Network-Guided Discovery of Extensive Epistasis betweenTranscription Factors Involved in AliphaticGlucosinolate Biosynthesis

Baohua Li,a Michelle Tang,a,b Ayla Nelson,a Hart Caligagan,a Xue Zhou,a Caitlin Clark-Wiest,a Richard Ngo,a

Siobhan M. Brady,b and Daniel J. Kliebensteina,c,1

a Department of Plant Sciences, University of California, Davis, Davis, California 95616bDepartment of Plant Biology and Genome Center, University of California, Davis, Davis, California 95616cDynaMo Center of Excellence, University of Copenhagen, DK-1871 Frederiksberg C, Denmark

ORCID IDs: 0000-0001-7235-0470 (B.L.); 0000-0001-9424-8055 (S.M.B.); 0000-0001-5759-3175 (D.J.K.)

Plants use diverse mechanisms influenced by vast regulatory networks of indefinite scale to adapt to their environment. Theseregulatory networks have an unknown potential for epistasis between genes within and across networks. To test for epistasiswithin an adaptive trait genetic network, we generated and tested 47 Arabidopsis thaliana double mutant combinations for20 transcription factors, which all influence the accumulation of aliphatic glucosinolates, the defense metabolites that controlfitness. The epistatic combinations were used to test if there is more or less epistasis depending on gene membership withinthe same or different phenotypic subnetworks. Extensive epistasis was observed between the transcription factors,regardless of subnetwork membership. Metabolite accumulation displayed antagonistic epistasis, suggesting the presenceof a buffering mechanism. Epistasis affecting enzymatic estimated activity was highly conditional on the tissue andenvironment and shifted between both antagonistic and synergistic forms. Transcriptional analysis showed that epistasisshifts depend on how the trait is measured. Because the 47 combinations described here represent a small sampling of thepotential epistatic combinations in this genetic network, there is potential for significantly more epistasis. Additionally, themain effect of the individual gene was not predictive of the epistatic effects, suggesting that there is a need for further studies.

INTRODUCTION

To adapt and maximize fitness, plants perceive and respond toa myriad of signals that in combination provide an image of theenvironment. These signals can arise from the biotic environment,includingbacteria, fungi, insects, andother plants, plus stimuli fromthe abiotic environment, including light, temperature, water, andnutrient availability (Goldwasser et al., 2002; Shinozaki et al., 2003;Jones and Dangl, 2006; Howe and Jander, 2008; Vidal andGutiérrez, 2008; Harmer, 2009; Chory, 2010;Mengiste, 2012; Xuanet al., 2017). Critically, each specific signal is typically perceived bya separate mechanism that stimulates a downstream regulatorynetwork involving at least tens of genes (Li et al., 2006; Hickmanet al., 2017). The current models often suggest that these geneticregulatorynetworks coalescearoundmaster regulators that are thecentral controllers for specific pathways and/or phenotypes (Guet al., 2004; Kazan and Manners, 2013). Often these master reg-ulators are transcription factors (TFs) that are both necessaryand sufficient for the changes in expression of genes or pathwaysthat modulate the growth, defense, and metabolic phenotype ofthe plant to adapt to that specific environment. We call this themaster regulator hypothesis. This concept is predominant within

developmental regulatory networks that often exhibit switch-likebehavior, shifting from one state to another. It is not clear how thisconcept may translate to metabolic pathways that may insteaddisplay a rheostatbehavior,where there isa continuousadjustmentin response to external and internal stimuli. However, in spite of theadvanced knowledge about specific regulatory networks in plants,the exact size and interconnected structure of these genetic net-works is a key unanswered question in systems biology (Phillips,2008). The size of networks is of critical importance for adaptivetraits because as genetic networks increase in size and inter-connectivity, the concept of a single master regulator at the be-ginning point of a specific regulatory network is less essential.Additionally, as gene membership increases, there is a concurrentincrease inthepotential forepistasisbetweenthesegenes (Mackay,2014; Gaudinier et al., 2015). In this context, we are definingepistasis as any nonadditive interaction between genotypes at twoor more loci influencing a trait. Thus, there is a need to understandhow large regulatory networks may be influenced by epistasis,especially for adaptive metabolic traits.One set of adaptive traits that could be used to study these

questions of network scale and epistasis are plant secondarymetabolites (Wink, 1988; Burow et al., 2010; Kroymann, 2011).Recent work has shown that plant secondary metabolites havestrong epistatic interactions that can influence fitness in the field(Brachi et al., 2015; Kerwin et al., 2015, 2017). Additionally,mechanistic and quantitative genetic studies are showing thatplant defense metabolites have vast genetic regulatory networks(Chan et al., 2010, 2011; Harper et al., 2012; Riedelsheimer et al.,2012; Wurschum et al., 2013; Wen et al., 2016). These studies

1 Address correspondence to kliebenstein@ucdavis.edu.The author responsible for distribution of materials integral to the findingspresented in this article in accordance with the policy described in theInstructions for Authors (www.plantcell.org) is: Daniel J. Kliebenstein(kliebenstein@ucdavis.edu).www.plantcell.org/cgi/doi/10.1105/tpc.17.00805

The Plant Cell, Vol. 30: 178–195, January 2018, www.plantcell.org ã 2018 ASPB.

provide an alternative hypothesis where regulation occurs viaa promoter integration model. In this model, the pathway iscontrolled by suites of TFs that interact with distinct subsets ofpromoters within a metabolic pathway. This promoter integrationmodel leads to a greatly extended gene network influencingametabolicpathwayandallows forpotentially increasedprecisionin the regulation of metabolic pathways. Furthermore, this raisesthe potential for there to be different types of epistasis acrossa pathway, depending upon the promoter/gene that influencesthat part of the pathway. For example, if two TFs bind differentpromoters within a pathway without interacting molecularly, theyhave the potential to show nonadditive epistasis at themetabolitelevel, as they are influencing multiple enzymatic reactions withinthe pathway. In the absence of metabolite-triggered transcrip-tional feedback, this metabolic epistasis might not be mirrored atthe transcript level, which may display an additive model. Thus,metabolic pathways where it is possible to measure differentoutputs fromasinglepathwaycanenable thedissectionofgeneticnetworks andepistatic interactions andhow theycompare at boththe metabolic and transcriptional levels.

In thisstudy,weusedthealiphaticglucosinolate (GLS)pathwaytotest the extent of epistasis within an adaptive regulatory network.GLSs are becoming amodel system for the study of plant adaptionto ever-changing environments (Hopkins et al., 2009; Kliebenstein,2009;Kroymann, 2011). AliphaticGLSsarederived frommethionine,

and genetic variation influencing aliphatic GLS composition is a keymechanismusedbyplants toadapt totheirecologicalniches (Lankauand Kliebenstein, 2009; Burow et al., 2010; Züst et al., 2012). Fur-thermore, the almost complete elucidationof themethionine-derivedaliphatic biosynthesis pathway in the model plant Arabidopsisthaliana has provided a unique system to test systems biologyconcepts (Sønderby et al., 2010a). Combining the full catalog ofbiosyntheticgeneswith large-scalesystemsbiologyapproacheshasallowedarapidcharacterizationof theregulatorynetworkscontrollingthis pathway to address plants’ defense and survival challenges inconnected regulatory networks. Previous studies identified andconfirmed the critical importance of transcriptional regulation of theGLS pathway, including the cloning of TF genes MYB28, MYB29,and MYB76, which regulate the accumulation of aliphatic GLS(Gigolashvili etal.,2007,2008;Hiraietal.,2007;Sønderbyetal.,2007;Malitskyetal., 2008;Sønderbyetal., 2010c).More recently,TFs in thejasmonatesignalingpathway,MYC2,MYC3,andMYC4,wereshownto be important regulators of both aliphatic and indolic GLS(Dombrecht et al., 2007; Fernández-Calvo et al., 2011; Schweizeret al., 2013). These key MYB and MYC regulators of GLS pathwaysare positive regulators and belong to evolutionarily conserved sub-sets of their corresponding families (Stracke et al., 2001; Fernández-Calvo et al., 2011). Intriguingly, while mutants of these proposedmaster regulators abolish the accumulation of the GLS metabolites,theyonlyabolish theexpressionofa fewkeygenes in thebiosynthetic

Figure 1. Genetic Networks under Investigation.

The20TFsunder investigationareshownasnodes.LinesconnectingTFsshowwheredoublemutantsweregenerated.The labeledclustersA toGrepresentthe previously identified phenotypic subnetworks for these TFs. Large-effect TFs that were not linked to specific clusters are shown in the centers of thenetworksasMYB28,MYB29,andANT.The fourdifferentepistaticgroupsarehighlightedwithdifferentcolors:within-cluster epistatic testsareshown in limegreen, between-cluster epistatic tests are shown in dark orange, MYB epistatic tests are shown in gray, and ANT epistatic tests are shown in black.

Network-Guided Investigation of Epistasis 179

pathway and do not affect the expression of other genes in thebiosynthetic pathway (Dombrecht et al., 2007; Sønderby et al.,2010b). As such, they do not fit the classical definition of a mastertranscriptional regulator and suggest the necessary involvement ofother TFs. A yeast one-hybrid approach identified numerous addi-tional TFs that bound to promoters of genes involved in GLS bio-synthesisand influencedtheaccumulationofaliphaticGLS, including

newTFfamilieswithdiversefunctions(Lietal.,2014a).ThesenewTFsare predominantly negative regulators with pathway-specific GLSeffects, allowing them to be clustered into distinct phenotypicmodules.How thesenewTFs interactwith eachother eitherwithinorbetween phenotypic modules and how they interact with the keyaliphatic GLS MYBs to structure the epistatic regulatory networkremain to be determined.

Figure 2. Epistatic Networks Controlling GLS Traits.

The genetic network from Figure 1 is used to represent the significant epistatic interactions for the four major GLS traits. Only connections that showsignificant epistasis aremaintained,while nonsignificant connectionsaredropped from thenetwork. A solid line shows that only theTF xTF interaction termwassignificant in theANOVAmodel.Adashed line showsepistatic interactionswhere therewasasignificant tissuexTFxTF interaction. Adotted lineshowsepistatic interactions where there was a significant environment x TF x TF interaction. A line of arrows shows that the interaction was conditional on bothtissueandenvironment. Thecolor of thenode indicateswhichmaineffect termsaresignificant for the individual TFs:Skyblue indicatesonly aTFmaineffect,orange indicatesonly a tissuexTF interaction,purple indicatesbothTFand tissuexTF, yellow indicatesbothTFand treatment xTF, and red indicates that allthree terms are significant. Gray indicates that no terms for the individual TF were significant.(A) Epistatic network for SC GLS.(B) Epistatic network for GLS OX.(C) Epistatic network for GLS Elong.(D) Epistatic network for indolic GLS.

