversity are inconclusive, certain taxa of bacteria and fungi showed consistent responses in some studies. Elevated CO2 reduced the relative abundance of Acidobacteria (Dunbar et al., 2012; Hayden et al., 2012), while Bacteroidetes and Actinobacteria were found to rise (Nguyen et al., 2011; Hayden et al., 2012). Till now, very few studies have documented the alteration in the abundance of fungal taxa. Few have reported a rise in the relative abundance of Basidiomycota under elevated CO2 conditions (Lesaulnier et al., 2008; Tu et al., 2015). Drought and high temperature might bring similar alterations in plant physiology and possibly cause changes in microbial composition, abundance, or function. Earlier long-term warming studies on the field as well as forests have found an increased abundance of Acidobacteria, Alphaproteobacteria, and Actinobacteria (DeAngelis et al., 2015; Hayden et al., 2012). Under drought stress, soil enriched with the Acidobacteria and alphaproteobacteria, whereas Actinobacterial population decreased, yet quickly achieved their initial abundance level (de Vries and Griffiths, 2018). Wagner et al. (2015) reported that flooding events greatly reduced the biomass of fungi and Gram-negative bacteria in the soil.

1.3 Meta-genomics and its application in sustainable agriculture

Meta-genomics employs to assess the diversity of microbial genome from environmental samples. Initially, metagenomic studies were performed using molecular markers to differentiate archaea and bacteria into taxonomic groups based on families or genera. 16S Ribosomal RNA genes (16S rRNA) have been utilized extensively for taxonomic classification as they are extremely stable and conserved genes with hypervariable regions. Additionally, molecular markers such as gene encoding subunits of cytochrome C oxidase enzyme and some housekeeping genes (gyrB, rpoB, rpoD, recA, atpD, infB, groEL, pmoA, sodA), having conserved function, are often used to find greater microbial diversity (Cortés-López et al., 2020). The advent of high-throughput sequencing technology has modernized microbial diversity study and brought classical environmental studies to different levels. High-throughput sequencing is updated in speed, price, and quality. Meta-genomics describes microbial ecosystem from the perspective of diversity, potential, and population dynamics. It also provides reference genes and genomes to facilitate meta-transcriptome analysis (Shi et al., 2011). The generated metagenomic information applies to many fields, including environmental research, agriculture, therapeutics, etc. It provides insight into microbial community structure and the evolutionary relationship of community members, hence, addresses basic scientific queries related to beneficial microorganisms as well as plant−pathogen interactions. The meta-genomics approach offers to unravel the rhizosphere-associated microbiome diversity and its deployment to increase crop productivity and resistance under different stress (Dubey et al., 2019; Kumar and Dubey, 2020). Suyal et al. (2015) have identified diversified cold adaptive diazotrophs from rhizosphere of Phaseolus vulgaris by targeting nitrogen fixation (nifH), cold shock proteins (csp). In Valverde (2016) and coworkers proved that a small core microbial community dwelling in the rhizosphere together with an arbuscular mycorrhizal fungus along with diverse beneficial microbes may be employed as a biofertilizer because they interplay synergistically and endorse plant growth. Meta-genomics has been applied to decipher microbiome associated with different crop hosts including watermelon (Saminathan et al., 2018), barley (Bulgarelli et al., 2015), corn (Peiffer et al., 2013), and potato (İnceoğlu et al., 2011) for various developmental prospects. Johnston et al. (2019) have conducted in situ warming experiment at approximately 1.1°C above ambient temperature in Alaskan tundra soils for 4.5 years and assessed microbial community shift using meta-genomics. They observed changes in terms of increased abundances of carbohydrate utilization (respiration) genes in surface layer while increased methanogenesis potential in receded permafrost/active layer boundary. The shift of microbial community composition and structure under elevated CO2 conditions has also been studied using metagenomics (He et al., 2010; Deng et al., 2012). In Luo (2014) and coworkers performed a comparative metagenomic analysis to study microbial community shifts in decades of warming. They found that the heated communities exhibited a considerable change in composition and potential function, and the changes were at the community level rather than attributable to only a few taxa (Fig. 1.1).

Figure 1.1 Workflow for meta-genomic analysis for microbial meta-genomic analysis.

