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TRPLSC-963; No. of Pages 4
Editorial
Special Issue: Specificity of plant–enemy interactions
Synthesizing specificity: multiple approaches to
understanding the attack and defense of plants
Anurag A. Agrawal1 and Martin Heil2
1
2
Department of Ecology and Evolutionary Biology, Cornell University, E425 Corson Hall, Ithaca, NY 14853-2701, USA
Departamento de Ingenierı́a Genética, CINVESTAV - Irapuato, México
The concept of specificity in plant–herbivore and plant–
pathogen interactions excites plant pathologists, molecular biologists and animal ecologists alike. This excitement
grows out of the notion that individual plant and enemy
species (or populations) are reciprocally interacting in a
way that shapes their traits and evolution [1,2]. Why is it
that most herbivores and pathogens attack a minute fraction of the plants or even plant organs available to them?
How do plants manage to defend against diverse enemies?
Why are plant enemies specialized at all, given that specialization seems to simply limit the number of available
hosts? Are most current plant–enemy interactions the
result of a coevolutionary history, and can these be manipulated to protect agricultural crops from pest insects and
disease, and ecosystems from invasive species? These are
the questions central to this Special Issue of Trends in
Plant Science. Here, we combine perspectives of the plant
with that of its enemies in order to focus on the traits that
allow for successful plant defense versus successful exploitation of plant tissues. Although the topic is often
approached from different research traditions (ecology,
genetics, physiology, etc.), scientists studying herbivores
and plant pathogens have occasionally joined forces and
should continue to do so, because there is much to be
learned by crossing traditional academic boundaries
[3–5]. In addition, we now realize that co-infection, multiple attack, and interactions between herbivores and pathogens are themselves commonplace [6,7].
At the core of issues relating to specificity are two
contrasting views, one from the perspective of the plant
and one from the perspective of the enemy. First, from a
plant’s perspective, there are myriad primary protective
barriers, and some of these will be effective against many,
if not most attackers [8]. For example, the plant cuticle
represents the first barrier encountered by most herbivores and pathogens. Even once this is breached, general
strategies, such as the production of hydrogen peroxide,
are used to strengthen cell walls. Phenolics occur in most
plants, and many of these compounds likely serve some
protective role. Even more common, perhaps ubiquitous,
are defensive proteins, which are frequently induced upon
attack and, depending on their specific structure, can
protect plants from pathogens and/or herbivores. Other
fairly general forms of defense include the production of
hydrogen cyanide and latex, each found in nearly 10%
Corresponding author: Agrawal, A.A. ([email protected]).
of all flowering plants, and both activated upon tissue
damage [9].
Despite these general barriers against enemies, the
diversity of enemies that are likely to attack any given
plant begs two important questions: (i) Do some traits that
protect the plant against one attacker make the plant more
susceptible to attack from others?; and (ii) Given the
limited resources available, even if the first question does
not apply, could a plant simultaneously defend against all
attackers? This leads to further questions on the strategies
of the attackers. What are the central traits of enemies
enabling them to overcome these multiple defensive strategies? Are enemies that are particularly efficient at exploiting one specific host necessarily suffering from a lower
performance on others, and what are the causal mechanisms that underlie this ‘jack of all traits – master of none’
principle?
The good news is that we know some of the answers. It is
reasonably certain that some plant traits that are
expressed in a defensive context are a double-edged sword
from the plant’s perspective [10,11], and there is no way to
simultaneously deploy even a fraction of the total plant
defensive traits in a plant genome. As a result, phenotypically plastic means of defense are the norm in plants. Two
articles in this Special Issue by Matthias Erb and colleagues, and Noah Whiteman and colleagues are devoted to
understanding the mechanisms and evolution of attacker
specific responses and how they may be coordinated. From
a more ecological perspective, the extent to which plant
traits (driven by genotypic variation) impact single enemies, guilds of attackers and entire communities, is
addressed by Thomas G. Whitham and colleagues. The
extent to which we expect specific plant responses to
specific enemies will depend on the extent of natural
selection imposed by each enemy and this, in turn, may
be driven by the extent to which enemies are specialized on
particular plants (Figure 1).
The issue of specificity, from the perspective of the
enemy, is tied up in the potential benefits of being able
to make a living on a plant resource that is perhaps less
available to more generalized enemies or more predictable
in its chemical composition [12]. For generalists, in the
extreme form, some microbes can infect both plant and
animal hosts. In a provocative opinion piece in this issue,
Adam Schikora and Heribert Hirt explores the specifics of
growth and propagation by Salmonella in plant and animal
hosts. Although there are a few super-generalist insect
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TRPLSC-963; No. of Pages 4
Editorial
Trends in Plant Science xxx xxxx, Vol. xxx, No. x
Effective plant defense?
Yes
No
Herbivore diet breadth
Specialist
Cycling coevolution
(gene-for-gene)
Some pathogens,
gallers and plants with
hypersensitive
responses
Plant hormonal
signaling used in
concert with
recognition, indirect
defense could be
effective, but not
well-studied
Arms-race coevolution
Gallers, sequesterers
Many classic cases
Specialists are tolerant of some
defense, but the plant has
effective traits. Hormonal
signaling can be effective for
direct and indirect defense
No coevolution; plant is well defended (via
effective general barriers) and there are
minimal reciprocal impacts because the
generalist has other options
Generalist
Evolution of reduced
herbivore virulence and
plant tolerance
A very common interaction, many mobile
herbivores, each of which has relatively little
impact (e.g., grasshoppers)
Plant hormonal
defenses are inactive,
ineffective, or
manipulated
Unspecialized arms-race coevolution
Relatively less mobile generalists (larvae)
that suppress or deactivate plant defenses
using highly conserved plant traits and
pathways
Plant hormonal signaling coopted by
herbivores
Plant hormonal signaling is effective
(indirect defense may be ineffective for
mobile herbivores, but generalist natural
enemies may work)
TRENDS in Plant Science
Figure 1. A preliminary scheme for conceptualizing specificity in multitrophic interactions. For specialist and generalist herbivores, we outline the predicted evolutionary
interaction (purple), types of species involved (blue), and mechanisms and roles of plant defense signaling in mediating such interactions (green). Arms-race coevolution
sits in the middle because herbivores are adapted to plant defense, but selection continually favors more effective defenses (including indirect defense).
herbivores (typically some of the worst agricultural pests),
most herbivores specialize to species in one plant family,
and often one or a few genera, and generalists are typically
limited to utilizing defined plant organs [13]. Some of the
classic predictions in plant–enemy interactions revolve
around not only why organisms specialize, but also how
specialists differentially interact with their host plants
compared with generalists. This is the subject of the papers
by Luke G. Barrett and Martin Heil, and Jared G. Ali and
Anurag A. Agrawal.
At the extreme, some specialist herbivores use plant
defenses for host finding or even to sequester secondary
compounds that are then used as defenses against their
own enemies [10,11]. Although it has been rarely considered in the past, a sequestering versus non-sequestering
specialist herbivore is predicted to have different relations
with plants, both in terms of the attacker’s tolerance of
plant defenses and the most adaptive defense strategy by
plants. Indeed, the paradigms about specialists are changing, and it is now well documented that at least some
generalist herbivores and pathogens are highly manipulative of their host plants [14]. The papers in this Special
Issue elaborate on these issues, and demonstrate why such
interactions may make sense in the evolutionary context,
despite a muddled interpretation in past literature.
2
Novel approaches that span phylogenetic to transgenic
methods will greatly aid in our progress of understanding
specificity in the interactions of different species. Many
modern theories of plant–enemy interactions typically
invoke three trophic levels, with the potential for several
specific linkages between any of the potential pairings
(plant–enemy, enemy–predator and plant–predator) [15].
Jonathan Gershenzon and colleagues focus on the issue of
indirect defense (i.e. plant–predator signaling), and whether natural selection could well have resulted in specific
adaptations across trophic levels. Demonstration of such
specificity has long been a holy grail and we still have
remarkably few examples of adaptive specificity in plant
defense against herbivores. Nonetheless, most researchers
believe that there is the potential for such adaptive defense
tailoring by plants, as it is commonly seen in effectormediated and highly specialized interactions between
plants and pathogens. Similarly, we don’t yet understand
why specialist herbivores specialize, and what the consequences may be for interactions with plants or predators.
However, all is not lost. Technological advances, such as
those outlined by Marcel Dicke and colleagues and, perhaps more importantly, conceptual advances, some of
which are outlined in this Special Issue, provide a roadmap
for how to proceed.
TRPLSC-963; No. of Pages 4
Editorial
An evolutionary framework
Among all the papers in this Special Issue, there is an
underlying evolutionary framework. To solidify this here,
we offer a novel conceptualization for plant–enemy interactions (Figure 1). Although ‘arms race coevolution’ has been
the dominant paradigm in plant–enemy interactions for
decades, we posit that such interactions only occupy a small
space of the conceptual landscape (Figure 1). In particular,
arms race coevolution is only expected when plants interact
with specialist parasites, where plant traits can directly
impact the fitness of the parasite, and when the parasite is
virulent and abundant enough to impose fitness losses to the
plant. As suggested by several papers in this Special Issue,
specialist enemies may be somewhat tolerant of certain
plant defenses, but plants nonetheless can defend against
them, often using hormonal signaling to upregulate direct
and indirect defenses. By contrast, when specialists have
truly broken the code of plant defense, most of the secondary
compounds that have evolved as induced defenses may be
ineffective, and plant parasites typically evolve low virulence (and plants evolve tolerance).
The view of coevolution in plant–pathogen interactions
is dominated by the gene-for-gene concept [4]; in this
alternative to arms race coevolution, virulence and avirulence genes in a population may show stable cycles due to
frequency-dependent selection (Figure 1). In particular,
plant and pathogen phenotypes do not ‘escalate’ here,
but instead persistent attack results in the evolution of
a novel plant resistance (typically alleles that are unrecognized or impervious to particular alleles in pathogens).
Ultimately, a matching allele evolves in the pathogen and
sweeps through the population, conferring virulence
against the plant. This interaction cycles via frequency
dependence, because the critical phenotypes have to do
with allele matching, rather than increasingly virulent
parasites and increasingly defended host plants. Although
such cycling is a hallmark of coevolutionary interactions
between plants and pathogens, relatively little is known
about such interactions of plants with herbivores.
The evolutionary landscape for generalist plant enemies
is often different to that of specialists [11]. Yes, occasionally generalists attack large parts of local host populations,
leaving them with reduced fitness to the point where
selection favors defense. However, such defenses are likely
only maintained by natural selection in the cases where
generalized defenses will be effective against several, taxonomically unrelated enemies. Thus, the reciprocal impact
of plant traits on generalists is often composed of general
barriers (Figure 1). In other words, we argue that the main
lines of general defense of plants may well be effective
against generalist herbivores, but these are likely to be
well conserved among plant species. Hormonally regulated
defense expression may indeed be effective against generalists (in an attempt to send these herbivores away), but
these same means, because they are highly conserved, may
be subject to manipulation and suppression by some generalist enemies [16]. As some of the articles in this Special
Issue discuss, it is still too early to know whether such
suppression is most common among generalist enemies.
However, perhaps the key point to emphasize is that
the outcome of plant–enemy interactions depends not
Trends in Plant Science xxx xxxx, Vol. xxx, No. x
only on the strategy of the enemy, but also of the plant’s
ability to recognize that enemy and defend appropriately.
Too often we take a single perspective, that of the enemy
or the plant, and assume the other party is static. The
way it appears however, and this should not surprise
anyone, is that coevolution proceeds as a reciprocal evolutionary interaction. Such coevolutionary interactions
play out in ecological time as a back-and-forth, as envisioned in Dangl and Jones’ zig-zag model [17]. Plants and
their enemies each produce substances involves in recognition and signaling of plant defense [18,19]. Similarly,
both plants and their enemies can respond in a highly
phenotypically plastic manner to interactions with specific partners, adding a further level of complexity to
studies that aim at understanding the reasons of specificity in plant–enemy interactions or its consequences for
future evolution [20]. Thus, there will be surprises! In
addition, there is a strong need for solid predictions and
rigorous analyses that integrate research at the molecular, physiological and ecological level to span the measure
of plant traits, resistance to enemies and fitness impacts.
We hope that this Special Issue contributes to this needed
new synthesis.
The Guest Editors
References
1 Janzen, D.H. (1980) When is it coevolution? Evolution 34, 611–612
2 Dodds, P.N. and Rathjen, J.P. (2010) Plant immunity: towards an
integrated view of plant–pathogen interactions. Nat. Rev. Genet. 11,
539–548
3 Rausher, M.D. (2001) Co-evolution and plant resistance to natural
enemies. Nature 411, 857–864
4 Brown, J.K.M. and Tellier, A. (2011) Plant–parasite coevolution:
bridging the gap between genetics and ecology. Ann. Rev.
Phytopathol. 49, 345–367
5 Erb, M. et al. (2011) Synergies and trade-offs between insect and
pathogen resistance in maize leaves and roots. Plant Cell Environ.
34, 1088–1103
6 Thaler, J.S. et al. (2010) Salicylate-mediated interactions between
pathogens and herbivores. Ecology 91, 1075–1082
7 Garcia-Guzman, G. and Dirzo, R. (2001) Patterns of leaf-pathogen
infection in the understory of a Mexican rain forest: incidence,
spatiotemporal variation, and mechanisms of infection. Am. J.
Botany 88, 634–645
8 Walters, D. (2011) Plant Defense: Warding Off Attack by Pathogens,
Pests and Vertebrate Herbivores, Wiley-Blackwell
9 Agrawal, A.A. (2011) Current trends in the evolutionary ecology of
plant defence. Funct. Ecol. 25, 420–432
10 Dobler, S. et al. (2011) Coping with toxic plant compounds – the insect’s
perspective on iridoid glycosides and cardenolides. Phytochemistry 72,
1593–1604
11 Lankau, R.A. (2007) Specialist and generalist herbivores exert
opposing selection on a chemical defense. New Phytol. 175, 176–184
12 Singer, M.S. (2008) Evolutionary ecology of polyphagy. In The
Evolutionary Biology of Herbivorous Insects. Specialization,
Speciation, and Radiation (Tilmon, K., ed.), pp. 29–42, University of
California Press
13 Schoonhoven, L. et al. (2005) Insect–Plant Biology, (2nd edn), Oxford
University Press
14 Musser, R.O. et al. (2005) Evidence that the caterpillar salivary enzyme
glucose oxidase provides herbivore offense in Solanaceous plants. Arch.
Insect Biochem. Physiol. 58, 128–137
15 Heil, M. (2008) Indirect defence via tritrophic interactions. New Phytol.
178, 41–61
16 Zarate, S.I. et al. (2007) Silverleaf whitefly induces salicylic acid
defenses and suppresses effectual jasmonic acid defenses. Plant
Physiol. 143, 866–875
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17 Jones, J.D.G. and Dangl, J.L. (2006) The plant immune system. Nature
444, 323–329
18 Heil, M. et al. (2012) How plants sense wounds: damaged-self
recognition is based on plant-derived elicitors and induces
octadecanoid signaling. PLoS ONE 7, e30537
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19 Alfano, J.R. and Collmer, A. (2004) Type III secretion system effector
proteins: double agents in bacterial disease and plant defense. Ann.
Rev. Phytopathol. 42, 385–414
20 Agrawal, A.A. (2001) Phenotypic plasticity in the interactions and
evolution of species. Science 294, 321–326
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Scientific Life: TrendsTalk
Interview with Anurag A. Agrawal
Anurag Agrawal, born in Allentown, Pennsylvania (USA),
received his BA (Biology) and MA (Conservation Biology)
from the University of Pennsylvania, where he was inspired by Daniel Janzen, a pioneering evolutionary ecologist, and became intrigued with plant–animal interactions.
He completed his PhD (Population Biology) with Rick
Karban at the University of California, Davis, and held
a Postdoctoral Fellowship at the University of Amsterdam
before becoming an Assistant Professor of Botany at the
University of Toronto. In 2004, he joined the Department of
Ecology & Evolutionary Biology at Cornell, where he is
currently a Professor. His research has been broad, embracing chemical ecology, quantitative genetics, phylogenetic analyses, community dynamics and the nascent field
of community genetics. Making his work a hobby and some
of his hobbies his work has made being a plant biologist
and naturalist an immense pleasure.
What turned you on to plant biology in the first
place?
It’s hard to know how this happened to me, but I suppose it
was spending much of my childhood outdoors, my mother’s
intense love of vegetable gardening, and then some key
serendipitous moments, like stumbling into Dan Janzen’s
introductory biology course at Penn. I am the type of person
who can get interested (and obsessed) by a lot of things, so I
feel lucky to have landed here!
What paper influenced you most?
Ehrlich and Raven 1964. Not because of its specific content,
but because of the conceptual linkage between something
so mechanistic (plant-produced secondary compounds and
their defensive impacts on insects) and something so bigpicture and central to patterns of biodiversity (how new
species are formed, generating clades of hyperdiverse
plants and herbivores). Remarkably, we don’t know if their
hypotheses were correct, but evolutionary chemical ecology
has certainly come of age, and great strides are being made
right now.
What big questions interest you in the long term?
To what extent can we generalize in plant science? Are
there laws that regulate the ways in which plants respond
to the environment, evolve and diversify? I am a huge
believer in the integration of work on mechanisms in model
organisms and the study of patterns across many species.
For example, it is remarkable that what we know about
highly conserved traits from some of our model organisms
(e.g. Arabidopsis and tomato) indicate that they interact
with the environment in divergent ways. I think we have to
reconcile the highly conserved blueprint of most plants
with the diversity of how they actually behave. In part that
means a move towards non-model-omics, but also a conscious decision to value the study of patterns in wild
species studied in their natural environment.
What is the best (professional) advice you have been
given?
Two things, one specific and one general. As an assistant
professor, ‘do another thesis project’. It is about the same
time frame, results in the same thing (a novel, cohesive and
advanced body of work), and is a concrete goal when
thrown into academia on our own. More generally, succeeding in science isn’t easy, but it shouldn’t be a mystery. ‘Do
whatever you have to in order to learn the culture of being a
scientist.’
And what advice would you give?
Be sure to play to your strengths and continually work on
your weaknesses. There isn’t a single formula for success in
science, but again, it shouldn’t be a mystery, and your
recipe will require self-study. Most scientists could
improve one or two things that are rate limiting steps
(e.g. writing faster), which could be a major improvement.
Oh, and you must be prepared to accept a steady stream of
criticism and rejection, but there is no limit to what we can
accomplish through dreaming and taking risks.
What is the biggest hindrance to science?
Two things, one general and one specific. First, there is often
difficulty in accepting change. A colleague once told me that
one of the great things about a life in science is that we have
the ability to change what we work on, our approach and
philosophy, and what we find inspiring. I couldn’t agree
more. Nonetheless, sometimes change is thrust upon us, in
terms of funding streams, technology, what questions are
hot or passé, etc. A challenge for the academic industry is
allowing creative freedom while maintaining an environment where accepting change is facilitated. Second, most
scientists sit on too much unpublished data... although there
are many reasons for this (negative results, other things
more pressing, student left the lab without finishing the
project); it is a bit of a tragedy for the work to be done but not
be available in the commons.
What has been your biggest mistake in research?
Letting my own impatience get the best of me. Was it really
a mistake? A few times, yes. Like most things, there are
tradeoffs, and my impatience has occasionally been beneficial as well. This interview is done.
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Scientific Life: TrendsTalk
Trends in Plant Science May 2012, Vol. 17, No. 5
Special Issue: Specificity of plant–enemy interactions
Interview with Martin Heil
Martin Heil is professor and senior researcher at the
CINVESTAV-Irapuato in México, leading the plant ecology
laboratory. His group studies co-adaptations in mutualisms
and induced defences of plants against pathogens and
herbivores.
What influenced your path into plant biology?
My way into biology was predictable ever since my early
years in kindergarden, in Hanau, Germany, when I already considered plants and animals way more interesting
than cars and ball games. Therefore, I studied Biology,
Geology and Philosophy at Würzburg University.
So why then did it become plant biology?
Well, I always felt more comfortable in a botanical garden
than in a zoo, and plants do not run away when a curious
scientist is approaching. However, I always saw my interest
at the interface between plants, microorganisms and animals and therefore focused in my PhD field work, in
Malaysia, on a protective ant–plant mutualism. I continued
to work on this topic during my postdoc at CEFE/CNRS in
Montpellier, France. I then joined Wilhelm Boland’s Department of Bioorganic Chemistry at the Max-Planck-Institute for Chemical Ecology in Jena, Germany as junior group
leader. At the age of 36, I accepted a full professorship at
University of Duisburg-Essen, where it took me less than
three years to find out that for me research is more interesting than administrative duties. For this reason, in 2007, I
moved to CINVESTAV-Irapuato in México, where I am
currently leading the plant ecology laboratory.
What was the driving force for you to move to Mexico
for your research?
The degree of freedom that I have in my work and my
private life and the very low load of administrative duties.
Moreover, the level of appreciation that I receive here for
my research is by orders of magnitude higher than before,
the authorities of the institute give me the impression that
they do whatever they can to support my research, and as
long as I maintain my productivity I can practically do
whatever I want. Fund raising is comparably easy, because
Mexico invests relatively more in science than for example
the USA, at least when we divide national funds for
science by the number of scientists that are competing
for these funds. Another important aspect is that the
Mexican system as a whole appreciates your scientific
productivity, in terms of publications, citations and graduated students, which not only strongly influences the personal salary, but also the chance to get projects accepted.
How did you decide on your current research topics?
I have no major strategy at all, but rather follow a stochastic, phenomena-based approach. My decisions are
244
mainly guided by curiosity, that is, when I see an interesting phenomenon in the field or read about a strange
observation in the literature, it might easily occur that I
start a new project in order to find out what is going on.
Has your work been affected by the genomics
revolution?
Recently, yes, because the new high-throughput sequencing
approaches allow the investigation of ecologically relevant
phenomena in non-model species under field conditions. For
example, our ant-Acacias are ecologically extremely interesting plants that possess multiple adaptations to maintain
the ant–plant mutualism. However, because they are taxonomically very distant from any model plant, classical genetic tools were not suitable to approach these phenomena
at the genetic level. With the recently developed highthroughput sequencing techniques, we are now able to
search for the genetic control mechanisms that underlie a
functioning ant–plant mutualism.
What would you be if you were not a biologist?
My way into biology seemed predestined; at least, I cannot
remember ever actively considering any alternatives. However, if I had not pursued an academic career, being a ballet
dancer or opera singer (tenor) would have been attractive
possibilities. In fact, I personally feel more like an artist
than a classical scientist, a phenomenon that perhaps also
underlies my ‘‘emotion-guided’’ approach in the selection of
research projects.
What is your favorite book?
Difficult to mention only one. If four are allowed, the
selection would be: ‘‘Der Zauberberg’’ by Thomas Mann,
‘‘La tia Julia y el escribidor’’ de Mario Vargas Llosa, ‘‘100
años de soledad’’ de Gabriel Garcı́a Marquez and, well, to
be honest... ‘‘Die Stadt der träumenden Bücher’’ by Walter
Moers.
Do you have a scientific hero?
Immanuel Kant and Werner Heisenberg, because I think
that both had the most dramatic impact on our understanding of what science can achieve and of its limitations,
and of the way in which we perceive our position in reality.
Are scientific controversies helpful?
Of course they are, as long as they are maintained free of
personal attacks and avoid the abuse of political positions
(including the position as a referee) to suppress the unwanted ideas. If we believe Thomas Kuhn, revolutions are
the mechanism by which science proceeds.
1360-1385/$ – see front matter
http://dx.doi.org/10.1016/j.tplants.2012.03.013 Trends in Plant Science, May 2012, Vol. 17, No. 5
TRPLSC-959; No. of Pages 5
Opinion
Special Issue: Specificity of plant–enemy interactions
Plants as alternative hosts for
Salmonella
Adam Schikora1, Ana V. Garcia2 and Heribert Hirt2
1
Institute for Plant Pathology and Applied Zoology, Research Centre for BioSystems, Land Use and Nutrition, JL University
Giessen, Heinrich-Buff-Ring 26-32, 35392 Giessen, Germany
2
URGV Plant Genomics, INRA/CNRS/University of Evry, 2 rue Gaston Crémieux, 91000 Evry, France
Recent findings show that many human pathogenic
bacteria can use multiple host organisms. For example,
Salmonella Typhimurium can use plants as alternative
hosts to humans and other animals. These bacteria are
able to adhere to plant surfaces and actively infect the
interior of plants. Similarly to the infection of animal
cells, S. Typhimurium suppresses plant defense
responses by a type III secretion mechanism, indicating
that these bacteria possess a dedicated multi-kingdom
infection strategy, raising the question of host specificity. In addition, evidence is accumulating that the interaction of Salmonella with plants is an active process with
different levels of specificity, because different Salmonella serovars show variations in pathogenicity, and
different plant species reveal various levels of resistance
towards these bacteria.
Plant-originated salmonellosis
Several reports indicated that bacteria, which are pathogenic to humans and other mammals, are able to infect
plants. Salmonella enterica, Pseudomonas aeruginosa, Burkholderia cepacia, Erwinia spp., Staphylococcus aureus,
Escherichia coli O157:H7 and Listeria monocytogenes infect
animals and plants [1–5]. Amongst these pathogens, Salmonella bacteria are the major cause of food poisoning.
These Gram-negative enteropathogenic bacteria can successfully colonize animals, humans and plants. Their genus
is divided into two species: Salmonella bongori and Salmonella enterica, encompassing several hundred isolates,
which are typically named after the place of origin [6].
The species S. enterica is additionally divided into seven
subspecies, one of them, S. enterica subsp. enterica, is the
major cause of salmonellosis in humans. The most common
mode of infection is ingestion of contaminated food or water.
Moreover, many reports have linked food poisoning with the
consumption of Salmonella-contaminated raw vegetables
and fruits (for review see [2,7]). Studies in various European
countries revealed that in 2007, 0.3–2.3% of raw vegetables
were infected with Salmonella bacteria [8]. In the USA, the
proportion of raw food-associated salmonellosis outbreaks
increased from 0.7% in the 1960s to 6% in the 1990s [9], and
crossed 25% in recent years [10]. Most studies on Salmonella–plant interactions suggested an epiphytic lifestyle of
Salmonella on plants. However, a growing body of evidence
Corresponding author: Hirt, H. ([email protected]).
points to a directed process in which the bacteria infect
various plants and use them as viable hosts (Table 1)
[11–22]. The ability to infect and grow on such diverse hosts
is a remarkable example of the lack of specificity seen in so
many other microbes (Figure 1).
Do plants serve as alternative hosts or are they part of
the Salmonella life cycle?
Adhesion is typically the first step of an infection by
Salmonella. Diverse S. enterica serovars have been shown
to adhere to plant surfaces, and many Salmonella serovars
bind to plants significantly better than for instance the
pathogenic E. coli strain O157:H7 [23]. Evidence suggests
that Salmonella actively attach to plant tissues and only
viable bacteria can successfully colonize plants [19]. In a
screen of 6000 S. Newport mutants, 20 mutants were
identified with lower attachment ability to Medicago sativa
(alfalfa) sprouts [12]. Interestingly, some of the genes
identified in this study code for the surface-exposed aggregative fimbria nucleator curli (agfB) and for the global
stress regulator rpoS which regulates the production of
curli, cellulose and other adhesins that are important also
for animal pathogenicity. AgfD, which was also identified
in this study, plays not only a central role in the ability to
attach to plant surfaces [24], but also in the environmental
fitness and the pathogenicity of the bacteria toward animals [25]. In addition, it was shown that yihO (involved in
O-antigen capsule formation) and bcsA (coding for a cellulose synthase) are also important for adhesion to alfalfa
sprouts [24], whereas cellulose and curli are involved
in transmission of S. Typhimurium from water onto parsley (Petroselinum hortense) leaves [26]. In another study,
two previously uncharacterized genes (STM0278 and
STM0650) were characterized as important factors for
the infection of alfalfa sprouts, due to their essential role
in biofilm formation and swarming [11]. It is thus becoming
evident that the genetic equipment of Salmonella, previously thought to be animal-infection specific, plays an
important role in the infection of animals and plants alike.
Surprisingly, a comparative study on the internal colonization in lettuce (Lactuca sativa) leaves by five S. enterica
serovars (Dublin, Enteritidis, Montevideo, Newport and
Typhimurium) indicated significant differences between
the different serovars, indicating that distinct genetic
backgrounds have an impact on the pathogenic behavior
towards plants [16]. A similar study conducted on the
1360-1385/$ – see front matter ß 2012 Elsevier Ltd. All rights reserved. doi:10.1016/j.tplants.2012.03.007 Trends in Plant Science xx (2012) 1–5
1
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Trends in Plant Science xxx xxxx, Vol. xxx, No. x
Table 1. Known interactions between Salmonella and plantsa
Salmonella strain
S. Anatum DMST 19600
S. enterica Dublin
S. enterica, diverse serovars
S. enterica, diverse serovars
S. enterica, diverse serovars
S. enterica, diverse serovars
S. Newport
S. Newport
S. Newport, Enteriditis, mutants
S. Thompson RM1987
S. Typhimurium
S. Typhimurium
S. Typhimurium 14028
S. Typhimurium 14028
S.
S.
S.
S.
S.
S.
S.
Typhimurium
Typhimurium
Typhimurium
Typhimurium
Typhimurium
Typhimurium
Typhimurium
14028
14028, 1344
DT104
MAE110, MAE119
SL1344
SL1344
14028
Infected plant
Cabbage
Lettuce
Lettuce
Arabidopsis
Tomato, pepper
Lettuce, cabbage
Alfalfa
Alfalfa
Alfalfa
Lettuce
Barley
(Hordeum vulgare)
Potato
(Solanum tuberosum)
Tomato fruits
Arabidopsis
Arabidopsis
Tobacco
Lettuce
Tomato
Lettuce
Diverse
Arabidopsis
Main finding
Temperature-dependent susceptibility to infection
Colonization of lettuce and transcriptome of response to infection
Different serovars vary in their colonizing capacity
Strains from O-serogroup induce chlorosis and wilting in Arabidopsis
Cultivar-dependent colonization, trichomes as infection point
Serovar-dependent divergences in attachment to leaves
Identification of two new genes required for attachment to plants
Screen of 6000 mutants for their ability to attach to plant surface
Cellulose and O-antigen capsule play role in the attachment to plants
Increased infection was observed in elderly leaves
Colonization of barley roots
Refs
[46]
[47]
[16]
[30]
[13]
[27]
[11]
[12]
[24]
[48]
[49]
Attachment to plant surface is an active process
[19]
Screen for bacterial genes expressed upon plant infection
Plants induce defense mechanisms after infection, bacteria
internalized in plants cells
Suppression of plant immune system is T3SS-dependent
Wild type bacteria suppress plant defense reactions
A passage via lettuce increased attachment capacity to epithelial cells
Bacteria spread systemically and colonize non-infected leaves and fruits
Internalization via stomata is light dependent and requires chemotaxis
Internalization of bacteria varies between leafy vegetables
Plant defense is required for resistance towards Salmonella
[18]
[20]
[21]
[45]
[50]
[22]
[17]
[14]
[15]
a
The majority of the studies focus on different serovars of S. enterica subspecies enterica interacting with Arabidopsis or plants traditionally associated with salmonellosis
outbreaks such as lettuce, tomato and alfalfa. The list presented here summarizes the research on the interaction between Salmonella and the plant immune system, as well
as the genetic requirement to infect plants. Due to length restrictions, it is impossible to cover comprehensively the broad literature of different plant-originated outbreaks
and the anti-microbial activity of diverse plants.
serovars Braenderup, Negev, Newport, Tennessee and
Thompson, likewise revealed differences between the tested serovars [27]. Interestingly, the authors pointed out a
correlation between the capacity to produce biofilms and
the attachment to leaves, with S. Thompson producing the
strongest biofilms and showing the most efficient adhesion
to lettuce leaves [27].
Salmonella can live inside plants
In animals, Salmonella actively enter epithelial and other
cell types in order to replicate and spread through the
organism. The question whether Salmonella use a similar
strategy to infect plants is therefore of great interest.
Salmonella were found to form biofilm-like structures on
the surface of roots, preferentially colonizing regions
around emerging lateral roots and wounded tissues
[15,20]. The formation of biofilms of Salmonella on leaves
was also reported. Recently, three reports presented the
possible entry points of bacteria to the inner layers of
leaves [13,14,17] and it was postulated that trichomes
are preferential colonization sites [13]. By contrast, it
was shown that Salmonella use stomata as entry points
in order to penetrate lettuce leaves [17]. Moreover, bacterial aggregation near stomata occurs only under light
conditions when the stomata are open. Artificial opening
of the stomata in the dark had no impact on the bacterial
behavior, suggesting that bacteria are attracted to photosynthesis-dependent products. Previously, we showed that
the GFP-marked S. Typhimurium 14028s bacteria can be
observed inside root hairs at 3 h, and bacterial titers
increased at 20 h after inoculation of Arabidopsis plants
[20].
2
Additional tests revealed that motility and the ability of
chemotaxis are essential for Salmonella to colonize the
interior of lettuce leaves [17]. In a follow-up report, the
same group demonstrated that not all plants are equally
susceptible (or resistant) to Salmonella internal infection.
Using GFP-marked bacteria, the authors analyzed the
internalization of the S. Typhimurium strain 1344 in many
leafy vegetables and herbs [14]. In the same year, another
study reported that S. Typhimurium strain MAE110 is
able to translocate within tomato (Solanum lycopersicum)
plants, infecting distal, non-infected leaves and fruits
without visible symptoms and only slightly reducing plant
growth [22]. Interestingly, while some plant species [e.g.
arugula (Diplotaxis tenuifolia)], allowed Salmonella to
internalize, some others (e.g. parsley), seemed to have
effective means to prevent infection [14]. Studies on lettuce, cabbage (Brassica oleracea) and tomato demonstrated significant differences in the susceptibility to
Salmonella infection [13,16], pointing to an important role
of plant innate immunity in modulating the response to
infection by these bacteria.
By contrast, pathogenic bacteria often use type III
secretion system (T3SS)-dependent injection of effector
proteins in order to modulate host physiology and suppress the immune system. To answer the question whether Salmonella rely on T3SS for infection of plants, mutants
in two Salmonella T3SS were tested for their performance
on plants. Both of the T3SS mutants are unable to inject
effector proteins into host cells and are therefore not
virulent for animal hosts [28,29]. Although these T3SS
mutant strains showed normal proliferation rates
when grown in standard medium, their proliferation in
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Trends in Plant Science xxx xxxx, Vol. xxx, No. x
H+
ROS
T3SS
H+
PRRs
MAMPs
H+
MAPKKK
MAPKK
MAPK
MAPK
TF
Defense related genes
TRENDS in Plant Science
Figure 1. Wild type Salmonella are able to attach to plant surfaces and infect plants via stomata openings or roots. Upon infection, Salmonella hinder the enhanced
production of reactive oxygen species (ROS) and prevent pH changes in the apoplast. Moreover, Salmonella actively prevent the transcriptional activation of defenserelated genes. Abbreviations: MAMP, microbe-associated molecular pattern (yellow circles); MAPK, mitogen-activated protein kinase; PRR, pattern recognition receptor;
T3SS, type III secretion system; TF, transcription factor; red circles represent Salmonella effectors; green circles represent products of defense-related genes.
Arabidopsis (Arabidopsis thaliana) plants was compromised, indicating that both SPI-1- and SPI-2-encoded type
III secretion systems are needed for successful plant infection [21].
Plant responses to Salmonella infection
Upon inoculation, Arabidopsis responds to Salmonella
with a rapid induction of defense responses, including
the activation of mitogen-activated protein kinases
MPK3, MPK4 and MPK6 that is followed by the expression
of a number of defense genes, such as PDF1.2 or the
pathogenesis-related genes PR2 and PR4 [20]. Transcriptome analysis of Arabidopsis plants showed differential
expression of about 250 and 1300 genes at 2 and 24 h after
Salmonella infection, respectively. With the exception of 32
genes, the Salmonella-induced differentially expressed
genes were also affected by inoculation with the non-pathogenic E. coli laboratory strain DH5a and the pathogenic
Pseudomonas syringae strain DC3000 [21]. Among the
genes that were induced by E. coli DH5a, S. Typhimurium
14028 and P. syringae DC3000, about 160 (including various WRKY and bZIP transcription factors as well as protein kinases and phosphatases) could be identified as a core
set of Arabidopsis genes responsive to common bacterial
exposure [21].
Towards identification of the plant Salmonella
receptors
A recent study examined the macroscopic symptoms of
wilting and chlorosis in Arabidopsis plants after infiltration with different serovars of S. enterica subsp. enterica, as
well as S. enterica subsp. arizonae and diarizonae [30].
Infiltration with S. Senftenberg and also with S. Cannstatt, Krefeld and Liverpool, all of which belong to the
serogroup E4 (O: 1, 3, 19) possessing the O-antigen,
resulted in rapid wilting and chlorosis. By contrast, infiltration with serovars lacking the O-antigen provoked no
symptoms [30]. In addition, the authors stated that the
response to Salmonella infiltration is independent of the
most prominent and studied pattern recognition receptors,
suggesting that specific receptors for Salmonella O-antigen could exist in Arabidopsis.
