Trade-Offs Between Plant Growth and Defense Against Insect Herbivory: An Emerging Mechanistic Synthesis

ANNUAL REVIEWS Further Click here to view this article's online features: Download figures as PPT slides Navigate linked references Download citations Explore related articles Search keywords Annu. Rev. Plant Biol. 2017. 68:513 34 First published online as a Review in Advance on January 30, 2017 The Annual Review of Plant Biology is online at plant.annualreviews.org https://doi.org/10.1146/annurev-arplant-042916-040856 Copyright c 2017 by Annual Reviews. All rights reserved Trade-Offs Between Plant Growth and Defense Against Insect Herbivory: An Emerging Mechanistic Synthesis Tobias Züst 1 and Anurag A. Agrawal 2 1 Institute of Plant Sciences, University of Bern, 3013 Bern, Switzerland; email: tobias.zuest@ips.unibe.ch 2 Department of Ecology and Evolutionary Biology and Department of Entomology, Cornell University, Ithaca, New York 14853; email: aa337@cornell.edu Keywords resource allocation, growth rate estimation, induced plant defense, leaf economics spectrum, plant insect interactions, signal transduction networks Abstract Costs of defense are central to our understanding of interactions between organisms and their environment, and defensive phenotypes of plants have long been considered to be constrained by trade-offs that reflect the allocation of limiting resources. Recent advances in uncovering signal transduction networks have revealed that defense trade-offs are often the result of regulatory decisions by the plant, enabling it to fine-tune its phenotype in response to diverse environmental challenges. We place these results in the context of classic studies in ecology and evolutionary biology, and propose a unifying framework for growth defense trade-offs as a means to study the plant s allocation of limiting resources. Pervasive physiological costs constrain the upper limit to growth and defense traits, but the diversity of selective pressures on plants often favors negative correlations at intermediate trait levels. Despite the ubiquity of underlying costs of defense, the current challenge is using physiological and molecular approaches to predict the conditions where they manifest as detectable trade-offs. 513

Trade-off: a fitness-imposed constraint on two competing functions, resulting in patterns of negative association between the functions Contents INTRODUCTION............................................................... 514 THEORETICAL CONSIDERATIONS OF COSTS OF DEFENSE ANDGROWTH DEFENSETRADE-OFFS... 515 GENETICCOSTSOFDEFENSE... 518 COSTS OF RESISTANCE ACROSS PLANT COMMUNITIES AND THE LEAFECONOMICSSPECTRUM... 518 INDUCEDDEFENSEANDPHENOTYPICCOSTS... 521 THE MOLECULAR SIGNAL TRANSDUCTION NETWORK THAT REGULATESGROWTHANDDEFENSE... 521 GROWTHRATEESTIMATION... 525 CONCLUSION... 527 INTRODUCTION For the past several decades, research on costs of plant defense has been a highly active area in plant biology, ecology, and evolution (16, 23, 25, 49, 103, 123). The basis for this interest comes from a variety of questions asked by plant scientists, key among which are (a) why plant individuals or communities express intermediate levels of defense and do not evolve higher levels (78, 93), (b) why breeding for enhanced yield can result in the unintentional loss of defenses (21), and (c) why levels of defense typically increase after damage (induced defense) (55). The commonly invoked answer to all three questions is that defensive traits have a cost that results in trade-offs with other plant functions, such as growth or reproduction. In this review, we revisit the theoretical considerations that underlie growth defense trade-offs and place them in the context of recent findings from plant physiological and molecular biology to outline an updated predictive framework for the study of costs of defense. Growth of vegetative tissue is a key physiological process for the majority of a plant s lifetime and an important component of overall plant performance (Figure 1). Plant growth rate the gain in biomass per unit time is a function of the conversion of primary metabolites into cells and cellular building blocks and depends on the balance of resource sources (leaves and roots for carbohydrates and nitrogen, respectively) and sinks (122). Herbivory can severely impair plant growth and often favors better-defended individuals, yet any redirection of energy and matter from primary metabolism to defense should reduce growth and reproduction. Thus, understanding the mechanisms that govern trade-offs between growth and defense is important, particularly given that rapid growth is a desirable trait in many agricultural crop species. For costs of defense to affect evolutionary processes in natural systems, only those costs that ultimately impact plant fitness are relevant, but true plant fitness (i.e., the number of successful offspring in future generations) is exceedingly difficult to measure. For practical reasons, most experiments therefore rely on surrogate fitness measures. Measures of seed set are commonly used (e.g., 76, 78, 94), but these capture only female fecundity and typically neglect the other half of fitness (siring of seeds by pollen). Alternatively, plant growth is often used as a measure of plant performance (e.g., 6, 54, 88, 137), particularly in long-lived species. It is crucial to note that all surrogate measures are highly context dependent, and their use may be more or less appropriate in different experiments. Differences in growth rate can be highly relevant for success at key stages, such as the colonization of new habitat patches involving a scramble for resources. 514 Züst Agrawal

Defense Herbivory Resources Growth Competition Annu. Rev. Plant Biol. 2017.68:513-534. Downloaded from www.annualreviews.org Figure 1 Reproduction Fitness Conceptual diagram of plant fitness determination via the combined effects of resource allocation to competing plant functions (green arrows), resource-based trade-offs (gray lines), and ecological interactions (black arrow and lines). Arrows indicate positive effects; lines with bars at the ends indicate negative effects. The sizes of the green arrows represent the relative amounts of allocated resources. Although growth, defense, and reproduction should ultimately trade off, equal allocation to two of the three functions may also result in positive covariance. Allocation to growth returns resources that may be reallocated to other functions. A higher growth rate may increase competitive dominance and reduce the negative impacts of competitors. Allocation to defense is diffuse, and individual defense traits may impose only minor drains on the overall resource pool, but overall allocation to defense should negatively affect growth. The costs of defense are offset at least partly by a reduced impact of herbivory. At such key stages, small differences in growth can have large impacts on the overall fate of a plant (opportunity costs) (25). For example, in cases of simultaneous germination of annual plants, growth rates measured on individual plants proved to be the best predictor of the outcome of direct plant plant competition (136) and of the eventual genotypic dominance in a selection experiment with intense competition (33). In this review, we specifically focus on trade-offs between investment in defense and plant growth (as opposed to other components of fitness) for two reasons. First, as discussed above, we assume that growth ultimately impacts fitness under many conditions and that growth represents a common currency that can be readily measured in all plant species. Second, and more importantly, plant growth serves as an intermediate stage between resource allocation and fitness and is of key importance to link the molecular underpinnings and physiological basis of plant allocation to trade-offs. We highlight recent progress in the understanding of plant regulatory networks that provide a mechanism for plants to fine-tune their phenotype and maximize fitness under limited resources. Finally, we discuss a set of essential methods that should be part of the toolbox of biologists interested in studying costs of defense. THEORETICAL CONSIDERATIONS OF COSTS OF DEFENSE AND GROWTH DEFENSE TRADE-OFFS Metabolic costs involved in the expression of a defense are ubiquitous and can be considerable. Each defensive trait or compound requires not only precursor molecules from a plant s primary metabolism for synthesis, but also energy and precursors for the machinery involved in synthesis, modification, transport, and maintenance or storage (38). Gershenzon (39) estimated the expenditures on all metabolic pathways involving terpenoid synthesis and concluded that these compounds are among the most energetically expensive defensive metabolites a plant may produce, even though they consist largely of carbon, which is not typically a limiting resource in plants. Similarly, Bekaert et al. (14) used a mathematical metabolic model of Arabidopsis thaliana to demonstrate that synthesis of the main defensive compounds of the Brassicaceae, the glucosinolates, increases the plant s photosynthetic requirement by at least 15%. Nonetheless, metabolic Opportunity costs: losses of competitive status early in plant ontogeny as a result of allocation to defense instead of growth Metabolic costs: the total energy and resource expenditures for the production and maintenance of a defense trait, including metabolic pathways and building blocks www.annualreviews.org Trade-Offs Between Plant Growth and Defense 515

