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. 2019 Dec 18;214(2):529–541. doi: 10.1534/genetics.119.302996

Epistatic Transcription Factor Networks Differentially Modulate Arabidopsis Growth and Defense

Baohua Li *,†,1, Michelle Tang *,‡,1, Céline Caseys *,1, Ayla Nelson *, Marium Zhou *, Xue Zhou *, Siobhan M Brady ‡, Daniel J Kliebenstein *,§,2
PMCID: PMC7017016  PMID: 31852726

How a plant regulates the relationship between plant growth and plant defense is critical for understanding plant fitness or yield. Yet, little about the required complex underlying interactions are understood....

Keywords: epistasis, glucosinolates, plant defense, plant growth, transcription factor

Abstract

Plants integrate internal and external signals to finely coordinate growth and defense for maximal fitness within a complex environment. A common model suggests that growth and defense show a trade-offs relationship driven by energy costs. However, recent studies suggest that the coordination of growth and defense likely involves more conditional and intricate connections than implied by the trade-off model. To explore how a transcription factor (TF) network may coordinate growth and defense, we used a high-throughput phenotyping approach to measure growth and flowering in a set of single and pairwise mutants previously linked to the aliphatic glucosinolate (GLS) defense pathway. Supporting a link between growth and defense, 17 of the 20 tested defense-associated TFs significantly influenced plant growth and/or flowering time. The TFs’ effects were conditional upon the environment and age of the plant, and more critically varied across the growth and defense phenotypes for a given genotype. In support of the coordination model of growth and defense, the TF mutant’s effects on short-chain aliphatic GLS and growth did not display a simple correlation. We propose that large TF networks integrate internal and external signals and separately modulate growth and the accumulation of the defensive aliphatic GLS.

GROWTH and defense are essential biological processes necessary for plant survival. Optimizing fitness requires plants to coordinate growth and defense phenotypes in response to specific environments. Efforts to understand the relationship between plant growth and defense are often modeled as a trade-off where resistance and growth are a cost on each other (Karban and Baldwin 1997; Agrawal 2011a,b; Huot et al. 2014). This model assumes that the available resources for plants are limited, suggesting that any flux of resources, energy and elements into plant defense would be at the cost of plant growth. Support for this model comes from the observations that some constitutive defense mutants generally grow smaller and suffer from yield and/or fitness losses.

A developing model is emerging from research in ecology, evolutional biology, and molecular genetics that suggests a dynamic relationship between plant defense and growth (Singh et al. 2002; de Lucas et al. 2016). This model began with multiple reports showing that defense metabolism does not show a universal negative correlation with plant growth (Bergelson and Purrington 1996; Almeida-Cortez et al. 1999; Koricheva 2002). Additionally, mechanistic manipulations of defense metabolism can show little to no effect on plant growth. Further, diminutive constitutive defense mutants can have their growth rescued by second-site mutations that maintain the constitutive defense (Hemm et al. 2003; Paul-Victor et al. 2010; Züst et al. 2011; Joseph et al. 2013; Campos et al. 2016; Kliebenstein 2016). Finally, support for this model came from the identification of specific transcription factors (TFs) that simultaneously increase growth and defenses (Campos et al. 2016; Wang et al. 2018). Together, these observations suggest that the relationship between plant defense and growth is a complex and active internal decision process involving regulatory and signaling pathways in planta (Kliebenstein 2016; Züst and Agrawal 2017).

Several recent studies provide important insights about the roles TFs play in integrating and transducing internal and external signals essential to growth and defense (Singh et al. 2002; Pajerowska-Mukhtar et al. 2012; Lozano-Duran et al. 2013; Fan et al. 2014; de Lucas et al. 2016). Mutations in JAZs (transcriptional repressors in jasmonic acid signaling pathway) in combination with altered photoreceptor PhyB result in fast-growing plants with enhanced plant defense responses (Campos et al. 2016). In rice, the Ideal Plant Architecture 1 TF activates yield-related genes while promoting both plant defense and rice yield (Jiao et al. 2010; Wang et al. 2018). These case studies demonstrate that growth and defense are under complex regulation and that TFs can be key integrators to coordinate these two important biological processes. However, it is not clear if these examples are isolated instances or a more generalizable view of how TFs modulate growth and defense. Systemic studies on TFs and their regulatory networks are needed to test how TFs may or may not coordinate between plant defense and growth in diverse environmental settings.