Salmonella factors interacting with the plant immune
system
In humans, salmonellosis develops after the bacteria enter
epithelial cells of the intestine [31]. Although a typical
infection usually leads to a self-limiting gastroenteritis,
Salmonella can cause systemic infections by invading
spleen, liver and other organs in susceptible hosts. Studies
of the infection mechanisms in animals have shown that
3
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Salmonella actively remodel the host cell physiology and
architecture, and suppress the host immune system by
injecting a cocktail of effectors delivered by T3SS. A recently published literature survey revealed a standard list
of 62 protein–protein interactions between 22 Salmonella
proteins and numerous human proteins [32]. Salmonella
enterica subsp. enterica has two distinct T3SSs, T3SS-1
and T3SS-2, encoded by the Salmonella Pathogenicity
Islands (SPI) SPI-1 and SPI-2, respectively [33,34].
T3SS-1 secretes at least 16 proteins of which six were
shown to interact with the host signaling cascades and the
cytoskeleton. T3SS-2 secretes at least 19 S. entericaspecific effector proteins that are involved in survival
and multiplication within the host cell [35,36]. The expression and the secretion of SPI-1 and SPI-2 encoded
effectors are tightly regulated. Recently, the cytoplasmic
SpaO–OrgA–OrgB complex was identified as the sorting
platform for T3SS effectors that determines the appropriate hierarchy for protein secretion [37]. This complex
enables the sequential delivery of translocases before
the secretion of the actual effectors. The authors also
described the role of specific chaperones in the recognition
and loading of effectors into the sorting SpaO–OrgA–OrgB
complex, and postulated that similar sorting platforms
might exist in other bacteria [37]. Even though many
reports suggest that the mechanisms used by Salmonella
to infect animal and plant hosts could be similar, the role of
Salmonella T3SS effectors during plant infections
remains unclear. To date, 44 Salmonella effectors have
been described to be injected into animal and human cells
through one or both T3SSs (reviewed in [38]). Several of
these effectors target MAPK cascades, which are important regulators of the immune response in animals and
plants. SpvC from Salmonella spp. belongs to the OspF
family initially identified in Shigella spp. OspF encodes a
phosphothreonine lyase that dephosphorylates the
pTXpY double phosphorylated activation loop in the
ERK1/2 kinases [39–41]. Interestingly, P. syringae
HopAI1 is a homolog of SpvC/OspF, and encodes a phosphothreonine lyase that dephosphorylates the threonine
residue in the activation loop of activated MAPKs [42].
When expressed in Arabidopsis, HopAI1 directly interacts
with MPK3 and MPK6, attenuating flg22-induced MAPK
activation and downstream defense responses [40–42].
Besides OspF/SpvC/HopAI1, also the Pseudomonas effector HopPtoD2 has homologs in human pathogenic bacteria. HopPtoD2 is a tyrosine phosphatase which inhibits
pathogen-triggered programmed cell death [43], while its
homolog from Salmonella SptP, inhibits phosphorylation
and membrane localization of Raf kinase and therefore the
activation of ERK2 [44]. It is tempting to speculate that
the biochemical features of these effectors are conserved
between animal and plant hosts, providing Salmonella
and other pathogenic bacteria with efficient tools for suppressing the host immune systems. A suppression of the
defense responses was recently reported during the infection of tobacco (Nicotiana tabacum) plants with S. Typhimurium. In contrast to living Salmonella, dead or
chloramphenicol-treated bacteria elicited an oxidative
burst and pH changes in tobacco cells [45], indicating that
Salmonella actively engages in the suppression of the
4
Trends in Plant Science xxx xxxx, Vol. xxx, No. x
plant defense responses. Similar conclusions were reached
when comparing the Arabidopsis responses against
S. Typhimurium wild type and the T3SS mutants invA or
prgH, which lack a functional T3SS [21,45]. Whereas
Salmonella wild type and prgH mutants provoke changes
in more than 1600 Arabidopsis genes after 24 h, a group of
649 genes is specifically induced by infection with the T3SS
mutant. Many of these prgH-specific genes encode proteins
related to pathogen responses and ubiquitin-mediated protein degradation. This group of genes also includes BAK1,
BIK1, WRKY18 and WRKY33, EIN3, PR4 and PUB23, all of
which are marker genes that are upregulated upon plant
pathogen infections. The lower expression level of those
genes upon infection with wild type Salmonella suggests
that the T3SS mutant is unable to employ an effective
immune suppression mechanism. These results suggest
that Salmonella depend on the T3SS during plant infection
and actively suppress immune responses.
Concluding remarks
Along with E. coli, Salmonella belong to the best-studied
bacteria today. The growing number of human infections
with pathogenic bacteria derived from vegetables and
fruits raise the question of the host specificity mechanisms
of these bacteria. Recent reports clearly demonstrate that
Salmonella not only passively survive, but also actively
infect plants. Moreover, infection of plants depends on the
active suppression of the host immune responses by
Salmonella. Further studies are clearly warranted to uncover the extent to which the factors and mechanisms
employed by Salmonella to infect animals are also used
against plants and will likely lead to a better understanding of the evolution of specificity.
Acknowledgments
The work of AVG and HH is supported from a grant of the ERANET
Systems Biology project SHIPREC (Salmonella Host Interaction Project
European Consortium). The authors would like to apologize to all
colleagues whose work could not be cited because of space limitations.
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5
Review
Special Issue: Specificity of plant–enemy interactions
Role of phytohormones in
insect-specific plant reactions
Matthias Erb1, Stefan Meldau2 and Gregg A. Howe3
1
Root–Herbivore Interactions Group, Max Planck Institute for Chemical Ecology, Hans-Knöll-Str. 8, 07745 Jena, Germany
Department of Molecular Ecology, Max Planck Institute for Chemical Ecology, Hans-Knöll-Str. 8, 07745 Jena, Germany
3
Biochemistry & Molecular Biology, 122 Plant Biology Lab, Michigan State University, East Lansing, MI 48824-1219, USA
2
The capacity to perceive and respond is integral to
biological immune systems, but to what extent can
plants specifically recognize and respond to insects?
Recent findings suggest that plants possess surveillance
systems that are able to detect general patterns of
cellular damage as well as highly specific herbivoreassociated cues. The jasmonate (JA) pathway has
emerged as the major signaling cassette that integrates
information perceived at the plant–insect interface into
broad-spectrum defense responses. Specificity can be
achieved via JA-independent processes and spatio-temporal changes of JA-modulating hormones, including
ethylene (ET), salicylic acid (SA), abscisic acid (ABA),
auxin, cytokinins (CK), brassinosteroids (BR) and gibberellins (GB). The identification of receptors and ligands
and an integrative view of hormone-mediated response
systems are crucial to understand specificity in plant
immunity to herbivores.
Know your enemy: a golden rule of plant defense?
‘If you know your enemies and know yourself, you can win a
hundred battles without a single loss’, states Sun Tzu in his
ancient military treatise The Art of War. Plants, as primary
producers of organic matter in terrestrial ecosystems, must
continuously resist a multitude of attackers and, unlike the
armies of Sun Tzu, do not have the option of retreating to
safe ground. Have plants nevertheless evolved the capacity
to ‘know’ the attacking enemies and adjust their defenses
accordingly? In this review, we use the paradigm of molecular specificity in plant–pathogen interactions as a framework to discuss potential mechanisms by which plants
specifically recognize and respond to insect herbivores.
Plants recognize herbivores via mechanical and
chemical cues
An appropriate defense response to a biotic threat requires
initial recognition. Pathogens are recognized when conserved patterns of microbial molecules, called microbe- or
pathogen-associated molecular patterns (MAMPs or
PAMPs), are detected by pattern recognition receptors
(PRRs) on the surface of the host plant cell, leading to
PAMP-triggered immunity (PTI; Figure 1). Damage-associated molecular patterns (DAMPs), which are endogenous
molecules that are produced by the plant after infection,
Corresponding author: Erb, M. ([email protected]).
250
are also recognized by PRRs to trigger defensive reactions
[1]. Pathogens can evade this innate immune response
through the action of effector proteins that, upon delivery
into the host cell, suppress PTI. Some plant genotypes
Glossary
Appropriate response: a phenotypical change following herbivory that
provides a benefit to the plant. This benefit can be realized either by increasing
resistance and fending off the attacker, or by changing the primary metabolism
to enable a more effective regrowth after attack. Appropriate responses are not
necessarily based on specific recognition and specific metabolic changes, as
plants can use general mechanisms to defend themselves against a variety of
attackers. Different chewing herbivores are likely to be susceptible to the same
defensive mechanisms. By contrast, phloem feeders might require different
measures of protection because they only feed on specialized cells. From an
adaptive point of view, truly specific responses can be expected to be
appropriate.
Direct crosstalk: a phenomenon in which two or more hormone pathways either
share a common signaling component; for example, the use of corepressor
TOPLESS (TPL) by both the JA and auxin pathways, or contain components that
physically interact to modify the signal output (e.g. the JAZ–DELLA interaction).
The biological significance of direct crosstalk in shaping the outcome of plant–
insect interactions remains to be demonstrated.
Hormone crosstalk: a phenomenon in which a signal transmitted through one
hormone pathway stimulates or represses signal output (e.g. a physiological or
defense-related response) from another signaling pathway. Interactions between the hormone signals can be direct or indirect (see direct and indirect
crosstalk).
Indirect crosstalk: a common phenomenon in which two or more hormone
pathways are integrated at the hormone response gene–network level rather
than at the upstream level of signal transduction. One example is the JAinduced expression of NtPYL4, which affects the ability of ABA to regulate
alkaloid production in tobacco.
Specificity of recognition: the extent to which a plant can discriminate the
presence of and/or attack by different herbivores. Specific recognition of arthropod herbivores can occur at different levels, ranging from phyla (i.e. distinct
detection of arthropods compared with vertebrates) to species (i.e. distinct
detection of two different herbivore species). Little is known about the molecular
mechanisms underlying plant recognition of herbivores; however, generalized
examples based on the PTI/ETI paradigm are informative. For example, a high
degree of specificity in recognition could be achieved by R gene products that
evolved to recognize effector molecules in adapted insects. Low-level specificity
might involve the action of mechanosensors that detect insect movement on the
leaf surface. Receptor-mediated recognition of HAMPs and/or DAMPs produced
at the site of insect feeding is expected to provide an intermediate level of
specificity because these signals are common in plant interactions with multiple
insect species.
Specificity of response: the extent to which plant physiological and/or metabolic
changes elicited by the specific perception of a given herbivore are distinct from
changes elicited by the perception of other attackers. Unlike the adaptive immune
system in animals, which creates an immunological memory of a specific invading
pathogen, the recognition of many insects (e.g. chewing herbivores) is channeled
into a general defense response. Many measured differences in responses are not
based on specific perception but are likely to be artifacts of secondary stress
factors. These responses are referred to as ‘distinct’ or ‘different’ but not ‘specific’.
Examples of specific responses are the different phenotypical changes triggered
by different putative aphid receptors [122].
1360-1385/$ – see front matter ß 2012 Elsevier Ltd. All rights reserved. doi:10.1016/j.tplants.2012.01.003 Trends in Plant Science, May 2012, Vol. 17, No. 5
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PAMPs
HAMPS
Eggs
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8
ETI
HTI
R
ETI
PTI
Plant
leaf
HTI
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Effectors
Phytohormone signaling and induced resistance
TRENDS in Plant Science
Figure 1. Molecular recognition of pathogens and herbivores by plants. 1. Microbe-, pathogen- and damage-associated molecular patterns (MAMPs, PAMPs and DAMPs)
are recognized by pattern recognition receptors (PRRs) and lead to PAMP-triggered immunity (PTI). 2. Pathogen effectors suppress PTI. 3. Resistance gene products
recognize effectors and lead to effector-triggered immunity (ETI). 4. Oviposition-associated compounds are recognized by unknown receptors and trigger defensive
responses. 5. Putative herbivore-associated molecular patterns (HAMPs) are recognized by receptors and lead to herbivore-triggered immunity (HTI). 6. Wounding leads to
the release of DAMPs and to wound-induced resistance (WIR). 7. Effector-like molecules from insects can suppress HTI and WIR. Uncharacterized elements are indicated by
broken lines.
again contain disease resistance (R) proteins that specifically recognize pathogen effectors, resulting in effectortriggered immunity (ETI) [2]. Although the PTI/ETI model
is sometimes regarded as an oversimplification [3], the
molecular identification of the involved ligands and receptors has enabled conclusions to be drawn about the specificity of recognition in plant–pathogen interactions: in
general, PTI is based on non-specific recognition of common microbial molecules, whereas ETI is triggered by
highly pathogen-specific compounds [4].
To what extent can the PTI/ETI model inform research
aimed at elucidating the specificity of recognition in plant–
herbivore interactions? In comparison to pathogens,
insects are highly complex multicellular organisms with
various lifestyles and behavioral patterns. Cues emanating from these patterns may be used by the plant to
recognize the threat of herbivory and to mount appropriate
defensive responses [5] (Figure 1).
The first contact with the herbivore often occurs when
the tarsi of an arriving insect touch the leaf surface.
Landing and walking on a plant will exert pressure, break
trichomes and deposit chemicals from tarsal pads on the
leaf [5]. Plants have evolved mechanisms to sense pressure. The Venus fly trap (Dionaea muscipula), for example,
closes immediately when its sensory hairs are stimulated
by insects [6]. Non-carnivorous plants are also highly
sensitive to touch [7]. In at least some cases, mechanostimulation by repeated touching is sufficient to induce the
accumulation of jasmonic acid (JA) [8], the precursor of the
defense hormone jasmonoyl-L-isoleucine (JA-Ile). Breaking of tomato (Solanum lycopersicum) leaf trichomes by
adult moths or caterpillars induces hydrogen peroxide
(H2O2) formation and expression of defensive proteinase
inhibitors [9]. To date, there is no indication that this type
of ‘early warning’ response is specific for particular insect
species, and the observed effects may be mostly related to
DAMP-like effects (see below).
Oviposition represents another opportunity for plants to
detect insect herbivores. The formation of necrotic zones
following egg deposition has been observed in black mustard
(Brassica nigra) and certain potato (Solanum spp.) clones
[10,11]. In pea (Pisum sativa) plants, long-chain alpha,omega-diols (bruchins) deposited during oviposition by pea
weevils (Bruchus pisorum) on pea pods trigger the formation
of undifferentiated cells beneath the eggs, which increase
plant resistance by hindering the larvae when they try to
burrow into the pod [12]. Oviposition can be accompanied by
wounding, and in the interaction between the elm leafbeetle (Xanthogaleruca luteola) and the field elm (Ulmus
minor), for example, oviduct secretions induce defenses only
when they are released into oviposition wound sites [13].
Overall, some oviposition-associated cues seem to act as
MAMP-like molecular patterns that can be used by plants
to recognize and predict herbivore attack. Consistent with
the PTI/ETI framework, oviposition effectors may be produced by herbivores to suppress the plant immune response
(Box 1). Taken together, these findings suggest that oviposition events trigger plant defense reactions in an insectand potentially even species-specific manner.
Herbivory disrupts the integrity of plant tissue, and
many plant defense responses can be triggered by mechanical wounding alone [14–16], leading to wound-induced
resistance (WIR). Extensive studies of the wound response
in model plants such as tomato and Arabidopsis (Arabidopsis thaliana) have identified plant-derived compounds
that trigger anti-insect defense responses. Such compounds are potentially recognized by PRRs and, thus,
can be defined conceptually as DAMPs [17]. Among the
DAMPs shown to activate anti-insect defenses in tomato
are cell wall-derived oligosaccharides and the peptide
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Review
Box 1. Herbivore effectors
Just as plants recognize a variety of herbivore-derived cues, there is
evidence that herbivores can use effector molecules to suppress
plant defenses:
Oviposition fluids trigger the SA pathway in Arabidopsis, which
increases the growth of Egyptian cotton leafworm (Spodoptera
littoralis) larvae [58,123]. Oviposition can also suppress herbivoreinduced plant volatiles in maize [124].
During feeding, insects secrete effector-like compounds to suppress plant immunity. The best-known example is glucose
oxidase produced by the salivary glands of various lepidopteran
insects [26]. Many aphids also produce effector-like compounds
[125], and other examples of herbivore-mediated suppression of
plant defenses via as yet unknown mechanisms have also been
documented [36,72].
Herbivores can produce plant hormones or hormone mimics to
manipulate the host defense responses [126].
Insect herbivores are hosts to microorganisms (e.g. endosymbionts) and surface-dwelling parasites that produce compounds
that potentially interfere or otherwise affect plant immunity [36].
For example, a recent study suggests that Wolbachia endosymbionts suppress the induction of maize genes involved in defense
against the Western corn rootworm (Diabrotica virgifera), which
feeds on roots [127]. Bacterial symbionts are also involved in the
production of cytokinins that are secreted by the larvae of a leafminer moth (Phyllonorycter blancardella) to inhibit leaf senescence to maintain a source of food for the larvae [128].
The PTI/ETI paradigm indicates that plants have evolved various
ways to recognize and respond defensively to pathogen effectors.
Have plants also acquired the capacity to recognize insect effectors?
Although there is evidence to suggest that this is indeed the case for
hymenopterans [25], the ligands and receptors that constitute this
form of recognition have yet to be identified and characterized [125].
Future research focusing on the identification of insect effectors and
their mechanism of action is likely to mark a new phase in plant–
insect interaction research.
signal systemin [18]. These studies lend support to the idea
that many plant defense responses against herbivores are
mediated by recognition of the ‘damaged self’ of the plant
[17,19]. Analogous to danger signal models in the vertebrate immune system, when plant tissue suffers mechanical damage, this is likely to disrupt intracellular
compartmentalization in ways that lead to the production
of molecules that trigger general plant immune responses.
It is becoming clear that a second layer of perception in
addition to WIR can enable plants to detect herbivores
more specifically: plants seem to recognize compounds that
are released by herbivores during feeding. Extensive genetic analysis of the Hessian fly (Mayetiola destructor)–
wheat (Triticum spp.) interaction [20,21] and the cloning of
receptor-like R genes have demonstrated that there is a
high degree of specificity of perception in this case [22–24].
The recognition systems for hemipteran and dipteran
parasites, together with the identification of possible effectors (Box 1), appear to conform to the general PTI/ETI
theory [25]. Less is known about the mechanisms by which
plants perceive chewing insects, such as beetles and caterpillars, which constitute the vast majority of insect herbivore species. Numerous studies have shown that insect
oral secretions, when applied to artificial wound sites,
amplify the wound response of the plant [26–29]. Identified
elicitors include fatty acid-amino acid conjugates (FACs),
sulfur-containing fatty acids (caeliferins), peptides from
digested plant proteins, and lipases [30–34]. Based on their
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Trends in Plant Science May 2012, Vol. 17, No. 5
eliciting activity, at least the insect-derived compounds can
be conceptually classified as herbivore-associated molecular patterns (HAMPs), which presumably are recognized
by PRRs at the cell surface [35,36] to trigger HAMPinduced immunity (HTI). Even though HAMP receptors
have not been identified, several trends have emerged
concerning the specificity of elicitor-mediated recognition
of chewing herbivores by plants. First, herbivore-derived
elicitors boost the amplitude of wound-induced defense
responses [30,31,33,34,37]. Second, different herbivore
species produce qualitatively and quantitatively different
elicitor combinations [38,39]. Third, the activity of the
different known elicitors varies between plant species
[37]. Taken together, these observations suggest the potential for elicitor-mediated, species-specific recognition of
chewing insects by plants.
Plant hormone regulatory networks integrate different
herbivore recognition cues
Following the recognition of an attacker, plants use different signaling cascades to reprogram their phenotype. Extended PTI/ETI models in plant–pathogen interactions
suggest that, although the recognition of pathogens can
be very specific, plants have a ‘common downstream signaling machinery’ [40] that is activated upon recognition of
many different attackers. To what extent is this paradigm
valid for plant–insect interactions? The JA signaling cascade, including the wound hormone JA-Ile, is widely considered to be a master regulator of plant resistance to
arthropod herbivores as well as various pathogens
[15,17,41–45], and JAs may therefore represent the core
signaling pathway for activating resistance to insects.
Disruption of plant tissue integrity during insect feeding triggers the production of JA-Ile and the activation of
a well-defined signal transduction chain, leading to transcriptional activation of defense responses. Thus, it is the
defining feature of most, if not all herbivores, namely the
need to obtain nutrition from plant tissues, which betrays
the presence of the attacker to the host. A major unresolved question is the extent to which herbivore-induced
production of JA-Ile is promoted by signals originating
from the signals of the plant (i.e. DAMPs, ‘self’) versus
those from the herbivores (i.e. HAMPs, ‘non-self’) [19].
Mechanical wounding is sufficient to trigger robust
local and systemic increases in JA-Ile levels within minutes of leaf injury, which indicates that herbivore-associated cues are not strictly required to activate the
response [17,46,47]. However, the severity of crushingtype wounds typically used in these studies may bypass
a requirement for HAMPs in the elicitation of herbivoreinduced responses. Research aimed at identifying herbivore-derived elicitors has therefore relied on the application of insect oral secretions to wound sites created by
mild wounding regimens that do not elicit a strong
defense response in the absence of oral secretion [35,
36,48–50]. Importantly, defense responses are attenuated
in leaf-feeding lepidopteran herbivores that lack known
elicitors in their oral secretions, lending support to the
concept that wound-induced responses at an ecologically
relevant intensity are potentiated by recognition factors
[51].
Review
Trends in Plant Science May 2012, Vol. 17, No. 5
Hormone accumulation
[(Figure_2)TD$IG]
(a)
(b)
(c)
(d)
Time
TRENDS in Plant Science
Figure 2. Perception triggers herbivore- and tissue-specific hormonal network responses. (a–d) Conceptual kinetics of three hypothetical phytohormones are shown by
solid and broken, black and gray lines. (a–c) with a white background represent the same tissue, whereas (d) with a darker background represents a different tissue or tissue
age. (a,b) Different herbivores can elicit different hormonal responses. (c,d) Hormonal responses to the same herbivore can show tissue- or age-specific differences. A
hypothetical hormone-responsive transcriptional network is then triggered by the different hormones. This network is represented here by specific transcripts (black
circles), which differ in their expression intensity (different sizes of the circles) and interact in space and time (unbroken black lines represent strong interactions and broken
black lines represent weak interactions). Gray ellipses denote specific groups of transcripts that are functionally related. The integration of spatiotemporal changes of
hormone signaling into the downstream transcriptional network can lead to herbivore-specific plant responses.
We argue here that the JA pathway represents a conserved core-signaling machinery that is activated by both
non-specific and specific recognition patterns following herbivore attack. However, how do plants fine tune their defense machinery to mount appropriate herbivore-specific
responses beyond JAs? We propose two potential answers
to this question. First, plants may use JA-independent,
parallel pathways to create distinct response patterns. Second, specificity may be mediated through the activation of
spatio-temporal modulators of the JA response (Figure 2).
Evidence for the first concept comes from studies on the
recognition and response system of tomato to the potato
aphid Macrosiphum euphorbiae. Mi-1, a putative receptor,
triggers SA-mediated signaling [52] and resistance independently of the JA pathway [53]. Plant recognition of, and
response to, many other hemipterans seems to follow a
similar pattern [54], which suggests that plants use JAindependent hormone response pathways to achieve specific resistance against phloem feeders.
However, most herbivores inflict much greater cell damage than do phloem feeders, and will activate JA signaling
and resistance. In this case, specificity may be achieved via
cross-talk with other hormones (Figure 3). Indeed, JAinduced changes in gene expression typically depend on
the context in which the hormone is perceived [55,56]. The
best-studied hormones that alter JA-mediated defense
responses and herbivore resistance are SA and ET. In
general, SA antagonizes JA-induced resistance, whereas
ET can have both positive and negative effects. For ET,
some of the transcriptional responses that are modulated
by crosstalk with JA were recently shown to be mediated
by the ET-stabilized transcriptional regulator EIN3 [57].
Several studies have shown that SA and ET are specifically
modulated by different herbivore elicitors [29,37] and may
thereby provide a degree of hormone-mediated specificity.
A striking example of how herbivore-induced SA signaling
can modulate JA-dependent defenses comes from research
on A. thaliana: Oviposition by the cabbage butterfly (Pieris
brassicae) induces SA accumulation and reduces the induction of JA-responsive genes, leading to reduce plant
resistance against S. littoralis [58]. SA–JA–ET crosstalk
has been reviewed in detail elsewhere [59,60] (also see
other reviews in this special issue). However, ABA, auxins,
GB, CK and BR have received less attention as potential
factors that modulate herbivore resistance. The following
discussion highlights examples from the recent literature
that indicate that these hormones also play an important
role in mediating specificity in herbivory-induced defense
responses.
Abscisic acid
ABA levels in maize (Zea mays) are increased during attack
by the specialist root herbivore western corn rootworm
(Diabrotica virgifera virgifera), but not by mechanical
wounding alone [27,61], and in Arabidopsis after induction
with wounding and the oral secretions of the desert locust
(Schistocerca gregaria), a generalist herbivore [34].
ABA levels also increased in a goldenrod species (Solidago
altissima) after induction by the tobacco budworm
(Heliothis virescens) caterpillar, but not by the gall-inducing
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Secondary
stress effects
Receptor-mediated
perception
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Desiccation
pH
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BRs
CKs
SCFCOI1
EIN3
ET
MYB
MYC
Phenotypical
changes
JA-dependent responses
Specific responses
Non-specific responses
TRENDS in Plant Science
Figure 3. The jasmonate (JA) core pathway and its modulating factors. A conceptual, non-exhaustive overview is presented. General and specific herbivore-associated
patterns, including herbivore-associated molecular patterns (HAMPs), damage-associated molecular patterns (DAMPs) and wounding, activate the JA pathway (blue area).
Increased accumulation of jasmonoyl-L-isoleucine (JA-Ile) promotes the interaction of JAZ proteins with the SCF ubiquitin ligase SCFCOI1. Ubiquitin-dependent degradation
of JAZs by the 26S proteasome releases transcription factors from their JAZ-bound repressed state, thereby activating the expression of transcriptional regulons that
promote defense and inhibit vegetative growth. JA-independent hormonal pathways are also induced (purple area), and several hormones, including salicylic acid (SA),
ethylene (ET), auxin, gibberellins (GA), cytokinins (CK) and brassinosteroids (BR) modulate JA metabolism and signaling (light-blue area). Herbivory also leads to oxidative
stress, changes in intracellular pH and desiccation, which modulate the JA pathway either directly or indirectly through other hormones. Together, this leads to complex
phenotypic changes that comprise both specific and general responses, the majority of which can be linked back to the JA pathway.
caterpillar Gnorimoschema gallaesolidaginis [62]. ABA synthesis and signaling affect herbivore-induced transcript
levels and JA biosynthesis in Arabidopsis [63,64], JA-inducible defense responses in maize [27] and resistance to herbivores in tomato [65]. ABA and JA synergistically induce
MYC2-dependent gene expression during wound responses.
MYC2 encodes a nuclear localized basic helix-loop-helix–
type transcription factor that acts as both activator and
repressor of JA-mediated gene expression and serves as an
integration point between ABA and JA signaling [66–68]. In
tobacco (Nicotiana tabacum), JA also regulates the expression of NtPYL4, a gene belonging to the PYR/PYL/RCAR
family, which encodes an ABA receptor protein, thereby
affecting ABA-induced levels of root alkaloids [69]. The same
study demonstrated that AtPYL4 and AtPYL5 mutants in
Arabidopsis are more sensitive to JA-induced growth inhibition and less sensitive to JA-induced anthocyanin accumulation. Although the molecular mechanisms behind
ABA–JA crosstalk are still elusive, recent findings suggest
that both pathways share similar regulatory proteins. The
co-repressor TOPLESS (TPL) interacts with ethylene-responsive element binding factor-associated amphiphilic
repression (EAR)-motif proteins to repress transcription
of genes involved in several hormone pathways [70].
The EAR-motif protein Novel Interactor of JAZ (NINJA)
254
connects TPL to the JAZ complex, thereby mediating repression of genes demarcated by JAZ-bound transcription
factors, such as MYC2. TPL also interacts with NINJArelated proteins that are part of a complex that mediates
ABA-induced degradation of negative transcriptional regulators [71]. Taken together, these findings indicate that
ABA and JA are tightly interconnected and that regulation
of ABA levels in response to herbivory can modulate JAdriven defense responses (Figure 3). However, because ABA
is also an important signal for responses to desiccation,
which is an effect that accompanies herbivore attack in
many cases, it remains to be determined to what extent
this stress hormone is involved in recognition-mediated
responses to insect feeding. The application of Egyptian
cotton leafworm (Spodoptera littoralis) regurgitant to Arabidopsis can reduce wound-induced stomatal closure and
water loss [72], whereas S. gregaria oral secretions induce
ABA levels [34], which suggests specific elicitor-mediated
regulation of this hormone.
Auxin
Levels of the auxin indole-3-acetic acid (IAA) are elevated
in plants attacked by gall-feeding insects [62,73]. By contrast, IAA levels in the leaves of a species of wild tobacco
(Nicotiana attenuata) are reduced within three days after
Review
simulated herbivory [74]. It is known that plant resistance
to pathogens can be modulated through changes in auxin
sensitivity. For example, the perception of the bacterial
elicitor flagellin decreases auxin sensitivity, thereby elevating resistance to Pseudomonas syringae [75]. Concomitantly, P. syringae suppresses host defense by promoting
auxin production via delivery of effectors into the plant cell
[76,77]. Treatments with synthetic auxin directly suppress
SA-induced defense responses [78], which can be linked to
SA-mediated resistance to phloem-feeding insects [52].
Whether insects that are negatively affected by SA-mediated defenses can alter auxin homeostasis or signaling to
suppress host defense is not known.
In addition to regulation of SA signaling, studies have
suggested that there is an intimate molecular interplay
between auxin and JA signaling: auxin formation in Arabidopsis roots is enhanced by JA-mediated induction of
genes involved in auxin biosynthesis and transport [68,79].
In N. attenuata leaves, JA negatively regulates woundinduced decreases in auxin content [74], demonstrating
that the effects of JA on auxin biosynthesis are tissue
specific and possibly also species specific [55]. Importantly,
these data suggest that herbivore-induced JA levels affect
auxin homeostasis. Conversely, there is evidence to indicate that auxin enhances JA biosynthesis and signaling
[80–82]. JAZ1 and MYC2 are coregulated by auxin and JA,
demonstrating the potential for crosstalk between both
hormones [81,82]. Analogous to ABA signaling, EAR-motif-containing AUX/IAA proteins, which are negative regulators of auxin-induced responses, also interact with TPL
[71], suggesting that TPL acts as an integrator of multiple
hormone pathways.
Another protein that links auxin and JA responses is
suppressor of G-two allele of SKP1 (SGT1), which connects
chaperone-mediated protein assembly and ubiquitin-mediated protein degradation. SGT1 mutants of Arabidopsis
are compromised in their sensitivity to both auxin and JA
[83], and silencing SGT1 in N. attenuata attenuates JA
levels, defense metabolite accumulation, and resistance to
the tobacco hornworm (Manduca sexta) caterpillar [84].
Auxin can also regulate plant defense responses independently of SA and JA [85,86]. These findings demonstrate
that auxin is a potent modifier of herbivore-relevant defense responses and indicate that plants may modulate
auxin levels to mediate attacker specificity.
Gibberellins
Studies with plants altered in GB signaling have suggested
a role for GB in herbivore-induced defense responses.
DELLA proteins are negative transcriptional regulators
of gibberellic acid (GA)-induced gene expression and are
considered to play key roles in integrating plant responses
to diverse developmental and environmental stimuli [87].
Remarkably, GAs affect JA signaling through competitive
binding of DELLAs to JAZ proteins, thereby preventing
JAZ–MYC2 interaction and promoting MYC2-induced
transcriptional responses [88]. GA perception leads to
degradation of DELLAs, which ultimately leads to inhibition of MYC2 and diminished JA responses. Accordingly,
alteration of DELLA levels affects JA biosynthesis and
signaling [89,90].
Trends in Plant Science May 2012, Vol. 17, No. 5
Cytokinins
In N. attenuata, CK-related transcripts are among the
genes that are most strongly regulated by FAC elicitors
[91,92], suggesting that CK has a role in the hormonal
regulatory network. In addition, gall-forming insects and
possibly some leaf miners modulate plant CK levels, presumably to maintain the sink status of the infected tissues
[62,73,93]. Isopentenyltransferases (IPT) represent the
rate-limiting step in CK biosynthesis, and IPT overexpression increases resistance of common tobacco to M. sexta
[94]. Several lines of evidence also support an important
role for CK in the activation of JA biosynthesis. Transgenic
tobacco (N. tabacum cv. Xanthi nc) plants that overexpress
a small GTP-binding protein accumulate high levels of CK,
resulting in increased rates of JA production after wounding, a response that can be mimicked by long-term CK
treatments [95]. Furthermore, CK treatments of hybrid
poplar (Populus sp.) leaves increase the wound-induced
JA burst and the expression of genes involved in JA
biosynthesis [96]. The same study also shows that wounding and CK treatments of sink but not source leaves
impairs gypsy moth (Lymantria dispar) larval performance, suggesting that CK-mediated resistance to insects
depends on leaf ontogeny. CK levels in leaves are thought
to be regulated by ontogenic constraints because the hormone accumulates to high levels in younger leaves, whereas reduced CK levels promote leaf senescence [97,98].
Because CKs modulate herbivore-induced defenses, the
CK status of a given tissue might determine the intensity
of the defense response of that particular tissue after
perception of herbivory and, thus, contribute to tissuespecific responses in herbivory-induced signaling [99]
(Figure 2).
Brassinosteroids
Recent findings have also suggested important roles for
BRs in herbivore resistance. BRs antagonize JA-mediated
trichome density and defense metabolite accumulation in
tomato [100]. BRs are also known to repress JA-governed
inhibition of root growth [101]. BR are perceived by BR
insensitive 1 (BRI1), a leucine-rich repeat receptor-like
kinase [102,103]. BRI1-associated kinase 1 (BAK1) interacts with BRI1 and plays an essential role in BR signaling
[104]. Apart from BR signaling, BAK1 also interacts with
the flagellin receptor FLS2 and is required for multiple
MAMP-elicited responses [105]. Silencing BAK1 in N.
attenuata reduces wound- and herbivory-induced JA
and JA-Ile levels and JA-induced trypsin proteinase inhibitor (TPI) activity [106]. Whether BAK regulates
HAMP or DAMP perception to modulate JA levels, or
whether the effects in BAK1-silenced plants are the result
of changes in BR perception, requires further analysis
[106].
Taken together, these examples illustrate the many
possibilities that plants have to modulate the JA pathway
to achieve specific responses. However, apart from JA/SA
crosstalk (see Whitham and colleagues in this special
issue), clear examples of specific induction of JA-modulators following herbivore recognition remain scarce. The
identification of herbivore receptors followed by in-depth
analysis of their downstream targets should help to fill
255
Review
this knowledge gap. Another open question is whether the
JA pathway and its modulating factors function in a
similar manner in different plant species. Interspecific
comparative approaches would be important to be
construct generalized hormonal networks that mediate
specificity.
Do recognition-induced plant hormone networks
trigger specific and appropriate defense responses?
The recognition systems used by plants to perceive herbivore attack are integrated with hormone response pathways that reprogram the plant. However, what evidence
is there that the resulting responses are specific and
appropriate (see Glossary) for defense against the attacking herbivore? Again, for hemipterans, compelling examples of gene-for-gene resistance link specific recognition
to both specific and appropriate responses [54]. In the
case of chewing herbivores, many studies have also demonstrated the differential responses of plants to different
insect species [107–110], but to date evidence that these
responses provide specific resistance is rare. Given the
complexity of host transcriptional responses to herbivory,
some of these differences may be attributed to a variety of
experimental factors that influence the response. Plant
growth conditions, plant and insect developmental stage,
herbivore density and treatment duration are among the
parameters that are expected to have major effects on
transcript profiles. Also, in some cases, different insects
from the same feeding guild elicit similar or converging
responses via the general JA-signaling cassette [111–
114]. The question thus arises whether, from an adaptive
point of view, plants benefit from tailoring their response
to different chewing herbivores, or whether a generalized
response following recognition is the most pertinent
strategy?
There is evidence to indicate that, among the multitude of defenses induced by one herbivore species, some
responses target specific types of herbivore, even within
the same feeding guild. One example comes from work on
the JA-regulated defensive enzyme threonine deaminase
(TD2), which degrades the essential amino acid Thr in the
lepidopteran gut [115]. Although TD2 expression in tomato leaves is induced in response to attack by both beet
armyworm (Spodoptera exigua) and cabbage looper (Trichoplusia ni) caterpillars and by the Colorado potato
beetle (Leptinotarsa decemlineata), the defensive activity
of the enzyme is only activated in the gut of lepidopteran
herbivores, not in the gut of the coleopteran herbivore
[116]. Levels of the JA-regulated non-protein amino acid
Nd-acetylornithine in Arabidopsis increase in response to
feeding by larvae of the small white (Pieris rapae) and the
diamondback moth (Plutella xylostella) as well as the
green peach aphid (Myzus persicae), but this defense
appears to target only the aphids [45]. Also, distinct
patterns of volatile compounds have been shown to be
induced different herbivores, leading to specific attraction of natural enemies [117,118] (see McCormick and
colleagues in this special issue). However, also in this
case, it remains open whether the differences in the
reactions of the plants are based on specific recognition
patterns.
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Trends in Plant Science May 2012, Vol. 17, No. 5
Overall, generalist and specialist herbivores might be
susceptible to different types of defense, and plants may
benefit from detecting highly adapted herbivores and
adjusting their regulation of quantitative and qualitative
direct and indirect defenses. Specialist herbivores in particular may have found ways to suppress plant defenses
(Box 1) or to circumvent them via behavioral adaptations,
which, in analogy to the PTI/ETI model in plant–pathogen
interactions, may have led to the counter-evolution of
specifically adapted defense mechanisms in plants
[119,120]. Until today, few mechanistic examples of
plant-counter adaptations to specialists are known (but
see other articles in this special issue), and further research is required to disentangle whether differential
responses of plants to chewing herbivores are truly specific,
and whether plants have evolved to tailor their response to
different chewing attackers.