Allocation costs: reductions in growth or reproduction as a direct consequence of diversion of limiting resources to defense processes Genetic costs: trade-offs between growth and defense traits based on underlying (quantitative or discrete) genetic variation Ecological costs: side effects of a plant s defense investment on its ecological interactions, such as increased susceptibility to specialist herbivores or negative impacts on mutualists costs will not always result in detectable trade-offs with growth (7, 16, 63), or indeed affect fitness. Understanding the conditions under which trade-offs manifest is thus of central importance. A trade-off between two functions could originate from at least three, non-mutually-exclusive mechanisms: (a) Traits may be directly limited by competition for resources (allocation costs), (b) traits may be linked genetically through pleiotropy or linkage disequilibrium (genetic costs), or (c) coexpression of traits may be penalized depending on a plant s environment (energetic limitation or ecological costs). The relative importance of the different mechanisms depends at least in part on the hierarchical level of a trait. Defense as a whole-plant phenotype (often quantified as the inverse of herbivore consumption) and growth rate are the product of many underlying traits and functions (26). The overall defense level of a plant is a function of lower-level traits related to primary metabolism, such as physiological leaf properties (e.g., protein and lignin content), as well as the concentrations of defensive secondary metabolites. Similarly, growth rate is a function of traits related to nutrient acquisition and photosynthetic function. With decreasing trait hierarchy, fewer genes are generally involved in the expression of a trait, making direct genetic costs more likely to occur. Indeed, trade-offs with other functions involving genetic costs have been frequently demonstrated for low-level traits (6, 76, 101, 107, 129). Direct competitive resource limitation of two traits (allocation costs) is perhaps the most commonly considered causal driver of trade-offs (102), but this view may be an oversimplification of complex plant allocation processes (55) (Figure 1). For example, if a plant is able to compensate for an increase in allocation to either growth or defense by reducing allocation to a third process, no growth defense trade-off may be observed, despite significant changes to the overall plant phenotype. Pleiotropy and linkage disequilibrium may explain how trade-offs can have a genetic basis but provide little explanation for why they are maintained over evolutionary time. We therefore propose a conceptual framework that emphasizes selective pressures as key elements in the creation and maintenance of trade-offs at the physiological level. Within the physiological limits determined by the total amount of resources available, the relative fitness achieved by a specific trait combination is primarily determined by past natural selection and current environmental conditions (Figure 2). Very low values of either growth or defense are commonly disfavored by natural selection, as evidenced by the competitive advantage of faster-growing individuals in colonization scenarios (33, 136) and the severe herbivore damage experienced by plants with defenses that have been artificially abolished (58, 59). Opposing the selection to increase trait levels are a set of mechanisms such as diminishing returns of traits with increasing investment, as well as extrinsic selective pressures such as ecological costs (102) (see sidebar titled Trait Limitation by Ecological Costs). The balance between opposing selective pressures may favor intermediate levels of both growth and defense traits (Figure 2). Indeed, stabilizing selection on intermediate defense levels because of the associated costs has been repeatedly demonstrated (20, 78, 91, 124), but note that strong selection may occasionally drive trait levels to the edge of physiological limits (93, 112). High levels of coexpression for growth and defense may thus be physiologically possible at the expense of a third function, but will likely be maladaptive in most natural environments and result in the removal of a trait combination from a population by natural selection. This framework highlights three key considerations: First, trade-offs may be absent in environments that lack specific selection pressures (such as environments lacking competition); second, trade-offs may be undetectable if plants are examined outside of their natural environment; and third, if plant growth and defense are the only traits of interest, high levels of both may be achieved by selective breeding. In the following sections, we provide an overview of some of the major approaches that have been used to detect and quantify costs of defense and place them in the context of our framework. 516 Züst Agrawal

Competition Costs Physiological trait limit Growth Annu. Rev. Plant Biol. 2017.68:513-534. Downloaded from www.annualreviews.org Figure 2 Herbivory Defense Hypothetical relationships among growth, defense, and plant fitness. The expression of traits is constrained by an upper physiological limit (solid line), at which all resources are allocated exclusively to either growth or defense. Reduced resource availability may shift this limit inward (dashed line). Within physiological limits, the fitness landscape is variable, and the shape of the fitness ridge (blue) is affected by natural selection (green arrows). Pervasive costs of defense in generally resource-limited environments will often favor intermediate trait levels. Natural selection will favor genotypes at peak fitness (black symbols) while penalizing more extreme genotypes (gray symbols), resulting in negative correlations and detectable trade-offs. Costs TRAIT LIMITATION BY ECOLOGICAL COSTS Ecological costs are easily overlooked in the study of plant growth and defense because they manifest themselves only through interactions with the biotic and abiotic environment of a plant (102). For example, resource allocation to defense can have negative impacts on other fitness-relevant functions that affect such interactions e.g., high levels of a defense trait may deter mutualists such as pollinators and indirectly reduce seed production (109). But even if herbivory is the main environmental interaction of a plant, allocation to any one defense trait may not necessarily increase plant resistance to all herbivores, particularly in interactions involving coevolved, specialized insects with high resistance to their host plant s defensive arsenal (32, 100). High allocation to a defense trait in response to a generalist herbivore may in fact increase susceptibility to specialists (e.g., 40, 43, 100). This effect is likely exaggerated if different defensive compounds are subject to trade-offs. As is the case for defense, growth rates are often well below the physiological maxima in many species (8, 81), and although direct costs are likely important (98), growth rates are also likely to be subject to ecological costs. Increased growth rates are generally the direct result of increased photosynthetic efficiency (quantified as the net assimilation rate), which is achieved by thicker, more chlorophyll-rich leaves (137). For any given level of defense, herbivores will thus likely prefer faster-growing, more nutritious individuals and exert negative selection on the growth phenotype. www.annualreviews.org Trade-Offs Between Plant Growth and Defense 517