To explore the interplay between plant growth and defense, we used the well-characterized plant secondary metabolic pathway, the Arabidopsis methionine-derived aliphatic glucosinolate (GLS) (Sønderby et al. 2010b). Aliphatic GLS provide defense against numerous herbivorous insects in both the laboratory and the field (Lambrix et al. 2001; Schlaeppi et al. 2008; Kos et al. 2012; Beran et al. 2014; Falk et al. 2014; Kerwin et al. 2015, 2017; Zalucki et al. 2017). Aliphatic GLS are critical for fitness in Brassicales, and their specific composition and accumulation across developmental stages are intricately controlled by genetic variation and pleiotropically influence a variety of growth- and defense-related pathways (Kliebenstein et al. 2002a; Bidart-Bouzat and Kliebenstein 2008; Wentzell and Kliebenstein 2008; Burow et al. 2010; Kerwin et al. 2011; Züst et al. 2011). Since aliphatic GLS contain both sulfur and nitrogen, flux-based modeling suggests that they are expensive to produce as they can accumulate at high concentrations. However, mutants missing these compounds have at most a slight change in early growth, indicating that aliphatic GLS do not display a tight trade-off with growth (Paul-Victor et al. 2010; Züst et al. 2011; Bekaert et al. 2012). Instead, the regulatory complexity of the aliphatic GLS better fits with the coordination model. For example, a large-scale yeast one-hybrid study identified a large collection of TFs that bind the aliphatic GLS enzyme promoters and regulate aliphatic GLS accumulation, directly arguing against a single dominant TF model (Li et al. 2014). Further, these TFs showed extensive epistatic interactions in influencing the accumulation of GLS (Li et al. 2018). This suggests that the regulation of this defense pathway is highly complex. While aliphatic GLS levels are influenced by perception of jasmonic acid and other herbivory-related signals, the majority of their accumulation is a steady-state response to the environment with genetic variation between natural accessions having larger effects on accumulation than specific signal perception events (Kliebenstein et al. 2002b; Mikkelsen et al. 2003; Wentzell and Kliebenstein 2008; Chan et al. 2011; Guo et al. 2013; Pangesti et al. 2016). Thus, we focused on using genetic perturbation of aliphatic short-chain (SC) GLS accumulation, the major form of aliphatic GLS, as an optimal way to test the system and how epistasis may link defense, growth, and flowering.

To test the conditional model of growth and defense, we used high-throughput phenotyping to measure growth and flowering time of 20 single TF mutants and 48 double mutants previously shown to influence the Arabidopsis aliphatic GLS pathway in two environments (Sønderby et al. 2007; Li et al. 2018). We show that 17 of the 20 TFs significantly influence plant growth and/or plant flowering time. While most TFs influence growth and defense, there was no clear correlation between the aliphatic SC GLS accumulation and plant growth and flowering time in our study. This indicates that each TF has specific and independent influences on both growth and aliphatic GLS. Our findings support the coordination model for the relationship between plant growth and aliphatic GLS, and provide novel insights on how these critical and complex biological processes are integrated to optimize fitness in different environments.

Materials and Methods

Plant materials, growth conditions, and experimental design

The Arabidopsis thaliana transfer DNA (T-DNA) insertion lines of the 20 TFs were ordered from Arabidopsis Biological Resource Center (Sussman et al. 2000; Alonso et al. 2003) and homozygous lines were validated in previous studies (Sønderby et al. 2010a; Li et al. 2014). The full description of how the 48 double mutants were generated and validated and planted is provided in a previous study (Li et al. 2018). Briefly, the Arabidopsis plants were grown in two independent chambers with 16-hr light at 100–120 µEi light intensity with temperature set at a continuous 22°. The two growth chambers were set to identical abiotic environments but contain dramatically different biotic environments: one pest-free, with a clean Controlled Environment Facility (CEF) chamber as a typical laboratory growth condition; and one with an endogenous pest population, with a stress Life Sciences Addition building (LSA) chamber mimicking more natural growth condition. Seeds were imbibed in water at 4° for 3 days and sown into Sunshine Mix (Sun Gro Horticulture). Seedlings were thinned to one plant per pot (6 cm × 5 cm) at 7 days after planting. The pots were in 36 cell flats and each flat was considered as a blocking unit. All four genotypes (wild type, two single mutants, and one double mutant) for a double mutant combination were grown together in a single flat along with genotypes for other double mutant combinations, such that there was only a single representative of each genotype within the flat. This was replicated across eight flats with randomization within each flat to create eight replicates of each genotype for each comparison. This experiment was conducted independently in the clean CEF and stress LSA chamber to generate a minimum of 16 biological repeats in total per genotype. The total combination of all genotypes required 24 flats per growth chamber. All genotypes were independently randomized within a flat.