Conclusions and future directions: piecing together the
recognition–response puzzle
A recurring theme in all spheres of plant–herbivore biology
is the ability of each player to perceive and respond to cues
generated by the other; this exchange of information
provides an excellent focal point for elucidating the basic
chemical and molecular principles of plant–herbivore interactions. Understanding the mechanisms behind perception
and response may also inform studies about their evolutionary history. In contrast to well-established models describing the evolution of plant–pathogen interactions [121], our
understanding of how molecular recognition and response
systems shape plant–herbivore relationships is still in its
infancy, and many important questions remain unanswered
(Box 2). Nevertheless, the literature supports several general conclusions about specificity in plant–herbivore interactions. First, plants perceive different arthropods by
integrating various environmental cues, ranging from
mechanostimulation by insects walking on plant surfaces
to contact with salivary components during feeding. Second,
the perception of herbivores triggers regulatory responses
that include different phytohormones, with the JA pathway
playing a dominant role in host resistance. Third, although
JA signaling is highly conserved, it is becoming increasingly
clear that multiple hormone response pathways interact to
translate initial perception events into appropriate
responses that increase plant fitness in the presence of
hostile aggressors.
Box 2. Outstanding questions
Which receptors are involved in plant perception of herbivores?
How prevalent are herbivore effectors and how do they act to
suppress the plant immune system?
Are herbivore effectors recognized by plant R genes in accordance
with the ETI/PTI model in plant–pathogen interactions?
How do plants integrate information derived from multiple
herbivore- and plant-derived cues?
Which JA-independent processes mediate specific plant responses to herbivore attack?
What is the precise role of growth hormones (GBs, CKs, auxin and
BRs) in modulating plant immunity to herbivores?
Is the recognition of a specific chewing herbivore translated into a
distinct defense response?
Review
Acknowledgments
We thank Martin Heil and Anurag Agrawal for the invitation to
contribute to this special issue. Georg Jander and Ian Baldwin provided
helpful comments on an earlier version of this manuscript. This work is
supported by a Swiss National Science Foundation Fellowship to ME
(PBNEP3-134930). Plant–insect interaction research in the GAH
laboratory is supported by grants from the National Institutes of
Health (R01GM57795), the Chemical Sciences, Geosciences and
Biosciences Division, Office of Basic Energy Sciences, Office of Science,
US Department of Energy (grant DE-FG02-91ER20021) and the US
Department of Agriculture (2007-35604-17791).
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TRPLSC-952; No. of Pages 11
Review
Special Issue: Specificity of plant–enemy interactions
Evolution of jasmonate and salicylate
signal crosstalk
Jennifer S. Thaler1, Parris T. Humphrey2 and Noah K. Whiteman2
1
2
Department of Entomology and Department of Ecology and Evolutionary Biology, Cornell University, Ithaca, NY, USA
Department of Ecology and Evolutionary Biology, University of Arizona, Tucson, AZ, USA
The evolution of land plants approximately 470 million
years ago created a new adaptive zone for natural enemies (attackers) of plants. In response to attack, plants
evolved highly effective, inducible defense systems. Two
plant hormones modulating inducible defenses are salicylic acid (SA) and jasmonic acid (JA). Current thinking
is that SA induces resistance against biotrophic pathogens and some phloem feeding insects and JA induces
resistance against necrotrophic pathogens, some phloem feeding insects and chewing herbivores. Signaling
crosstalk between SA and JA commonly manifests as a
reciprocal antagonism and may be adaptive, but this
remains speculative. We examine evidence for and
against adaptive explanations for antagonistic crosstalk,
trace its phylogenetic origins and provide a hypothesistesting framework for future research on the adaptive
significance of SA–JA crosstalk.
Attack, hormonal signaling and plant defense
Sessile organisms, such as terrestrial green plants, are
subject to pervasive attack by diverse attackers. These
attackers include microbial pathogens (e.g. viruses, bacteria and fungi), macroscopic herbivores and parasites (e.g.
parasitic plants and arthropods) and browsing herbivores
(e.g. ungulates). The vast majority of attackers are relatively specialized in terms of the number of host species
that they utilize (specialists), and a minority are less
restricted in host range (generalists) [1,2]. Over the past
470 million years [3], plants have evolved effective inducible defense systems [4] to cope with attack by these
diverse and abundant enemies. Yet, the specific match
between particular attackers and plant defense traits,
and whether attackers have the upper hand in these
interactions, is poorly understood [5]. The specificity of
plant–attacker interactions, from both sides of the equation, has important implications for understanding the
evolution of resistance in plants and the evolution of virulence in the enemies [6].
Plants have to balance the costs and potential benefits of
investing in defense in an environment where enemy attack
is variable. On the one hand, defenses are costly to produce;
in the absence of enemies, deploying defenses reduces plant
fitness [7]. Because they are costly to produce, natural
selection is presumed to favor the evolution of inducibility,
meaning that these defenses are only produced in the
Corresponding author: Whiteman, N.K. ([email protected]).
presence of attack. On the other hand, having an immediate
impact on an attacker could be paramount to deterring
further attacks. Plants generally strike a balance and maintain constitutive and inducible defenses. However, individual plants are likely to be attacked by more than one
organism. Microbial pathogens, which are typically endophagous and single-celled, require vastly different defenses
than macroscopic herbivores, which may even move among
plant individuals while feeding. Among herbivores, different defenses are required for different guilds. For example,
defense traits that are effective against aphids, which feed
on plant phloem, are distinct from those that are effective
against caterpillars, which typically defoliate plants [8].
Characterization of the specificity of the plant response is
a focus of intense research among ecologists and plant
scientists [5,9,10]. Of particular interest in this review is
whether adaptive tailoring of the response occurs, or if
tailoring is a byproduct of manipulation by enemies.
Despite the caveats discussed above, the inducible plant
defense system can be generally divided into two branches –
one effective primarily against biotrophic (feeding on living
tissue) pathogens and one against herbivores and necrotrophic (feeding on dead tissue) pathogens [11]. Inducible
defenses are incredibly diverse and include morphological
structures such as trichomes, fast-killing toxins such as
alkaloids, digestibility reducers such as proteinase inhibitors and indirect defenses such as extrafloral nectaries and
plant volatiles that can recruit other insects that deter
herbivores [1,12–14]. Several plant hormones regulate the
production of downstream resistance molecules in each
branch. The SA pathway is primarily induced by and effective in mediating resistance against biotrophic pathogens
and the JA pathway is primarily induced by and effective in
mediating resistance against herbivores and necrotrophic
pathogens [9]. This is an overly simplistic view of the
complex repertoire of plant hormones that probably play
a role in mediating inducible defenses, including abscisic
acid (ABA), auxin, brassinosteroids, cytokinins, ethylene
(ET) and gibberellic acid [15]. Interestingly, evidence from
several distantly related plant species suggests that there
can be evolutionarily conserved SA- and JA-signaling crosstalk resulting in reciprocal antagonism between the SA and
JA signaling pathways [9]. The adaptive significance of this
crosstalk, if any, is the focus of this review.
The dynamics and genetic bases of SA–JA crosstalk,
including the reciprocal antagonism often observed as a
1360-1385/$ – see front matter ß 2012 Elsevier Ltd. All rights reserved. doi:10.1016/j.tplants.2012.02.010 Trends in Plant Science xx (2012) 1–11
1
TRPLSC-952; No. of Pages 11
Review
result, has mainly been dissected in the model plant Arabidopsis thaliana (Arabidopsis) [16–18]. The genetic basis
of the reciprocal antagonism is extremely complex and an
overview is presented below, in the context of the evolution
of each of the major genetic players. Here we focus on SA
and JA; however, ET is a critical third player from the
perspective of understanding how plants prioritize and
tailor their responses to diverse attackers and a brief focus
on its role in mediating crosstalk is warranted. SA is
typically prioritized over JA in Arabidopsis [19]. However,
plants use ET to fine tune defenses by prioritizing JA
induction over SA in response to multiple attackers [20].
ET also modifies the effect of a key protein (NPR1; NONEXPRESSOR OF PATHOGENESIS-RELATED GENES
1) involved in SA suppression of JA. In Arabidopsis, NPR1
is necessary for expression of SA-responsive genes and for
repression of JA by SA. However, when ET is present,
NPR1 function is no longer required for SA suppression of
JA [20,21], suggesting that ET signaling acts to suppress
JA in the presence of SA by bypassing NPR1. Many other
plant hormones are also important in mediating the crosstalk, but the genetic bases of this crosstalk are less well
studied. Recent approaches that examine genetic interaction networks in Arabidopsis have been fruitful for identifying candidate loci to be studied in detail for their
potential role in defense signaling crosstalk [22].
The SA–JA crosstalk that often results in reciprocal
antagonism between these two pathways has been interpreted as being an adaptive plant strategy, representing
a cost-saving measure given that phenotypically different
enemies are susceptible to distinct defense strategies.
However, specific defenses that induce resistance to
one attacker may render the plant more susceptible to
another if alternative defenses are repressed by crosstalk
[23]. We first focus on the phylogenetic distribution of
crosstalk, candidate loci underlying crosstalk and the
nature of the evidence used to assay for crosstalk. We
then evaluate adaptive and nonadaptive evidence for the
SA–JA reciprocal antagonism and illuminate a research
path that integrates phylogenetic, genetic and ecological
approaches towards testing explicit hypotheses on the
origins and adaptive value of signal crosstalk. We end
with a discussion of SA–JA signal interactions as a
mechanism that generates specificity in plant–attacker
interactions.
Distribution of SA–JA reciprocal antagonism
Although the SA–JA antagonism is clearly present in
many plant species, an open question is whether there is
a common genetic basis to this crosstalk and if so, whether
the trait is conserved across all plants. Similarly, although
it can be a reciprocal antagonism, the strength of the
downregulation from each side of the SA and JA equation
is not identical and may not be antagonistic across plants.
We searched for all studies that tested for antagonisms
in SA–JA signaling (Table 1). A paper was included as
presenting evidence for SA–JA antagonism if there was a
genetic or biochemical measure widely believed to be regulated by the jasmonate and salicylate pathways, or if one
pathway was genetically manipulated and a response was
measured in the other. Our survey included papers that
2
Trends in Plant Science xxx xxxx, Vol. xxx, No. x
measured JA, SA or their derivatives, gene expression or
chemical end-products known to be regulated by one of the
pathways. In some studies, one pathway was elicited and
had direct effects on the other pathway. In other studies,
SA–JA antagonism was seen when induction of one pathway reduced the response to elicitation of the other pathway. We did not include studies that only found
antagonisms in resistance to bioassay organisms if there
was not evidence that the antagonism was due to SA–JA
crosstalk. From the well-studied systems including Arabidopsis, tomato (Solanum lycopersicum) and tobacco (Nicotiana spp.), a subset of studies is included to highlight the
ecological conditions under which antagonism can occur.
There are systems that show conditionality in the antagonism and these were scored as having SA–JA antagonism
for the purposes of Table 1 and are discussed in the text. It
is important to point out that although there are a growing
number of studies using biological inducers, most of the
evidence for antagonism is based on treating plants with
exogenous SA and JA, either singly or in combination. In
most cases, there have not been studies that test whether
there is a common genetic basis or a correlated gene
expression phenotype that underlies the gross SA–JA
antagonism reported across plants. Therefore, the results
of this survey and our inferences on trait evolution need to
be interpreted with caution because of the inherent limitations of screening for SA–JA antagonism using chemical
elicitors and the lack of direct evidence for a common
mechanism. The evolutionary interpretations below and
our interpretations are hypotheses to be tested.
The pathways that produce both hormones at the center
of this story have ancient origins. SA is produced downstream of isochorismate synthase (ICS), which occurs in
many green and red algae as well as in bacteria, and may
have a plastid origin in plants [24]. By contrast, jasmonates are end-products of the ancient octadecanoid (C18)
oxylipin pathway. Oxylipins are bioactive lipid derivatives
that are used as signaling molecules in plants, animals,
fungi [25], as well as in several marine algae species [26].
An allene oxide synthase (AOS) homolog (the second enzyme in the octadecanoid biosynthetic pathway) has been
discovered in the moss Physcomitrella patens [27,28], and
distant structural homologs to AOS have been putatively
identified in three metazoan lineages [29]. The specific
compounds JA and methyl JA also have been detected
in P. patens [27,30–32], as well as in ferns [33], suggesting
that JA production arose at least in the common ancestor of
mosses, ferns and seed plants (Figure 1).
Despite the ancient origins of each hormone, the antagonism between SA and JA may have more recent origins. SA–
JA antagonism has been reported in a total of 17 plant
species, including 11 crop plants and six wild species (Table
1). Ancestral state reconstruction [34] indicates that SA–JA
antagonism evolved at least at the base of angiosperms, but
possibly before the split of gymnosperms and angiosperms
(Figure 1; using data from Table 1). The presence of orthologs of genes known to be involved in the SA–JA antagonism
including NPR1, WRKY70 (WRKY DNA-binding protein
70), GRX480 (Glutaredoxin 480), ERF1 (ETHYLENE RESPONSE FACTOR 1), MYC2 (JASMONATE INSENSITIVE 1, JIN1), ORA59 (OCTADECANOID-RESPONSIVE
Method of JA elicitation
Arabidopsis thaliana
Method of
SA elicitation
Pieris brassicae
eggs/egg extracts
SA
Arabidopsis thaliana
SA
Arabidopsis thaliana
Hyaloperonospora parasitica
Pseudomonas syringae
Arabidopsis thaliana
Arabidopsis thaliana
Arabidopsis thaliana
Arabidopsis thaliana
Arabidopsis thaliana
Solanum
lycopersicum
(tomato)
Solanum
lycopersicum
Solanum
lycopersicum
Solanum
lycopersicum
Nicotiana tabacum
(tobacco)
Nicotiana tabacum
Nicotiana attenuata
Hordeum vulgare
(barley)
–
–
Pathogens:
Alternaria brassicola,
Botrytis cinerea, insects:
Frankliniella occidentalis,
Pieris rapae
MeJA
–
–
PDF1.2 expression decreases
–
[59]
MeJA
–
–
[10]
Mutant plants with
elevated or
suppressed SA
SA
Cucumber mosaic virus
SA
–
–
–
MeJA
–
–
–
Genome wide effects
JA inducible transcripts decrease
Proteinase inhibitors decrease
Decreased resistance
to Trichoplusia ni
Trichoplusia ni resistance
decreased as SA
expression increased
–
–
–
BTH
JA
PR4 (PATHOGENESIS RELATED 4)
transcripts downregulated
Oxidative enzymes decrease
[53,55]
Botrytis cinerea
–
SA induced
Parasitic plant Cuscuta
pentagona and SA
deficient plants
BTH
–
SA induced
Proteinase inhibitor transcripts
decrease
JA and herbivore induced plant
volatiles decrease
JA
–
Polyphenol oxidase activity
decrease
Decreased resistance
to Spodoptera exigua
and Trichoplusia ni
B. cinerea disease
increased
Spodoptera exigua
performance not
affected
Spodoptera exigua
performance not
affected
Mechanical damage
–
–
Tobacco mosaic
virus inoculation
Genetically
reduced SA
production
–
–
–
Increased JA correlates with
decreased SA
JA and nicotine decrease
–
–
Fatty acid–amino acid
conjugates from
Spodoptera exigua
oral secretion
–
SA
JA and systemin
JA pathway inducibility
measurement
Ten insect-induced JA regulated
transcripts decrease
Peroxidase, polyphenol oxidase,
chitinase, glucosinolates
decrease
PDF 1.2 (PLANT DEFENSIN 1.2)
decreases
Bioassay result
Refs
Decreased resistance
to Spodoptera littoralis
Decreased resistance
to Spodoptera exigua
[44]
–
[59]
[84]
[65]
[85]
[45]
[86]
[47]
[48]
[52]
[70]
[46]
JA, nicotine, polyphenol
oxidase increase
Decreased resistance
to Manduca sexta
Increased resistance
to Heliothis virescens
SA decreases
–
–
[80]
–
13-Hydroxyoctadecatri(di)enoic
(JA suppressor) increase
–
[88]
[87]
3
Trends in Plant Science xxx xxxx, Vol. xxx, No. x
Solanum
lycopersicum
cv. cerasiforme
(wild tomato)
Oryza sativa (rice)
–
SA pathway inducibility
measurement
SA induced
TRPLSC-952; No. of Pages 11
Plant species
Review
Table 1. Evidence for SA–JA antagonism across plant species
Method of
SA elicitation
BTH
Method of JA elicitation
Pisum sativum (pea)
SA
Phaseolus lunatus
(lima bean)
Gossypium hirsutum
(cotton)
Cucumis sativus
(cucumber)
Sorghum bicolor
(sorghum)
Ginkgo biloba
Brassica carinata
(Ethiopian mustard)
Brassica nigra
(black mustard)
Brassica oleracea
(cabbage)
Brassica napus
(oilseed rape)
Asclepias tuberosa
(butterfly milkweed)
SA pathway inducibility
measurement
Reduced chitinase levels on
dual-elicited plants
JA pathway inducibility
measurement
–
Wounding, JA
–
Whitefly, SA
JA
–
JA, polyphenol oxidase
downregulated
JA, volatiles
Phenacoccus
solenopsis
(mealy bugs)
SA
–
SA-induced volatiles and upregulation
of SA-dependent transcripts
Gossypol and other transcripts
downregulated
MeJA
Some SA transcripts downregulated
Transgenic
suppression of SA
Sclerotinia
sclerotiorum
(white mold)
SA applied to
roots
SA applied to
roots
SA
–
–
JA
Bioassay result
Refs
Colletotrichum
orbiculare disease
severity lower on
dual elicited plants
–
[58]
[51]
Predatory mite
attraction reduced
–
[49]
some JA transcripts downregulated
–
[56]
JA, OPDA levels decrease
–
[72]
–
[89]
–
[66]
[50]
Sclerotinia sclerotiorum
JA transcripts upregulated after
SA transcripts downregulated
–
–
SA transcripts upregulated only
after JA transcripts are
downregulated
JA downregulated in roots
–
–
JA downregulated in roots
–
[66]
Mechanical wounding,
Methyl jasmonate
–
–
[90]
Danaus plexippus
(monarch) herbivory
SA decreases
Myrosinase-associated protein
downregulated in dual-elicited
plants
JA upregulated
–
A.A. Agrawal,
unpublished
Abbreviations: BTH, benzothiadiazole; JA, jasmonic acid; SA, salicylic acid.
TRPLSC-952; No. of Pages 11
Plant species
Review
4
Table 1 (Continued )
Trends in Plant Science xxx xxxx, Vol. xxx, No. x
TRPLSC-952; No. of Pages 11
Review
E
SA vid
–J enc
A e
an for
ta
go
ni
sm
Trends in Plant Science xxx xxxx, Vol. xxx, No. x
Last common ancestor
of land plants probably
produced SA and JA.
Bryophytes
? Physcomitrella patens
? Selaginella moellendorffii
? Ferns
Lycophytes
Pteridophytes
Orthologs of
Arabidopsis
genes important
in SA-JA antagonism
found in all
available land
plant genomes
(see Table 2).
Ginkgo biloba
Gymnosperms
0.50
Picea abies
Sorghum bicolor
0.50
Monocots
0.59
0.84
0.99
Zea mays
Oryza sativa
Hordeum vulgaris
Earliest
node with
support for
origin of
SA-JA antagonism.
0.99
Angiosperms
0.62
Pisum sativum
0.98
Rosids
Cucumis sativus
0.86
0.96
Eudicots
Gossypium hirsutum
Asterids
NPR-1 modulates SA–JA antagonism
0.## Probability of SA–JA antagonism as ancestral state
Arabidopsis thaliana
Brassica spp. **
0.95
0.70
0.50
Key:
Phaseolus lunatus
Asclepias tuberosa
Asclepias exaltata
0.83
Solanum lycopersicum
0.98
0.99
Nicotiana tabacum
Nicotiana attenuata
SA–JA antagonism present
SA–JA antagonism absent
?
No data
TRENDS in Plant Science
Figure 1. Phylogeny of green plants showing putative and reconstructed ancestral states for key aspects of SA–JA antagonism. Topology is based on that published on the
Angiosperm Phylogeny Website [96]. Antagonism between SA and JA signaling has only been investigated in seed plants, and only sparsely among gymnosperms (see
Table 1 for details and references). The ancestral state of the antagonism was inferred using the ace function in the R library APE [34] using maximum likelihood with branch
lengths set to 1. Node labels are probabilities (between 0 and 1) of trait presence given equal gain/loss transition probabilities. The antagonism was probably present in the
ancestor of all angiosperms, and in the ancestor of all seed plants, but whether the antagonism is present in the gymnosperms is equivocal given poor taxon sampling. To
our knowledge, there are no data addressing the existence of SA–JA antagonism in sister taxa of seed plants. This is despite the occurrence of close genetic orthologs of
many genes known to affect the antagonism in angiosperms (Table 2). BLASTs of Arabidopsis thaliana genes (Table 2) were conducted using blastp searches against the
following taxa: Physcomitrella patens (NCBI taxon id: 3218), Selaginella moellendorffii (taxon id: 88036), Sorghum bicolor (taxon id: 4558), Zea mays (taxon id: 4577), Oryza
sativa var. Japonica (taxon id: 39947), Solanum lycopersicum (taxon id: 4081), as well as an expressed sequence tag database for the fern Pteridium aquilinum. For all
genes, a hit was found to all taxa (except P. aquilinum) with an e-value < e–10. Hits (e-value < e–6) to P. aquilinum were found for AtGRX480 and AtMPK4 using blastn
searches against the nonhuman, nonrodent EST database; additional genes in this fern were possibly missed due to low coverage of the P. aquilinum transcriptome.
Because of extensive gene and genome duplications across plants, BLAST results convey conservation of gene families, members of which were inherited by the ancestor
of all land plants, although the vast majority of hits from the taxa above represent reciprocal best blast hits back to the Arabidopsis thaliana genes used as queries. Thus, in
principle, the genetic machinery underpinning the SA–JA antagonism was available early on in the evolution of land plants. This in itself is not evidence of SA–JA
antagonism. An NPR1 ortholog in Oryza sativa modulates the SA–JA antagonism, which is similar to NPR1 in Arabidopsis, suggesting this aspect of the antagonism may
have been present before the split between monocots and eudicots. More extensive taxon sampling is required before evaluating the evolution of this function for NPR1
across plants. **Four Brassica species (B. carinata, B. nigra, B. oleracea and B. napus) all exhibit SA–JA antagonism (Table 1).
ARABIDOPSIS AP2/ERF 59), JAZ1-JAZ3 (JASMONATE
ZIM-DOMAIN) are predicted, based on reciprocal best
blastp searches using Arabidopsis proteins as subjects (Table 2), to have been present in the first land plants, after this
lineage split with green algae. This suggests that many
regulatory features of SA–JA crosstalk have diverse and
potentially ancient roles in the cell. An ortholog of the
canonical crosstalk regulator NPR1 was probably present
in the ancestor of all land plants, indicating that the potential for this gene to mediate SA–JA antagonism exists in all
species in which the antagonism has been found (Table 1,
Figure 1). NPR1 exhibits unique roles in SA–JA crosstalk in
different extant plant species. Unlike in Arabidopsis, tobacco (Nicotiana attenuata) NPR1 acts as a negative regulator
of signal crosstalk in the presence of herbivory by preventing
SA from suppressing JA-responsive defenses [35]. In this
study, herbivory induced SA and JA, as well as NPR1 gene
expression [35]. This functioned to prevent SA from repressing JA defenses against the herbivore, thus prioritizing JA
over SA.
5
AT gene
symbol
GRX480
Arabidopsis
thaliana
NP_174170.1
Solanum
lycopersicum
NP_001233988.1
Sorghum
bicolor
XP_002440249.1
Oryza sativa
Zea Mays
Physcomitrella
patens
XP_001770429.1
NP_001043812.1
ERF1
NP_188965.1
NP_001234695.1
XP_002463464.1
NP_001051973.1
NP_001170395.1
XP_002967934.1
XP_001779786.1
MYC2
NP_174541.1
AAF04917.1
XP_002467448.1
NP_001065478.1
AAD15818.1
XP_002987548.1
XP_001754025.1
NPR1
NP_176610.1
NP_001234558.1
XP_002455011.1
NP_001042286.1
NP_001152107.1
XP_002992598.1
XP_001778211.1
ORA59
NP_172106.1
NP_001234695.1
XP_002461637.1
NP_001051973.1
NP_001170395.1
XP_002966804.1
XP_001779786.1
WRKY70
NP_191199.1
NP_001234530.1
XP_002441930.1
NP_001055192.1
NP_001147748.1
XP_002961829.1
XP_001778254.1
MPK4
NP_192046.1
NP_001234660.1
XP_002467591.1
NP_001061028.2
NP_001105239.1
XP_002976336.1
XP_001763232.1
JAZ1
NP_564075.1
NP_001234883.1
XP_002465159.1
NP_001060268.1
NP_001150658.1
XP_002984538.1
XP_001785097.1
JAZ2
NP_565096.1
NP_001234883.1
XP_002461012.1
NP_001050322.1
NP_001148852.1
XP_002984538.1
XP_001785091.1
JAZ3
NP_566590.1
NP_001234373.1
XP_002462352.1
NP_001063121.1
NP_001141029.1
XP_002975031.1
XP_001754769.1
Role in JA–SA crosstalk in
Arabidopsis
This SA- and NPR1-induced
glutaredoxin represses JA-responsive
PDF1.2 in a TGA-transcription
factor-dependent manner.
This ET- and JA-responsive factor
suppresses MYC2-dependent JA
responses. ERF1 is suppressed by
NPR1.
This JA-induced transcription factor is
inhibited by ET/JA-mediated ERF1
expression.
SA suppression of JA in Arabidopsis is
NPR1-dependent.
This ET- and JA-responsive transcription
factor is necessary for preventing SA
suppression of JA in the presence of ET.
NPR1-mediated suppression of JA is
controlled by WRKY70 and downstream
TGA transcription factors.
This MAP kinase is a negative regulator of
SA and a positive regulator of JA by
suppressing the SA activators/JA
repressors PAD4 and EDS1.
JAZ proteins mediate JA crosstalk
with a variety of other pathways,
including SA, ET, Auxin and Gibberellin.
In the absence of JA, JAZ proteins
repress the JA-responsive TFs
EIN3/EIL1, which suppresses SA
synthesis through effects on ICS (SID2).
JAZ3 (JAI3) was the first JAZ protein
identified to repress the JA-responsive
transcription factor, MYC2.
Refs
[91]
[91,92]
[92]
[93]
[20]
[94]
[95]
[39]
[39]
[39]
Trends in Plant Science xxx xxxx, Vol. xxx, No. x
NP_001147414.1
Selaginella
moellendorffii
XP_002988222.1
TRPLSC-952; No. of Pages 11
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6
Table 2. Phylogenetic distribution of orthologs to Arabidopsis thaliana genes important in the JA–SA antagonism
TRPLSC-952; No. of Pages 11
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Functional data, based on gene expression and/or other
studies, show that NPR1 modulates SA–JA antagonism in
rice, Arabidopsis and tomato, suggesting that this subfunction for NPR1 may have ancient origins in the common
ancestor of monocots and eudicots. Another Arabidopsis
gene involved in crosstalk, WRKY70, is present as a ortholog in rice (Oryza sativa WRKY13). These orthologs positively regulate SA-induced and negatively regulate JAinduced responses [36]. Although OsWRKY13 is not a
one-to-one ortholog of WRKY70 in Arabidopsis, the
central role of a WRKY transcription factor in modulating
between SA- and JA-dependent responses is important in
both species. In addition, a microarray analysis on
OsWRKY13-overexpression rice lines found suites of SAand JA-regulated genes displaying reciprocal antagonism
[37]. These are patterns similar to those found in Arabidopsis. Among their many functions, JAZ proteins repress
the JA-responsive ethylene-signaling genes EIN3 (ETHYLENE INSENSITIVE 3)/EIL1 (ETHYLENE INSENSITIVE 3 LIKE1), which when expressed lead to suppression
of SA synthesis [38]. Upon activation of the JA receptor
COI1 (CORONATINE INSTENSITIVE 1), JAZ repressor
proteins are degraded, allowing for the activation of JAresponsive signaling cascades [39]. JAZ-mediated repression and derepression appears to be important in mediating not only SA–JA crosstalk but also JA–ET, JA–
Gibberellin and JA–auxin signal interactions [39]. This
suggests that signal crosstalk may be a fundamental attribute of plant genetic networks [22,40] and may be commonly achieved through JAZ-mediated repression [39].
Orthologs of JAZ proteins identified in Arabidopsis have
been discovered in P. patens and other early diverging land
plants [41] (and this study). Together, the role of NPR1,
WRKY and JAZ genes in regulating SA–JA and SA–JA–ET
crosstalk from rice to eudicots suggests a generally conserved core genetic architecture to defense signaling in
flowering plants. Nonetheless, the presence/absence of
these genes is not sufficient evidence of any SA–JA antagonism. Although the antagonism frequently occurs, it also
appears to be absent in several lineages: Picea abies (Pinaceae) (J. Arnerup, PhD thesis, Swedish University of
Agricultural Sciences, 2011), Zea mays (Poaceae) [42]
and Asclepias exaltata (Apocynaceae) (Table 1, Figure 1).
For two closely related milkweed species studied in the
same experiment, Asclepias tuberosa showed the antagonism whereas A. exaltata did not (A.A. Agrawal, personal
communication).
Antagonisms are common when chemical elicitors are
the inducing agents [43] and when one pathway is genetically suppressed (ginkgo, Arabidopsis, tomato, tobacco)
(Table 1). There is also widespread evidence that an antagonism occurs following induction by a biological agent.
An extensive range of inducers in Arabidopsis has been
investigated and the antagonism has been found following
infection by bacteria, virus and fungi, leaf damage by
thrips (Thysanoptera) and lepidopteran larvae, and oviposition of lepidopteran eggs [16,44,10,45]. Virus infection
reduces induction of the JA pathway in tobacco [46]. In
tomato, the antagonism occurs following infection by a
parasitic plant and a fungus [47,48]. In lima bean (Phaseolus lunatus) and cotton plants (Gossypium hirsutum),
Trends in Plant Science xxx xxxx, Vol. xxx, No. x
SA induction by whiteflies and mealybugs decreased
sensitivity to JA [49,50]. Finally, in milkweed plants
(A. tuberosa) monarch caterpillar feeding increased JA
levels and decreased SA (A.A. Agrawal, personal communication).
Does antagonism at the level of gene expression or
hormone levels translate into a change in actual resistance
level? In a small subset of these examples antagonism is
inferred based on monitoring readouts of an end-product
such as gossypol levels in cotton [50], polyphenol oxidase
activity in pea (Pisum sativum) [51] and volatiles in cultivated tomato (Solanum lycopersicum) [48]. There are few
examples that also include a bioassay to test for an antagonism, and when they do, an antagonism at the level of
gene expression sometimes resulted in reduced resistance
and sometimes it did not. Specifically, in Arabidopsis,
cultivated tomato and tobacco, the antagonism has been
shown to decrease resistance to a future attacker, yet in
wild tomato there was no effect [52]. In the cases where the
antagonism occurred following a biological inducer and
resulted in decreased resistance, the inducing agent was
usually a generalist attacker (whiteflies, aphids, Pseudomonas syringae; Table 1). Very few papers examined SA–
JA antagonism in a field setting [53,35] and we found no
study that measured the consequences of the antagonism
for plant fitness.
Although SA induction frequently suppresses JA induction, and plants have long been hypothesized to prioritize
SA over JA induction, there are seven species in which JA
responses were associated with the suppression of SA
induction [54–58]. The sequence in which SA and JA are
added exogenously in experiments influences the strength
of the reciprocal antagonism [20], and the timing [59] or
dosage [60] of hormone application is important for realization of the antagonism [59]. In some cases, SA and JA
pathways are each upregulated by one attacker species,
but their induction is not simultaneous. For example,
following infection by Fusarium spp., a hemibiotrophic
fungal pathogen, both the SA and JA pathways are induced
after infection, but SA is important in establishing resistance early on, and JA is important in facilitating resistance during later time points [61]. Thus, although both
the SA and JA pathways are induced by the same pathogen, the responses are temporally disconnected. Screens
across genotypes of Arabidopsis revealed variation in priming of the SA and JA pathways that manifested as coinduction of SA and JA when a fungal species was used as the
inducer [62]. However, these genotype-specific effects were
only in the context of actual pathogen attack and were not
observed when hormones were applied to plants [59]. All of
this work points to the fact that the antagonism is highly
context-dependent, both in terms of what is used to elicit
SA and JA, the timing of the elicitation, and possibly with
respect to genetic variation underlying the antagonism.
The suppression of SA by JA is either triggered by a
biological inducer (Arabidopsis, milkweed and Brassica),
or follows after chemical or genetic manipulation of the SA
pathway (tomato, millet, tobacco, cucumber). For example,
the jasmonate mimic coronatine produced by Pseudomonas
syringae activates the JA pathway and suppresses the SA
pathway in Arabidopsis [63].
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Within-plant factors including the timing, concentration and location of induction influence whether crosstalk
and an antagonism occurs. Effects of timing have been
shown with elicitor studies that temporally manipulate the
sequence of application, and with studies showing that
endogenous JA and SA levels change inversely with each
other [59,64]. Most studies only test for local but not
systemic interactions. However, systemic antagonism in
Arabidopsis is induced by Pseudomonas infection [10,65],
and insect eggs and egg extracts only induced antagonism
locally [44]. Root SA elicitation decreased JA inducibility
within the root but did not reduce JA inducibility in shoot
tissues [66]. We know little about how intensity of induction [55,67,68] and factors such as plant sectoriality and
phenology influence signal antagonism. Because these
aspects of plant form and growth influence hormone induction per se [69], they will probably influence the interaction between hormonal pathways.
Adaptive and nonadaptive hypotheses for the
antagonism
Is the SA–JA antagonism an artifact of complex
signaling?
Plants have a limited number of hormone signal molecules,
which by chance may sometimes interact to affect gene
expression positively or negatively. In this scenario, different environmental conditions such as the location and
timing of attacked generate specificity in the antagonism.
Although this is possible, the existence of conserved genes
(e.g. NPR1), conserved across several distantly related
plant taxa that regulate SA–JA interactions in diverse
taxa (e.g. rice, tobacco, Arabidopsis) makes this hypothesis
unlikely.
Is the SA–JA antagonism an ancient constraint found in
plants and animals?
Lipid-derived, jasmonate-like animal hormones such as
prostaglandins are inhibited in animals by aspirin (i.e.
acetylsalicylic acid). Because a similar antagonism is also
widespread in plants (Figure 1), it may represent an
ancient evolutionary constraint [70]. In addition, several
genes that underpin crosstalk regulation in Arabidopsis
have close homologs in the moss P. patens and the lycophyte Selaginella moellendorffii, in addition to several
angiosperms (Table 2, Figure 1). This indicates that the
genetic machinery to express and regulate crosstalk is
widely conserved to this day and was probably ancestral
to all land plants. However, gene presence/absence does
not imply functional conservation. Because there is variation in whether the antagonism is expressed even between
closely related taxa (Figure 1), expression of SA–JA antagonism is not an unbreakable constraint.
Is SA–JA antagonism due to resource allocation costs of
induction?
There are fitness costs associated with the induction of SA
and JA defenses in the absence of a natural enemy attack
[71]. Thus, the SA–JA antagonism could be viewed as
either a limitation of or adaptation to a resource-limited
environment. There are at least two scenarios to consider
whereby JA and SA pathways either regulate different
8
Trends in Plant Science xxx xxxx, Vol. xxx, No. x
defense products or the same defense products. When
crosstalk limits production of a product, antagonisms in
the induction of each may prevent simultaneous induction
[72]. When the crosstalk limits production of a product
regulated by only one pathway, signal crosstalk can be a
means of maintaining production of one product instead of
another. The effect of elicitor concentration and exposure
time on whether the antagonism is found supports this
hypothesis [43].
Resource allocation costs are probably partially responsible for shaping the patterns of induction following attack,
but several lines of evidence suggest they are not likely to
be the only factor. The SA and JA pathways do not utilize
the same precursors or components for their signal transduction pathways, which makes specific resource limitation less likely to be the explanation at that level. However,
more importantly, strict competition for precursors, such
as amino acids, should result in downregulation of many
plant functions, not only particular JA or SA regulated
genes. Therefore, costs alone do not explain the apparent
specificity in the antagonism: decreased inducibility of the
jasmonate pathway following light limitation is due to
specific hormonal modulation [73], not simply reduced
resource availability. Similarly, decreasing nitrogen availability actually increased the expression of the jasmonate
pathway due to altered interactions between jasmonate
and ethylene. Thus, decreasing nutrient levels can even
increase defense expression, evidence against strict resource mediated SA–JA crosstalk [74].
Is SA–JA antagonism a means for the plant to adaptively
tailor its responses to different enemies and also a target
for manipulation by enemies?
Downstream defenses that are modulated by the SA and
JA pathways affect pathogens and herbivores, and each
attacker may be affected by a different subset of these
defense products. Thus, the adaptive tailoring hypothesis
predicts that the plant should induce the components of
each pathway that are most effective against the current
attacker. This implies some degree of specificity on the
plant’s part – if the plant is tailoring its defense response
adaptively then different enemies must be recognized as
distinct by the plant [5,75]. Many of the patterns described
above almost make specificity axiomatic, such as the general asymmetry of SA and JA suppression, the important
role of other hormones, the effects of the pattern of damage
on the expression of crosstalk, and the effect of other
enemies present on the plant [76].