180 The Plant Cell

Figure 3. Distribution of Genetic Variance Ascribed to Distinct Model Terms.

The variances attributable to all terms including a genetic factor were summed together, and the percentage of this total genetic variance ascribed to eachgenetic termwas calculated, as shown in violin plots. Themedian in each violin is shown as a dot. The phenotypes tested are shown individually, as labeledon the x axis.

Network-Guided Investigation of Epistasis 181

Previous work in yeast has shown that the modularity of genesinfluencing a phenotype, primary metabolism, could be used topredict the presence of epistasis. Specifically, epistatic interactionswerepredominantly foundwhen studying doublemutants involvinggenes associated with different phenotypic clusters or modules,whilegeneswithin thesamecluster rarelydisplayedepistasis (Segrèet al., 2005). This was hypothesized to be caused by genes withina cluster having more redundancy than genes between clusters.This observation was also noted in global studies investigatingpairwise interactions amongall yeastgenes, showing that negative/antagonisticepistasiswaspredominantly foundamonggenes in thesamecomplex/bioprocess(Costanzoetal.,2010,2016). Incontrast,the molecular underpinnings of positive/synergistic epistasis wereless decipherable. These yeast studies focused on growth as thetrait, raising the question of how epistasis transitions from geneexpression to enzyme to metabolite to fitness. Because we havea large collection of TFs influencing a defense metabolite class,aliphatic GLSs, within Arabidopsis and these TFs cluster intospecificgroupsbasedon their phenotypic effects on themetaboliteaccumulation, we can now test if network architecture influencesepistasis in adaptive plant defensemetabolism, as observed for theyeast genome. The central hypothesis tested is that TFs acting indifferent phenotypic clusters or modules are more likely to displayepistasis than those within a phenotypic cluster or module.

In this study, to explore the potential for epistasis in regulatorynetworks of the plant secondary metabolite GLSs, we focused onthewell-established and highly variable aliphatic GLS pathway bysystematically constructing 47 double mutants and 4 triplemutants using 20 selected TFs controlling aliphatic GLS accu-mulation. These TFs include two key TF regulators, MYB28 andMYB29, and 18 additional TFs that were previously ascribed tospecific phenotypic clusters depending upon their single mutantGLS phenotype (Figure 1) (Li et al., 2014a). The 24 GLS pheno-types of the 47 double mutants and 4 triple mutants were sys-tematically explored in 4 contrasting tissue and treatmentcombinations (Supplemental Data Set 1). Epistatic networks differfor the major aliphatic GLS phenotypic clusters. The absence ofaliphatic GLS inmyb28 myb29 could not be revived by the testedrepressor TFs in triple mutants, even though the expression ofbiosynthesis genes of aliphatic GLS could bemodulated by theserepressor TFs, as predicted. Our findings provide insights intoepistatic networks, contribute new genetic resources to thecommunity, and elicit research questions on the regulatory net-works of the model plant secondary metabolites, GLSs.

RESULTS

Selection and Construction of Epistatic Networks

To test our hypothesis, we systematically generated 47 doublemutants and 4 triple mutants using 20 representative TFs in Ara-bidopsis (Figure 1; Supplemental Data Set 2). All of these T-DNAmutants exhibited altered GLS accumulation in single mutants (Liet al., 2014a) (Supplemental Data Set 2). These TFs had previouslybeen grouped based on their mutants’ phenotypic effect on theaccumulation of all aliphatic GLS metabolites across multiple tis-sues and environments. These selected TFs belong to diverse TFfamilies with diverse gene functions, including AINTEGUMENTA(ANT) of the AP2/EREBP family, which controls cell proliferation(Elliott et al., 1996; Krizek et al., 2000; Liu et al., 2000;Mizukami andFischer, 2000; Horstman et al., 2014), IAA-LEUCINE RESISTANT3(ILR3) of the bHLH family, which regulates iron deficiency (Rampeyetal.,2006;Longetal.,2010),G-BOXBINDINGFACTOR2 (GBF2)ofthe bZIP family, which regulates response to blue light (Schindleret al., 1992; Menkens and Cashmore, 1994; Terzaghi et al., 1997),HMGBD15 of the ARID family, which regulates pollen tube growth(Xia et al., 2014), HOMEOBOX PROTEIN21 (HB21) of the ZF-HDfamily,whichregulatesabscisicacid-activatedsignaling (González-Grandíoetal.,2017),NAC102ofNAC,whichregulates responsestolow oxygen stress (hypoxia) in germinating seedlings (Christiansonet al., 2009), and ATE2F2 of E2F/DP, which controls the balancebetween cell division and endoreduplication and xylem cell de-velopment (del Pozo et al., 2006; Berckmans et al., 2011; Taylor-Teeples et al., 2015). The 47 double mutants represent 4 differentepistatic test sets: (1) within cluster epistasis, 13 double mutantswereobtainedbycrossingmutants inTFs thatwerewithin thesamephenotypic cluster; (2) between cluster epistasis, 16 double mu-tantswereobtainedbycrossingmutants inTFs thatwere indifferentclusters; (3)MYBepistasis,10doublemutantsobtainedbycrossingthe new TFs to myb28 or myb29; and (4) ANT epistasis, 8 doublemutants obtained by crossing new TFs to ant. The double mutantswith the known MYBs and strong-effect TF ANT were included toassess how these new TFs may interact with described masterregulators of glucosinolate biosynthesis. We also generated fouradditional triple mutants to test the consequence of adding mu-tations in a repressor TF to the double mutant myb28 myb29 thatabolishes GLS accumulation, includingmyb28 myb29 ant,myb28myb29 zfp4,myb28myb29 zfp7, andmyb28myb29hb21. All of themutants were validated as being homozygous for the specific

Figure 3. (continued).

(A) Percentage of genetic variation controlled by individual TF main effects.(B) Percentage of genetic variation controlled by tissue x TF interactions.(C) Percentage of genetic variation controlled by treatment x TF interactions.(D) Percentage of genetic variation controlled by TF x TF epistatic interactions.(E) Percentage of genetic variation controlled by tissue x TF x TF epistatic interactions.(F) Percentage of genetic variation controlled by treatment x TF x TF epistatic interactions.(G)Visualization of individual epistatic variance componentswithin the genetic network for SCGLS. Thewidth of the line connecting twoTFs is proportionalto the variance linkedwith the TF xTF term for that specific interaction. The highest proportion is the interaction betweenHMGBD15 andHB34, with 49%ofthe total genetic variance. Solid lines show that there was a significant interaction term, while dashed lines show combinations with no significantinteractions.

182 The Plant Cell

genotype and grown concurrently with the wild-type and singlemutant genotypes to age match all seed stocks.

Epistatic Networks Mediating GLS Traits

To test the genotypes for epistasis, we measured leaf and seedGLS contents for the wild-type control, all single mutants, and alldouble and triple mutants in two different chambers using a ran-domized complete block design, as previously described (Li et al.,2014a). The two growth chambers have a controlled abiotic en-vironment but contain different biotic environments. The CEF(controlled environment facility) chamber is maintained as pestfree, while the LSA (life sciences addition) chamber has an en-dogenouspestpopulationprovidedbycontinuouspropagation oftomato (Solanum lycopersicum) and Brassica napus plants. Thisgenerates amix of mites, aphids, flea beetles, and fungus gnats inthe LSA chamber and allows us to test for the effect of a blend ofbiotic interactions rather than specific biotic interactions. Mea-suring GLS in two tissues and two environments has previouslyenhanced our ability to identify significant effects and test if theyare tissue or environmentally sensitive (Li et al., 2014a). We thenused all measured phenotypes with linear models to specificallytest for epistatic interactions (Supplemental Data Sets 3 to 5). Forthe ensuing analysis, we focused on five summary variables thatdescribe the majority of the variance in aliphatic GLS (Wentzellet al., 2007). These summary GLS traits are as follows: (1) accu-mulation of short-chainGLS (SCGLS), the sumof the three-carbonandfour-carbonsidechainaliphaticGLSs; (2)accumulationof long-chainGLS (LCGLS), thesumof theseven-carbonandeight-carbonside chain aliphatic GLSs; (3) accumulation of indolic GLS, the sumof all indolic GLSs; (4) GLS Elong, the percentage of three-carbonGLSs in SC GLS, which is an indication of the enzyme activities ofthe elongation cycle (Haughn et al., 1991; de Quiros et al., 2000;Kroymann et al., 2003); (5) GLS OX, the percentage of 4-methylthiobutyl glucosinolate to the total of all GLSs with fourcarbons, an indication of the GLS OX enzyme activities (Hansenet al., 2007; Li et al., 2008, 2011) . These five traits are quantifiablein all tissues and environments and allow an analysis of distinctbiochemical processes within the pathway.

Mapping the epistatic interactions of the TFs based on thedifferent phenotypes highlighted several patterns. First, thereweredifferences in the frequencyof epistasis,withSCGLShavingsignificant epistasis for 42 of 47 pairs of interactions comparedwith only 11 of 42 for indolicGLS (Figures 2A and 2D). Importantly,thereweredifferences in thepattern andconditionality of epistasisbetween the pathway (SC GLS) and specific enzyme activityestimates of enzymes (GLS OX and GLS Elong). For the wholepathway, most epistatic interactions were independent of thetissue or environment in which the phenotype was measured(Figure 2A). In contrast, themajority of epistatic interactions for theinferred enzymatic activities within the pathway were highly de-pendent upon the tissue (Figures 2B and 2C). This difference inepistatic patterns between parts of the pathway agrees with theprevious observation that the TFs regulate a subset of steps in thepathway and not the whole pathway. Thus, there is significantepistasis in all four phenotypic categories tested in this collection,and this epistasis shows distinct properties and unique patternsfrom the level of enzyme activity to metabolite accumulation.