GENETIC COSTS OF DEFENSE Because of their importance for the microevolutionary dynamics of populations, a wide range of studies have focused on genetic costs of specific defensive traits of interest (e.g., 6, 54, 88, 137). In these studies, traits of low-defense and high-defense plant genotypes are typically measured in environments without herbivores, and the cost of defense is inferred as the relative growth or fitness reductions suffered by high-defense genotypes relative to low-defense genotypes. Such genotype comparisons allow for direct predictions of population change in response to herbivore selection pressures. However, as discussed above, the detection of potential trade-offs using this approach relies to a large extent on the specific selective environment in the source population, and thus may not unequivocally demonstrate a mechanistic, causal basis of costs of defense. Stressful environmental conditions and nutrient limitation are hypothesized to increase the likelihood of the manifestation of trade-offs (15, 63). Decreasing levels of resources will likely constrain the potential trait space of the fitness landscape (Figure 2) and increase penalties for high trait expression levels (13). Indeed, a set of milkweed genotypes grown at two nutrient levels exhibited a stronger negative correlation between growth rate and defense (cardiac glycoside concentration) at low nutrient availability (137). As an alternative to using natural genotypes from environments with uncertain selective pressures, a powerful method to demonstrate trade-offs is the manipulation of selection regimes using either artificial selection (107, 108) or natural selection by manipulating the presence of herbivores (2, 135). Studies that manipulate selection have the advantage of starting from a known phenotypic point of reference that serves as a control for the comparison with the altered genotypic composition of a population. If a specific selection regime favors one trait and increases its expression, a concordant decrease in another trait provides strong support for genetic trade-offs between these traits. Guiding natural selection via herbivore manipulation has the added benefit of removing the need for the experimenter to artificially select traits of interest, because herbivore feeding and any associated ecological effects will favor traits with the strongest effect on herbivore performance and plant fitness. For example, selection by two specialist aphids exerted strong opposing selection on a biochemical genetic locus in A. thaliana that had not previously been associated with resistance to aphids (135). Among the caveats that make both natural and artificial selection approaches challenging is the often long life span of plants. Genetic approaches have therefore emerged as an alternative method to manipulate trait levels and observe corresponding changes in other traits (22, 92, 106, 130, 136). Genetic manipulation generally limits the differences between a manipulated genotype and a control genotype to a single altered gene, and such approaches may more thoroughly explore the full range of physiologically possible trait levels. Nonetheless, genetic manipulation studies of constitutive defense have thus far employed mostly a sledgehammer approach by targeting major-effect loci that radically change the defensive phenotype of a plant (e.g., 59, 136), but at least in some cases, natural selection may act on such major-effect loci (17, 31, 118). Exploring the link between targets of genetic manipulation and naturally occurring genetic variants that are subject to natural selection is a critical frontier to make the study of costs of defense both more rigorous and evolutionarily relevant. COSTS OF RESISTANCE ACROSS PLANT COMMUNITIES AND THE LEAF ECONOMICS SPECTRUM In an attempt to find general patterns in biology, growth defense trade-offs are increasingly studied not only in individual plants or species, but also across many species of an ecosystem for 518 Züst Agrawal

example, trees in a tropical rain forest (24, 37) or plants in a grassland community (71). A major axis of cross-species leaf physiology not directly related to defense has been termed the leaf economics spectrum (96, 125). This concept describes correlations among leaf physiological traits that govern resource investments by plants to determine strategies ranging from resource acquisitive (fast growing, short lived) to resource conservative (slow growing, long lived). The leaf economics spectrum has had some success in predicting trade-offs between growth and defense investment (44, 82), and multiple studies using cross-species comparisons have found evidence for such trade-offs (24, 37, 71). The selective pressures that shape a plant s fitness landscape may vary widely at a local scale, but at a more global scale, they may average out and reveal a general disfavoring of high coexpression of defense and growth (Figure 2). However, although large-scale species comparisons can provide important support for the existence of trade-offs, there are both conceptual and methodological pitfalls that require careful interpretation and make this approach prone to false conclusions from a mechanistic perspective. The implicit assumption of comparative studies with a community focus is that growth and defense are major axes of differentiation for all species of that community. Herbivory should thus act as a strong force that reduces plant performance, herbivores should respond to plant defensive traits in similar ways, and herbivore impacts should be negatively correlated with plant defense investment. In reality, most plant species are likely to have differentiated along many different axes in response to diffuse coevolution with a whole community of herbivores and other environmental variables (52, 89, 90). Thus, although trade-offs are likely drivers of growth and defense levels among genotypes within each species, the same is not always true across species. Because different plant species produce widely different mixtures of defensive chemicals, broad comparative studies among distantly related species of a natural community often forgo quantification of such specific defenses and instead focus on traits that are easily quantifiable across a wide range of species (e.g., leaf thickness or ash content) (72, 80). Such analyses can result in the erroneous conclusion that secondary metabolites are not important for plant defense (see 5). A common alternative approach is to focus on a high-level measure of defense, such as the inverse of plant damage by reference herbivores in a bioassay or a community of attackers in the field (57, 103). The results of biodetector studies that use generalist herbivores or total damage in the field come with several caveats. First, the relative selective impacts of generalist herbivores compared with specialists are unclear. Even though slugs and browsing mammals are prevalent and can be devastating when they feed on a plant, they may be too undiscriminating to exert consistent selective pressures on individual plant species. Because specialists and generalists may respond divergently to the same plant secondary metabolites, measures of total damage in the field are typically difficult to interpret (5). Second, few insect herbivores are true generalists, and laboratory favorites such as Spodoptera caterpillars are not significant herbivores in most natural plant communities. Finally, simple measures of total herbivore damage can often be driven by variation in plant nutritional quality, which may not be directly related to other measures of defense investment. All three points highlight that although generalist herbivory can be a useful measure of defense, it may often be unrelated to the costs and benefits of the defensive traits that are most prominently under natural selection in the field. The implementation of the leaf economics spectrum for the study of costs of defense is perhaps most promising in a phylogenetically controlled set of related species. For example, Mason et al. (77) demonstrated that within a large group of wild sunflower species native to North America, higher levels of leaf toughness and tannins were associated with a more resource-conservative, slower-growing leaf economics strategy. Species in a monophyletic group originated from the same ancestral state, and thus, as long as environmental variation is accounted for, trade-offs present among these species likely reflect underlying costs of defense. Even so, results from such www.annualreviews.org Trade-Offs Between Plant Growth and Defense 519