Sequencing of 16S rRNA gene provided insights into microbial diversity. The Next Generation Sequencing technologies helps in sequencing millions of DNA molecules at once, which significantly helps in studying the microbial diversity (Escobar-Zepeda et al., 2015). About 454 pyrosequencing was one of the first high-throughput sequencing technologies used for sequencing ribosomal RNA gene amplicons (Logares et al., 2014). The Illumina platform is used in second-generation sequencing and is the best known sequencing technology, albeit it needs much sophisticated bioinformatic analysis than other platforms (Pacheco-Arjona and Sandoval-Castro, 2018). The mass sequencing technologies whole-genome shotgun sequencing (WGS) and shotgun metagenomics sequencing (SMS) provide a clear picture at the taxonomic level by studying metagenomes, in which metagenomic DNA is isolated and sequenced (Zinicola et al., 2015; Ross et al., 2012). Metagenomic sequences provide an advantage that having all the metagenomic DNA, those corresponding to the 16S rRNA gene, and other constituent polymorphic markers are selected for use as molecular taxonomic markers to better classify the microorganisms (Ranjan et al., 2016).

The sequencing methods provide massive data; therefore, it is imperative to use various bioinformatics tools to analyze it (Escobar-Zepeda et al., 2015) (Table 1.1). Since its launch, metagenomics Rapid Annotation using Subsystem Technology (MG-RAST) server is one of the most used applications (Meyer et al., 2008). It performs functional annotations of analyzed sequences and compares with protein as well as nucleotide homology databases besides allowing phylogenetic analysis. Other commonly used bioinformatics tools in the metagenomic studies are MOTHUR (Schloss et al., 2009), which relies on metagenomic information that users add a database with monthly updates, and quantitative insights into microbial ecology (QIIME), which is utilized to analyze microbial communities from bacterial and archaeal data.

Table 1.1

PhaME (Ahmed et al., 2015) is a tool that may be utilized to assess interspecies and interstrain divergence as well as reduce sequencing and assembly errors. PhaME performs phylogenetic and molecular evolutionary analysis using an SNP-based method with whole genomes from databases, raw sequences, and assembled sequences (contigs). This program combines methods for genome-wide alignment, reading mapping, and phylogenetic tree construction, and it employs intrinsic commands to predict the main genome and SNPs, infer trees, and do additional molecular evolution analysis (Ahmed et al., 2015). Other tools, such as VITCOMIC1 (Mori et al., 2018), emphasize the analysis of hypervariable regions of the 16S rRNA gene, combining information from targeted 16S rRNA gene sequencing as well as massive WGS or SMS sequencing to better visualize the phylogenetic composition of metagenomic samples and generate a more accurate record of the microbiome. Similarly, based on 16S rRNA gene−based metagenomic sequencing data, the 16SPIP (Miao) program has been utilized for rapid identification of pathogenic bacteria in clinical samples. As for predictive metagenomics approaches, the PICRUSt (Langille et al., 2013) algorithm exploits evolutionary models to predict metagenomes from 16S rRNA gene data and a reference genome database. This tool has been used with data from soil microbiome samples, mammalian intestines, microbial mats, and humans (Langille et al., 2013).

1.4 Meta-transcriptomics and its application in sustainable agriculture

Gene expression analysis has traveled a long way from a single gene expression to a global transcriptome level through the use of microarrays (Schena et al., 1995). The inception of mass sequencing and RNA-seq has opened new dimensions in the field of the transcriptome, providing insights into the host gene expression profile and expression analysis of complex bacterial communities in a given environment (Wang et al., 2009). Metatranscriptome analysis offers an advantage over metagenomic study as it provides information about transcriptionally active populations (Franzosa et al., 2014). In a study conducted to identify functionally active microbial community in native prairie soil, metatranscriptome analyses revealed that less abundant Acidobacteria were very active than highly abundant Verrucomicrobia (White III et al., 2016). This approach is also suitable to elucidate the shift in the microbial diversity in the contaminated environment. Sharma and Sharma (2018) reported high abundance of bacterial genera exhibiting pesticide degradation and heavy metals detoxification property. They also found that bacteria in this contaminated ecosystem rely on organo-sulfonated compounds for their growth and development. Comparative metatranscriptome analysis performed by Sharma et al. (2019) unveiled a wide response of microbial communities in agriculture and organic soil.