A major unanswered question is whether crosstalk is
adaptive for the plant [42]. If crosstalk tailors the plant’s
response to a particular attacker this specificity should
increase the plant’s resistance to that attacker. However,
the selective advantage of manipulating crosstalk from the
perspective of a particular attacker must be high. Thus, the
specificity of response is a complex phenotype mediated by
plant and attacker. The elicitors present in, for example,
the saliva or accessory gland secretions from a particular
herbivore species that is attacking a plant often determine
the specificity of these responses in the plant [10,77].
Manipulation of hormonally regulated pathways may be
a mechanism by which enemies can suppress induced
TRPLSC-952; No. of Pages 11
Review
defenses in biochemically divergent plants [78]. Given that
the SA–JA antagonism appears to be phylogenetically
widespread and ancient, this method of manipulating
the host plant has been available for a long time and
may work against a wide diversity of plants [78]. Consistent with this hypothesis, generalist enemies have been
found to induce SA–JA crosstalk in a way that benefits
them [10,79–81].
Thus, experiments are required to understand who
benefits (the plant, the plant’s attacker or neither?) and
yet few studies explicitly connect the plant’s specific response to an effect on plant resistance [82] or performance.
We propose that testing the adaptive value of specificity
will require experiments that incorporate a ‘neutral’ inducer such as mechanical damage or pure hormonal application as controls. The effects of ‘neutral’ induction and
induction in response to the biological organism can then
Box 1. What experiments are needed to effectively test the
adaptive significance of SA–JA antagonism?
Constraints hypothesis
(i) Do simple phylogenetic constraints explain SA–JA antagonism?
Analysis of phylogenetic distribution of the SA–JA antagonism
using common elicitors would illuminate repeated losses and
gains. SA–JA antagonism is widespread across plants, but
evidence is missing from early diverging lineages.
(ii) Do the same pathways exist across plants for modulating the
antagonism? This can be tested by measuring patterns of gene
expression in candidate SA, JA and crosstalk modulator loci in
dual elicitation experiments across plant diversity in a common
environment [9]. If orthologous loci show common patterns of
expression during dual elicitation, it is unlikely to be adaptive
tailoring and more likely to be a constraint.
Resource allocation costs of induction hypothesis
(i) Resource limitation. Isotope tracer studies measuring flux of
resources and precursors between the pathways would directly
demonstrate resource diversion [43,83]. Resource limitation
could also be tested by manipulating resource availability and
addressing if SA–JA antagonism is weaker in resource-rich
conditions.
(ii) Cost of single versus dual elicitation. Is inducing both pathways
more costly than inducing one? Plant fitness should be
measured following single and dual elicitation.
Trends in Plant Science xxx xxxx, Vol. xxx, No. x
be compared. For specificity to be adaptive for the plant,
the plant’s response to the neutral and biological inducer
must differ and this tailoring must benefit the plant. For
example, evidence was found against adaptive specificity
when chemical elicitation caused a similar pattern of crosstalk as biological induction [59]. If the attacker benefits, it
may be manipulating the plant to its benefit. Alternatively,
the response may not be adaptive for the plant or the
attacker. Consistent with the adaptive tailoring hypothesis is that there is extensive variation in patterns of
induction across plant diversity, which is a prerequisite
for, but not yet evidence of, adaptation. In summary,
critical data on the consequences of SA–JA antagonism
for plants in the field are too scant to address this adaptive
tailoring hypothesis at present.
A prospectus on future experiments
Our most important conclusion is that in order to test the
various hypotheses proposed above: (i) measurements of
the SA–JA reciprocal antagonism in the form of gene
expression and biochemical activity must be coupled with
pathogen and herbivore bioassays and simultaneous measurements of plant fitness, and (ii) that these experiments
must be conducted in ecologically relevant settings and
across plant diversity. From an evolutionary perspective,
future experiments should attempt to test if the SA–JA
antagonism arose in a reciprocal manner or sequentially
with unidirectional antagonisms arising separately. Future genome sequencing of plant species where there is no
evidence for the antagonism could reveal if, and perhaps
how, SA–JA antagonism was lost or if there are other
conditions under which the antagonism is expressed.
Researchers should focus on understanding if indeed
SA–JA reciprocal antagonism arose once and if there is
a common genetic basis to this phenomenon across the
plants in which it occurs. Specific recommendations are
given in Box 1.
Acknowledgments
We thank Martin Heil, Anurag Agrawal, the Cornell Plant-Interactions
Group and members of the Whiteman Laboratory at the University of
Arizona for comments. We also thank Will Petry (UC-Irvine) for
comments on the manuscript.
References
Adaptive tailoring hypothesis
(i) Biological elicitors result in varied expression levels and
patterns of loci involved in the antagonism relative to chemical
elicitors or mechanical elicitation. Greater variance and distinct
patterns in the antagonism across attacker species would
support the tailoring hypothesis. If chemical and biological
inducers show similar patterns, this would not support adaptive
tailoring. Higher resistance and plant performance following
biological induction of SA–JA antagonism compared with
chemical elicitation would be evidence for adaptive tailoring.
(ii) Is genetic variation in the antagonism adaptive? Genotypes
varying in the antagonism could be placed into environments
varying in attacker composition. In environments with one
attacker, the antagonism is more likely to benefit the plant
compared with environments with multiple enemies (unless the
antagonism is induced to the benefit of the attacker). Artificial
selection experiments and forcing induction of the alternative
pathway could reveal how natural selection shapes the antagonism.
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reductase pathway by salicylate. FEBS Lett. 464, 133–137
89 Yang, B. et al. (2010) Characterization of defense signaling pathways of
Brassica napus and Brassica carinata in response to Sclerotinia
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90 Taipalensuu, J. et al. (1997) Regulation of the wound-induced
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91 Ndamukong, I. et al. (2007) SA-inducible Arabidopsis glutaredoxin
interacts with TGA factors and suppresses JA-responsive PDF1.2
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92 Lorenzo, O. et al. (2004) JASMONATE-INSENSITIVE1 encodes a
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jasmonate-regulated defense responses in Arabidopsis. Plant Cell 16,
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93 Spoel, S.H. et al. (2003) NPR1 modulates cross-talk between salicylateand jasmonate-dependent defense pathways through a novel function
in the cytosol. Plant Cell 15, 760–770
94 Li, J. et al. (2004) The WRKY70 transcription factor: a node of
convergence for jasmonate-mediated and salicylate-mediated signals
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11
Review
Special Issue: Specificity of plant–enemy interactions
Community specificity: life and afterlife
effects of genes
Thomas G. Whitham, Catherine A. Gehring, Louis J. Lamit, Todd Wojtowicz,
Luke M. Evans, Arthur R. Keith and David Solance Smith
Department of Biological Sciences and Merriam-Powell Center for Environmental Research, Northern Arizona University, Flagstaff,
AZ 86011, USA
Community-level genetic specificity results when individual genotypes or populations of the same species
support different communities. Our review of the literature shows that genetic specificity exhibits both life and
afterlife effects; it is a widespread phenomenon occurring in diverse taxonomic groups, aquatic to terrestrial
ecosystems, and species-poor to species-rich systems.
Such specificity affects species interactions, evolution,
ecosystem processes and leads to community feedbacks
on the performance of the individuals expressing the
traits. Thus, genetic specificity by communities appears
to be fundamentally important, suggesting that specificity is a major driver of the biodiversity and stability of
the world’s ecosystems.
Genetic specificity by communities
Specificity is often defined as the number of different host
species with which a plant enemy or mutualist associates.
Researchers in diverse fields have expanded this definition
to include factors such as phylogenetic relationships
among hosts [1,2]. We propose that specificity should be
broadened in two additional ways. First, we provide evidence that specificity by plant associates such as pathogens, herbivores or mutualists frequently occurs below the
species level. Studies of this genetic specificity are important for understanding the process of speciation [3]. Second, we show that entire communities of organisms can
exhibit specificity for plants below the species level, in
which different genotypes of plants support different communities (see Glossary). In turn, these communities can
feed back to affect the performance of the individual genotypes with which they interact. Studies of specificity at
this level demonstrate novel links between ecology and
evolution.
Figure 1 shows an example of specificity in which a
diverse community of arthropods (103 species from 12
orders) exhibit specificity for individual tree genotypes
[4]. Replicate clones of the same genotypes of narrowleaf
cottonwood (Populus angustifolia) showed significant
broad-sense heritability of the community phenotype. Similarly, repeated censuses across years showed high repeatability, demonstrating that the colonizing communities of
arthropods responded similarly each year to individual
tree genotypes. Specificity among species has long been
Corresponding author: Whitham, T.G. ([email protected]).
studied in the context of coevolution. At the community
level, coevolutionary dynamics undoubtedly play a role;
however, we predict that the effects of plant genetics on the
associated community is highly asymmetric [5] in which
only a few species are coevolved and the genetically based
interactions of a few species (e.g. plant–enemy interactions) are likely to define much of the community. Many
associated species may have no individual feedbacks to the
plant, whereas the whole community of arthropods or soil
microbes, acting together, does affect the fitness of individual genotypes.
Although several reviews have documented the extended community and ecosystem phenotypes of individual
plant genotypes [6,7], this review emphasizes the breadth
of model and non-model systems that have demonstrated
genetic effects at the community level and is the first to
place these within the context of specificity. Community
specificity is largely an outgrowth of community heritability, which is the tendency for related individuals to support
similar community members and ecosystem processes [6].
Thus, with low heritability, community specificity should
be weak, but as heritability increases, specificity should
also increase. Our review examines: (i) how subspecific
levels of plant genetic variation (populations, genotypes)
differentially affect community structure, ecosystem processes, diversity and stability across a range of organisms,
Glossary
Afterlife effects: the phenotypic effects of a plant that extends beyond the life
of the individual plant or plant part such as leaf litter.
Broad-sense heritability: the contribution of all genetic factors (additive, dominant, epistatic) to the total variance in phenotype. H2 is the broad-sense
heritability of a traditional phenotype and H2C is the broad-sense heritability
of a community or ecosystem phenotype [35].
Community and ecosystem phenotypes: the effects of genes at levels higher
than the population [6].
Community genetics: the study of the genetic interactions that occur between
species and their abiotic environment in complex communities [6].
Community specificity: the genetically based tendency for individual populations or individual genotypes within a species to support different communities
of organisms and ecosystem processes.
Community stability: the similarity in the community composition of associated
species across years for individual plant genotypes or populations [4].
Deme: genetically distinct populations that form despite close proximity to one
another [3].
Foundation species: ‘a single species that defines much of the structure of a
community by creating locally stable conditions for other species. . .’ [72]. Other
terms such as keystone, ecosystem engineers or dominant species have similar
meanings and overlap in their definitions.
1360-1385/$ – see front matter ß 2012 Published by Elsevier Ltd. doi:10.1016/j.tplants.2012.01.005 Trends in Plant Science, May 2012, Vol. 17, No. 5
271
Review
[(Figure_1)TD$IG]
Trends in Plant Science May 2012, Vol. 17, No. 5
Yr3
Yr3
Yr1
Yr1
Yr3
Yr1
Yr3
Yr2
Yr2
Yr1
Tree
genotype
Yr2
Key:
Yr2
1000
HE-8
1020
1008
More stable
community
Less stable
community
TRENDS in Plant Science
Figure 1. Non-metric multidimensional scaling (NMDS) analysis showing replicated genotypes of narrowleaf cottonwood (Populus angustifolia) vary in arthropod
community composition and stability. Points represent the mean arthropod communities on replicate clones of an individual tree genotype and bars represent 95%
confidence limits. These censuses were repeated each year for 3 years, so the mean community of each tree genotype is represented three times, once for each year (circles
encompass the three years of each genotype). Because the within genotype variance is much less than the between genotype variance, individual genotypes differed
significantly in arthropod community composition (Analysis of similarity (ANOSIM) R for community composition = 0.21, P < 0.0001). Furthermore, because the
communities of some genotypes changed significantly less over the 3 years of study than other genotypes (see size of encompassing circles), there is also a significant
genetic component to community stability (Restricted estimated maximum likelihood (REML) for community stability P < 0.0001). Replicate clones also showed significant
broad-sense heritability of the community phenotype (species composition; H2C = 0.65). Images of the 11 most common arthropods from the study are shown clockwise
from bottom left: Anthocoris antevolens, Chrysopa sp. 1, Harmonia axyridis, Boisea trivitatta, Tortricidae sp. 2, Listrus sp. 1, Ceratopogonidae sp. 1, Dasyhelia sp. 1,
Thecabius populomonilus, Tortricidae sp. 1, Trombiculidae sp. 1, Gypona sp. 1, Pemphigus betae. We know that at least one member of the community, the bud-galling
mite, Aceria parapopuli has evolved and adapted to individual tree genotypes indicating that specificity has both ecological and evolutionary implications. Photos courtesy
of A. Keith, J. Dombroskie and K. Matz. Reproduced, with permission, from [4].
both in the context of the living organism and effects that
endure after the organism has died; (ii) the effect of specificity on the evolution of dependent organisms and how
genetically based species interactions among relatively few
species can define a larger community; (iii) how the community can feed back to affect the genotypes expressing
specific community and ecosystem phenotypes; and (iv) the
key postulates that are necessary to demonstrate that
specific genes are responsible for community and ecosystem phenotypes. Because community and ecosystem ecology have been largely genetics free, while molecular
ecology has been largely community and ecosystem free,
the demonstration of genetic specificity across these levels
represents an important merger of disciplines.
Community specificity to plant genotype
Community genetics studies have revealed a diverse suite of
plant-associated communities that exhibit specificity below
the level of plant species. Initially, studies that focused on
specificity of communities had a strong emphasis on arthropod herbivores, reflecting the field’s roots in plant–enemy
interactions. However, this research has expanded to
272
include organisms such as fungal endophytes [8], mycorrhizal fungi [9], epiphytic and terrestrial plants [10,11], soil
microbes [12] and terrestrial invertebrates [13]. Table 1
highlights the diversity of study systems where community
specificity has been examined. Because studies based on
whole communities within a genetics context are rare, to
reflect more complex communities only studies with five or
more species were included. Of the 75 communities shown in
Table 1, 85% responded to genetic variation in focal plant
species from 28 genera within 15 plant families, including
angiosperms and gymnosperms. Aboveground arthropod
and plant communities seemed particularly responsive,
with 93.5% and 88.9%, respectively, showing a significant
effect, whereas litter/soil invertebrates and microbial communities responded to plant genetics approximately 75% of
the time. In addition to their phylogenetic diversity, organisms exhibiting community specificity represent a range of
relationships with the focal plant, including mutualism,
parasitism, commensalism, facilitation and competition.
Evidence of community specificity comes from ecosystems around the world. Genetic variation in plants as
varied as neotropical canopy trees, Tasmanian eucalypts,
Review
Trends in Plant Science May 2012, Vol. 17, No. 5
Table 1. Plant genera within which at least one focal plant species has been examined for community specificitya
Focal plant taxa Growth Genetic scale
form
Asteraceae
Shrub
Intraspecific
Artemisia c
hybridization;
population;
subspecies
Shrub
Genetically based
Baccharis
phenotype
Forb e
Genotype
Borrichia
Subspecies
Chrysothamnus Shrub
Biome and habitat
Study region
Study
Communities responding
method b
Refs
Temperate semi-arid
shrubland
North America
CG; F
[73–75]
Temperate coastal
dune
Temperate coastal
Temperate conifer
forest
Subspecies
Temperate grassland
and savannah
Genotype
Temperate coastal
Genotype; ploidy; Temperate old-field f
half-sibs
North America
CG; F
North America
North America
CG
F
Herbivores (stem, bud and
leaf arthropods, deer); soil
bacteriad; fungal endophytes
(root, shoot and leaf) d
Leaf, bud and stem arthropods;
herbaceous and woody plants
Leaf and stem arthropods
Leaf, stem and bud arthropods
North America
CG
Leaf, stem and seed arthropods [79]
North America
North America
CG
CG; F
Leaf and stem arthropods d
Herbaceous plants; leaf, bud
and stem arthropods; litter
arthropods; arthropod
pollinators
[77]
[16,25,33,
80,81]
North America
CG
Leaf, flower and stem
arthropods
[82]
Fungal leaf endophytes; leaf
and stem herbivores
(arthropods and small
mammals)
[8,83]
Mycorrhizal fungi; soil bacteria;
soil fungi
Herbaceous plants; mycorrhizal
fungi; soil bacteria; soil fungi;
foliar and inflorescence
herbivores (arthropods and
mollusks)
[84]
Helianthus
Forb
Iva
Solidago c
Shrub
Forb
Apocynaceae
Asclepias
Forb
Full-sibs
Temperate old-field
Tree;
shrub
Genotype;
half-sibs
Subarctic birch forest; Northern Europe CG; L
boreal forest
Brassicaceae
Alliaria
Forb
Brassica
Forb
Genetically based Temperate forest
phenotype
Genetically based Temperate grassland/
phenotype
old-field; temperate
coastal cliffs
Betulaceae
Betula
Fabaceae
Acacia c
North America
G
North America;
Europe
CG; F; G
[17,76]
[77]
[78]
[14,85,86]
Tree;
shrub
Population
Arid tropical savanna; Western Africa;
temperate forest
Australia
CG; G
Soil nematodes; foliar
arthropods d
[87,88]
Tree
Population
South America
CG
Foliar arthropods
[89]
Tree;
shrub
Genotype;
half-sibs;
genetically based
phenotype
Temperate deciduous
forest
Temperate forest
North America;
Japan; Europe
CG; F
Herbivores (leaf and stem
arthropods, deer); litter
microarthropodsd; soil
bacteria d
[21,90–92]
Moraceae
Brosimum
Tree
Genotype
Tropical rainforest
Central America
F
Litter and trunk arthropods,
epiphytic plants
[10]
Myrtaceae
Eucalyptus c
Tree
Sib-families;
population
Temperate forest
Australia
CG
[13,15,
24,93]
Metrosideros c
Tree
Genetically based Tropical rainforest
phenotype
Polynesia
CG
Trunk, litter and foliar
arthropods; foliar pathogens;
decomposer macro fungi d
Foliar arthropods
Onagraceae
Oenothera
Forb e
Full-sibs
Temperate meadow
and old-field
North America
CG
Leaf and inflorescence
arthropods; herbaceous
plants d
[19,55,95]
Pinaceae
Picea c
Tree
Genotype;
genetically based
phenotype
Boreal forest
Europe
CG
[9,96,97]
Pinus c
Tree
Population;
genetically
based phenotype
Temperate semi-arid
woodland; temperate
forest; boreal forest
North America;
Europe
CG; F
Mycorrhizal fungi; soil
microbes (bacteria and
fungi); needle endophytic
fungid; litter decay fungid;
understory plants
Mycorrhizal fungi; litter
arthropods; soil bacteria;
soil fungi; understory plants
Fagaceae
Nothofagus c
Quercus c
[94]
[12,31,
98,99]
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Review
Trends in Plant Science May 2012, Vol. 17, No. 5
Table 1 (Continued )
Focal plant taxa Growth Genetic scale
form
Piperaceae
Shrub
Genotype
Piper
Poaceae
Grass
Population
Ammophila
Biome and habitat
Study region
Study
Communities responding
method b
Refs
Tropical rainforest
Central America
CG
Leaf arthropods
[18]
Temperate coastal
dune
Temperate meadow
Temperate coastal
wetland
Europe
CG
[100]
North America
North America
CG
CG
Root nematodes; foliar
arthropods and mollusks
Foliar arthropods
Herbaceous plants
[101]
[102]
North America
F
Herbaceous plants
[11]
[4,22,23,
26,35,
36,103]
[104]
Festuca
Spartina c
Grass
Grass
Genotype
Genotype
Rosaceae
Geum c
Forb
Genetically based Temperate alpine
phenotype
tundra
Salicaceae
Populus c
Tree
Genotype;
genetically based
phenotype
Temperate forest;
temperate riparian
forest
North America,
Europe
CG; F
Tree,
shrub
Half-sibs
Temperate swamp
North America
CG
Leaf, bud and stem
arthropods; root fungal
endophytes; soil bacteria;
soil fungi; mycorrhizal fungi;
aquatic invertebratesd;
aquatic fungi
Leaf and stem arthropods
Solanaceae
Datura
Forb
North America
CG
Foliar arthropods
[105]
Solanum
Forb
Genetically based Temperate semi-arid
phenotype
canyons and dry
stream channels
Genotype;
Temperate old-field
population
North America
CG
Herbivores (stem, flower,
leaf and fruit arthropods,
voles)
[20]
Salix c
a
Studies focus on non-agricultural systems where the response of at least five community members were measured. Additionally, studies were chosen to represent the
diversity of plant genera that have been examined.
b
Study method codes: CG = common garden, F = field, L = laboratory, G = greenhouse or nursery (communities in soils only).
c
Taxa where status as a foundation species is established.
d
A community that has not shown a significant response in any study for a particular focal plant taxa.
e
Broad-leaved, non-woody flowering plants as distinguished from grasses and sedges.
f
Abandoned agricultural fields in various stages of recovery.
coastal dune shrubs, boreal conifers, alpine cushions and
old-field (i.e. abandoned agricultural fields) forbs influences associated communities (Table 1). The scale of focal
plant genetics varies among studies, including subspecies,
populations, genotypes and sib-families. Although community specificity to each of these genetic levels has different
ecological and evolutionary implications, these studies
emphasize that genetic effects on diverse communities
are common. In some systems, quantitative traits of the
focal plant, including chemistry [14], bark characteristics
[15], productivity [16] and architecture [17], are identified
as potential mechanisms linking communities to plant
genetics. Focal plants examined in this research are often
foundation species (Table 1). Because of their strong influences on communities and ecosystems, genetic variation in
the traits of foundation species is most likely to have a
large impact on associated communities [6].
The extended effects of genes on community specificity
also occur within plant species that do not define habitats.
In a hyperdiverse tropical forest, the communities of trunk
and litter arthropods, as well as epiphytic bromeliads and
orchids, were more similar on genetically similar breadnut
trees (Brosimum alicastrum) relative to genetically dissimilar individuals [10]. In addition, in a Costa Rican rainforest, communities of arthropod herbivores were found to
274
vary among genotypes of the understory shrub Piper
arieianum [18]. Even non-foundation species such as evening primrose (Oenothera biennis) and horsenettle (Solanum carolinense) exhibit genetically variable effects on
associated arthropod communities [19,20]. These examples
illustrate two important points: intraspecific genetic variation in non-foundation plant species can exhibit community specificity, and these effects can occur in diverse
systems, including species-rich rainforests.
Specificity of afterlife effects
Some of the longest lasting effects of plant genetic identity
on communities and processes may occur after the death of
either the whole plant or its components (e.g. litter, Table
1). All seven plant genera and families referenced in this
section showed some afterlife response by biota or processes to subspecific genetic variation in plants. The afterlife
effects of genetically based differences in leaf litter properties, in particular chemistry, are well documented
[17,21–23], although the afterlife effects of bark and dead
wood have also been examined [15,24]. Subspecific genetic
variation in plants can influence soil and litter microbial
biomass, microbial biomass nitrogen, microbial communities and litter arthropod communities [10,13,22,23,25,26];
however, soil and litter biota are not always sensitive to the
Review
afterlife effects of fine-scale plant genetic variation [21].
These biotic responses may influence decomposition and
nutrient dynamics, extending the effects of specificity to
ecosystem processes [21,22].
The response of soil organisms and soil processes to
genetically based differences in litter expands the temporal footprint of plant genetic identity beyond that of living
tissue. Researchers have documented afterlife effects of
litter lasting for days to years [21,22]. Genotypic variation
in turkey oak (Quercus laevis) litter chemistry altered soil
and litter nutrient dynamics during an 18–36 month sampling period [21]. We hypothesize that afterlife effects may
last longer than have been reported to date, particularly if
recalcitrant compounds such as lignin and polyphenols
vary by genotype or population, as is often the case [21–
23], or if soil processes are influenced by repeated litter
deposition by long-lived plants.
Genetic specificity by plant enemies can also result in
afterlife effects in litter and soil. Genetically based susceptibility to herbivores [3,27,28] results in damage to living
leaves that translates to altered litter chemistry, which then
influences litter and soil nutrient dynamics, soil microbial
communities, microarthropod abundance and decomposition rates [29–32]. For example, the abundance of galls made
by the rosette gall midge (Rhopalomyia solidaginis) on tall
goldenrod (Solidago altissima) is dependent upon plant
genotype and ploidy [27,33]. Leaf litter associated with galls
contains higher initial carbon concentration, exhibits shortterm lower mass loss and retains more nitrogen than litter
not associated with galls [32]. These studies provide evidence that life and afterlife effects of genetic specificity do
not operate independently of each other. They can be linked
by other organisms, and plant enemies may be particularly
important in this context.
Specificity of species interactions
The community phenotypes described above result from
interactions among species that can lead to greater specificity. It is important to distinguish between ecological [34]
and genetic interactions [35] among species in which ecologists and geneticists define interactions differently for
their purposes. In the ecological sense, two general types
of indirect interactions have been described: interaction
chains and interaction modifications [34]. These ‘indirect
ecological interactions’ are distinct from ‘indirect genetic
interactions’ in that they require more than two species,
and do not consider the particular effect plant genotype
may have on affected species. Yet indirect ecological interactions [34] can be linked to indirect genetic interactions
[35] as follows. First, as an example of an interaction chain,
plant genotypes can influence communities through another species [14]. With cottonwood (P. angustifolia), genetically based resistance and susceptibility to the galling
aphid (Pemphigus betae) differentially shape the surrounding community and ecosystem [4,28,30,36]. Using experiments with naturally occurring tree genotypes, aphidsusceptible trees supported a different community of foliar
arthropods, different rates of litter decomposition and
more stable communities through time than resistant
trees. Furthermore, the experimental removal of aphids
from susceptible trees showed that the genotypic effects on
Trends in Plant Science May 2012, Vol. 17, No. 5
the community were mediated by the aphids [36]. This
example shows that plant–enemy interactions, specific to
particular plant genotypes, can shape a much larger community. In particular, it shows the importance that plant
genotype, environment and selection in a community context can have on community organization. Second, in what
has been termed interaction modification [34], plant genotype may mediate the interaction between two other species. For example, interactions between aphids and
tending ants on common milkweed (Asclepias syriaca)
ranged from antagonistic to mutualistic depending on
plant genotype [37]. Thus, not only did the quantity of
species change but the type of species interactions changed
across plant genotypes, a result that has also been observed in other systems [19,28]. Here too, plant genotype,
environment and selection in a community context significantly define community organization.
In contrast to the ecological requirement that indirect
effects are mediated by a third species, for geneticists, two
classes of indirect genetic effects are identified. Indirect
genetic effects (IGEs) refer to the influence that an individual genotype has on the expression of phenotype in
other individuals within the same species, whereas interspecific indirect genetic effects (IIGEs) refer to the influence that an individual genotype in one species has on
phenotypic expression among individuals in another species, with consequent effects on fitness [35]. Thus, in an
evolutionary sense, IIGE theory shows that genetic variation in one species can influence the fitness and distribution of other species. Again, using the cottonwood (P.
angustifolia), species interaction models showed that plant
genotype can affect the fitness of other community members and that the accumulation of these fitness effects on
multiple species can shape the unique communities that
individual plant genotypes support [35]. This represents
an important step towards incorporating genetics into
community analyses and better understanding the genetic
basis of ‘indirect effects’ in ecology.
Although the extended consequences of these differing
interactions on the rest of the community have not yet been
shown in the wild, it is noteworthy that simply quantifying
the abundances of species in a community is not sufficient
to characterize the differing community dynamics among
plant genotypes. Furthermore, most studies have focused
on just a few species; considering the community in its
entirety could reveal a larger and more realistic suite of
genetically based interactions. For example, network theory may be a powerful tool to explore genetically based
interactions in whole communities [38]. Network analysis
revealed that communities were generally resilient to
random elimination of species, but communities collapsed
with the removal of well-connected species [39], suggesting
that community stability is tied to the fate of a few, wellconnected foundation species.
Specificity affects the evolution of dependent
organisms
The community context of the evolution between plants and
their enemies can shape specificity as well as be a product
of it. Specialization occurs in microbes, arthropods and
vertebrates to plant populations and genotypes [3,40–42],
275
Review
which can be influenced by the community context [43]. This
community-driven evolution and specificity at the population level is evident in the interactions between lodgepole
pine (Pinus contorta) and its seed predators. Red squirrels
(Tamiasciurus hudsonicus) are dominant seed predators
and independently drive the evolution of cone shape; however, in their absence crossbill (Loxia curvirostra complex)
bill size and cone shape show evidence of a coevolutionary
arms race [41]. Where squirrels are absent, a moth seed
predator (Eucosma recissoriana) also influences cone evolution, underscoring the importance of a community context in
specialization [44]. Selection on cones from squirrels influences serotiny (i.e. cones that release their seeds after fire) so
extensively that it may shape fire dependency of seedling
establishment and stand dynamics, resulting in community
and ecosystem consequences [45].
Community context can also influence the evolution of
dependent species in response to genetic variation at a finer
scale than populations. The adaptive deme hypothesis [3]
posits that individual host plants within a single population
represent heterogeneous environments, which can lead to
the formation of adapted demes of dependent herbivores and
microbes. Reciprocal transfer experiments have demonstrated adaptation to individual plant genotypes across a
broad range of arthropod and microbial taxa [3,46–48].
Where genetic interactions between species are affected
by a third species [49], they indicate that community context
drives genetic specificity [50]. When these interactions involve foundation species, for example, narrowleaf cottonwood (P. angustifolia) and the galling aphid (P. betae) [4,28],
adaptive deme formation may influence the specificity of
entire communities at the subspecies level.
The community context in which differentiation of hostassociated herbivore lineages occurs may lead to differentiation of taxa outside the direct interaction between
plants and their enemies. For example, parasitoids of
two herbivores, each consisting of genetically divergent
lineages on different Solidago species, have themselves
differentiated in response to herbivore evolution, demonstrating evolutionary consequences at the community level
driven by plant genetic variation [51]. Similar interactions
are likely in response to herbivore specificity at finer levels
of plant genetic variation, and a test of this would represent a major step forward in community genetics. Although
most studies have examined species pairs when investigating the evolutionary consequences of plant genetic variation, it is clear that the community context of evolution is
a frontier of community genetics research.
Specificity affects community diversity and stability
Specificity to genotypes can extend beyond community and
ecosystem phenotypes to affect biodiversity and community stability. Often considered emergent properties, community diversity and stability can result from the sum of
communities over all individual host genotypes (i.e. additive) or as a result of complex interactions of multiple
communities and many genotypes that cannot be predicted
by simple summation (i.e. non-additive) [52]. A clearer
understanding of how genetically based traits may promote biodiversity has conservation value and could help
prevent the loss of biodiversity.
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Trends in Plant Science May 2012, Vol. 17, No. 5
Plant genetic diversity at both the individual and population levels can alter associated species diversity. Although some studies have found negative or no effects of
plant genotypic diversity on associated community biodiversity [25,53,54], a greater number show that increased
plant genetic diversity positively affects species richness
and diversity (Table 1). With these patterns becoming
more apparent, research is now focusing on potential
mechanisms. For example, in tall goldenrod (S. altissima),
increases in genotypic diversity led to greater aboveground net primary productivity, resulting in non-additive increases in arthropod community diversity [55]. A
similar focus on the potential mechanisms by which genetic diversity can affect associated communities is necessary to predict when effects will be absent, additive or
non-additive.
Community specificity can also result in variation in
community stability among genotypes and populations. A
recent study showed that individual plant genotypes differed significantly in the stability of their associated arthropod communities across multiple years of study. They
also showed that stability could be considered a heritable
plant trait and that differences were likely to be the result
of an indirect interaction with the galling aphid (P. betae), a
foundation herbivore [4]. These results emphasize that
although the composition of a large community can be
structured by host genetic differences, there may be one
or a few species that respond to those differences, which in
turn define much of the remaining community. Genotypes
of the biennial evening primrose (O. biennis) also varied in
arthropod community structure, but the effects of particular genotypes changed across years, potentially due to
large changes in phenotype [56]. Expanded to the stand
or patch scale, variation among plant genotypes in diversity and stability could provide an additional mechanism
linking diversity and stability. Furthermore, just as plant
diversity has been shown to affect the stability of plant
communities [57], it seems probable that genotypic diversity within a species will affect the stability of associated
communities such as arthropods across multiple scales.
Although studies are limited, it appears that plant
genetic differences influence biodiversity and stability
[4,56,58]. They suggest that complex community properties may be understood by considering the potential specificity of entire communities to host genetic variation.
Because natural selection acting on individual tree genotypes can affect the associated community properties of
diversity and stability, the diversity–stability hypothesis
itself [59] may be genetically based and subject to natural
selection. The conservation consequences of specificity are
important because the choice of genotypes used in restoration could stabilize or destabilize a community as well as
determine overall biodiversity.
Genetic specificity and community feedbacks
The community and ecosystem phenotypes that result from
genetic specificity may feed back to affect the fitness of the
individuals generating those phenotypes, resulting in an
eco-evolutionary feedback, or ‘bidirectional interaction that
unifies ecology and evolution’ [7,60]. For example, genotypes of P. angustifolia differentially affect soil microbial
Review
Trends in Plant Science May 2012, Vol. 17, No. 5
communities and the nitrogen transformations they mediate [26,30]. A reciprocal transplant experiment showed that
P. angustifolia seedlings survived twice as well and were
larger when grown in their local (maternal) soils, providing
evidence of a positive feedback [61]. Similarly, research in
plant–pollinator systems has demonstrated selection by the
pollinator community on flowering phenology [62] and a
gene associated with such changes [63], suggests that
a mechanistic understanding of these relationships is
attainable.
Community–genotype feedbacks can be positive or negative, with fundamentally different consequences for community specificity. If feedbacks are positive between individual
plant genotypes and their associated community, and of
similar strength among plant genotypes, both genotypic
and community diversity will be maintained. However, if
some genotype–community associations have higher fitness
than others, the former will be selected for, ultimately
reducing genotypic variation and species diversity. For
example, P. angustifolia seedling genotypes varied more
[(Figure_2)TD$IG]
Natural variation in nicotine
(b)
Relative expression
Nicotine (µg/g)
(a)
400
300
200
100
0
Experimental gene silencing
1
0.5
0
WT
Accession
(c)
pmt expression
(d)
Herbivore community
Pollinator community
(i)
1
Nicotine
H
N
N
CH3
µg/mg leaf
0.5
Anatabine
1
0.5
H
N
N
0
H
0
WT
WT Silenced
1
Nectar volume (µl) after
exposure to pollinators
(i)
µg/mg leaf
Silenced
Silenced
0.75
0.5
0.25
0
WT
20
All herbivores
15
10
5
0
WT
Silenced
Relative repellence when
nicotine added to nectar
Leaf area damaged (%)
(ii)
Silenced
(ii)
0
−10
−20
−30
−40
−50
Ha
s
h
ird
ot
m
wk
i
b
ng
m
ts
An
m
Hu
TRENDS in Plant Science
Figure 2. Genetic variation in the nicotine defenses of tobacco (Nicotiana attenuata) affects entire communities. (a) Different tobacco accessions exhibit natural variation in
the production of nicotine. Photograph of N. attenuata in the wild (courtesy of USDA-NRCS PLANTS Database). (b) Silencing the putrescine N-methyl transferase ( pmt)
genes responsible for this variation in the laboratory leads to altered production of the pyridine alkaloids, nicotine and anatabine (photo of silenced pmt plant courtesy of
Anke Steppuhn). (c) Silenced pmt plants produce lower foliar nicotine levels (i), leading to higher levels of herbivory by a large suite of herbivores (ii). Herbivore examples
include, clockwise from top in (c) (ii), Manduca sexta (Clemson University – USDA Cooperative Extension Slide Series, Bugwood.org), Timeroptropis sp. (courtesy of David
J. Ferguson), Diabrotica undecimpunctata (Creative Commons Attribution License), Epitrix hirtipennis (Clemson University – USDA Cooperative Extension Slide Series,
Bugwood.org), Spodoptera exigua (courtesy of Anke Steppuhn). (d) (i) Nicotine also alters pollinator preference, with pmt silenced plants preferred over wild type because
of the lower level of nicotine in the nectar. (d) (ii) Nicotine is a deterrent for common pollinators of N. attenuata, such as the hawkmoth, hummingbirds and ants, as
demonstrated by the experimental addition of nicotine to the nectar of pmt silenced plants. Data and images modified, with permission, from [66–68].
277
Review
than twofold in the survival advantage they gained by
associating with their local versus foreign soil community
[61], setting the stage for selection against the less favorable
genotype–community combinations. By contrast, negative
feedback is unlikely to result in community specificity, but
instead may result in strong selection against some genotypes. The cushion plant (Geum rossii) has two distinct
architectural phenotypes. Common garden studies indicate
that there is a genetic basis to these architectures and they
are associated with distinct associated plant community
phenotypes in the field. The more species-rich plant community associated with cushions with an open architecture
significantly reduces their fitness, potentially leading to
directional selection against open cushions and the plant
community associated with them [11].
