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RESEARCH ARTICLE

Network-guided Discovery of Extensive Epistasis Between Transcription Factors Involved in Aliphatic Glucosinolate Biosynthesis Baohua Li1, Michelle Tang1,2, Ayla Nelson1, Hart Caligagan1, Xue Zhou1, Caitlin Clark-Wiest1, Richard Ngo1, Siobhan M. Brady2, and Daniel J. Kliebenstein1,3 x

1Department of Plant Sciences, University of California, Davis, One Shields Avenue, Davis, CA, 95616, USA 2Department of Plant Biology and Genome Center, University of California, Davis, One Shields Avenue, Davis, CA, 95616, USA 3DynaMo Center of Excellence, University of Copenhagen, Thorvaldsensvej 40, DK-1871, Frederiksberg C, Denmark xCorresponding Author: Kliebenstein@ucdavis.edu

Short title: Network-guided investigation of epistasis

One-sentence summary: A large effort was made to systemically test, identify, and study genetic interactions between regulatory genes for a plant defense metabolism network controlling fitness.

The author responsible for distribution of materials integral to the findings presented in this article in accordance with the policy described in the Instructions for Authors (www.plantcell.org) is: Daniel J. Kliebenstein (Kliebenstein@ucdavis.edu).

ABSTRACT Plants use diverse mechanisms influenced by vast regulatory networks of indefinite scale to adapt to their environment. These regulatory networks have an unknown potential for epistasis between genes within and across networks. To test for epistasis within an adaptive trait genetic network, we generated and tested 47 Arabidopsis thaliana double mutant combinations for 20 transcription factors, which all influence the accumulation of aliphatic glucosinolates, the defense metabolites that control fitness. The epistatic combinations were used to test if there is more or less epistasis depending on gene membership within the same or different phenotypic sub-networks. Extensive epistasis was observed between the transcription factors, regardless of sub-network membership. Metabolite accumulation displayed antagonistic epistasis, suggesting the presence of a buffering mechanism. Epistasis affecting enzymatic estimated activity was highly conditional on the tissue and environment and shifted between both antagonistic and synergistic forms. Transcriptional analysis showed that epistasis shifts depend on how the trait is measured. Because the 47 combinations described here represent a small sampling of the potential epistatic combinations in this genetic network, there is potential for significantly more epistasis. Additionally, the main effect of the individual gene was not predictive of the epistatic effects, suggesting that there is a need for further studies.

INTRODUCTION 1

To adapt and maximize fitness, plants perceive and respond to a myriad of signals that in 2

combination provide an image of the environment. These signals can arise from the biotic 3

Plant Cell Advance Publication. Published on January 9, 2018, doi:10.1105/tpc.17.00805

©2018 American Society of Plant Biologists. All Rights Reserved

2

environment, including bacteria, fungi, insects and other plants, plus stimuli from the abiotic 4

environment, including light, temperature, water and nutrient availability (Goldwasser et al., 5

2002; Shinozaki et al., 2003; Jones and Dangl, 2006; Howe and Jander, 2008; Vidal and 6

Gutierrez, 2008; Harmer, 2009; Chory, 2010; Mengiste, 2012; Xuan et al., 2017). Critically, each 7

specific signal is typically perceived by a separate mechanism that stimulates a downstream 8

regulatory network involving at least tens of genes (Li et al., 2006; Hickman et al., 2017). The 9

current models often suggest that these genetic regulatory networks coalesce around master 10

regulators that are the central controllers for specific pathways and/or phenotypes (Gu et al., 11

2004; Kazan and Manners, 2013). Often these master regulators are transcription factors (TFs) 12

that are both necessary and sufficient for the changes in expression of genes or pathways that 13

modulate the growth, defense and metabolic phenotype of the plant to adapt to that specific 14

environment. We call this the master regulator hypothesis. This concept is predominant within 15

developmental regulatory networks that often exhibit switch-like behavior, shifting from one 16

state to another. It is not clear how this concept may translate to metabolic pathways that may 17

instead display a rheostat behavior, where there is a continuous adjustment in response to 18

external and internal stimuli. However, in spite of the advanced knowledge about specific 19

regulatory networks in plants, the exact size and interconnected structure of these genetic 20

networks is a key unanswered question in systems biology (Phillips, 2008). The size of networks 21

is of critical importance for adaptive traits because as genetic networks increase in size and 22

interconnectivity, the concept of a single master regulator at the beginning point of a specific 23

regulatory network is less essential. Additionally, as gene membership increases, there is a 24

concurrent increase in the potential for epistasis between these genes (Mackay, 2014; Allison 25

Gaudinier, 2015). In this context, we are defining epistasis as any non-additive interaction 26

between genotypes at two or more loci influencing a trait. Thus, there is a need to understand 27

how large regulatory networks may be influenced by epistasis, especially for adaptive metabolic 28

traits. 29

One set of adaptive traits that could be utilized to study these questions of network scale 30

and epistasis are plant secondary metabolites (Wink, 1988; Burow et al., 2010; Kroymann, 2011). 31

Recent work has shown that plant secondary metabolites have strong epistatic interactions that 32

can influence fitness in the field (Brachi et al., 2015; Kerwin et al., 2015; Kerwin et al., 2017). 33

Additionally, mechanistic and quantitative genetic studies are showing that plant defense 34

3

metabolites have vast genetic regulatory networks (Chan et al., 2010; Chan et al., 2011; Harper 35

et al., 2012; Riedelsheimer et al., 2012; Wurschum et al., 2013; Wen et al., 2016). These studies 36

provide an alternative hypothesis where regulation occurs via a promoter integration model. In 37

this model, the pathway is controlled by suites of TFs that interact with distinct subsets of 38

promoters within a metabolic pathway. This promoter integration model leads to a greatly 39

extended gene network influencing a metabolic pathway and allows for potentially increased 40

precision in the regulation of metabolic pathways. Further, this raises the potential for there to be 41

different types of epistasis across a pathway, depending upon the promoter/gene that influences 42

that part of the pathway. For example, if two TFs bind different promoters within a pathway 43

without interacting molecularly, they have the potential to show non-additive epistasis at the 44

metabolite level, as they are influencing multiple enzymatic reactions within the pathway. In the 45

absence of metabolite-triggered transcriptional feedback, this metabolic epistasis might not be 46

mirrored at the transcript level, which may display an additive model. Thus, metabolic pathways 47

wherein it is possible to measure different outputs from a single pathway can enable the 48

dissection of genetic networks and epistatic interactions and how they compare at both the 49

metabolic and transcriptional levels. 50

In this study, we utilized the aliphatic glucosinolates (GLS) pathway to test the extent of 51

epistasis within an adaptive regulatory network. GLS are becoming a model system for the study 52

of plant adaption to ever-changing environments (Hopkins et al., 2009; Kliebenstein, 2009; 53

Kroymann, 2011). Aliphatic GLS are derived from methionine, and genetic variation influencing 54

aliphatic GLS composition is a key mechanism used by plants to adapt to their ecological niches 55

(Lankau and Kliebenstein, 2009; Burow et al., 2010; Züst et al., 2012). Further, the almost 56

complete elucidation of the methionine-derived aliphatic biosynthesis pathway in the model 57

plant Arabidopsis thaliana has provided a unique system to test systems biology concepts 58

(Sønderby et al., 2010a). Combining the full catalog of biosynthetic genes with large-scale 59

systems biology approaches has allowed a rapid characterization of the regulatory networks 60

controlling this pathway to address plants’ defense and survival challenges in connected 61

regulatory networks. Previous studies identified and confirmed the critical importance of 62

transcriptional regulation of the GLS pathway, including the cloning of TF genes MYB28, 63

MYB29 and MYB76, which regulate the accumulation of aliphatic GLS (Gigolashvili et al., 2007; 64

Hirai et al., 2007b; Sønderby et al., 2007; Gigolashvili et al., 2008; Malitsky et al., 2008; 65

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Sønderby et al., 2010c). More recently, TFs in the jasmonate signaling pathway, MYC2, MYC3, 66

and MYC4, were shown to be important regulators of both aliphatic and indolic GLS (Dombrecht 67

et al., 2007a; Fernández-Calvo et al., 2011; Schweizer et al., 2013). These key MYB and MYC 68

regulators of GLS pathways are positive regulators and belong to evolutionarily conserved 69

subsets of their corresponding families (Stracke et al., 2001; Fernández-Calvo et al., 2011). 70

