**3.1 Phenomics based imaging and analytical toolkits**

The phenotype exhibited by plants at certain stages of growth/developments are the function of gene × environment interaction that govern a peculiar trait of interest expressed from the plant's genome [67]. The term "phenotype" corresponds to precise and rigorous recording of the distinct phenotypic parameters from single cell to whole plant level, which if conducted explicitly can help facilitate identification/classification of novel traits in several plant species. Phenomics is a sub-discipline of plant biology that deals with phenotyping under controlled green-house conditions as well as field experimentation using advanced imaging technologies and imaging tools [67]. Phenomics study is a three-step process involving (i) setting up experimental plot, light intensities, nutrition acquisition and temperature (ii) rigorous monitoring/phenotyping such as growth, stress response, photosynthesis, chlorophyll and secondary metabolite contents etc. using advanced imaging tools and (iii) computer-assisted data visualisation, interpretation and storage [68]. Recent technological advancements have paved the way for the development of high-resolution imaging platforms aided with advanced bio-informatic tools for the phenotyping several important traits in plants for cellular and functional analysis [69]. Therefore, phenomics has now been recognised as an indispensable tool that can provide valuable insights into plant's morphology and physiology which can be further integrated with functional genomics data for analysing key traits such as secondary metabolites production and other economically important traits [68].

Several informatorily databases and analytical toolkits have been developed to facilitate phenomics and taxonomic studies in various cultivated and under-utilised crops at a greater pace. For example., PlantCLEF (2019) is an online repository that contain wide variety of images of plant's organs with the sole purpose to facilitate identification and classification of underutilised crop plants having distinct features [70]. PlantCLEF act like a real-life computerised program that can identify and classify plant species using raw images by extracting similar traits/characteristics and matching them defined plant species and family [70]. Similarly, MPID (Medicinal plant images database) which is a premium database maintained by Hong Kong Baptist University that is known to accommodate vast range of phenotypic data related to medicinal and therapeutically important plants [71]. Furthermore, in addition to phenotypic data, it also acts as a repertoire of scientific/botanical names, therapeutic values, physiological and ecological parameters of more than 1000 medicinal plants. Likewise, MPDB (Medicinal plant database of Bangladesh) database is also specifically dedicated to store phenotypic and physiological data associated with medicinal and aromatic plants found in Bangladesh [72].

*Unlocking Pharmacological and Therapeutic Potential of Hyacinth Bean (*Lablab purpureus *L.)… DOI: http://dx.doi.org/10.5772/intechopen.99345*

Apart from databases, several computer-based analytical tools and techniques have also been developed and implemented for recording high-resolution images and morpho-physiological parameters in selected plants [70]. Plant computer vision (PlantCV) is a freeware software package written explicitly in python language that provide valuable algorithms for analysing phenotypic data [71]. It can analyse phenotypic data for multiple plant species and compare them with in the database for identification of novel traits/characteristics in genetically un-explored crops [67]. Similarly, ImageJ is a Java based program equipped with various algorithms such as image enhancer, graphic correction, segmentation and measurement to facilitate accurate phenotyping of plant species [73]. HTPheno is an algorithm of ImageJ that allows monitoring of plant's growth and development in terms colour spectrum. It captures image related to growth and fitness by various angles, time period and temperature/light conditions in the form high-resolution images [74]. However, despite of these technological breakthroughs, the implantation of these state-of-the art techniques are limited certain plant species. Therefore, efforts are needed to establish, standardise and implement these advanced phenomics techniques in various under-utilised medicinally important crops in order to facilitate comprehensive analysis of their physiological, morphological and cellular functions.

#### **3.2 Functional genomics approach**

Identification of hereditary determinants governing morphological, physiological and biochemical properties are of astute importance to uncover genetic potential of plant species. With the advent of next-generation sequencing techniques it has now become possible to perform in-depth studies on economically/therapeutically important under-utilised crops [75]. Till date whole genome sequencing projects has led to the development of draft genomes and chloroplast genomes of various medicinally important plants which can be efficiently exploited in-conjunction with advanced bio-informatic tools to obtain information about gene families, gene regulatory networks, miRNA and non-coding RNAs involved in gene regulation in those plants whose genome sequence is not available [76]. Furthermore, it can also result in the development of DNA markers for DNA fingerprinting and DNA barcoding to facilitate efficient taxonomic identification of plant under study using specific region of DNA [77]. Several DNA fingerprinting/barcoding primers such as 18-S-rRNA, 5S-rRNA, rupture of the cranial cruciate ligament (rccl), maturase K (matK), internal transcribed spacer (ITS), intergenic spacer (trnH-psbA) have been successfully implemented for identification and classification of medicinal plants. In addition, several dominant and co-dominant markers such as single nucleotide polymorphism (SNP), sequence characterised amplified region (SCAR), amplified fragment length polymorphism (AFLP), inter simple-sequence repeat (ISSR) and random amplified polymorphic DNA (RAPD) have also facilitated identification and authentication of medicinal plants [76].