The interactions between genotypes and their associated communities vary with environmental context, leading to complex spatial and temporal dynamics. In G. rossii,
open architecture cushions persist because they are favored in locations where environmental disturbance is
high, showing the importance of spatial variation to feedback dynamics [11]. Studies of garlic mustard (Alliaria
petiolata) across its introduced range provide an example
of the importance of temporal variation. Genotypes of
A. petiolata vary in their production of a family of allelochemicals, the glucosinolates. Glucosinolates alter the soil
microbial community, particularly the arbuscular mycorrhizal fungi upon which many native plants depend for
resource uptake [64]. Early in the invasion of a site,
genotypes with high glucosinolate production are favored
because they make A. petiolata more competitive against
native plants. However, later in the invasion when A.
petiolata interacts mostly with conspecifics, genotypes
that invest less in the costly production of glucosinolate
are favored [64]. Variation in the selection pressures
associated with plant–plant interactions through time
results in altered geographic patterns of genotype–microbial community feedbacks. These studies, along with similar research in other plant–soil systems [23] suggest that
community and ecosystem feedbacks can influence our
understanding of the evolution of specificity and its ecological impacts.
Postulates of genetic specificity at higher levels
Four postulates (analogous to Koch’s postulates for demonstrating the causal relationship between a microbe and
a disease) have been proposed for testing the hypothesis
that specific genes have community and ecosystem phenotypes [65]. These include: (i) the demonstration that a
target organism affects other community members and/or
ecosystem processes; (ii) the demonstration of key traits in
the target organisms that are heritable; (iii) the demonstration of genotypic variation in these traits that result in
different communities and/or ecosystem processes; and
(iv) the identification of target gene(s) or their expression
to evaluate a community and ecosystem effect experimentally. The fourth postulate could involve the use of quantitative trait loci mapping, genome-wide and fine-scale
association mapping, knockouts, knockins, or other technologies tailored to the practical and ethical concerns of a
given study.
278
Trends in Plant Science May 2012, Vol. 17, No. 5
Although evidence in support of two or more of these
postulates has been found in numerous systems, only a few
studies have tested all four. Using transformed native
tobacco (Nicotiana attenuata) with silenced putrescine Nmethyl transferase ( pmt) genes, the production of nicotine
defenses was reduced by 95% relative to the wild type
(Figure 2) [66,67]. When planted in their native habitat
and exposed to their natural community of herbivores,
transformed plants suffered three times greater defoliation than wild-type plants. The silencing of nicotine genes
also affected native insect and avian pollinators as well as
nectar robbers [68], supporting all four postulates. Similar
connections between plant genes and diverse community
members have been observed in native populations of
Arabidopsis thaliana [69]. With the increased use of transgenic plants on a global scale, the fourth postulate will
receive widespread testing. For example, genetically modified aspen (Populus tremula P. tremuloides) expressing
Bacillus thuringiensis (Bt) toxins that affect insect herbivores have unintended effects on the non-target aquatic
insect community [70]. Such multidisciplinary research
and experimental confirmation is likely to become common
and should allow us to expand specificity studies to new
levels.
Concluding remarks
Several major findings and future directions have
emerged. (i) Genetic specificity at the community and
ecosystem level has been demonstrated in diverse taxonomic groups, aquatic to terrestrial ecosystems, and from
species-poor to species-rich systems (Table 1). Future
work should assess how common this type of specificity
is in different environments and investigate the cause(s) of
significant differences. (ii) The effects of plant genetics can
extend after the death of living plant tissue, but the
breadth of the temporal and spatial footprints of these
effects remains unknown. (iii) The genetically based interactions of relatively few species (e.g. foundation species)
appear to drive the structure of a much larger community.
For most systems, particularly species-rich systems, we
have yet to characterize the highly interactive species,
particularly those that might be ‘hidden’ players such as
microbes. Network analysis at the population and genotype levels could provide a powerful tool for identifying key
interactions. (iv) Specificity in one species for individual
host genotypes or populations can affect the evolution of
other species, which in turn can affect a larger community.
Importantly, these genetically based interactions can
change across the landscape to result in a geographic
mosaic of evolution. The community context of evolution
remains a frontier of ecology and evolutionary biology. (v)
Because different genotypes support different communities, even emergent properties such as community
stability are, in part, defined by genetic specificity.
Understanding the links between biodiversity and genetic
diversity is an important challenge. (vi) Because communities can differentially affect the performance of the
individual genotypes they are associated with, feedbacks
provide a major mechanism for evolution. In combination,
it appears that genetic specificity is a fundamental feature
of most ecosystems that has important ecological, evolu-
Review
tionary and applied consequences. For example, if individual genotypes or populations differ in their response to
climate change [71], such interactions could alter community structure, biodiversity, stability and specificity. Because of community specificity, losses of plant genetic
diversity, even in common species, may cascade to affect
whole communities.
Acknowledgments
Our work was supported by NSF DEB-0425908, NSF DEB-0816675 and
Science Foundation Arizona. A National Science Foundation Integrative
Graduate Education and Research Traineeship grant provided support
for L.J.L., D.S.S., L.M.E. and A.R.K. We apologize for not being able to
reference more of the research by our colleagues owing to space and
citation limitations. We thank Steve Shuster for his comments on the
manuscript.
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104 Roche, B.M. and Fritz, R.S. (1997) Genetics of resistance of
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1490–1498
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Plant Science Conferences in 2012
23rd International Conference on Arabidopsis research (ICAR)
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Vienna, Austria
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Plant Biology 2012
20 – 24 July, 2012
Austin, USA
http://www.aspb.org/meetings
EPSO meeting
29 July – 3 August
Freiburg, Germany
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281
Review
Special Issue: Specificity of plant–enemy interactions
Unifying concepts and mechanisms
in the specificity of plant–enemy
interactions
Luke G. Barrett1 and Martin Heil2
1
2
CSIRO Plant Industry, GPO Box 1600, Canberra ACT, 2601, Australia
Departamento de Ingenierı́a Genética, CINVESTAV, Irapuato, México
Host ranges are commonly quantified to classify herbivores and plant pathogens as either generalists or specialists. Here, we summarize patterns and mechanisms
in the interactions of plants with these enemies along
different axes of specificity. We highlight the many
dimensions within which plant enemies can specify
and consider the underlying ecological, evolutionary
and molecular mechanisms. Host resistance traits and
enemy effectors emerge as central players determining
host utilization and thus host range. Finally, we review
approaches to studying the causes and consequences of
variation in the specificity of plant–enemy interactions.
Knowledge of the molecular mechanisms that determine host range is required to understand host shifts,
and evolutionary transitions among specialist and generalist strategies, and to predict potential host ranges of
pathogens and herbivores.
The importance of specificity in plant–enemy
interactions
Herbivores and plant pathogens make use of only a subset
of the plant species and organs to which they are exposed.
Such specialization is ubiquitous in plant–enemy interactions (see Glossary) and can have important consequences
for their ecological and evolutionary dynamics. In a broad
sense, specialization to the many different niches represented by plant communities has facilitated the evolution
of the enormous diversity of herbivorous animals and
microbial pathogens [1,2]. In turn, the specialization of
plant enemies can influence rates of encounter with hosts
[3] and with competitors or members of the third trophic
level [4–6], and the local coexistence of plant species [3,7,8].
Specialization is also important from a broad array of
applied perspectives. In particular, questions concerning
the potential host range of plant enemies become crucial in
a world in which both plants and their enemies have highly
increased mobility, mainly because of human activities [9].
Despite the general importance of specificity in plant–
enemy interactions, clearly defining the term ‘specialization’ is surprisingly challenging, and understanding the
causes and consequences of specialization on ecological or
evolutionary timescales remains an even more difficult
Corresponding author: Heil, M. ([email protected]).
282
task. In part, this is because specialization can evolve
along multiple axes, often simultaneously [10]. Specialization is usually considered as the process of adaptation to a
limited spectrum of potential resources, although evidence
is accumulating that the adaptations required by generalists might be as complex as those required by specialists
[3]. Research into the molecular mechanisms of host utilization by plant enemies and the ecology and evolution of
specificity has progressed mostly independently. Likewise,
plant–herbivore and plant–pathogen interactions have
only rarely been subject to general synthesis [11]. This
is despite many commonalities: herbivores and pathogens
often exploit the same plant species or plant organs, must
overcome the same defence mechanisms, have similar
effects on plant fitness and share clear demographic similarities.
In this review, we identify concepts and mechanisms
of general importance to the evolution of specificity in
interactions between plants and their enemies. We first
Glossary
Effector: a molecule secreted by a plant enemy to manipulate host resistance.
Effectors are commonly polymorphic among strains of the same species of
pathogen or herbivore.
Effector-triggered immunity (ETI): a plant resistance response that is activated
upon recognition of enemy effectors by NB-LRRs.
Enemy (plant enemy): used here to denominate herbivores and plant
pathogens; that is, animals and microorganisms that form the second trophic
level.
Host range (potential): the host species or organs that could be used by an
enemy in the absence of all other (usually geographical, behavioural or
temporal) barriers.
Host range (realized): the current host range of a plant enemy.
Microbe-associated molecular pattern (MAMP): synonym to PAMP.
Nucleotide-binding and leucine-rich repeat protein (NB-LRR): plant resistance
proteins that act as receptors for effector molecules. NB-LRRs are often
polymorphic among races or populations of plants.
Pathogen-associated molecular pattern (PAMP): phylogenetically conserved
molecular motifs, such as chitin and flagellin, that are recognized by plants as
indicators of attacking pathogens.
Pattern recognition receptor (PRR): proteins serving the perception of PAMPs,
usually conserved within a host species or larger taxonomic group.
Specificity (geographic): differences in host ranges among populations of an
enemy.
Specificity (ontogenetic): specificity in host use among different developmental stages of the enemy.
Specificity (phylogenetic): specificity concerning the phylogenetic distances
among host species.
Specificity (structural): specificity concerning different structures or organs of
the host.
1360-1385/$ – see front matter ß 2012 Elsevier Ltd. All rights reserved. doi:10.1016/j.tplants.2012.02.009 Trends in Plant Science, May 2012, Vol. 17, No. 5
Review
Trends in Plant Science May 2012, Vol. 17, No. 5
highlight the many axes along which specificity can evolve
and discuss the fitness benefits of different strategies.
We then review the common ecological, evolutionary and
molecular mechanisms that determine patterns of host
specificity. Only a mechanistic understanding of the determinants of host range will identify the reasons behind host
shifts, and transitions between specialist and generalist
strategies, and enable researchers to predict the potential
host ranges of geographically isolated enemies. Effectors
emerge as a common molecular concept that determines
the host spectra of pathogens and herbivores [12–15].
Specialist pathogens and many specialist herbivores have
highly specific effectors that facilitate the exploitation of
specific hosts, but which in turn are often recognized as
‘avirulence’ (avr) genes by resistant hosts [14,16]. By contrast, generalist enemies commonly have multiple or promiscuous effectors or digestive enzymes that successfully
suppress or overcome resistance responses in many different hosts [14,17–22].
Thus, host resistance traits and enemy strategies to
overcome these traits are central players in defining host
range. Based on these observations, we question the general hypothesis that specialists are more adapted than
generalists and suggest that generalists are better understood as ‘multi-host specialists’. We finish with concrete
suggestions as to how next-generation sequencing techniques can be used to investigate natural host ranges of
herbivores and plant pathogens and to understand the
molecular mechanisms that explain why certain plant
enemies utilize specific organs of specific hosts.
The multifaceted nature of specificity
The specificity of the interactions between plants and their
enemies can range from tightly coupled associations
among species pairs, through to diffuse relationships
among diverse communities of prospective partners (for
recent reviews, see [3]). However, the number of host
species that an enemy can exploit (Figure 1) is only one
aspect of its specificity. Specificity can manifest in different
ways, and simple similarities in the overall number of host
species attacked can mask fundamental differences in the
biology of the organisms involved (Box 1).
Potential and realized host ranges
The host species utilized by a plant enemy in nature (the
realized host range) does not necessarily reflect the species
that it could attack in principle (its potential host range). A
modern history of repeated invasions by plant enemies
attests to the importance of geographical barriers in limiting realized host ranges [9]. As a consequence of geographical and behavioral limitations, plant enemies in nature
seldom utilize all potential hosts. For example, the recent
arrival of a single genotype of the endemic Brazilian rust
pathogen Puccinia psidii in Australia (to which a wide
range of species in the family Myrtaceae are potential
hosts) has added more than 100 species to the realized
host range of this pathogen (http://www.outbreak.gov.au/
pests_diseases/pests_diseases_plant/myrtle-rust/national_
host_list.html). However, despite being a growing problem
worldwide, researchers currently lack the ability to predict
potential host spectra accurately. Pathogens in particular
[(Figure_1)TD$IG]
Agonopterix
alstroemeriana (1)
1
2
3
Phascolarctos Myzus persicae
cinereus (2–3) (several hundred)
4
10
Spodoptera
littoralis (>500)
100
Bemisia
tabaci (>500)
500
Capra aegagrus
hircus (>1000)
1000
Number of host species
Cephaloleia
placida (1)
Tetraopes
tetraophthalmus (1)
Cephaloleia
belti (11)
Popilia
japonica (>300)
TRENDS in Plant Science
Figure 1. Host species number as the conventional concept of specialization. The number of host species (in parentheses after species names) that can be utilized by a plant
enemy rank from a single one to over 1000. Neither feeding mode nor size or taxonomic position appear to be good predictors of the position of a plant enemy on this first
axis of specialization. Images reproduced, with permission, from: Eric M. Coombs (Agonopterix alstroemeriana); W. Billen (Bemisia tabaci); Biologische Bundesanstalt für
Land- und Forstwirtschaft (Spodoptera littoralis); S. Bauer (Myzus persicae); and David Cappaert (Popilia Japonica) (all published at http://www.bugwood.org under
Creative Commons Attribution 3.0 License); Edith Freitag and Matthias Goeke (Capra aegagrus hircus; http://www.tiere-der-heimat.de); Marc E. Ellis at H2O pictures (http://
www.h2opictures.com) (Phascolarctos cinereus); [62] and Carlos Garcı́a-Robledo (Cephaloleia beetles); and Anurag A. Agrawal (Tetraopes tetraophthalmus).
283
Review
are being increasingly discovered in association with novel
host species, and often cause either unfamiliar or no obvious
symptoms [23]. Humanopathogenic bacteria, such as Salmonella, Escherichia coli or Klebsiella pneumoniae, can also
develop in plants [17,24,25], and endophytic fungi that were
isolated from surface-sterilized, symptom-free leaves of diverse hosts commonly comprise multiple strains of the
plant-pathogenic genera Alternaria [26–28], Colletotrichum
[26,28,29] and Fusarium [26,28,30]. Are such endophytes
non-pathogenic relatives of common pathogens, or are they
pathogenic only under certain conditions, and what are the
consequences of these alternative life stages for disease
establishment and spread? In the ‘Perspectives’ section,
we discuss how next-generation sequencing can be applied
to investigate realized host ranges of herbivores and pathogens and how research into the molecular mechanisms used
by plant enemies to overcome the resistance of their hosts
will help to understand and reliably predict potential host
ranges.
Axes of specificity
As stated by Daniel H. Janzen [31], plant enemies do not
simply ‘eat latin binomials’. Rather, they are adapted to
exploit selected parts of selected organs of selected plants,
and the evolutionary relationships among host species
commonly affect the probability that a given plant species
can be attacked by a particular enemy species. This poses
the question ‘to what is the specialist specialized?’
Specialization may manifest along various axes (Box 1).
First, it can vary throughout the development of the enemy
or host. Larval and adult stages of many insect herbivores
[32,33] and different pathogenic spore stages [34] often
have only partially overlapping or even completely
Trends in Plant Science May 2012, Vol. 17, No. 5
separate host ranges (‘ontogenetic specificity’), and most
plant enemies can utilize only defined host developmental
stages or organs (‘structural specificity’). If the traits that
make a host suitable for a particular enemy tend to be
distributed among phylogenetically related hosts, then the
‘phylogenetic specificity’ of the enemy is high. In fact, the
capacity of most herbivores and pathogens to exploit multiple hosts decreases with the phylogenetic distance among
host species [14,35–39]. By contrast, few species are ‘true
generalists’ that are capable of exploiting numerous
completely unrelated host taxa. Examples include Phytophthora cinnamomi, which attacks more than 1000 plant
species in numerous families, including Myrtaceae, Coniferales and Fagaceae [40]; the Japanese beetle, Popillia
japonica, whose adults feed on the foliage, fruits and
flowers of over 300 species of plants from at least 79 plant
families (http://pubs.ext.vt.edu/2909/2909-1411/2909_
1411_pdf.pdf), and classic ‘model’ generalists, such as
whitefly (Bemisia tabaci) and Spodoptera littoralis. Finally, host ranges might differ according to specific environments or habitats (‘geographic specificity’) [41].
Importantly, specialization can vary almost independently
along all the axes that we describe here. Thus, simply
counting susceptible species limits one’s ability to understand the processes that drive the evolution of specialization in plant–enemy interactions.
Generalist species as conglomerates of specialized
genotypes
The observation that different populations of plant enemies can attack different host species indicates the potential importance of within-species genetic structure to an
understanding of the evolution of host range [10,41]. In
Box 1. How can one define a specialist? The conceptual part of the problem
We identify an urgent need for obtaining standardized (and, hence,
comparable) methods for the quantification of host ranges along
different axes of specialization. Ideally, one would quantify how well
every developmental stage of the enemy does on any of its potential
host organs, or species. Useful data in this context would be growth
rates, population densities of pathogens, time required to conclude
certain developmental stages or, ultimately, fitness. As suggested in
[36], specificity then can be quantified using classic diversity indices
(e.g. Simpson and Shannon-Weaver indices) along at least four axes as
ontogenetic, structural, phylogenetic and geographic specificity (Figure
I). Their quantitative comparison is important when considering
ecological and evolutionary consequences of specialization and will
provide the basis for the understanding of underlying mechanisms.
For the enemy, ontogenetic specificity denominates specificity at
different developmental stages (Figure Ia) such as, for example,
beetle larvae and adults that perform differently on the same plant
species [32]. Structural specificity means the degree of specialization
on a specific host organ, or developmental stage of the host (Figure
Ib). For example, Peronospora downy mildews, which are solely
restricted to infecting flowers [76], have a higher structural specificity
than does the Japanese beetle, Popillia japonica, whose adults feed
on foliage, fruits and flowers (http://pubs.ext.vt.edu/2909/2909-1411/
2909_1411_pdf.pdf). Phylogenetic specificity (Figure Ic) expresses the
general tendency of herbivores and plant pathogens to restrict their
host range to related species and can be high even for enemies that
utilize multiple host species. Examples are Puccinia psidii, which
attacks a large number of species that all belong to the family
Myrtaceae [112] and the beetle Cephaloleia belti, which attacks 15
host species but only from the Zingiberales [62]. By contrast, the
284
above-mentioned Peronospora downy mildews have been isolated
from diverse plants belonging to the Orobanchaceae, Lamiaceae,
Asteraceae and Dipsacaceae [76] and so showed higher structural
than phylogenetic specialization. Finally, geographic specificity
quantifies the differences among host uses of geographically
disparate populations of a plant enemy (Figure Id) [41] and will be
particularly strong for apparent generalist species that in fact
represent groups of locally adapted cryptic specialists.
Importantly, the consequences of these different levels of specificity
for the realized and potential host range and geographic range of a
species are very different, as can easily be illustrated in terms of set
theory (Figure Ie). The host range of an individual comprises the host
ranges of all of its ontogenetic stages, meaning that its niche
represents the intersection of the niches of its life stages (Figure
Iei). At least one host for every ontogenetic stage must be present at
the same site and in the correct temporal order to allow an individual
to express positive fitness. As stated in [32], ‘the breadth of
environments in which a species can succeed is ultimately determined by the full pattern of its vital rates in each environment’. By
contrast, the host range of a species is the sum of the host ranges of
all of its geographic populations or genetic races (i.e. Figure Ieii).
These aspects are important for invasion biology, for example, where
an enemy that utilizes different hosts during its different developmental stages can invade only a region in which all hosts are present.
By contrast, all individual populations or genetic lines of an enemy
represent the source of potentially invasive founder individuals;
therefore, species with very different host ranges in their different
habitats, or genetic lines, have a higher level of releasing invasive
progeny.
Review
Trends in Plant Science May 2012, Vol. 17, No. 5
[(Figure_I)TD$G]
Ontogenetic specificity
(a)
(b)
Structural specificity
High
High
Low
Flower
Young leaf
Old leaf
Low
Fruit
Shoot
Root
Host species
Ontogenetic stage 1 Ontogenetic stage 2
0
0
Enemy performance
Host organs
Phylogenetic specificity
(c)
High
(d)
Low
Geographic specificity
High
Low
0
0
Enemy performance
(e)
(i)
Population 2
Population 3
Host species
Host range of individual
Host range
of stage 1
(ii)
Population 1
Host range
of stage 2
Host range of species
Host range of
population 1
Host range of
population 2
TRENDS in Plant Science
Figure I. Levels of specialization and their consequence for host ranges. Specialization can occur as (a) ontogenetic specificity, which describes specificity at different
enemy developmental stages, (b) structural specificity towards certain host organs or developmental stages of the host, (c) phylogenetic specificity, and (d) geographic
specificity, which describes different degrees of specialization among populations of the same species. Set theory (e) illustrates that the host range of an individual is
formed by the intersection of the host ranges of all of its ontogenetic stages, whereas the overall host range of a species is represented by the union of the host ranges
of all its populations.
particular, enemies recorded on a wide number of hosts
(e.g. P. cinnamomi or P. japonica) should not be classified a
priori as ‘mega-generalists’, because such species may exist
as complexes of genetically discrete, host-specialized
lineages. For example, a recent study reported a strong
assortment of specific genotypes of the fungus Beauveria
bassiana with specific hosts and other environmental conditions [42], and the generalist herbivore B. tabaci is also
likely to represent a species complex [43]. In fact, many
species with apparently wide host ranges exist as (often
cryptic) complexes of closely related subspecies, host-associated lineages, locally adapted populations and individual
specialists, all of which individually utilize a narrower host
spectrum than does the species as a whole [41,44–47]. Such
species can be termed ‘generalists’ only when considered as
an entire, taxonomically defined unit [41] and might be
better described and understood as a complex of functionally disparate, but closely related, specialists [41,48]. This
intraspecific variability in host plant utilization is likely to
be critical to understanding the emergence of true specialists, because specialization, similar to all adaptive processes, requires genetic variation within populations upon
which evolution can act (see the section below on biotic
heterogeneity and the emergence of specialists and the
‘jack of all trades – master of none’ principle).
Given such complexity, it is clear that categorically
assigning enemies to generalist or specialist strategies is
problematic. Rather, these terms need to be considered as
end points along various continua of specificity. However,
we argue that it remains important to differentiate among
strategies, at least in a relative sense, because there are
numerous ecological and evolutionary consequences of
285
Review
Trends in Plant Science May 2012, Vol. 17, No. 5
specialist versus generalist life-histories. Although the
terms are undoubtedly relative, evolution and ecology also
act at relative scales (e.g. the same absolute number of
offspring means a higher fitness in one environment, and
lower fitness in a second environment). Therefore, distinguishing among strategies enhances one’s ability to understand important processes and interpret patterns of
specialization.
Ecological and evolutionary patterns
The ecological and evolutionary mechanisms that determine variation in host range mirror more general processes
that drive the evolution of ecological specialization and the
maintenance of biological diversity [10]. Here, we review
the most widely cited hypotheses dealing with the phenomenon of specialization and how recent phylogenetic
studies have challenged their universal applicability.
Adaptive radiation, ecological fitting and enemy-free
space
The concept that is perhaps most widely cited to explain how
the interactions of plants with their enemies enhance specialization is adaptive radiation, sensu Ehrlich and Raven
[49]. Under this model, plants evolve new resistance traits in
response to selective pressure exerted by herbivores; following herbivores then evolve specific counter-adaptations that
enable them to overcome the new resistance, and so on. The
same ‘zigzag’ model of coevolution of plant resistance with
enemy counter-adaptations is also likely to be an important
driver of the multiple layers of inducible resistance traits
that plants exhibit against pathogens [14,16] and so potentially represents a major driving force in the process of
adaptive radiation and ecological specialization. Thus, a
common prediction is that coevolution will promote specialization via optimization of performance on a restricted
subset of hosts. This outcome implies the existence of
trade-offs between the capacity to attack a host and another
component of fitness [50].
A necessary prerequisite of any host shift is that the
plant enemy initially has the equipment to experience
positive net fitness on the new host, before evolving specific
adaptations that facilitate its utilization. The concept of
ecological fitting was originally formulated by Daniel H.
Janzen [51] to scrutinize the fact that observing a functioning plant–enemy interaction in nature does not necessarily indicate any coevolutionary history [52,53]. In fact,
each of the thousands of invasive herbivores and plant
pathogens represents independent empirical support of
the importance of ecological fitting: all these species have
initially experienced positive net fitness on new hosts that
were probably never encountered before throughout their
evolutionary history. At the ecological level, ecological
fitting represents a ‘black box’ that is unlikely to be mechanistically understood, or let alone reliably predicted.
However, recent studies have revealed molecular mechanisms that are likely to causally underlie ecological fitting
and host shifts (see the section below on effectors and the
determination of host specificity and host shifts).
Trade-offs
Low specificity increases the number of individual hosts
available, probably enhances the geographic range that the
enemy can occupy, reduces the time required to search for
new hosts and lowers the risk of extinction should any one
host be unavailable (Table 1). However, all plant enemies
show some level of specialization and most are characterized by high phylogenetic conservatism [14,35–39]. The
existence of trade-offs is perhaps the most frequently
advanced hypothesis regarding the evolution towards specialization in plant–enemy interactions [6]. For example,
adaptive radiation requires that a plant enemy that is well
adapted to a specific new host will perform suboptimally on
the ancestral species, and the concept of enemy-free space
assumes different performances on the original versus the
new host species [4,49].
In other words, genotypes that perform well on one host
should perform relatively poorly on alternate hosts, and
specialists should outperform generalists on any given host
species (i.e. the ‘jack of all trades – master of none’ principle) [54]. Perhaps the clearest empirical support for these
predictions comes from studies of microbial pathogens. For
example, a trade-off has been demonstrated between host
range and the mean number of infective spores produced
by the pathogen Melampsora lini, such that strains infecting a wider range of hosts were generally less fecund [55].
Studies of plant viral pathogens [56,57] further provide
evidence that trade-offs can be important for the maintenance of different specialist pathogen lineages, such that
experimentally passaged viral populations that evolved
increased capacity to exploit novel hosts suffered negative
effects on the original hosts. Another study found that less
virulent strains of Pseudomonas syringae had a higher
probability of survival in non-host conditions than did
more virulent strains [58]. For black bean aphids (Aphis
fabae), studies comparing different clones reported tradeoffs in lifetime fitness between two different host plants
Table 1. Advantages and disadvantages of specialist versus generalist strategies
Advantages
Disadvantages
286
Specialist
Higher optimal performance
Reduced interspecific competition
Better able to respond to changes in host resistance
Reliance on fewer hosts increases potential for
heterogeneity in terms of resource availability
Increased intraspecific competition
Reduced capacity to establish in new environments
and exploit novel hosts (niche contraction)
Increased chance of evolutionary constraint?
Generalist
Diet mixing can improve development (herbivores)
Decreased resource heterogeneity
Increased capacity to establish in new environments and
exploit novel hosts (niche expansion)
Lower optimal performance (jack of all trades – master of none)
Metabolically costly
Promiscuous enzymes (e.g. for detoxification) are less efficient
Review
[59]. Similarly, fitness of pea aphid (Acyrthosiphon pisum)
races was negatively correlated among different hosts and,
consequently, it has been argued that antagonistic pleiotropy is likely to be generally important [60]. However,
strains of the bacterial pathogen Salmonella typhimurium
that were passaged though multiple generations in plant
hosts did not alter their virulence for animals cells [24],
suggesting that pathogens can evolve to exploit different
hosts without measurable reductions in their performance.
Specialization originating via trade-offs requires that the
fitness of genotypes within populations be negatively correlated among different hosts. However, trade-offs at the
interspecific or interpopulation level might be more likely
to represent the consequence of adaptation to specific hosts
rather than its cause [6,54]. Thus, the generality of tradeoffs as mechanisms that limit the emergence of generalist
enemies is the subject of much ongoing debate [10]. Indeed,
many studies seeking empirical support for trade-offs within populations report results that conflict with expectations.
For example, one study searched for performance trade-offs
in a population of the moth Rothschildia lebeau, whose
larvae feed on several host species [61], whereas another
study compared the performance of various Cephaloeia leaf
beetles on native and invasive Zingiberaceae [54]. Both
studies found mainly positive rather than negative genetic
correlations in cross-host performance, meaning that genotypes that performed particularly well on one host also
performed better on the alternative host [54,61]. Surprisingly, this phenomenon applied to both generalist and specialist beetle species [54].
How can these contrasting results be explained? First,
the above-cited studies might indicate that trade-offs are
more relevant for enemies that are tightly associated with
their host plant, such as pathogens and sap suckers.
However, the available data are not sufficient to decide
whether these contrasting results really indicate a general
and biologically relevant difference among guilds of plant
enemies. Second, it is possible that trade-offs might only be
important under certain conditions and so are difficult to
detect. For example, many plant–herbivore studies that
find no support for trade-offs have commonly used comparisons among genotypes within populations [54]. Third,
many studies have used generalists with an arguably
narrow host spectrum. For example, the generalist beetle
used in [62] feeds naturally on 15 host species from five
families; however, all the species fall within the order
Zingiberales. Fourth, because ecologically realistic studies
depend on already established plant–enemy interactions,
they are focused on enemies that exhibit ecological fitting
towards the new host. Finally, detecting trade-offs requires
picking the appropriate measure of performance, measured under appropriate ecological conditions; many
experiments might fail to meet these criteria.
Further mechanisms independent of, or reinforcing,
trade-offs
Although it might depend on the detailed experimental
design whether the capacity to perform on one specific host
can be demonstrated to be negatively correlated with the
capacity to utilize another one, the empirical evidence is
overwhelming: the ‘jack of all trades – master of none’
Trends in Plant Science May 2012, Vol. 17, No. 5
principle does not apply in all situations and a negative
correlation among the performances of a given enemy
genotype on different hosts is not sufficient as a sole
explanation for the evolution of specificity. Indeed,
trade-offs are by no means the only evolutionary mechanism proposed to influence variation in host range. In
particular, demographic events and population-level processes, such as bottlenecks and assortative mating, might
reinforce, and perhaps even drive, the evolution of specialization [10,63]. For example, populations of plant enemies
that utilize different host plants have a reduced probability
of encounter and mating even before any genetic isolation
mechanisms can act, and this isolation can become total if
hosts are geographically separated or exhibit non-overlapping phenologies [64]. Enemies that utilize geographically separated discrete host populations can secondarily
specialize via genetic drift and assortative mating, evolve
genetic differentiation and, consequently, divergent patterns of specificity towards these host populations [3,65].
The accumulation of deleterious mutations that degrade
performance on alternate hosts might further reinforce the
effects of drift and assortative mating to the point that
specialization may evolve even in the absence of other
selective processes [63].
Therefore, major patterns in the specialization of plant
enemies can be explained by host–enemy coevolution.
However, host shifts are common and can be favored if
the new host represents an enemy-free space [4–6]. Insect
herbivores might shift to nutritionally suboptimal host
species when enemy encounters are less likely to occur
on the new hosts. For example, caterpillars of the swallowtail butterfly (Papilio machaon aliaska) were found on new
host plants that allowed for lower survival rates than the
original hosts, more commonly in habitats with lower antmediated lethality [66]. Such observations indicate the
importance of the third trophic level in host choice. Herbivores might also shift onto new hosts on which the encounter rates with competitors, rather than predators, are
reduced [6]. These mechanisms also apply to microbial
pathogens, whose performance often is impaired by plant
endophytic fungi, which outcompete or directly attack the
pathogens [23]. Thus, in principle, pathogens might also
search for enemy-free space and specialize on new hosts
when these contain lower competitor, parasite or predator
loads.
Biotic heterogeneity and the emergence of specialists
As highlighted above, host range can be viewed as the
result of a trade-off in the ability to exploit individual hosts
optimally and the ability to utilize the maximum number of
hosts encountered [10,50]. As soon as plant enemies exhibit at least some intraspecific variability of the underlying
traits, selection can act upon the different genotypes
and favor an evolution towards more specialized or more
generalist species, depending on the detailed selective
pressures. In the classic ‘arms-race’ model of adaptive
variation, this selection should normally cause the evolution of a more generalist ancestor towards a group of
closely related specialists.
However, heterogeneities in selective pressure experienced in complex plant communities might make it difficult
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for generalist enemies to counteradapt to the emergence of
new host resistance traits. Costs apply when enemies must
search for suitable hosts or express specific effectors or
digestive enzymes to invade or utilize changing host plants.
Therefore, it might be expected that generalist strategies
should evolve when suitable hosts are infrequent or ephemeral, whereas specialist strategies should be favored when
susceptible hosts are abundant and predictable [67]. It has
been argued that interactions between generalist pathogens
and rare or ephemeral hosts should favor the host in any
evolutionary arms race, because although common generalist enemies might be important to the population biology of
rare hosts, the reverse is unlikely to be true [47].
In addition, the demography of enemy populations is
likely to be tightly linked to variation in host community
structure. In particular, enemies with a large population
size have an increased probability of colonizing new hosts
and areas, and of generating new mutations [68]. Thus,
whereas extreme variability in host availability might
favor generalist enemies, the emergence of such strategies
may be more probable at intermediate levels of resource
heterogeneity [69]. Although decreased population size is
likely to be associated with a concomitant decrease in the
supply of mutants from which capacity to attack new hosts
might emerge, this should be countered by an increased
selective advantage to those enemies capable of infecting
novel hosts. Thus, the probability that a generalist enemy
will evolve and then be maintained may be highest at some
intermediate level of host community complexity.
Phylogenetic patterns
In summary, classic theories predict a general tendency to
evolve towards a higher degree of specialization, but certain conditions might also favor a widening of host range.
In fact, both scenarios have been reported in phylogenetic
studies. Given the general propensity for host range conservatism (see above), many hypotheses advanced to explain the remarkable levels of specificity in interactions
between plants and their enemies have been based on the
assumption of tightly coupled, pairwise coevolution and
subsequent co-speciation [70]. Furthermore, specialization
is often predicted to be an evolutionary ‘dead-end’ because,
due to the costly accumulation of host-specific adaptations,
specialized enemies should have increasingly lower fitness
on hosts to which they are not specialized (see [38] for a
recent empirical example). In Box 2, we review evidence for
such phylogenetic constraints on the evolution of different
strategies. By contrast, phylogenetic studies using ancestral state mapping increasingly reveal evidence for host
switching [71–77] and examples of generalists that have
evolved from a specialist ancestor [78,79]. These observations make it increasingly apparent that life-history evolution in species interactions can be highly dynamic.
Molecular mechanisms determine specificity in plant–
enemy interactions
As we have highlighted above, specialization along one or
more axes is inherent to all plant enemies, and trade-offs are
one of the key evolutionary mechanisms that are likely
to underlie the maintenance of specialized strategies. A
specialist that very efficiently utilizes one host is commonly
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Trends in Plant Science May 2012, Vol. 17, No. 5
Box 2. Phylogenetic constraints and the evolution of
specialization
If specialization evolves due to trade-offs in performance, then
optimized performance on one host should limit performance on
others. Thus, host specialization has classically been thought to be
an evolutionary outcome that strongly selects for further specialization [67,78]. If these predictions are correct, then phylogenetic
reconstructions should reveal that host shifts are rare in specialists,
that transitions from specialist to generalist strategies are uncommon, and that specialist lineages are phylogenetically derived.
Despite the intuitive appeal of these predictions, it is becoming
increasingly clear that transitions from specialist to generalist
strategies are common, host shifts are frequent and generalist
lineages are equally likely to be phylogenetically derived [14,70,113].
Several authors report generalists and specialists within the same
phylogenetic line [14], and models assuming irreversible evolution
of generalists to specialists are usually strongly rejected [78].
Genomic plasticity and rapid evolution of the mechanisms underlying specialization are emerging as key drivers of this evolutionary
dynamism. Horizontal gene transfer can radically alter the genomes
of microbial pathogens, and many host jumps involve horizontal
transfer of large effector complements [14]. Furthermore, conserved
effector loci often undergo strong diversifying selection and display
unusually high sequence polymorphism, suggesting rapid evolution
in genes underlying host specificity [106,114].
less efficient on a second host, generalists are often less
successful than specialists on highly defended plants, and
the potential to encounter an enemy-free space favors shifts
towards hosts that are less optimal as a food source than the
original host.
However, why is it so difficult in a proximate sense to
utilize multiple plant species, or host organs, and which
traits promote ecological fitting? Rather than enemies
being limited by primary metabolic demands, performance
on a host is mainly determined by the interplay of host
resistance and enemy counteradaptation. Thus, host resistance traits and the capacity of the enemies to deal with
them emerge as central factors in the determination of host
ranges. The use of any specific host assemblage (which can
be narrow or broad) requires particular adaptations [80].
Due to their different size and mobility, pathogens and
herbivores have different strategies to avoid being affected
by host resistance traits. Here, we discuss how host resistance traits and the molecular basis of their suppression or
avoidance by plant enemies are fundamentally involved in
the determination of host spectra and, thus, the evolution
of specialist versus generalist strategies.