Intriguingly, while mutants of these proposed master regulators abolish the accumulation of the 71

GLS metabolites, they only abolish the expression of a few key genes in the biosynthetic 72

pathway and do not affect the expression of other genes in the biosynthetic pathway (Dombrecht 73

et al., 2007b; Sønderby et al., 2010b). As such, they do not fit the classical definition of a master 74

transcriptional regulator and suggest the necessary involvement of other TFs. A yeast one-hybrid 75

(Y1H) approach identified numerous additional TFs that bound to promoters of genes involved 76

in GLS biosynthesis and influenced the accumulation of aliphatic GLS, including new TF 77

families with diverse functions (Li et al., 2014a). These new TFs are predominantly negative 78

regulators with pathway-specific GLS effects, allowing them to be clustered into distinct 79

phenotypic modules. How these new TFs interact with each other either within or between 80

phenotypic modules and how they interact with the key aliphatic GLS MYBs to structure the 81

epistatic regulatory network remain to be determined. 82

Previous work in yeast has shown that the modularity of genes influencing a phenotype, 83

primary metabolism, could be used to predict the presence of epistasis. Specifically, epistatic 84

interactions were predominantly found when studying double mutants involving genes associated 85

with different phenotypic clusters or modules, while genes within the same cluster rarely 86

displayed epistasis (Segre et al., 2005). This was hypothesized to be caused by genes within a 87

cluster having more redundancy than genes between clusters. This observation was also noted in 88

global studies investigating pair-wise interactions among all yeast genes, showing that 89

negative/antagonistic epistasis was predominantly found amongst genes in the same 90

complex/bioprocess (Costanzo et al., 2010; Costanzo et al., 2016). In contrast, the molecular 91

underpinnings of positive/synergistic epistasis were less decipherable. These yeast studies 92

focused on growth as the trait, raising the question of how epistasis transitions from gene 93

expression to enzyme to metabolite to fitness. Because we have a large collection of TFs 94

influencing a defense metabolite class, aliphatic GLS, within Arabidopsis and these TFs cluster 95

into specific groups based on their phenotypic effects on the metabolite accumulation, we can 96

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now test if network architecture influences epistasis in adaptive plant defense metabolism, as 97

observed for the yeast genome. The central hypothesis tested is that TFs acting in different 98

phenotypic clusters or modules are more likely to display epistasis than those within a 99

phenotypic cluster or module. 100

In the current study, to explore the potential for epistasis in regulatory networks of the 101

plant secondary metabolites GLS, we focused on the well-established and highly variable 102

aliphatic GLS pathway by systematically constructing 47 double mutants and 4 triple mutants 103

using 20 selected TFs controlling aliphatic GLS accumulation. These TFs include 2 key TF 104

regulators MYB28, MYB29, and 18 additional TFs that were previously ascribed to specific 105

phenotypic clusters depending upon their single mutant GLS phenotype (Figure 1)(Li et al., 106

2014a). The 24 GLS phenotypes of the 47 double mutants and 4 triple mutants were 107

systematically explored in 4 contrasting tissue and treatment combinations (Supplemental Data 108

Set 1). Epistatic networks differ for the major aliphatic GLS phenotypic clusters. The absence of 109

aliphatic GLS in myb28 myb29 could not be revived by the tested repressor TFs in triple mutants, 110

even though the expression of biosynthesis genes of aliphatic GLS could be modulated by these 111

repressor TFs, as predicted. Our findings provide insights into epistatic networks, contribute new 112

genetic resources to the community, and elicit research questions on the regulatory networks of 113

the model plant secondary metabolites, GLS. 114

115

RESULTS 116

Selection and construction of epistatic networks 117

To test our hypothesis, we systematically generated 47 double mutants and 4 triple 118

mutants using 20 representative TFs in Arabidopsis (Figure 1, Supplemental Data Set 2). All of 119

these T-DNA mutants exhibited altered GLS accumulation in single mutants (Li et al., 120

2014a)(Supplemental Data Set 2). These TFs had previously been grouped based on their 121

mutants’ phenotypic effect on the accumulation of all aliphatic GLS metabolites across multiple 122

tissues and environments. These selected TFs belong to diverse TF families with diverse gene 123

functions, including AINTEGUMENTA (ANT) of the AP2/EREBP family, which controls cell 124

proliferation (Elliott et al., 1996; Krizek et al., 2000; Liu et al., 2000; Mizukami and Fischer, 125

2000; Horstman et al., 2013), IAA-LEUCINE RESISTANT3 (ILR3) of the bHLH family, which 126

regulates iron deficiency (Rampey et al., 2006; Long et al., 2010), G-BOX BINDING FACTOR2 127

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(GBF2) of the bZIP family, which regulates response to blue light (Schindler et al., 1992; 128

Menkens and Cashmore, 1994; Terzaghi et al., 1997), HMGBD15 of the ARID family, which 129

regulates pollen tube growth (Xia et al., 2014), HOMEOBOX PROTEIN21 (HB21) of the ZF-130

HD family, which regulates abscisic acid-activated signaling (Gonzalez-Grandio et al., 2017), 131

NAC102 of NAC, which regulates responses to low oxygen stress (hypoxia) in germinating 132

seedlings (Christianson et al., 2009), and ATE2F2 of E2F/DP, which controls the balance 133

between cell division and endo-reduplication and xylem cell development (del Pozo et al., 2006; 134

Berckmans et al., 2011; Taylor-Teeples et al., 2015). The 47 double mutants represent 4 different 135

epistatic test sets: 1) Within cluster epistasis, 13 double mutants were obtained by crossing 136

mutants in TFs that were within the same phenotypic cluster; 2) Between cluster epistasis, 16 137

double mutants were obtained by crossing mutants in TFs that were in different clusters; 3) MYB 138

epistasis, 10 double mutants obtained by crossing the new TFs to myb28 or myb29; and 4) ANT 139

epistasis, 8 double mutants obtained by crossing new TFs to ant. The double mutants with the 140

known MYBs and strong-effect TF ANT were included to assess how these new TFs may 141

interact with described master regulators of glucosinolate biosynthesis. We also generated four 142

additional triple mutants to test the consequence of adding mutations in a repressor TF to the 143

double mutant myb28 myb29 that abolish GLS accumulation, including myb28 myb29 ant, 144

myb28 myb29 zfp4, myb28 myb29 zfp7 and myb28 myb29 hb21. All of the mutants were 145

validated as being homozygous for the specific genotype and grown concurrently with the wild-146

type and single mutant genotypes to age match all seed stocks. 147

148

Epistatic networks mediating GLS traits 149

To test the genotypes for epistasis, we measured leaf and seed GLS contents for the wild-150

type control, all single mutants and all double and triple mutants in two different chambers using 151

a randomized complete block design, as previously described (Li et al., 2014a). The two growth 152

chambers have a controlled abiotic environment but contain different biotic environments. The 153

CEF (Controlled Environment Facility) chamber is maintained as pest free, while the LSA (Life 154

Sciences Addition) chamber has an endogenous pest population provided by continuous 155

propagation of tomato and brassica plants. This generates a mix of mites, aphids, flea beetles, 156

and fungus gnats in the LSA chamber and allows us to test for the effect of a blend of biotic 157

interactions rather than specific biotic interactions. Measuring GLS in two tissues and two 158

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environments has previously enhanced our ability to identify significant effects and test if they 159

are tissue or environmentally sensitive (Li et al., 2014a). We then used all measured phenotypes 160

with linear models to specifically test for epistatic interactions (Supplemental Data Set 3-5). For 161

the ensuing analysis, we focused on 5 summary variables that describe the majority of the 162

variance in aliphatic GLS (Wentzell et al., 2007). These summary GLS traits are as follows: 1) 163

accumulation of short chain GLS (SC GLS), the sum of the 3 carbons and 4 carbons side chain 164

aliphatic GLS; 2) accumulation of long chain GLS (LC GLS), the sum of the 7 carbons and 8 165

carbons side chain aliphatic GLS; 3) accumulation of indolic GLS, the sum of all indolic GLS; 4) 166

GLS Elong, the percentage of 3 carbons GLS in SC GLS, which is an indication of the enzyme 167

activities of the elongation cycle (Haughn et al., 1991; de Quiros et al., 2000; Kroymann et al., 168