Flowering time and plant growth measurement

All the plants were checked daily, and the flowering time, the first day of flowering after planting, was recorded for each of the plants. Pictures of all the plants were taken every other day from day 9 to 27, when some of the early flowering lines in the stress LSA chamber started to flower. The circling area of the rosette of each plant in each growth condition was manually measured in ImageJ across the days. The total circling leaf area was used as an indicator of the plant size. The plant flowering time and plant sizes were normalized relative to Col-0, and further visualized using the iheatmapr package in R software (R Development Core Team 2014).

Statistics

Each pair of TFs were separately analyzed using a repeated measures general linear model with lme4 to test for epistasis affecting plant growth (Bates et al. 2015). For this analysis each double mutant was tested using both single mutants and wild type grown concurrently with the following model: yabc = µ + Aa +Bb + Chc + Dn + Aa×Bb + Aa×Chc + Bb×Chc + Aa×Dn + Bb×Dn+ Chc×Dn + Aa×Bb×Dn + Aa×Chc×Dn + Bb×Chc×Dn + Aa×Bb×Chc×Dn + Pi + εabc, where Pi is the random variable of Plant, the experimental unit from which measurements were taken over time. εabc, the error term is assumed to be normally distributed with mean 0 and variance σε2. In this model, yabc denotes the rosette area of each plant, genotype A represents the presence or absence of a T-DNA insert in one TF gene (wild type vs. mutant of locus A), and genotype B represents the presence or absence of a T-DNA insert in another TF gene (wild type vs. mutant of locus B) in the double mutant from chamber Chc (clean CEF chamber or stress LSA chamber). Time is represented by days, Dn. This model was run individually for each double mutant test and only the single mutants and wild-type Col-0 grown alongside the double mutants in the same flats were used in each model. This means that each double mutant test uses matched wild-type and single mutant control plants. Similarly, a general linear model was used to test epistasis affecting flowering time in each pair of TFs: yabc = µ + Aa +Bb + Chc + Aa×Bb + Aa×Chc + Bb×Chc + Aa×Bb×Chc + Chc:Ui + εabc, where Ui is the random effect of blocks nested in chamber Chc. The ANOVA table, least-square means, and SE for each genotype by treatment combinations were obtained with lme4 and emmeans packages in R (Searle et al. 1980; Bates et al. 2015). The type III sums of squares from these models were used to calculate the variance and percent variance attributable to each term in the model. For the percent variance, this was calculated by comparing to the total variance in the model as the denominator. All network representations were generated using Cytoscape version 2.8.3 (Shannon et al. 2003).

To determine the power to identify significant epistatic and epistatic × conditional terms given our experimental design, we used the pwr.f2.test function from the pwr package (https://github.com/heliosdrm/pwr). Using the median number of error degrees of freedom across all models of 542 and a significance level of 0.05, we have a 0.8 power to detect significant epistatic and epistatic * conditional terms if the terms effect size (f2) is 0.0145 or higher. This is within the range of effect sizes linked to the epistatic or epistatic × conditional terms in our models (Supplemental Material, Figure S1). This indicates that we had sufficient power to identify epistatic × conditional using this experimental design (Figure S1).

In these analyses, we considered the 20 models for main effects or 48 models for the different epistatic comparisons as our independent tests. Using a nominal P value across 48 tests would suggest between two and three potential false positives per trait for the epistatic analyses. However, there was on average 20 (±2 SE) tests below 0.05 for the main epistatic interactions per trait and on average 8 (±1 SE) tests below 0.05 for the chamber × epistasis terms. This is much higher than the two or three expected false positives. To maximize our ability to compare across traits and genes, we have opted to utilize the less conservative nominal P values for the comparisons. Further, all P values and least-square means are reported to allow for direct assessment of the results (Tables S1 and S2).

