Revealing insect herbivory-induced phenolamide metabolism: from single genes to metabolic network plasticity analysis



The phenylpropanoid metabolic space comprises a network of interconnected metabolic branches that contribute to the biosynthesis of a large array of compounds with functions in plant development and stress adaptation. During biotic challenges, such as insect attack, a major rewiring of gene networks associated with phenylpropanoid metabolism is observed. This rapid reconfiguration of gene expression allows prioritized production of metabolites that help the plant solve ecological problems. Phenolamides are a group of phenolic derivatives that originate from diversion of hydroxycinnamoyl acids from the main phenylpropanoid pathway after N–acyltransferase-dependent conjugation to polyamines or aryl monoamines. These structurally diverse metabolites are abundant in the reproductive organs of many plants, and have recently been shown to play roles as induced defenses in vegetative tissues. In the wild tobacco, Nicotiana attenuata, in which herbivory-induced regulation of these metabolites has been studied, rapid elevations of the levels of phenolamides that function as induced defenses result from a multi-hormonal signaling network that re-shapes connected metabolic pathways. In this review, we summarize recent findings in the regulation of phenolamides obtained by mass spectrometry-based metabolomics profiling, and outline a conceptual framework for gene discovery in this pathway. We also introduce a multifactorial approach that is useful in deciphering metabolic pathway reorganizations among tissues in response to stress.


Over the last three decades, it has become abundantly clear that the plethora of small molecules produced by plants that are not directly required for growth play important roles as signal and defense molecules rather than being ‘waste’ products (Pichersky and Gang, 2000; Hartmann, 2007; Berenbaum and Zangerl, 2008; Pichersky and Lewinsohn, 2011). In contrast to those derived from ‘primary’ metabolic pathways, biosynthesis of these metabolites is restricted to selected plant taxa. This suggests that specific biosynthetic pathways have been positively selected throughout the course of evolution in particular plant lineages when a compound or group of compounds addressed specific ecological needs. Consistent with this view, ecological and/or evolutionary insights obtained from field studies and laboratory-based functional genomic analyses have clearly established that plant interactions with insects have sculpted many aspects of plant metabolism. This includes the composition and size of metabolic classes as well as the regulatory networks that determine their fluxes (Berenbaum and Zangerl, 2008; Agrawal et al., 2012; Prasad et al., 2012). The frequently invoked functional distinction between secondary and primary metabolites is therefore a reflection of our ignorance of the genes controlling their biosynthesis and their biological function (Pichersky and Gang, 2000). Metabolites derived from the shikimate/phenylpropanoid core pathway illustrate how sophisticated rearrangements of central metabolic pathways allow plants to solve ecological challenges imposed by their interactions with insects.

Phenylpropanoid derivatives are found ubiquitously across the plant kingdom, and contain at least one aromatic hydrocarbon ring with one or more hydroxyl groups attached to it, a common feature derived from the skeleton of phenylalanine (Vogt, 2010). After oxidation, reduction, methylation, decoration with various kinds of small molecules and/or polymerization, simple hydroxycinnamoyl acids and esters produced by the core part of the shikimate pathway serve as metabolic units for production of an enormous array of compounds such as flavonoids, coumarins, lignin and phenolamides. These metabolic classes play essential roles in development, and also as plant defenses against biotic challenges. For example, phenolic-derived floral scents and pigments attract insect or bird pollinators and are thus essential determinants of a plant's fertility and outcrossing rates (Dudareva and Pichersky, 2000; Kessler et al., 2013). On the other hand, many structurally different phenolics rapidly accumulate to higher levels as components of an induced defense arsenal against herbivore attack, processes that involve plant species-specific transcriptionally mediated rearrangements of metabolic pathways (Howe and Jander, 2008). Important insights into the structural and regulatory genes of the core phenylpropanoid pathway have been summarized in several review articles (Costa et al., 2003; Vogt, 2010; Tohge et al., 2013). In contrast, how stressed cells re-channel metabolic fluxes of the phenylpropanoid core pathway for production of a specific spectrum of metabolites required to solve ecological problems remains largely unknown. The same holds for the well-studied downstream branches for which some branch-specific transcription factors have been identified.

In this review, we describe recent insights into the regulation of phenolamide production, a pathway that originates from diversion of hydroxycinnamoyl acids from the main phenylpropanoid pathway after conjugation to polyamine or aryl monoamine molecules by hydroxycinnamoyl CoA:amine N–(hydroxycinnamoyl)transferases, hereafter referred to as N–acyltransferases (Facchini et al., 2002; Bassard et al., 2010). These metabolites, sometimes referred to as phenylamides or more accurately as N–hydroxycinnamoyl-amine conjugates, are a diverse group of phenolic-derived secondary metabolites that are found in many dicotyledonous as well as monocotyledonous plant lineages (Martin-Tanguy et al., 1978; Martin-Tanguy, 1985; Facchini et al., 2002; Edreva et al., 2007; Bassard et al., 2010). Organ-specific pools of phenolamides were originally thought to function only in developmental homeostasis. High levels of polyamine-based phenolamides have long been known to be a characteristic feature of developing flower buds of many species, and floral organ-specific phenolamide profiles are known to correlate with floral developmental stages (Fellenberg et al., 2008, 2009, 2012a,b; Grienenberger et al., 2009; Matsuno et al., 2009). Phenolamides appear to be absent from mutants that do not flower or produce abnormal flowers, suggesting that these metabolites fulfill important roles during normal flower development; however, the exact function of these metabolites in these tissues, including the pollen coat, where they may be particularly abundant, remains puzzling.