Genetic Variance Controlled by Epistasis

To obtain more quantitative insights into how epistasis influencesthe traits, we estimated the genetic variance that could be as-cribed to epistasis (Figure 3; Supplemental Data Set 4). As

Figure 4. Synergistic and Antagonistic Epistatic Patterns in SC GLSAccumulation.

The levels of SC GLSs in the corresponding genotypes and tissues areshown.Different letters showgenotypeswithsignificantlydifferentSCGLSlevels (P < 0.05 using post-hoc Tukey’s test after ANOVA). The P value ofthe epistatic interactionand the epistasis value are shownon theplots. SE isshown with 16 samples across two experiments for each genotype.(A) erf9 x rap2.6l antagonistic epistasis for SCGLS accumulation in leavesfrom the stress LSA chamber.(B) ant x hmgbd15 synergistic epistasis for SCGLS accumulation in seedsfrom the clean CEF chamber.

Network-Guided Investigation of Epistasis 183

Figure 5. Epistatic Effects for SC GLS.

Epistasis values were calculated for all pairwise combinations individually in all treatment and tissue combinations. For (B) to (E), different letters indicatestatistically different average epistatic values across the cluster tests, as determined by ANOVA at P value of 0.05.

184 The Plant Cell

expected from the high incidence of epistasis, metabolite accu-mulation from the SC GLS pathway has the highest fraction ofgenetic variance present in the TF x TF epistatic term (SC GLS inFigures3Aand3D). Incontrast,mostof thevarianceattributable toepistasis for the enzyme activity traits was in the conditional TF xTF x environment or x tissue terms (GLS Elong and GLS OX inFigures 3B and 3E). To visualize how the epistatic variance wasinfluenced by the network topology, we mapped the epistaticvariance for the metabolite accumulation for SC GLS (Figure 3G).This plot showed that the proposed master regulatory TFs werenot the keydrivers of theepistatic network. For example, themajorregulator of SC GLS,MYB28, does not play a key role in shapingthe epistatic network. In contrast, MYB29, which is typicallyconsidered tohavea less significant role thanMYB28 in regulatingSCGLS accumulation, has amuch stronger effect thanMYB28 inthe epistatic networks (Figure 3G). In contrast, ERF107,RAP2.6L,and ZFP7, which have relatively small single mutant phenotypiceffects, had major epistatic roles (Figure 3G). This suggests thatthe phenotypic consequences of single gene mutants are notsufficient to predict epistatic importance as genes. Thus, to fullyunderstand a genetic network, both large and small effect locishould be analyzed when directly testing for epistasis.

Quantification of Epistasis Indicates a BipartiteRegulatory System

The detection of epistasis does not allow us to distinguish be-tween different types of epistatic effects. The epistatic effects onan individual trait could be synergistic, by which the phenotypicvalue of doublemutantswas higher than the linear combination ofsingle mutants. Alternatively, the epistatic effects could be an-tagonistic epistasis, by which the phenotypic value of doublemutants was lower than the linear combination of single mutants(Hartman et al., 2001; Segrè et al., 2005; Costanzo et al., 2010,2016). To differentiate between these forms of epistasis, wesubtracted the measured double mutant phenotype from thepredicteddoublemutant phenotypeunder anadditivemodel. Thisvalue was then normalized to the wild-type phenotype to developanepistasis value.This epistasis valuewasmeasured for eachpairofmutants for each trait thatwasmeasured (Supplemental Figures1 to 4 and Supplemental Data Set 5). This epistasis value will bepositive when there is synergistic epistasis and negative for an-tagonistic epistasis (Figure 4).

Using this approach,we found that formetabolite accumulationin the SC GLS pathway, almost all of the epistasis was antago-nisticepistasis,with largervalues in leavesversusseeds (Figure5).Comparing theepistasis valuewhen testingpairsof TFs thatcomefrom the same phenotypic cluster versus different phenotypic

clusters showed that therewasnodifference in these groups. Thisis in contrast to yeast primary metabolism, where there was moreepistasis in crosses from different clusters than from crossesinvolving genes within a cluster (Segrè et al., 2005). Of thecomparisons, the only crosses that were significantly differentwere the crosses to the putative master regulators myb28 ormyb29. Thus, the new TFs largely interact additively with thepreviously identified MYBs while epistatically interacting witheach other to control metabolite accumulation (Figure 5). Thisabsence of epistasis between the new TFs and the MYBs sug-gests that there may be a buffering structure that allows the twogroups of TFs to function independently.In contrast to the exclusively antagonistic epistasis for SCGLS,

for GLS Elong and GLS OX, epistatic effects could shift fromsynergistic to antagonistic depending on the tissue and the en-zyme activity being measured (Figure 6). For GLS Elong, therewereconditional effectswithpredominantlyantagonistic epistasisin leaf tissueandsynergistic epistasis in seed tissue,whileGLSOXshowed antagonistic versus synergistic epistasis within the leaf inthe two different environments (Figure 6). Similar to the situationformetabolite accumulation, therewasnodifference in the level ofepistasis within and between TF clusters (Figure 6). One possibleexplanation for the difference in epistatic patterns is that theenzymatic activities are expressed as ratios while metaboliteaccumulation is expressed as absolute abundance. Arguing thatthese ratios are providing biological insight is the observation thatboth ratio traits, GLSElong andGLSOX, showdifferential epistasisin the leaf samples between the two environments. If this weresolely a mathematical issue, these traits should show similarbehaviors. This argues that the same set of TFs have distinctpatterns of epistatic effects upon different components of thesame metabolic pathway, as the two estimated enzyme activitieshave opposing patterns and the resulting total accumulation of thecompounds in this pathway have a distinct antagonistic epistasis.This suggests that epistasis of TFs is frequent within the aliphaticGLS pathway and that the effects of this epistasis change de-pending upon the specific portion of the pathway being measured.

Absence of Aliphatic GLS in Triple Mutants betweenRepressors and Activators

The observation that the putative master regulators, MYB28 andMYB29, had limited epistasis with the rest of the TFs suggestedthat they function largely additively with the other TFs. Consid-ering that the MYBs are activators, as the myb28 myb29 doublemutant has no aliphatic GLS while the other TFs are largely re-pressors (mutants have higher GLS), we hypothesized that triplemutants should be additive. Thus, the triple mutant under this

Figure 5. (continued).

(A) Epistasis values for all pairwise mutant combinations plotted in a heat map using hierarchical clustering; the gene combinations are listed to the right ofthe diagram. The first vertical column shows if the pairwise mutant interaction is testing epistasis from within a cluster (green), between clusters (orange),MYB (gray), or ANT (black). The next three columns show which epistatic interaction term is significant (purple) or not significant (gray) (ANOVA, P < 0.05).(B) Average and SE of epistatic value for all pairwise mutant combinations measured from leaf samples from the clean CEF chamber.(C) Average and SE of epistatic value for all pairwise mutant combinations measured from leaf samples from the stressed LSA chamber.(D) Average and SE of epistatic value for all pairwise mutant combinations measured from seed samples from the clean CEF chamber.(E) Average and SE of epistatic value for all pairwise mutant combinations measured from seed samples from the stressed LSA chamber.

Network-Guided Investigation of Epistasis 185

hypothesis should restore detectable aliphatic GLS. To test thishypothesis and to assess if it is possible to roughly predict triplemutant interactions from pairwise combinations, we generatedfour triplemutants,myb28myb29 ant,myb28myb29 zfp4,myb28myb29 zfp7, andmyb28myb29 hb21 because ANT, ZFP4, ZFP7,andHB21 are negative regulators that function in different parts ofthe pathway and have strong effects on aliphatic GLS accumu-lation.Measuring aliphaticGLSaccumulation in the triple, double,and single mutants in all four contrasting conditions showed thatnone of the triple mutants rescued the accumulation of aliphaticGLS in any tissueor condition. This included 4MSOGLS, themostabundant aliphatic GLS in leaf tissue, and 4-methylthiobutylglucosinolate GLS, the most abundant aliphatic GLS in seeds(Figure 7; Supplemental Figure 5 andSupplemental Data Set 5). Incontrast to the pairwise epistasis analysis, which suggestedadditivity, this indicates that all four TFs show classical recessiveepistasis to themaster regulators and require a functionalMYB28andMYB29 to induce GLS metabolite accumulation. Thus, theseadditional TFs do genetically interact with both MYB28 andMYB29 to influence SC GLS accumulation, in contrast to thepairwise mutant evidence, and they appear to function down-stream of the MYBs.

Presence of Aliphatic GLS Transcripts in Triple Mutantsbetween Repressors and Activators

The above epistatic analysis is solely built upon measuring theaccumulation of the metabolites, raising the question of how thismaybe reflected in transcript accumulation,whichshouldbemoreproximal to the TFs. To test if the underlying transcriptionalchanges mirrored the metabolic consequences, we conductedtranscriptomic analysis of the ant myb28 myb29 genotypes. Weused RNA-seq to measure the transcript abundance of all knownenzyme-encoding genes in the GLS pathway in the wild type andall single, double, and triple mutants using leaf tissue. This wasdone using leaf tissue from the CEF chamber where the strongestthree-way epistasis between these genes occurs (Figure 8;Supplemental Data Set 6). The transcript analysis showed that, aspreviously observed,myb28 had a stronger effect thanmyb29 onpathway transcript abundance (Sønderby et al., 2007, 2010c) In

Figure 6. Epistatic Effects for GLS Elong and GLS OX.

Epistasis values were calculated for all pairwise combinations individually inall treatment and tissue combinations. Different letters indicate statisticallydifferent average epistatic values across the cluster tests, as determined byANOVAat P value of 0.05. The average and SEof the epistatic values for eachgroup of epistatic groups are shown in all box plots, with orange showingbetween-cluster crosses, green showing within-cluster crosses, grayshowing crosses involvingMYB, and black showing crosses involving ANT.