2003); 5) GLS OX, the percentage of 4MT to the total of all GLS with 4 Carbons, an indication 169

of the GLS OX enzyme activities (Hansen et al., 2007; Li et al., 2008; Li et al., 2011) . These 170

five traits are quantifiable in all tissues and environments and allow an analysis of distinct 171

biochemical processes within the pathway. 172

Mapping the epistatic interactions of the TFs based on the different phenotypes 173

highlighted several patterns. First, there were differences in the frequency of epistasis, with SC 174

GLS having significant epistasis for 42 of 47 pairs of interactions compared to only 11 of 42 for 175

indolic GLS (Figure 2A and 2D). Importantly, there were differences in the pattern and 176

conditionality of epistasis between the pathway (SC GLS) and specific enzyme activity estimates 177

of enzymes (GLS OX and GLS Elong). For the whole pathway, most epistatic interactions were 178

independent of the tissue or environment in which the phenotype was measured (Figure 2A). In 179

contrast, the majority of epistatic interactions for the inferred enzymatic activities within the 180

pathway were highly dependent upon the tissue (Figure 2B and C). This difference in epistatic 181

patterns between parts of the pathway agrees with the previous observation that the TFs regulate 182

a subset of steps in the pathway and not the whole pathway. Thus, there is significant epistasis in 183

all 4 phenotypic categories tested in this collection, and this epistasis shows distinct properties 184

and unique patterns from the level of enzyme activity to metabolite accumulation. 185

186

Genetic variance controlled by epistasis 187

To obtain more quantitative insights into how epistasis influences the traits, we estimated 188

the genetic variance that could be ascribed to epistasis (Figure 3, Supplemental Data Set 4). As 189

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expected from the high incidence of epistasis, metabolite accumulation from the SC GLS 190

pathway has the highest fraction of genetic variance present in the TF x TF epistatic term (SC 191

GLS in Figure 3A, D). In contrast, most of the variance attributable to epistasis for the enzyme 192

activity traits was in the conditional TF x TF x Environment or x tissue terms (GLS Elong and 193

GLS OX in Figure 3B and E). To visualize how the epistatic variance was influenced by the 194

network topology, we mapped the epistatic variance for the metabolite accumulation for SC GLS 195

(Figure 3G). This plot showed that the proposed master regulatory TFs were not the key drivers 196

of the epistatic network. For example, the major regulator of SC GLS, MYB28, does not play a 197

key role in shaping the epistatic network. In contrast, MYB29, which is typically considered to 198

have a less significant role than MYB28 in regulating SC GLS accumulation, has a much stronger 199

effect than MYB28 in the epistatic networks (Figure 3G). In contrast, ERF107, RAP2.6L and 200

ZFP7, which have relatively small single mutant phenotypic effects, had major epistatic roles 201

(Figure 3G). This suggests that the phenotypic consequences of single gene mutants are not 202

sufficient to predict epistatic importance as genes. Thus, to fully understand a genetic network, 203

both large and small effect loci should be analyzed when directly testing for epistasis. 204

205

Quantification of epistasis indicates a bipartite regulatory system 206

The detection of epistasis does not allow us to distinguish between different types of 207

epistatic effects. The epistatic effects on an individual trait could be synergistic, by which the 208

phenotypic value of double mutants was higher than the linear combination of single mutants. 209

Alternatively, the epistatic effects could be antagonistic epistasis, by which the phenotypic value 210

of double mutants was lower than the linear combination of single mutants (Hartman et al., 2001; 211

Segre et al., 2005; Costanzo et al., 2010; Costanzo et al., 2016). To differentiate between these 212

forms of epistasis, we subtracted the measured double mutant phenotype from the predicted 213

double mutant phenotype under an additive model. This value was then normalized to the wild 214

type phenotype to develop an epistasis value. This epistasis value was measured for each pair of 215

mutants for each trait that was measured (Supplemental Figure 1-4 and Supplemental Data Set 5. 216

This epistasis value will be positive when there is synergistic epistasis and negative for 217

antagonistic epistasis (Figure 4). 218

Using this approach, we found that for metabolite accumulation in the SC GLS pathway, 219

almost all of the epistasis was antagonistic epistasis, with larger values in leaves versus seeds 220

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(Figure 5). Comparing the epistasis value when testing pairs of TFs that come from the same 221

phenotypic cluster versus different phenotypic clusters showed that there was no difference in 222

these groups. This is in contrast to yeast primary metabolism, where there was more epistasis in 223

crosses from different clusters than from crosses involving genes within a cluster (Segre et al., 224

2005). Of the comparisons, the only crosses that were significantly different were the crosses to 225

the putative master regulators myb28 or myb29. Thus, the new TFs largely interact additively 226

with the previously identified MYBs while epistatically interacting with each other to control 227

metabolite accumulation (Figure 5). This absence of epistasis between the new TFs and the 228

MYBs suggests that there may be a buffering structure that allows the two groups of TFs to 229

function independently. 230

In contrast to the exclusively antagonistic epistasis for SC GLS, for GLS-Elong and GLS-231

OX, epistatic effects could shift from synergistic to antagonistic depending on the tissue and the 232

enzyme activity being measured (Figure 6). For GLS Elong, there were conditional effects with 233

predominantly antagonistic epistasis in leaf tissue and synergistic epistasis in seed tissue, while 234

GLS-OX showed antagonistic versus synergistic epistasis within the leaf in the two different 235

environments (Figure 6). Similar to the situation for metabolite accumulation, there was no 236

difference in the level of epistasis within and between TF clusters (Figure 6). One possible 237

explanation for the difference in epistatic patterns is that the enzymatic activities are expressed as 238

ratios while metabolite accumulation is expressed as absolute abundance. Arguing that these 239

ratios are providing biological insight is the observation that both ratio traits, GLS-Elong and 240

GLS-OX, show differential epistasis in the leaf samples between the two environments. If this 241

were solely a mathematical issue, these traits should show similar behaviors. This argues that 242

the same set of TFs have distinct patterns of epistatic effects upon different components of the 243

same metabolic pathway, as the two estimated enzyme activities have opposing patterns and the 244

resulting total accumulation of the compounds in this pathway have a distinct antagonistic 245

epistasis. This suggests that epistasis of TFs is frequent within the aliphatic GLS pathway and 246

that the effects of this epistasis change depending upon the specific portion of the pathway being 247

measured. 248

249

Absence of aliphatic GLS in triple mutants between repressors and activators 250

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The observation that the putative master regulators, MYB28 and MYB29, had limited 251

epistasis with the rest of the TFs suggested that they function largely additively with the other 252

TFs. Considering that the MYBs are activators, as the myb28 myb29 double mutant has no 253

aliphatic GLS while the other TFs are largely repressors (mutants have higher GLS), we 254

hypothesized that triple mutants should be additive. Thus, the triple mutant under this hypothesis 255

should restore detectable aliphatic GLS. To test this hypothesis and to assess if it is possible to 256

roughly predict triple mutant interactions from pairwise combinations, we generated 4 triple 257

mutants, myb28 myb29 ant, myb28 myb29 zfp4, myb28 myb29 zfp7 and myb28 myb29 hb21, 258

because ANT, ZFP4, ZFP7 and HB21 are negative regulators that function in different parts of 259

the pathway and have strong effects on aliphatic GLS accumulation. Measuring aliphatic GLS 260

accumulation in the triple, double and single mutants in all 4 contrasting conditions showed that 261

none of the triple mutants rescued the accumulation of aliphatic GLS in any tissue or condition. 262

This included 4MSO GLS, the most abundant aliphatic GLS in leaf tissue, and 4MT GLS, the 263

most abundant aliphatic GLS in seeds (Figure 7, Supplemental Figure 5 and Supplemental Data 264

Set 5). In contrast to the pairwise epistasis analysis, which suggested additivity, this indicates 265

that all four TFs show classical recessive epistasis to the master regulators and require a 266

functional MYB28 and MYB29 to induce GLS metabolite accumulation. Thus, these additional 267

TFs do genetically interact with both MYB28 and MYB29 to influence SC GLS accumulation, in 268

contrast to the pairwise mutant evidence, and they appear to function downstream of the MYBs. 269

270

Presence of aliphatic GLS transcripts in triple mutants between repressors and activators 271

