The Role of Soil Microorganisms in Plant Mineral Nutrition—Current Knowledge and Future Directions

Abstract

In their natural environment, plants are part of a rich ecosystem including numerous and diverse microorganisms in the soil. It has been long recognized that some of these microbes, such as mycorrhizal fungi or nitrogen fixing symbiotic bacteria, play important roles in plant performance by improving mineral nutrition. However, the full range of microbes associated with plants and their potential to replace synthetic agricultural inputs has only recently started to be uncovered. In the last few years, a great progress has been made in the knowledge on composition of rhizospheric microbiomes and their dynamics. There is clear evidence that plants shape microbiome structures, most probably by root exudates, and also that bacteria have developed various adaptations to thrive in the rhizospheric niche. The mechanisms of these interactions and the processes driving the alterations in microbiomes are, however, largely unknown. In this review, we focus on the interaction of plants and root associated bacteria enhancing plant mineral nutrition, summarizing the current knowledge in several research fields that can converge to improve our understanding of the molecular mechanisms underpinning this phenomenon.

Full text

fpls-08-01617 September 14, 2017 Time: 17:15 # 1
REVIEW
published: 19 September 2017
doi: 10.3389/fpls.2017.01617
Edited by:
Girdhar Kumar Pandey,
University of Delhi, India
Reviewed by:
Raffaella Balestrini,
Consiglio Nazionale delle Ricerche
(CNR), Italy
Ingo Dreyer,
Universidad de Talca, Chile
*Correspondence:
Stanislav Kopriva
skopriva@uni-koeln.de
†Present address:
Manuela Peukert,
Department of Safety and Quality of
Meat, Federal Research Institute of
Nutrition and Food, Max
Rubner-Institut (MRI), Kulmbach,
Germany
Specialty section:
This article was submitted to
Plant Physiology,
a section of the journal
Frontiers in Plant Science
Received: 02 July 2017
Accepted: 04 September 2017
Published: 19 September 2017
Citation:
Jacoby R, Peukert M, Succurro A,
Koprivova A and Kopriva S (2017)
The Role of Soil Microorganisms
in Plant Mineral Nutrition—Current
Knowledge and Future Directions.
Front. Plant Sci. 8:1617.
doi: 10.3389/fpls.2017.01617
The Role of Soil Microorganisms in
Plant Mineral Nutrition—Current
Knowledge and Future Directions
Richard Jacoby, Manuela Peukert†, Antonella Succurro, Anna Koprivova and
Stanislav Kopriva*
Botanical Institute, Cluster of Excellence on Plant Sciences (CEPLAS), University of Cologne, Cologne, Germany
In their natural environment, plants are part of a rich ecosystem including numerous
and diverse microorganisms in the soil. It has been long recognized that some of these
microbes, such as mycorrhizal fungi or nitrogen fixing symbiotic bacteria, play important
roles in plant performance by improving mineral nutrition. However, the full range of
microbes associated with plants and their potential to replace synthetic agricultural
inputs has only recently started to be uncovered. In the last few years, a great progress
has been made in the knowledge on composition of rhizospheric microbiomes and
their dynamics. There is clear evidence that plants shape microbiome structures, most
probably by root exudates, and also that bacteria have developed various adaptations to
thrive in the rhizospheric niche. The mechanisms of these interactions and the processes
driving the alterations in microbiomes are, however, largely unknown. In this review,
we focus on the interaction of plants and root associated bacteria enhancing plant
mineral nutrition, summarizing the current knowledge in several research fields that can
converge to improve our understanding of the molecular mechanisms underpinning this
phenomenon.
Keywords: plant–microbe interactions, plant nutrition, microbiome, root exudates, natural variation,
mathematical modeling
INTRODUCTION
The Interconnection of Plants with Soil Microbes
Although plant physiologists sometimes view soil as simply a source of nutrients to plants, it
is actually a complex ecosystem hosting bacteria, fungi, protists, and animals (Bonkowski et al.,
2009;Muller et al., 2016). Plants exhibit a diverse array of interactions with these soil-dwelling
organisms, which span the full range of ecological possibilities (competitive, exploitative, neutral,
commensal, mutualistic). Throughout modern plant science, most interaction studies have focused
on alleviating pathogenic effects such as herbivory and infection (Strange and Scott, 2005;Zhang
et al., 2013), or attenuating abiotic stress conditions (Yaish et al., 2016;Meena et al., 2017). However,
there has also been longstanding interest in characterizing the positive ecological interactions
that promote plant growth. For instance, mycorrhizal fungi as well as the bacteria present in
nodulated legumes were both recognized as root symbionts from the second half of 19th century
(Morton, 1981). Already in the 1950s, crop seeds were coated with bacterial cultures (Azotobacter
chroococcum or Bacillus megaterium) to improve growth and yield (Brown, 1974). By the 1980s
many different bacterial strains, mainly Pseudomonas but also Azospirillum, had been described
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as having plant growth promoting effects (Burr et al., 1978;