(A) GLS Elong epistatic value for all pairwise mutant combinationsmeasured from leaf samples from the clean CEF chamber.(B) GLS Elong epistatic value for all pairwise mutant combinations mea-sured from leaf samples from the stressed LSA chamber.(C) GLS Elong epistatic value for all pairwise mutant combinationsmeasured from seed samples from the clean CEF chamber.(D) GLS Elong epistatic value for all pairwise mutant combinationsmeasured from seed samples from the stressed LSA chamber.(E)GLS OX epistatic value for all pairwise mutant combinations measuredfrom leaf samples from the clean CEF chamber.(F)GLS OX epistatic value for all pairwise mutant combinations measuredfrom leaf samples from the stressed LSA chamber for GLS OX.(G)GLSOX epistatic value for all pairwise mutant combinations measuredfrom seed samples from the clean CEF chamber for GLS OX.(H)GLSOX epistatic value for all pairwise mutant combinations measuredfrom seed samples from the stressed LSA chamber for GLS OX.

186 The Plant Cell

contrast to the observation that ANT is a repressor of the me-tabolite, ant had contrasting effects on the transcripts, with someshowing higher and some lower accumulation in the single antmutant versus the wild type (Figures 8B to 8D; i.e., compareGS-OX2 versus GS-OX3). Supporting the idea that epistasis canchange dependent on the molecular traits (i.e., transcripts versusmetabolites), ant showed more epistasis with myb28 for thetranscripts, while ant was mainly epistatic to myb29 for the me-tabolites (Figure 8). Further support came from the observationthat ant is able to induce the transcription of pathway genes in themyb28 myb29 double mutant in contrast to its dependency formetabolite accumulation (Figures 8B to 8E). Thus, ANT canfunction at least in parallel toMYB28MYB29 to regulate transcriptlevels while genetically, it appears to occupy a downstream role inmetabolite accumulation. This suggests that the epistasis betweenthese TFs is likely a complex blend of potential direct interactionsand indirect interactions that could be caused by the metabolites’accumulationbeingconstrainedby thestructureof thebiosyntheticpathway and the relative fluxes of the different enzymes. As such,theepistasismeasuredat themetabolite levelwouldbe the result ofhow the promoter-level epistasis is translated to enzyme activityepistasis and correspondingly how this equates to the accumu-lationof thefinalmetabolite.Thissuggests thatmolecularmodelsofepistasisat the levelofhowTFsdoordonot interactwitheachotheratasinglepromotermaynot translate to thepredictionofmetaboliteaccumulation within a single pathway.

DISCUSSION

In this work, we used a large network of TFs that regulate aliphaticGLS biosynthesis to measure pairwise and higher-order epistaticinteractions. This pathway had previously been identified ashaving master regulators of GLS biosynthesis: MYB28 andMYB29 (Hirai et al., 2007; Traka et al., 2013). In contrast, wepreviously identified a large collection of TFs that had muchmoremodest impacts on thepathway via bindingdistinct subsets of thepromoters in the pathway (Li et al., 2014a; Gaudinier et al., 2015).Within this network, the vast majority of TFs showed pairwiseepistasis. The strongest interactions involved interactions amongTFs with smaller single mutant effects, while the putative masterregulatory MYB had the lowest level of epistasis. For a relatedpathway that involves some overlap but also a distinct set of TFsand enzyme-encoding genes, indolic GLS, there was far lowerepistasis, suggesting that the prevalence of epistasis is notcaused by indirect pleiotropies but is a general property of thespecificnetworkbeingstudied. Theepistasisdetectedchanged interms of frequency, direction, and strength depending on how thenetwork’s phenotypewasmeasured, initial transcript abundance,intermediate enzyme activity estimates, or final metabolite ac-cumulation. This suggests that epistasis may be a commonfeature of large regulatory networks that influence adaptive traits.Furthermore, the idea of a master regulator does not appear totranslate into a central functionwithin anepistatic network. Instead,it appears that epistasis is favored within a collection of small tomoderate effect TFs that interact with partially overlapping sets ofpromoterswithin the pathway. This epistasis needs tobe assessedwhen working to develop predictive models that translate fromsingle gene studies to higher order studies on entire networks.

Figure 7. Leaf GLS Accumulation in Allmyb28/myb29/ant CombinatorialGenotypes.

Leaf GLS levels in the corresponding genotypes, asmeasured in the cleanCEF chamber. Different letters indicate genotypes with significantly dif-ferentSCGLS levels (P<0.05usingpost-hocTukey’s test afterANOVA). SEis shown with 16 samples across two experiments for each genotype.(A) SC GLS.(B) LC GLS.(C) Indolic GLS.

Network-Guided Investigation of Epistasis 187

Figure 8. Transcript Levels for Aliphatic Glucosinolate Biosynthesis Genes in All myb28 myb29 ant Combinatorial Genotypes.

SE for each data point is shown from three independent biological samples.

188 The Plant Cell

Epistasis Varies across a Network’s Molecular Hierarchyfrom Transcript to Metabolite

Biochemical pathways can be considered to have multiple traitsthat can be measured across the hierarchy proceeding froma gene encoding an enzyme to the accumulation of the finalmetabolite. This includes measuring the transcript abundance foreach and every gene in the pathway, approximating enzymeactivities linked to these transcripts, and finally measuring thedetectable metabolites produced by the pathway. Frequently,regulatory studies focus on the TF to transcript link, with theimplicit assumption that the other steps in this hierarchy will in-herently follow the same logic. However, with posttranscriptionalregulatory processes at the RNA, protein, and activity level inaddition to the competition by other metabolic processes for thesame precursor compounds and energy, this is not inherently thecase. This is especially the case when most TFs are not limited toregulating a singlemetabolic pathway but instead have numerousother regulatory links. As such, it is an open question how reg-ulatory processes may translate from gene to enzyme to me-tabolite in a complex multicellular organism. We measuredtranscripts and metabolites and inferred enzymatic activities totest how genetic epistasis changes through the molecular hier-archyof thealiphaticGLSpathway. This showed that theepistasiswas highly dependent upon the specific molecular step beingmeasured. While the metabolite and enzymatic activity steps hadasimilar frequencyofepistasis, theyshoweddifferingsensitivity tothe environment and development. The metabolites revealedepistatic effects that were consistent across tissues and envi-ronment, while the enzymatic efficiencies were largely conditional(Figures 4 and 6). As amore focused example, we summarized allthe epistatic information linked to the ant myb28 myb29 combi-nation of genotypes (Figure 9). This illustrates how main effectsand epistatic interactions shift from transcript to metabolite. ANThas no main effect on methylthioalkylmalate synthase transcriptsbut is a key player in the resulting GLS Elong activity and SC GLSaccumulation. Similarly, ANT had significant main effects onGS-OX transcripts, but this was only displayed as epistatic in-teractions with no main effect on GLS OX efficiency. This showsthat epistasis within large regulatory networks can have con-trasting effects, depending upon the specific molecular outputbeing measured.

Naive Pairwise Tests Find High Levels of Epistasis

Within this study, we attempted to cross the majority of availableTFmutantsknown toaffectaliphaticGLS regardlessofmechanistic

or homology information. In most published epistasis tests, thechoiceofmutants to cross is frequently guidedby thegenes havinga known function in the same regulatory pathway. Alternatively, thegenesmay be chosen based on their membership in a gene family,and the mutants are crossed to test for redundancy. While theseguided approaches frequently reveal epistasis, they do not test forhow often epistasis occurs outside of these guiding rules. In thiswork, the majority of the interactions involve TFs belonging todifferent TF families, and there is little to nomechanistic informationsuggesting that they function in a single pathway (Figure 1). Thus,the explicit goal of this designwas to test for epistasis betweenTFsthat may function independently and are only connected by influ-encing the same trait. This naive design identified a high level ofepistasis, including connections between processes not typicallylinked. For example, ANT and ILR3 show significant epistasis butare typicallyconsidered tofunction indifferentbiologicalprocesses.ANT belongs to the AP2/EREBP TF family and controls cell pro-liferation (Elliott et al., 1996; Krizek et al., 2000; Liu et al., 2000;Mizukami and Fischer, 2000; Horstman et al., 2014), while ILR3belongs to the bHLH TF family and controls responses to irondeficiency (Rampey et al., 2006; Long et al., 2010). Our resultssuggest that these two TFs somehow have regulatory effects thatinteract to modulate GLS accumulation. More intriguing is the ideathat if this interaction is not specific to GLS, is it possible that ANTand ILR3 have epistatic interactions affecting the regulation of cellproliferation and/or responses to iron deficiency? This raises thepotential that conductingnaive crossesofTFmutantsmayopenupa unique avenue to investigate how processes such as growth,nutrient acquisition, and biotic resistance are coordinated acrosslarge regulatory networks that are frequently studied in isolation.

Epistasis, Heritability, and Fitness

AsGLS accumulation is an adaptive trait, it is tempting to attemptand translate these results directly to their potential fitness con-sequences. Mathematically, this is relatively simple using theequation R = h2S, where the response to any selection, R, is theadditive heritability, h2, multiplied by the strength of selection, S(Falconer andMackay, 1996;Mackay, 2001, 2014). In our system,the total variance controlled by any genetic term averaged;30%across the models, but this included all terms, both additive andnonadditive.The typical singlegeneadditiveheritabilitywas;5%,which would suggest that this system may have small effects onfitness in Arabidopsis. However, this direct comparison is com-plicated by several factors. The first is that by measuring GLS inmultiple environments and multiple tissues, we are constrainingour estimateof heritability. In thesemodels, tissue andenvironment

Figure 8. (continued).

(A)Theheatmapdisplays the foldchange in transcript levels in themutantscomparedwith theCol-0control,with redshowing increasedaccumulationandblueshowingdecreasedaccumulation. The columnson the right display the statistical significance (purple, significantP<0.05; gray, not significant) for each term intheANOVAmodel,as listedat thebottom.TheexpressionofMYB29 in themyb29background lines isshownasNM(notmeasurable)duetotheT-DNAinsertion.(B) Transcript levels of BCAT4. Lines show the values in the ANT (red) and ant (blue) genotypes across the four different myb28 myb29 backgrounds.(C) Transcript levels of MAM3. Lines show the values in the ANT (red) and ant (blue) genotypes across the four different myb28 myb29 backgrounds.(D) Transcript levels of CYP79F1. Lines show the values in the ANT (red) and ant (blue) genotypes across the four different myb28 myb29 backgrounds.(E) Transcript levels of GS-OX2. Lines show the values in the ANT (red) and ant (blue) genotypes across the four different myb28 myb29 backgrounds.