More recently, these products have been shown to accumulate during stress and to function as induced defenses (Martin-Tanguy, 1985; Newman et al., 2001; Muroi et al., 2009; Demkura et al., 2010; Kaur et al., 2010). Various plant species have been shown to accumulate phenolamides during insect herbivory, but important advances in knowledge of the defensive function of these metabolites have been mainly obtained in transgenic Nicotiana attenuata plants, for which precise genetic manipulations of phenolamide transcriptional regulation and structural genes have been performed (Onkokesung et al., 2012). The dramatic increases in production of some of these metabolites during insect herbivory in N. attenuata (Gaquerel et al., 2010; Kaur et al., 2010; Onkokesung et al., 2012) have been shown to (i) reshape many other connected metabolic pathways (Gaquerel et al., 2013), (ii) be indicators of herbivory-induced hormonal signals spreading throughout a plant, and (iii) decrease insect performance (Kaur et al., 2010).

In a nutshell, probing this complex metabolic grid may provide an interesting framework for assessing transcriptional and metabolic controls that prioritize the activation of metabolic branches when a plant is attacked by insect herbivores. In the conceptual framework outlined here, understanding metabolite regulation through metabolomics approaches is the first step in the gene discovery process, which is illustrated by recent work in N. attenuata, a plant that is known to display herbivory-induced signaling (Wu and Baldwin, 2010). As the herbivory-specific co-expression patterns among genes affecting phenolamide metabolism deployed throughout the plant are probably organ-specific, we describe a newly developed multifactorial approach for deciphering whole-organism metabolic pathway reorganizations.

Mass Spectrometry-Based Profiling of the Phenolamide Metabolic Grid

Phenolamide structural diversity has been summarized by Bassard et al. (2010). Briefly, phenolamides have been identified in many dicotyledonous plants as well as in monocots, including wheat (Triticum aestivum), barley (Hordeum vulgare), rice (Oryza sativa) and maize (Zea mays) (Martin-Tanguy et al., 1978; Martin-Tanguy, 1985; Facchini et al., 2002; Edreva et al., 2007; Bassard et al., 2010). The abundance and organ-specific profiles of phenolamides in phenolamide-producing plant lineages have been carefully documented in several studies (e.g. Martin-Tanguy et al., 1978; Martin-Tanguy, 1985, 1997). Activated forms of coumaric, caffeic and ferulic acids in combination with either aryl monoamines (tyramine, tryptamine, anthranilate, etc) or polyamines (putrescine and spermidine) are the most commonly encountered building blocks. In the case of spermidine-containing metabolites, the phenolamide ‘codebook’ produces mono-, di- and tri-substituted metabolites, which results in a large repertoire of structures that may be additionally decorated after conjugation (Fellenberg et al., 2008, 2009, 2012a; Matsuno et al., 2009). The degree of acylation, and, in turn, the resulting number of free residual amino groups, determines the physicochemical properties of the phenolamides as well as the biological functions mediated by these metabolites. Representative acylated putrescine and spermidine phenolamide structures are presented in Figure 1. Profiling phenolamide levels across tissue types by medium- to high-throughput MS-based metabolomics profiling has been decisive in revealing the genomic basis of phenolamide biosynthesis. For example, ontogenically tuned increases in phenolamide levels have been reported during herbivory (Kaur et al., 2010; Onkokesung et al., 2012).

Figure 1.

Mass spectrometry-based metabolomics of main phenolamides in N. attenuata leaves.

Phenolamides may be readily analyzed by UHPLC coupled with mass spectrometry. A representative UHPLC TOF MS full-scan chromatogram recorded in positive ionization mode for an extract of an herbivory-induced leaf of Nicotina attenuata is shown. Coumaroyl (m/z 147.05), caffeoyl (m/z 163.04) and feruloyl (m/z 177.05) moieties resulting from cleavage from various core molecules may be queried rapidly to compute extracted ion currents from the chromatogram. Specific m/z signals corresponding to either coumaroyl-, caffeoyl- and feruloyl-containing mono-acylated putrescine molecules or mono- and diacylated spermidines (N′,N″-coumaroyl caffeoylspermidine, N′,N″-dicaffeoylspermidine, N′,N″-diferuloylspermidine) may be queried to reveal phenolamide peaks (indicated by black dots). Representative structures are shown.