Calculation of epistasis value

To study the effect of epistasis, we use epistasis value to describe the direction and strength of the epistasis. The epistasis value was calculated by normalizing the difference of observed double mutant phenotype vs. the predicted double mutant phenotype, assuming additivity of the single mutants, then normalized to the wild type as reported before (Segrè et al. 2005; Li et al. 2018). The phenotype for wild type was set as w, mutant TFa as a, mutant TFb as b, and double mutant TFa/TFb as ab. The epistasis value is calculated as {ab – [w + (a−w) + (b−w)]/w}. If the epistasis value is positive, this shows evidence for synergistic epistasis, while antagonistic epistasis is reflected in negative values. Larger the epistasis value signifies stronger the epistasis effects. The epistasis value were further visualized using the iheatmapr package in R software (R Development Core Team 2014).

Data availability

All genotypes are available upon request. File S1 contains all supplemental figures and tables mentioned within the text. Supplemental material available at figshare: https://doi.org/10.25386/genetics.11388195.

Results

Conditional growth effects of the 20 TF mutations

The 20 selected TFs were originally identified as binding aliphatic GLS-related promoters and their mutants influence aliphatic GLS accumulation (Li et al. 2014, 2018). We focused on the aliphatic GLS as they represent ∼90% of the total GLS content within Arabidopsis leaves and therefore the majority of the metabolic flux. To measure the plant size in this mutant collection, we utilized digital image analysis on plants from two chambers, coded by their location in the CEF (clean chamber) and LSA (stress chamber) (Li et al. 2018). The two chambers, both built by Conviron, are similar in size, with identical light and humidity settings. The two chambers differ in their pest population: the clean CEF chamber is sterilized monthly after each planting while the stress LSA chamber contains longer-lived plants that maintain endogenous populations of pests likes fungus gnats, various flea beetles, aphids, etc. This creates a chamber that presents the plant with a blend of biotic pressures. Further, we acknowledge potential differences in the abiotic environments due to variation in ventilation or other parameters that were not controllable. Additionally, the mutant collection provides a large range of perturbation in defense chemistry independently of the different chambers that is similar to or even larger than the range of GLS variation in natural accessions (Figure 1) (Kliebenstein et al. 2001; Chan et al. 2010). This mutant collection generates a wider range of SC GLS defense variation than observed in multiple years of field trials for Col-0 (Kerwin et al. 2015). Thus, the LSA and CEF chambers test how the growth and defense in the mutant collection may or may not be sensitive to complex environmental perturbations (Figure 1).

Figure 1.

Figure 1

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Extensive range of variation in short-chain GLS content of wild type, single mutants, and double mutants in CEF and LSA. The average absolute SC GLS content across all the genotypes in each growth chamber is shown. Wild type (Col-0) is highlighted as a green dashed line, while all the other genotypes are shown as gray solid lines.

Growth was measured every other day from 9 days postgermination to 27 days, at which time the leaves were overlapping. Plants were organized in a randomized complete block design with eight measurements per genotype per chamber (Li et al. 2018). The plant growth measurement spans the majority of vegetative growth providing a dynamic analysis of growth. Combining the data with repeated measure linear models, we tested for significant effects of all single gene TF mutants on growth across the conditions (Figure 2, Figure S2, and Supplemental Data Sets 1 and 2).

Figure 2.

Figure 2

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The effects of the 20 TFs on plant growth. The heatmap displays the fold change of plant size in single mutants, from day 9 to 27, in both the clean CEF chamber and stress LSA chamber. Red shows increased plant size and blue decreased plant size in comparison to Col-0. The columns on the right display the statistical significance (purple, significant P < 0.05; gray, not significant) for each term in the statistical model as listed at the bottom using the repeated measure model. Specific framed genotypes have mean phenotypes shown for reference in Figure S2.