The above epistatic analysis is solely built upon measuring the accumulation of the metabolites, 272

raising the question of how this may be reflected in transcript accumulation, which should be 273

more proximal to the TFs. To test if the underlying transcriptional changes mirrored the 274

metabolic consequences, we conducted transcriptomic analysis of the ant myb28 myb29 275

genotypes. We used RNAseq to measure the transcript abundance of all known enzyme-276

encoding genes in the GLS pathway in WT and all single, double and triple mutants using leaf 277

tissue. This was done using leaf tissue from the CEF chamber where the strongest three-way 278

epistasis between these genes occurs (Figure 8 and Supplemental Data Set 6). The transcript 279

analysis showed that, as previously observed, myb28 had a stronger effect than myb29 on 280

pathway transcript abundance (Sønderby et al., 2007; Sønderby et al., 2010c) In contrast to the 281

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observation that ANT is a repressor of the metabolite, ant had contrasting effects on the 282

transcripts, with some showing higher and some lower accumulation in the single ant mutant 283

versus WT (Figure 8B, C, D; i.e. compare GS-OX2 versus GS-OX3). Supporting the idea that 284

epistasis can change dependent on the molecular traits (i.e., transcripts vs. metabolites), ant 285

showed more epistasis with myb28 for the transcripts, while ant was mainly epistatic to myb29 286

for the metabolites (Figure 8). Further support came from the observation that ant is able to 287

induce the transcription of pathway genes in the myb28 myb29 double mutant in contrast to its 288

dependency for metabolite accumulation (Figure 8B-E). Thus, ANT can function at least in 289

parallel to MYB28 MYB29 to regulate transcript levels while genetically, it appears to occupy a 290

downstream role in metabolite accumulation. This suggests that the epistasis between these TFs 291

is likely a complex blend of potential direct interactions and indirect interactions that could be 292

caused by the metabolites’ accumulation being constrained by the structure of the biosynthetic 293

pathway and the relative fluxes of the different enzymes. As such, the epistasis measured at the 294

metabolite level would be the result of how the promoter-level epistasis is translated to enzyme 295

activity epistasis and correspondingly how this equates to the accumulation of the final 296

metabolite. This suggests that molecular models of epistasis at the level of how TFs do or do not 297

interact with each other at a single promoter may not translate to the prediction of metabolite 298

accumulation within a single pathway. 299

300

DISCUSSION 301

In this work, we used a large network of TFs that regulate aliphatic GLS biosynthesis to 302

measure pair-wise and higher-order epistatic interactions. This pathway had previously been 303

identified as having master regulators of GLS biosynthesis - MYB28 MYB29 (Hirai et al., 2007a; 304

Traka et al., 2013). In contrast, we previously identified a large collection of TFs that had much 305

more modest impacts on the pathway via binding distinct subsets of the promoters in the 306

pathway (Li et al., 2014a; Gaudinier et al., 2015). Within this network, the vast majority of TFs 307

showed pairwise epistasis. The strongest interactions involved interactions among TFs with 308

smaller single mutant effects, while the putative master regulatory MYB had the lowest level of 309

epistasis. For a related pathway that involves some overlap but also a distinct set of TFs and 310

enzyme-encoding genes, indolic GLS, there was far lower epistasis, suggesting that the 311

prevalence of epistasis is not caused by indirect pleiotropies but is a general property of the 312

12

specific network being studied. The epistasis detected changed in terms of frequency, direction 313

and strength depending on how the network’s phenotype was measured, initial transcript 314

abundance, intermediate enzyme activity estimates or final metabolite accumulation. This 315

suggests that epistasis may be a common feature of large regulatory networks that influence 316

adaptive traits. Further, the idea of a master regulator does not appear to translate into a central 317

function within an epistatic network. Instead, it appears that epistasis is favored within a 318

collection of small to moderate effect TFs that interact with partially overlapping sets of 319

promoters within the pathway. This epistasis needs to be assessed when working to develop 320

predictive models that translate from single gene studies to higher order studies on entire 321

networks. 322

323

Epistasis varies across a network’s molecular hierarchy from transcript to metabolite 324

Biochemical pathways can be considered to have multiple traits that can be measured 325

across the hierarchy proceeding from a gene encoding an enzyme to the accumulation of the final 326

metabolite. This includes measuring the transcript abundance for each and every gene in the 327

pathway, approximating enzyme activities linked to these transcripts and finally measuring the 328

detectable metabolites produced by the pathway. Frequently, regulatory studies focus on the TF 329

to transcript link, with the implicit assumption that the other steps in this hierarchy will 330

inherently follow the same logic. However, with post-transcriptional regulatory processes at the 331

RNA, protein and activity level in addition to the competition by other metabolic processes for 332

the same precursor compounds and energy, this is not inherently the case. This is especially the 333

case when most TFs are not limited to regulating a single metabolic pathway but instead have 334

numerous other regulatory links. As such, it is an open question how regulatory processes may 335

translate from gene to enzyme to metabolite in a complex multicellular organism. We measured 336

transcripts and metabolites and inferred enzymatic activities to test how genetic epistasis changes 337

through the molecular hierarchy of the aliphatic GLS pathway. This showed that the epistasis 338

was highly dependent upon the specific molecular step being measured. While the metabolite 339

and enzymatic activity steps had a similar frequency of epistasis, they showed differing 340

sensitivity to the environment and development. The metabolites revealed epistatic effects that 341

were consistent across tissues and environment, while the enzymatic efficiencies were largely 342

conditional (Figures 4 and 6). As a more focused example, we summarized all the epistatic 343

13

information linked to the ant myb28 myb29 combination of genotypes (Figure 9). This illustrates 344

how main effects and epistatic interactions shift from transcript to metabolite. ANT has no main 345

effect on methylthioalkylmalate synthase (MAM )transcripts but is a key player in the resulting 346

GLS Elong activity and SC GLS accumulation. Similarly, ANT had significant main effects on 347

GS-OX transcripts, but this was only displayed as epistatic interactions with no main effect on 348

GLS OX efficiency. This shows that epistasis within large regulatory networks can have 349

contrasting effects, depending upon the specific molecular output being measured. 350

351

Naive pair-wise tests find high levels of epistasis 352

Within this study, we attempted to cross the majority of available TF mutants known to 353

affect aliphatic GLS regardless of mechanistic or homology information. In most published 354

epistasis tests, the choice of mutants to cross is frequently guided by the genes having a known 355

function in the same regulatory pathway. Alternatively, the genes may be chosen based on their 356

membership in a gene family, and the mutants are crossed to test for redundancy. While these 357

guided approaches frequently reveal epistasis, they do not test for how often epistasis occurs 358

outside of these guiding rules. In this work, the majority of the interactions involve TFs 359

belonging to different TF families, and there is little to no mechanistic information suggesting 360

that they function in a single pathway (Figure 1). Thus, the explicit goal of this design was to test 361

for epistasis between TFs that may function independently and are only connected by influencing 362

the same trait. This naïve design identified a high level of epistasis, including connections 363

between processes not typically linked. For example, ANT and ILR3 show significant epistasis 364

but are typically considered to function in different biological processes. ANT belongs to the 365

AP2/EREBP TF family and controls cell proliferation (Elliott et al., 1996; Krizek et al., 2000; 366

Liu et al., 2000; Mizukami and Fischer, 2000; Horstman et al., 2013), while ILR3 belongs to the 367

bHLH TF family and controls responses to iron deficiency (Rampey et al., 2006; Long et al., 368

2010). Our results suggest that these two TFs somehow have regulatory effects that interact to 369

modulate GLS accumulation. More intriguing is the idea that if this interaction is not specific to 370

GLS, is it possible that ANT and ILR3 have epistatic interactions affecting the regulation of cell 371

proliferation and/or responses to iron deficiency? This raises the potential that conducting naïve 372

crosses of TF mutants may open up a unique avenue to investigate how processes such as growth, 373

14

nutrient acquisition and biotic resistance are coordinated across large regulatory networks that 374

are frequently studied in isolation. 375

376

Epistasis, Heritability and Fitness 377

As GLS accumulation is an adaptive trait, it is tempting to attempt and translate these 378

results directly to their potential fitness consequences. Mathematically, this is relatively simple 379

using the equation 𝑅 = ℎ2𝑆, where the response to any selection, R, is the additive heritability, 380

h2, multiplied by the strength of selection, S (Falconer and Mackay, 1996; Mackay, 2001, 2014). 381

In our system, the total variance controlled by any genetic term averaged approximately 30% 382

across the models, but this included all terms, both additive and non-additive. The typical single 383

gene additive heritability was approximately 5%, which would suggest that this system may have 384

small effects on fitness in Arabidopsis. However, this direct comparison is complicated by 385

several factors. The first is that by measuring GLS in multiple environments and multiple tissues, 386

we are constraining our estimate of heritability. In these models, tissue and environment and 387

their interaction averaged ~50% of the total variance, which places a constraint on additive 388

heritability. The second complication is that the above equation largely relies on the species 389

being a random outbreeding population, whereas Arabidopsis is species that has a low level of 390

outbreeding, which typically occurs within narrow local populations (Charlesworth et al., 1997; 391

