integrative approaches to enhance understanding of plant ... · here, we provide an overview of how...

Update on Integrative Studies

Integrative Approaches to Enhance Understanding ofPlant Metabolic Pathway Structure and Regulation1

Takayuki Tohge*, Federico Scossa, and Alisdair R. Fernie

Max-Planck-Institute of Molecular Plant Physiology, 14476 Potsdam-Golm, Germany (T.T., A.R.F.); andConsiglio per la Ricerca e Analisi dell’Economia Agraria, Centro di Ricerca per la Frutticoltura, 00134 Rome,Italy (F.S.)

Huge insight into molecular mechanisms and biological network coordination have been achieved following the application ofvarious profiling technologies. Our knowledge of how the different molecular entities of the cell interact with one anothersuggests that, nevertheless, integration of data from different techniques could drive a more comprehensive understanding ofthe data emanating from different techniques. Here, we provide an overview of how such data integration is being used to aidthe understanding of metabolic pathway structure and regulation. We choose to focus on the pairwise integration of large-scalemetabolite data with that of the transcriptomic, proteomics, whole-genome sequence, growth- and yield-associated phenotypes,and archival functional genomic data sets. In doing so, we attempt to provide an update on approaches that integrate dataobtained at different levels to reach a better understanding of either single gene function or metabolic pathway structure andregulation within the context of a broader biological process.

The diversity of metabolites in the plant kingdom isstaggering: a commonly quoted estimate is that plantsproduce somewhere in the order of 200,000 uniquechemical structures (Dixon and Strack, 2003; Yonekura-Sakakibara and Saito, 2009; Tohge et al., 2014). Of these,only a relatively small subset will be abundant in anygiven tissue or any one species (Fernie, 2007); however,certain species have evolved a particularly rich meta-bolic diversity, presumably in response to environ-mental features of their habitat (for examples, seeFutuyma andAgrawal, 2009;Moore et al., 2014; Li et al.,2015). Given these facts, it is unsurprising that ourcurrent understanding of the metabolic structure of alarge number of pathways remains fragmentary; not tomention our current views of regulatory mechanismsunderlying metabolite accumulation, which cover, atbest, a very limited fraction of the metabolic network.This statement is especially true for the highly special-ized pathways of secondary metabolism, although anumber of gaps still remain to be filled also concerningimportant sectors of plant primary metabolism. Asdetailed in other Update articles within this issue, theadoption of various broad-scale profiling technolo-gies to assess the gene, transcript, protein, and smallmolecule complement of the cell has started to minethis metabolic complexity. Additionally, the same ap-proaches have also started to shed light on the evolu-tion of gene and metabolite regulatory networks acrossthe plant kingdom. In addition to large-scale profilingapproaches, classical reductionist biochemistry andreverse genetic approaches retain, in any case, great

utility in enhancing our understanding of enzymemechanisms (and their regulation) and about the invivo functions of enzymes, respectively. To give just acouple of recent examples from organic acid metabo-lism, a detailed study of the effect of phosphorylation ofphosphoenolpyruvate carboxylase reveals an importantanaplerotic control point in developing castor bean(Ricinus communis) endosperm (Hill et al., 2014), whilethe enzyme pyruvate orthophosphate dikinase wasrecently demonstrated to represent a second gatewayfor organic acids into the gluconeogenic pathway inArabidopsis (Arabidopsis thaliana; Eastmond et al., 2015).We aim to provide examples from both primary andsecondary metabolism and to illustrate the power ofsuch approaches both in (1) gene functional annotationand (2) enhancing our understanding of the systems-level response to cellular circumstances. We will addi-tionally discuss recent studies combining genomesequence data with metabolomics in order to highlightthe utility of such approaches in metabolic quantitativeloci analyses. Finally, we will detail insight that can beobtained from fusing archived data that can be down-loaded from databases with experimental data gener-ated de novo. Given that, as documented previously(Fernie and Stitt, 2012), a number of complicating fac-tors still exist when attempting such analyses, we willdiscuss these on an approach-by-approach basis.

INTEGRATING METABOLITE ANDTRANSCRIPTOME DATA

The earliest integrative approaches with relevance toplant metabolism featured the combination of datafrom transcript and metabolite profiling (Urbanczyk-Wochniak et al., 2003; Achnine et al., 2005; Tohge

1 This work was supported by the Max Planck Society (to T.T. andA.R.F.) and an Alexander von Humboldt grant (to T.T.).

* Address correspondence to [email protected]/cgi/doi/10.1104/pp.15.01006

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et al., 2005). Such studies were initially restricted tomodel species for which ESTs or oligonucleotides wereavailable; early transcriptomics approaches relied infact on differential hybridization of complementaryDNA samples to known sequences immobilized onsolid supports. The advent of next-generation se-quencing technologies, however, has removed thisbarrier, and far more exotic species are beginning to bestudied using this approach (Góngora-Castillo et al.,2012; Gechev et al., 2013; Li et al., 2015). Two basicquestions are commonly addressed by combining tran-script and metabolite data. The first concerns whether agene functions within a given metabolic pathway.When a better characterization of the pathway is achieved,it becomes fundamental to investigate also the extentof transcriptional control (except in some cases, for ex-ample, regulation by posttranscriptional modificationsof the enzyme and positive/negative feedback reg-ulation by substrates/products) under various physio-logical conditions and how it is distributed across thevarious enzymatic steps.