Network-Guided Investigation of Epistasis 189

and their interaction averaged ;50% of the total variance, whichplacesaconstraintonadditiveheritability.Thesecondcomplicationis that the above equation largely relies on the species beinga random outbreeding population, whereas Arabidopsis is speciesthat has a low level of outbreeding, which typically occurs withinnarrow localpopulations (Charlesworth et al., 1997;Nordborgetal.,2002). As such, in this species, nonadditive epistatic variance may

actually contribute more to selection responses than the simpleequation would suggest (Rieseberg et al., 1999, 2003).A final complication in this direct comparison is that in this

analysis, there is abuilt-in assumption that because theGLSare thesame compounds in the leaf and seed, they must be a single trait.However, fitness effects in a complex environment may actuallysuggest that leaf and seed GLS are distinct traits. This is best

Figure 9. Shifting Epistatic Interactions of ANT, MYB28, and MYB29 across the Molecular Aliphatic GLS Accumulation Processes.

Thebottomof thefigure shows thegenes involved in the synthesis of aliphaticGLSwithinArabidopsis. Thedifferentmolecular phenotypesmeasuredwithinthis study that pertain to the SC GLS, GLS Elong, and GLS OX processes are shown, from the key transcripts involved in each process to the estimatedenzyme activity to the final accumulation of SC GLS. A summary of the statistical effects of the ANT, MYB28, and MYB29 main effects (dots), pairwiseinteractions (lines between dots), and three-way interaction (triangle) are shown as per the legend. Purple indicates that the specific termwas significant forthe represented phenotype, and gray indicates nonsignificance. The different processes in aliphatic GLS biosynthesis are shown as follows: blue forelongation-related steps, green for core structure synthesis, and yellow for side chain modification.

190 The Plant Cell

exemplifiedbythenonrandomdistributionofbioticattackersacrossthe different tissues and their potential differential sensitivitytospecificglucosinolates (Janderetal.,2001;KimandJander,2007;Hansenetal.,2008).Forexample,most lepidopteran larvaefocusoneating the leaves of Arabidopsis, while granivores, i.e., seed-predators such as weevils, solely focus on the developing seed. Assuch, if fitness was driven by lepidopteran larvae, then only the leafGLS effects would contribute to fitness. In contrast, if granivoreswerethedominantselectivepressure, thenonly theseedGLSwouldcontribute to fitness. In both cases, the heritability estimated acrossboth tissues would be inaccurate for predicting responses to se-lection in these environments. Thus, there is a significant amount ofwork needed to understand the complexity of the biotic interactiondriving plant adaptation and equally, the complexity of the un-derlying genetic network controlling this response.

Genetic Network Size for a Single Metabolic Pathway?

Within this project,we tested the epistasis of 20TFs that havebeenlinked to the accumulation of aliphatic GLS. While this is a largecollection, this does not represent anywhere near the true scope ofthe regulatory network. The original yeast one-hybrid analysis thatfocused solely on root stele-expressed TFs suggested that therewere likely dozens of other TFs that influence the accumulation ofthis metabolite but that were not able to be tested (Gaudinier et al.,2011; Li et al., 2014). Furthermore, genome-wide associationstudies performed to estimate the size of the genetic networksinfluencing GLS accumulation regardless of gene activity sug-gested that therewereat leasthundredsof likelygenes thatcausallyinfluence this pathway (Chan et al., 2010, 2011). In the naturalvariation studies, there are also extensive epistatic interactionsamongthe identifiedloci (Kliebensteinetal.,2002a,2002b;Wentzellet al., 2007). Together, these findings suggest that the geneticnetwork influencing this metabolite is vastly larger than 20 TFs and34 promoters. Yet these crosses identified a high level of epistasiswithin this subnetwork. This raises the question of how this mighttranslate to a complete genetic network for aliphatic glucosinolatesand all possible epistatic combinations within that network. Thiswould go far beyond three-way epistatic interactions and requirevast experimental populations. The other key question is how thistranslates to other metabolic pathways or other biological pro-cesses. Is this a unique property of metabolites that provide ad-aptation to biotic stresses, or is this a general property ofmetabolism and/or genetic networks in a multicellular organism?

METHODS

Plant Materials

The Arabidopsis thaliana T-DNA insertion lines of the 20 TFs were initiallyordered from the Arabidopsis Biological Resource Center (Sussman et al.,2000;Alonsoet al., 2003) andvalidatedashomozygous inprevious studies(Supplemental Data Set 2) (Sønderby et al., 2010c; Li et al., 2014). The47 double mutants and four triple mutants were generated by crossing thecorresponding single mutants and validating the double mutant homo-zygosity in the F2 generation using PCR-based markers for each mutant.The confirmed homozygous double and triple mutants were grown to-gether with single mutants and the wild type to bulk seeds and providematching seed batches for the downstream GLS profiling experiments.

Plant Growth Conditions

The Arabidopsis plants were grown in two independent chambers with 16 hlight at 100- to 120-mE light intensity for the GLS profiling experiment. Bothgrowth chambers were set at a continuous 22°C and utilized high-outputfluorescent bulbs. Thesewere thesamegrowthchambersandconditionsasutilized in the original report of these TFs. The use of two growth chambersallowed for a test of biotic environmental effects. The two growth chamberswere set to identical abiotic environments but contain dramatically differentbiotic environments, one pest free, CEF, and one with an endogenous pestpopulation,LSA.Theendogenouspestpopulation isprovidedbycontinuouspropagation of tomato (Solanum lycopersicum) and Brassica napus plantsthat generates amix of mites, aphids, flea beetles, and fungus gnats. This isnot meant to test a specific biotic interaction but the general effect of bioticinteractionswith a blendof biotic interactions. This use of the clean chamberCEF and stress chamber LSA increases our ability to detect significant GLSphenotypes conditioned on the variation in environmental factors (Li et al.,2014a). Briefly, seeds were imbibed in water at 4°C for 3 d and sown intoSunshine Mix 1 (Sun Gro Horticulture). Seedlings were thinned to one plantper pot (63 5 cm) at 7 d after planting. For each experiment, at least eightreplicates of Col-0, single, double, and triple mutants were planted usinga randomized complete block design. Each flat had one plant per genotypeleading to eight flats per replication. This experiment was conducted in-dependently in the clean CEF and stress LSA chamber to generate a mini-mum of 16 biological repeats in total for most of the genotypes. This designmeans that each individual biological replicate is derived from a single in-dependent plant that was planted in a randomized design.

GLS Extraction and Analysis

Theharvest andcollectionofplant samples forGLSanalysiswereperformedas described before (Kliebenstein et al., 2001a, 2001b, 2001c). Briefly, onefullymature leaf fromeach 4-week-old plant was removed, placed in 400mL90% (v/v) methanol, and stored at 220°C before extraction. The plantsfinished their life cycle and the seedswere harvested. Forty seeds from eachplantwerecountedandstoredat220°Cbeforeextraction.Thesampleswerebroken with two 2.3-mm metal ball bearings in a paint shaker at roomtemperature and incubated at room temperature for 1 h. The tissues werepelleted by centrifugation for 15min at 2500g and the supernatantwas usedfor anion exchange chromatography in 96-well filter plates. After methanolandwaterwashingsteps, thecolumnswere incubatedwithsulfatasesolutionovernight. Desulfo-GLSs were eluted and analyzed by HPLC according toa previously described method (Kliebenstein et al., 2001c).

Statistics

To test for epistasis of the TFs in controlling GLS biosynthesis, the GLSphenotypes for each epistatic combination were separately analyzed byANOVAusingageneral linearmodelbySAS.The followingmodelwasusedto test for the epistasis for the GLS phenotypes in the double mutants,with each double mutant having both single mutants and the wild typegrown concurrently: yabtc = m +Aa +Bb + Tt +Chc + AaxBb + AaxTt + AaxChc +BbxTt + BbxCc + Tt xChc + AaxBbxTt + AaxBbxChc + BbxTtxChc+ AaxTtxChc +AaxBbxTtxChc+«abtc,where«rgt is theerror termandisassumedtobenormallydistributed with mean 0 and variance se

2. In this model, yabtc denotes theGLS phenotype in each plant, genotype A represents the presence orabsence of a T-DNA insert in one TFgene (wild type versusmutant of locusA), andgenotypeB represents thepresenceor absenceof aT-DNA insert inanother TF gene (wild type versus mutant of locus B) in the double mutantfrom tissue Tt (leaf or seed) and chamberChc (cleanCEF chamber or stressLSA chamber). TheANOVA table, least-square (LS)means, and SE for eachgenotype x tissue x treatment combinationswere obtained usingSAS. Thetype III sumsof squares from thismodelwereused tocalculate thevarianceand percent variance attributable to each term in the model. For the

Network-Guided Investigation of Epistasis 191

percentage of variance, this was calculated by comparing to the totalvariance in themodel as thedenominator.All network representationsweregenerated using Cytoscape.v2.8.3 (Shannon et al., 2003).

For all metabolites, we utilized the absolute abundance for all calcu-lations, as the residuals for these tests were normally distributed in all butthe model testing myb28/myb29 epistasis, which displays classical re-cessive epistasis. Adjusting to a log scale did not improve the residuals inthe myb28/myb29 epistatic model and as such, we maintained the mostdirect link to the absolute metabolite abundance for the other pairwisemodels using the absolute abundance. In addition, we utilized an additiverather than a multiplicative scale for the epistatic tests because naturalArabidopsis accessions and recombinant inbred lines generated withCol-0 crosses can accumulate up to 20-foldmore glucosinolatemetabolitesthan the Col-0 accession. This suggests that there is not a physiologicalmaxima that we are approaching in our data that would necessitateamultiplicative scale (Kliebenstein et al., 2001a, 2001b, 2002c; Chan et al.,2010, 2011). Transcripts were tested for epistasis using log-adjustednormalized expression values, as is the standard requirement for tran-scriptomicanalysis. The ratio tests for theGS-OXandGS-Elong locuswerealso conducted using the unscaled data, as like for the metabolite, theresiduals in these tests were largely normally distributed. These ratios arederived by having the value in the numerator (4-methylthiobutyl for GS-OX)also in the denominator to force the ratio to be between 0 and 1. In ourprevious work, we found that these two ratios are largely uncorrelated withthe absolute content of any specific glucosinolate, allowing for them tobehave as independent variables focusedon the specificenzymatic step inquestion (Kliebenstein et al., 2001a, 2002c; Wentzell et al., 2007; Chanet al., 2010, 2011).