Nordborg et al., 2002). As such, in this species, non-additive epistatic variance may actually 392

contribute more to selection responses than the simple equation would suggest (Rieseberg et al., 393

1999; Rieseberg et al., 2003). 394

A final complication in this direct comparison is that in this analysis, there is a built-in 395

assumption that because the GLS are the same compounds in the leaf and seed, they must be a 396

single trait. However, fitness effects in a complex environment may actually suggest that leaf and 397

seed GLS are distinct traits. This is best exemplified by the non-random distribution of biotic 398

attackers across the different tissues and their potential differential sensitivity to specific 399

glucosinolates (Jander et al., 2001; Kim and Jander, 2007; Hansen et al., 2008). For example, 400

most lepidopteran larvae focus on eating the leaves of Arabidopsis while granivores, i.e., seed-401

predators such as weevils, solely focus on the developing seed. As such, if fitness was driven by 402

lepidopteran larvae, then only the leaf GLS effects would contribute to fitness. In contrast, if 403

granivores were the dominant selective pressure, then only the seed GLS would contribute to 404

15

fitness. In both cases, the heritability estimated across both tissues would be inaccurate for 405

predicting responses to selection in these environments. Thus, there is a significant amount of 406

work needed to understand the complexity of the biotic interaction driving plant adaptation and 407

equally, the complexity of the underlying genetic network controlling this response. 408

409

Genetic network size for a single metabolic pathway? 410

Within this project, we tested the epistasis of 20 TFs that have been linked to the 411

accumulation of aliphatic GLS. While this is a large collection, this does not represent anywhere 412

near the true scope of the regulatory network. The original Y1H analysis that focused solely on 413

root stele-expressed TFs suggested that there were likely dozens of other TFs that influence the 414

accumulation of this metabolite but that were not able to be tested (Gaudinier et al., 2011; Li et 415

al., 2014b). Furthermore, genome-wide association studies carried out to estimate the size of the 416

genetic networks influencing GLS accumulation regardless of gene activity suggested that there 417

were at least hundreds of likely genes that causally influence this pathway (Chan et al., 2010; 418

Chan et al., 2011). In the natural variation studies, there are also extensive epistatic interactions 419

amongst the identified loci (Kliebenstein et al., 2002a; Kliebenstein et al., 2002b; Wentzell et al., 420

2007). Together, these findings suggest that the genetic network influencing this metabolite is 421

vastly larger than 20 TFs and 34 promoters. Yet these crosses identified a high level of epistasis 422

within this sub-network. This raises the question of how this might translate to a complete 423

genetic network for aliphatic glucosinolates and all possible epistatic combinations within that 424

network. This would go far beyond three-way epistatic interactions and require vast experimental 425

populations. The other key question is how this translates to other metabolic pathways or other 426

biological processes. Is this a unique property of metabolites that provide adaptation to biotic 427

stresses, or is this a general property of metabolism and/or genetic networks in a multi-cellular 428

organism? 429

430

METHODS 431

Plant materials 432

The Arabidopsis thaliana T-DNA insertion lines of the 20 TFs were initially ordered from 433

Arabidopsis Biological Resource Center (Sussman et al., 2000; Alonso et al., 2003) and 434

validated as homozygous in previous studies (Supplemental Data Set 2) (Sønderby et al., 2010c; 435

16

Li et al., 2014b). The 47 double mutants and 4 triple mutants were generated by crossing the 436

corresponding single mutants and validating the double mutant homozygosity in the F2 437

generation using PCR-based markers for each mutant. The confirmed homozygous double and 438

triple mutants were grown together with single mutants and wild type to bulk seeds and provide 439

matching seed batches for the downstream GLS profiling experiments. 440

441

Plant growth conditions 442

The Arabidopsis plants were grown in two independent chambers with 16-h light at 100- to 120-443

µEi light intensity for the GLS profiling experiment. Both growth chambers were set at a 444

continuous 22ºC and utilized high output fluorescent bulbs. These were the same growth 445

chambers and conditions as utilized in the original report of these TFs. The use of two growth 446

chambers allowed for a test of biotic environmental effects. The two growth chambers were set 447

to identical abiotic environments but contain dramatically different biotic environments, one pest 448

free, CEF, and one with an endogenous pest population, LSA. The endogenous pest population is 449

provided by continuous propagation of tomato and brassica plants that generates a mix of mites, 450

aphids, flea beetles, and fungus gnats. This is not meant to test a specific biotic interaction but 451

the general effect of biotic interactions with a blend of biotic interactions. This use of the clean 452

chamber CEF and stress chamber LSA increases our ability to detect significant GLS phenotypes 453

conditioned on the variation in environmental factors (Li et al., 2014a). Briefly, seeds were 454

imbibed in water at 4°C for 3 days and sown into Sunshine Mix 1(Sun Gro Horticulture). 455

Seedlings were thinned to one plant per pot (6cm x 5cm) at 7 d after planting. For each 456

experiment, at least 8 replicates of Col-0, single, double and triple mutant were planted using a 457

randomized complete block design. Each flat had one plant per genotype leading to eight flats 458

per replication. This experiment was conducted independently in the clean CEF and stress LSA 459

chamber to generate a minimum of 16 biological repeats in total for most of the genotypes. This 460

design means that each individual biological replicate is derived from a single independent plant 461

that was planted in a randomized design. 462

463

Glucosinolates extraction and analysis 464

The harvest and collection of plant samples for GLS analysis were performed as described before 465

(Kliebenstein et al., 2001a; Kliebenstein et al., 2001b; Kliebenstein et al., 2001c). Briefly, one 466

17

fully mature leaf from each 4-week-old plant was removed, placed in 400 µl 90% (v/v) methanol 467

and stored at -20°C before extraction. The plants finished their life cycle and the seeds were 468

harvested. 40 seeds from each plant were counted and stored at -20°C before extraction. The 469

samples were broken with 2 2.3mm metal ball bearings in a paint shaker at room temperature 470

and incubated at room temperature for 1 hour. The tissues were pelleted by centrifugation for 15 471

minutes at 2500 g and the supernatant was used for anion exchange chromatography in 96-well 472

filter plates. After methanol and water washing steps, the columns were incubated with sulfatase 473

solution overnight. Desulfo-GLS were eluted and analyzed by HPLC according to a previously 474

described method (Kliebenstein et al., 2001d). 475

476

Statistics 477

To test for epistasis of the TFs in controlling GLS biosynthesis, the GLS phenotypes for each 478

epistatic combination were separately analyzed by ANOVA using a general linear model by SAS. 479

The following model was used to test for the epistasis for the GLS phenotypes in the double 480

mutants, with each double mutant having both single mutants and wild type grown concurrently: 481

yabtc = µ + Aa +Bb + Tt + Chc + AaxBb + AaxTt + AaxChc + BbxTt + BbxCc + Tt xChc + AaxBbxTt 482

+ AaxBbxChc + BbxTtxChc+ AaxTtxChc + AaxBbxTtxChc + εabtc, where εrgt is the error term and is 483

assumed to be normally distributed with mean 0 and variance σε2. In this model, yabtc denotes the 484

GLS phenotype in each plant, Genotype A represents the presence or absence of a T-DNA insert 485

in one TF gene (WT versus mutant of locus A), and Genotype B represents the presence or 486

absence of a T-DNA insert in another TF gene (WT versus mutant of locus B) in the double 487

mutant from tissue Tt (Leaf or Seed) and Chamber Chc (Clean CEF chamber or Stress LSA 488

Chamber). The ANOVA table, least-square (LS) means and standard error for each genotype x 489

tissue x treatment combinations were obtained using SAS. The type III sums-of-squares from 490

this model were used to calculate the variance and percent variance attributable to each term in 491

the model. For the percent variance, this was calculated by comparing to the total variance in the 492

model as the denominator. All network representations were generated using Cytoscape.v2.8.3 493

(Shannon et al., 2003). 494

For all metabolites, we utilized the absolute abundance for all calculations, as the 495

residuals for these tests were normally distributed in all but the model testing myb28/myb29 496

epistasis, which displays classical recessive epistasis. Adjusting to a log scale did not improve 497