Initial observations about the role of differential geneexpression in tuning the synthesis of metabolites dateback to the 1990s. Some specific pathways, such ashormone, glucosinolate, and flavonoid biosynthesis,were the initial focus of these investigations. For ex-ample, differential mechanisms of gene expressionhelped clarify in Arabidopsis the involvement of twodifferent nitrilase genes in regulating the synthesis ofauxin (Bartling et al., 1994). Similarly, the contributionsof gene duplication and inducible gene expression(differential activation of subsets of biosynthetic genes)were shown to impact the amount and the compositionof glucosinolates (Kliebenstein et al., 2001). An addi-tional early evidence of the role of specific transcriptaccumulation on a metabolic phenotype came from theelucidation of the role that different regulation mecha-nisms affecting Trp synthase a and b had on theamount of 2,4-dihydroxy-7-methoxy-1,4-benzoxazin-3-one, a natural pesticide synthesized in maize (Zea mays)leaves (Melanson et al., 1997). Another example of thecoordination between transcripts and metabolite accu-mulation came from the analysis of maize anthers,where a strong correlation was found between the ex-pression of a structural gene (flavanone 3-hydroxylase)and the appearance of specific flavonols (mainly quer-cetin and kaempferol; Deboo et al., 1995). These sameapproaches have also been used to select a number ofcandidate genes involved in the biosynthesis of cap-saicinoids, a group of vanillylamides conferring pun-gency to hot peppers (Capsicum spp.). In this case, thecomparison between sweet and hot pepper varietiesfacilitated the identification of some placenta-specific,differentially expressed genes that were directly corre-lated with the accumulation of capsaicinoids (Curryet al., 1999). The examples cited above laid the foun-dation for large-scale studies using the parallel analysisof transcripts andmetabolites. One of the first examplesof this approach focused on the identification of tran-scripts strongly correlated with the abundance of given

metabolites across tuber development, irrespective ofwhether the transcript was associated with the meta-bolic pathway under question or not (Urbanczyk-Wochniak et al., 2003).

This approach was indeed able to identify sometranscripts that exhibited very high correlation with theexpression of certain genes and, as such, proved effec-tive in identifying a number of candidate genes forbiofortification. By corollary, the same approach can be,and indeed has been, used to elucidate the variation ingene-to-metabolite networks following short- and long-term nutritional stresses in Arabidopsis (Hirai et al.,2004) or to identify metabolic regulators of gene ex-pression (Hirai et al., 2007). Cryptoxanthin, for exam-ple, was identified as highly correlating with a broadnumber of genes across diverse environmental condi-tions in Arabidopsis (Hannah et al., 2010), and the or-ganic acid malate was putatively identified (Carrariet al., 2006) and subsequently confirmed (Centeno et al.,2011) to be important in mediating the ripening processin tomato (Solanum lycopersicum). Such current studiesare all examples of the guilt-by-association approach,which in essence postulates biological entities as beingfunctionally related if they exhibit strong correlation orcoresponse across a wide range of cellular circum-stances. The power of this approach is that, given that itdoes not rely on a priori pathway knowledge, it canhave great utility in identifying novel metabolic inte-gration and/or novel regulatory mechanisms (Hiraiet al., 2007; Tohge et al., 2007; Yonekura-Sakakibaraet al., 2008; Tohge and Fernie, 2010). However, adrawback of the approach is that, in the absence ofsubsequent rounds of experimentation, it is difficult togain any insight into the mechanistic links underlyingthe observed behavior, given that correlation betweenbiological entities does not always imply causation orthe existence of functional links (Sweetlove and Fernie,2005; Sweetlove et al., 2008; Stitt, 2013). In this regard, itbecomes imperative to validate the outputs of coex-pression analyses with follow-up approaches in orderto prove the existence of putative functional links. Ar-guably, the greatest advances made to date followingapproaches to integrate transcript and metabolite datahave been achieved in gene annotation and the struc-tural elucidation of plant intermediary and secondarymetabolism.

Two early studies of particular note are those fromthe Saito and Dixon laboratories investigating Arabi-dopsis anthocyanin and Medicago truncatula triterpenemetabolism, respectively (Achnine et al., 2005; Tohgeet al., 2005). In the case of the anthocyanin pathway,prior to the study of Tohge et al. (2005), no late bio-synthetic genes involved in anthocyanin decorationsteps had been identified in Arabidopsis, although allearly biosynthetic genes have been characterized byvisible phenotype screening. A combination of tran-script and metabolite profiling on a Production of An-thocyanin Pigment1 activation-tagged line alongsidevalidatory experiments involving both heterologouslyexpressed enzymes and knockout mutants resulted in

