Keywords

9.1 Introduction

Plants produce around 2,00,000 types of secondary metabolites as a defense response and they are useful sources of drugs, fragrances, pigments, food additives, and pesticides for mankind (Dixon and Strack 2003; Kutchan and Dixon 2005). It is estimated that 70–80% of the people worldwide rely mainly on herbal medicines for their primary healthcare (Canter et al. 2005). Reports document that out of 50,000–70,000 plants that are used worldwide for medicinal purposes, nearly 10,000 plants have become endangered (Brouwer et al. 2002; Edward 2004). World Health Organization (WHO) estimated that the market of herbal medicine will grow up to US$5 trillion by the year 2050 with an annual growth of 5–15% (Kumar and Gupta 2008). Due to complex chemical structures of the metabolites, they are difficult to synthesize chemically, and metabolites such as ajmalicine, ajmaline, artemisinin, berberine, colchicines, digoxin, ginsenosides, morphine, quinine, shikonin, taxol, vincristine, vinblastine, etc., are still extracted from plants (Rao and Ravishankar 2002). However plants synthesize metabolites in low concentrations and are restricted to a particular species or genus (Verpoorte et al. 2002). Thus to fulfill the demand, a large number of plants are collected from the wild which depletes the plants from natural habitat. Another problem faced by industries is the requirement of a large quantity of material for extraction of metabolites e.g., 2.5 kg of taxol requires 27,000 tons of Taxus brevifolia bark and thus the availability of plants for herbal medicines becomes a major problem (Rates 2001).

Synthesis of metabolites is under the control of different genes that are expressed in a particular tissue or cell type (Pichersky and Gang 2000). The plant genome contains 20,000–60,000 genes of which around 15–25% are involved in the synthesis of secondary metabolites (Bevan et al. 1998; Somerville and Somerville 1999). Metabolic engineering of pathways has key applications in alleviating the demands for limited natural resources (Lau et al. 2014). The secondary metabolite pathways are chain reactions catalyzed by enzymes that convert substrates into products with one or more branched points (Farré et al. 2014). Thus main challenge in manipulating the pathways is their complex nature which involves many regulatory factors (Kooke and Keurentjes 2012). Different strategies like blocking a competitive pathway, over-expressing regulatory genes/transcription factors, or inhibiting the catabolism of molecules can be used for the enhancement of metabolites (Koffas et al. 1999; Gomez-Galera et al. 2007).

9.2 Metabolic Engineering

Metabolic engineering is defined as the ‘directed improvement of product formation or cellular properties through the modification of specific biochemical reactions or the introduction of new genes with the use of recombinant DNA technology’ (Stephanopoulos 1999). The main aim of this technique is to redirect the precursor pool toward the synthesis of the desired compound(s) through alteration in the gene expression, and it is done either in positive (over-expression) or negative (down-regulation) manner (Pickens et al. 2011; Farré et al. 2014). The metabolic flux of the pathways can be regulated by the metabolites themselves, which in turn influences the activity of enzymes, transcription factors, and signaling proteins. The chemical diversity mainly arises through alkaloid, phenylpropanoid, and terpenoid pathways, thus number of studies have been carried out for identification of their regulatory genes and transcription factors (Wu and Chappell 2008; Nagegowda 2010). High throughput ‘omics’ technologies like genomics, transcriptomics, proteomics, and metabolomics are being used for elucidation of the pathways (Vemuri and Aristidou 2005; Caspi et al. 2013). In non-model plants where whole genome sequencing is not available, gene identification is done by a comparatively cheaper technique like expressed sequence tags (ESTs) (Joshi and Pathak 2019). Thus, the process of metabolic engineering in medicinal plants research is divided into three steps: (i) selection of plant species and elucidation of the pathways through ‘omics’ technology, (ii) targeting the gene of interest through genetic engineering tool, and (iii) screening the plants for metabolite content (Lau et al. 2014) (Fig. 9.1).

Fig. 9.1
figure 1

Steps of metabolic engineering

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One of the key ways to reduce the levels of undesirable metabolites is recessive gene disruption and dominant gene silencing (Tang and Galili 2004). But the latter is a more promising approach to decrease the synthesis of undesirable compounds by suppression of branch-point gene which redirects the enzymatic reactions to increase the metabolite(s) of interest (DellaPenna 2001). Silencing the expression of a particular gene can be done in three different ways: (i) transcriptional gene silencing (TGS), (ii) post-transcriptional gene silencing (PTGS), and (iii) translation inhibition (Hamilton and Baulcombe 1999; Mansoor et al. 2006). But the central dogma of life suggests that if mRNA is silenced, further synthesis of secondary metabolites will be stopped (Abdurakhmonov 2016). RNA interference (RNAi) also known as post-transcriptional gene silencing (PTGS) is frequently used for gene down-regulation and thus known as the ‘knock-down’ method (Tang and Galili 2004).