studies must be interpreted with care. Leaf physiological traits may well show different results at different scales. High leaf density, as expressed by leaf mass per area, is associated with slow growth of species along the leaf economics spectrum, and the same association can be found for single species grown in different environments (3, 137). For example, milkweed plants under nutrient limitation grew more slowly and produced smaller, thicker leaves than those with the same genotypes and high nutrient availability (137) (Figure 3a). However, variation in functional traits likely has different causes between and within environments. Within an environment, photosynthetic efficiency [net assimilation rate (NAR)] may be fixed, but because of the functional and mathematical relationships between these traits, genotypes with low NAR could hypothetically increase their growth by enlarging leaf area for example, via cell extension (Figure 3b). Such plasticity may reverse predicted correlations of the leaf economics spectrum within single-species, single-environment comparisons (137) (Figure 3a,c). Annu. Rev. Plant Biol. 2017.68:513-534. Downloaded from www.annualreviews.org a Nutrient availability b RGR 0.4 0.3 0.2 0.1 Low 0.0 2.0 2.5 3.0 3.5 4.0 4.5 5.0 LMA Low LMA (mg cm 2 ) 4.4 5.1 3.1 4.1 NAR (mg cm 2 day 1 ) 0.7 1.8 0.8 1.8 RGR (mg mg 1 day 1 ) 0.12 0.19 0.20 0.23 c RGR 0.30 0.25 0.20 0.15 0.10 High 3.0 3.5 4.0 4.5 5.0 5.5 LMA High Figure 3 (a) Effects of the environment on leaf traits. Plants growing at reduced nutrient availability (left) produce smaller, thicker leaves and achieve lower growth than plants with high nutrient availability (right). Photosynthetic conversion efficiency [quantified here as net assimilation rate (NAR)] is a genotype-specific trait, but lower efficiency may be compensated for in part by water-driven cell elongation [decreasing the leaf mass per area (LMA)]. Values are extreme genotype means selected from a set of milkweed genotypes grown at two fertilization levels (137). (b) Visualization of hypothetical compensation for reduced photosynthetic efficiency. At a common leaf density, reduction of photosynthetic efficiency by 50% (gray circles) results in a 50% decrease in growth rate relative to a control state (blue circle and dashed line). Conversely, increasing leaf area via a reduction in LMA by cell elongation may increase growth rate. (c) Because of this plasticity, comparisons between species or environments may predict a negative relationship between LMA and growth (solid line), whereas within-species comparisons are more likely to uncover positive relationships (blue circles and dashed lines). Additional abbreviation: RGR, relative growth rate. 520 Züst Agrawal

INDUCED DEFENSE AND PHENOTYPIC COSTS The third common approach to studying growth defense trade-offs takes advantage of induced responses to herbivory, or the increased production of plant secondary metabolites following attack by herbivores. Defense induction itself is viewed as a cost-saving strategy in the face of unpredictable and variable herbivore pressures, where high costs may limit constitutive expression of defense (45). Defense pathways are highly conserved across many plant species, and although species differ in their defensive traits, damage caused by chewing herbivores generally triggers a spike in the production of jasmonic acid ( JA), which induces a signaling cascade to increase plant defense levels (50). JA induces a remarkably diverse set of defensive traits in different plant species, including alkaloids, oxidative enzymes, glucosinolates, cardenolides, trichomes, volatile organic compounds, and extrafloral nectar (1). Exogenous application of JA to a plant can therefore stimulate defense induction without damaging plants or reducing their photosynthetic leaf area. A large number of studies using a wide range of plant species have demonstrated inhibitory effects of JA and defense induction on growth (10, 27, 29, 46, 48, 51, 94, 116, 133). Activation of the JA signaling pathway results in complex changes in a plant s metabolism in addition to inducing defenses. For example, Halitschke et al. (42) demonstrated that elicitation of the JA pathway in Nicotiana attenuata directly resulted in a downregulation of photosynthesis, whereas other studies showed little change in photosynthetic rate following JA elicitation in A. thaliana (10, 19). JA-mediated defense induction can also affect carbon fluxes between shoots and roots (11, 36, 41, 79, 97, 99). For example, induction of N. attenuata by JA increased defensive compounds in leaves but decreased sugar and starch reserves in roots, impairing subsequent regrowth potential from roots (74). Similarly, treatment of A. thaliana leaves with JA resulted in a temporary increase in the allocation of new photoassimilates to roots and a more long-term allocation to treated leaves, resulting in increased accumulation of defensive compounds (36). The changes in allocation and decrease in growth following induction are generally interpreted as a cost of increasing the levels of a defense, although the comparison of plant growth between induced and uninduced plants may include other strategic allocation changes in the plant (4, 13, 47, 84). The altered carbon allocation and storage may thus be driven partly by costs of defense and partly by the plant s anticipation of future herbivory and regrowth. Studies of induced defense generally focus on single genotypes (or uncontrolled mixtures of genotypes), and costs detected in this approach are phenotypic. Estimates of phenotypic costs will often be different from genetic costs, even when considering the same defensive traits. Nonetheless, the major advantage of such comparisons is the potential for a more mechanistic understanding of the underlying metabolic costs, particularly if the study uses targeted approaches, such as the quantification of 15 N allocation to nicotine in tobacco following induction (12). A combination of approaches and quantification of induction-mediated trade-offs across natural genotypes of a species promises to be a powerful tool to elucidate the full fitness landscape that drives plant defensive phenotypes. Phenotypic costs: negative correlations between measures of growth and defense with unknown or no genetic contributions to trait variation THE MOLECULAR SIGNAL TRANSDUCTION NETWORK THAT REGULATES GROWTH AND DEFENSE Recent advances in the investigation of regulatory pathways that mediate growth defense tradeoffs have renewed interest in the study of costs of defense (46, 51). Different phytohormones have long been known to affect each other through networks of stimulating and inhibiting interactions (65) (Figure 4), including the prominent example of antagonism (or crosstalk) between the defense www.annualreviews.org Trade-Offs Between Plant Growth and Defense 521