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the identification of five genes and the identification ofup to 11 anthocyanins. Such confirmatory experimentsare essential in order to unequivocally assign genefunction. The combination of reverse genetic strategieswith the characterization of enzyme activity when thegene is expressed in a heterologous system remains thegold standard for the molecular identification of novelenzyme-catalyzed reactions (Tohge et al., 2005; Luoet al., 2007; Yonekura-Sakakibara et al., 2012). Subse-quent follow-up studies have identified some six genesassociated with flavonol metabolism, and some 24compounds (among 35 compounds found) of this classhave now been identified in Arabidopsis (Tohge et al.,2007; Yonekura-Sakakibara et al., 2007, 2008, 2014;Nakabayashi et al., 2009; Tohge and Fernie, 2010; Saitoet al., 2013; Fig. 1). While the expansion of the charac-terized triterpenoid metabolism in M. truncatula is notquite so impressive, the study of Achnine et al. (2005)allowed the functional annotation of 30 different sa-ponins, and currently, over 70 metabolites of this com-pound class have been identified in M. truncatula(Pollier et al., 2011; Gholami et al., 2014; Watson et al.,2015). The utility of this approach is at its greatest forthe relatively unchartered pathways of specializedmetabolism; however, it is worth noting that slightvariations on this strategy independently identified thegene encoding plant Thr aldolase (Fernie et al., 2004;Jander et al., 2004) in Arabidopsis and 2,4-dihydroxy-7-methoxy-1,4-benzoxazin-3-one glucoside methyltrans-ferase in maize (Meihls et al., 2013). A decade later, thenumber of species and pathways for which this ap-proach has been adopted has expanded massively toinclude several crops and medicinal plants. Strategiescombining transcript and metabolite profiling haveproved effective in elucidating the structure of severalmetabolic pathways involved in the synthesis of pri-marymetabolites, flavonoids, terpenoids, and alkaloids(Osorio et al., 2011, 2012; Shelton et al., 2012; Lin et al.,2015).On a broader level, the combination of transcript and

metabolite profiling has commonly been used formultilayered descriptions of plant responses, partic-ularly those to abiotic stress (Gibon et al., 2006;Maruyama-Nakashita et al., 2006; Kusano et al., 2011;Gechev et al., 2013; Bielecka et al., 2014; Nakabayashiet al., 2014). In this vein, a number of studies have beencarried out that assess the combined transcript andmetabolite responses to water stress, temperaturestress, light stress, and limitations of nutrient supply(Urano et al., 2009; Caldana et al., 2011; Kusano et al.,2011; Nakabayashi et al., 2014). Such studies, while bynature descriptive, can afford insight into global met-abolic variations under certain conditions as well asidentify which pathways are under tight and which areunder loose transcriptional control. Given the highlyinterconnected nature and nonlinearity of metabolicpathways in the global network structure, and even inthe absence of flux profiling data, the integration oftranscriptomics with wide metabolic profiling can, inany case, narrow down which metabolic steps could

be active under specific conditions. Occasionally,however, they can also provide more mechanistic in-formation. One prominent example of this is the de-tailed analysis of several transgenic Arabidopsis lineswith altered flavonoid levels via transcriptomic andmetabolomics analyses, including hormone analysis,which revealed that the overaccumulation of fla-vonoids exhibiting strong oxidative capacity in vitroalso confers oxidative stress and drought tolerance(Nakabayashi et al., 2014; Nakabayashi and Saito,2015). In addition, a range of developmental processeshave been followed at high resolution by a combinationof transcript and metabolite profiles. Such studies aredominated by studies of fruit ripening (Zamboni et al.,2010; Lin et al., 2015; Vallarino et al., 2015) and leafdevelopment (Pick et al., 2011; Wang et al., 2014);however, they are not limited to these processes, withstudies also covering the development of various or-gans, lignin deposition, and the establishment ofarbuscular mycorrhizal symbiosis (Vanholme et al.,2013; Laparre et al., 2014; Nakamura et al., 2014; Wanget al., 2014). In this regard, these approaches proveinformative in clarifying the relative importance ofseemingly redundant pathways of biosynthesis and thedegradation of specific metabolites or may also helpto define the role of those primary metabolites (e.g.g-aminobutyrate) for which a signaling role was hy-pothesized (Batushansky et al., 2014). For example,ascorbate biosynthesis, which is one of the well-studiedmetabolisms in several higher plants, especially inArabidopsis (Wheeler et al., 1998; Gatzek et al., 2002;Laing et al., 2004; Conklin et al., 2006; Dowdle et al.,2007), has been revealed as the dominant route ofascorbate biosynthesis during ripening in tomato(Carrari et al., 2006). Another example could be found inthe elucidation of the arogenate pathway as an alterna-tive route for Phe biosynthesis (Dal Cin et al., 2011). Asimilar approach in Arabidopsis, based on feedingstudies and coexpression analysis, allowed an alterna-tive pathway to be proposed for Lys degradation indark-induced senescent leaves (Araújo et al., 2010).

However, despite the fact that these examples illus-trate that combined transcriptome/metabolome stud-ies provide increases in our understanding of theregulation of metabolic networks, we contend that theyremain at their most powerful in gene functional an-notation and in the elucidation of species- and/ortissue-specific metabolic pathway structures.