The modular structure of phenolamides makes them easily identifiable by MS, as spectrometry fragmentation patterns measured using high-resolution and even low-resolution mass spectrometers are frequently highly diagnostic and sufficient for identification of building blocks (Matsuno et al., 2009; Onkokesung et al., 2012; Gaquerel et al., 2013). Many members of the phenolamide library, especially poly-acylated metabolites, undergo fragmentation during in-source ionization so that coumaroyl (m/z 147.05), caffeoyl (m/z 163.04) or feruloyl (m/z 177.05) moieties, for example, resulting from cleavage of various core molecules (e.g. polyamines and monoarylamines, but also sugars or quinate molecules), may be queried to compute extracted ion currents from MS-based chromatograms recorded in profiling mode without bias towards specific metabolic classes (Onkokesung et al., 2012). More specific m/z signals corresponding to either the mono-acylated spermidine molecules or fragments from polyacylated spermidines (e.g. m/z 308.21 for a mono-caffeoylspermidine molecule or fragment) may be used to visualize acylated spermidine profiles within an extract (Figure 1). To comprehensively mine these data, compound-specific pseudo-spectra collected in profiling mode may be deconvoluted in an automated fashion by open access programs such as the R package CAMERA (Kuhl et al., 2012) as a preliminary step before statistical analysis to evaluate the enrichment of phenolamide diagnostic ions (Onkokesung et al., 2012). The location of the phenolic moieties within the polyamine skeletons cannot be rigorously assigned to positions N1, N5 or N10 of spermidine from MS data alone due to rearrangements occurring during fragmentation (Fellenberg et al., 2009; Gaquerel et al., 2010). Characterization of the m/z signals of the phenolamide building blocks makes these metabolites amenable to fragmentation rule-based de-replication approaches (the analytical process by which known metabolites are identified from novel matrices based on established analytical rules).

Gene Discovery in the Phenolamide Metabolic Grid

The biosynthetic pathways of the amine moieties and the phenolic blocks from deaminated phenylalanine have been thoroughly reviewed (Kusano et al., 2008; Bassard et al., 2010; Vogt, 2010). Conjugation of phenolic units to a polyamine or an aryl monoamine molecule represents the key metabolic entry point in phenolamide biosynthesis, but the N–acyltransferase enzymes catalyzing these important conjugation reactions have remained unknown for many years. Recent functional genomic work on floral organ-specific phenolamide biogenesis has identified a range of N–acyltransferases that catalyze phenolamide biosynthesis, most of which belong to the BAHD gene family (D'Auria, 2006). Phylogenetic relationships among the characterized hydroxycinnamoyl CoA:amine transferases and Arabidopsis BAHD genes are presented in Figure 2. Phylogenetic clustering of these genes according to the type of amine molecule used as the acyl acceptor has been reported (Luo et al., 2009), but species-specific divergences are also known among N–acyltransferases that act on the same amine skeleton. Three N–acyltransferases for phenolamide biogenesis that compete for an overlapping set of phenolic acyl donors during herbivory (Figures 3 and 4) have recently been identified in N. attenuata (Onkokesung et al., 2012). AT1 controls the production of N–coumaroylputrescine and N–caffeoylputrescine. N–hydroxycinnamoyl CoA:putrescine transferase enzymatic activities were first characterized by Negrel (1989), Negrel et al. (1991, 1992) in tobacco. DH29 controls the first acylation step on spermidine, and silencing its expression disrupts the accumulation of most wound- and herbivory-induced spermidine-based phenolamides. CV86 is involved in the production of certain diacylated spermidine isomers from DH29-dependent mono-acylated spermidines, suggesting that additional N–acyltransferases are required to produce the full spectrum of metabolites found in N. attenuata. Dh29 and CV86 genes are distantly related to Arabidopsis N–acyltransferases that use spermidine as an acyl acceptor to catalyze sequential synthesis of specific polyacylated spermidine-based phenolamides in a single gene manner (Luo et al., 2009; Onkokesung et al., 2012).

Figure 2.

Phylogenetic relationships among phenolamide-forming N–acyltransferases.

A phylogenetic analysis was performed for Arabidopsis BAHD (BAHD refers to the first letter of each of the first four biochemically characterized enzymes of this family: BEAT, AHCT, HCBT, and DAT) (green branches of the tree) and functionally characterized N–acyltransferases, including phenolamide-forming ones (acyl acceptor indicated) summarized in Bassard et al. (2010) and additional characterized N–acyltransferases reported by Luo et al. (2009). The phylogenetic tree shows that N. attenuata DH29 and CV86 (highlighted in blue), which control the two-step synthesis of diacylated spermidines, cluster far from the Arabidopsis polyacylated spermidine-forming N–acyltransferases (AtSCT and AtSHT). Phenolamide N–acyltransferases with different acyl acceptor specificities (as indicated) are located on different parts of the tree. Sequences were aligned using Muscle ( and the alignment was trimmed using Gblocks ( to obtain 133 positions in 16 blocks, which were used to calculate the phylogenetic tree using mega 4 (, and the neighbor-joining clustering method with 1000 iterations was used to calculate bootstrap values (Onkokesung et al., 2012). Colored ellipses of the tree connected to gene name in bold indicate characterized phenolamide-forming N–acyltransferases (plant species names are reported in the color key). Plant species names are abbreviated as follows: At, Arabidopsis thaliana; As, Avena sativa; Ca, Capsicum annuum; Cr, Catharanthus roseus; Cb, Clarkia breweri; Cm, Curcumis melo; Dc, Dianthus caryophyllus; Fa, Fragaria anassa; Hv, Hordeum vulgare; La, Lupinus albus; Mp, Malus pumila; Na, Nicotiana attenuata; Nt, Nicotiana tabacum; Ps, Papaver somniferum; Ss, Salvia splendens; Sl, Solanum lycopersicum; Tc, Taxum cupsidata; Vl, Vitis labrusca.

Figure 3.

Herbivory-induced changes in N. attenuata leaf phenolamide metabolism.