Shade Herbivory HAMP FAC DAMP phyb WRKY signaling in rice WRKY70 SIPK WIPK GA20ox7 ICS ACS2 LOX SA ET GA JA Annu. Rev. Plant Biol. 2017.68:513-534. Downloaded from www.annualreviews.org Figure 4 PIF Growth GID1 DELLA JAZ JA-Ile COI1 MYC Defense Working model of the plant defense signaling network for regulation of growth defense trade-offs. Herbivory-associated cues, such as wounding [damage-associated molecular patterns (DAMPs)] and oral secretion [herbivory-associated molecular patterns (HAMPs) and fatty acid conjugates (FACs)], activate mitogen-activated protein kinases (MAPKs). Wound-induced protein kinases (WIPKs) primarily stimulate jasmonic acid ( JA), and salicylic-acid-induced protein kinases (SIPKs) additionally stimulate ethylene (ET) and suppress salicylic acid (SA) synthesis. In rice (light yellow highlight), an early trade-off is regulated by the transcription factor WRKY70, which activates JA and ET synthesis genes but suppresses gibberellin (GA) and SA synthesis genes. Note that the function of WRKY70 appears to be highly species specific. The GA pathway is partially under the control of external stimuli (e.g., shading) and results in the activation of growth-promoting transcription factors, such as phytochrome-interacting factor (PIF), whereas the JA pathway activates the defense-promoting transcription factor MYC. Phytohormones further interact via largely unknown mechanisms (dashed lines). In particular, the GA and JA pathways act as reciprocal suppressors via an interaction of their respective transcription factors and suppressor proteins JASMONATE ZIM DOMAIN ( JAZ) and DELLA. Arrows indicate positive effects; lines with bars at the ends indicate negative effects. Additional abbreviations: ACS2, 1-AMINO-CYCLOPROPANE-1- CARBOXYLATE SYNTHASE 2; COI1, CORONATINE INSENSITIVE; GA20ox7, gibberellin 20 oxidase 7; GID1, GIBBERELLIN-INSENSITIVE DWARF 1; ICS, isochorismate synthase; JA-Ile, JA-isoleucine conjugate; LOX, lipoxygenase; phyb, phytochrome B. hormone JA and salicylic acid (SA) (111). Additional components of the plant signaling network continue to be identified and understood, including feedback loops of defensive metabolites on phytohormones (e.g., 18, 56). An emerging consensus highlights the close regulatory control of growth and defense by the plant and identifies negative associations between growth and defense not as the direct result of allocation costs, but rather as prioritization of one process over another (19, 51, 60, 73, 99, 104, 121, 128). Trade-offs between growth and defense thus may more often reflect the range of trait combinations that achieve optimal fitness, rather than representing hard physiological limits. 522 Züst Agrawal

Levels of defense are regulated by central nodes in the plant signaling network, and manipulation of these nodes can have significant consequences for the defensive phenotype of a plant. For example, induction of defensive nicotine in N. attenuata is mediated by JA and involves conjugation of JA with isoleucine (Ile) to create JA-Ile and the binding of JA-Ile to the receptor CORONATINE INSENSITIVE 1 (COI1). Mutants deficient in JA biosynthesis show an 80% reduction in induced nicotine levels (74, 75, 120). Plants impaired in the conjugation of JA to JA- Ile exhibited only a 30% reduction of induced nicotine (120), whereas silencing of COI1 resulted in a 50% reduction of induced nicotine (86). The variable phenotypic effects of the various nodes highlight their potential usefulness for the manipulation of growth defense phenotypes, but also the need for a better understanding of unintended side effects of such manipulations under natural conditions. The study of natural variation in growth defense prioritization among genotypes promises to provide microevolutionary insights into the fate of different defense allocation strategies, yet to our knowledge, few studies have attempted this. As an example, Todesco et al. (113) demonstrated how variation at a single genetic locus associated with the JA signaling pathway accounted for a considerable fraction of the variation in the growth defense phenotypes of a large set of natural genotypes of A. thaliana. A comprehensive approach to studying the growth defense signal transduction network should include both among-genotype and across-species comparisons and should therefore allow the combination of the currently separate fields of inducible defenses, genetic costs, and community-wide plant defensive strategies. Below, we discuss the current knowledge of key nodes in the plant signal transduction network that most likely determine a plant s allocation decisions and represent the targets for such comparative studies. Herbivore recognition and defense responses in plants involve complex regulatory and phytohormonal pathways (69, 79, 128, 131). Herbivore damage releases general damage-associated molecular patterns (DAMPs) and often involves specific herbivore-associated molecular patterns (HAMPs) such as salivary proteins [e.g., fatty acid conjugates (FACs)] (127). DAMPs, HAMPs, and FACs activate mitogen-activated protein kinases (MAPKs) (127, 132), which initiate signal transduction pathways that result in both increased defense and a slowed rate of growth (79). MAPKs are therefore important switches that can be disabled or overexpressed to manipulate induced responses or constitutive levels of defense to elucidate the phenotypic costs of defense induction. MAPKs differ in their specificity and connectivity with the signal transduction network and thus offer the potential for sophisticated manipulation of plant defense strategies. For example, wound-induced protein kinase (WIPK) is a direct positive regulator of JA and downstream defense responses, whereas salicylic-acid-induced protein kinase (SIPK) acts as both a positive regulator of JA and ethylene and an inhibitor of SA (79). In a defense-elicitation experiment using oral secretions of caterpillars, WIPK-silenced N. attenuata plants accumulated less JA and benefited from higher growth and seed production relative to control plants. By contrast, SIPK-silenced plants accumulated high levels of SA and did not benefit from reduced JA sensitivity, presumably because of the higher costs associated with SA-mediated defense (79). It is unclear to what extent genetic variation in MAPKs is linked to genotypic variation of natural plants in constitutive defense levels and their inducibility. The WRKY transcription factors represent a second important class of key regulators of defense (69, 131). A comprehensive study of signaling in rice demonstrated that the MAPK-activated transcription factor WRKY70 is an early positive regulator of JA signaling following herbivory by leaf chewers but is also a suppressor of growth (69). WRKY70 acts to upregulate the lipoxygenase (LOX) gene involved in JA synthesis (134) (Figure 4) and concurrently inhibits the plant growth hormone gibberellin (GA) by suppressing the GA synthesis gene gibberellin 20 oxidase 7 (GA20ox7), resulting in a stunted phenotype (69). Supplementing exogenous GA restores a normal www.annualreviews.org Trade-Offs Between Plant Growth and Defense 523