INTEGRATING METABOLITE AND PROTEOME/ENZYME ACTIVITY DATA

Less commonly used to date than combined tran-scriptome and metabolome analyses are combinedproteome and metabolome analyses. They are addi-tionally largely used in amanner analogous to the moredescriptive studies reviewed above. That said, consid-erable insight into metabolic network structure as wellas into general aspects of metabolic regulation have

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been gained in thismanner. Here, wewill describe eightstudies that illustrate how the integration of proteomicand metabolomic data sets has been used to informour understanding of systems regulation. In the firstof these examples, metabolite data were studied in

parallel to enzyme data (and transcriptomics data)across varying diurnal cycles in wild-type and astarchless mutant of Arabidopsis, revealing that rapidchanges in transcripts are integrated over time to gen-erate essentially stable changes in many sectors of

Figure 1. Current model of flavonol/anthocyanin biosynthesis in Arabidopsis. Colors are as follows: blue, early biosynthetic genes;green, flavonol-specific biosynthetic genes; and purple, anthocyanin-specific biosynthetic genes. CHS, Chalcone synthase, At5g13930; CHI,chalcone isomerase, At3g55120; CHIL1, At5g05270; F3H, flavanone-3-hydroxylase, At3g51240; F39H, flavonoid 39-hydroxylase, At5g07990;DFR, dihydroflavonol reductase, At5g42800; ANS, anthocyanidin synthase, At4g22880; F3GlcT, flavonoid-3-O-glucosyltransferase,UGT78D2, At5g17050; A5GlcT, anthocyanin-5-O-glucosyltransferase, UGT75C1, At4g14090; A3Glc299XylT, anthocyanin-3-O-glucoside-299-O-xylosyltransferase, UGT79B1, At5g54060; A5Glc69999MalT, anthocyanin-5-O-glucoside-69999-O-malonyltransferase,At3g29590; A3Glc699pCouT, anthocyanin-3-O-glucoside-699-O-p-coumaroyltransferase, At1g03940, At1g03495; A3Glc299XylSinT,anthocyanin-3-O-(299-O-xylosyl)-glucoside-6999-O-sinapoyltransferase, At2g23000; A3Glc699pCouT, anthocyanin-3-O-(699-O-coumaroylglucoside-O-glucosyltransferase,At4g27830; FLS1, flavonol synthase,At5g08640; F3RhaT,flavonol-3-O-rhamnosyltransferase,UGT78D1, At1g30530; F3AraT, flavonol-3-O-arabinosyltransferase, UGT78D3, At5g17030; F7RhaT, flavonol 7-O-rhamnosyltransferase,UGT89C1, At1g06000; F7GlcT, flavonol 7-O-glucosyltransferase, UGT73C6, At2g36790; OMT1,O-methyltransferase, At5g54160.


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metabolism (Gibon et al., 2006). The same group wenton to apply this approach to tomato fruit developmentand natural variance inArabidopsis. In tomato, enzymeprofiles were sufficiently characteristic to allow stagesof development and cultivars and the wild species to bedistinguished, but comparison of enzyme activity andmetabolites revealed remarkably little connectivitybetween the developmental changes of enzyme andmetabolite levels, suggesting the operation of post-translational modification mechanisms (Steinhauseret al., 2010). In Arabidopsis, they documented highlycoordinated changes between enzyme activities, par-ticularly within those of the Calvin-Benson cycle, aswell as significant correlations in specific metabolitepairs and between starch and growth. On the otherhand, few correlations, and thus low overall connec-tivity, were observed between enzyme activities andmetabolite levels (Sulpice et al., 2010), but strong linkswere seen between starch levels and growth, which wedescribe below. In an alternative approach, proteomicand metabolic data were used merely to extend therange of molecular entities in order to demonstrate thatfascicular and extrafascicular phloem are isolated fromone another and divergent in function (Zhang et al.,2010). A similar approach was taken to identify root asthe major organ involved in alkaloid biosynthesis inMacleaya spp. (Zeng et al., 2013). Three further studiesof note are more similar to that of Gibon et al. (2006) inthat they use a combination of proteomics and meta-bolomics as a means to define the complex response ofthe cell to varying circumstances, be they iron nutritionin Arabidopsis (Sudre et al., 2013), the drought re-sponse in maize xylem (Alvarez et al., 2008), or heatstress acclimation in the model alga Chlamydomonasreinhardtii (Hemme et al., 2014). The fact that many ofthese studies were published in the last 2 years reflectsthe growing uptake of such strategies. That said, in ouropinion, it remains an underexploited research ap-proach to date.