9.3 RNA Interference (RNAi)

RNAi is a quick, easy, and sequence-specific homology-based tool to down-regulate the expression of targeted mRNA (Small 2007). Initially it was thought to function as a part of the defense mechanism against viruses when discovered in plants (Mansoor et al. 2006). The history of RNAi is nearly three decades old where Napoli and co-workers in 1990 transformed petunia plants with chalcone synthase (CHS) gene and the flower color changed from dark purple to white/chimeric, and this phenomenon was named as co-suppression. After five years, Guo and Kemphues (1995) reported knock-down of par-1 gene expression in Caenorhabditis elegans through both sense and antisense RNA. The reason behind gene silencing remained unknown till Andrew Fire and Craig Mello reported that potent and specific genetic interference can be done by double-stranded RNA (dsRNA) in C. elegans which triggered the silencing of genes as it had identical sequences to the mRNA. This type of gene silencing was termed as ‘RNA interference (RNAi)’ (Fire et al. 1998) and in 2006 Fire and Mello received the Nobel Prize for discovering it (Allen et al. 2004). At the same time similar phenomenon was also reported in plants by Waterhouse et al. (1998) where dsRNA induced gene silencing which was more efficient than either sense or antisense RNA. RNAi technology suppresses the expression of enzymes that are expressed in the number of tissues at different developmental stages, whereas sense or antisense RNA fails to block the activity of enzymes that are encoded by multigene family (Larkin et al. 2007). Wesley et al. (2001) compared the silencing efficiency of hpRNA (dsRNA) and antisense RNA, and reported that hpRNA increases gene silencing by 90–100%. Thus it was confirmed that RNAi became the most promising tool for the suppression of dominant gene expression (Smith et al. 2000). One advantage of this tool is its dominant nature and the silenced gene is passed on in the T1 generation which created new opportunities in agriculture and production of metabolites (Lessard et al. 2002; Verpoorte et al. 2002). Many researchers use in vitro cultures to down-regulate the gene as it reduces the risk of contaminating food sources and environment, and provides a platform to test a metabolic engineering strategy that will be utilized for large scale production of metabolites (Wu and Chappell 2008). The main aims of RNAi technology for engineering secondary metabolites synthesis is given in Fig. 9.2.

Fig. 9.2
figure 2

Uses of RNAi technology in manipulating secondary metabolite pathway

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9.3.1 Mechanism

Micro RNA (miRNA), short interfering RNA (siRNA), and small hairpin RNA (hpRNA) are types of small non-coding RNAs that are mainly involved in RNAi mechanism (Aukerman and Sakai 2003; Palatnik et al. 2003). Artificial microRNA (amiRNA)-based vectors have also proved to be effective for gene silencing since the last decade (Warthmann et al. 2008). Smith et al. (2000) suggested that a more feasible approach is to clone both sense and antisense sequences separated by an intron region which forms a hairpin RNA (hpRNA) molecule upon transcription and triggers gene silencing. Aberrant single-stranded RNA (ssRNA) with an intron-hairpin construction triggers the generation of dsRNA by RNA-dependent RNA polymerase (RdRP) and activates the RNAi pathway (Waterhouse et al. 2001). Dicer, a ribonuclease III-type enzyme, is activated by ATP which recognizes dsRNA and cuts them into smaller segments of 21–25 bp. These small RNAs are then incorporated into a nuclease complex known as the ‘RNA-induced silencing complex’ (RISC) which contains argonaute protein (AGO). Then one of the strands of siRNA (guide strand) becomes stably associated with AGO and the other strand (passenger strand) is degraded. The guide strand then leads RISC to its target mRNA and AGO protein binds the guide strand to the target sequence for complementary base pairing. Successful docking of the RISC-siRNA complex with mRNA will then either block the translation or degrade mRNA using exonucleases (Kusaba 2004). Reports suggest that the directionality of dsRNA processing and the target RNA cleavage sites are predefined, and the sequence complementary to the guide siRNA will be recognized and cleave the target mRNA in the central region which is 10-12 nt from the 5’ end of siRNA (Elbashir et al. 2001). Lastly, the siRNA molecules are amplified via RdRp on the target mRNA and these siRNAs will, in turn, induce a secondary RNA interference i.e., transitive RNAi (Denli and Hannon 2003).

9.3.2 Vector and Transformation Methods

Different vectors are used to suppress gene expression in plants and the vector-based RNAi technology was improved by using an intron as the linker (Waterhouse et al. 1998; Smith et al. 2000). These RNAi vectors are specifically designed to generate long dsRNA with the same sequence as the target genes. Similarly, vectors designed to express hairpin RNAs (hpRNAs) are also successfully applied to silence the corresponding target genes (Wesley et al. 2003). Nowadays biotechnology companies are developing specialized vector constructs for RNA interference in plants (see table), which after transformation into host plant converts into dsRNAs and triggers efficient silencing.