Gene names and functions are given in the main text. Red lines show metabolite accumulation patterns in W+OS-treated leaves, and blue dashed lines show responses in a systemic leaf from the same plant. Putrescine conjugates show greater induced changes than do spermidine conjugates. Dynamics differ among the predicted isomers.

Figure 4.

Current view of the regulation of herbivory-induced phenolamide biosynthesis in N. attenuata as mediated by the core jasmonic acid biosynthetic and transcriptional pathway.

MAPK signaling and interactions among other hormonal signaling networks shape the amplitude of the jasmonate bursts and downstream signaling. The role of ethylene in cross-regulating polyamine metabolism has yet to be rigorously investigated. Specific JAZ proteins inhibiting MYB8 transcription are not yet known. MYB8 regulates induced changes in the core phenylpropanoid pathway and DH29, CV86, AT1 and possibly also unknown phenolamide-forming N–acyltransferases.

Gene and metabolite expression datasets are increasingly being integrated to accelerate gene discovery in this pathway. Previous MS-based metabolomics investigations in Arabidopsis thaliana using a multiple sampling experimental design, publicly available at AtMetExpress ( and compatible with the developmental and single tissue-based experiments available in AtGenExpress that are commonly used by the Arabidopsis community, demonstrated that transcriptional programs largely regulate the tissue-specific production of diverse phytochemicals, with the phenolamides being a case in point (Matsuda et al., 2010).

In this study, visualization of gene-to-metabolite co-linearity patterns was enabled by use of an ‘electronic fluorescent pictogram’ browser (Winter et al., 2007) and co-expression analysis based on self-organizing maps (Hirai et al., 2004). These tools pave the way for bioinformatics association studies for discovery of N–acyltransferases with regio-isomer-specific activity for acylation of spermidine skeletons, as the isomeric profiles of polyacylated spermidines are frequently tissue-specific. Following a similar approach (Ehlting et al., 2008), Matsuno et al. (2009) identified the role of two tandem duplicated cytochrome P450 genes, CYP98A8 and CYP98A9, arising from successive retroposition and duplication events. These two genes with pollen-specific expression act downstream of spermidine phenolamide-forming N–acyltransferases, and control meta-hydroxylation of the hydroxycinnamoyl moieties of specific phenolamides (Matsuno et al., 2009). Co-expression analysis using these two genes as baits identified an alternative phenylpropanoid pathway specifically supplying hydroxycinnamoyl units for production of pollen coat phenolamides (Matsuno et al., 2009).

Phenolamide Metabolism as Part of the Anti-Herbivore Defensive Arsenal

Robust increases in the levels of N–caffeoylputrescine and certain N′,N″-dicaffeoylspermidine isomers occurring during insect feeding have been used to screen for alterations in herbivory-induced signaling networks in leaves of N. attenuata transformants. MS-based metabolomics profiling of the herbivory-regulated metabolome of this plant has shown that almost every aspect of the phenolamide metabolic grid are reconfigured during insect herbivory, but these changes occur differently in locally treated and systemic leaves of the same plant (Figure 3) (Gaquerel et al., 2010; Kaur et al., 2010; Onkokesung et al., 2012). Increased phenolamide production after insect herbivory has also been reported in maize (Marti et al. 2013). Wounding a source leaf with a fabric pattern wheel on both sides of the mid-rib and immediately applying Manduca sexta oral secretions to the fresh puncture wounds (hereafter referred to as W+OS or OS elicitation) provides a convenient means of accurately standardizing herbivore elicitation (McCloud and Baldwin, 1997) and activating the associated defense/tolerance/escape responses in N. attenuata plants. This procedure recapitulates the major reconfigurations in the phenolamide metabolic network that are repeatedly induced during insect feeding, and allows researchers to perform replicated time-series metabolomics analysis. After appropriate data processing, the resulting metabolic traces may be used to reconstruct metabolic networks in which phenolamide responses are resolved as the main induced responses.

Most previous analyses on the complete phenolamide profile of N. attenuata leaves have shown that, in line with the amplitude of the responses of their underlying biosynthetic genes, putrescine-based phenolamides exhibit more dramatic responses to the W+OS treatment than do spermidine conjugates, and that caffeic acid-containing metabolites accumulate to higher levels in N. attenuata leaves than other types of phenolamides (Figure 3). Putrescine-based phenolamides occur at low levels in non-stressed leaves, whereas high amounts of developmentally regulated spermidine conjugates are regularly detected in most vegetative and reproductive tissues (Keinanen et al., 2001; Kaur et al., 2010; Onkokesung et al., 2012). The dynamics of spermidine-based phenolamides are relatively complex, which may not only reflect the multiple metabolic interconnections existing among these metabolites but may also be a signature for their highly specific functions. Only small amounts of mono-acylated spermidine are typically detected during W+OS treatment although these intermediates accumulate during insect feeding, suggesting rapid conversion into diacylated forms (Onkokesung et al., 2012). Perhaps, as proposed by Bassard et al. (2010), these complex patterns illustrate a plant's ability to separately control the accumulation of various diacylated spermidine isomers for specific functions in plant defense and/or development. The turnover and interconversions among phenolamides and their respective free precursors remain to be explored, and these may also contribute to the changes in metabolite levels seen during herbivory.