INTEGRATING METABOLITE AND GENOME DATA

Given that the advent of metabolomics more or lessparalleled the release of the first plant genome, theintegration of metabolomics and whole-genome se-quence data is perhaps unsurprising. The true potentialof this approach has been realized only within the lastfew years; we will not describe it again in detail, giventhat it is discussed in a previous correspondence inPlant Physiology (Fernie and Stitt, 2012). Suffice it to say,there are considerable complexities in such combina-tions; tellingly, early studies aimed at computationalprediction of the size of the Escherichia coli metabolomeestimated a complement of approximately 750 metab-olites, while subsequent experimental approaches haverevealed many metabolites that were not computedfrom the genome (van der Werf et al., 2007). Severalpotential reasons could be put forward to explain thisdiscrepancy (for review, see Fernie and Stitt, 2012;

Tohge et al., 2014); we contend here that an additionalreason to explain this (partial) lack of concordance inthe integrative approaches involving metabolism couldlie within the incomplete annotation of most genomes,including those of model organisms. However, we be-lieve the most likely reason to be the lack of linearrelationship between genes, their protein products, andmetabolites and, secondly, the fact that most genomes,even those of model organisms, remain incompletelyannotated. Despite this serious drawback, we hopeto illustrate in this section that the integration ofmetabolomics and genomic data can be incrediblypowerful in understanding natural variation in metab-olism and its regulation.

Whole-genome sequences are available formore than100 plant species (including microalgae; Tohge et al.,2014); this massive acceleration afforded by next-generation technologies cannot currently be matchedby metabolomics, especially if high-quality species-optimized approaches are adopted (Fukushima et al.,2014). The KNApSAcK database, which is one of thelargest curated compendia of phytochemicals, containsover 700 compounds for early sequenced plants likeArabidopsis and rice (Oryza sativa) but no entries forrecently sequenced species such as goatgrass (Aegilopstauschii) and wild tobacco (Nicotiana tomentosiformis).In this section, we will describe insight gained fromcombining metabolomic data with genome sequencesin three different case studies: (1) a simple comparisonof a reference genome with metabolomics data; (2) acomparison of natural allelic and metabolic variance;and (3) integrating genome sequence data into quanti-tative genetics approaches. The first of these has beencovered in considerable detail recently (Fukushimaet al., 2014; Tohge et al., 2014), so we will only brieflydescribe it here. The starting point is to performgenome-wide ortholog searches using functionally an-notated genes; best practice is to use cross-speciescluster-based BLAST searches such as those housed inthe PLAZAdatabase (Proost et al., 2009) or, in the case ofphotosyntheticmicrobes, pico-PLAZA (Vandepoele et al.,2013). Illustrations of how such analyses have beenperformed for central, shikimate, phenylpropanoid,terpenoid, alkaloid, and glucosinolate metabolism havebeen presented (Hofberger et al., 2013; Tohge et al.,2013a, 2013b, 2014; Cavalcanti et al., 2014; Boutanaevet al., 2015). Thereafter, comparison of these gene in-ventories with metabolite profiles of the species underevaluation allows the construction of putative meta-bolic pathway structures that can be further tested viareverse genetics or heterologous expression, as de-scribed in “IntegratingMetabolite and Transcript Data”above. Important insights into pathway evolution canbe gained from such approaches, as illustrated by therecent cross-kingdom comparison of ascorbate biosyn-thesis (Wheeler et al., 2015).

The second case study, that of evaluating allelic andmetabolic variance across natural diversity, is similar inscope yet far more targeted than genome-wide associ-ation studies, which we describe below. The majority of





recent examples of its utility come from the analysis ofwild species tomato; however, it is important to notethat the approach itself is essentially just a modificationof that adopted over decades in the cloning of naturalcolor mutants (Fernie and Klee, 2011). In the last fewyears, understanding of primary as well as secondaryand cuticular cell wall metabolism has been enhancedconsiderably via this approach (Schauer et al., 2005;Matas et al., 2011; Kim et al., 2012, 2014; Koenig et al.,2013), albeit the greatest insight into the latter wasultimately elucidated via the use of an introgression linepopulation, as described below. In essence, this ap-proach starts with the identification of metabolic vari-ance within a population of ecotypes, cultivars, orsimilarly related species and attempts to link this withallelic diversity or gene duplication, as has beenachieved for acyl-sugar metabolites (Schilmiller et al.,2015), terpenes (Matsuba et al., 2013), and isoprenoids(Kang et al., 2014), or even with the presence or absenceof genes, as described recently for methylated flavo-noids of glandular trichomes (Kim et al., 2014). Thepreceding list documents the success of this approach;until recently, however, it was constrained by the limitsof our a priori knowledge, which is needed in order toselect the candidate genes in which we search for allelicvariance. The development of RNA sequencing tech-nologies means that we are no longer limited by theamount of sequence data; a potential hurdle to theseintegrative approaches, however, can still be presentwhen comparing highly genetically divergent individ-uals, since the number of genetic polymorphisms istoo great to evaluate one by one. For this reason, thequantitative trait loci approach is a powerful alternativemethod of associating phenotypes to their underlyinggenetic variance. The use of such approaches in plantmetabolism has been the subject of several recentcomprehensive reviews (Kliebenstein, 2009; Scossaet al., 2015); however, we will provide a couple ofexamples of their utility for advancing the under-standing of metabolite accumulation and metabolicregulation.