The models indicated that the influence of these TFs on growth is highly conditional on both plant age and growth chamber (Figure 2 and Figure S3). Of the 20 aliphatic GLS TF mutants, 16 had a significant influence on growth. Strong positive growth effects were observed more often in the stress LSA chamber than in the clean CEF chamber for a number of TFs, like HMGBD15, AT5G52020, ZFP4, CBF4, NAC102, and GBF2 (Figure 2). In contrast, ABF4 had strong negative growth effects in both chambers. In addition to differences between the growth chambers, most of the TFs had differential influence across plant age. Some mutants combined these conditionalities as illustrated by ilr3, which had a transition at 21 days postgermination from positive to negative growth effects, but only in the LSA chambers (Figure S3). This conditionality illustrates the importance of large-scale phenotyping across different conditions to generate a broad view of how mutational effects may change dynamically (Figures S2 and S3). In an energetic trade-off, we would expect that most of these TF mutations should lead to smaller plants as these mutations increase aliphatic GLS content (Li et al. 2014). In contrast, the mutants had a mix of positive and negative growth effects. In addition, three mutants with strong aliphatic GLS phenotypes, ant, myb28, and myb29, have little to no detectable main effect on growth. One proposed alternative explanation for the myb28 and myb29 mutant is that these mutations often have a twofold increase in indolic GLS accumulation that may balance the loss in aliphatic GLS. However, because aliphatic GLS represent ∼90% of the total GLS production, the GLS flux is still reduced by 80% in these mutants. The links between plant growth and defense via the aliphatic GLS in this TF collection supports the coordination model of growth and defense.

Dynamic epistatic networks underlying plant growth

We previously generated a set of 48 pairwise mutant combinations from 20 TFs controlling aliphatic GLS that form an extensive antagonistic epistatic network (Sønderby et al. 2007; Li et al. 2018). As these TFs affect growth individually, we used repeated measures linear models to assess if the epistatic effects were as prevalent on growth as on aliphatic GLS accumulation. We plotted the potential epistatic effects for each mutant pair and represented these interactions as a connectivity plot based on the mutants’ main effects on aliphatic GLS (Figure 3, Figure 4, and Supplemental Data Set 3 and, 4). This analysis identified extensive epistatic interactions influencing growth. As observed in the single mutants, the epistatic interactions were equally conditional across ontogeny and growth condition (Figure 3 and Figure 4). These epistatic interactions followed a similar clustering as that observed when using GSL phenotypes for clustering. Particularly, the TFs in group F, ERF9, DF1, ABF4, and Rap2.6L, showed frequent epistatic interactions with each other for growth. Thus, this set of TFs identifies and illustrates an epistatic system that influences plant growth depending on age and growth condition. The effects of these TFs on growth are more conditional than the aliphatic GLS, suggesting that while the traits of growth and defense are both controlled by the epistatic network, they are independent regulatory outputs.

Figure 3.

Figure 3

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Epistatic interactions between TF genes modeled for plant growth. Epistatic networks for (A) TF × TF × chamber, (B) TF × TF × day, and (C) TF × TF × chamber × day. A line connecting two TFs correspond to a significant epistatic interaction. Node color indicates individual TFs’ significance. Sky blue means that the individual TF has a significant genotype × chamber × day term, green means significant in both genotype × chamber × day and genotype × chamber terms, while red means significant in genotype × chamber × day, genotype × chamber, and genotype terms. Gray means not significant. (A–C) To maximize comparison across publications, the TFs are laid out in the network as groups A–G based on their modular effect on aliphatic GLS accumulation (Li et al. 2018).

Figure 4.

Figure 4

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Epistatic effects on plant growth. Heatmap of epistasis values for all pairwise TF combinations from day 9 to 27 in each treatment condition. The genotypes are clustered using hierarchical clustering. The columns to the right of the heatmap show epistatic interaction terms as significant (purple) or not significant (gray) (P < 0.05). Framed genotypes are plotted in Figure 5.