Genetic and physiological trade-offs
In specialized interactions, constraints on the use of certain host plants might be evident as pleiotropic trade-offs
in the performance on alternate hosts (i.e. genes that
promote the ability to utilize one partner, impair the
ability to utilize another) [10,81]. For example, in the
interaction between the plant Linum usitatissimum and
its fungal pathogen M. lini, several interacting host resistance (R) and pathogen effector gene loci, which provide
alternate resistance and infectivity specificities, have been
identified [82]. Importantly, allelic variants at the AvrP123
effector locus that escape recognition from one R gene,
usually confer recognition to a different R gene [82]. Thus,
specialization via antagonistic pleiotropy is seemingly
built into the system. Analogous trade-offs might also
Review
mediate broader patterns of resistance specificity, such as
resistance to insect herbivores versus microbial pathogens
[83,84], and biotrophic versus necrotrophic pathogens [85].
Interestingly, evidence is mounting that some enemies can
directly exploit constraints imposed on hosts by such pleiotropic costs [86–88].
Genetic constraints can also limit the success of generalist strategies, via trade-offs between the capacity to
utilize a wide range of hosts and optimal performance
on any one host [81,88]. A mechanism that causally underlies the ‘jack of all trades – master of none’ principle would
be the fact that specialist herbivores can utilize specialized
enzymes for the detoxification of the ingested food whereas
generalists require either multiple [20–22] or widely effective digestive enzymes [19,89]. Because promiscuous
enzymes are less efficient than those that catalyze only
one distinct chemical reaction [18], and the synthesis of
multiple enzymes comes at a high metabolic cost, generalists are usually less efficient than specialists in utilizing
any given host species.
Effectors are used to overcome host resistance
Pathogens and herbivores have evolved some common molecular mechanisms to evade or suppress host resistance.
Perhaps most universal is the concept of the ‘effector’: a term
used to denominate all molecules that are released from
plant enemies for host manipulation [13,14]. The concept of
effectors and their role in host invasion and activation of
resistance is most advanced in the context of plant–pathogen interactions [14,16,82,90,91]. In general, plants have
evolved the capacity to perceive two classes of molecule
(elicitors) that indicate attack by a pathogen. Conserved
microbial molecules, known as ‘pathogen-associated molecular patterns’ (PAMPs) or ‘microbe-associated molecular
patterns’ (MAMPs), are perceived by host receptor proteins
known as pattern recognition receptors (PRRs). PAMPS are
typically common structural components of a class of pathogen, such as chitin and flagellin, and their recognition causes
PAMP-triggered immunity (PTI). To overcome this problem,
many successful pathogens have evolved the capacity to
deliver effectors into host cells to suppress PTI and other
defence responses. In turn, many hosts have acquired the
capacity to recognize either the changes that are inflicted by
the action of these elicitors (‘modified-self recognition’), or to
recognize directly and specifically the effectors via their
interaction with a class of plant receptor proteins that
contain nucleotide-binding (NB) domains and leucine-rich
repeat (LRR) receptor kinases. The recognition of effectors
by plant NB-LRR proteins results in further layers of more
specific (‘gene-for-gene’) resistance responses denominated
‘effector-triggered immunity’ (ETI) [16,82].
Plant NB-LRR proteins confer resistance to both microbial pathogens and insects [82]. The emerging pattern is
that R genes in general confer resistance to herbivores in a
similar manner to that described above for pathogens
[12,13,15], although it is likely that the relative importance
of effectors for host utilization is higher for insect herbivores that are more intimately associated with their host
plant, such as leaf miners, phloem feeders and single-cell
feeders. By contrast, classic, leaf-chewing folivores are
likely to depend more on behavioral, detoxification and
Trends in Plant Science May 2012, Vol. 17, No. 5
sequestration strategies, although specific effectors, such
as b-glucosidases and proteases, are likely to play a crucial
role in this process.
As for pathogens, plants can recognize herbivore-derived
effectors either directly, or by monitoring their action. In the
first case, the recognition of ‘herbivore-associated patterns’
(HAMPs) induces relatively general resistance responses,
which often depend, at least partly, on induction of a jasmonic acid (JA) pathway. Plants also recognize the action of
herbivores, similar to the above-mentioned ‘modified-self
recognition’ strategy, by perceiving fragmented or delocalized molecules associated with damage caused by their
action. The responses that are elicited by plant ‘damagedself recognition’ are very general ones and are commonly
based on JA induction [92,93]. By contrast, sucking insects
cause little mechanical damage. However, they are intimately associated with the plant cells and need multiple
effectors to evade recognition of their HAMPs [13]. As such,
herbivores such as the hessian fly (Mayetiola destructor) and
aphids were the first for which resistance mediated by R
genes was reported [13,15,94,95]. The targets of these R
genes are likely to be insect-derived effectors. For example,
based on their similarity to pathogen effectors, 48 effector
candidates have been identified in the green peach aphid
(Myzus persicae) [96]. At least one of these functions as the
target of recognition in certain plant hosts [96]. Correspondingly, several NB-LRR proteins have been identified that
are required for a successful resistance induction against
insect herbivores, including whitefly and aphids ([15,97] and
references therein).
Effectors and the determination of host specificity and
host shifts
As essential microbial components, PAMPS are highly
conserved within and among pathogen species and plant
PRRs are also highly conserved [98]. By contrast, the
overall effector repertoire of pathogens can be highly variable, particularly among species and host-specific lineages
[14,90] and, when conserved, often displays unusually high
levels of sequence polymorphism [3,75]. Similarly, NBLRR resistance protein repertoires are variable among
species and can be highly polymorphic [14] or deleted
entirely within host species [99]. As we note above, there
are well-established patterns of evolutionary conservatism
in the range of hosts used by any given pathogen. It has
been hypothesized that the interplay among highly conserved PRR-triggered immunity, and highly specific
NB-LRR protein-triggered immunity can explain the phylogenetic specialization of plant enemies [14]. In particular, as phylogenetic distances among hosts increase, the
effector repertoire carried by a pathogen becomes increasingly ineffective, first at suppressing specific NB-LRRmediated resistance, followed by increasing basal PRRmediated resistance. Together with trade-offs and ongoing
antagonistic coevolution, such dynamics could strongly
promote evolution towards increasing specialization in
plant–enemy interactions.
Despite the general appeal of the above scenario, some
enemies do have truly wide host ranges, and host shifts can
involve quite distantly related hosts [70,76]. What mechanisms facilitate the ecological fitting that underlies these
289
Review
seeming anomalies? One strategy that may be important to
the maintenance of wide host ranges is to suppress host
resistance mechanisms at an early stage of induction. In
pathogens, the type III secretion system is used to inject
multiple effectors into host cells and helps Salmonella to
colonize plant and animal hosts [17,24]. This system represents a common trait of numerous plant and animal pathogens, many of which are characterized by wide host ranges
[100–104]. Because it can be also used by specialists to
inject specific cocktails of coevolved effectors [105], the type
III secretion system does not represent a ‘generalist strategy’ per se, but represents instead an apparatus that might
facilitate a true ‘generalists’ strategy. For example, many
Pseudomonas strains inject coronatine, a JA mimic. Coronatine manipulates the crosstalk between the JA and
salicylic acid (SA) pathways [84], resulting in the suppression of SA-dependent responses. Thus, this process renders
hosts generally susceptible to this pathogen [11]. Similarly,
many insects and necrotrophic pathogens release hormones that suppress JA-dependent defence responses
[11,84]. Such strategies mean that enemies can avoid
the consequences of the expression of hundreds of defence-related genes, thereby greatly enhancing their ability to utilize a wide range of hosts. Other generalists may
rely on carrying a broad spectrum of effectors, only a subset
of which might be effective against any given host (e.g.
Botrytis cinerea), and generalist herbivores often use multiple, or highly promiscuous, enzymes to detoxify their food
[18–22], although such strategies presumably come at a
cost [106]. Thus, host shifts and ecological fitting likely
involve mechanisms that suppress resistance strategies
that are shared between the old and new host. Moreover,
shifts within and among closely related species may be
achieved by mutations or deletion of single effector genes
[106], whereas more distant jumps often seem to involve
horizontal transfer of large complements of effectors [14].
Perspectives: new approaches to studying specificity in
plant–enemy interactions
In the above section, we reviewed the most common molecular mechanisms that underlie the specificity in host
use by herbivores and plant pathogens and discussed how
recently developed molecular concepts can help to explain
classic ecological and evolutionary hypotheses, such as
adaptive radiation, phylogenetic conservatism and ecological fitting. However, molecular tools remain underutilized
in the ecological and evolutionary disciplines and more
could be done to identify the molecular determinants of
specificity of host use by plant enemies.
As we highlight in Box 1, the various axes along which
plant enemies evolve specificity are important because
they provide insight into the underlying ecological and
evolutionary mechanisms. However, there is a real deficiency of empirical data on host range under natural conditions.
Such data will be required to inform theory and to develop
capacity to predict host shifts and potential for invasion of
plant enemies. For pathogens, one way to develop a better
understanding of host utilization under field conditions
might be intensive sampling and unbiased sequencing of
microbial DNA resident in plants. This approach has been
applied recently to discover asymptomatic endophytes,
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Trends in Plant Science May 2012, Vol. 17, No. 5
many of which are pathogens in crops [27–30]. However,
to determine precise outcomes, such studies will need to be
accompanied by experiments examining the effects of colonization under common environmental conditions. For herbivores, realized host ranges can be assessed by unbiased
collection strategies (fogging, etc.) and food web construction, preferably accompanied by feeding trials (e.g. [107]).
An alternative way to determine realized host ranges of
herbivores will be DNA barcoding or another sequencingbased approach to determine species ranges of ingested
food items in the digestive tracts of animals living in
the wild.
The continued development of next-generation sequencing platforms will revolutionize research into the functional
and evolutionary genetics of specialization in plant–enemy
interactions. As well as the identification of realized host
ranges of herbivores and pathogens, DNA barcoding and
other sequencing-based strategies can be used to identify
cryptic species and patterns in the association of certain
genotypes of plant enemies with specific hosts [42,44]. Recently, these techniques have successfully been applied to
understand the specificity and virulence of the over 50
pathovars of the ‘generalist’ pathogen, P. syringae [104,108].
Large-scale phylogenies are increasingly becoming
available and can be subjected to ancestral trait mapping
to identify host shifts and truly ‘phylogenetically conservative’ plant enemies [71–77]. Enemies from different
populations, or species that have recently diverged and
specialized onto different hosts [109], can be compared at
the genomic, transcriptomic and phenotypic level, to investigate directly the genetic changes that are involved in host
specialization. Perhaps the most powerful tool is represented by phylogenetically controlled comparisons among
transcriptomes of specialists and generalists or in enemies
that have recently been subject to a major shift in their host
range. In particular, pathogens that have evolved higher
specialization following a host shift [73,108], pathogens
that have changed their life style from pathogen to asymptomatic endophyte [110] or vice versa [111], and related
herbivores that represent the same feeding guild but differ
strongly in host range [22], are promising models to screen
for adaptations that allow generalists and specialists to
fulfill successfully all the specific tasks that are required
for their respective strategy. As is the case for many other
disciplines, research into host ranges of plant enemies
urgently requires multidisciplinary approaches to gain a
causal understanding of why a particular enemy can, or
cannot, successfully attack certain hosts and to predict
potential host shifts and changes among specialist and
generalist strategies.
Acknowledgments
We thank Carlos Garcı́a-Robledo for discussing critical parts of the
manuscript and CONACyT de México (project 129678) and the Australian
Research Council (DP1097256) for financial support. The following people
generously provided photographs for Figure 1: Eric M. Coombs, W. Billen,
S. Bauer, David Cappaert, Edith Freitag, Matthias Goeke, Marc E. Ellis
and Anurag A. Agrawal.
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Review
Special Issue: Specificity of plant–enemy interactions
Specialist versus generalist insect
herbivores and plant defense
Jared G. Ali and Anurag A. Agrawal
Department of Ecology and Evolutionary Biology, Cornell University, E425 Corson Hall, Ithaca, NY 14853-2701, USA
There has been a long-standing hypothesis that specialist and generalist insects interact with plants in distinct
ways. Although many tests exist, they typically compare
only one species of each, they sometimes confound
specialization and feeding guild, and often do not link
chemical or transcriptional measures of the plant to
actual resistance. In this review, we synthesize current
data on whether specialists and generalists actually
differ, with special attention to comparisons of their
differential elicitation of plant responses. Although we
find few consistencies in plant induction by specialists
versus generalists, feeding guilds are predictive of differential plant responses. We outline a novel set of
predictions based on current coevolutionary hypotheses
and make methodological suggestions for improved
comparisons of specialists and generalists.
Why might specialist and generalist herbivores have
distinct interactions with plants?
‘Jack of all trades is master of none’. Here lies the theoretical basis for why ecologists and plant scientists have long
argued that specialist insect herbivores, as compared with
generalists, will have distinct and predictable interactions
with their host plants (Box 1). With specialization, it was
proposed that alongside the loss of ability to use many host
plants, herbivores would gain the ability to tolerate plant
defenses, manipulate hosts to their benefit and evolve ways
to reduce predation and parasitism [1,2]. This powerful
and seductive hypothesis has been a mainstay of coevolutionary studies for over 40 years, and yet little resolution
has been reached on certain predictions. In particular, we
argue below that ecologists and plant scientists have been
too quick to position the specialist–generalist dichotomy as
a paradigm, and often uncritically. Below we evaluate the
current evidence and provide a roadmap for future studies.
There have been several specific predictions made about
the specialist–generalist paradigm. First, specialists
should be less impacted by a given plant defense compared
with a generalist [2] (Figure 1). In addition to being less
affected by particular defense traits, some specialist herbivores have even evolved the capacity to use these same
traits in host finding or protection from predators (sequestration or fecal shields). Second, generalists should have
‘general’ mechanisms to tolerate an array of plant defenses
and also possess mechanisms to manipulate plants via
highly conserved plant pathways [1,2]. The notion behind
Corresponding author: Ali, J.G. ([email protected]).
this prediction is that although generalists do not master
any one defense, many aspects of plant defense can be
overcome because plants possess a common evolutionary
history leading to shared physiological features in core
signal transduction chains [e.g. jasmonate (JA) signaling]
[3]. Third, upon damage, induced plant responses to specialists will be distinct compared with responses to generalists. This general prediction is complicated by
coevolution: are observed plant responses adaptive for
the plant or manipulated by herbivores? The perspective
from which we view the interaction distinctly shapes our
predictions (Figure 2).
Although we will touch on the first prediction above, the
focus of our review is on the latter two: how and why
specialists and generalists might elicit differential plant
responses (or manipulate the plants in different ways).
Since the origin of the specialist–generalist paradigm,
there have been hundreds of studies of insect tolerance
and detoxification of plant defense [4]. However, it is only
in the past 20 years that plant biologists have realized that
induced responses are a crucial component of plant defense, and ideas about how specialists and generalists
differ in this regard are continuing to develop. In addition,
Box 1. Who’s who on the diet breadth continuum?
Insect herbivores have been conventionally grouped into categories
based on their degree of dietary specialization. When limited to only
one or a few closely related plant taxa, often a single genus,
herbivores are considered monophagous (or highly specialized).
Insect herbivores that feed on several plant species, usually within
one botanical family, are designated oligophagous. Finally, polyphagous (or highly generalized) species are insects that feed on
species in more than one plant family. Although these terms are
helpful for generalizing broad groups of herbivores into simpler
categories, their basis is drawn on fairly arbitrary observations and
may lead to inherit limitations in their use. Nonetheless, some
groups of herbivores, such as aphids, leaf hoppers and leaf miners
are dominated (>75%) by monophages [71]. Across all herbivorous
insects, it is estimated that <10% feed on plants in more than three
different plant families [72].
The distribution of feeding on one plant species to a diversity of
plants is truly a graded continuum. We also recognize that
polyphagous individuals can show preferences over their distribution of hosts, where herbivores may be more limited than we
predict. Here we have adopted the terms specialists and generalists
to focus on the extremes, usually meaning species that consume a
few related species to species in several plant families, respectively.
Nonetheless, the comparison is a relative one and the specific
contrast of a specialist and generalist should be explained as fully as
possible for each case.
1360-1385/$ – see front matter ß 2012 Elsevier Ltd. All rights reserved. doi:10.1016/j.tplants.2012.02.006 Trends in Plant Science xx (2012) 1–10
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defense, both generalists and specialists can overcome
some digestibility reducers, although it is unclear how
common this is [12–14]. In addition, some generalists
possess remarkable abilities to consume highly toxic host
plants [15–17]. Thus, based on the literature and the
commonly used experimental designs, few conclusions
can be reached about the relative impact of different
classes of plant defense on specialists and generalists.
Toxicity
B
A
Monophagous
Polyphagous
Host range
TRENDS in Plant Science
Figure 1. A conceptualization of the impacts of plant defensive compounds on
specialist and generalist herbivores, based on a meta-analysis of >290 empirical
studies [4]. A Compounds within the normal host range: for specialists that
normally encounter a particular defense, toxicity is lower compared with the
impact on generalists. B Novel compounds: for specialists that do not typically
encounter a particular defense, toxicity to the specialist is greater than or equal to
that compared with the generalist. In other words, it is empirically the case that
specialists are less impacted by the toxicity of the plant defenses they typically
consume compared with generalists; nonetheless, specialists can be highly
susceptible to novel plant secondary compounds.
modern studies that span bioassays of insect preference
and performance, plant production of hormonal signals
and defensive secondary metabolites, and transcriptional
responses have the potential to aid us in making rapid
progress in understanding how and why specialists and
generalist herbivores differ.
Impacts of plant defense on specialists and generalists
The notion that specialists are immune to the defenses of
the host plant is widespread but incorrect. Cases where
specialist herbivores are negatively impacted by defense
compounds include: parsnip webworms (Depressaria pastinacella) eating furanocoumarins [5], buckeye caterpillars
(Junonia coenia) ingesting iridoid glycosides [6], monarch
caterpillars (Danaus plexippus) on cardenolide-containing
sandhill milkweed (Asclepias humistrata) [7], cabbage
white caterpillars (Pieris rapae) being poisoned by isothiocyanates [8] and tobacco hornworms (Manduca sexta) fed
artificial diets containing nicotine [9]. In nearly all of these
cases, the specialists do have physiological adaptations to
cope with the plant defenses, which allow greater tolerance
than most generalists. Indeed, on average, specialist herbivores are less negatively impacted by defense compounds
than generalists [4] (Figure 1). Our main message is that
tolerance of specialist insects to low levels of toxins is to be
expected; however, at higher levels of defense, few insects
are immune to the deleterious effects of plant toxins.
It is unclear whether certain classes of plant defense are
more effective against generalists or specialists. A study 35
years ago suggested that although toxins could be overcome by specialists, digestibility reducers are likely to be
effective against all attackers [10]. Others have argued
that indirect defense (i.e. attracting enemies of herbivores)
is likely to be more difficult to overcome compared with
direct defense (e.g. digestibility reducers and toxins) [11].
Although most plants produce all of these classes of
2
Do specialists and generalists elicit different defensive
responses?
A hypothesis that grew out of the specialist–generalist
paradigm is that specialist herbivores will cause distinct
induction of plant defenses compared with those induced
by generalists [18–20]. Nonetheless, there have been few
explicit predictions in the literature about how and why
specialists will differ from generalists with regard to elicitation of induced defenses. Given that generalists are
typically more sensitive to plant toxins than specialists,
from the perspective of the insect, one prediction is that
generalists should suppress induced plant responses,
whereas specialists should only minimize the induction
of high levels of defense (Figure 2). From the perspective of
the plant, the predicted responses are less consistent:
induction of direct defenses could be variable against
specialists (Figure 2), but induction of indirect defenses
[e.g. extrafloral nectar and parasite-attracting volatile
organic compounds (VOCs)] should be strong if the specialist is not sequestering. Nonetheless, it is presumably
adaptive for plants to respond, as strongly as possible, to
most generalists (Figure 2).
Experiments comparing phenotypic or transcriptional
responses to both specialist and generalist herbivores often
include only one specialist and one generalist species,
making rigorous conclusions impossible; in addition, many
studies compare specialist and generalist species from
different feeding guilds [21–23]. We found 20 studies comparing the phenotypic or transcriptional responses of a
plant to both specialist and generalist herbivores using one
feeding guild (Table 1). Although we interpret these results
in light of the predictions in Figure 2, we recommend
caution because nearly every result can be interpreted
in an adaptive context, because what is beneficial for the
plant and beneficial for the insect herbivore can be different. In addition, we assumed that the authors were careful
to match the amount and timing of damage by the two
herbivores; we highly recommend that future studies explicitly address this issue (Box 2).
A few generalizations emerged from our review. First,
there are few studies linking mechanistic plant response to
impacts on herbivores; however, these links are crucial for
interpreting specific consequences of plant defenses. For
example, some studies in the Brassicaceae found that
generalist and specialist elicited a similar plant response
[20,24], whereas other studies that only measured impacts
on herbivores found differential induction of resistance
[19]. Second, of the generalist chewers, 14 out of 16 studies
used only noctuid agricultural pests in one of a few genera.
All four studies of generalist and specialist aphids used the
same two species on Brassicaceae hosts (Table 1). Aside
from the potential taxonomic bias in herbivores, there was
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Trends in Plant Science xxx xxxx, Vol. xxx, No. x
Impacts of plant toxins on herbivores
(a)
Predicted induced plant responses
Sequestering specialist
Insect performance
Insects benefit from intermediate
induction
Plants benefit from weak or strong
induction (indirect defense may only
be effective at low toxin levels)
(b)
Insect performance
Non-sequestering specialist
Insects indifferent to all but high levels
of induction
Plants benefit from strong induction
and indirect defense
Generalist
Insect performance
(c)
Insects benefit from suppressing
induction
Plants benefit from any induction
Toxin level produced by plant
TRENDS in Plant Science
Figure 2. Three herbivore strategies (a–c) and their expected relationships with plant toxins. Sequestering specialists benefit from the toxins at intermediate levels (via
protection from predation) and nonsequestering specialists are tolerant of toxins at low levels; however, in both cases toxins eventually impose a cost. From the perspective
of the insect, induction should maximize their own growth, and across all herbivore strategies induction should be low (either intermediate, minimal or suppressed (a–c),
respectively). From the perspective of the plant, maximizing defense, induction responses can be more variable and alternative strategies (i.e. indirect defense via induction
of volatile organic compounds) might be the most effective defense against specialists. We note that there are special cases that might not fit this model; for example, some
generalists benefit from feeding on toxic plants, even if they do not sequester the toxins [59].
Table 1. Comparison of plant defensive response to at least one specialist and one generalist insect herbivore from the same
feeding guild
Plant
(Brassicaceae)
A. thaliana
Generalist
(Aphididae)
Myzus persicae
Specialist
(Aphididae)
Brevicoryne
brassicae
Measure of plant response
Transcriptional responses,
glucosinolates (GS)
(Brassicaceae)
Brassica
oleraceae
(Aphididae)
M. persicae
(Aphididae)
B. brassicae
GS
Results a
The generalist caused slightly more changes in gene
expression than did the specialist (sequesterer).
General stress-responsive genes and octadecanoid and
indole GS synthesis genes were similarly induced by
generalist and specialist [22,32]. The specialist induced
a lower GS response than did the generalist [26].
Induction pattern by the two species depended on water
status of the plant [58].
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Table 1 (Continued )
Plant
(Brassicaceae)
A. thaliana
Generalist
(Noctuidae)
Spodoptera
exigua
Specialist
(Piridae)
Pieris rapae
Measure of plant response
Transcriptional response,
GS
(Brassicaceae)
A. thaliana
(Brassicaceae)
A. thaliana
(Noctuidae)
S. littoralis
(Noctuidae)
S. exigua
Transcriptional response
(Brassicaceae)
A. thaliana
(Noctuidae)
T. ni,
S. exigua
(Brassicaceae)
Brassica nigra
(Noctuidae)
Mamestra
brassicae
(Brassicaceae)
B. nigra
(Noctuidae)
Trichoplusia ni
(Piridae)
P. rapae
(Piridae)
P. rapae
(Plutellidae)
P. xylostella
(Piridae)
P. rapae,
(Plutellidae)
P. xylostella
(Piridae)
P. rapae,
(Plutellidae)
Plutella
xylostella
(Piridae)
P. rapae
(Brassicaceae)
Boechera
divaricarpa
(Noctuidae)
T. ni
(Plutellidae)
P. xylostella
Transcriptional response
(Brassicaceae)
Raphanus
sativus
(Noctuidae)
T. ni,
S. exigua
(Piridae)
P. rapae,
(Plutellidae)
P. xylostella
Induced resistance,
herbivore performance
(Brassicaceae)
Sinapis alba
(Noctuidae)
S. frugiperda
(Tenthredinidae)
Athalia rosae
GS, myrosinase (MYR)
(Lauraceae)
Lindera benzoin
(Noctuidae)
S. exigua
(Plantaginaceae)
Plantago
lanceolata
Poaceae
Zea mays
(Nymphalidae)
Junonia ceonia
(Geometridae)
Epimecis
hortaria
(Erebidae)
Spilosoma
congra
(Chrysomelidae)
Diabrotica
virgifera
virgifera
(Sphingidae)
Manduca sexta
Peroxidase activity (POD),
C/N ratio, protein content,
insect bioassays
Iridoid GS (IrGS), protein,
foliar nitrogen
(Solanacae)
Nicotiana
attenuata
(Solanacae)
N. attenuata
(Solanacae)
N. attenuata
(Solanacae)
N. tabacum
a
(Chrysomelidae)
Diabrotica
balteata
(Noctuidae)
S. exigua
Parasitoid specificity for
herbivore induced plant
volatiles (HIPVs)
Transcriptional responses,
GS
Results a
Expression of GS genes was similar for generalist and
specialist, but GS levels only showed an increase in
response to S. exigua. Mean aliphatic GS levels were
equal. P. rapae caused a higher increase in indolyl GS
content [22].
Transcription profiles were indistinguishable [24].
Parasitoid attracted to damaged plants over controls for
both generalists and specialists. Parasitoids only
discriminate between induction by insects in different
guilds [21].
Transcriptional responses and GS were not consistently
influenced by degree of insect specialization [26].
GS
Indole GS was significantly higher after feeding by
P. rapae and M. brassicae than after P. xylostella feeding
[60].
Foliar trichomes, sinigrin,
foliar nitrogen
Differential induction by specialist versus generalist led
to increased trichomes, but the effect reversed on
different leaf positions [61].
Specialist induced SA- and ethylene-associated genes,
whereas generalist induced JA and ET genes [36]. The
specialist might be well adapted, but the plant defends
against the generalist.
Variation in induction was found, but it was not
associated with insect specialization. P. xylostella and
S. exigua induced resistance to all, whereas P. rapae
only induced resistance to P. rapae and S. exigua.
T. ni did not induce resistance [19].
Specialist (sequesterer) and mechanical wounding
induced GS and MYR threefold, whereas generalist
induced only GS (twofold) [37] – generalist might be
adaptively suppressing defense.
POD activity was more strongly induced by generalist
than specialist (no difference in bioassay) [62] – plant
might be adaptively defending against generalist.
Higher IrGS induced by specialist (sequesterer)
compared with generalist [63] – plant might be
adaptively defending against generalist.
Natural enemies preferred roots attacked by specialist
over roots damaged by generalist. The specialist
induced significantly more (E)-b-caryophyllene than the
generalist.
Specialist induced JA/ET burst, generalist induced SA
[64] – might be adaptive for generalists to suppress
resistance by activating SA.
Despite large overlap, the plant response to the
generalists was more similar than the response to the
specialist. This was correlated to FACs/oral secretions.
Both generalists were noctuids and downregulated a
large number of similar genes [54].
M. sexta induced a JA and SA response, whereas
S. littoralis and T. ni induced stronger SA responses [33].
Parasitoid specificity for
herbivore induced plant
volatiles
Phytohormones
(Noctuidae)
Heliothis
virescens,
S. exigua
(Sphingidae)
M. sexta
Transcriptional response
(Noctuidae)
T. ni,
S. littoralis
(Noctuidae)
Helicoverpa
armigera
(Sphingidae)
M. sexta
Phytohormones
(Noctuidae)
Helicoverpa
assulta
Lipoxygenase (LOX),
proteinase inhibitors (PIs),
nicotine, peroxidase
(POD), polyphenol oxidase
(PPO)
Both herbivores induced a similar defensive response,
but response intensity of plants was different: specialist
induced a lower PPO response and more intensive
nicotine and POD response than generalist (JA, LOX
and PIs were not different) [65].
Color-coding reflects consistency with the hypotheses in Figure 2 (green = consistent, but only two species are compared). Yellow indicates no consistent pattern and red
indicates that the level of specialization was not predictive of plant responses.
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Box 2. Testing for differences in induced plant defense among specialist and generalist herbivores
If the goal is to test the hypothesis that specialists elicit differential plant
resistance compared with generalists, we recommend the following
experimental design (Figure I). Ideally, a comparison of more than two
species is necessary because any two species will differ in a myriad of
ways. We suggest a minimum of comparing four species that are all
from one feeding guild (e.g. leaf chewers or phloem-suckers) and in two
taxonomic pairs. As an example, consider the plant defense response
induced by two Helicoverpa spp. (one specialist and one generalist) and
two sawfly species (Tenthredinidae) (one specialist and one generalist).
Note that the two pairs in this case are grouped at differing taxonomic
levels (within genus versus within family). Nonetheless, the comparisons are both valid because within each group, a specialist and
generalist are compared. A common shortcoming of studies is that
both specialists (or both generalists) are from one group (e.g. noctuids),
confounding the comparison between specialists and generalists and
taxonomic grouping. A benefit of having the four species in two
taxonomic groups is that a two-way analysis of variance approach can
be used to partition the relative impact of herbivore specialization and
taxonomic grouping in the plant response.
To test for the differences in the induction of the defense response, it
is crucial to conduct all treatments at the same time and intermixed
within the experimental arena, for example, a growth chamber (in our
scenario of four species there would be six treatments: a control,
mechanical damage and damage by each of the four herbivores). The
reason for this approach is that differences between the induced
defense responses are often subtle and, thus, it is important to have
treatments intermixed. The timing, location, extent of herbivory (and
mechanical damage), developmental stage and diet on which the
insects are raised must also be highly controlled because differences
can arise because of differences in feeding style unrelated to specialization. Finally, we strongly recommend some measure of the plant
responses (e.g. chemical and transcriptional) be coupled with some
biological effect (i.e. a bioassay). An important benefit of this approach
is the connection between complex (often multivariate) response
measures being linked to the hypothesized effect on organisms.
We note that although the proposed experimental design appears
onerous, there should be possibilities, particularly for crop plants and
trees with well-known insect faunas.
Relative significance of host range comparisons
Herbivore specialists
Optimal
comparison
A
Herbivore generalist
Sub-optimal
comparison
Herbivore specialists
B
Herbivore generalist
Time
TRENDS in Plant Science
Figure I. A phylogenetic representation for suggested comparisons in studies comparing herbivores with different levels of specialization. In one scenario, A represents
a chewing herbivore lineage and B represents a piercing-sucking lineage; here, the optimal comparison between specialists and generalists is within guild (and also
within lineage). In a second scenario, all represented herbivores are chewers, but A are Lepidoptera and B are sawflies; again, the within lineage comparison is superior
to the across lineage comparison because it controls for many other differences between the species.
also a very limited range in the plant species, as all species
were herbaceous, and most were representatives of the
Brassicaceae or Solanaceae. Third, few studies compared
induction of indirect defenses [21,25]. This area requires
further studies because the adaptive value of indirect
defenses, particularly VOCs, can be associated with the
ability of a specialist to sequester toxins (Figure 2). The
additional trophic level further complicates generalizations
of plant–herbivore interactions because the involvement
of a natural enemy incorporates dynamics of foraging behavior and signal reliability (see review by Jonathan Gershenzon and colleagues in this special issue). Finally,
despite efforts to align appropriate comparisons (i.e. within
taxon and guild), we found no consistent pattern of differential elicitation based on the degree of host plant specialization (Table 1). None of the studies that compared more
than two herbivores showed consistency with regard to
responses associated with insect specialization.
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The strongest studies compared at least two herbivores
each from both the generalist and specialist categories and
within the same feeding guild; only two studies met this
criteria, and neither found consistent differential induction
by specialists and generalists [19,26]. Although one of the
studies [19] found specificity of induction among four caterpillar species [the beet armyworm (Spodoptera exigua)
and cabbage looper (Trichoplusia ni), which are generalists, and the diamondback moth (Plutella xylostella) and
the cabbage white (Pieris rapae), which are specialists]
damaging wild radish (Raphanus sativus), this was not
associated with diet specialization. The other study [26]
approached the paradigm with a rigorous analysis of plant
response to three specialists [diamondback moth, small
cabbage white and the cabbage aphid (Brevicoryne brassicae)] and three generalists [cabbage looper, beet armyworm and the green peach aphid (Myzus persicae)],
taking into consideration the role that feeding-guild might
play. This excellent study on Arabidopsis (Arabidopsis
thaliana) was able to partition the relative effects of specialists and generalists and simultaneously compare the
induction by two guilds [26]. Nonetheless, an examination
of genome-wide transcriptional responses, major defenserelated pathways and phenotypic responses in terms of
glucosinolate levels revealed that plant responses were not
consistently influenced by the degree of specialization. In
summary, given the methodological issues with testing the
generalist–specialist hypothesis, it is premature to draw
any strong conclusions about differential induction based
on host plant specialization (Box 2).
To address whether alternative categorizations (irrespective of specialization) of herbivores, namely feedingguild (e.g. chewers versus phloem-feeders), can have consistent predictive value for differential induction, we
reviewed the recent literature (Table 2). Indeed, it has
been widely suggested that depending on the feeding mode
of a herbivore, different plant responses will be induced,
resulting in the activation of different plant defense mechanisms [26,27]. Many studies have suggested the involvement of salicylic acid (SA) in defense against phloemsucking insects [27,28], whereas chewing larvae (mainly
Lepidopterans) are often shown to cause extensive tissue
damage and JA and ethylene (ET) induction [29,30]. Of the
13 studies that directly compared chewers and suckers,
there was a strong trend for phloem-feeding insects to
induce fewer genes associated with the JA pathway,
whereas the chewers induced fewer genes associated with
the SA pathway. This is consistent with the prediction that
phloem-feeding herbivores, such as aphids, leafhoppers
and whiteflies, cause only minor tissue damage and induce
defense signaling pathways resembling those activated
against pathogens (SA regulated) [27,31,32].
A second emerging trend is that phloem-feeders cause a
less drastic, more subtle response in the plant. Often they
suppress more genes than the chewing herbivores (e.g.
[23,29,33]), suggesting that they minimize the activation
of plant defenses. Again (Table 1), we found few studies
linking observations of plant responses to herbivore performance [34,35]. An exception is a study that compared adult
potato aphids (Macrosiphum euphorbiae) and beet armyworm caterpillars attacking tomato (Solanum lycopersicum)
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Trends in Plant Science xxx xxxx, Vol. xxx, No. x
[35], where alongside experiments that compared transcriptional and chemical responses of the plant, bioassays were
conducted on the caterpillars. Aphid feeding changed the
level of expression of 2.8 times more plant genes than
caterpillar feeding, downregulating significantly more
genes, and yet increasing the expression of fewer herbivore
defense-related genes (and secondary metabolites). Accordingly, caterpillars were heavier and had a lower mortality on
aphid-damaged plants compared with controls, but weighed
less and had increased mortality on plants previously damaged by caterpillars compared with controls [35]. By linking
plant responses to herbivore performance, the authors provided evidence of aphids minimizing the magnitude of induction by reducing the ability of the plant to respond to
caterpillar feeding.
Who is in charge: insects or plants?
Interpreting which party is ‘in charge’ is of crucial importance when attempting to understand the induction of the
plant defense response by specialists and generalists. For
example, observing a minimal induced response might be
adaptive for the plant because a sequestering herbivore
benefits from plant toxins (Figure 2). However, this response might be adaptive for a generalist insect that
suppresses the potentially harmful defenses of the plant.
It is important to consider the respective qualities of the
herbivore (e.g. sequestering or stealthy) and the consequences of a given plant response in the context of each
herbivore in order to distinguish the roles of a plant
response. Moreover, the fitness impact of each herbivore
is likely to dictate the extent to which a coevolutionary
process is likely between any two herbivores. We suggest
that one way to specifically address this problem of the
response being interpreted as beneficial to different parties
is to include an extra control treatment in induction studies. In particular, treatments that provide a baseline for
induction in the absence of herbivore-specific cues allow for
greater interpretation of differential induction by different
herbivores.
Such controls can involve: (i) mechanical damage, typically realistic maceration of leaves, exactly matching the
amount of maceration to that in real herbivory treatments,
and with treatments that span the timing of real herbivory;
(ii) a JA or other phytohormone treatment; or (iii) insect
manipulations that reduce the salivary activity of the
herbivore (e.g. ablation of the spinnerets). For example,
in a study comparing the transcript profiles after insect
herbivory, wounding and response to JA, SA and ET in
Boechera divaricarpa (Brassicaceae), analyses revealed
that responses to the specialist diamondback moth
(P. xylostella) were determined by effects associated with
the ET and SA pathways, whereas responses to the generalist cabbage looper (T. ni) were determined by the ET and
JA pathways [36]. Mechanical damage induced all three
pathways, yet was dominated by a JA effect. Thus, each
herbivore appears to elicit a distinct response from mechanical damage. Another study investigated specificity in
induction patterns of chemical defenses from plants damaged by a sequestering specialist herbivore (turnip sawfly,
Athalia rosae), a generalist herbivore (fall armyworm, Spodoptera frugiperda) or mechanical wounding (cork borer) in
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Table 2. Comparisons of plant defense elicitation by chewing versus phloem-feeding insectsa
Herbivores
Plutella xylostella,
Pieris rapae,
Spodoptera exigua,
Brevicoryne brassicae,
Myzus persicae
M. persicae,
B. brassicae,
S. exigua,
P. rapae
M. persicae,
P. rapae
Measure of plant response
Transcriptional responses
Results b
Chewers upregulated defense-related pathways involving JA
signaling, sulfate metabolism and aliphatic glucosinolate
biosynthesis. Phloem-feeders downregulated the above [26].