Calculation of Epistasis Value

To study the effect of epistasis, we utilized an algebraic approximationdescribing the direction and strength of the epistasis by normalizing thedifference of observed double mutant phenotype versus the predicteddouble mutant phenotype, assuming additivity of the single mutants. Thisepistasis value was then normalized to the wild type, as done with otherepistasis terms (Segrè et al., 2005). The phenotype for thewild typewas setasw,mutant TFa as a,mutant TFb as b, and doublemutant TFa/TFb as ab.The epistasis value is calculated as (ab 2 (w + (a-w) + (b-w))/w). If theepistasis value is positive, this shows evidence for synergistic epistasis,while antagonistic epistasis is reflected in negative values. The larger theepistasis value, the stronger the epistasis effects. The epistasis valueswere further visualized using the iheatmapr package in R software (R CoreTeam, 2015).

RNA-Seq Analysis

Arabidopsis plants, including Col-0, myb28, myb29, ant, myb28 ant,myb29 ant, andmyb28 myb29 ant, were grown in CEF clean chambers ina randomized complete block design using two independent experiments.Leaves were harvested from two individual plants per genotype from eachexperiment and used to make four independent RNA-seq libraries pergenotype. Total leaf RNAwasextracted usingTrizol (Invitrogen) and storedat 270°C before constructing the library. The RNA sequencing librarieswere created with a QuantSequation 39 mRNA-Seq Library Prep Kit(Lexogen). Each library had unique indexing primers, and the libraries werepooled and sequenced on the HiSeq 4000 platform at UC Davis DNATechnologies Core Facility. Fastq files from individual HiSeq lane wereseparated by adapter index into individual libraries. The alignment andgene counting were done with the BlueBee pipeline accompanying theLexogen kit using the Arabidopsis (TAIR10) LexogenQuantSeq 2.2.1 FWDreferencegenome.Statistical analysisof theRNA-seqdatawasconductedusing the R V3.4.1 statistical environment (R Core Team, 2015). The genecount data from RNA-seq were subjected to a previously described

statistical approach (Zhangetal., 2017).Normalizationongenecountswasfirst conducted using the TMMmethod in function calcNormFactors() fromthe edgeR package (Robinson and Smyth, 2008; Robinson et al., 2010;Robinson and Oshlack, 2010; Nikolayeva and Robinson, 2014), andnormalized pseudo-counts were then obtained for downstream analysis.The linearmodelwasconductedonnormalizedgenecountsusing functionglm.nb() from the MASS package (Venables and Ripley, 2002). Model-corrected means and standard errors for each transcript were determinedusing the lsmeansV2.19 package (Lenth, 2016). RawP values for F- and x2

tests were determined by type III sums of squares using the functionANOVA() from the car package (Fox and Weisberg, 2011). Transcript Pvalueswere false discovery rate (P value <0.05) corrected formultiple testsof significance (Benjamini et al., 2001; Strimmer, 2008).

Accession Numbers

Transcriptomicsdata fromthisarticlecouldbefound inSupplementalDataSet6.The accession numbers for the genes analyzed are as follows: MYB28,AT5G61420;MYB29, AT5G07690; ANT, AT4G37750; ILR3, AT5G54680; ZFP4,AT1G66140; ERF107, AT5G61590; CBF4, AT5G51990; GBF2, AT4G01120;HMGBD15, AT1G04880; ZFP7, AT1G24625; HB21, AT2G02540; RAP2.6L,AT5G13330; HB34, AT3G28920; NAC102, AT5G63790; ERF9, AT5G44210;ATE2F2, AT1G47870; ABF4, AT3G19290; DF1, AT1G76880; MYB76,AT5G07700; MYC2, AT1G32640; MYC3, AT5G46760; and MYC4, AT4G17880.

Supplemental Data

Supplemental Figure 1. Epistatic effects for GLS Elong.

Supplemental Figure 2. Epistatic effects for GLS OX.

Supplemental Figure 3. Epistatic effects for LC GLS.

Supplemental Figure 4. Epistatic effects for indolic GLS.

Supplemental Figure 5. The absence of aliphatic GLS in triplemutants between repressor TFs and myb28/myb29.

Supplemental Data Set 1. Glucosinolate abbreviations, descriptions,and chemical structures.

Supplemental Data Set 2. The selected transcription factors and theirT-DNA insertion lines.

Supplemental Data Set 3. P values of the ANOVA of the double andtriple mutants.

Supplemental Data Set 4. The sum of squares of the ANOVA of thedouble and triple mutants.

Supplemental Data Set 5. LS means of the ANOVA of the double andtriple mutants.

Supplemental Data Set 6. RNA-seq analysis of the full set of ant-related mutants.

ACKNOWLEDGMENTS

We thank Wei Zhang for the discussions and help with the use of thepipeline to analyze the RNA-seq data. This work was funded by NationalScience Foundation Grants MCB1330337 to S.M.B. and D.J.K. and DBI0820580 to D.J.K., by the USDA National Institute of Food and Agricul-ture, Hatch Project CA-D-PLS-7033-H to D.J.K., by Danish NationalResearch Foundation Grant DNRF99 to D.J.K., by the NationalScience Foundation GRFP to M.T. via NSF DGE 1148897 to Jeffery C.Gibeling, Dean and Vice Provost of Office of Graduate Studies at UCDavis, and by the UCDavis Department of Plant Sciences Jastro ShieldsResearch Award to M.T.

192 The Plant Cell

AUTHOR CONTRIBUTIONS

B.L. and D.J.K. conceived and designed the experiments. B.L., M.T., A.N.,H.C., X.Z., C.C.-W., and R.N. performed the experiments. B.L. and D.J.K.analyzed the data. B.L., M.T., S.M.B., and D.J.K. wrote the article.

Received October 16, 2017; revised December 7, 2017; accepted January5, 2018; published January 9, 2018.

REFERENCES

Alonso, J.M., et al. (2003). Genome-wide insertional mutagenesis ofArabidopsis thaliana. Science 301: 653–657.

Benjamini, Y., Drai, D., Elmer, G., Kafkafi, N., and Golani, I. (2001).Controlling the false discovery rate in behavior genetics research.Behav. Brain Res. 125: 279–284.

Berckmans, B., Lammens, T., Van Den Daele, H., Magyar, Z.,Bögre, L., and De Veylder, L. (2011). Light-dependent regulation ofDEL1 is determined by the antagonistic action of E2Fb and E2Fc.Plant Physiol. 157: 1440–1451.

Brachi, B., Meyer, C.G., Villoutreix, R., Platt, A., Morton, T.C., Roux, F.,and Bergelson, J. (2015). Coselected genes determine adaptive varia-tion in herbivore resistance throughout the native range of Arabidopsisthaliana. Proc. Natl. Acad. Sci. USA 112: 4032–4037.

Burow, M., Halkier, B.A., and Kliebenstein, D.J. (2010). Regulatorynetworks of glucosinolates shape Arabidopsis thaliana fitness. Curr.Opin. Plant Biol. 13: 348–353.

Chan, E.K., Rowe, H.C., Corwin, J.A., Joseph, B., and Kliebenstein,D.J. (2011). Combining genome-wide association mapping andtranscriptional networks to identify novel genes controlling gluco-sinolates in Arabidopsis thaliana. PLoS Biol. 9: e1001125.

Chan, E.K.F., Rowe, H.C., and Kliebenstein, D.J. (2010). Un-derstanding the evolution of defense metabolites in Arabidopsisthaliana using genome-wide association mapping. Genetics 185:991–1007.

Charlesworth, B., Nordborg, M., and Charlesworth, D. (1997). Theeffects of local selection, balanced polymorphism and backgroundselection on equilibrium patterns of genetic diversity in subdividedpopulations. Genet. Res. 70: 155–174.

Chory, J. (2010). Light signal transduction: an infinite spectrum ofpossibilities. Plant J. 61: 982–991.

Christianson, J.A., Wilson, I.W., Llewellyn, D.J., and Dennis, E.S.(2009). The low-oxygen-induced NAC domain transcription factorANAC102 affects viability of Arabidopsis seeds following low-oxygentreatment. Plant Physiol. 149: 1724–1738.

Costanzo, M., et al. (2010). The genetic landscape of a cell. Science327: 425–431.

Costanzo, M., et al. (2016). A global genetic interaction network mapsa wiring diagram of cellular function. Science 353: aaf1420.

del Pozo, J.C., Diaz-Trivino, S., Cisneros, N., and Gutierrez, C.(2006). The balance between cell division and endoreplication de-pends on E2FC-DPB, transcription factors regulated by the ubiquitin-SCFSKP2A pathway in Arabidopsis. Plant Cell 18: 2224–2235.

de Quiros, H.C., Magrath, R., McCallum, D., Kroymann, J.,Schnabelrauch, D., Mitchell-Olds, T., and Mithen, R. (2000).a-Keto acid elongation and glucosinolate biosynthesis in Arabi-dopsis thaliana. Theor. Appl. Genet. 101: 429–437.

Dombrecht, B., Xue, G.P., Sprague, S.J., Kirkegaard, J.A., Ross,J.J., Reid, J.B., Fitt, G.P., Sewelam, N., Schenk, P.M., Manners,J.M., and Kazan, K. (2007). MYC2 differentially modulates diversejasmonate-dependent functions in Arabidopsis. Plant Cell 19: 2225–2245.

Elliott, R.C., Betzner, A.S., Huttner, E., Oakes, M.P., Tucker, W.Q.,Gerentes, D., Perez, P., and Smyth, D.R. (1996). AINTEGUMENTA,an APETALA2-like gene of Arabidopsis with pleiotropic roles inovule development and floral organ growth. Plant Cell 8: 155–168.

Falconer, D.S., and Mackay, T.F.C. (1996). Introduction to Quanti-tative Genetics. (Essex, UK: Longman, Harlow).

Fernández-Calvo, P., et al. (2011). The Arabidopsis bHLH tran-scription factors MYC3 and MYC4 are targets of JAZ repressorsand act additively with MYC2 in the activation of jasmonate re-sponses. Plant Cell 23: 701–715.

Fox, J., and Weisberg, S. (2011). An R Companion to Applied Re-gression. (Thousand Oaks, CA: SAGE).

Gaudinier, A., Tang, M., and Kliebenstein, D.J. (2015). Transcrip-tional networks governing plant metabolism. Curr. Plant Biol. 3–4:56–64.