Conditional epistatic networks underlie plant growth

To quantify the epistatic effects on plant growth, we used a previously established epistasis value to measure the direction and magnitude of each epistatic interaction (Li et al. 2018). Briefly, we subtracted the measured double mutant phenotype from the predicted double mutant phenotype under an additive model. This value was then normalized to the wild-type phenotype. This epistasis value was measured for each pair of mutants for all the growth data (Supplemental Data Set 5). The epistasis value will be positive when there is synergistic epistasis and negative for antagonistic epistasis (Figure 4). Previous work showed that epistasis for SC GLS, the dominant form of aliphatic GLS, was almost entirely negative/antagonistic (Li et al. 2018). Unlike SC GLS, epistasis for growth was a mix of antagonistic and synergistic values that shift depending upon the conditions and developmental stages. For example, rap2.6l was involved in several epistatic interactions with other TFs from group F that were positive in the LSA growth condition but negative in the CEF growth condition (i.e., rap2.6/erf9). In contrast, epistatic interactions involving hmgbd15 had negative interactions in the LSA and positive interactions in the CEF growth conditions (i.e., ilr3/hmgbd15) (Figure 4 and Figure 5). This argues that this epistatic network of TFs influences growth in both conditions, but that the environmental signals in the two conditions permeate differently through the TF network to generate variable growth outputs.

Figure 5.

Figure 5

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Epistatic growth phenotypes at day 15. The average rosette size on day 15 for three pairs of TFs in the clean (CEF) and stress (LSA) chambers. Each TF’s pairwise combination has a wild-type (Col-0) genotype. Letters indicate genotypes with significantly different plant sizes. (P < 0.05 using post hoc Tukey’s test). SE was calculated for eight samples per genotype and treatment. Day 15 was chosen to provide a common date across which to illustrate key differences. (A) Rosette size of single and double mutants of rap2.6l and erf9. (B) Rosette size single and double mutants of ilr3 and hmgbd15. (C) Rosette size single and double mutants of myb28 and myb29.

Epistatic networks influence flowering independent of tested environments

To test if these TFs and their network influence reproduction, we measured flowering time in all of the plants from all of the genotypes in the two contrasting chambers, and showed that 12 of the 20 tested TFs significantly influenced flowering (Figure 6). However, unlike growth, the effects on flowering time were almost entirely toward early flowering in both chambers, with only ANT, ILR3, and ERF9 having an environmental conditionality (Figure 6 and Figure S4). Plotting the epistatic effects for flowering time showed predominantly synergistic epistasis with 18 statistically significant interactions and only two interactions having environmental conditionality (Figure 7, A and B). This indicates that the genetic control of flowering time is less influenced by the environmental conditions than are either growth or aliphatic GLS. To further visualize how the epistatic variance was influenced by the network topology, we mapped the epistatic variance for the plant flowering (Figure 7C). Using previously ascribed groupings of the TFs based on their GLS phenotypes, TFs in groups A and B have more significant epistatic interactions partly overlapping with the epistatic interactions of growth phenotype, and TFs with strong main effects on flowering time also have higher genetic variance in their epistatic interactions.

Figure 6.

Figure 6

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TF effects on flowering time. The heatmap displays the fold change of flowering time in the single mutants in both clean CEF chamber and stress LSA chamber. Red shows increased flowering time and blue decreased flowering time in comparison to Col-0. The columns on the right display the statistical significance (purple, significant P < 0.05; gray, not significant) for each term in the statistical model as listed at the bottom.

Figure 7.

Figure 7

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Epistatic network and flowering time effects. (A) Heatmap of epistasis values for all pairwise combinations individually in both treatment conditions. The genotypes are clustered using hierarchical clustering. The columns to the right display interactions as significant (purple) or not significant (gray) (P < 0.05). To maximize comparison across publications, the TFs are laid out in the network as groups A–G based on their modular effect on aliphatic GLS accumulation (Li et al. 2018). (B) A representation of the significant epistatic networks for flowering time. Solid lines indicate significant TF × TF interaction. Dotted lines indicate significant treatment × TF × TF interaction. Node color indicates individual TFs significance: sky blue, TF; green, treatment × TF; yellow, TF + treatment × TF; gray, not significant. (C) Visualization of individual epistatic variance components within the genetic network for flowering time. The width of the line connecting two TFs is proportional to the variance linked with the TF × TF term for that specific interaction.