One of the major issues in plant genetic transformation is to obtain a stably transformed plant which depends on the transformation methods. The first choice is Gram-negative, soil-borne pathogen Agrobacterium spp., which is also known as ‘natural genetic engineer’ is commonly used to transform numerous dicotyledonous plants (Zupan et al. 2000). But the wild-type Ti plasmid is very large (200 kb) and difficult to manipulate, which was overcome by the development of binary vectors (Bevan 1984). In such a system, the Ti plasmid of Agrobacterium has been disarmed by removing the T-DNA and keeping vir regions intact. Simultaneously, a separate binary vector is constructed which carries an origin of replication that is compatible with the Ti plasmid of Agrobacterium. When the binary vector is introduced into Agrobacterium the vir genes of Ti plasmid will act in trans to transfer the recombinant T-DNA from the binary vector to the host plant cell. As the binary vectors are smaller and comparatively easier to construct than wild-type Ti plasmids, the Agrobacterium-mediated transformation is considered as a reliable technique (Lessard et al. 2002).

Transient gene expression in majority of the plant species can be done via particle bombardment and electroporation. These techniques are useful especially when long term expression is not required for e.g., to test the effectiveness of various gene constructs before stable transformation (Lessard et al. 2002). One of the advantages of this method is high transformation frequency, which resulted in the successful transformation of plastids in tobacco and tomato (Maliga 2001). But these methods require the use of tissue culture protocols to regenerate transgenic plants/callus whereas Agrobacterium-mediated transformation overcomes this limitation by directly transforming germ-line cells or seeds and is one of the first choice for RNA interference in plants (Tague 2001).

RNAi is a promising way to manipulate the metabolite pathway (Borgio 2009) and it was first used by Mahmoud and Croteau (2001) in Mentha x piperita to reduce the level of menthofuran through antisense suppression of the mfs gene which codes for the cytochrome P450 (+) menthofuran synthase, which in turn increased the content of essential oils in plants. Later on many studies documented that the content of various volatiles can be increased in Mentha spp. by silencing different genes or transcription factors (Mahmoud et al. 2004; Wang et al. 2016; Reddy et al. 2017). Since the beginning of this technique, berberine bridge enzyme (BBE) is the gene of interest for RNAi research as many scientists knock-down the expression of this gene to study its effect on the content of different alkaloids, especially benzophenanthridine type in many plant species (Park et al. 2002; Frick et al. 2004; Fujii et al. 2007). Waterhouse et al. (1998) documented that this technology can be useful to alter the flower colors as compared to conventional breeding and genetic transformation. RNAi has been applied to suppress the genes of anthocyanin biosynthesis like anthocyanidin synthase (ANS) in Torenia spp. which changed the flower color in transgenic plants (Nagira et al. 2006; Nakamura et al. 2006). Similarly, other genes of flavonoid pathways like isoflavone synthase (IFS), flavone synthase II (FNSII), flavonol synthase (FLS), flavanone 3-hydroxylase (F3H), flavonoid 3’-hydroxylase (F3’H), flavonoid 3’,5’-hydroxylase (F3’5’H), flavone 6-hydroxylase (CYP82D1.1), flavone 8-hydroxylase (CYP82D2), chalcone isomerase (CHI), chalcone synthase (CHS), etc., were silenced and their effect on flavonoids was reported by many workers (Subramanian et al. 2005; Nakatsuka et al. 2007; Seitz et al. 2007; Park et al. 2011; Jiang et al. 2014; Zhang et al. 2015; Zhao et al. 2018). Recently Hu et al. (2020) reported that the down-regulation of one of the flavonoid biosynthetic pathway gene laccase gene (Lac1) affects the cotton fiber development. Whereas Liu et al. (2002) down-regulated the expression of two fatty acid desaturase genes i.e., stearoyl-acyl-carrier protein Δ9-desaturase (SAD) and oleoyl-phosphatidylcholine ω6-desaturase (FAD) in cotton seeds, which increased the content of stearic acid and oleic acid for better oil quality. Similarly, the content of different types of ginsenosides was increased or decreased in different species of Panax (P. ginseng, P. notoginseng and P. quinquefolium) by RNAi technique to identify the roles of different genes in ginsenoside biosynthetic pathway (Han et al. 2006; Zhao et al. 2015; Wang et al. 2017). This strategy has been used for commercial-scale production of desired plant products e.g., decaffeinated Coffea arabica and Coffea canephora plants were produced by silencing theobromine synthase gene using RNAi (Ogita et al. 2003, 2004). Table 9.1 depicts the plant species in which RNAi has been used to silence the secondary metabolite genes as well as the vector and transformation methods used for the same.

Table 9.1 Down-regulation of plant secondary metabolite pathways using RNAi
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9.4 Conclusion

RNAi is the choice of present-day researchers to manipulate the genes synthesizing secondary metabolites. Since RNAi is a sequence-specific process, this requires the selection of a unique or conserved region of the target gene which ensures that the multiple gene families can be silenced. But the major bottleneck is that the complete information about the genomes of many non-model plants for secondary metabolite synthesis is lacking. The major drawback of RNAi tool is its unintended targets as 21–25 nt homology is required to suppress the gene function, even then it is still being used for identifying the gene functions and to increase the content of the desired metabolite.