The fruit of tomato, as the model species for ripen-ing of fleshy fruits, has been the subject of combinedlarge-scale genomic, physiological, and metabolic in-vestigations, often making use of specific biparentalpopulations or large sets of unrelated individuals, inan attempt to understand the causal variants of themetabolic variations (Schauer et al., 2005; Lin et al.,2014; Sauvage et al., 2014). In particular, the use of apopulation of introgression lines, obtained from thecross between tomato and Solanum pennellii (a wildtomato species), has greatly aided the identification ofquantitative trait loci for a large number of physio-logical and metabolic traits. Profiling data of primaryand secondary metabolites in this population werecollected over several years (along with some classi-cal yield-related traits), revealing more than 1,500metabolic quantitative trait loci affecting the levelsof several sugars, amino acids, organic acids, vita-mins, phenylpropanoids, and glycoalkaloids. The

availability of the sequences of both parental genomes(Bolger et al., 2014) narrowed down the origin of themetabolic variation to specific genetic polymorphismsin some selected metabolic quantitative trait loci(Quadrana et al., 2014; Alseekh et al., 2015). The inte-gration between genotypic and metabolic variance canbe, and has actually been applied, also on large col-lections of unrelated individuals (metabolite-basedgenome-wide association studies): as in the case ofbiparental populations, also with this strategy, severalcases of polymorphological variants of genomic se-quences have been identified and related to metabolicvariation. These two approaches, based either on bi-parental populations or on large collections of naturalaccessions, have been used in Arabidopsis and cropspecies (maize, rice, wheat [Triticum aestivum], and fruittrees; Gong et al., 2013; Li et al., 2013; Wen et al., 2014;Matsuda et al., 2015; for review, see Luo, 2015). Theboon that new sequences will provide, especially fromwild relatives or locally adapted varieties, will be rep-resented by the possibility to dissect the genetic basis ofmetabolite variation, with a view to introgress benefi-cial traits in crop improvement.

INTEGRATING METABOLITE ANDPHYSIOLOGICAL DATA

While the above examples concentrate on the inte-gration of various types of profiling data with oneanother in order to advance our understanding ofmetabolic pathway structure and/or metabolic regu-lation, relatively few studies have attempted to cor-relate metabolite content with physiological data,including growth and yield (for review, see Stitt et al.,2010; Carreno-Quintero et al., 2013). One of the earlieststudies to do so was the above-described metabolicquantitative trait loci analysis of the S. pennellii intro-gression lines, in which yield-associated plant traitswere measured alongside primary metabolite contentof the fruit (Schauer et al., 2006). In this study, networkanalysis based on cartographic modeling algorithmsdeveloped by Guimerà and Nunes Amaral (2005)identified that yield-associated traits were positivelycorrelated to a range of previously defined signal me-tabolites, compounds that have signaling as well asmetabolic functions, including Suc, hexose, and inositolphosphates, Pro, and g-aminobutyrate. In addition, thisstudy indicated that the harvest index (i.e. the ratio ofharvestable product to total biomass) negatively cor-related with the content of the vast majority of aminoacids. This relationship was confirmed in an indepen-dent population and following experiments that artifi-cially altered the fruit load per truss (Do et al., 2010).However, as would perhaps be anticipated, subsequentevaluation of the relationship between growth andsecondary metabolite content revealed far less correla-tion (Alseekh et al., 2015). Using essentially the sameapproach in an Arabidopsis recombinant inbred linepopulation, Meyer et al. (2007) found that, although no


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single metabolite exhibited a very high correlation withbiomass, canonical correlation analysis in which thedata of a linear combination of metabolites allowed theimprovement of this correlation by a factor of 10, thusdefined a metabolic signature of growth. Intriguingly,the hexose phosphates Glc-6-P and Fru-6-P as well asSuc were among the 20 top metabolites contributingto this signature. When similar approaches were ap-plied to maize, strong genome-wide association linkswere found between coumaric and caffeic acids andcinnamoyl-CoA reductase, while these precursors alsosignificantly correlated with lignin content plant heightand drymatter yield, presenting another example of thenarrowing of the genotype-phenotype gap of complexagronomic traits (Riedelsheimer et al., 2012). The samegroup revealed that models based on data obtained for130 metabolites gave highly accurate predictions ofagronomic traits and suggested that combined metab-olite, genomic, and agronomic phenotyping representsan important screening tool for the identification ofparental lines for the creation of superior hybrid crops(Riedelsheimer et al., 2012).Returning to Arabidopsis, evaluation of the variation