growth phenotype, suggesting the causal role of GA suppression by WRKY70 (69). Overexpression of WRKY70 results in high levels of JA and resistance to chewing herbivores but simultaneously increases susceptibility to sucking herbivores of rice plants. WRKY70 also acts as an inhibitor of isochorismate synthase (ICS) (Figure 4), a key enzyme of SA synthesis (126) that is generally required for resistance to sucking herbivores (119). WRKY70 could thus be partly responsible for SA JA crosstalk (e.g., 62), although the early positioning of WRKY70 in the rice signaling cascade suggests additional antagonism between the two hormones further downstream (Figure 4). Current work suggests that WRKY transcription factors have roles in defense signaling in diverse plant species, and WRKY70 is generally associated with SA JA crosstalk (67, 68). However, the signal connectivity of WRKY70 and its relative position within the regulatory network appear to be highly species specific. Overexpression of WRKY70 in A. thaliana has the opposite effect as it does in rice, suppressing JA synthesis and upregulating an SA response (67, 68). Similarly, although Li et al. (69) demonstrated that WRKY70 in rice is unaffected by exogenous JA or SA treatment and thus concluded it to be an early regulator of plant defense signaling (Figure 4), WRKY70 in A. thaliana is upregulated by SA and downregulated by JA (68). Given the evidently important role of WRKY70 in growth defense trade-offs, we stress the need for cross-species comparative studies. More generally, WRKY transcription factors appear to be key decision nodes determining plant defensive strategies, and manipulation of these nodes is increasingly used to study the role of different growth and defense pathways in resistance to herbivores (61, 64). However, effective use of this approach will require a more comprehensive understanding of the action of different WRKYs within one species as well as of the same WRKYs in different species. Downstream of MAPK and WRKY in the signal transduction network, additional nodes act to further enforce the regulatory crosstalk between growth and defense (Figure 4). Upon activation of JA synthesis, some JA is enzymatically modified to JA-Ile (105), which indirectly activates transcription factors (such as MYCs) that initiate the plant s defense responses. In uninduced plants, MYC transcription factors are suppressed by JASMONATE ZIM DOMAIN ( JAZ) proteins (34). JA-Ile-activated COI1 binds to JAZ, after which the protein complex is degraded and expression of MYC commences (Figure 4). In an equivalent cycle, the growth-promoting phytochromeinteracting factor (PIF) is inactivated by the DELLA repressor protein (35). In full sunlight, the shade (red:far red ratio) receptor phytochrome B (phyb) suppresses both PIF and GA (19), but upon shading, phyb suppression is released and GA synthesis is stimulated. Activation of the GA pathway initiates the formation of complexes between free GA and the proteins GIBBERELLIN- INSENSITIVE DWARF 1 (GID1) and DELLA. This releases PIFs from DELLA suppression and allows them to bind to their target gene promoters and activate a growth response (35) (Figure 4). Reduced phyb activity also causes degradation DELLAs to further stimulate PIFs (66). The GA-GID1-DELLA complex interacts with SA in A. thaliana (83), and in fact shading of plants can suppress both SA and JA responses and increase susceptibility to natural enemies (30). Interestingly, a mutually negative interaction takes place between JAZ and DELLA (110, 128); therefore, because the activation of the JA pathway results in the COI1-mediated degradation of JAZ, JAZ no longer suppresses DELLA, which in turn strengthens the suppression of PIFs and plant growth (Figure 4). Campos et al. (19) recently demonstrated that a dual inactivation of JAZ and phyb in A. thaliana results in a phenotype that is both well defended and fast growing. Similarly, GA complementation of JA-induced N. attenuata plants is sufficient to restore a normal growth phenotype while maintaining higher defense levels (R.A.R. Machado, I.T. Baldwin & M. Erb, manuscript in preparation). These examples demonstrate that, at least under optimal laboratory conditions, negative trait associations are not allocation-based trade-offs, and plants can be forced to grow and defend at the same time. In an important next step, such genotypes with high coexpression 524 Züst Agrawal

of both traits should be taken to the field (or other stressful conditions) to test whether they suffer fitness consequences. The power to directly manipulate aspects of primary and secondary metabolism highlights the need for increased understanding of the plant s regulatory network as well as the typical challenges that plants face in field environments. Nonetheless, there may be tremendous potential for targeted manipulation of plant defense strategies and growth that extends beyond the typical fitness-imposed boundaries (Figure 2). Annu. Rev. Plant Biol. 2017.68:513-534. Downloaded from www.annualreviews.org GROWTH RATE ESTIMATION Growth rates are often the most convenient measures of plant performance, particularly for perennial plants. Growth rates that are measured over a relatively short period compared with a plant s full lifetime will vary in their predictive power for fitness, just as allocation decisions of a plant will have different consequences at different life stages. Perhaps the strongest link between growth rate and fitness occurs at an early stage in a plant s life, when clumps of seeds often fall into small patches of habitat, resulting in potentially strong competition. Differences in germination time and growth rate at this stage can therefore have significant effects on lifetime success (e.g., 117). However, growth rate is a relevant part of plant performance at any point in time, as it represents a measure of resource allocation in real time. As our understanding of the molecular mechanisms that underlie growth and defense increases, we must take care that the methods used to measure and quantify these phenotypes remain appropriate. Growth rate estimation has undergone a paradigm shift from being viewed not as a fixed genetic trait but as a plastic response of the plant to its environment, at least in part because of new technology that has allowed researchers to capture growth responses nondestructively. Historically, growth has often been approximated as the total biomass gained after an arbitrary amount of time selected for the duration of the experiment. However, single biomass measures are inappropriate measures of growth rate because differences in starting size are not accounted for and the final size may be related more to the soil volume available (e.g., pot size) than to the growth rate per se (87, 88). As an improvement over single biomass measures, the growth rate of a plant has often been quantified as the relative growth rate. In its classic form, this rate is the difference between two log-transformed plant size measurements from different time points of plant development divided by the amount of time between the two time points. Because this method requires only two size measurements, its simplicity and ease of use have played a major part in its persistence. Unfortunately, this method is based on the flawed assumption that plant growth follows an exponential function. For exponential growth (linear on the log scale), the rate of growth is constant, and regardless of which time points are chosen for the size measurements, relative growth rate will always reflect this true rate of growth. However, plants are virtually always limited by resource availability and size-dependent slowing of growth resulting from self-shading or increased investment in structural tissues, and they normally grow at a less-than-exponential rate (98, 115). As a consequence, the traditional measure of relative growth rate has repeatedly been demonstrated to provide inaccurate or even false estimates of the true growth rate of plants (88, 114), which may have contributed to the debate about growth defense trade-offs. A more appropriate method to quantify plant growth rate is to fit biologically relevant growth functions (such as power-law or logistic functions) to plant size data and then extract the growth rate from the model parameters (85). Importantly, because these functions are more realistic, they require more model parameters, which increases the necessary size measurements that need to be recorded over the period of interest during plant development. Because the primary growth rate of a plant describes the gain of biomass per unit of existing biomass and time, the most direct methods www.annualreviews.org Trade-Offs Between Plant Growth and Defense 525

