4.1 Introduction

The natural ecosystems which are modified for the production of food and fiber are known agroecosytems. There are several biotic and abiotic factors that are also present in the natural ecosystems. Agroecosytems supports the production of many crops but the environmental factors affect the productivity of crops (Ptaszek 2013). The interaction of biotic and abiotic stress component of environment affects the life as well productivity of crops, and hence, it is quite necessary to study the role of interference and underlying mechanisms of plants to sustain against the challenges. The agroecosysystems also interact positively and negatively with insects, birds, and weeds and contribute in sustainability of crops (GANS 2005).

Functional genomics is the study of function and interactions of genes/proteins by using genome-wide approaches by integrating the data obtained from different processes like DNA sequence, gene expression, transcriptome profiling, and DNA-protein, RNA-protein, and protein-protein interactions. On the basis of above-mentioned information, one can build the model of interaction that regulates the gene expression and other biological processes (Bunnik and Le Roch 2013). In 1998 Weinstein (Weinstein 1998) coined the term “omics” and classified it into genomics, transcriptomics, and proteomics. Functional genomics fills the research gap between the classical gene expression and genome-wide expression and it is the correlation with biological process (Gasperskaja and Kučinskas 2017).

Plants are ultimate source of energy, food, and other valuable compounds. Systems biology allows us to understand how plants used to synthesize the various valuable compounds as well as it also correlate the phenotype and genotype (Kell 2002; Dhondt et al. 2013). The productivity of various plants is decreasing day by day due to the biotic and abiotic factor of environment, and this includes cold, drought, heat, salinity, and heavy metals (Bebber et al. 2013). Plants are very much susceptible to stresses and sometimes all these stress acts simultaneously and plants act accordingly if they unable to process it may die (Ramegowda and Senthil-Kumar 2015; Mushegian 2017).

Various omics approaches as shown in Fig. 4.1 enable the researcher to identify the stress-responsive genes, pathways, and secondary metabolites using genomics and systems biology tools (Pandey et al. 2015). The integrative approach of transcriptomics, proteomics, metabolomics, phenomics, and interactomics with systems biology is used to understand the underlying mechanism in plants during various stress conditions (Kumar et al. 2015; Ben-Amar et al. 2016; Pandey et al. 2016). The systems of plants are well studied and understood by using the omics and systems biology approach and are useful for discovering the marker genes associated with stress and it is the very first step to develop the tolerance variety of crops (Saito and Matsuda 2010; Dhondt et al. 2013).

Fig. 4.1
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Various aspects of functional genomics that are used to understand the effect of stress on plants to understand the mechanism of defence

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In this chapter, we have focused the application of different omics and systems biology approach during biotic and abiotic stress on plants to understand the tolerance strategy of plants.

4.2 Transcriptomics

Gene expression is also affected by modification in DNA by DNA methylation without changing the DNA sequence. This can be determined by using methylation-dependent restriction enzyme (MDRE) or bisulfite conversion and its PCR (Schuster 2007; Zilberman and Henikoff 2007). Other modifications like acetylation, phosphorylation, and ubiquitination also regulate the gene expression (Bannister and Kouzarides 2011), and it can be investigated using chromatin immunoprecipitation (CHIP) (Shendure and Aiden 2012). The information content of an organism is present in its genomic DNA and is expressed through transcription, where transcriptome requires the information content of genome at a particular time and allow us to study the differential expression pattern at a specific condition (Lowe et al. 2017). The term transcriptome was used in 1990 (Piétu et al. 1999), and before this scientists were using serial analysis of gene expression (SAGE) based on Sanger sequencing (Velculescu et al. 1995).

Next-generation sequencing (NGS) is the massively parallel sequencing of the genome under specific conditions to study the genomes using RNA sequencing (RNA Seq) which is high-throughput sequencing technology (Ozsolak and Milos 2011), and it intakes the RNA in nanogram (Hashimshony et al. 2012). It does not require the prior information of genomic sequencing whose sequencing/analysis has to be done (Stefano 2014) and has high accuracy up to 90% in sequencing. NGS methods are also able to detect the SNPs with high technical reproducibility of 99% (Marioni et al. 2008) as explained in Fig. 4.2 the various aspects of NGS.