Interestingly, the levels of some of these spermidine-based phenolamides rapidly decrease following simulated herbivory in N. attenuate, such as the unidentified isomers of N′,N″-dicaffeoylspermidine, while others, such as N′,N″-caffeoyl,feruloylspermidine, exhibit inversely correlated accumulation patterns. Several studies have shown that further decoration may be added to phenolic residues when conjugated to polyamines. Notably, Fellenberg et al. (2008) identified an O–methyltransferase from the Arabidopsis CcOAMT gene family that controls the terminal methylation of tri-(5-hydroxyferuloyl)spermidine into N1,N6-di(hydroxyferuloyl)-N10-sinapoylspermidine in the tapetum. Such enzyme-dependent interconversions between pre-existing diacylated spermidine pools (Fellenberg et al., 2008; Matsuno et al., 2009) most likely shape the complex dynamics seen in accumulation of various spermidine-based phenolamides during herbivory, and may involve the methylation of N′,N″-dicaffeoylspermidine into N′,N″-caffeoyl,feruloylspermidine.

Regulation via the Jasmonate–MYB8 Transcriptional Module

The accumulation of N–caffeoylputrescine in various solanaceous plants (Tebayashi et al., 2000), and the more recently characterized profound reconfigurations of most branches of the phenolamide metabolic network of N. attenuata during insect feeding, are transcriptionally regulated by the jasmonic acid (JA) signaling pathway (Keinanen et al., 2001; Paschold et al., 2007; Stitz et al., 2011; Onkokesung et al., 2012; Ullmann-Zeunert et al., 2013) (Figure 4). Previous work with transformants or mutants directly impaired in jasmonate accumulation or perception showed that they accumulate much lower levels of N–caffeoylputrescine and stress- or herbivory-inducible phenolamides, but exhibit less pronounced changes in the basal levels of several spermidine-based phenolamides (Paschold et al., 2007; Onkokesung et al., 2012; Ullmann-Zeunert et al., 2013). In general, alterations due to jasmonate signaling deficiency are more pronounced in systemic leaf positions, where induced phenolamide accumulation is thought to translate from major transcriptional adjustments initiated by jasmonate-dependent mobile signals transmitted from OS-elicited leaves. Jasmonate-dependent phenolamide accumulation requires the F–box protein COI1. After interaction with jasmonoyl isoleucine (JA–Ile), this receptor protein targets JAZ transcriptional repressors for degradation by the proteasome, a transcriptional machinery that controls many secondary metabolic pathways (De Geyter et al., 2012). The strict requirement for JA–Ile in this process is clearly discernible in lines ectopically expressing an Arabidopsis JA-specific methyltransferase that specifically depletes JA–Ile accumulation (Stitz et al., 2011).

Approaches exploiting natural variations are commonly used in Arabidopsis to infer associations between genes or phytohormone signals, including jasmonates, and quantitative traits of a plant's phenotype. Recent work on naturally variable traits in N. attenuata populations has highlighted important variations in the amplitude of the JA and JA–Ile bursts produced after simulated herbivory in these populations (Machado et al., 2013). Figure 5(b) shows that herbivory-induced levels of N–caffeoylputrescine, as well as of other phenolamides, vary greatly in native populations when grown under controlled laboratory conditions, indicating the existence of genetically determined variations at the level of regulation and biosynthesis of these metabolites. These patterns of natural variation are significantly positively correlated with the amplitude of JA–Ile bursts.

Figure 5.

Novel approaches based on metabolomics for discovery of regulatory mechanisms for herbivory-induced changes in phenolamide metabolism.

(a) High-throughput non-targeted metabolite profiling of herbivory-induced changes in a large collection of RNAi transgenic lines reveals regulators of metabolite accumulation. Processed data may be classified using hierarchical clustering and clusters of m/z signals of interest screened across the library of transgenic lines. The jasmonate regulation of N′,N″-caffeoyl,feruloylspermidine is shown as an example.

(b) Natural variation in W+OS-induced levels of N–caffeoylputrescine and an unknown spermidine-based phenolamide in 176 natural accessions of N. attenuata positively correlate with natural variation in the OS-induced JA–Ile bursts.

Jasmonate-dependent activations during phenolamide metabolism are not solely the result of increases in expression of genes of the phenylpropanoid pathway, most of which are well-known expression markers for wound and jasmonate responses. The expression of N–acyltransferases required for phenolamide production is also controlled by the jasmonate pathway through the transcriptional activity of N. attenuata MYB8 and Nicotiana tabacum MYBJS1, members of the R2R3 MYB transcription factor family (Galis et al., 2006; Kaur et al., 2010). The DNA-binding domain of the homologous gene of MYB8 in N. tabacum, MYBJS1, has been shown to bind to the promoter regions of copies of the PAL gene to regulate the expression of core genes of the phenylpropanoid pathway as well as a few from the polyamine pathway (Galis et al., 2006). Additionally, silencing of MYB8 in N. attenuata abolishes herbivory-induced elevations as a result of strong reductions in AT1, DH29 and CV86 expression (Onkokesung et al., 2012). MYB8-silenced plants do not show developmental alterations, indicating that this transcription factor may control herbivory-induced elevations in the phenylpropanoid flux guided towards phenolamide production rather than steady-state parameters of this pathway. In this respect, the current view is that COI1-based perception of JA–Ile alleviates a negative transcriptional control exerted by one or several yet to be characterized JAZ proteins, which then leads to expression of MYB8 (Figure 4). Recent work suggests that N. attenuata MYC2, a basic helix-loop-helix Leu zipper transcription factor that regulates several jasmonate-dependent responses, regulate the expression of MYB8, but only minor alterations of the phenolamide profiles were detected in MYC2-transiently silenced plants (Woldemariam et al., 2013). As already demonstrated for other pathway-specific transcription factors (Mehrtens et al., 2005; Dal Cin et al., 2011), the high specificity of MYB8 in the regulation of phenolamide metabolism opens up interesting perspectives for increasing the rate of gene discovery in this pathway using transcriptional screens.