18

the residuals in the myb28/myb29 epistatic model and as such, we maintained the most direct link 498

to the absolute metabolite abundance for the other pairwise models by using the absolute 499

abundance. In addition, we utilized an additive rather than a multiplicative scale for the epistatic 500

tests because natural Arabidopsis accessions and recombinant inbred lines generated with Col-0 501

crosses can accumulate up to 20 fold more glucosinolate metabolites than the Col-0 accession. 502

This suggests that there is not a physiological maxima that we are approaching in our data that 503

would necessitate a multiplicative scale (Kliebenstein et al., 2001b; Kliebenstein et al., 2001c; 504

Kliebenstein et al., 2002a; Chan et al., 2010; Chan et al., 2011). Transcripts were tested for 505

epistasis using log-adjusted normalized expression values, as is the standard requirement for 506

transcriptomic analysis. The ratio tests for the GS-OX and GS-Elong locus were also conducted 507

using the unscaled data, as like for the metabolite, the residuals in these tests were largely 508

normally distributed. These ratios are derived by having the value in the numerator (4-509

methylthiobutyl for GS-OX) also in the denominator to force the ratio to be between 0 and 1. In 510

our previous work, we have found that these two ratios are largely uncorrelated with the absolute 511

content of any specific glucosinolate, allowing for them to behave as independent variables 512

focused on the specific enzymatic step in question (Kliebenstein et al., 2001c; Kliebenstein et al., 513

2002a; Wentzell et al., 2007; Chan et al., 2010; Chan et al., 2011). 514

515

Calculation of epistasis value 516

To study the effect of epistasis, we utilized an algebraic approximation describing the direction 517

and strength of the epistasis by normalizing the difference of observed double mutant phenotype 518

versus the predicted double mutant phenotype, assuming additivity of the single mutants. This 519

epistasis value was then normalized to the wild type, as done with other epistasis terms (Segre et 520

al., 2005). The phenotype for WT was set as w, mutant TFa as a, mutant TFb as b, and double 521

mutant TFa/TFb as ab. The Epistasis Value is calculated as (ab - (w + (a-w) + (b-w))/w). If the 522

epistasis value is positive, this shows evidence for synergistic epistasis, while antagonistic 523

epistasis is reflected in negative values. The larger the epistasis value, the stronger the epistasis 524

effects. The Epistasis Value were further visualized using the iheatmapr package in R software 525

(R Development Core Team, 2015). 526

527

RNAseq Analysis 528

19

Arabidopsis plants, including Col-0, myb28, myb29, ant, myb28 ant, myb29 ant, and myb28529

myb29 ant, were grown in CEF clean chambers in a randomized complete block design using 530

two independent experiments. Leaves were harvested from two individual plants per genotype 531

from each experiment and used to make four independent RNAseq libraries per genotype. Total 532

leaf RNA was extracted using Trizol (Invitrogen) and stored at -70°C before constructing the 533

library. The RNA sequencing libraries were created with a QuantSeq 3’ mRNA-Seq Library Prep 534

Kit (Lexogen). Each library had unique indexing primers, and the libraries were pooled and 535

sequenced on the HiSeq4000 platform at UC Davis DNA Technologies Core Facility. Fastq files 536

from individual HiSeq lane were separated by adapter index into individual libraries. The 537

alignment and gene counting were done with the BlueBee pipeline accompanying the Lexogen 538

kit using the A. thaliana (TAIR10) Lexogen QuantSeq 2.2.1 FWD reference genome. Statistical 539

analysis of the RNAseq data was conducted using the R V3.4.1 statistical environment (R 540

Development Core Team, 2015). The gene count data from RNASeq were subjected to a 541

previously described statistical approach (Zhang et al., 2017). Normalization on gene counts was 542

first conducted using the TMM method in function calcNormFactors() from the edgeR package 543

(Robinson and Smyth, 2008; Robinson et al., 2010; Robinson and Oshlack, 2010; Nikolayeva 544

and Robinson, 2014), and normalized pseudo-counts were then obtained for downstream analysis. 545

The linear model was conducted on normalized gene counts using function glm.nb() from the 546

MASS package (Venables, 2002). Model-corrected means and standard errors for each transcript 547

was determined using the lsmeans V2.19 package (Lenth, 2016). Raw p-values for F- and ChiSq-548

tests were determined by Type III sums of squares using the function anova () from the car 549

package (Fox and Weisberg, 2011). Transcript p-values were False discovery rate (FDR) (p-550

value < 0.05) corrected for multiple tests of significance (Benjamini et al., 2001; Strimmer, 551

2008). 552

553

Accession numbers 554

Transcriptomics data from this article could be found in Supplemental Data Set 6. 555

The accession numbers for the genes analyzed are as follows: MYB28, AT5G61420. MYB29, 556

AT5G07690. ANT, AT4G37750. ILR3, AT5G54680. ZFP4, AT1G66140. ERF107, AT5G61590. 557

AT5G52020, AT5G52020. CBF4, AT5G51990. GBF2, AT4G01120. HMGBD15, AT1G04880. 558

ZFP7, AT1G24625. HB21, AT2G02540. RAP2.6L, AT5G13330. HB34, AT3G28920. NAC102, 559

20

AT5G63790. ERF9, AT5G44210. ATE2F2, AT1G47870. ABF4, AT3G19290. DF1, 560

AT1G76880. AT2G44730, AT2G44730. MYB76, AT5G07700. MYC2, AT1G32640. MYC3, 561

AT5G46760. MYC4, AT4G17880. 562

563

Supplemental Data 564

Supplemental Figure 1. Epistatic effects for GLS Elong 565

Supplemental Figure 2. Epistatic effects for GLS OX566

Supplemental Figure 3. Epistatic effects for LC GLS 567

Supplemental Figure 4. Epistatic effects for indolic GLS 568

Supplemental Figure 5. The absence of aliphatic GLS in triple mutants between repressor TFs 569

and myb28/myb29570

Supplemental Data Set 1. Glucosinolate abbreviations, descriptions, and chemical structures. 571

Supplemental Data Set 2. The selected transcription factors and their T-DNA insertion lines.572

Supplemental Data Set 3. P-values of the ANOVA of the double and triple mutants.573

Supplemental Data Set 4. The Sum of Squares of the ANOVA of the double and triple mutants.574

Supplemental Data Set 5. LS means of the ANOVA of the double and triple mutants.575

Supplemental Data Set 6. RNAseq analysis of the full set of ant-related mutants. 576

577

ACKNOWLEDGMENTS 578

We thank Wei Zhang for the discussions and help with the use of the pipeline to analyze the 579

RNA sequencing data. This effort was funded by NSF grants MCB1330337 to SMB and DJK 580

and DBI 0820580 to DJK, the USDA National Institute of Food and Agriculture, Hatch project 581

number CA-D-PLS-7033-H to DJK and by the Danish National Research Foundation DNRF99 582

grant to DJK, the NSF GRFP to MT via NSF DGE 1148897 to Jeffery C. Gibeling, Dean and 583

Vice Provost of Office of Graduate Studies at UC Davis, and the UC Davis Department of Plant 584

Sciences Jastro Shields Research Award to MT. 585

586

AUTHOR CONTRIBUTIONS 587

BL and DJK conceived and designed the experiments. BL, MT, AN, HC, XZ, CCW, and RN 588

performed the experiments. BL and DJK analyzed the data. BL, MT, SMB, and DJK wrote the 589

paper. 590

21

591

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885

886

28

Figure Legends 887

Figure 1. Genetic networks under investigation 888

The 20 TFs under investigation are shown as nodes. Lines connecting TFs show where double 889

mutants were generated. The labeled clusters A to G represent the previously identified 890

phenotypic sub-networks for these TFs. Large-effect TFs that were not linked to specific clusters 891

are shown in the centers of the networks as MYB28, MYB29 and ANT. The 4 different epistatic 892

groups are highlighted with different colors: within-cluster epistatic tests are shown in lime green, 893

between-cluster epistatic tests are shown in dark orange, MYB epistatic tests are shown in grey, 894

and ANT epistatic tests are shown in black. 895

896

Figure 2. Epistatic networks controlling GLS traits 897

The genetic network from Figure 1 is used to represent the significant epistatic interactions for 898

the four major GLS traits. Only connections that show significant epistasis are maintained, while 899

non-significant connections are dropped from the network. A solid line shows that only the TF x 900