Fig. 4.2
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The various role of Next generation sequencing used for analysis and understanding the biological process in plants and animals

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DNA microarray has the ability to measure the simultaneous expression of thousands of genes in at the particular stage of cells (Schena et al. 1995). This technique is used to investigate the diagnostic or prognostic biomarkers, disease-associated genes, and the response of gene against a particular drug/stress in plants and animals to understand the mechanism involved in a particular condition (Aguan et al. 2000; Zhang et al. 2005). The first mRNA is isolated from a normal and experimental condition which are transcribed into cDNA and labeled with dye and finally allowed to hybridize with a probe attached on chip to measure the level of mRNA in control and experimental condition (Gibson and Muse 2009). Lots of studies have been performed on various plants during biotic/abiotic stress using transcriptomics approach, and some important studies are listed in Table 4.1.

Table 4.1 List of transcriptomics studies available on various plants to understand the abiotic stress using omics approach
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It has been revealed by a transcriptomic study that many genes undergo differential exposure during exposure of environmental stress. The genes which are upregulated and downregulated are a key player in several biological processes and molecular function. Stress-responsive gene which is overexpressed is involved in different defense mechanism against the environmental stress while underexpressed genes are generally involved in the storage process. Certain metabolic pathways are overexpressed and underexpressed for providing tolerance against heavy metal stress in chickpea plant (Szklarczyk et al. 2017).

4.3 Proteomics

In understanding the biological process, it is necessary to understand the function of the protein in a biological process. Sometimes transcriptomics study does not correlate with proteomics study due to posttranslational modification which changes the function of the protein. Two-dimensional gel electrophoresis can be used to analyze the protein content on a cell where proteins are first separated by size followed by mass spectrometry. LC-MS is also one of the approaches where proteins are first separated by one-dimensional SDS PAGE, then protein is digested and separated by LC and analyzed by MS. Multidimensional protein identification technology is high-throughput technology used for separating protein from complex mixture by digesting proteins into peptides followed by separation on the basis of charge and hydrophobicity and finally analyzed by MS. (Washburn et al. 2001). The signal obtained from MS data is compared with a database for identification of the protein.

Stress associated proteins were differentially expressed during abiotic stress as reported in various literature contributing towards the stress tolerance (Witzel et al. 2009; Hossain et al. 2012; Pérez-Clemente et al. 2013). The different proteomic study has been performed for, e.g., drought stress (Caruso et al. 2008; Mirzaei et al. 2012; Mohammadi et al. 2012; Cramer et al. 2013; Zhang et al. 2016), salt stress (Nam et al. 2012; Zhu et al. 2012), water lodging (Komatsu et al. 2009, 2010, 2013a, b, 2014, Alam et al. 2010a, b), and heat stress (Rollins et al. 2013; Xuan et al. 2013). The complex biological process is analyzed using proteomics in plants during stress (Aghaei and Komatsu 2013; Ghosh and Xu 2014; Gong et al. 2015). The methodology of the proteomics approach for identifying candidate protein during stress condition is shown in Fig. 4.3.

Fig. 4.3
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Flow chart of methodology to perform the proteomics study on plants during stress conditions

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4.4 Interactomics

The union of informatics, biochemistry, and engineering technology enables the researcher to understand the interactions of proteins used to study under interactomics. The omics technology enable the researcher to understand the biological system and interaction of expressed proteins in a cell (the proteome) and genome encoded product along with its interaction in complex biological network i.e. the interactome. It is a fast-growing area of systems biology for understanding the biological process and regulatory network during biotic and abiotic stress in plants. The study of interactions in signal transduction, transcriptional regulation, metabolic pathways, and other biological processes is said as interactomics. Lots of approaches have been developed for studying the interactome like in silico, in vivo, and in vitro (Rao et al. 2014). The first approach includes the computational analysis and text mining; in vivo includes Y2H hybrid system, while the in vitro method experiments are performed on the living organism to understand the biological functions interactome.

