Introduction
Plant pathogens represent a constant and major threat to global food production, with
20%–30% global crop losses estimated, principally in food-deficit areas [1]. Pesticide
use, breeding of resistance genes, and genetic manipulation of plant immune components
have helped to mitigate this threat. However, rapid evolution of pathogen resistance
and virulence, together with host range expansion and host jumps, contribute to severe
disease outbreaks, especially in the context of current agricultural practices [2].
This underscores the need to reduce the lag time between the appearance of new diseases
and development of protective measures effective on a broad range of pathogens and
host plants.
In this context, microbial products and inoculants for plant protection have recently
gained attention thanks to the large efforts made to systematically isolate, identify,
and characterize plant-associated microbes that engage in intimate association with
healthy plants [3]. Recent findings indicate that individual- and community-level
features provided by plant microbiota members can confer extended immune functions
to the plant host. Importantly, the traits lent by “beneficial” microbes strongly
depend on the interplay between the soil nutrient status and the plant immune system
[4, 5]. Thus, the successful implementation of microbiota-mediated disease protection
will depend on our mechanistic understanding of how microorganisms interact with their
hosts and with one another in natural environments. Here, we seek to synthesize the
state of this cross-disciplinary field to bridge the gap toward rational design of
synthetic microbial communities (SynComs) [6] with broad, durable, and flexible plant
protective activities.
Microbiota-modulated immunity (MMI) enhances disease resistance
Land plants have evolved a complex innate immune system comprising membrane-localized
receptors (pattern recognition receptors [PRRs]) and intracellular receptors (nucleotide-binding
domain and leucine-rich repeat-containing receptors [NLRs]) that detect apoplastic
and cytoplasmic elicitors, respectively, and activate immune outputs against microbial
pathogens (
Fig 1
). Although some studies identified commensals that are able to evade PRR-triggered
immunity, the conserved nature of microbe-associated molecular patterns (MAMPs) suggests
that PRRs cannot efficiently discriminate commensal from pathogenic microbes [7].
Because healthy plants in nature are extensively colonized by these commensals, it
is conceivable that the immune system and the microbiota may instruct each other [7]
beyond the simple coevolutionary arms race between plants and pathogens. Consistent
with this observation, recent evidence indicates that bacterial commensals can activate
or suppress plant immune responses [8, 9] and that an intact immune system is required
for the accommodation of beneficial microbes [10, 11].
10.1371/journal.ppat.1007740.g001
Fig 1
Microbiota-mediated extension of the plant immune system.
Extracellular PRRs recognize MAMPs and DAMPs at the cell membrane. Intracellularly,
NLRs recognize pathogen effectors either directly or indirectly by monitoring host
proteins targeted by effectors. This innate immune system is modulated by the microbiota
(i.e., MMI), which induces systemic resistance and enhances plant resistance to pathogens.
In addition, the plant microbiota provides direct protective activity against microbial
pathogens via DMC. DMC includes competition for nutrients and space as well as secretion
of antimicrobials, and these interactions are integrated into a complex network that
dictates pathogen growth in planta. ADP, adenosine diphosphate; ATP, adenosine triphosphate;
DAMP, damage-associated molecular pattern; DMC, direct microbial competition; NLR,
nucleotide-binding domain and leucine-rich repeat-containing receptor; MAMP, microbe-associated
molecular pattern; MMI, microbiota-modulated immunity; PRR, pattern recognition receptor.
Root microbiota–mediated stimulation of plant innate immunity has been extensively
described to confer resistance against various microbial leaf pathogens (a phenomenon
referred to as priming or induced systemic resistance [ISR]) [12, 13]. ISR has been
well described in Arabidopsis thaliana, and the identified mechanisms controlling
its onset appear to be conserved for different organisms. Particularly, the transcription
factor MYB72 plays a key role in the regulation of ISR triggered by Trichoderma spp.