of growth, metabolite levels, and enzyme activities wasalso carried out across 94 accessions, revealing thatbiomass correlated negatively with many metabolites,including starch and protein and to amuch lesser extentSuc (Sulpice et al., 2009). However, further experimentsin which 97 accessions grown in near-optimal carbonand nitrogen supply, restricted carbon supply, and re-stricted nitrogen supply and analyzed for biomass and54 metabolic traits revealed that robust prediction ofphenotypic traits (biomass, starch, and protein) is mosteffective (and reliable) when metabolite data (uponwhich predictions are based) are collected from thesame growth environments (Meyer et al., 2007; Sulpiceet al., 2009; Korn et al., 2010; Steinfath et al., 2010).Clearly, attempting to predict biomass, for example,from metabolic profiles collected in a different growthenvironment generally yields fewer (and weaker) cor-relations (Sulpice et al., 2013). Therefore, the predictionof biomass across a range of conditions would bet-ter require condition-specific measurement of meta-bolic traits to take account of environment-dependentchanges of the underlying networks (Sulpice et al.,2013). Data from this study were subsequently ana-lyzed with respect to the tradeoffs between metabolismand growth, specifically comparing increasing sizewithincreasing protein concentration, demonstrating thataccessions with high metabolic efficiency lie closer tothe Pareto performance frontier (the optimal solutionfor the two contending tasks) and hence exhibit in-creased metabolic plasticity (Kleessen et al., 2014). Arelated study addressing an ecological tradeoff betweensecondary metabolism and fitness relates to the accu-mulation of capsaicinoids in the placenta of pepperfruits (Capsicum spp.). Capsaicinoids constitute a classof vanillylamides derived from Phe; they accumulatein ripening pepper fruit and are responsible for thepungency sensation occurring upon ingestion. In

natural environments, the accumulation of capsaicinoidsin populations of Capsicum chacoense (a wild pepperspecies) is inversely correlated with seed set; thesemetabolites, however, have a defensive role in highlyhumid environments, where their accumulation detersthe attack of phytopathogenic fungi. Across a geo-graphical gradient of decreasing rainfall (with a grad-ual decreasing pressure of the pathogens, which thriveonly in humid environments), the accumulation ofcapsaicinoids also decreases in Capsicum spp. popula-tions, while seed set, on the other hand, increases. Thisstudy is an example of the combination of targetedmetabolic approaches with population ecology in dis-secting the basis of natural polymorphic traits (Haaket al., 2012). Further studies that address the concept ofmetabolism and growth tradeoffs have used reciprocalcrosses to assess the contribution of the organellar ge-nome to the processes and came to the conclusion thatthere is far greater diversity in defense chemistry thanprimary metabolism (Joseph et al., 2013, 2015). The in-terrogation of such tradeoffs is only possible via theintegrated approach described here and appears to bevery powerful; as such, we would expect considerableadvances in our understanding of this phenomenon tobe gained following its application. Not just the lastthree studies but all of the above studies have beenpublished within the last 6 years, reflecting the factthat such analyses are in their infancy. Given therecognized complexity of the metabolism-to-growthinteractions, a comprehensive understanding of theintricate networks that coordinate this interface islikely some time off. That said, as the above examplesillustrate, the integration of growth data into metab-olite profiling data as well as that of simpler physio-logical processes such as photosynthetic or respiratoryrates (Florez-Sarasa et al., 2012) has already presenteda number of key findings.

POSTGENOMIC INTEGRATION OF DATABASE-HOUSED RESEARCH WITH NOVEL EXPERIMENTS

The examples described above rely on the integrationof data obtained in parallel using different experimen-tal approaches. While such approaches are ideal foraddressing a number of questions, particularly thoseconcerning the temporal aspects underlying dynamicresponses to a systems perturbation, the integrationof novel experimental data with different types ofarchived data can also prove highly informative, pro-viding an appropriate amount of caution is used ininterpreting the results. Here, we will provide severalexamples illustrative of such approaches, which largelyfit into two major types of approaches: (1) those usingcorrelative approaches and (2) those using genome-scale stoichiometric models. The first study we willdescribe fits into the former category, being an attemptto define the storage metabolome of the vacuole (Tohgeet al., 2011; Fig. 2). In this research, a combination ofgas chromatography-mass spectrometry and Fourier





transform mass spectrometry was used to detect andquantify some 59 primary metabolites and 200 sec-ondary metabolites (defined on the basis of strongchemical formulae predictions) in either silicon oil-purified barley (Hordeum vulgare) vacuoles or the pro-toplasts from which these were derived. Of the 259putative metabolites, 12 were exclusively detected inthe vacuole, 34 were exclusively in the protoplast, and213 were common to both samples. At the quantitativelevel, the difference between vacuole and protoplastwas yet more striking, with secondary metabolites be-ing differentially abundant between the two sampletypes. As a next step to predict the underlying cytosolic-vacuolar transporters, tonoplast proteins predicted tohave a transport function were evaluated within thecontext of the metabolic profiling data. Specifically, 88proteins reported to be tonoplast proteins in barley(Endler et al., 2006) were evaluated after conversion toAffymetrix probe identifiers and coexpression analysisof the resultant 128 probe sets was carried out usingPlaNet for barley (Mutwil et al., 2011; http://aranet.mpimp-golm.mpg.de). Coexpressed networks of theseprobes separated into 13 subgroups, with the mostdense cluster being highly correlated with aromaticamino acid-related genes and the second most densecluster including several vacuolar ATP synthase pro-teins and tricarboxylic acid cycle-related genes. Inaddition, clear associations were found between theexpression of transport proteins and that of pathwaysof flavonoid and mugineic acid synthesis as wellas storage protein functions (Tohge et al., 2011). Thisstudy was thus able to putatively assign function to

previously described transporter proteins as well as tohighlight the dynamic nature of the storagemetabolome.The coexpression approach has also been combinedwith metabolic profiling in the annotation of plasmamembrane lignin and plastidial glycolate/glycerateand bile acid transporters (Gigolashvili et al., 2009;Sawada et al., 2009; Alejandro et al., 2012) as well asa multitude of cell wall-associated proteins (Perssonet al., 2005). Moreover, this approach has also been usedto identify process, as opposed to pathway-specific, pro-teins, identifying proteins involved in dark-inducedsenescence (Araújo et al., 2011) and in the response toUV-B irradiance (Kusano et al., 2011).