Herbivory-Induced Phenolamide Profiles Reveal Interaction Between Phytohormone Signaling Pathways and Nitrogen Metabolism Trade-Offs

Virtually any signaling nodes influencing jasmonate pools may alter induced phenolamide levels (Heinrich et al., 2013). In this respect, high-throughput metabolomics profiling of transgenic lines for which there is sufficient knowledge regarding disturbed signaling pathways may rapidly contribute to our understanding of phenolamide regulation (Figure 5a). This includes the possibility of reviewing how phytohormone cross-talk and downstream transcriptional regulators affect defense metabolite production. The role of ethylene in regulating phenolamide production is particularly noteworthy, because, in addition to its signaling function, ethylene biosynthesis connects with the putrescine to spermidine conversion (Kumar et al., 1996).

Phenolamide biosynthesis interacts with nitrogen metabolism through the polyamine component of phenolamide metabolism (Matsuno et al., 2009; Fellenberg et al., 2012b; Ullmann-Zeunert et al., 2013). Activation of herbivory-induced responses in tobacco plants in which patterns of stress-induced nitrogen accumulation have been tracked in various tissue compartments represents an ideal system for testing the nature of this interaction. The phenolamide profile is also strongly influenced by the soil type used. We have shown that, in sand-grown plants, the induced spermidine and putrescine-based phenolamide pools are replaced by tyramine-based ones (Kim et al., 2011). It is currently unclear whether this metabolic shift is related to the differential nitrogen supplies between soils and allocation in the plant (Lou and Baldwin, 2004). A recent flux study has demonstrated that elevations in phenolamide levels involve significant trade-offs for nitrogen allocation during insect herbivory (Ullmann-Zeunert et al., 2013). This study is of central importance to understand how nitrogen allocation costs affect phenolamide metabolism inducibility throughout a plant's development. The mechanisms behind this metabolic trade-off may be investigated using transcriptomic approaches such as the one presented below.

New Systems-Based Approaches for Discovery of Gene Regulation in Phenolamide Metabolism

As frequently detected in transcriptomic screens, herbivore attack activates specific reorganizations of metabolic pathways that are different between locally attacked and distal tissues from the same plant (Schittko et al., 2001; Gulati et al., 2013b). Spatially coordinated modulations in gene expression networks may be a key mechanism to regulate changes in metabolite pools throughout the plant, but this remains under-studied. Influential work on regulation of glucosinolate biosynthesis and distal networks connected to it has shown that upstream genes of the pathway with important flux control and that are subject to intense purifying selection (Olson-Manning et al., 2013) are central in shaping the glucosinolate chemotype according to the ‘genomic context’ or network of genes with which they are co-expressed (Malitsky et al., 2008). Instrumental data analysis tools for mining these gene networks are described in a series of inspiring recently published ‘evo-devo’ transcriptomic studies. Several recent studies have notably highlighted the unprecedented perspective into the developmental regulation of genes that appropriate statistical analysis of transcriptomic datasets provides. For instance, the elegant statistical and data visualization approach developed by Chitwood et al. (2013) has been used to demonstrate that changes in gene networks, rather than sequence divergence patterns, are responsible for the significant anatomical differences between cultivated and wild tomato species. Here we discuss the insights into phenolamide metabolism that resulted from implementation of such a bioinformatic approach.

Many ‘Interactive Effect’ Genes Play important function in metabolism

Surprisingly, the fact that most herbivory-inducible secondary metabolites also increase in systemic tissues has hardly been exploited in the context of gene function analysis. The case of phenolamides is particularly germane, as the dynamics of these metabolites differ between local and systemic leaf tissues and these differences are known to be essential determinants of systemic defense induction in N. attenuata. Onkokesung et al. (2012) successfully selected N–acyltransferase candidates for production of phenolamides based on their greater amplification by insect OS cues in systemic tissues compared to mechanical wounding. Indeed, most induced defense secondary metabolism genes investigated in N. attenuata in the context of W+OS treatments show amplified expression in response to by OS-activated mobile signals that are transported into systemic tissues from OS-elicited ones (Schittko et al., 2001; Schittko and Baldwin, 2003; Kim et al., 2011; Gulati et al., 2013b). This further confirms that only OS perception alone, but not mechanical wounding alone, leads to deployment of robust systemic signals (Gulati et al., 2013a,b).