Meta-transcriptomics also revealed that reclamation of land by phytoremediation depends on an extremely complex and unequivocally interacting community of microbial kingdom (Gonzalez et al., 2018). For plant−microbes interaction studies, meta-trascriptome characterizes the microbiome attributing to specific function and deciphers the genes responsible for interactions between microbiota and host. Meta-transcriptomics approach has identified many genes responsible for mutulism of seagrass and microbiota (Crump et al., 2018). Hayden et al. (2018) attempted to reveal the process of suppressive and nonsuppressive Rhizoctonia solani infection in wheat using meta-transcriptomics. In this study, a group of genes was identified, having association with suppression and nonsuppression phenotypes, which could be used as molecular targets for disease management. Meta-transcriptomic survey of mineral and organic soils helped characterize the taxonomic diversity of active protist community (Geisen et al., 2015) (Fig. 1.2).

Figure 1.2 Workflow of meta-transcriptomics from microbial communities.

Meta-transcriptome analysis requires mRNA, constituting only 2%–5% of the total RNA (Peano et al., 2013). Therefore, to enrich mRNA, several approaches have been developed and implemented. Nowadays, mRNA enrichment using magnetic beads that selectively remove rRNA presents an attractive option. In prokaryotes, poly-A tail is lacking, making its selection for cDNA synthesis inapplicable. Meta-transcriptomics generally requires reverse transcription to synthesize cDNA; that is further sequenced by employing similar sequencing platforms as for meta-genomics. The resulting RNA sequence reads are mapped against reference genomes, and the expressed genes are specified according to the sequence reads covering these regions (Benítez-Páez et al., 2014; Peano et al., 2013). Data obtained from metatranscriptomic experiments increase consistently in number and size. Therefore computerized, potent, and high-throughput analyses are necessary to obtain significant results from datasets (Korf, 2013). In recent years, several comprehensive analysis suites, for example, HUMAnN (Abubucker et al., 2012) and MG-RAST (Keegan et al., 2016), came into existence, are extensively used, and provide an end-to-end solution. Table 1.2 presents the list of different bioinformatics tools used in meta-transcriptomic analysis and their comparison.

Table 1.2

1.5 Meta-proteomics and its application in sustainable agriculture

The expression of genes that are located on chromosomes depends on loci, timing, and stimulus. All the genes will not express at the same time. The study of these factors along with the total complement of proteins of any microbial community is known as meta-proteomics. It involves the identification and quantification of total proteins expressed in the microbial community at any particular time or with a particular stimulus. Genomics involves the study of total genome of a cell is a major field of omics, but it has some drawbacks; therefore, we need to go for omics appraoches to understand and elaborate the concept of gene expression. Considering the importance of genomics, information from expressed genes in the form of proteins is more reliable and useful for the analysis of the microbial ecosystem coupled with the microbial genetic and functional variability. The synergistic approach of metagenomics and metaproteomics will improve our understanding of the pattern of gene expression and metabolic pathways (Maron et al., 2007; Wilmes and Bond, 2006). Meta-proteomics functions as bioindicator in various environmental conditions of organisms and comprehension pertinent microbial analysis with the assistance of a sequencing project. Nowadays, the investigation of ecological parameters such as litter decomposition and biogeochemical cycles like carbon cycle is performed by studying meta-proteome (Schneider et al., 2012; Overy et al., 2021). The analytical techniques such as sodium dodecyl-sulfate (SDS) polyacrylamide gel electrophoresis, chromatography, and mass spectrometry (Ms) along with bioinformatic pipelines are the important tools for proteins analysis, which gives information about the microbial community and phylogenetic linkage. Extraction of proteins is a crucial step in the meta-proteomics studies. Among various protocols for protein extraction, SDS-Phenol method has been reported to extract higher numbers of proteins from samples (Keiblinger et al., 2012). The major challenge is database analysis with an algorithm such as estimation of error rate and identification with annotations of peptides (Schiebenhoefer et al., 2019). The meta-proteome analysis by Prophane, PeptideShaker, Unipept, MetaProteomeAnalyzer with the database UniProt, CAZy, EggNOG, and NCBI made the metaproteome analysis more robust and easier (Schiebenhoefer et al., 2020; Van et al., 2020; Verschaffelt et al., 2021). The comparative study of meta-proteome data by metaQuantome and Unipept is easy to use and has the potential to analyze the collected data from variable conditions and stimuli (Verschaffelt et al., 2021; Mehta et al., 2021). To generate a database for comparative study of meta-proteome profiles, various extraction protocols have been evaluated.