TF interaction term was significant in the ANOVA model. A dashed line shows epistatic 901

interactions where there was a significant Tissue x TF x TF interaction. A dotted line shows 902

epistatic interactions where there was a significant Environment x TF x TF interaction. A line of 903

arrows shows that the interaction was conditional on both Tissue and Environment. The color of 904

the node indicates which main effect terms are significant for the individual TFs; sky blue 905

indicates only a TF main effect, orange indicates only a tissue x TF interaction, purple indicates 906

both TF and tissue x TF, yellow indicates both TF and treatment x TF, and red indicates that all 907

three terms are significant. Grey indicates that no terms for the individual TF were significant. 908

A: Epistatic network for SC GLS 909

B: Epistatic network for GLS OX 910

C: Epistatic network for GLS Elong 911

D: Epistatic network for indolic GLS 912

913

Figure 3. Distribution of genetic variance ascribed to distinct model terms 914

The variances attributable to all terms including a genetic factor were summed together, and the 915

percentage of this total genetic variance ascribed to each genetic term was calculated, as shown 916

29

in violin plots. The median in each violin is shown as a dot. The phenotypes tested are shown 917

individually, as labeled on the X-axis. 918

A: Percent of genetic variation controlled by individual TF main effects. 919

B: Percent of genetic variation controlled by tissue x TF interactions. 920

C: Percent of genetic variation controlled by treatment x TF interactions. 921

D: Percent of genetic variation controlled by TF x TF epistatic interactions. 922

E: Percent of genetic variation controlled by tissue x TF x TF epistatic interactions. 923

F: Percent of genetic variation controlled by treatment x TF x TF epistatic interactions. 924

G: Visualization of individual epistatic variance components within the genetic network for SC 925

GLS. The width of the line connecting 2 TFs is proportional to the variance linked with the TF x 926

TF term for that specific interaction. The highest proportion is the interaction between 927

HMGBD15 and HB34, with 49% of the total genetic variance. Solid lines show that there was a 928

significant interaction term, while dashed lines show combinations with no significant 929

interactions. 930

931

Figure 4. Synergistic and antagonistic epistatic patterns in SC GLS accumulation 932

The levels of SC GLSs in the corresponding genotypes and tissues are shown. Different letters 933

show genotypes with significantly different SC GLS levels (p <0.05 using post-hoc Tukey’s test 934

after ANOVA). The p-value of the epistatic interaction and the epistasis value are shown on the 935

plots. Standard error is shown with 16 samples across two experiments for each genotype. 936

A: erf9 x rap2.6l antagonistic epistasis for SC GLS accumulation in leaves from the stress LSA 937

chamber. 938

B: ant x hmgbd15 synergistic epistasis for SC GLS accumulation in seeds from the clean CEF 939

chamber. 940

941

942

Figure 5. Epistatic effects for SC GLS 943

Epistasis values were calculated for all pairwise combinations individually in all treatment and 944

tissue combinations. For B-E, different letters indicate statistically different average epistatic 945

values across the cluster tests, as determined by ANOVA at p-value of 0.05. 946

30

A: Epistasis values for all pairwise mutant combinations plotted in a heatmap using hierarchical 947

clustering; the gene combinations are listed to the right of the diagram. The first vertical column 948

shows if the pairwise mutant interaction is testing epistasis from within a cluster (green), 949

between clusters (orange), MYB (grey) or ANT (black). The next three columns show which 950

epistatic interaction term is significant (purple) or not significant (grey) (ANOVA, P < 0.05). 951

B: Average and standard error of epistatic value for all pairwise mutant combinations measured 952

from leaf samples from the clean CEF chamber. 953

C: Average and standard error of epistatic value for all pairwise mutant combinations measured 954

from leaf samples from the stressed LSA chamber. 955

D: Average and standard error of epistatic value for all pairwise mutant combinations measured 956

from seed samples from the clean CEF chamber. 957

E: Average and standard error of epistatic value for all pairwise mutant combinations measured 958

from seed samples from the stressed LSA chamber. 959

960

Figure 6. Epistatic effects for GLS Elong and GLS OX 961

Epistasis values were calculated for all pairwise combinations individually in all treatment and 962

tissue combinations. Different letters indicate statistically different average epistatic values 963

across the cluster tests, as determined by ANOVA at p-value of 0.05. The average and standard 964

error of the epistatic values for each group of epistatic groups are shown in all boxplots, with 965

orange showing between cluster-crosses, green showing within-cluster crosses, grey showing 966

crosses involving MYB and black showing crosses involving ANT. 967

A: GLS Elong epistatic value for all pairwise mutant combinations measured from leaf samples 968

from the clean CEF chamber. 969

B: GLS Elong epistatic value for all pairwise mutant combinations measured from leaf samples 970

from the stressed LSA chamber. 971

C: GLS Elong epistatic value for all pairwise mutant combinations measured from seed samples 972

from the clean CEF chamber. 973

D: GLS Elong epistatic value for all pairwise mutant combinations measured from seed samples 974

from the stressed LSA chamber. 975

E: GLS OX epistatic value for all pairwise mutant combinations measured from leaf samples 976

from the clean CEF chamber. 977

31

F: GLS OX epistatic value for all pairwise mutant combinations measured from leaf samples 978

from the stressed LSA chamber for GLS OX. 979

G: GLS OX epistatic value for all pairwise mutant combinations measured from seed samples 980

from the clean CEF chamber for GLS OX. 981

H: GLS OX epistatic value for all pairwise mutant combinations measured from seed samples 982

from the stressed LSA chamber for GLS OX. 983

984

985

Figure 7. Leaf GLS accumulation in all myb28/myb29/ant combinatorial genotypes986

Leaf GLS levels in the corresponding genotypes, as measured in the clean CEF chamber. 987

Different letters indicate genotypes with significantly different SC GLS levels (p <0.05 using 988

post-hoc Tukey’s test after ANOVA). Standard error is shown with 16 samples across two 989

experiments for each genotype. 990

A: SC GLS. 991

B: LC GLS. 992

C: Indolic GLS. 993

994

Figure 8. Transcript levels for aliphatic glucosinolate biosynthesis genes in all myb28 myb29 995

ant combinatorial genotypes. 996

Standard error for each data point is shown from three independent biological samples. 997

A: The heatmap displays the fold change in transcript levels in the mutants compared to the Col-998

0 control, with red showing increased accumulation and blue showing decreased accumulation. 999

The columns on the right display the statistical significance (purple – significant p<0.05, grey – 1000

not significant) for each term in the ANOVA model, as listed at the bottom. The expression of 1001

MYB29 in the myb29 background lines is shown as NM (not measurable) due to the T-DNA 1002

insertion. 1003

B: Transcript levels of BCAT4. Lines show the values in the ANT (red) and ant (blue) genotypes 1004

across the four different myb28 myb29 backgrounds. 1005

C: Transcript levels of MAM3. Lines show the values in the ANT (red) and ant (blue) genotypes 1006

across the four different myb28 myb29 backgrounds. 1007

32

D: Transcript levels of CYP79F1. Lines show the values in the ANT (red) and ant (blue) 1008

genotypes across the four different myb28 myb29 backgrounds. 1009

E: Transcript levels of GS-OX2. Lines show the values in the ANT (red) and ant (blue) 1010

genotypes across the four different myb28 myb29 backgrounds. 1011

1012

Figure 9. Shifting epistatic interactions of ANT, MYB28 and MYB29 across the molecular 1013

Aliphatic GLS accumulation processes. 1014

The bottom of the figure shows the genes involved in the synthesis of aliphatic GLS within 1015

Arabidopsis thaliana. The different molecular phenotypes measured within this study that pertain 1016

to the SC GLS, GLS Elong and GLS OX processes are shown, from the key transcripts involved 1017

in each process to the estimated enzyme activity to the final accumulation of SC GLS. A 1018

summary of the statistical effects of the ANT, MYB28 and MYB29 main effects (dots), pairwise 1019

interactions (lines between dots) and three-way interaction (triangle) are shown as per the legend. 1020

Purple indicates that the specific term was significant for the represented phenotype, and grey 1021

indicates non-significance. The different processes in aliphatic GLS biosynthesis are shown as 1022

follows: blue for elongation-related steps, green for core structure synthesis, and yellow for side-1023

chain modification.1024

B

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F G

Figure 1. Genetic networks under investigation The 20 TFs under investigation are shown as nodes. Lines connecting TFs show where double mutants were generated. The labeled clusters A to G represent the previously identified phenotypic sub-networks for these TFs. Large-effect TFs that were not linked to specific clusters are shown in the centers of the networks as MYB28, MYB29 and ANT. The 4 different epistatic groups are highlighted with different colors: within-cluster epistatic tests are shown in lime green, between-cluster epistatic tests are shown in dark orange, MYB epistatic tests are shown in grey, and ANT epistatic tests are shown in black.