PP68CH19-Agrawal ARI 6 April 2017 9:38 Annu. Rev. Plant Biol. 2017.68:513-534. Downloaded from www.annualreviews.org to quantify growth rates are destructive sequential harvest experiments, in which subsets of plants are harvested at regular intervals (88, 136). The main disadvantages of destructive sequential harvests are (a) that high temporal resolution in the sampling quickly results in prohibitively large experiments and (b) that growth rates can be estimated only at the genotype or species level because each plant individual harvested contributes a single temporal data point. Nondestructive methods can also be used to generate repeated size data, ranging from manual measurement of leaf size (137) or quantification of rosette (leaf ) area from photographs (53) to a b c d Figure 5 Example of low-tech leaf area quantification using standard and infrared photography of a small crucifer species. (a) A standard photograph of a potted plant, illustrating potential issues of a complex soil matrix and uneven lighting. (b) The leaf area (black) after supervised leaf classification in the image analysis software ImageJ. A technician chooses color thresholds to maximize correct classification of pixels. Overlaps in color ranges commonly lead to false classification of the background matrix as leaf area, requiring further manual processing to correct. (c) An infrared photograph of the same plant that highlights only photosynthetically active tissue. The plant is illuminated with a red monochromatic light source and photographed with a digital camera modified for infrared light capture. (d) Automated leaf area classification of the infrared photograph based on the whiteness of pixels, which requires virtually no postprocessing. 526 Zu st Agrawal

fully automated plant phenotyping systems (9, 10, 28). As part of the introduction for their own phenotyping system, Cruz et al. (28) provided a useful overview of the various advanced spectroscopic methods used for plant phenotyping and highlighted their advantages and shortcomings. However, although continuous, high-accuracy monitoring of plant growth can provide valuable insights into the dynamics of plant growth (28), such automated systems are often expensive, which has limited their adoption by the plant biology community. We believe that there is a middle ground between labor-intensive manual methods and expensive automated systems. Leaf area quantification based on photographs is already commonly used (e.g., 53), but image processing still requires large amounts of manual input (Figure 5a,b). By contrast, infrared imaging selectively captures photosynthetically active leaf tissue (28) (Figure 5c), which removes the need for labor-intensive image processing (Figure 5d ). Because standard digital cameras can easily be converted to capture infrared spectra, this method is simple to use and readily available. Two-dimensional photography is particularly powerful for quantifying the early leaf growth of rosette-forming plants; although plants naturally minimize leaf overlap to prevent self-shading, this overlap does become more significant as they grow, which consequently reduces the accuracy of leaf area estimates. Nonetheless, two-dimensional infrared photography promises to be useful even for larger plants and more complex growth forms as long as standardized photographs are validated with plant biomass measures. As growth rate estimation has become more sophisticated and closer to biological reality, it has become clear that growth rate is not a fixed trait in plants. As discussed above, for all growth functions except exponential growth, a plant s growth rate intrinsically decreases as its size increases. Furthermore, growth rates likely respond directly to the plant s environment. The vast majority of studies of growth defense trade-offs measure growth rates under standard conditions in the absence of herbivores and correlate them with the levels of defense (e.g., 88, 137). Estimating more sophisticated growth rates comes with the added benefit of enabling their partitioning into physiological components relevant to the leaf economics spectrum, such as net assimilation rate, specific leaf area, and leaf/mass ratio (70, 95, 137). Thus, whereas relative growth rate captures growth at the whole-plant scale, net assimilation rate measures the efficiency of the photosynthetic leaf area and as such may be much more directly related to plant allocation. CONCLUSION Growth defense trade-offs are prevalent across biological systems, and their study is gaining renewed attention through the discovery of the underlying regulatory mechanisms that govern these trade-offs. However, it is becoming apparent that detectable negative associations may only occasionally arise from direct limitation of resource allocation or pleiotropy. More commonly, growth defense trade-offs appear to result from plant allocation decisions that are intended to maintain optimal fitness while responding to a variable environment. Hardwired phytohormonal and regulatory signaling cascades have evolved over evolutionary time to manage resource allocation among all plant functions that ultimately determine fitness. Although physiological limitations in the natural environment of a species are the ultimate drivers of growth defense trade-offs, these trade-offs are often attenuated under favorable conditions. A comprehensive understanding of the growth defense signal transduction network across natural genotypes and plant species, supported by available technology to accurately quantify plant growth, promises to resolve the long-standing debate about the relevance of these trade-offs and to provide valuable insights for crop breeding. www.annualreviews.org Trade-Offs Between Plant Growth and Defense 527

SUMMARY POINTS 1. In the quest to understand costs of defense with relevance to a plant s evolutionary biology, plant growth rate is a critically important measure to capture real-time resource allocation decisions. 2. Meaningful measures of plant growth are crucial for the study of trade-offs, and require frequent (nondestructive) size measurements and biologically appropriate statistical models. 3. Although plant growth and defense can be inevitably (negatively) linked by pleiotropy, the more typical scenario may be that plant genotypes maximize allocation to many functions, and that in nature, natural selection has favored genotypes that typically fall along the growth defense trade-off continuum with intermediate investment in both functions. 4. Plants employ a series of regulatory switches to prevent costly coexpression of high levels of growth and defense, but targeted selection or manipulation may disable these switches and result in high levels of both traits, at least in conditions of high resource availability and in the absence of other stressors. 5. The leaf economics spectrum provides a valuable physiological framework to partition aspects of plant growth into components, some of which are highly relevant to understanding trade-offs with investment in plant defense. Species with different leaf economics strategies typically have distinct defense strategies. 6. Induced plant defense against herbivory, typically under the control of jasmonic acid, is a powerful tool to study resource allocation and phenotypic costs of defense, but caution is needed in interpreting such physiological approaches in an evolutionary context. DISCLOSURE STATEMENT The authors are not aware of any affiliations, memberships, funding, or financial holdings that might be perceived as affecting the objectivity of this review. ACKNOWLEDGMENTS During the writing of this review, we were supported by the Swiss National Science Foundation (grant PZ00P3-161472 to T.Z.), the National Science Foundation (grant NSF-DEB-1513839 to A.A.A.), and the Templeton Foundation (grant to A.A.A.). We thank Mark Rausher, Lindsay Turnbull, and Matthias Erb for helpful discussion and comments on the manuscript. LITERATURE CITED 1. Agrawal AA. 2011. Current trends in the evolutionary ecology of plant defence. Funct. Ecol. 25:420 32 2. Agrawal AA, Hastings AP, Johnson MTJ, Maron JL, Salminen J-P. 2012. Insect herbivores drive realtime ecological and evolutionary change in plant populations. Science 338:113 16 3. Agrawal AA, Kearney EE, Hastings AP, Ramsey TE. 2012. Attenuation of the jasmonate burst, plant defensive traits, and resistance to specialist monarch caterpillars on shaded common milkweed (Asclepias syriaca). J. Chem. Ecol. 38:893 901 528 Züst Agrawal