With the advancement in proteomics study, it assists to exploit microbial research in multidimensional investigations such as community structure, function, ecology, and evolution. The protein-based study will allow the genotype−phenotype correlations of the environmental microbial community to explore the diversity and their functions in a particular niche (Wilmes et al., 2015). In the soil ecosystem, the plant−microorganism interaction could be studied for a better understanding of rhizospheric metaproteomics. Such studies will assist the purpose of crop designing and the second green revolution for food security (Wang et al., 2011). The pathways and proteins involved in the host−pathogen interactions can be studied by meta-proteomics, which can further aid in drug designing. In the past years, gene expression analysis was the major insight of meta-proteomics, but at present, the study of proteins and their quantification is being used for biomass analysis to determine the microbial community structure, carbon flow, and in-situ study of substrate uptake (Kleiner, 2019). The study of collective proteins from microbes and their analysis for biogas production from agricultural waste biomass will be of great use to meta-proteomics. To enhance the agricultural yield sustainably, study of functional microbes and their diversity is a need in the present time. Understanding the phylogeny, taxonomy, and ecosystem of soil via meta-proteomics can play a very crucial role for further application of agriculturally important microorganisms. The disposals of agricultural wastes are the major problem, which can be solved by identifying and characterizing the fermentation related microbes via meta-proteomics. Further meta-proteomics will help in the comparative study of rhizospheric microorganisms, which would play important role in the agriculture sector.

1.6 Metabolomics and its application in sustainable agriculture

There are different end products of cellular metabolism, which influence the genetic variations and environmental adaptation of microbiome. These cellular end products are said to be metabolomes that include biomolecules such as amino acids, fatty acids, carbohydrates, vitamins, and inorganic chemical species. Structural identification and quantification of the total metabolites of an organism are known as metabolomics. The techniques such as nuclear magnetic resonance (NMR) spectroscopy, chromatography, and Ms are powerful techniques to analyze the metabolites (Idle and Gonzalez, 2007). Metabolic profiling and fingerprinting are the link between phenotypes and genotypes (Fiehn, 2002). In this process, the desired sample is collected, data are processed and analyzed to interpret the biological and functional information. Modern analytical tools such as high-performance liquid chromatography, ultra-performance liquid chromatography, gas chromatography with Ms and NMR spectroscopy help in the detection and quantification of metabolites. No single technology can be used for all metabolites; therefore, an integrated approach must be followed for proper separation, identification, and quantification (Zhang et al., 2012). Further, the generated data need to be managed through bioinformatics tools for better comprehension, analysis, and modeling. Mainly, the metabolome can be categorized into two groups, namely untargeted and targeted metabolomics. The untargeted involves the analysis of all measurable analytes while the targeted metabolomics involves analysis of chemically characterized and annotated metabolites (Roberts et al., 2012). Functional analysis of these metabolites of individuals can be used for diagnostic tools in the form of biomarkers. Due to downstream gene expression, the study of metabolome complements transcriptomics. Metabolomics has wide applications such as modeling microbial, plant, and animal systems for synthetic biology study (Putri et al., 2013). Determination and quantification of target metabolites by best analytical tools are the first conceptual step of metabolic approaches followed by the analysis of unknown and identified metabolites, that is, metabolic profiling. The third step is the analysis by chromatographic, spectrometric, and NMR technologies called metabolomics. Finally, metabolic fingerprinting where the mass profile of metabolites is generated and comparative analysis is performed. Further, it could be used to study the morphology of organisms, in functional genomics and response to various stresses in the biological system (Roessner and Bowne, 2009). The complete metabolic profile is useful for estimation of nutrient composition and yield-related characteristics, which may assist to give new direction toward yield enhancement. Liquid chromatography/Ms principal component analysis–based drug discovery, identification of natural products, and bioprospecting are very common nowadays (Hou et al., 2012). PRIMe is the key software being used in the study of metabolomics. The bioremediation study could also be carried out by metabolomics via isotope distribution analysis and study of mineralization process with biodegradation. Various factors such as pH, temperature, and nutrient concentration affect the extracellular metabolites, which lead to the alteration in agriculturally important microorganism’s metabolism. In the current era of climate change, metabolomics can be applied for the study of microbial interaction with plants and tolerance against various stresses to develop novel nutritive crops. Meta-bolomics can be applied for phenotyping of cultivars, grains quality estimation, enhancement of biostimulant and bioinoculant potential, disease diagnostics, fertilization management for sustainable agriculture, and enrichment of secondary metabolites in crops (HamanyDjande et al., 2020; Chaudhary and Shukla,