Transcriptional responses,
glucosinolates (GS)
Phloem-feeders increased aliphatic GS. Chewers increased
indolyl and aliphatic GS (P. rapae did not induce aliphatics)
[22].
Phytohormones,
transcriptional responses
Parasitoid specificity for
herbivore-induced plant
volatiles (HIPVs)
(Brassicaceae)
Brassica nigra
P. xylostella,
P. rapae,
S. exigua,
M. persicae
P. rapae,
B. brassicae
Phloem-feeders downregulated genes significantly and did
not induce detectable changes in SA, JA and ET, whereas
chewers induced JA-dependent responses [29].
Parasitoids preferred chewer damaged over phloem-feeder
damaged plants [21].
(Fabaceae)
Glycine max
Cerotoma trifurcata,
Spissistilus festinus
Oxidative enzymes
(Malvaceae)
Gossypium
hirsutum
(Plantaginaceae)
Plantago lanceolata
Bemisia tabaci,
S. exigua
HIPVs
Dysaphis cf. Plantaginea,
Grammia incorrupta,
Heliothis virescens
Spodoptera littoralis,
Rhopalosiphum maidis
Macrosiphum
euphorbiae,
Helicoverpa zea
Secondary metabolites
Plant
(Brassicaceae)
Arabidopsis
thaliana
(Brassicaceae)
A. thaliana
(Brassicaceae)
A. thaliana
(Brassicaceae)
A. thaliana
(Poaceae)
Zea mays
(Solanacae)
Lycopersicon
esculentum
Transcriptional responses
HIPVs
Oxidative enzymes,
herbivore performance
(Solanacae)
S. lycopersicum
Macrosiphum
euphobiae,
S. exigua
Transcriptional responses,
biochemistry, herbivore
performance
(Solanacae)
Solanum
tuberosum
M. persicae,
Leptinotarsa
decemlineata
HIPVs, oxylipin synthesis
(Solanacae)
N. attenuata
Manduca sexta,
S. littoralis,
Trichoplusia ni,
Myzus nicotianae
Transcriptional responses
Caterpillars induced more genes (JA-dependent), repressed
fewer genes (SA dependent), whereas phloem-feeder
repressed ET-dependent genes [28].
Phloem-feeders caused increases in the activities of LOX,
POD, ascorbate oxidase and PPO, the chewers induced LOX
only [66].
Phloem-feeders did not induce volatile emissions or affect the
density of pigment glands, whereas chewers strongly
induced volatiles [67].
Chewers had stronger effects and upregulated many
compounds. Aphids mainly downregulated compounds [23].
Chewers induced many volatiles, whereas aphids induced no
measurable emissions (even after heavy infestation) [68].
Aphid feeding induced POD and LOX, but had no effect on PI
and reduced PPO activities; the chewers induced PPO, PI and
LOX, but did not induce POD. Prior aphid feeding had
decreased resistance to S. exigua. Prior chewer feeding
increased resistance to S. exigua [69].
Aphids changed the expression of more genes than
caterpillars, yet caterpillar defense induction was higher (PIs).
Prior aphid feeding decreased resistance. Prior chewers
increased resistance via JA-regulated genes. Aphid feeding
had weak JA pathway responses [35].
Chewers induced fewer genes (no JA-dependent responses),
whereas the phloem-feeders induced JA-dependent
responses. Volatile signatures and biochemical precursors
associated with stress signaling were distinct [70].
Chewers induced JA-dependent genes, whereas the phloemfeeders reduced some JA-dependent genes and increased
SA-dependent genes [33].
a
Each comparison is from a single study.
b
Color-coding reflects consistency with the hypothesis that the phloem-feeders induced a weaker defensive response than the chewers (green). Yellow indicates no
consistent pattern and red indicates rejection of the hypothesis.
white mustard (Sinapis alba) [37]. Specialist feeding and
mechanical damage induced threefold increases of the
glucosinolate–myrosinase system, whereas generalist feeding induced up to twofold increases in glucosinolate only.
Although these studies did not have replication at the level
of specialists and generalists, because of the additional
controls, we can speculate that specialists might have
different mechanisms based on their strategy to evade
(diamondback moth) or sequester (turnip sawfly) the
plant’s defenses. Although the herbivore treatments alone
in both experiments would have demonstrated differences
between the two species, having relative bases of comparison allows for a stronger interpretation. Ultimately a link
between these differential induced responses and the
impacts on the herbivores would be needed to assess which
parties benefit.
One of our major predictions is that generalist herbivores use mechanisms to suppress plant defenses more so
than specialists, allowing them to feed on a broad range of
species (Figure 2). This hypothesis was advocated some
time ago with regards to behavioral trenching, a method by
which some generalist herbivores attack plants that exude
latex [16]. Generalists that trench were able to feed on a
diversity of host plants with latex, whereas generalists
that did not trench had poor performance on these same
plants. Recent developments confirm other, less visually
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apparent, mechanisms by which generalists can also suppress plant defense [38].
Mandibular glands of the noctuid caterpillar Helicoverpa zea were found to secrete salivary glucose oxidase
(GOX) [38,39], which has been implicated as an effector
responsible for suppression of defense by eliciting an SA
burst (which, in turn, attenuates JA and ET levels). When
the ability of caterpillars to introduce GOX to their host
plants is removed (via ablating the saliva-producing
spinnerets), tobacco (Nicotiana tabacum) plants mount a
response that reduces herbivore performance, thus demonstrating a benefit for generalists to reduce the ability of
the plant to respond to herbivore attack [40]. A recent
survey of GOX levels in 85 species (across 23 families of
Lepidoptera) found that highly polyphagous species have
relatively higher levels of GOX compared with more specialized species [39]. Thus, the production of GOX as a
suppressor of induced plant defenses appears to follow our
prediction of generalists being more suppressive of plant
defense than specialists.
An additional example of generalists suppressing plant
defense was found in Arabidopsis plants infested by the
phloem-feeding silverleaf whitefly (Bemisia tabaci). Whitefly feeding increased SA-responsive gene transcripts,
whereas JA- and ET-dependent pathways were repressed
or not modulated [41]. Mutant plants with higher activity
of JA defenses or impaired in SA defenses slowed nymphal
development, whereas those that activate SA and impair
JA increased nymphal development [41]. Thus, generalist
whitefly feeding strategies appear to benefit the whiteflies
at the expense of plant defense. Given the similarity of this
result with that of the generalist potato aphid on tomato
discussed above [35], we advocate a critical comparison of
plant responses to generalist versus specialist aphids. All
of the examples of comparisons thus far have been between
the generalist green peach aphid (M. persicae) and the
specialist cabbage aphid (B. brassicae) on Brassicaceae,
and none have linked plant responses with aphid performance (Table 1).
But wait, are specialists not specialists?
Given that specialist herbivores share an intimate evolutionary history with their host plants, are specialists more
manipulative as herbivores than generalists? The answer
to this question is complicated by three issues: (i) specialists can be somewhat tolerant of defenses (and, thus,
might not need to be manipulative); (ii) specialists can
maximize their fitness in nonobvious ways (e.g. phenology,
location of feeding); and (iii) from a coevolutionary standpoint, plants might recognize specialists (particularly
those with strong fitness impacts on the plant) and defend
appropriately. Of course there are examples of specialists
that manipulate their hosts [42,43]. Insect gallers perhaps
epitomize highly manipulative specialist herbivores. The
conventional view is that gallers reprogram both primary
and secondary plant metabolism to their benefit [43].
Indeed, most gallers are highly specialized, more so even
then their endophagous (but nongalling) relatives [44].
Thus, specialists can either be highly manipulative or
not so manipulative. As discussed above, directly comparing specialist induction to some mechanical damage and to
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Trends in Plant Science xxx xxxx, Vol. xxx, No. x
elicitor-free control should aid in addressing this issue.
More generally, we are in need of direct contrasts of
specialists and generalists, testing whether generalists
are more sensitive to particular defenses and, hence, can
manipulate them effectively.
Plants in charge? Fatty acid amino acid conjugates and
beyond
Plants are able to perceive a wide range of herbivoreassociated elicitors resulting in the activation of specific
plant responses, although the adaptive value of such specificity is unclear [39]. Most elicitors and their respective
responses differ from responses to mechanical damage and
appear to be restricted to particular plant–insect associations [45]. There have been four documented elicitors
produced by insects: b-glucosidase [46], fatty acid amino
acid conjugates (FACs) [47], inceptins [48] and caeliferins
[49]. The most broadly investigated and described elicitors
to date have been FACs from lepidopteran larvae (generalists and specialists) and these constituents (typically
obtained from oral secretions or regurgitate) are thought
to betray the insects presence (and perhaps identity) to the
plant [45,47,50]. The first well-characterized FAC was
volicitin [N-(17-hydroxylinolenoyl)-L-glutamine], which
was identified from the beet armyworm [47] and induces
direct and indirect plant defenses in several plants [51].
FACs (particularly volicitin) have a strong impact on plant
hormone levels as well as on the induction of plant volatiles
in a variety of plant species, unlike caeliferin and inceptin,
two newly identified elicitors that appear to be more
restricted in the plants for which they are active [48,52].
We presume that insect elicitors, although potentially
harmful to the insect in the plant–herbivore interaction,
are produced (and not lost because of natural selection)
because they are an essential part of the primary metabolism of the insect. For example, FACs in the noctuid moth
Spodoptera litura play an active role in nitrogen assimilation by regulating the amount of glutamine in the larval
midgut [53]. A recent FAC screen of 29 Lepidoptera species
found that some species do not produce these elicitors [51].
Additional categories of elicitors are combinations of plant
and insect constituents, which might be a highly stable
mechanism for plant recognition of attack. For example,
inceptins are derived from fragments of digested plant
tissues. Peptides released from proteolytic fragments of
chloroplastic ATP synthase were found in the oral secretions of the fall armyworm (S. frugiperda) [48], thus giving
the plant a direct role in the perception of a specific
attacker.
It is unclear if generalist and specialist herbivores differ
in their elicitors. A study has shown that the transcriptional responses of Nicotiana attenuata to attack from two
generalist herbivores [the tobacco budworm (Heliothis virescens) and the beet armyworm (S. exigua)] was more
similar than that of the tobacco hornworm (M. sexta),
which is a specialist herbivore, and that this difference
was linked to their FACs (although in this case, the two
generalists were closely related and thus shared many
traits) [54]. Regurgitates of the generalists were virtually
identical [55], whereas that of the specialist differed, lacking volicitin and dominated by fatty acid–glutamic acid
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conjugates that were not present in the regurgitates of the
generalists [56,57]. FACs from the specialist M. sexta are
involved in suppressing the nicotine response in tobacco,
but do not suppress indirect defensive responses (VOCs),
and this has been interpreted as adaptive on the part of the
plant [58,11]. It would be interesting to evaluate the degree
of specificity of insect recognition in plants and to assess
whether plants tend to have more fine-tuned degrees of
recognition (e.g. via mechanisms specifically associated
with FACs or saliva produced by labial and mandibular
glands) for specialists and more broad feedback mechanisms (plant-derived byproducts of herbivore digestion,
e.g. inceptins or regurgitants) for generalists.
Concluding remarks
For plants to ‘be in charge’ we assume that after integrating signals from a given attack they will activate pathways
that provide the most defensive response. The predictions
of the specialist–generalist paradigm suggest that there
can be consistency in herbivore elicitation and plant recognition among different types of attackers. Yet, to date,
evidence for distinct groupings of generalists and specialists is not so clear, in part because of methodological
limitations. A ubiquitous problem with interpreting the
specialist–generalist paradigm is that there are two sides
to every story (that of the herbivore and of the plant), there
are also potentially different predictions based on the type
of specialist (sequestering or not?) and the fact that coevolutionary interactions can modify the dynamics in space
and time. Nonetheless, we are optimistic. As detailed in
this review, we advocate the use of real species level
replication, strong controls and links between measures
of plant responses with insect performance. It is premature
to kill the specialist–generalist paradigm, but perhaps also
too early to celebrate its generality.
Acknowledgments
We thank Martin Heil, Sergio Rasmann, Andre Kessler, Jennifer Thaler
and the Plant-Interactions Group at Cornell for helpful comments and the
United States National Science Foundation (DEB-1118783) for financial
support.
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Review
Special Issue: Specificity of plant–enemy interactions
The specificity of herbivore-induced
plant volatiles in attracting herbivore
enemies
Andrea Clavijo McCormick, Sybille B. Unsicker and Jonathan Gershenzon
Department of Biochemistry, Max Planck Institute for Chemical Ecology, Hans-Knöll Strasse 8, D-07745 Jena, Germany
Plants respond to herbivore attack by emitting complex
mixtures of volatile compounds that attract herbivore
enemies, both predators and parasitoids. Here, we explore whether these mixtures provide significant value
as information cues in herbivore enemy attraction. Our
survey indicates that blends of volatiles released from
damaged plants are frequently specific depending on the
type of herbivore and its age, abundance and feeding
guild. The sensory perception of plant volatiles by herbivore enemies is also specific, according to the latest
evidence from studies of insect olfaction. Thus, enemies
do exploit the detailed information provided by plant
volatile mixtures in searching for their prey or hosts, but
this varies with the diet breadth of the enemy.
Plant volatiles from an herbivore enemy perspective
Plants emit complex blends of volatile compounds from
many of their organs, and there is considerable variation in
volatile composition among species and other taxonomic
levels [1]. For these reasons, volatiles are commonly used
by herbivores as cues in choosing host plants. However, as
well as herbivores, organisms at the next trophic level,
herbivore enemies, also use plant volatiles as cues. Two
types of herbivore enemy, predatory arthropods and parasitoids, are known to exploit plant odors to find plants
harboring their prey and hosts [2–4]. These enemies take
advantage of the fact that, after herbivory, many plants
emit blends of volatiles differing in both quantity and
composition from those emitted before herbivory. Over
50 different species of plants are reported to produce
distinct blends of herbivore-induced volatiles, and these
are known to attract a range of herbivore enemies, including predators and parasitoids drawn from five insect
orders, plus predatory mites, nematodes and birds [3,4].
Much of the research on volatile-mediated attraction of
herbivore enemies to damaged plants has centered on the
ecological and evolutionary significance of volatile emissions
to plants. There has been an ongoing debate in the literature
about whether volatiles increase plant reproductive fitness
by attracting herbivore enemies, and so act as defenses
[2,5,6] (Box 1), or instead play other roles in the lives of
the plants that produce them [7,8]. By contrast, herbivoreassociated plant volatiles have always been assumed to have
Corresponding author: Gershenzon, J. ([email protected]).
real value for herbivore enemies, although this has rarely
been subject to detailed analysis. Over 20 years ago, L.E.M.
Vet and M. Dicke [9] proposed that plant volatiles are useful
cues for herbivore enemies at medium to long range because
they can be detected more readily than volatile cues emitted
from herbivores. The authors reasoned that plants usually
have a greater biomass than their herbivores and typically
emit volatiles systematically from a greater area than that
actually damaged. Shortly thereafter, Dicke and colleagues
also considered the potential of volatiles to provide specific
information to enemies about the prey and hosts present
[10]. Yet, knowledge of the information content of volatile
blends has advanced little since then. It is sometimes
asserted that the composition of the volatile blend might
furnish specific details for enemies about the kinds of herbivore(s) present and their age, developmental state and
abundance, but this possibility is not easy to demonstrate.
In this review, we consider the specificity of herbivoreinduced plant volatiles as cues for herbivore enemies in
light of the latest results on enemy behavior and insect
olfaction. We cover specificity at three stages of enemy
attraction: in the blend of volatiles emitted from the plant,
in the response of enemies to volatiles, and in how enemies
perceive volatiles. Our goal is to understand whether the
complex volatile mixtures emitted from plants under herbivore attack have significant information value to herbivore enemies.
Specificity in plant volatile emission
Given the enormous variety of substances reported in volatile mixtures [1,11], it is easy to imagine that each plant
species could emit a distinct blend of volatile compounds,
and thus be recognizable to herbivores and their enemies.
However, perusal of the major herbivore-induced volatiles
shows that the same constituents (Box 2) are released by
most plant species, irrespective of their taxonomic affinities.
For example, the monoterpenes (E)-b-ocimene and linalool,
the sesquiterpenes (E, E)-a-farnesene and (E)-b-caryophyllene, the C11 homoterpene (E)-4,8-dimethyl-1,3,7-nonatriene (DMNT), and the fatty acid derivatives known as
green leaf volatiles (GLVs), including (Z)-3-hexen-1-ol and
(Z)-3-hexenyl acetate, are frequent components of volatile
blends released after herbivore damage from a wide range of
plant species [12–17]. Nevertheless, the relative amounts of
these substances vary greatly among species and there are
1360-1385/$ – see front matter ß 2012 Elsevier Ltd. All rights reserved. doi:http://dx.doi.org/10.1016/j.tplants.2012.03.012 Trends in Plant Science, May 2012, Vol. 17, No. 5
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Review
Box 1. Herbivore enemy attraction to volatiles from the
plant perspective: ecological and evolutionary ambiguities
Despite all the reports on volatile-mediated attraction of herbivore
enemies to damaged plants, its ecological and evolutionary
significance for plants is still uncertain. Unlike for herbivore
enemies, where exploitation of volatile cues attracting them to their
prey or hosts is assumed to bring benefits, there has been no clear
proof of whether volatiles increase plant reproductive fitness and so
can actually be considered a defense [2,6,5]. In part, this is because
most studies have been carried out under laboratory conditions or
with agricultural systems where true evolutionary inference is not
possible. Field studies have demonstrated that herbivore-induced
volatiles increase the attraction of herbivore enemies [93,94], and
this can result in decreased herbivore survival and performance [95],
but there is still no evidence that volatile emission positively affects
plant fitness.
Another difficulty in showing the fitness benefits of herbivore
enemy recruitment to plant volatiles is that emission has also been
reported to have additional roles for plants in direct defense,
internal signaling or communication with neighboring plants
[8,11,96,97]. These may complement or conflict with a role in
herbivore enemy attraction, and so it is hard to assign precise costs
or benefits to volatile emission.
Furthermore, there is some doubt as to the effectiveness of one
type of enemy frequently attracted by herbivore-mediated volatiles:
koinobiont parasitoids. These do not kill their herbivore hosts
immediately, but wait until they are about to pupate or become an
adult. Koinobiont parasitoids might even decrease the fitness of
individual plants by stimulating herbivores to feed more [98], but
could benefit plants by reducing future herbivore populations.
Finally, recent research at the community level reveals an unexpected number of plant volatile-mediated interactions among
species at various trophic levels causing further complications in
assessing the fitness benefits of volatiles. [99,100].
typically many differences in less abundant compounds that
could contribute to specificity. If these differences are perceived by herbivore enemies, they could facilitate species
recognition.
Within a single species, plant volatile emission can vary
with the herbivore present, as noted many years ago by
Dicke and colleagues [10]. This variation provides herbivore enemies with valuable information on the identity of
prey or hosts available on a plant and their feeding guilds
[18–21]. For example, Brassica rapa (turnip rape) plants
damaged by the root herbivore Delia radicum (cabbage
rootfly) emit a distinct blend of volatiles from their aboveground tissue that differs significantly from the blend that
is released when the plants are attacked aboveground by
caterpillars of Pieris brassicae (large cabbage white butterfly) [22]. 4-Methyltridecane and salicylaldehyde are
dominant compounds in the blend of D. radicum-damaged
plants, whereas methyl salicylate is characteristic for
P. brassicae-damaged cabbage. When roots and shoots
are attacked simultaneously, the GLV hexyl acetate is
released in high relative amounts [22].
The distinctive profiles induced by various herbivores
could be caused by specific elicitors in oral secretions applied
during the process of feeding. In addition to the well-known
fatty acid–amino acid conjugates and b-glucosidases in the
oral secretions of lepidopteran larvae, several new elicitors
associated with herbivore oviposition and feeding have
recently been reported [23–26]. However, the elicitors
known so far seem insufficient to explain most of the differences in plant volatile emission patterns. Specificity in
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Trends in Plant Science May 2012, Vol. 17, No. 5
elicitor recognition could arise from differential induction
of defense-signaling pathways and associated phytohormones [27]. In general, leaf-chewing insects activate jasmonic acid-dependent signaling, whereas phloem feeding
induces salicylic acid-dependent signaling, which sometimes antagonizes jasmonic acid signaling [28,29]. More
research on hormones, signaling pathways, new elicitors
and other factors affecting the biosynthetic pathways of
volatile emission is required to judge what drives specificity
in plant volatile emission.
Further evidence of specificity in volatile release comes
from plants attacked by different developmental stages of
the same insect herbivore species [30,31]. For instance,
larvae of Plagiodera versicolora (the willow leaf beetle)
trigger young Salix eriocarpa (wollypod willow) trees to
emit six out of 17 detected volatile compounds in significantly higher amounts than after adult beetle herbivory.
Larval feeding also results in higher overall emission rates
than does adult beetle damage [31]. Egg deposition has
also been reported to affect volatile blends [24,32,33], with
egg-induced volatile blends distinct from those induced by
larval feeding [34].
The volatile blend released from plants can also give
valuable information on the number of herbivores that are
currently feeding on a plant because the rate of emission of
particular compounds is often positively correlated to the
amount of inflicted damage [35,36]. In addition, volatile
release can even indicate whether herbivores have already
been attacked by parasitoids and the identity of the attacking species [37], which could represent valuable information for other parasitoids.
Despite these impressive examples of specificity in the
herbivore-induced emission of plant volatiles, there are
several reports in which different herbivore species, feeding guilds, developmental stages and number of attackers
were not found to alter volatile emission significantly
[6,38,39]. For example, the spectrum of Nicotiana attenuata (coyote tobacco) volatiles induced by herbivory from a
lepidopteran, Manduca quinquemaculata (five-spotted
hawkmoth), a beetle, Epitrix hirtipennis (tobacco flea beetle) and an hemipteran, Tupiocoris notatus (suckfly) is
similar with compounds being released in only slightly
different proportions [39].
To judge whether herbivore-induced emission is ultimately specific enough to be useful to herbivore enemies,
experimental methods have to be improved. A more thorough chemical analysis of plant volatile mixtures is necessary that includes even minor compounds, because the
abundance of individual compounds may not be correlated
with their information value. In addition, explicit statistical
procedures are needed to establish whether blends differ
significantly. These must take into account the fact that
abundance and composition change at the same time, that
compounds derived from the same biosynthetic pathway
may be autocorrelated, and that data are often non-normally
distributed and heteroscedastic [40]. A major improvement
in analyzing induced volatile emission would come if collections were made in the field to assess how blend composition
is affected by the typical biotic and abiotic factors prevailing
there. Most herbivore-induced volatile collections have been
carried out under the controlled conditions of the laboratory
Review
Box 2. Plant volatiles associated with herbivore enemy
attraction
Plants produce a large range of metabolites that are volatile because
of their high vapor pressure under standard conditions. Aside from
simple gases, such as O2, CO2, water vapor and ethylene, over 1700
volatiles are reported from plants [1], but only a fraction of these are
emitted by individual plants after herbivore damage. These can be
grouped into four categories.
Terpenes
Comprising the largest class of plant volatiles, terpenes or
terpenoids are classified by the number of branched C5 units in
their structures. Major terpene volatiles emitted from vegetative
tissue include the C5 compound isoprene (one C5 unit), C10
monoterpenes, such as (E)-b-ocimene and linalool (two C5 units),
and C15 sesquiterpenes, such as (E)-b-caryophyllene and (E,E)-a farnesene (three C5 units). Two terpenes that occur frequently after
herbivore damage have irregular structures, the C11 (E)-4,8dimethyl-1,3,7-nonatriene (DMNT) and the C16 (E,E)-4,8,12-trimethyl- 1,3,7,11-tridecatetraene (TMTT).
Fatty acid derivatives
The oxidation of fatty acids leads to the formation of a large family
of volatile derivatives. After herbivore damage, sequential lipoxygenase and hydroperoxide lyase action results in the production of
C6 compounds, such as (E)-2-hexenal, (Z)-3-hexenal, (Z)-3-hexen-1ol and (Z)-3-hexenyl acetate, called GLVs because they impart the
typical odor of green leaves.
Aromatic compounds
The metabolism of phenylalanine leads to a group of compounds
with simple aromatic rings and C1–C3 side chains, whereas an
offshoot of tryptophan biosynthesis leads to indole derivatives. The
most important representatives of this group after herbivore
damage are methyl salicylate and indole.
Amino acid derivatives
After herbivore damage, various amines, oximes, nitriles, isothiocyanates and sulfides are released that are produced from amino
acids. These compounds are often not as well recovered in standard
headspace collections as terpenes, GLVs and aromatics, and may be
more abundant than is currently realized.
For chemical structures and more information on the chemistry and
biochemistry of herbivore-induced volatiles, several good references are available [101,102].
or greenhouse, where demonstration of specificity is only
inferential.
Ultimately, the specificity of volatile blends for herbivore enemy attraction must be determined by the enemy
and not the plant. In the following two sections, we focus on
the behavior of herbivore enemies and their perception of
plant volatiles.
Specificity in exploitation of plant volatile cues by
herbivore enemies
We have discussed above that herbivore-damaged plants
may release distinct volatile blends that vary with the
plant species, the herbivore species, and the age, stage
and number of herbivores present. Do herbivore enemies
take advantage of these cues to find the prey or hosts they
are seeking?
A review of the literature reveals many examples in
which herbivore enemies use herbivore-induced plant volatiles to discriminate among different plant species [41,42],
different cultivars or varieties of the same species [43,44]
and even different phenological states of the plant [45,46].
The behavior of enemies is also influenced by qualitative
changes in volatile profiles caused by different types [47,48]
and growth stages of herbivores [30], and by quantitative
Trends in Plant Science May 2012, Vol. 17, No. 5
changes in volatiles associated with varying host or prey
density [35,49]. It can be inferred that this behavior helps
enemies to find prey and hosts.
Although herbivore enemies are ultimately seeking
herbivores rather than plants, their attraction to plants
has been rationalized on the basis of the detectability and
reliability of volatile cues [9]. Volatile signals from herbivore-induced plants, which are often systemically emitted,
are stronger and easier to detect than are signals from the
arthropod prey or the hosts themselves, even if they are not
as reliable. Enemies able to perceive the cues released by
the plant upon attack can optimize their foraging efficiency
by concentrating their searching behavior [50]. Whether
differential attraction towards plant volatiles is directly
connected to increased herbivore enemy fitness is so far
unknown, but it has been reported that the species, genotype or nutritional status of plants upon which herbivores
develop can have a great impact on enemy performance
[51–55]. For example, parasitization of herbivores that
feed on plants with a high level of defense compounds,
such as alkaloids, iridoid glycosides or furanocoumarins,
may have adverse effects on the growth or survival of the
parasitoid, and so parasitoids may be under selection to
avoid chemically well-defended plants in host searching
and to be differentially attracted by volatiles to plants with
lower levels of defense compounds [55]. By contrast, some
parasitoids perform better on hosts feeding on chemically
well-defended plants, perhaps because the ability of the
host to encapsulate the parasitoid is compromised [56,57].
Although there is little evidence for the ability of herbivore enemies to discriminate among plants based on their
nutritional value to herbivores, enemies are sometimes
observed to prefer healthy, infested plants compared with
those under abiotic stress [58,59], and have reduced attraction to plants that are simultaneously infested by other
herbivores or pathogens [60–62]. For example, predatory
mite (Phytoseiulus persimilis) attraction to lima bean (Phaseolus lunatus) infested with spider mites (Tetranychus
urticae) was reduced when the plants were also infested
with tobacco whitefly (Bemisia tabaci), which reduced
monoterpene emission [21]. The attraction of the hymenopteran parasitoid Diadegma semiclausum to the P.
rapae-induced volatiles of Arabidopsis thaliana is diminished by the presence of methyl salicylate, which might
serve as an indicator of salicylate-signaling arising from coinfestation of a pathogen or phloem-feeder [63]. Herbivore
enemies living below ground show similar behavior. Attraction of the entomopathogenic nematode, Heterorhabditis megidis, to its prey, the larvae of Diabrotica virgifera
virgifera (the western corn rootworm) feeding on maize
roots is reduced by shoot herbivory, which decreases emission of the principal volatile, the sesquiterpene (E)-b-caryophyllene [60]. The presence of additional, non-target
herbivores on a plant may decrease its attractiveness for
enemies owing to induction of defenses or nutrient reallocation, which can negatively impact the development of
target herbivores.
It seems likely that the use of plant volatile cues by
herbivore enemies to choose plants for prey or host searching has fitness value. However, studies on the attraction of
herbivore predators and parasitoids to volatile cues rarely
305
Review
incorporate measurements of growth, development or
other aspects of performance. Thus, more research is
needed on the relationship between enemy response to
volatile cues and performance (e.g. [64]) before one can
appreciate the ecological and evolutionary significance of
this behavior.
The way in which herbivore enemies exploit plant volatile cues to locate their prey or hosts depends, at least for
parasitoids, on their degree of host specificity [65,66]. For
example, the parasitoid Cardiochiles nigriceps, which is
specialized on the noctuid Heliothis virescens (tobacco
budworm), can discriminate between plant volatiles emitted after host feeding versus feeding of a non-host, the
closely related Helicoverpa zea (corn earworm) [67]. By
contrast, the generalist parasitoid Campoletis chlorideae
was equally attracted to the volatiles emitted by the feeding of two other noctuid moth larvae, Helicoverpa armiguera (cotton bollworm) and Pseudaletia separata (oriental
armyworm), even though the composition of the two blends
was not the same [68]. Among aphid enemies, the specialist
parasitoid Aphidius ervi recognizes its host Acyrthosiphon
pisum (pea aphid) over the non-host Aphis fabae (bean
aphid) on Vicia faba (broad bean), whereas a generalist
aphid parasitoid Diaeretiella rapae did not discriminate
between two aphid species based on plant volatiles alone
[69].
It has already been mentioned that plants attacked by
different developmental stages of the same herbivore can
release different blends of volatiles. These differences
appear to be exploited more by specialists than by generalists. For example, the specialist parasitoid Cotesia kariayi
was more attracted to maize plants fed upon by earlyinstar larvae of P. separata, which are suitable for parasitization, than to plants fed upon by late-instar larvae,
which are less suitable for parasitization [30]. A similar
situation was observed for the specialist predator Aiolocaria hexaspilota [31]. By contrast, the generalist parasitoids Microplitis rufiventris and Cotesia glomerata, which
also attack early stages of their hosts, were unable to
discriminate between different instars based on plantemitted volatiles [70,71].
Within a plant, parasitoids may also use volatile information to locate suitable hosts more precisely and, once
again, specialists exploit this cue more than generalists do.
Females of the specialist parasitoid, Cotesia rubecula, were
observed to land more frequently on leaves infested with
unparasitized caterpillars of their host Pieris rapae (lesser
cabbage white) than on leaves infested with parasitized
caterpillars, whereas the generalist parasitoid C. glomerata could not make this discrimination [72]. Taken together, these examples demonstrate that the ability of
parasitoids to exploit plant-derived volatiles as cues to
locate their hosts is higher when host ranges are narrow.
Nevertheless, other factors such as habitat complexity and
competition might be important selective pressures on
parasitoid behavior, and more experiments in this area
are warranted.
Specificity in herbivore enemy perception of volatiles
Herbivore enemies can only respond to variations in plant
volatile blends if their sensory apparatus can perceive the
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Trends in Plant Science May 2012, Vol. 17, No. 5
differences among them. Thus, it is important to understand how enemies detect volatiles and discriminate
among different types and quantities of compound. Olfactory perception in arthropods occurs when volatile compounds bind to olfactory receptors in the antennae,
maxillary palps or legs. One or few olfactory receptors
are expressed in each olfactory neuron, and neurons bearing similar receptors converge in distinct structures called
glomeruli in the olfactory bulb or antennal lobe, which are
functional units for odorant coding and processing [73,74].
The number of such glomeruli is variable among insect
taxa, with approximately 40 in the fruit fly Drosophila
melanogaster [75], approximately 160 in the honeybee Apis
mellifera [76], between 14 and 21 in the predatory mite P.
persimilis [74] and approximately 190 in the parasitoid
wasps C. glomerata and C. rubecula [77]. Thus, at least
some herbivore enemies have complex machinery for olfactory perception.
By extrapolating from studies with herbivores, herbivore enemies are likely to employ olfactory cues to locate
the food plants of their prey and hosts via two different
modes of perception (Figure 1). In one mode, called ‘speciesspecific odor recognition’, single compounds that are characteristic of a certain plant species or a group of related
species are used for plant recognition, whereas in a second
mode, called ‘ratio-specific odor recognition’, enemies detect a set of plant volatiles ubiquitous to many families and
are tuned to discriminate differences in ratios among these
compounds to find their hosts [78].
Evidence for species-specific odor recognition in herbivores has been obtained from studies with herbivores
specialized on Brassicaceae [79]. These insects are attracted
[(Figure_1)TD$IG]
?
Attractive blend
Non-attractive blend
Species-specific
odor recognition
Presence versus
absence
4
: 6
Ratio-specific
odor recognition
3
Whole-blend
odor recognition
: 4
: 1
versus
: 4
: 4
: 1
versus
TRENDS in Plant Science
Figure 1. Possible modes of odor discrimination by herbivore enemies.
Mechanisms of odor discrimination are depicted using colored symbols to
represent individual volatile compounds in an odor blend. In species-specific
odor recognition, discrimination is based on compounds restricted to a single
species or group of related species. In ratio-specific odor recognition,
discrimination is based on the ratios of blend compounds. In whole-blend odor
recognition, discrimination is based on the entire blend or many of its components
perceived as a whole.
Review
to isothiocyanates, hydrolysis products of the characteristic
glucosinolate metabolites of the Brassicaceae that are sometimes volatile, and have specific olfactory receptor neurons
tuned to isothiocyanate perception. Among herbivore enemies associated with the Brassicaceae, species-specific odor
recognition might also occur, because the specialist parasitoid Diaeretiella rapae, a braconid wasp that attacks aphids
living on Brassicaceae, displays electroantennogram (EAG)
responses to various isothiocyanates and is behaviorally
attracted to them [69,80]. It was suggested that D. rapae
has specific receptors for isothiocyanates and uses these
compounds as cues to locate its hosts. Interestingly, isothiocyanate receptors are also present in herbivores that do not
use Brassicaceae as hosts [81], suggesting the role of single
compound recognition not only in attracting herbivores to
hosts, but also in avoidance of non-host plants, which may
make host searching more efficient. For herbivore enemies,
the role of repellents has so far received less attention, but
might also play a crucial role in host selection. For example,
an investigation of the parasitoid Diadegma semiclausum,
an ichneumonid wasp, revealed that the volatile compound
isoprene is perceived by olfactory receptors and acts in a
repellent manner [82].
Where herbivore enemies do not use plant-specific compounds as attractants or repellents, the second mode of host
perception, ratio-specific odor recognition, may be dominant
(Figure 1). EAG and gas chromatography-electroantennographic detection (GC-EAD) have shown that most enemies
tested respond to a comparable list of widely occurring plant
volatiles (Box 2), including linalool, DMNT, (E, E)-a-farnesene, (E)-2-hexenal, (Z)-3-hexen-1-ol and, (Z)-3-hexenyl acetate, but to different extents [83–87]. Thus, their ability to
discriminate among odor blends is not likely to be due to the
presence or absence of individual substances, but instead to
the differences in the relative proportions of individual
constituents. In addition, enemies such as hymenopteran
parasitoids have different sensitivities to volatiles, and do
not necessarily respond most strongly to the most abundant
compounds. Differences in relative perception have been
linked to the range of host choice, with generalist parasitoids
having stronger GC-EAD responses to compounds emitted
by a large variety of species immediately after damage, such
as the green leaf volatiles, and specialist parasitoids
responding more strongly to volatiles emitted several hours
after damage [84,87,88].
Ratio-specific odor recognition of a plant by an herbivore
enemy is well illustrated by the case of Closterocerus
ruforum, an egg parasitoid of the pine sawfly Diprion pini
[89]. The parasitoid is more attracted to pine foliage on
which D. pini eggs have been laid than to foliage on which
no eggs have been laid. The volatile blend of the egg-laden
foliage differs from that of the no-egg foliage only in an
increased amount of (E)-b-farnesene, but this sesquiterpene alone is not attractive. However, (E)-b-farnesene is
attractive when present as a mixture with five other
terpenes [b-phellandrene, (E)- and (Z)-b-ocimene, (E)-bcaryophyllene, and a-humulene], whose amounts are not
increased on D. pini oviposition. The mixture of these five
compounds plus (E)-b-farnesene was significantly attractive to the parasitoid, but only when the ratios mimicked
those emitted from egg-laden pine foliage. The parasitoid
Trends in Plant Science May 2012, Vol. 17, No. 5
Cotesia vestalis is also attracted to a complex mixture of
volatiles. In this case, a response requires four herbivoreinduced volatiles [n-heptanal, a-pinene, sabinene, and (Z)3-hexenyl acetate] present in ratios similar to those emitted by a cabbage plant infested with diamondback moth
(Plutella xylostella) and presented against a background of
non-infested cabbage [90]. None of the compounds alone is
attractive. These results suggest that even when there are
one or a few key attractive compounds, these may only be
active as host-finding cues when present in natural ratios
against a background of other volatiles emitted by the
plant.