Uhrig, Williams, and Bowles in 2006 performed the protein-protein interaction network study on Arabidopsis that was based on literature mining and co-expression approach (Williams and Bowles 2004; Uhrig 2006). Jane Geisler-Lee in 2007, predicted Arabidopsis protein interactome based on the interolog method. They also concluded that the predicted proteins were co-localized and co-expressed by analyzing existing experimental data from Arabidopsis and decipher the significant role of signaling and cellular function by enabling hypothesis generation Arabidopsis interactome (Geisler-Lee et al. 2007).

4.5 Metabolomics

The study of metabolites at the particular instant in agroecosystem is known as metabolomics. It is important to understand the plant stress response in terms of metabolites. Lots of studies have been done for deciphering the role of metabolite during different abiotic stress condition. In 2004, Rizhky performed the metabolic profiling of plants during drought, heat, and combined stress and reported accumulation of sucrose and other sugar like starch (Rizhsky 2004). NMR based metabolic fingerprinting of plants during heavy metal stress were performed by Bailey in 2003 and responsible metabolic pathways were explored (Bailey et al. 2003). The microarray data of chickpea during heavy metal stress were analyzed and responsible pathways which were providing tolerance were also identified. It was seen that Nitrogen metabolism, Starch sucrose metabolism, and Riboflavin metabolism were altered. Several metabolomics approacheses was also used to understand the production of metabolite during different stress condition in plants like water salinity (Cramer et al. 2007), oxidative stress (Baxter et al. 2006), and heavy metal stress (Le Lay et al. 2006).

The metabolomics data is similar to transcriptomics and proteomics data. It requires lots of computational work including file handling, data mining, and finally comparative analysis. Lots of online server/databases/tools are available for analysis and visualization of metabolic pathway for understanding the agroecosystem shown in Table 4.2. Once the function of metabolite will be known, one can directly correlate with the function of the gene during particular stress condition. It is a rapidly growing technology to understand the metabolic pathways in plants by using different strategies like targeted analysis, metabolite profiling, and metabolic fingerprinting (Fiehn 2002; Halket et al. 2005; Shulaev 2006).

Table 4.2 List of various online tools and their web address that are used for analysis of various metabolic pathways in plants
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4.6 Phenomics

Generally, plants interact with the biotic and abiotic component of the ecosystem. As a result, there is a change in genotype and phenotype of plants. The alteration in phenotype is due to combined interaction of genome with the environment. The study of phenotype during the interaction of biotic and abiotic factor of the environment at a particular instant is known as phenomics. Invasive and noninvasive techniques are available to understand the change in phenotype in model plants (Großkinsky et al. 2015). Phenomics technology is used in basic plant research during different stress conditions and crop breeding (Furbank and Tester 2011). A noninvasive method of plant phenomics methodology is shown in Fig. 4.4.

Fig. 4.4
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Various steps involved in generation and analysis of Phenomics data in plants for understanding the effect of stress

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In image acquisition, digital image of plants is taken to study the effect of biotic and abiotic effect on plants. It is done by several approaches like tomography imaging (Bovik 2005), thermography imaging (Padhi et al. 2012), LIDAR (Lenco 1982), and time-of-flight camera (Klose et al. 2011). After image acquisition, processing of the image is done for removing the noise and improving the contrast (Hamuda et al. 2016). Image cropping, contrast improvement, and dimensional reduction are major operation done during image processing (Singh et al. 2016). After the image preprocessing, segmentation of image is done, in which objects are identified and isolated by removing irrelevant background present in the objects (Singh and Misra 2017). In the feature extraction process, the image of interest is used to extract the numerical value where different algorithms can be applied to understand the phenotype (Van Der Heijden et al. 2012). Finally, the generated data is analyzed by a machine-learning approach (Ghatak 2017).