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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 show significant epistasis are maintained, while non-significant connections are dropped from the network. A solid line shows that only the TF x TF interaction term was significant in the ANOVA model. A dashed line shows epistatic interactions where there was a significant Tissue x TF x TF interaction. A dotted line shows epistatic interactions where there was a significant Environment x TF x TF interaction. A line of arrows shows that the interaction was conditional on both Tissue and Environment. The color of the node indicates which main effect terms are significant for the individual TFs; sky blue indicates only a TF main effect, orange indicates only a tissue x TF interaction, purple indicates both TF and tissue x TF, yellow indicates both TF and treatment x TF, and red indicates that all three terms are significant. Grey 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

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 each genetic term was calculated, as shown in violin plots. The median in each violin is shown as a dot. The phenotypes tested are shown individually, as labeled on the X-axis. A: Percent of genetic variation controlled by individual TF main effects. B: Percent of genetic variation controlled by tissue x TF interactions. C: Percent of genetic variation controlled by treatment x TF interactions. D: Percent of genetic variation controlled by TF x TF epistatic interactions. E: Percent of genetic variation controlled by tissue x TF x TF epistatic interactions. F: Percent of genetic variation controlled by treatment x TF x TF epistatic interactions. G: Visualization of individual epistatic variance components within the genetic network for SC GLS. The width of the line connecting 2 TFs is proportional to the variance linked with the TF x TF term for that specific interaction. The highest proportion is the interaction between HMGBD15 and HB34, with 49% of the total genetic variance. Solid lines show that there was a significant interaction term, while dashed lines show combinations with no significant interactions.

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LS, ,

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a

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Figure 4. Synergistic and antagonistic epistatic patterns in SC GLS accumulation The levels of SC GLSs in the corresponding genotypes and tissues are shown. Different letters show genotypes with significantly different SC GLS levels (p <0.05 using post-hoc Tukey’s test after ANOVA). The p-value of the epistatic interaction and the epistasis value are shown on the plots. Standard error is shown with 16 samples across two experiments for each genotype. A: erf9 x rap2.6l antagonistic epistasis for SC GLS accumulation in leaves from the stress LSA chamber. B: ant x hmgbd15 synergistic epistasis for SC GLS accumulation in seeds from the clean CEF chamber.

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zfp4 zfp7 ant ilr3 zfp4 nac102 myb29 zfp7 zfp4 rap2.6l zfp4 cbf4 zfp4 erf107 ant cbf4 myb28 zfp7 myb29 erf107 myb29 at5g52020 zfp4 ate2f2 myb28 myb29 myb29 ant myb28 hb21 myb28 zfp4 myb29 hb21 myb29 zfp4 myb28 ant erf107 hmgbd15 erf107 hb21 rap2.6l df1 hmgbd15 rap2.6l erf9 df1 ilr3 hmgbd15 hmgbd15 zfp7 hmgbd15 hb34 abf4 df1 ate2f2 at2g44730 gbf2 hb21 gbf2 at5g52020 ilr3 gbf2 ant at5g52020 hb21 at5g52020 ilr3 hb21 ant hb21 ant gbf2 ant zfp4 erf107 ate2f2 ant hmgbd15 rap2.6l abf4 zfp4 hmgbd15 erf107 zfp7 rap2.6l erf9 ilr3 zfp7 zfp7 rap2.6l ant zfp7

Figure 5. Epistatic effects for SC GLS Epistasis values were calculated for all pairwise combinations individually in all treatment and tissue combinations. For B-E, different letters indicate statistically different average epistatic values across the cluster tests, as determined by ANOVA at p-value of 0.05. A: Epistasis values for all pairwise mutant combinations plotted in a heatmap using hierarchical clustering; the gene combinations are listed to the right of the diagram. The first vertical column shows if the pairwise mutant interaction is testing epistasis from within a cluster (green), between clusters (orange), MYB (grey) or ANT (black). The next three columns show which epistatic interaction term is significant (purple) or not significant (grey) (ANOVA, P < 0.05). B: Average and standard error of epistatic value for all pairwise mutant combinations measured from leaf samples from the clean CEF chamber. C: Average and standard error of epistatic value for all pairwise mutant combinations measured from leaf samples from the stressed LSA chamber. D: Average and standard error of epistatic value for all pairwise mutant combinations measured from seed samples from the clean CEF chamber. E: Average and standard error of epistatic value for all pairwise mutant combinations measured from seed samples from the stressed LSA chamber.

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Figure 6. Epistatic effects for GLS Elong and GLS OX Epistasis values were calculated for all pairwise combinations individually in all treatment and tissue combinations. Different letters indicate statistically different average epistatic values across the cluster tests, as determined by ANOVA at p-value of 0.05. The average and standard error of the epistatic values for each group of epistatic groups are shown in all boxplots, with orange showing between cluster-crosses, green showing within-cluster crosses, grey showing crosses involving MYB and black showing crosses involving ANT. A: GLS Elong epistatic value for all pairwise mutant combinations measured from leaf samples from the clean CEF chamber. B: GLS Elong epistatic value for all pairwise mutant combinations measured from leaf samples from the stressed LSA chamber. C: GLS Elong epistatic value for all pairwise mutant combinations measured from seed samples from the clean CEF chamber. D: GLS Elong epistatic value for all pairwise mutant combinations measured from seed samples from the stressed LSA chamber. E: GLS OX epistatic value for all pairwise mutant combinations measured from leaf samples from the clean CEF chamber. F: GLS OX epistatic value for all pairwise mutant combinations measured from leaf samples from the stressed LSA chamber for GLS OX. G: GLS OX epistatic value for all pairwise mutant combinations measured from seed samples from the clean CEF chamber for GLS OX. H: GLS OX epistatic value for all pairwise mutant combinations measured from seed samples from the stressed LSA chamber for GLS OX.

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Figure 7. Leaf GLS accumulation in all myb28/myb29/ant combinatorial genotypes Leaf GLS levels in the corresponding genotypes, as measured in the clean CEF chamber. Different letters indicate genotypes with significantly different SC GLS levels (p <0.05 using post-hoc Tukey’s test after ANOVA). Standard error is shown with 16 samples across two experiments for each genotype. A: SC GLS. B: LC GLS. C: Indolic GLS.

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Statistical Test

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CYP79F1 CYP79F2 CYP83A1 GSTF11 GSTU20 GGP1 GGP3 C-S lyase UGT74C1

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Figure 8. Transcript levels for aliphatic glucosinolate biosynthesis genes in all myb28 myb29 ant combinatorial genotypes. Standard error for each datapoint is shown from three independent biological samples. A: The heatmap displays the fold change in transcript levels in the mutants compared to the Col-0 control, with red showing increased accumulation and blue showing decreased accumulation. The columns on the right display the statistical significance (purple – significant p<0.05, grey – not significant) for each term in the ANOVA model, as listed at the bottom. The expression of MYB29 in the myb29 background lines is shown as NM (not measurable) due to the T-DNA insertion. 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.

BCAT4 BAT5 MAM1 MAM3

IPMDH1 IPMDH3

BCAT3 CYP79F1 CYP79F2

CYP83A1 GSTF11 GSTU20

IPMI-LSU1 IPMI-SSU2 IPMI-SSU3

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C-S lyase UGT74B1 UGT74C1

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Figure 9. Shifting epistatic interactions of ANT, MYB28 and MYB29 across the molecular Aliphatic GLS accumulation processes. The bottom of the figure shows the genes involved in the synthesis of aliphatic GLS within Arabidopsis thaliana. The different molecular phenotypes measured within this 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 estimated enzyme activity to the final accumulation of SC GLS. A summary of the statistical effects of the ant, myb28 and myb29 main effects (dots), pairwise interactions (lines between dots) and three-way interaction (triangle) are shown as per the legend. Purple indicates that the specific term was significant for the represented phenotype, and grey indicates non-significance. The different processes in aliphatic GLS biosynthesis are shown as follows: blue for elongation-related steps, green for core structure synthesis, and yellow for side-chain modification.

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DOI 10.1105/tpc.17.00805; originally published online January 9, 2018;Plant 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

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