Connections between defense and growth

To further explore the connection between plant defense and development, we systemically tested for associations between growth and defense in this data. These TFs predominantly influence the accumulation of SC GLS, and we used this as our quantification for defense (Supplemental Data Set 1) (Li et al. 2018). The mutant effects of the 20 TFs on SC GLS, plant growth and flowering time in both chambers were calculated by taking the phenotypic difference between mutant and wild type and normalized by the wild-type value. We then tested for a relationship between growth and defense by testing for correlations between the mutant effects using these traits (Figure 8 and Supplemental Data Set 6). In contrast to the expectation that an energetic trade-off model was solely driving this system, the TFs’ effects on the accumulation of SC GLS and growth/flowering were largely uncorrelated (Figure 8, A and B). This was equally true in both the stress and clean chamber, indicating that stress did not illuminate a hidden relationship. As expected, there was a negative correlation between growth and flowering. Our findings show a highly complex relationship between plant defense and plant growth.

Figure 8.

Figure 8

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Lack of relationship between mutant effects on defense and growth. The main additive effect of the TF mutants in stress LSA chamber were selected to illustrate the absence of a relationship between growth, flowering time, and SC GLS. The predicted linear trend line and the statistical test results are shown. The axis shows the fold effect of the average mutant phenotype in comparison to the respective wild-type value. (A) Comparison of average gene effects on SC GLS and flowering time (r = −0.006, P = 0.980). This result was nonsignificant in both the presence and absence of the outlier point for SC GLS. (B) Comparison of average gene effects on SC GLS and growth (r = 0.131, P = 0.583). This result was nonsignificant in both the presence and absence of the outlier point for SC GLS. (C) Comparison of average gene effects on flowering time and growth (r = −0.867, P < 0.001).

The connection of single mutant and double mutant effects

Given the low correlation between the SC GLS accumulation and plant growth, we proceeded to investigate if there may be any connection between a TF’s main effect on a trait and its average epistatic effect on the trait. This allows us to investigate if there is any internal influence of single gene effects on the direction and value of their epistatic interactions. If there is a significant correlation, it would suggest that a gene’s main effects can provide information about epistatic networks. To do this, we calculated each TF’s average epistasis value for each trait across all the pairwise combinations involving that TF. Next, we correlated the TF’s estimated single mutant effects to their average epistasis value for each phenotype and chamber combinations (Figure 9 and Supplemental Data Set 6). The analysis showed that within all three tested traits, there are consistent negative correlations between a mutation’s main and epistatic effects. One possible interpretation of this result is that the genetic background is constraining the results that we have obtained. There may be a maximal effect size within a background that would cause larger main effect TFs to invariantly have smaller epistatic interactions. The prevalence of negative epistasis in the SC GLS phenotype agrees with this possibility. Future work is required to understand if this observation is a general property of this network or is a function of the specific Col-0 accession in which it was conducted.

Figure 9.

Figure 9

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Relationship of the main and epistatic effects for TF genes. The average epistatic effect of a TF across all its pairs in the stress LSA chamber was calculated and compared to the TFs main effects for SC GLS, flowering time and plant size on day 17. The predicted linear trend line and the statistical test results are shown. The average epistasis value for each single gene across all its epistatic pairs is plotted against that gene’s average single gene effect on the value as calculated against the respective wild type. (A) Comparison of average main gene and epistatic gene effects on SC GLS (r = −0.610, P = 0.004). This relationship was significant in both the presence and absence of the outlier point for SC GLS. (B) Comparison of average main gene and epistatic gene effects on flowering time (r = −0.732, P < 0.001). (C) Comparison of average main gene and epistatic gene effects on flowering time on growth (r = −0.841, P < 0.001).

Discussion

In this study, we tested 20 TF mutants and 48 paired double mutants that influence aliphatic GLS accumulation to systemically explore whether these TFs also influence plant growth and flowering time. Seventeen of these 20 TFs significantly influence plant growth and flowering time. Interestingly, the key aliphatic GLS regulators MYB28 and MYB29 have little influence on plant growth and flowering. In addition, no simple mechanistic connection between the TFs’ effects on the accumulation of aliphatic SC GLS, growth, or flowering was found. These findings fit with the emerging coordination model whereby a network is dynamic and highly responsive to the specific requirements of a specific environment. We propose that the internal and external signals are coordinated and perceived via connections between these diverse TFs. These connections create a decision matrix whereby growth and aliphatic GLS are interpreted and coordinated. Independent connections to growth and aliphatic GLS then proceed from this matrix to maximize plant fitness in a given environmental setting (Figure 10). Further, this suggests that TF networks provide an unappreciated potential to fine-tune both growth and defense to optimize modern agriculture.