A study involving an herbivore predator, the mite P.
persimilis, shows that mixtures are also important to this
class of herbivore enemies. When tests were performed
involving several major herbivore-induced lima bean volatiles, (E)- and (Z)-b-ocimene, (Z)-3-hexenyl acetate, DMNT,
TMTT and methyl salicylate, only the last of these compounds was attractive when presented alone. However, a
mixture of all five was more attractive than the individual
compounds or partial mixtures, as long as it was tested
with background odor from lima bean without herbivory.
Interestingly, the GLV (Z)-3-hexenyl acetate that was
repellent when tested alone, was a key component of the
attractive blend [91,92]. The results suggest that predatory
mites perceive plant volatiles not as a mixture of individual
attractive and repellent compounds, but as a synthetic
whole blend (Figure 1).
Thus, in future behavioral studies with herbivore enemies, individual plant volatile compounds should be tested
not only alone, but also as part of mixtures and in the
context of background odors. In addition, the perception of
Box 3. The importance of naı̈ve versus learned responses
The value of plant volatiles as cues for herbivore enemies may
depend on whether enemies respond innately or only after learning.
All herbivore enemies are likely to have fixed naı̈ve responses
towards a subset of odors that are most relevant for host or habitat
location [9]. Naı̈ve responses have been hypothesized to be
associated with highly reliable cues, such as host or host-related
odors (i.e. pheromones, honeydew or frass) [9,103], but there is
accumulating evidence that some parasitoid or predator species also
use naı̈ve responses in association with plant odors [41,104–107].
Associative learning of odors is also widespread among both
classes of herbivore enemy, parasitoids and predators [108–110].
Rewarding experiences, such as successful oviposition (parasitoids)
or prey capture (predators), in association with plant odors, have a
positive impact on foraging behavior by enhancing innate or naı̈ve
attraction to the compounds involved or generating attraction to
blends that are not attractive to inexperienced individuals. Unrewarding experiences do not seem to affect naı̈ve responses or
produce aversive learning [47,110], but encountering sequential
unrewarding and then rewarding experiences enhances the learning
value of positive associations [111,112].
Initial predictions assumed that learning of plant odors would
have a high adaptive value for generalist predators and parasitoids.
By contrast, specialized species were expected to exhibit a naı̈ve
response to odors and have little learning abilities [9]. However, it
now appears that all herbivore enemies make use of both naı̈ve and
learned responses, and their relative importance cannot be
explained solely on the basis of dietary specialization [50].
Furthermore the great variation in learning rate and memory
formation between closely related parasitoid species [65,113,114]
hints that the trade-off between learned and naı̈ve responses reflects
adaptations to specific ecological constraints [115].
307
Review
plant volatiles may not be static for a single insect, given
that enemies may be able to learn to associate odors with
prey or hosts (Box 3).
Concluding remarks and outlook
Ever since the first reports of the attraction of herbivore
enemies to volatile blends released from plants after herbivore damage, researchers have marveled at the complexity
of these blends and wondered whether blend composition
could be an informative cue for enemies in host and prey
searching. Here, we highlighted the potential information
content of volatile blends for herbivore enemies as well as
the ability of herbivore enemies to detect them. Enemies not
only detect individual major and minor compounds of herbivore-induced blends, but also respond to the blend as a
whole depending on the ratios of the components present.
The sensory capabilities of herbivore enemies should facilitate their use of complex plant volatile information in
searching for prey and hosts, as documented in many reports
in the literature. The greater use of plant volatiles by
specialist as opposed to generalist enemies is consistent
with the greater need for detailed information in locating
specific target prey or hosts.
To further understand the value of herbivore-induced
volatiles in herbivore enemy attraction, it is essential to
discover whether herbivore enemy behavior is linked with
performance: Can the response to plant volatiles be directly correlated with increased success in prey and host
searching and, ultimately, reproductive fitness? It is also
important to learn whether the plants releasing the volatiles obtain any fitness benefits for themselves by recruiting herbivore enemies (Box 1). If so, volatile blends may
be under selection to maximize signal intensity, detectability, reliability and information content to herbivore
enemies.
In most reviews of plant–herbivore–herbivore enemy
interactions, researchers are encouraged to conduct more
experiments under natural conditions to better understand the ecological significance of the results obtained.
This should also hold true for investigations of herbivore
enemy attraction to plant volatiles, because emission patterns often differ between laboratory and field conditions
[3,4,6]. The use of naturally co-occurring species in such
studies is also desirable as it would allow inferences to be
made about the evolution of herbivore enemy–plant volatile interactions. Additional work with herbivore predators
is also called for given that plant volatiles have usually
been studied in relation to herbivore parasitoids rather
than predators. Because predators kill herbivores immediately, they may be of greater value to plants than koinobiont parasitoids and plants may have been under
particular selection pressure to attract them with volatiles.
Acknowledgments
The authors gratefully acknowledge the International Max Planck
Research School in Jena, Germany and other Max Planck Society funds
for support during the preparation of this review. We also thank Andreas
Reinicke for helpful comments on the manuscript.
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Review
Special Issue: Specificity of plant–enemy interactions
Association mapping of plant
resistance to insects
Karen J. Kloth1,2,3*, Manus P.M. Thoen1,2,3*, Harro J. Bouwmeester2,
Maarten A. Jongsma3 and Marcel Dicke1
1
Laboratory of Entomology, Wageningen University, P.O. Box 8031, 6700 EH Wageningen, The Netherlands
Laboratory of Plant Physiology, Wageningen University, Droevendaalsesteeg 1, 6708 PB Wageningen, The Netherlands
3
Business Unit Bioscience, Plant Research International, Wageningen University and Research Center, P.O. Box 619, 6700 AP
Wageningen, The Netherlands
2
Association mapping is rapidly becoming an important
method to explore the genetic architecture of complex
traits in plants and offers unique opportunities for studying resistance to insect herbivores. Recent studies indicate that there is a trade-off between resistance against
generalist and specialist insects. Most studies, however,
use a targeted approach that will easily miss important
components of insect resistance. Genome-wide association mapping provides a comprehensive approach to
explore the whole array of plant defense mechanisms in
the context of the generalist–specialist paradigm. As
association mapping involves the screening of large
numbers of plant lines, specific and accurate highthroughput phenotyping (HTP) methods are needed.
Here, we discuss the prospects of association mapping
for insect resistance and HTP requirements.
Enhancing host–plant resistance against generalist and
specialist insects
Host–plant resistance is one of the cornerstones of environmentally benign pest management systems [1,2]. Devastating pests and diseases only rarely occur in nature, which
is due to the tremendous degree of natural variation in
plant defense mechanisms [3,4]. Only a relatively small
degree of such variation is contained in cultivated crop
populations [5], but wild populations provide ample opportunities for discovering novel mechanisms responsible for
resistance to insects. A wide range of resistance mechanisms against herbivorous insects has been described [1,2],
and the impact of mechanisms depends on the characteristics of the herbivore, such as insect diet breadth [6,7].
Although specialist insects, feeding on one or a few plant
species within one family, are considered to be resistant to
toxic compounds of their host [8], generalist insects are
thought to thrive on a wider range of hosts with relatively
low levels of allelochemicals [9,10]. Toxins, however, affect
the performance of specialists as well [11], and generalists
can cope with variable levels of secondary metabolites [10],
implying a more complex relationship between insect host
range and plant defense. More insight into plant defenses
against specialist and generalist insects is needed to
*
Corresponding author: Dicke, M. ([email protected])
These authors contributed equally to this review.
understand how plants deal with herbivorous insects that
differ in the degree of specialization and to improve host–
plant resistance of economically important crops against
insect pests. Most studies have addressed this topic with a
targeted approach, focusing on only one or a few types of
secondary metabolites and a restricted amount of natural
variation therein. To unravel the paradigm about resistance against specialists and generalists and to identify
new plant defense mechanisms, comprehensive technologies are needed that can explore the apparent natural
variation in multiple resistance mechanisms at the level
of the genotype and phenotype.
Association mapping (see Glossary) allows screening of
many different wild and cultivated populations for genes
involved in complex plant traits. Although association
mapping has hardly been used in plant–insect studies
thus far, it has the potential to allow new developments
in eco-genomic studies of plant–insect interactions. One of
the major prospects is the possibility to do genome-wide
association (GWA) mapping to retrieve functional genetic
loci involved in plant defenses against herbivorous insects
in an untargeted way. GWA mapping involves the screening of large numbers of plant lines, which is currently a
bottleneck because of the costs involved in this time- and
Glossary
Association mapping: a population-based method of mapping quantitative
trait loci (QTLs) that takes advantage of historic linkage disequilibrium to link
phenotypes to genotypes (also known as ‘linkage disequilibrium mapping’).
Candidate gene: a chromosome region suspected of being involved in the
expression of a trait of interest.
Genome-wide association (GWA) mapping: comprehensive approach to
systematically search the genome for causal genetic variation, using a large
number of markers, by association between genotypes at each locus and a
given phenotype.
High-throughput phenotyping (HTP): experimental setup in which large
amounts of specimens can be phenotypically screened, preferably automatic,
fast, accurate and with low costs.
Linkage disequilibrium: two loci that are in linkage disequilibrium (LD) are
inherited together more often or less often than would be expected by chance.
QTL: quantitative trait locus, a region in the genome that is responsible for
variation in the quantitative trait of interest.
QTL mapping: a family-based mapping method using well-known pedigrees to
generate crosses in which the genetic architecture of traits can be explored
(also known as traditional linkage mapping).
Quantitative genetics: the study of the heritability of quantitative traits, which
are the products of two or more genes.
1360-1385/$ – see front matter ß 2012 Elsevier Ltd. All rights reserved. doi:10.1016/j.tplants.2012.01.002 Trends in Plant Science, May 2012, Vol. 17, No. 5
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Trends in Plant Science May 2012, Vol. 17, No. 5
labor-intensive methodology. The large number of plant
lines to be screened in insect resistance studies will require high-throughput phenotyping (HTP) techniques
that succeed in accurately identifying different resistance
traits. Particularly in view of the high diversity in insectresistance mechanisms and their degree of specificity
towards their enemies, this will pose some challenges.
In this review, we discuss the perspectives of GWA mapping and HTP techniques in the context of insect resistance, with special reference to strategies against
specialist and generalist insects.
Association studies and linkage mapping
Understanding the genetic basis of phenotypic variation is
one of the key goals in evolutionary biology. Family-based
quantitative trait locus (QTL) mapping (which uses wellcharacterized pedigrees [12–14]) and association mapping
(which uses linkage disequilibrium among numerous individuals of different populations [15,16]) are the most commonly used tools for dissecting the genetic basis of
phenotypic trait variation. In QTL mapping only a limited
number of recombination events that have occurred within
families and pedigrees can be studied, whereas with association mapping the recombination events that have accumulated over thousands of generations can be exploited
[17]. Since the 1980s, QTL mapping has been used most
frequently, but association mapping is a promising alternative method for dissecting complex traits [18,19]. Increased mapping resolution, reduced research time and
larger allele numbers have been put forward as main
advantages over traditional QTL mapping [17,20]. Association studies can be divided into two broad categories: (i)
candidate gene association mapping, in which variation
in a gene of interest is tested for correlation with the
phenotypic trait of interest and (ii) GWA mapping, where
genetic variation is explored within the whole genome,
aiming to find signals of association with the complex trait
[17] (see Table 1 for an overview). Because GWA mapping
is less dependent on prior information about candidate
genes than QTL mapping and candidate gene association
mapping, this is a promising method to identify novel loci
involved in complex phenotypic traits. However, GWA
mapping should not be regarded as a replacement of
traditional QTL mapping. In fact, GWA mapping and
QTL mapping have complementary advantages and disadvantages, which can lead to a better understanding of
causal genetic polymorphism when these approaches are
combined [18,21].
Association mapping in plant sciences
In the past decade, GWA mapping has emerged as a tool for
studying the genetics of natural variation and economically important traits in plants [16]. Flowering time, chemical
composition, disease resistance, taste and many other
economically and evolutionarily important traits have
been studied in crop species (see [17] for an overview).
Apart from agriculturally relevant crops, the model plant
Arabidopsis (Arabidopsis thaliana) is of great value for
understanding complex traits using GWA mapping (Box 1).
The presence of recombination events that have accumulated in plants over thousands of generations is both an
advantage as well as a potential pitfall of GWA mapping,
because functional QTLs that are correlated with population structure can result in many false positives [21].
Several statistical methods have been developed that
use neutral genotypic information to account for confounding effects of population structure in GWA studies [22–24].
However, inadequate use of these models can lead to
Table 1. Comparison of family-based (QTL) and population-based (association mapping) methods that aim to unravel the genetic
basis of complex traits in plantsa
Main advantages
Main disadvantages
General requirements
Recent case study
in Arabidopsis
QTL mapping b
No population structure effects
Identification of rare alleles
Few genetic markers required
Limited genetic diversity
Not always possible to create crosses
Cannot distinguish between pleiotropic
and physically close genes
Small ‘original population size’, low
number of genetic markers, many
replicates needed
Generated mapping material, e.g.
F2 population, (AI-)RILs, MAGIC lines,
NILs, HIFs, etc.
QTL mapping with AI-RILs on flowering
time [14]
two AI-RIL populations (approximately
280 individuals each)
181 and 224 markers
12 to 70 replicates
Candidate gene association mapping
Allows fine mapping
Relatively low costs
Detailed functional knowledge
of trait is required
No novel traits will be found
Large population size, small
number of genetic markers,
the bigger the population size,
the less replicates needed
Prior genetic and biochemical
knowledge on trait of interest
Prior knowledge on LD, nucleotide
polymorphism, breeding system
and population structure
Candidate gene approach on
flowering time [86]
251 accessions
51 SNPs
10 replicates per accession
Genome-wide association mapping
Allows untargeted fine mapping
(blind approach)
Detection of common alleles
Confounding effects due to
population structure
Will miss rare and weak effect
alleles
Large population size, many genetic
markers, the bigger the population
size, the fewer replicates needed
Prior knowledge on LD, nucleotide
polymorphism, breeding system
and population structure
Whole-genome approach on multiple
phenotypic traits [16]
199 accessions in total
216 150 SNPs
Four replicates in general
a
Combinations of these three approaches can allow the identification of false positives and negatives, but is much more laborious: a recent dual QTL mapping–GWA study
[26] involved phenotyping nearly 20 000 individual plants, including 184 worldwide natural accessions genotyped for 216 509 SNPs and 4366 RILs derived from 13
independent crosses. See [25] for an overview of different linkage mapping populations mentioned in this table.
b
Abbreviations: AI-RIL, advanced intercross-recombinant inbred line; HIFs, heterogeneous inbred family; LD, linkage disequilibrium; MAGIC, multiparent advanced
generation intercross; NIL, near-isogenic line; QTL, quantitative trait locus; RIL, recombinant inbred line; SNPs, single nucleotide polymorphisms.
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Box 1. Arabidopsis–insect interactions as a model for GWA
studies
The model species, Arabidopsis (Arabidopsis thaliana), is often used
in plant–insect studies for obvious reasons, such as the availability
of extensive information about genetic variation and physiology,
and numerous mutants. Even though Arabidopsis is not a crop,
there are numerous devastating crop pest insects (such as the
generalist insect herbivores Frankliniella occidentalis and Myzus
persicae and the specialist insect herbivores Pieris rapae, Plutella
xylostella and Brevicoryne brassicae) that readily feed on Arabidopsis [41,62,87–89]. However, one disadvantage in the light of
insect–plant biology is that many accessions of Arabidopsis are
winter annuals, so the life cycle of Arabidopsis does not temporally
overlap with the life cycle of many herbivorous insects. It is known
that herbivore performance (quantified in terms of mortality and
developmental time) is commonly better on plants with such a
‘pausing’ strategy, indicating that such plants may invest less in
defense traits [90]. This has probably influenced the evolution of
signaling pathways in Arabidopsis, because the main biotic stresses
probably comprise pathogens such as oomycetes, bacteria and
fungi. Still, Arabidopsis is of great interest for studying insect
resistance, because many insect defense mechanisms have been
evolved within the Brassicaceae family, such as glucosinolates
[6,7,29], and many defense mechanisms against pathogens are also
effective against herbivorous insects. Leaf toughness is, for
example, effective against both microbial pathogens and insects
[2], and salicylic acid-, jasmonic acid- and ethylene-regulated
defenses are involved in defenses against both pathogen and insect
infestations [6,87,91,92].
overcorrection, resulting in false negatives which are
equally problematic [21]. Studies that have combined
GWA and QTL mapping strategies (dual linkage association mapping) revealed a false positive rate of 40% and a
false negative rate of 24% in assays that solely involved
GWA mapping [25]. A major drawback of such a dual
linkage association mapping, however, is that it requires
phenotyping of several thousands of individual plants and
the genesis of numerous linkage mapping populations [26].
GWA mapping in regional mapping populations (instead of
GWA mapping at the species scale) is an alternative
approach to reduce confounding due to population structure [25].
Another major impediment in GWA studies is the phenomenon of missing heritability. Often, the associated
QTLs can explain very little of the phenotypic variation,
even after accounting for the effects of population structure. This phenomenon is attributed to several factors,
including a scattered signal across numerous QTLs, each
contributing to only a marginal proportion of the phenotype. Complex traits, such as insect resistance, are likely to
encounter this problem [27,28]. Integrating association
mapping with transcriptional network analysis can decrease high false positive rates and increase the resolution
in scattered associations [19]. The scattering of genotype–
phenotype associations can also be reduced by phenotyping
multiple component traits instead of one multifactorial
trait, as will be further discussed in the section ‘Requirements for phenotyping’.
Association mapping of plant–insect interactions
The complexity in the orchestration of insect resistance
and its evolution in plants make it a difficult trait to study
in a genomic context [4]. So far, only few GWA studies have
Trends in Plant Science May 2012, Vol. 17, No. 5
been reported that deal explicitly with plant defense mechanisms against herbivorous insects (see [16] for an example
on aphids). One such study on glucosinolates (GSL) –
secondary defense metabolites within the Brassicaceae
family involved in resistance against herbivorous insects
[6,7,29] – was conducted using 96 Arabidopsis accessions
exhibiting 43 distinct GSL phenotypes and 230 000 single
nucleotide polymorphisms (SNPs) [18]. In this study, GWA
analysis successfully identified two major polymorphic loci
controlling GSL variation in natural populations, but variation in resistance to specialist and generalist insects
remains to be investigated for these accessions. This would
require an experimental setup in which GWA mapping and
HTP of insect resistance are integrated (Figure 1).
GWA mapping of insect resistance will probably encounter similar obstacles as recognized in other GWA
studies. Because insect resistance is generally under
strong positive selection pressure, GWA mapping of insect
resistance might, however, unlike GWA studies of human
diseases [15,27], be less affected by rare alleles that are not
included in the haplotype map. Nevertheless, a good representation of all (sub)populations is indispensable for
[(Figure_1)TD$IG]
(a)
(b)
(c)
Preference
#Eggs
Performance
Feeding
damage
(d)
Mobility
ATCCAGCT
ATCCTGCT
(e)
Overexpression
Gene silencing
TRENDS in Plant Science
Figure 1. Screening plants for insect resistance through GWA mapping. This
simplified overview shows how the genetic architecture underlying insect
resistance can be determined in five steps, using GWA mapping. (a) Genotype
SNPs for numerous accessions of the plant of interest; (b) develop HTP choice and
no-choice experiments to screen for insect preference and performance (using leaf
discs in this example); (c) screen for relevant insect-resistance parameters; (d) find
the genetic basis of phenotypic differences, using GWA mapping; (e) validate
candidate genes with reverse genetic tools, such as overexpression and gene
silencing.
313
Review
detecting variation in host–plant resistance and preventing them from having a too low allele frequency in the
experimental setup. Particularly, the confounding effects
of population structure can have a large effect on the
success of GWA studies of host–plant resistance, because
resistance against specific insects could have evolved independently and be based on different mechanisms in
different populations and habitats [30]. Moreover, confounding effects due to strong population differences can
be severe, when an intense evolutionary arms race between plant and herbivore has occurred as may be the case
for specialist herbivorous insects and their host plants
[30–33]. This will require statistical correction of population structure, which can enhance the chance of false
negatives due to overcorrection. This problem is expected
to be less evident with generalists, because they lack a
reciprocal evolutionary interaction with specific plants [9].
Resistance against specialist versus generalist insect
herbivores
Specialist and generalist insect herbivores have different
ways to deal with the defensive mechanisms of their host
plants, and this is expected to result in different associations. In addition to morphological and structural aspects,
chemical defenses involving secondary metabolites play a
major role in plant defense against insects [2]. Secondary
metabolites can be divided into two broad functional categories, based on their modes of actions: qualitative compounds, which can be interpreted as toxins, and
quantitative defensive compounds, with a dose-dependent
effect, such as digestibility reducers [9]. Recent studies
show that qualitative compounds (e.g. GSL and alkaloids)
often fail to affect specialist insects, because specialist
insects evolved ways to detoxify or tolerate these compounds [9]. In other words, if secondary metabolites play
a role in defense against specialist insects, predominantly
quantitative defensive compounds that reduce the digestibility are expected to be functional, whereas defense
against polyphagous insects is mainly achieved by qualitative compounds. Toxins are even used by specialist
insects to locate their host plants or sequester these toxins
for their own defense [1,2]. Thus, plants have to ‘choose’
between investing in substantial concentrations of qualitative compounds to deter polyphagous insects or marginal
concentrations of the same compounds to decrease preference by specialist insects [34,35]. The evolution of defensive traits against generalists could, therefore, lead to an
increased host–plant preference by specialists and vice
versa. This trade-off between resistance to specialists
and generalists is expected to be reflected in genotype–
phenotype associations of the host plant.
There are, however, many examples that do not support
the qualitative–quantitative dichotomy. The generalist
aphid Myzus persicae feeds on herbaceous plants in over
40 plant families, including families such as the Solanaceae
that are well-known producers of toxic alkaloids [10,36].
Moreover, specific toxins do affect specialist herbivores. For
instance, silencing nicotine production in tobacco (Nicotiana
tabacum) results in improved performance of the specialist
herbivore Manduca sexta [37] and overexpression of the
lectin agglutinin in tobacco negatively affected the larval
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Trends in Plant Science May 2012, Vol. 17, No. 5
performance of M. sexta [11]. Isothiocyanates, breakdown
products of GLS, negatively affect the performance of the
specialist herbivore Pieris rapae [38]. The performance of P.
rapae on the coi1 mutants of Arabidopsis, which is compromised in the JA signal transduction pathway, is significantly improved in comparison to wild-type plants, showing that
even a specialist is affected by inducible plant defenses [39].
Interestingly, the effects of quantitative and qualitative
defenses may interact: nicotine prevents a compensatory
response of the generalist herbivore Spodoptera exigua to
proteinase inhibitors and thus counters an insect adaptation to a qualitative defense [40].
The main deficiency in addressing the defense mechanisms of plants against specialist and generalist insect
herbivores is that most studies have used a targeted
approach, focusing on only one or a few types of secondary
metabolites in a limited number of plant lines. Because
resistance and tolerance are likely to be phenotypic traits
that are composed of multiple factors, a targeted approach
will easily miss important components. This is true for
resistance to both generalists and specialists, but comparing the components and their relative strength of resistance to specialists and generalists may reveal how these
traits are balanced.
A more comprehensive approach is, for example, taken
in transcript profiling studies, where gene expression signatures of infested plants and/or herbivorous insects are
analyzed in different treatments [39,41–43]. Although several studies did not find a different plant response to
specialist and generalist insects [39,41], one study [42]
found a differential response in the insects that foraged
on wild-type and mutant Nicotiana attenuata. The specialist M. sexta showed diet-specific alterations in gene expression, whereas the generalist Heliothis virescens regulated
similar transcripts over different diets, indicating that the
specialist is better adapted to both qualitative (nicotine)
and quantitative (trypsin protease inhibitor) compounds of
the host [42]. Another explorative approach is taken in a
recent study, where metabolite fingerprints of Plantago
lanceolata leaves differed after they were attacked by
specialist or generalist herbivores, and by insects belonging to different taxa [43]. These examples show that
untargeted approaches, such as transcript profiling, metabolic fingerprinting and GWA mapping, allow exploring a
large array of plant defense mechanisms in many plant
lines.
Requirements for phenotyping
Phenotyping is a prime factor in GWA mapping of host
plant resistance. Among vast numbers of genome-wide
markers, the aim is to achieve significant statistical power
for only those molecular markers that are located close to
the genes that influence the phenotypic trait of interest. In
reality, functional associations between phenotype and
molecular markers are often confounded, both in association and QTL mapping studies [18,44,45].
In the discussion about missing heritability of associations, where the identified genetic loci explain only little of
the phenotypic variation, little attention has been paid to
the role of phenotypes and phenotyping techniques. Some
association studies of crop yield, for example, resulted in
Review
Trends in Plant Science May 2012, Vol. 17, No. 5
the characterization of numerous minor functional genes
[46]. This confirms the infinitesimal model of Fischer [47],
which assumes a very large number of loci to be involved in
quantitative genetics, each with a marginal effect on the
phenotype. It is to be expected, however, that the number
of functional (low effect) QTLs involved is trait-specific. A
complex trait is generally the result of numerous processes,
which will result in a scattered association across multiple
genetic loci: numerous QTLs are involved, which have a
reduced statistical significance and each contribute to only
a marginal proportion of the effect size of the phenotypic
variation (Figure 2). Although a multifactorial character is
inherent to complex traits, the efficiency of association
mapping can be optimized by dissecting the phenotype
into quantitative components with a minimum expected
number of responsible mechanisms [48]. A genome-wide
screening within the scope of only a few mechanisms
attributing to the trait of interest will increase the success
of finding novel functional genes. A drawback is that it
narrows the scope of a genome-wide survey. Complex traits
are generally based on gene networks; therefore, the assessment of individual components will probably overlook
interactions between components, and the network as a
whole and its environment [49,50].
In insect resistance studies, typically multiple traits are
phenotyped and reduced to one resistance variable, R.
Most often, the total of life history parameters of the insect
are summarized in the variable rm, the intrinsic rate of
population increase [51,52]. This summary statistic is an
accurate parameter of the effect of resistance mechanisms
on the herbivorous insects. However, insect performance is
typically dependent on multiple plant traits (e.g. nutritional components of the host plant and multiple resistance
mechanisms of the plant [52]). Hence, rm may lack resolution in association studies and using this parameter may
[(Figure_2)TD$IG]
(a)
result in a high proportion of missing heritability due to
scattered signals (Figure 2). We expect that dissecting the
complex parameter in multiple specific phenotypic components, e.g. host preference, time interval before the insect
starts feeding, reproduction, larval development time and
mortality, will contribute to solving the problem of missing
heritability and will help to identify multiple underlying
mechanisms (Figure 2). The combination of these individual mechanisms will ultimately allow plant breeding to
achieve sustainable host–plant resistance in crops. Indeed,
multiparameter approaches, using a combination of phenotypic traits, for example both concentration of secondary
metabolites and insect performance, have been postulated
to deliver more significant relations to functional genetic
data [50,53]. Apart from the parameterization of the phenotype(s), increasing the number of plant lines is of major
importance for the statistical support of relevant associations [15,27]. So far, most studies have used sample sizes of
approximately 100 to 500 plant lines, but more genetic
lines will increase the number and frequency of functional
alleles and thereby improve the statistical power to detect
them [18,24,44,54,55]. Secondly, a larger number of replicates within plant lines will increase the accuracy of the
phenotype and the statistical support of genotype–phenotype associations. Particularly phenotyping insect resistance, involving the interaction among two or more
organisms and species, is sensitive to stochastic errors
and could result in relatively high levels of missing heritability. Although there is an example of successful GWA
mapping by assessing aphid offspring in four replicates on
96 Arabidopsis lines [16], more replicates will reduce confounding effects. Moreover, the quality of phenotypic data
can be improved by eliminating noise induced by the
environment [50,56]. Many studies have shown that insect
resistance is an adaptive response to several biotic and
(b)
Preference
Time before feeding
Leaf
toughness
VOCs
Leaf
toughness
VOCs
Plant
surface
Feeding
deterrents
Plant
surface
Nutrients
Feeding
deterrents
Insect life history
parameter rm
Nutrients
Larval development
Key:
Association signal
Egg
Larva
Pupa
Adult
Genome
Markers associated
with functional gene
TRENDS in Plant Science
Figure 2. Dissecting insect resistance into component traits. Association mapping of a complex trait such as insect resistance can result in numerous associations with low
statistical power. This is illustrated in (a) where the life history parameter rm of the insect is associated with many genetic loci. One approach to improve resolution in
genotype–phenotype associations is to dissect the complex phenotype into component traits (b), e.g. insect preference (detection of repellent VOCs), time before the insect
starts feeding (screening for the influence of leaf toughness and deterrent structures on the plant surface) and larval development (detection of, e.g. feeding deterrents,
toxins and nutrient content). Whereas the genetic architecture can overlap to some degree due to similar underlying processes, mapping these component traits will result
in fewer genotype–phenotype associations with larger statistical power and a higher proportion of functional associations. Genotype–phenotype associations can be
further elucidated with, for example, metabolite fingerprints of VOCs, plant tissues or epicuticular waxes.
315
Review
Box 2. Plant resistance to herbivorous insects
Host–plant resistance against herbivorous insects is generally
defined as ‘the relative amount of heritable qualities possessed by
the plant which influence the ultimate degree of damage done by
the insect in the field’ [1]. Herbivorous insects use host plants for
oviposition, feeding and shelter. Plants can achieve protection
against herbivorous insects by both indirect defense, i.e. the
attraction and facilitation of natural enemies of the insect herbivore,
and direct defense against the pest insect [93–95]. Three main
categories of resistance against insect herbivores are (i) antixenosis,
(ii) antibiosis and (iii) tolerance [1].
Antixenosis mechanisms deter the insect or, after the insect has
arrived on the plant, prevent it from settling. Generally, the insect
‘decides’ not to colonize the plant due to the absence or low
availability of an attractant, or the presence or quantity of a
deterrent. A wide range of components can act as attractants or
deterrents: volatile organic compounds (VOCs), color, topology of
the plant, chemicals and morphology of the plant surface (e.g.
trichomes, epicuticular waxes, substrate texture), and physical and
chemical characteristics of internal plant tissues (e.g. secondary
metabolites, nutrient content, toughness of the cell wall) [2].
Herbivorous insects use olfactory and visual cues in the prealighting stage, and assess olfactory, visual, tactile and gustatory
traits after arriving on the host plant. Plants that exhibit antixenosis
have a reduced number of initial colonizers and a relatively small
population of herbivorous insects.
After the insect has ‘decided’ to utilize the host, antibiosis
mechanisms of the host can affect insect performance (e.g. growth,
development, reproduction and survival) by toxins released after
tissue damaging, feeding deterrents (e.g. protease inhibitors),
nutritional imbalance or tissue toughness. Antibiosis causes a
decrease in the insect population size [1]. Plants can display
antixenosis and antibiosis mechanisms constitutively or after
induction by, e.g. herbivory or egg deposition [93,96].
Finally, tolerance represents the plant’s ability to compensate
insect damage by increased growth, reproduction or repair of the
damage. In contrast to antixenosis and antibiosis, tolerance does
not severely affect the insect herbivore, but rather minimizes the
impact of herbivory on the performance of the plant itself [1,2].
abiotic factors (Box 2) [19,35,57,58]. For example, it has
been shown that the developmental stage of the plant
altered the outcome of the GWA analysis, resulting in
the identification of different functional genetic loci in
different developmental stages [19]. This underlines the
need for an experimental setup with uniform conditions
among the genetic lines (Figure 1). Some noise will be
inevitable for plant species harboring a high diversity of
ecotypes that differ in optimal growth conditions and
development time. By contrast, ‘uniform’ laboratory assays
can deliver functional associations different from field
conditions [16] due to genotype-by-environment interactions [49]. Including several (a)biotic treatments or an
additional field assay could yield more field-predictive
outcomes.
High-throughput phenotyping
For quantitative traits such as insect resistance, reliable
phenotyping requires a substantial amount of space, time
and manpower, and this will be increasingly so in the
context of association studies that require large sample
sizes. There is, thus, a need for HTP methods that are
accurate and yet predictive of field performance. Particularly, in view of the differential impact of mechanisms to
specialist and generalist insects as discussed earlier, insect
and plant performance are not necessarily correlated with
316
Trends in Plant Science May 2012, Vol. 17, No. 5
each other, as high levels of deterrent compounds do not
always negatively affect the performance of herbivorous
insects [34,35], and good insect performance does not
always result in reduced plant performance (Box 2). Therefore, both plant and insect traits are relevant for assessing
the underlying mechanisms of insect resistance. Because
association mapping requires at least hundreds of plant
lines to be screened, it poses some challenges to the phenotyping efforts. Below some potential HTP techniques for
assessing insect resistance are discussed.
High-throughput phenotyping of plant defense
In the past decades, plant phenotyping techniques have
gone through major developments [59,60]. Several of these
methods can be applied to detect antixenosis, antibiosis or
tolerance against insects and the benefits and costs involved for the plant (Box 2). Metabolite profiling techniques, such as mass spectrometry and nuclear magnetic
resonance, are the most obvious methods for screening
primary and secondary proteins and metabolites in
large-scale experiments [60]. However, image processing
techniques are also highly suitable for HTP platforms [59–
61]. These techniques translate changes in the spectral
signature of a plant to quantify characteristics concerning
plant growth, yield and (a)biotic stress. In the visible
spectrum it is possible to detect damage caused by leafchewing insects or for example silver damage due to thrips
feeding [62]. Multicolor fluorescence imaging has been
used to assess feeding damage of mites and stylet penetrations of whiteflies [63]. In the near-infrared spectrum,
stress-related changes in plants and changes in organic
compounds can be detected [64–67].
High-throughput phenotyping of insect performance
and preference
Assessing insect performance rather than that of the host
plant, delivers the opportunity to study the direct and
indirect impacts of plant nutritional quality and defense
mechanisms on the dynamics of the herbivore population
[52]. Although a wide variety of insect phenotyping techniques is available, only a marginal proportion of these
techniques is translated into high-throughput devices.
This field in particular faces some challenges in developing
methodologies that have low demands in terms of space,
time and labor but yet are accurate and predictive of field
performance.
Most insect studies have focused on insect performance,
e.g. population density, insect growth, development rate,
fecundity, survival and the intrinsic rate of population
increase (rm) [52]. These parameters are correlated to both
antixenosis and antibiosis. Assessing insect performance
can be time consuming, depending on the generation time
and life cycle of the insect, and is usually done in a
nonautomated way [51,68]. Image analysis of photographs
or videos represents potential for automated indexing of
insect performance parameters (e.g. the number of eggs,
larvae and surviving adults).
A behavioral assay can, in contrast to just monitoring
insect performance or plant traits, result in a detailed
chronological dataset of the process of host selection and
food uptake. An additional advantage is that a behavioral
Review
assay can potentially be much shorter than an end-point
measurement of reproduction and survival [69,70]. Food
uptake is an important aspect of insect behavior, related
to insect performance and host–plant resistance [52]. Electronic monitoring of probing behavior in piercing–sucking
insects has proven to be successful in finding feeding deterrents [71,72], but is hardly feasible in large-scale experiments necessary for association mapping. Alternatively,
automated tracking of insect behavior allows measuring
multiple factors involved in host selection, e.g. host preference, mobility of the insect and the timing and duration of
food uptake. An additional advantage is that it allows
screening the behavior of multiple individual insects and
multiple arenas simultaneously [73–78]. The major challenge of high-throughput video-monitoring of insect behavior is to realize two- or three-dimensional arenas predictive
of field performance [79]. In large-field trials, a mark–release–recapture technique can be a cost-effective method to
assess host preference and population growth of insects [80].
Ultimately, the choice of a phenotyping technique will
largely depend on the study system and research focus.
Future perspectives
The development of accurate and field-predictive HTP will
allow GWA mapping to increase insight into the genetic
architecture of plant resistance to generalist versus specialist insects that will contribute to the development of
host–plant resistance in crops. ‘Blind’ screening, unbiased
by parental phenotypes and candidate genes, is the basis of
this method and opens the opportunity to analyze the full
scope of existing natural variation in resistance mechanisms. Although current studies mainly focus on one or a
few candidate mechanisms, the untargeted nature of GWA
mapping will include multiple factors that contribute to
resistance against generalist and specialist herbivores. We
expect that the current assumptions about differential
resistance mechanisms against specialists and generalists
can be addressed more comprehensively using such an
unbiased approach. A further step forward will be the
integration of association mapping with transcriptomics,
proteomics and metabolomics, to assess insect resistance
at the levels of the genotype, gene expression, and metabolite and protein networks [15,19,27,81–84]. However, a
major determinant of finding phenotype–genotype associations is imposed by the plant species itself. At present, next
generation sequencing technologies result in an increasing
amount of sequenced plant species and lines within a
species, so that the scope of plant–insect association studies will be expanded to additional biological systems with a
wider array of plant–insect interactions and resistance
mechanisms. In the near future, the genomes and genetic
variation of an increasing number of insect herbivores will
also become available [85]. Comparing functional mechanisms in insect and plant populations at the genomic level
will allow the development of ecological insights into the
evolution of plant–herbivore interactions and will take
host–plant resistance studies to the next level.
Acknowledgments
We thank Fred van Eeuwijk and three anonymous reviewers for
stimulating discussions and comments on a previous version of the
Trends in Plant Science May 2012, Vol. 17, No. 5
manuscript. The Netherlands Organization for Scientific Research
(NWO) is acknowledged for funding through the Technology Foundation
(grant STW10989, Perspectief Programme ‘Learning from Nature’).
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