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ANNUAL REVIEWS Connect With Our Experts New From Annual Reviews: Annual Review of Cancer Biology cancerbio.annualreviews.org Volume 1 March 2017 ONLINE NOW! Annu. Rev. Plant Biol. 2017.68:513-534. Downloaded from www.annualreviews.org Co-Editors: Tyler Jacks, Massachusetts Institute of Technology Charles L. Sawyers, Memorial Sloan Kettering Cancer Center The Annual Review of Cancer Biology reviews a range of subjects representing important and emerging areas in the field of cancer research. The Annual Review of Cancer Biology includes three broad themes: Cancer Cell Biology, Tumorigenesis and Cancer Progression, and Translational Cancer Science. TABLE OF CONTENTS FOR VOLUME 1: How Tumor Virology Evolved into Cancer Biology and Transformed Oncology, Harold Varmus The Role of Autophagy in Cancer, Naiara Santana-Codina, Joseph D. Mancias, Alec C. Kimmelman Cell Cycle Targeted Cancer Therapies, Charles J. Sherr, Jiri Bartek Ubiquitin in Cell-Cycle Regulation and Dysregulation in Cancer, Natalie A. Borg, Vishva M. Dixit The Two Faces of Reactive Oxygen Species in Cancer, Colleen R. Reczek, Navdeep S. Chandel Analyzing Tumor Metabolism In Vivo, Brandon Faubert, Ralph J. DeBerardinis Stress-Induced Mutagenesis: Implications in Cancer and Drug Resistance, Devon M. Fitzgerald, P.J. Hastings, Susan M. Rosenberg Synthetic Lethality in Cancer Therapeutics, Roderick L. Beijersbergen, Lodewyk F.A. Wessels, René Bernards Noncoding RNAs in Cancer Development, Chao-Po Lin, Lin He p53: Multiple Facets of a Rubik s Cube, Yun Zhang, Guillermina Lozano Resisting Resistance, Ivana Bozic, Martin A. Nowak Deciphering Genetic Intratumor Heterogeneity and Its Impact on Cancer Evolution, Rachel Rosenthal, Nicholas McGranahan, Javier Herrero, Charles Swanton Immune-Suppressing Cellular Elements of the Tumor Microenvironment, Douglas T. Fearon Overcoming On-Target Resistance to Tyrosine Kinase Inhibitors in Lung Cancer, Ibiayi Dagogo-Jack, Jeffrey A. Engelman, Alice T. Shaw Apoptosis and Cancer, Anthony Letai Chemical Carcinogenesis Models of Cancer: Back to the Future, Melissa Q. McCreery, Allan Balmain Extracellular Matrix Remodeling and Stiffening Modulate Tumor Phenotype and Treatment Response, Jennifer L. Leight, Allison P. Drain, Valerie M. Weaver Aneuploidy in Cancer: Seq-ing Answers to Old Questions, Kristin A. Knouse, Teresa Davoli, Stephen J. Elledge, Angelika Amon The Role of Chromatin-Associated Proteins in Cancer, Kristian Helin, Saverio Minucci Targeted Differentiation Therapy with Mutant IDH Inhibitors: Early Experiences and Parallels with Other Differentiation Agents, Eytan Stein, Katharine Yen Determinants of Organotropic Metastasis, Heath A. Smith, Yibin Kang Multiple Roles for the MLL/COMPASS Family in the Epigenetic Regulation of Gene Expression and in Cancer, Joshua J. Meeks, Ali Shilatifard Chimeric Antigen Receptors: A Paradigm Shift in Immunotherapy, Michel Sadelain ANNUAL REVIEWS CONNECT WITH OUR EXPERTS 650.493.4400/800.523.8635 (us/can) www.annualreviews.org service@annualreviews.org

Annual Review of Plant Biology Volume 68, 2017 Contents Firmly Planted, Always Moving Natasha V. Raikhel 1 Annu. Rev. Plant Biol. 2017.68:513-534. Downloaded from www.annualreviews.org Biogenesis and Metabolic Maintenance of Rubisco Andreas Bracher, Spencer M. Whitney, F. Ulrich Hartl, and Manajit Hayer-Hartl 29 The Epigenome and Transcriptional Dynamics of Fruit Ripening James Giovannoni, Cuong Nguyen, Betsy Ampofo, Silin Zhong, and Zhangjun Fei 61 Retrograde Signals: Integrators of Interorganellar Communication and Orchestrators of Plant Development Amancio de Souza, Jin-Zheng Wang, and Katayoon Dehesh 85 The Structural Basis of Ligand Perception and Signal Activation by Receptor Kinases Ulrich Hohmann, Kelvin Lau, and Michael Hothorn 109 Cell Biology of the Plant Nucleus Iris Meier, Eric J. Richards, and David E. Evans 139 Phloem-Mobile RNAs as Systemic Signaling Agents Byung-Kook Ham and William J. Lucas 173 Chemical Genetic Dissection of Membrane Trafficking Lorena Norambuena and Ricardo Tejos 197 Plant Mitochondrial Genomes: Dynamics and Mechanisms of Mutation José M. Gualberto and Kathleen J. Newton 225 Plastoglobuli: Plastid Microcompartments with Integrated Functions in Metabolism, Plastid Developmental Transitions, and Environmental Adaptation Klaas J. van Wijk and Felix Kessler 253 Strigolactone Signaling and Evolution Mark T. Waters, Caroline Gutjahr, Tom Bennett, and David C. Nelson 291 Zooming In on Plant Hormone Analysis: Tissue- and Cell-Specific Approaches Ondřej Novák, Richard Napier, and Karin Ljung 323 vi