Gaudinier, A., et al. (2011). Enhanced Y1H assays for Arabidopsis.Nat. Methods 8: 1053–1055.

Gigolashvili, T., Yatusevich, R., Berger, B., Müller, C., and Flügge,U.I. (2007). The R2R3-MYB transcription factor HAG1/MYB28 isa regulator of methionine-derived glucosinolate biosynthesis inArabidopsis thaliana. Plant J. 51: 247–261.

Gigolashvili, T., Engqvist, M., Yatusevich, R., Müller, C., and Flügge,U.I. (2008). HAG2/MYB76 and HAG3/MYB29 exert a specific and co-ordinated control on the regulation of aliphatic glucosinolate bio-synthesis in Arabidopsis thaliana. New Phytol. 177: 627–642.

Goldwasser, Y., Westwood, J.H., and Yoder, J.I. (2002). The Use ofArabidopsis to Study Interactions between Parasitic Angiospermsand Their Plant Hosts. Arabidopsis Book 1: e0035.

González-Grandío, E., Pajoro, A., Franco-Zorrilla, J.M., Tarancón,C., Immink, R.G., and Cubas, P. (2017). Abscisic acid signaling iscontrolled by a BRANCHED1/HD-ZIP I cascade in Arabidopsis ax-illary buds. Proc. Natl. Acad. Sci. USA 114: E245–E254.

Gu, Y., Wang, Z., and Yang, Z. (2004). ROP/RAC GTPase: an old newmaster regulator for plant signaling. Curr. Opin. Plant Biol. 7: 527–536.

Hansen, B.G., Kliebenstein, D.J., and Halkier, B.A. (2007). Identifi-cation of a flavin-monooxygenase as the S-oxygenating enzyme inaliphatic glucosinolate biosynthesis in Arabidopsis. Plant J. 50:902–910.

Hansen, B.G., Kerwin, R.E., Ober, J.A., Lambrix, V.M., Mitchell-Olds, T., Gershenzon, J., Halkier, B.A., and Kliebenstein, D.J.(2008). A novel 2-oxoacid-dependent dioxygenase involved in theformation of the goiterogenic 2-hydroxybut-3-enyl glucosinolateand generalist insect resistance in Arabidopsis. Plant Physiol. 148:2096–2108.

Harmer, S.L. (2009). The circadian system in higher plants. Annu. Rev.Plant Biol. 60: 357–377.

Harper, A.L., Trick, M., Higgins, J., Fraser, F., Clissold, L., Wells,R., Hattori, C., Werner, P., and Bancroft, I. (2012). Associativetranscriptomics of traits in the polyploid crop species Brassica na-pus. Nat. Biotechnol. 30: 798–802.

Hartman IV, J.L., Garvik, B., and Hartwell, L. (2001). Principles forthe buffering of genetic variation. Science 291: 1001–1004.

Haughn, G.W., Davin, L., Giblin, M., and Underhill, E.W. (1991).Biochemical genetics of plant secondary metabolites in Arabidopsisthaliana. The glucosinolates. Plant Physiol. 97: 217–226.

Hickman, R., et al. (2017). Architecture and Dynamics of the JasmonicAcid Gene Regulatory Network. Plant Cell 29: 2086–2105.

Hirai, M.Y., et al. (2007). Omics-based identification of ArabidopsisMyb transcription factors regulating aliphatic glucosinolate bio-synthesis. Proc. Natl. Acad. Sci. USA 104: 6478–6483.

Hopkins, R.J., van Dam, N.M., and van Loon, J.J. (2009). Role ofglucosinolates in insect-plant relationships and multitrophic inter-actions. Annu. Rev. Entomol. 54: 57–83.

Network-Guided Investigation of Epistasis 193

Horstman, A., Willemsen, V., Boutilier, K., and Heidstra, R. (2014).AINTEGUMENTA-LIKE proteins: hubs in a plethora of networks.Trends Plant Sci. 19: 146–157.

Howe, G.A., and Jander, G. (2008). Plant immunity to insect herbi-vores. Annu. Rev. Plant Biol. 59: 41–66.

Jander, G., Cui, J., Nhan, B., Pierce, N.E., and Ausubel, F.M. (2001).The TASTY locus on chromosome 1 of Arabidopsis affects feedingof the insect herbivore Trichoplusia ni. Plant Physiol. 126: 890–898.

Jones, J.D.G., and Dangl, J.L. (2006). The plant immune system.Nature 444: 323–329.

Kazan, K., and Manners, J.M. (2013). MYC2: the master in action.Mol. Plant 6: 686–703.

Kerwin, R., et al. (2015). Natural genetic variation in Arabidopsis thalianadefense metabolism genes modulates field fitness. eLife 4: e05604.

Kerwin, R.E., et al. (2017). Epistasis 3 environment interactionsamong Arabidopsis thaliana glucosinolate genes impact complextraits and fitness in the field. New Phytol. 215: 1249–1263.

Kim, J.H., and Jander, G. (2007). Myzus persicae (green peach aphid)feeding on Arabidopsis induces the formation of a deterrent indoleglucosinolate. Plant J. 49: 1008–1019.

Kliebenstein, D., Pedersen, D., Barker, B., and Mitchell-Olds, T.(2002a). Comparative analysis of quantitative trait loci controllingglucosinolates, myrosinase and insect resistance in Arabidopsisthaliana. Genetics 161: 325–332.

Kliebenstein, D., Lambrix, V., Reichelt, M., Gershenzon, J., andMitchell-Olds, T. (2001a). Gene duplication and the diversificationof secondary metabolism: side chain modification of glucosinolatesin Arabidopsis thaliana. Plant Cell 13: 681–693.

Kliebenstein, D.J. (2009). A quantitative genetics and ecologicalmodel system: understanding the aliphatic glucosinolate biosyntheticnetwork via QTLs. Phytochem. Rev. 8: 243–254.

Kliebenstein, D.J., Figuth, A., and Mitchell-Olds, T. (2002b). Geneticarchitecture of plastic methyl jasmonate responses in Arabidopsisthaliana. Genetics 161: 1685–1696.

Kliebenstein, D.J., Gershenzon, J., and Mitchell-Olds, T. (2001b).Comparative quantitative trait loci mapping of aliphatic, indolic andbenzylic glucosinolate production in Arabidopsis thaliana leavesand seeds. Genetics 159: 359–370.

Kliebenstein, D.J., Kroymann, J., Brown, P., Figuth, A., Pedersen,D., Gershenzon, J., and Mitchell-Olds, T. (2001c). Genetic controlof natural variation in Arabidopsis glucosinolate accumulation. PlantPhysiol. 126: 811–825.

Krizek, B.A., Prost, V., and Macias, A. (2000). AINTEGUMENTApromotes petal identity and acts as a negative regulator of AGAMOUS.Plant Cell 12: 1357–1366.

Kroymann, J. (2011). Natural diversity and adaptation in plant sec-ondary metabolism. Curr. Opin. Plant Biol. 14: 246–251.

Kroymann, J., Donnerhacke, S., Schnabelrauch, D., and Mitchell-Olds, T. (2003). Evolutionary dynamics of an Arabidopsis insectresistance quantitative trait locus. Proc. Natl. Acad. Sci. USA 100(suppl 2.): 14587–14592.

Lankau, R.A., and Kliebenstein, D.J. (2009). Competition, herbivoryand genetics interact to determine the accumulation and fitnessconsequences of a defence metabolite. J. Ecol. 97: 78–88.

Lenth, R.V. (2016). Least-squares means: TheRPackagelsmeans.J. Stat. Softw. 69: 1–33.

Li, B., Gaudinier, A., Tang, M., Taylor-Teeples, M., Nham, N.T.,Ghaffari, C., Benson, D.S., Steinmann, M., Gray, J.A., Brady,S.M., and Kliebenstein, D.J. (2014). Promoter-based integration inplant defense regulation. Plant Physiol. 166: 1803–1820.

Li, J., Kristiansen, K.A., Hansen, B.G., and Halkier, B.A. (2011).Cellular and subcellular localization of flavin-monooxygenases in-volved in glucosinolate biosynthesis. J. Exp. Bot. 62: 1337–1346.

Li, J., Hansen, B.G., Ober, J.A., Kliebenstein, D.J., and Halkier, B.A.(2008). Subclade of flavin-monooxygenases involved in aliphaticglucosinolate biosynthesis. Plant Physiol. 148: 1721–1733.

Li, S., Assmann, S.M., and Albert, R. (2006). Predicting essentialcomponents of signal transduction networks: a dynamic model ofguard cell abscisic acid signaling. PLoS Biol. 4: e312.

Liu, Z., Franks, R.G., and Klink, V.P. (2000). Regulation of gynoeciummarginal tissue formation by LEUNIG and AINTEGUMENTA. PlantCell 12: 1879–1892.

Long, T.A., Tsukagoshi, H., Busch, W., Lahner, B., Salt, D.E., andBenfey, P.N. (2010). The bHLH transcription factor POPEYE regu-lates response to iron deficiency in Arabidopsis roots. Plant Cell 22:2219–2236.

Mackay, T.F.C. (2001). The genetic architecture of quantitative traits.Annu. Rev. Genet. 35: 303–339.

Mackay, T.F.C. (2014). Epistasis and quantitative traits: using modelorganisms to study gene-gene interactions. Nat. Rev. Genet. 15:22–33.

Malitsky, S., Blum, E., Less, H., Venger, I., Elbaz, M., Morin, S.,Eshed, Y., and Aharoni, A. (2008). The transcript and metabolitenetworks affected by the two clades of Arabidopsis glucosinolatebiosynthesis regulators. Plant Physiol. 148: 2021–2049.

Mengiste, T. (2012). Plant immunity to necrotrophs. Annu. Rev.Phytopathol. 50: 267–294.

Menkens, A.E., and Cashmore, A.R. (1994). Isolation and charac-terization of a fourth Arabidopsis thaliana G-box-binding factor,which has similarities to Fos oncoprotein. Proc. Natl. Acad. Sci.USA 91: 2522–2526.

Mizukami, Y., and Fischer, R.L. (2000). Plant organ size control:AINTEGUMENTA regulates growth and cell numbers during or-ganogenesis. Proc. Natl. Acad. Sci. USA 97: 942–947.