The other type of examples we would like to discussare based on the integration of transcriptomic andmetabolomics level genome-scale models (Töpfer et al.,2014). In the first of these studies, microarray data fromArabidopsis exposed to eight different light and tem-perature conditions (data published in Caldana et al.,2011) were integrated into a genome-scale model(Mintz-Oron et al., 2012). Before discussing the out-come of this integration, we first digress to provide abrief description of how genome-scale models aregenerated. Essentially, a genome-scale model corre-sponds metabolic genes with metabolic pathways in amanner whereby a stoichiometrically balanced meta-bolic network is generated, which corresponds to allgene functions annotated for that organism. Suchmodels were originally published for microbes at theturn of the century (Edwards and Palsson, 2000), withmany models for plants species being subsequentlygenerated, including the model species Arabidopsis aswell as crop species such as rice and maize (for review,see Simons et al., 2014). Returning to the superimposi-tion of experimental data on the model, the addition oftranscriptomic data was able to predict flux capacitiesand statistically assess whether these vary under theexperimental conditions tested. Moreover, this studyintroduced the concepts of metabolic sustainers andmodulators, with the former being metabolic functionsthat are differentially up-regulated with respect to thenull model whereas the latter are differentially down-regulated in order to control a certain flux and, there-fore, modulate affected processes (Töpfer et al., 2013).In a follow-up study, predictions made from the inte-gration of transcriptomics were complemented withmetabolomics data from the same experiment. In doingso, the authors were able to bridge flux-centric andmetabolomics-centric approaches and, in so doing,demonstrate that, under certain conditions, metabolitesserving as pathway substrates in pathways defined aseither modulators or sustainers display lower temporalvariation with respect to all other metabolites (Töpferet al., 2013). These findings are thus in concordancewith theories of network rigidity and pathway robust-ness (Stephanopoulos and Vallino, 1991; Rontein et al.,2002; Williams et al., 2008). Furthermore, considerableevidence suggests that the levels of specific metabolites,such as Ala, pyruvate, 2-oxoglutarate, Gln, and sper-midine, are exceptionally stable across a massive range

Figure 2. Schematic overview of an integrative approach using me-tabolite profiling of storage metabolite and membrane proteomic data.Example of network: barley vacuole network from Tohge et al. (2011).


Tohge et al.


http://aranet.mpimp-golm.mpg.de

http://aranet.mpimp-golm.mpg.de


of cellular circumstances (Geigenberger, 2003; Stittand Fernie, 2003). They also are in keeping withobservations that the levels of metabolites such as Sercoordinately control the levels of expression of genesencoding multiple steps of the pathways to which they,themselves, belong (Timm et al., 2013). The high sta-bility of these metabolites is in keeping with their re-quirement across a range of different stresses. It alsohighlights the fact that the robust metabolites may wellbe the most biologically relevant for metabolic regula-tion; this is an important point, since it is at odds withthe manner in which the majority of the metabolomicscommunity assesses their data. This observation addi-tionally highlights the potential difficulties and chal-lenges in interpreting data from a single level of thecellular hierarchy and thus provides further groundsfor integrated models.

CURRENT AND FUTURE CHALLENGES INDATA INTEGRATION

The above sections document that integrative ap-proaches to further our understanding of metabolismhave proven very successful over the last decade or so,particularly when linked with genetic and/or envi-ronmental experiments. To date, the approaches takenhave been relatively straightforward and have gener-ally not been performed at a high level of spatial reso-lution. Several methods currently exist to obtain datafrom all of the methods described here at the tissue,cellular, and even subcellular levels (Aharoni andBrandizzi, 2012); while still technically challenging, itseems conceivable that such methods could providedata required to better understand the cell specializa-tion of metabolism. In addition, methods to gain accu-rate metabolic flux estimates following 13CO2 labelinghave recently been established (Young et al., 2008;Szecowka et al., 2013; Ma et al., 2014) but are not yetfully integrated with protein or transcript data. How-ever, it is important to note that such experiments, al-beit using [13C]Glc as a precursor, have already beencarried out in in vitro-cultivated Brassica napus em-bryos, providing considerable insight into the systems-level regulation of this organ (Schwender et al., 2015). Itadditionally seems highly likely that future researchwill drawmore heavily on archived genomics data thanit has to date; thus, the continued availability andquality-control curation of such data sets are imperativeif we are going to fully exploit their value.Received July 7, 2015; accepted September 10, 2015; published September 14,2015.

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