Transcriptome-wide profiling of genes of regulatory pathways can help researchers gain valuable insight into the functional mechanisms of plant's biosynthetic pathways. In the recent years, researchers have exploited expressed sequence tags (ESTs) for transcriptome wide analysis of important medicinal plants [77]. Later, the scientists began to use microarray which is probe hybridization-based technique for studying regulation of gene expression and candidate gene discovery [78]. Recently, various transcriptome-wide analysis studies have been conducted in several medicinally important plants and their sequencing and expression profiling data are available in various online databases such as GarlicESTdb (garlic EST database), GEO (gene expression omnibus), ArrayExpress, RASP (RNA atlas of structure probing), AgriSeqDB (RNA sequence database), EGENES

(EST database) that can help expedite transcriptomic research in those plants in which transcriptome wide analysis has yet not been completed [79]. Likewise, several toolkits have also been designed that explicitly analyse microarray data and can also be used in conjunction with other phenomics, transcriptomics, proteomics and epigenomics for the identification of functional biological pathways liked with secondary metabolite synthesis [79]. Notably used toolkits are iArray, BRB-Arraytools, KEGG (Kyoto encyclopaedia for genes and genomes), GENEVESTIGATOR, PLEXdb, ExPath are the ones which offers various features for microarray data analysis, visualisation, interpretation and annotation in the form of heat map, graph and tables [80].

In addition, few databases have also been developed such as CroFGD (*Catharanthus roseus* functional genomic database), TeaCon (database of gene coexpression network), PlaNet (plant co-expression network), AraNet (*Arabidopsis* co-expression network) for functional analysis and study of co-expression networks to identify functional biosynthetic pathways [81–83]. Furthermore, several non-coding RNAs (ncRNAs) such as small interfering RNAs (siRNA) and microR-NAs (miRNAs) have also been discovered and are thought to play pivotal role in the regulation of secondary metabolite synthesis in medicinal and crop plants [84]. Intriguingly, several transcriptome-wide analyses in medicinal plants have well indicated that these ncRNAs whether siRNA or miRNA indeed have therapeutic properties which if harnessed systematically can help in the prevention of various chronic diseases such as cancer and influenza A virus infection [84]. In this context, a group of researchers have developed a miRNA database (MepmiRDB; medicinal plant microRNA database) devoted specifically for medicinal plants that provide plethora of information regarding gene sequence, expression levels and target miRNA for 30 different medicinal plants [85]. Besides, several software packages have also been developed such as sRNA-Seq-data, NATpipe, PLncPRO and CNIT that can greatly facilitate the identification ncRNAs, siRNAs and miRNAs in various medicinal plants as well as in crop plants that specifically involved in the regulation of secondary metabolites of therapeutic importance [85].

Several protein-coding genes have also been qualitatively and quantitatively analysed for their corresponding products to generate a profile of their proteome to help researchers gain valuable insights into the mechanisms underlying cellular and metabolic pathways in medicinal plants [86]. Fewer studies have been conducted to develop a complete proteome map in the medicinal plants describing the proteins involved in the regulation of secondary metabolite synthesis. For example, a study conducted by Jacobs et al. [87] identified various proteins involved in alkaloid biosynthesis in *C. roseus* using 2D gel electrophoresis and mass spectrometry. Likewise, Chin [88] also performed in-depth proteomic study using Matrix-Assisted-Laser Desorption and Ionisation (MALDI) Time of Flight (TOF) analysis to unravel proteins involved in the secondary metabolite production in the germinating seeds of orchid plants. In addition, several online toolkits such as STRING (search tools for retrieval of interacting genes), PAIR (predicted Arabidopsis interactome resource), UniProt, Pfam (protein families), IntAct (molecular interaction database) can also be exploited in non-model crop plants such as hyacinth bean to gain functional insight into proteins involved in the secondary metabolite productions [79]. A list of putative genes/TFs involved in the regulation of bioactive metabolites in legumes are presented in **Table 1**.