Nikolayeva, O., and Robinson, M.D. (2014). edgeR for differentialRNA-seq and ChIP-seq analysis: an application to stem cell bi-ology. Methods Mol. Biol. 1150: 45–79.

Nordborg, M., Borevitz, J.O., Bergelson, J., Berry, C.C., Chory, J.,Hagenblad, J., Kreitman, M., Maloof, J.N., Noyes, T., Oefner,P.J., Stahl, E.A., and Weigel, D. (2002). The extent of linkage dis-equilibrium in Arabidopsis thaliana. Nat. Genet. 30: 190–193.

Phillips, P.C. (2008). Epistasis–the essential role of gene interactionsin the structure and evolution of genetic systems. Nat. Rev. Genet.9: 855–867.

R Core Team (2015). R: A Language and Environment for Statisti-cal Computing. (Vienna, Austria: R Foundation for StatisticalComputing).

Rampey, R.A., Woodward, A.W., Hobbs, B.N., Tierney, M.P.,Lahner, B., Salt, D.E., and Bartel, B. (2006). An Arabidopsis ba-sic helix-loop-helix leucine zipper protein modulates metal ho-meostasis and auxin conjugate responsiveness. Genetics 174:1841–1857.

Riedelsheimer, C., Czedik-Eysenberg, A., Grieder, C., Lisec, J.,Technow, F., Sulpice, R., Altmann, T., Stitt, M., Willmitzer, L.,and Melchinger, A.E. (2012). Genomic and metabolic prediction ofcomplex heterotic traits in hybrid maize. Nat. Genet. 44: 217–220.

Rieseberg, L.H., Archer, M.A., and Wayne, R.K. (1999). Trans-gressive segregation, adaptation and speciation. Heredity (Edinb.)83: 363–372.

Rieseberg, L.H., Widmer, A., Arntz, A.M., and Burke, J.M. (2003).The genetic architecture necessary for transgressive segregation iscommon in both natural and domesticated populations. Philos.Trans. R. Soc. Lond. B Biol. Sci. 358: 1141–1147.

Robinson, M.D., and Smyth, G.K. (2008). Small-sample estimationof negative binomial dispersion, with applications to SAGE data.Biostatistics 9: 321–332.

194 The Plant Cell

Robinson, M.D., and Oshlack, A. (2010). A scaling normalizationmethod for differential expression analysis of RNA-seq data. Ge-nome Biol. 11: R25.

Robinson, M.D., McCarthy, D.J., and Smyth, G.K. (2010). edgeR:a Bioconductor package for differential expression analysis ofdigital gene expression data. Bioinformatics 26: 139–140.

Schindler, U., Menkens, A.E., Beckmann, H., Ecker, J.R., andCashmore, A.R. (1992). Heterodimerization between light-regulatedand ubiquitously expressed Arabidopsis GBF bZIP proteins. EMBO J.11: 1261–1273.

Schweizer, F., Fernández-Calvo, P., Zander, M., Diez-Diaz, M.,Fonseca, S., Glauser, G., Lewsey, M.G., Ecker, J.R., Solano, R.,and Reymond, P. (2013). Arabidopsis basic helix-loop-helix tran-scription factors MYC2, MYC3, and MYC4 regulate glucosinolatebiosynthesis, insect performance, and feeding behavior. Plant Cell25: 3117–3132.

Segrè, D., Deluna, A., Church, G.M., and Kishony, R. (2005). Mod-ular epistasis in yeast metabolism. Nat. Genet. 37: 77–83.

Shannon, P., Markiel, A., Ozier, O., Baliga, N.S., Wang, J.T.,Ramage, D., Amin, N., Schwikowski, B., and Ideker, T. (2003).Cytoscape: a software environment for integrated models of bio-molecular interaction networks. Genome Res. 13: 2498–2504.

Shinozaki, K., Yamaguchi-Shinozaki, K., and Seki, M. (2003).Regulatory network of gene expression in the drought and coldstress responses. Curr. Opin. Plant Biol. 6: 410–417.

Sønderby, I.E., Geu-Flores, F., and Halkier, B.A. (2010a). Bio-synthesis of glucosinolates–gene discovery and beyond. TrendsPlant Sci. 15: 283–290.

Sønderby, I.E., Burow, M., Rowe, H.C., Kliebenstein, D.J., andHalkier, B.A. (2010b). A complex interplay of three R2R3 MYBtranscription factors determines the profile of aliphatic glucosino-lates in Arabidopsis. Plant Physiol. 153: 348–363.

Sønderby, I.E., Burow, M., Rowe, H.C., Kliebenstein, D.J., andHalkier, B.A. (2010c). A complex interplay of three R2R3 MYBtranscription factors determines the profile of aliphatic glucosino-lates in Arabidopsis. Plant Physiol. 153: 348–363.

Sønderby, I.E., Hansen, B.G., Bjarnholt, N., Ticconi, C., Halkier,B.A., and Kliebenstein, D.J. (2007). A systems biology approachidentifies a R2R3 MYB gene subfamily with distinct and overlappingfunctions in regulation of aliphatic glucosinolates. PLoS One 2:e1322.

Stracke, R., Werber, M., and Weisshaar, B. (2001). The R2R3-MYB genefamily in Arabidopsis thaliana. Curr. Opin. Plant Biol. 4: 447–456.

Strimmer, K. (2008). fdrtool: a versatile R package for estimating local andtail area-based false discovery rates. Bioinformatics 24: 1461–1462.

Sussman, M.R., Amasino, R.M., Young, J.C., Krysan, P.J., andAustin-Phillips, S. (2000). The Arabidopsis knockout facility at theUniversity of Wisconsin-Madison. Plant Physiol. 124: 1465–1467.

Taylor-Teeples, M., et al. (2015). An Arabidopsis gene regulatorynetwork for secondary cell wall synthesis. Nature 517: 571–575.

Terzaghi, W.B., Bertekap, R.L., Jr., and Cashmore, A.R. (1997).Intracellular localization of GBF proteins and blue light-inducedimport of GBF2 fusion proteins into the nucleus of cultured Arabi-dopsis and soybean cells. Plant J. 11: 967–982.

Traka, M.H., et al. (2013). Genetic regulation of glucoraphanin ac-cumulation in Beneforté broccoli. New Phytol. 198: 1085–1095.

Venables, W.N., and Ripley, B.D. (2002). Modern Applied Statisticswith S, Statistics and Computing, Vol. 12. (New York: Springer).

Vidal, E.A., and Gutiérrez, R.A. (2008). A systems view of nitrogennutrient and metabolite responses in Arabidopsis. Curr. Opin. PlantBiol. 11: 521–529.

Wen, W., Liu, H., Zhou, Y., Jin, M., Yang, N., Li, D., Luo, J., Xiao, Y.,Pan, Q., Tohge, T., Fernie, A.R., and Yan, J. (2016). Combiningquantitative genetics approaches with regulatory network analysisto dissect the complex metabolism of the maize kernel. PlantPhysiol. 170: 136–146.

Wentzell, A.M., Rowe, H.C., Hansen, B.G., Ticconi, C., Halkier,B.A., and Kliebenstein, D.J. (2007). Linking metabolic QTLs withnetwork and cis-eQTLs controlling biosynthetic pathways. PLoSGenet. 3: 1687–1701.

Wink, M. (1988). Plant breeding: importance of plant secondary me-tabolites for protection against pathogens and herbivores. Theor.Appl. Genet. 75: 225–233.

Wurschum, T., Maurer, H.P., Dreyer, F., and Reif, J.C. (2013). Effectof inter- and intragenic epistasis on the heritability of oil content inrapeseed (Brassica napus L.). Theor. Appl. Genet. 126: 435–441.

Xia, C., Wang, Y.J., Liang, Y., Niu, Q.K., Tan, X.Y., Chu, L.C., Chen,L.Q., Zhang, X.Q., and Ye, D. (2014). The ARID-HMG DNA-bindingprotein AtHMGB15 is required for pollen tube growth in Arabidopsisthaliana. Plant J. 79: 741–756.

Xuan, W., Beeckman, T., and Xu, G. (2017). Plant nitrogen nutrition:sensing and signaling. Curr. Opin. Plant Biol. 39: 57–65.

Zhang, W., Corwin, J.A., Copeland, D., Feusier, J., Eshbaugh, R.,Chen, F., Atwell, S., and Kliebenstein, D.J. (2017). Plastic tran-scriptomes stabilize immunity to pathogen diversity: The jasmonicacid and salicylic acid networks within the Arabidopsis/Botrytispathosystem. Plant Cell 29: 2727–2752.

Züst, T., Heichinger, C., Grossniklaus, U., Harrington, R.,Kliebenstein, D.J., and Turnbull, L.A. (2012). Natural enemies drivegeographic variation in plant defenses. Science 338: 116–119.

Network-Guided Investigation of Epistasis 195

DOI 10.1105/tpc.17.00805; originally published online January 9, 2018; 2018;30;178-195Plant Cell

Siobhan M. Brady and Daniel J. KliebensteinBaohua Li, Michelle Tang, Ayla Nelson, Hart Caligagan, Xue Zhou, Caitlin Clark-Wiest, Richard Ngo,

Aliphatic Glucosinolate BiosynthesisNetwork-Guided Discovery of Extensive Epistasis between Transcription Factors Involved in

 This information is current as of November 28, 2020

 

Supplemental Data

/content/suppl/2018/04/03/tpc.17.00805.DC3.html /content/suppl/2018/01/09/tpc.17.00805.DC1.html /content/suppl/2018/01/12/tpc.17.00805.DC2.html

References /content/30/1/178.full.html#ref-list-1

This article cites 95 articles, 40 of which can be accessed free at:

Permissions https://www.copyright.com/ccc/openurl.do?sid=pd_hw1532298X&issn=1532298X&WT.mc_id=pd_hw1532298X

eTOCs http://www.plantcell.org/cgi/alerts/ctmain

Sign up for eTOCs at:

CiteTrack Alerts http://www.plantcell.org/cgi/alerts/ctmain

Sign up for CiteTrack Alerts at:

Subscription Information http://www.aspb.org/publications/subscriptions.cfm

is available at:Plant Physiology and The Plant CellSubscription Information for

ADVANCING THE SCIENCE OF PLANT BIOLOGY © American Society of Plant Biologists