Experiments designed to assess the dynamic rewiring of the gene networks that control the spread of herbivory-induced systemic responses often have a complex factorial structure resulting from the different conditions/treatments and tissue types analyzed, and necessarily involve time-series analysis. Based on the targeted interpretation of metabolic gene regulation presented above, we recently designed a dimensionality reduction method based on multifactorial analysis to categorize genes according to their degree of tissue specificity and responses to W+OS elicitation (Gulati et al., 2013a,b) (Figure 6). The procedure utilizes bootstrap-based non-parametric anova models implemented in the R package tanova (Zhou et al., 2010; Zhou and Wong, 2011). We applied this method to the analysis of a time-course microarray dataset for tissues collected from control and W+OS-treated plants (Kim et al., 2011). When used for statistical comparison of gene expression between locally treated leaves and systemic tissues collected from the same plant, we identified four mutually exclusive groups of genes with different anova structures. We detected anova structures that were significant for an interactive effect (two leaf positions behaving differently across the time series in response to W+OS elicitation), an additive effect (W+OS-induced responses that are independent of tissue type), or corresponding to independent effects derived from the main experimental factors (major treatment effects in both treated and untreated tissues or differences in tissue type with no response to treatment) (Gulati et al., 2013b). The interactive gene set represents 69% of the non-constantly expressed genes analyzed, and is highly enriched in genes involved in metabolic processes. Most processes connected with secondary metabolic pathways map to this group of genes (red sector in Figure 6b). Remarkably, genes of the phenylpropanoid and phenolamide pathways are among those exhibiting the largest interactive effects. Additionally, many other metabolic pathways and their transcription factors share similar behavior and have yet to be explored. This necessitates classifying temporal dynamics within this large group of promising genes for metabolic pathway exploration that constitutes the interactive effect group.

Figure 6.

A multifactorial-based co-expression analysis workflow for delineating systemically induced secondary metabolic pathways.

(a) A multifactorial analysis workflow was developed by Gulati et al. (2013a,b)z. This strategy was applied to analysis of multidimensional transcriptomic datasets acquired from multifactorial experimental designs (different tissue types, treatment, etc…) including time-series experiments.

(b) Transcriptomic data collected at each time point are combined into a data matrix used for multifactorial analysis. The statistical group corresponding to the interactive effect genes (those genes that respond to the treatment differently according to the tissue type, here locally versus systemically treated leaves) is highly over-represented by metabolism-related genes (red sector).

(c) Self-organizing maps are used to impose structure and to cluster genes within this bin according to their temporal dynamics using a metric derived from the multifactorial analysis.

(d) Bait genes (here from the phenylpropanoid and phenolamide pathways) may be localized on the maps to identify clusters of genes of interest (phenylpropanoid genes: L1 for early interactive effects in local leaves; phenolamide genes: L1, S5a and S5b for local and then systemic interactive effects).

(e) These clusters of genes may be subsequently mined in accordance with the predictions of phylogenetic relationships.

(f) Genes from specific branches of the phenylpropanoid space may be classified according to the detection of interactive effect regulation. Most lignin-related genes, except HCT-like, do not show interactive effect regulation in response to herbivory, unlike the core phenylpropanoid and phenolamide genes.

Tissue × Treatment Self-Organizing Maps Show the Sequential Arrangement of Metabolic Pathways

Metabolic genes belonging to a common biosynthetic pathway tend to be co-regulated as a result of activation of a robust regulatory system (Saito et al., 2008). However, basic statistical approaches used to identify such strong co-expression patterns are often plagued by problems of gene prioritization (Bittner et al., 1999; Getz et al., 2000) that arise from performing clustering analysis of gene expression under all experimental conditions (Swindell, 2006). Indeed, patterns revealed by simple co-expression analysis essentially represent the static rewiring of the network, which does not realistically capture the plant's phenotypic plasticity that results from the ability of cells to activate transient gene associations that represent intermediate biological states. The need for condition-dependent algorithms to resolve functional gene associations that are affected by only a subset of experimental conditions, such as the transmission OS-induced signaling to systemic leaf positions, has been reviewed by Krouk et al. (2013).

We used the time-specific anova coefficients reflecting the degree of significance for the interactive effect between the treatment and leaf positions, and scaled them with the difference in amplitudes of responses to OS elicitation to obtain a metric that characterizes the behavior of a given gene in more than one tissue (here two leaf positions). We then applied self-organizing maps to delineate gene network assemblies. Self-organizing maps result from an iterative process in which neighboring clusters influence each other. The resulting maps clearly visualize the main expression patterns in the analysis of molecular responses to perturbations (Hirai et al., 2004, 2005; Chitwood et al., 2013; Gulati et al., 2013b). Self-organizing maps colored according to the cluster's mean intensity at each time point are presented in Figure 6. Gene network assemblies along the time course are visualized by changes in the size of the groups of clusters that are similarly colored according to the tissue specificity of gene expression. Of the sequential arrangements of the group of genes termed ‘interactive’ motifs, we isolated one motif (S5) that was detectable 5 h post-elicitation for systemic leaves and showed an over-representation of metabolic pathway-encoded processes (Figure 6e). From this motif, we delineated, in a previous study, the acyclic diterpene glycoside pathway, another route leading to production of anti-herbivore defense metabolites (Heiling et al., 2010). Here we confirm that PAL genes and downstream elements of the phenylpropanoid pathway map into a large interactive motif (L1) that is rapidly induced in locally treated leaves. Consistent with their induced regulation to supply phenolamide production in these tissues, MYB8, AT1, DH29 and CV86 map to different cells corresponding to interactive effects detected first in local leaves (L1) and then in systemic leaves (S5a and S5b).