Figure 10.

Figure 10

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Proposed model for TF coordination of plant development and aliphatic GLS-based plant defense. External environmental and internal developmental signals are coordinately perceived via a group of TFs. Direct and indirect connections between these TFs allow for a coordinated response to these complex signals. A theoretically cohesive response is then transmitted to growth and defense via separate outputs from this network.

Growth and defense vs. coordination

The canonical model for plant growth and plant defense is a trade-off model, which treats plant growth and defense as two competing biological processes under the assumption that the acquisition of elements and energy are limiting. As an alternative hypothesis, growth and defense are two separable outputs of the plant’s regulatory system that must be coordinated depending on the specific environment. The analysis of the phenotypes in this TF collection and epistatic network in aliphatic GLS pathway supported the existence of the coordination model. The complex interactions between growth and defense depended on the specific perturbed TF, the environment and their epistatic effects. We found no consistent evidence of any negative relationship between the accumulation of the SC GLS defense metabolites and any measurement of growth. Future experiments will need to incorporate more complex regulatory relationships to truly understand how growth and defense are related in the field.

One complication to the interpretation of these results is pleiotropy associated with the use of T-DNA mutants. Recent work shows that individual Arabidopsis T-DNA mutants can have chromosomal rearrangements that could potentially affect the phenotype via off-target effects linked to growth pleiotropy (Jupe et al. 2019). However, those are likely limited to individual TFs and are unlikely to affect a whole collection of 20 TFs. We show that the whole collection supported a lack of a correlation between growth and defense. Further, recent evidence suggests that TFs that directly control GLS genes or GLS metabolites themselves can directly influence growth (Zhao et al. 2008; Kerwin et al. 2011; Khokon et al. 2011; Wang et al. 2011; Urbancsok et al. 2017; Zhu and Assmann 2017; Salehin et al. 2019). For example, MYB29 can control growth in response to differential nitrate potentially by its direct interaction with the CCA1 promoter (Gaudinier et al. 2018). However, to fully understand how the plant is coordinating growth and defense will require a complete catalog of all TF-TF interactions at the protein and promoter levels. This catalog would clarify if genetic effects that we classify as indirect are in fact direct interactions that, instead of being a single molecular step, i.e., TF to promoter, utilize a longer cascade of connected molecular processes than are typically studied.

Using transcription factors to tune and optimize growth and defense

Putative costs of plant defenses on growth have been intensively researched, and recent findings suggest that it is possible to promote one without sacrificing the other (Campos et al. 2016; Wang et al. 2018). Our current study further expands the future potential to optimize plant growth and defense through TF manipulation. Specifically, we found that a vast array of potential TFs can be identified and potentially used to manipulate plant growth and defense. In our system, mutations in more than half of the tested TFs could significantly promote plant growth and defense together across diverse environmental conditions. Critically, this ability to promote both defense and growth is conditional on specific environments. If this conditionality is true in other genes and TFs involved in plant growth and defense, it will raise the importance of studying plants’ response to genetic manipulation under different growth conditions. This is particularly relevant to crop-breeding efforts in response to climate change and environmental stresses. However, studies involving larger collections of TFs and epistatic interactions across even more diverse environments are needed to further understand the coordination between growth and defense across fluctuating environments.

Acknowledgments

Funding for this work was provided by the National Science Foundation (awards IOS 1655810 and 1547796 to D.J.K.), the US Department of Agriculture National Institute of Food and Agriculture (Hatch project number CA-D-PLS-7033-H to D.J.K.) and by the Danish National Research Foundation (grant DNRF99 to D.J.K.). S.M.B. was partially funded by a Howard Hughes Medical Institute faculty scholar fellowship.

Footnotes

Supplemental material available at figshare: https://doi.org/10.25386/genetics.11388195.

Communicating editor: J. Birchler

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

All genotypes are available upon request. File S1 contains all supplemental figures and tables mentioned within the text. Supplemental material available at figshare: https://doi.org/10.25386/genetics.11388195.

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