4.7 Role of Systems Biology for Understanding Agroecosystem

For a comprehensive study of plants systems biology, the role of bioinformatics is crucial. “Omics” data analysis and interpretation required the good skill of bioinformatics and algorithm (Joyce and Palsson 2006). The omics data can be analyzed for understanding plants systems, and this requires lots of tools for visualization of networks and construction of pathways and analysis tools as shown in Fig. 4.5.

Fig. 4.5
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Integrative approach using omics and systems biology to understand the plants system during stress conditions

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Systems biology represents the graphical view of biomolecules and their interactions in the form of a biological network (BN). In BN, nodes of the graph represent the gene/protein/DNA/RNA and edges represent the way of interaction. The interaction may be direct (flow of information from one to another biomolecule) and undirected (having interaction but the direction is not sure). The way of interaction may be physical interaction, metabolic reaction, and regulatory connection (Joyce and Palsson 2006). The most connected node in networks is termed as hub node, which is a key player in BN (Barabási and Oltvai 2004). During stress condition in plants, the interaction between biomolecule changes and it can be well studied and understood by using systems biology by constructing or reconstructing gene to metabolite network, protein-protein interaction networks, gene regulatory networks, and transcriptional regulatory network (Yuan et al. 2008).

4.8 Gene to Metabolite Networks

Gene to metabolite networks is made on the basis of the coefficient of correlation between genes. The way of interaction is represented on the basis of the correlation value. A different biological process, molecular functions, and gene functions can be explored to understand plants systems biology during normal and experimental conditions. Various studies on different plants have been performed to understand the gene to metabolite network and candidate gene responsible for over- or underproduction of secondary metabolites were identified during stress and normal conditions (Goossens 2003; Scheible 2004; Zulak et al. 2007).

4.9 Protein-Protein Interaction Networks

In PPI networks, nodes are represented by the proteins and edges represents the physical or genetic interaction. The function of genes is explored on the basis of genetic interaction between the proteins (Boone et al. 2007) while physical interactions are used to understand protein-protein interaction and dimer formation (de Folter 2005). Analysis on the basis of biological networks construction in chickpea during cadmium and chromium exposure has been reported that most of the hub genes are involved in protein dimerization (Yadav and Mani 2018).

4.10 Transcriptional Regulatory Networks

Interaction of the transcription factor and downstream gene are studied in this type of networks. Here nodes are transcription factor or regulatory genes and represent the activation and deactivation (Babu et al. 2004). One transcription factor interacts with a large number of genes simultaneously. Different studies have been performed to understand the stress response in plants using transcriptional regulatory networks (Nakashima et al. 2009; Yun et al. 2010; Todaka et al. 2012).

4.11 Gene Regulatory Networks

In gene regulatory networks, node represents the genes/mRNA/proteins and edges are regulatory interaction like activation, repression, inhibition, or functional interactions (Long et al. 2008). Various studies reported that gene regulatory network having an important role to understand the underlying mechanisms during developmental and stress condition on the basis of gene regulatory network in different plants (Li et al. 2006; Meng et al. 2011; Pires et al. 2013). There are lots of tools available for construction and analysis of biological network in plants which are shown in Table 4.3.

Table 4.3 List of frequently used software and online tools used for construction of biological networks and their analysis using genomics data in plants
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4.12 Conclusion

In upcoming years, plants will be a solution of all problems like water and food scarcity. So, it is very important to understand the agroecosystem; for this one should know about functional genomics and systems biology. The functional genomics and systems biology can be used to develop a strategy to escape the plants from stress condition and new variety can also be developed. In addition to advantage, there are certain challenges like big data handling and its analysis. Due to the complexity of plants, wet lab experiment is not always possible; hence there is a need of lots of computational approaches that can be applied to functional genomics and systems biology to understand the agroecosystem in a well-defined manner.