In our previous study, a rigorous comparison of Pearson correlation patterns before and after extraction of the interactive effect metric by multifactorial analysis revealed that the method greatly improves detection of tight regulation between the phenylpropanoid pathway and its downstream phenolamide branch (Gulati et al., 2013b). We therefore propose that, after delimitation and selection of relevant interactive motifs using bait genes for specific branches of phenylpropanoid metabolism, self-organizing maps may be quickly mined for phenolamide gene discovery. Figure 6 shows the overall workflow, and phylogenetic relationships between predicted N. attenuata BAHD genes are used as queries for the self-organizing maps. This process, based on multidimensional clustering of gene expression, is specifically designed to mine enzyme-coding gene families for which substrate specificity and enzymatic functions are not readily predictable from phylogenetic relationships. Finally, the involvement in phenolamide biosynthesis and metabolism of a set of genes analyzed by this method may be tested by transient virus-induced gene silencing, a rapid technique with many advantages for screening the role of metabolic genes at the interface between developmental and defense processes (Steppuhn et al., 2010; Galis et al., 2013; Gaquerel et al., 2013). Genes inferred from this analysis in motifs L1, S5a and S5b and exhibiting high sequence similarity with AT1 and CV86 are currently being characterized for their involvement in the production of N–feruloylspermidine and specific N′,N″-dicaffeoylspermidine isomers.

Plastic Gene Networks Shape Developmental Versus Defensive Allocations of Phenolic Residues To Phenylpropanoid Sub-Branches

Loss-of-function approaches may in some cases highlight complex patterns of ‘metabolic tension’ and feedback regulation that exist between interconnected metabolic branches (Vanholme et al., 2012). Our previous study provided support for the existence of a strong competition in conjugation of phenolic residues to putrescine or spermidine molecules (Onkokesung et al., 2012). Silencing of one acyltransferase enzyme impairs the accumulation of several metabolites while increasing another set of metabolites. Using the same approach, we also uncovered complex interconnections between the lignin and phenolamide pathways by silencing hydroxycinnamoyl CoA:shikimate/quinate hydroxycinnamoyl transferase-like (HCT-like), which encodes an O–acyltransferase catalyzing the production of upstream intermediates in the lignin pathway that branch at the level of the phenylpropanoid pathway (Hoffmann et al., 2003, 2004). Interestingly, HCT-like expression is also controlled by the transcriptional activity of MYB8, but, consistent with its main function in developmentally controlled lignin deposition, its expression is less pronounced than that of phenolamide biosynthetic genes during herbivory and is also much less affected than these latter genes during herbivory in MYB8-silenced plants (Gaquerel et al., 2013). Widely targeted metabolomics analysis on plants transiently silenced for HCT-like revealed large metabolic shifts due to a large diversion of activated coumaric acid units from lignin production into production of developmentally and herbivory-induced coumaroyl-containing phenolamides (N′,N″-dicoumaroylspermidine, N′,N″- coumaroylputrescine, etc). The fact that metabolic shifts in the production of unusual coumaroyl-containing phenolamides are largest during herbivory in HCT-like-silenced plants identifies HCT-like as a large-effect gene within the gene network underlying phenylpropanoid metabolism plasticity.

Exploring the type of effect revealed by the multifactorial analysis for a given group of genes may be used to track the dynamic behavior of gene expression involved in connected branches of a metabolic pathway. This approach may be used to mine the above-mentioned interactions between the phenolamide and lignin branches. Interestingly, unlike the phenylpropanoid core pathway and phenolamide genes, preliminary work revealed that most previously characterized lignin biosynthetic genes tested, with the exception of HCT-like, do not exhibit interactive effect regulation following insect herbivory. Individual expression patterns are shown in Figure 6(f). These patterns suggest that steady-state coordinated expression patterns between the phenylpropanoid and lignin pathways are relaxed after herbivore attack. As a result of the profound reconfigurations of gene expression, tighter co-expression patterns appear to be established between the core phenylpropanoid module and the structural genes of the phenolamide pathway during herbivory compared to control conditions. More research is needed to understand the central function of MYB8 in assembling these co-expression networks between high-amplitude regulation genes of the phenylpropanoid and phenolamide pathways in order to prioritize phenolamide production during insect herbivore attack.


The importance of phenolamides as central players in a plant's defenses is rapidly being recognized. The advances outlined here in understanding the transcriptional regulation and biosynthesis of these metabolites offer new possibilities for manipulating these dynamic phenolamide pools and understanding the many subtle adjustments at the interface between development and stress metabolic responses that determine phenolamine levels. Tissue-specific genetic silencing approaches such as that recently established by Schafer et al. (2013) will probably reveal novel aspects of the functions of phenolamide metabolism.


We thank Aura Navarro Quezada (Max Planck Institute for Chemical Ecology, Department of Molecular Ecology) for help with the phylogenetic analysis. E.G.'s research in Heidelberg is funded by a Excellence Initiative grant from the Deutsche Forschungsgemeinschaft. E.G., J.G. and I.T.B. are funded by the Max Planck Society, and E.G. is additionally funded by advanced grant number 293926 from the European Research Council to I.T.B.