fungi and the bacterium Pseudomonas simiae [14, 15]. Interestingly, MYB72 is also
involved in A. thaliana’s response to iron deficiency [16], suggesting a direct interplay
between nutrient stress and immunity. ISR may occur because plants have evolved to
use microbial molecules as developmental signals for plant immune system maturation
[17], implying that early contact with microbe-derived molecules is needed for plant
survival in natural soils. Recent studies indicate that A. thaliana leaf infection
by the biotrophic pathogens Pseudomonas syringae and Hyaloperonospora arabidopsidis
results in root recruitment of beneficial ISR-inducing bacteria [18, 19], mediated
by modification of root exudation profiles [18]. Remarkably, the presence of ISR-inducing
strains can further drive root secretion of antimicrobial coumarin compounds that
shape the root microbiota and mobilize potentially beneficial bacteria, including
ISR-inducing strains [20]. This suggests a self-reinforcing immunity and recruitment
loop, which constitutes a promising tool to manipulate beneficial microbiota functions
for stable plant protection across generations. Interestingly, the same exudates that
shape root microbiota often function in abiotic stress responses [21, 22], reflecting
that root recruitment systems—similar to the plant immune system itself—have evolved
multiple functions.
Direct microbial competition (DMC) supplements plant innate immunity
Direct pathogen suppression by microbiota members has been repeatedly reported in
plant roots [23–26] and leaves [27, 28]. These interactions are highly diverse and
include secretion of antimicrobial compounds [28, 29], hyperparasitism [30], and competition
for resources like nutrients or space [31], which ultimately mitigate pathogen growth
(
Fig 1
). Recently, it has been shown that although roots of A. thaliana are colonized by
deleterious filamentous eukaryotes in natural populations, plants remain healthy because
of the presence of coresident bacteria that maintain fungal balance in plant roots
and promote host survival [32]. This raises the possibility that, in nature, plants
may equally rely on their resident bacteria and immune system to restrict pathogen
invasion. In tomato, microbiota structure and composition are predictors of disease
resistance, but trophic network architecture, which describes how resident microbiota
and pathogens interact with each other in terms of resource usage, was equally important
[33]. At the community level, DMC constitutes a complex network of interactions that
determines microbes’ coexistence and thereby ultimately dictates pathogen invasion
in plant tissues (
Fig 1
). Thus, it is becoming increasingly clear that potential applications for plant protection
depend on the understanding and manipulation of multispecies and multikingdom interactions
that are critical for microbiota-mediated disease resistance.
Rationally designed SynComs can provide robust plant protection
Single strains carrying specific functions under laboratory conditions have already
been used for their biocontrol activities under field conditions, but they provide
beneficial activities only in some cases [3]. This is due to the fact that microbiota-mediated
disease resistance occurs at the interface between plants and their local environments,
where plant-associated communities of prokaryotic and eukaryotic microbes are hyperdiverse
and changing over time. Therefore, how can microbiota-encoded traits like MMI or DMC
be successfully maintained in the field to increase crop resilience? Several examples
suggest that defined SynComs could confer more efficient plant protection than individual
strains [24, 32] and argue for the use of native, locally adapted plant-associated
microbes for efficient plant protection in the field [24, 32, 34]. The recent demonstration
that most abundant plant-associated microbes can be isolated and maintained axenically
[32, 35] opens new avenues to systematically screen for desirable traits using microfluidic
systems, high-throughput screens, or microbiota reconstitution experiments with SynComs
and germ-free plants (
Fig 2
). Thus, the design of SynComs based on traits involved in MMI and/or DMC represents
a promising direction to achieve robust plant protective activities against pathogens.
Here, we illustrate two emerging tools and concepts that could lead toward a more
targeted screening approach and to higher SynCom success in the field (
Fig 2
).
10.1371/journal.ppat.1007740.g002
Fig 2
Rational design of SynComs with predictable pathogen biocontrol activities.
Starting in the field where a pathogen outbreak has occurred, the microbiota of diseased
and healthy plants are characterized and isolated. The isolates are screened in binary
microbe–microbe and in planta ternary interactions to detect and catalogue traits
linked to DMC and MMI. From the obtained catalogue, activities of individual strains
can be used to design more complex SynComs while taking into account trait redundancy,
dominance, and modularity. In parallel, interaction networks are inferred from sequencing
data, and potential key organisms are identified based on hub structural properties
or functional modules. This network inference helps with prioritizing candidate strains
for targeted screening of DMC and MMI traits. Both trait-based and structure-based
approaches can inform the rational design of SynComs with stable and effective biocontrol
activities in the field. DMC, direct microbial competition; MMI, microbiota-modulated
immunity; SynComs, synthetic microbial communities.