#### **3.3 Metabolomics approach**

Metabolomics is also a functional genomics tool with the sole purpose to provide in-depth understanding of different cellular and metabolic pathways in various


#### *Unlocking Pharmacological and Therapeutic Potential of Hyacinth Bean (*Lablab purpureus *L.)… DOI: http://dx.doi.org/10.5772/intechopen.99345*


**Table 1.**

*List of putative genes/transcription factors and functional genomics tools involved in regulating biosynthesis of secondary metabolites in legumes.*

#### *Unlocking Pharmacological and Therapeutic Potential of Hyacinth Bean (*Lablab purpureus *L.)… DOI: http://dx.doi.org/10.5772/intechopen.99345*

organisms. Metabolomics is an advanced system biology tool with improved analytical methodologies, sensitivity and resolution that has been successfully exploited to understand biosynthesis of important metabolites in various plant species [103]. Several researchers have used this technique to discover candidate genes/proteins involved biosynthesis of specialised metabolites [104]. Furthermore, it has also provided great depth of understanding about the structural properties and diversity that exists among different metabolites as well as has facilitated to gain valuable insight into the type active ingredients that gives each metabolites its specific nutritional and medicinal properties [103]. Recent decades have witnessed the detailed characterisation of various medicinally important metabolites such as paclitaxel, artemisinin, vincristine, vinblastine, camptothecin and accuminata etc. from Pacific yew tree, *Artemisia annua*, *C. roseus, Camptotheca acuminata* and *Papaver somniferum* having anti-cancer and anti-malarial properties using this approach [104]. The identification of these medicinally important metabolites in above mentioned plants has served as model for studying the biosynthesis of specialised metabolites in other crop plants as well, which however, could not be possible by phenomics, genomics, transcriptomics and proteomics studies [105]. Metabolite profiling studies have been conducted in various transgenic plants by generating over-expression and gene-insertion based mutants to track the regulation of flavonoid biosynthesis (Nguyen et al. 20). In addition, metabolomics-based reverse genetic approach has also led to the identification of putative genes involved in the regulation of flavonoid synthesis driven by the conjugative action of posttranslational modifications such as acetylation, phosphorylation, methylation, ubiquitination and biotinylation [105].

Several metabolomic studies have been conducted in model as well as crop legumes such as *Medicago truncatula*, *Lotus japonicus*, *Glycine max* and *Pisum sativum* to identify functional metabolites that are involved in the imparting biotic and abiotic stress tolerance for thus improving their growth and productivity [106]. However, fewer studies have conducted for the identification of medicinally important metabolites in legume plants compared to other model and medicinal plants [93]. Nonetheless, efforts are being made to revamp, standardise and implicate these advanced system biology tools for the identification, characterisation and quantification of important metabolites in various under-utilised crops as well [106]. The techniques like gas-chromatography mass spectrometry (GC–MS), liquid chromatography-mass spectrometry (LC–MS), nuclear magnetic resonance (NMR), capillary electrophoresis-mass spectrometry (CE-MS) and high-performance liquid chromatography-time of flight-mass spectrometry (HPLC-TOF-MS) have been successfully used for the assessment of medicinal constituents of functional metabolites [106]. With the advent of technological breakthroughs, metabolomics has also facilitated the generation of various protein reference maps of various model plant species and legume crops which can expedite the functional genomic analysis of genes/proteins in those plant species whose genome sequence is not available [93]. Nevertheless, efforts are needed to generate more protein reference maps to unravel cellular and biochemical signalling pathways and to identify novel genes and their product through comparative proteomic approach. The integrative analysis of "OMICS" datasets is crucial for the implementation of system biology tools for identification and mapping of secondary metabolite pathways in medicinal plants as well as legume crops and mechanism by which it can be achieved is depicted in **Figure 1**. Therefore, it has now become imperative to generate resourceful OMICS database that can help in the advancement integrative omics technology for precise understanding of molecular mechanisms and their possible application in legume improvement through breeding programs.

#### **Figure 1.**

*Systemic workflow depicting the application of integrated OMICS tools as well as role of different biotic/ abiotic elicitors for improving the biosynthesis known bioactive compounds and identification of novel therapeutic metabolites in legume crops. In this process, tissue culture plants or plants grown in field are treated with different biotic or abiotic elicitors either independently of in combination. The plants are then analysed for the differential expression of genes involved in the regulation of secondary metabolites using integrated OMICS techniques. Candidate genes are discovered using various techniques such as cDNA-AFLP, SAGE, analysed by bioinformatics tools and are rewired using synthetic biology tool. The transformed plants are then exploited for sustainable production of important bioactive metabolites. GWAS: Genome wide association studies; MAS: Marker assisted selection; SNP: Single nucleotide polymorphism; QTLs: Quantitative trait loci's; miRNA: microRNA; siRNA: Small interfering RNA; NMR: Nuclear magnetic resonance; HPLC: High performance liquid chromatography; GC: Gas chromatography; LC: Liquid chromatography; MALDI-TOF-MS: Matrix assisted laser desorption ionisation-time of flight-mass spectrometry; cDNA AFLP: Complementary DNA amplified fragment length polymorphism (RNA finger printing technique); SAGE: Serial analysis of gene expression; DdPCR: Differential display PCR; SM: Secondary metabolites; CRISPR-CAS 9: Clustered regulatory interspaced short palindromic repeat, CRISPR associated protein 9; TFs: Transcription factors.*