The robustness of beneficial microbial trait expression is increased by redundancy
and dominance
In order to design SynComs with predictable beneficial outcomes for plant health in
the field, understanding whether selected traits are retained in a community context
is essential. Specifically, traits that are redundant (i.e., expressed by multiple
strains) and dominant (i.e., expressed whenever one strain has the respective function)
across SynCom members are likely to lead to robust expression of the trait in a community
context (
Fig 2
). In plants, several bacterial root commensals belonging to the families Comamonadaceae
and Pseudomonadaceae were able to protect A. thaliana against root-derived filamentous
eukaryotes, illustrating how one functional trait can be expressed by phylogenetically
distant bacteria [32]. Because deployed SynComs will be challenged by many environmental
microorganisms, redundant traits also need to be dominantly expressed in multiple
contexts. Garrido-Oter and colleagues (2018) [8] observed that root growth promotion
and MAMP response suppression by Rhizobiales root commensals are dominant traits,
the corresponding functions being expressed regardless of the presence of another
bacterial cocolonizer. These advances lend additional support to the idea that traits
observed in monoassociation with the host can at least partially inform the design
of SynComs with predictable outcomes, as was recently reported [36]. However, even
if the observed root growth promotion and MAMP response suppression phenotypes observed
in monoinoculation often predict dual-inoculation outcomes, some combinations can
result in synergistic effects [8]. Importantly, the emergence of such new properties
is a phenomenon that cannot be predicted from trait screening in binary interactions
but that can be tested experimentally in a SynCom context (
Fig 2
).
Network analysis as a tool for the design of stable and predictable SynComs
Screening for MMI and DMC traits can be a highly time-consuming approach considering
the very high diversity of microbes that can be isolated from plants. Analytic tools
have been recently developed that can be used to target such screening and to improve
the likelihood of SynCom success in the field. A good example is the significant progress
made in ecological network inference. In short, inferred networks break down complex
ecological systems into “interactions” (usually correlations) between measured entities,
revealing, for example, how microbial taxa, genes, and abiotic factors are linked
to host phenotypes. Analyzing these networks guides the formation of testable hypotheses
about which (groups of) entities drive phenotypes ahead of lab screening (
Fig 2
). For example, this approach was used to identify potential bacterial antagonists
of oak powdery mildew by characterizing plants between their healthy state and during
disease progression [37]. Inferred networks of microbial interactions have also been
used to reveal functionally related rhizosphere microorganisms [38] and keystone species
in microbial assemblies that influence community structures [39]. Given the influence
of keystone species, this approach can be used to identify possible points of control
to manipulate microbial diversity and to characterize strains based on their persistence
in the field. In summary, network inference provides the opportunity to take into
account the local context by linking microbial and abiotic factors to phenotypes,
providing a pathway to maximize SynCom persistence and trait expression success in
the field [39].
Concluding remarks
It is now critical to further develop bottom-up experimental approaches to improve
our understanding of dominance and modularity of desirable traits within SynComs.
Cataloguing such knowledge on microbial traits and their behavior in a community context
will allow the establishment of a microbial toolbox for microbiota-mediated pathogen
protection and the rational design of SynComs with modular functions related to MMI
and DMC. In parallel or in combination with this cataloguing, microbial network inference
will play a key role in identifying candidate biocontrol taxa and in the design of
SynComs with stable and predictable outcome in the field. In addition to the mentioned
benefits for direct application in the field, this approach is likely to provide insights
into the following unresolved fundamental questions: (1) Can we detect phylogenetic
signals for MMI and DMC traits that could be used to predict beneficial outcomes for
the plant host? (2) Do immunity-modulating microbes act through similar or different
immune pathways? And (3) as a consequence of the previous question, do the identified
MMI and DMC traits act independently, synergistically, or antagonistically?