Teintze et al., 1981;Lin et al., 1983). Since the 2000s, research
focus has somewhat shifted away from individual microbial
strains, and toward documenting the abundance and diversity
of the root microbiome through metagenomics. Results from
such sequencing studies have shown that the rhizospheric niche
is a hotspot of ecological richness, with plant roots hosting
an enormous array of microbial taxa (Bulgarelli et al., 2013).
In the last few years, research has swung toward assembling
rationally designed synthetic communities (SynComs) that
comprise strains representing the dominant rhizospheric taxa,
with the aim of re-capitulating favorable microbial functions
under controlled experimental conditions (Busby et al., 2017).
A major goal of this research field is to gain a mechanistic
understanding of how soil microbes boost plant growth and
defense, and then to use this knowledge to inform the optimal
design of microbial communities tailored to carry out specific
functions.
Microbial Traits and the Bioavailability of
Nutrients for Plants
Three mechanisms are usually put forward to explain how
microbial activity can boost plant growth: (1) manipulating the
hormonal signaling of plants (Verbon and Liberman, 2016);
(2) repelling or outcompeting pathogenic microbial strains
(Mendes et al., 2013); and (3) increasing the bioavailability
of soil-borne nutrients (van der Heijden et al., 2008). This
review will focus on the third mechanism, whereby soil
microbes metabolize recalcitrant forms of soil-borne nutrients
to liberate these elements for plant nutrition. In natural
ecosystems, most nutrients such as N, P, and S are bound
in organic molecules and are therefore minimally bioavailable
for plants. To access these nutrients, plants are dependent
on the growth of soil microbes such as bacteria and fungi,
which possess the metabolic machinery to depolymerize and
mineralize organic forms of N, P, and S. The contents of
these microbial cells are subsequently released, either through
turnover and cell lysis, or via protozoic predation (Bonkowski,
2004;Richardson et al., 2009). This liberates inorganic N, P,
and S forms into the soil, including ionic species such as
ammonium, nitrate, phosphate, and sulfate that are the preferred
nutrient forms for plants (van der Heijden et al., 2008). In
natural settings, these microbial nutrient transformations are
key drivers of plant growth, and can sometimes be the rate-
limiting step in ecosystem productivity (Schimel and Bennett,
2004).
Fertilization Practices and Environmental
Sustainability
In most contemporary agricultural systems, macronutrients
are provided through the application of mineral fertilizers.
However, unsustainable fertilization practices are contributing
to the large-scale alterations of Earth’s biogeochemical cycles,
through mechanisms such as soil degradation, waterway
eutrophication, and greenhouse gas emissions (Amundson et al.,
2015;Steffen et al., 2015). Furthermore, known reserves of
phosphate rock are rapidly diminishing and predicted to be
exhausted within a few decades (Cordell and White, 2014),
while N-fertilizer production via the energy-intensive Haber–
Bosch process relies upon fossil fuels and thus exacerbates
global warming and natural resource depletion (Erisman et al.,
2013). Due to the scale and severity of these fertilizer-
induced problems, a current research priority is for agricultural
science to develop alternative methods of sustaining plant
nutrition with dramatically lower inputs of mineral fertilizers
(Foley et al., 2011). One such possibility is to replace
mineral fertilizers by organic inputs, and to supplement
plants with specific root-associated microbes that depolymerize
and mineralize the organic-bound nutrients. The logic of
this idea is that organic inputs can be obtained more
sustainably than mineral fertilizers, because myriad agricultural,
industrial and municipal processes produce huge volumes of
nutrient-rich “waste” that are currently disposed of, but could
potentially be composted and applied as fertilizers (Paungfoo-
Lonhienne et al., 2012). Another factor is that organically
bound nutrients are more stable in the soil compared to
mineral fertilizers, and therefore less prone to leaching and
volatilization (Reganold and Wachter, 2016). Bio-fertilizers are
already used in organic farming systems, but there is currently
little mechanistic insight behind the choice of plant cultivars
and microbial inoculants (Bender et al., 2016;Reganold and
Wachter, 2016). This lack of precision occurs because of two
major knowledge gaps: (1) it is unclear what strategies plants
use to recruit beneficial microbes, and how much genetic
variation exists for this trait; and (2) There is insufficient
knowledge of which particular microbes are best partners for
boosting plant nutrition from organic sources of N, P, and S
(Figure 1).
We aim to understand how microbes contribute to plant
nutrition, and also how plants shape their microbiome to
maximize the nutritional benefits of this interaction. In this
review, we summarize current progress in approaches toward
FIGURE 1 | Interactions between plants, microbiota, and soil. Both plants and
microorganisms obtain their nutrients from soil and change soil properties by
organic litter deposition and metabolic activities, respectively. Microorganisms
have a range of direct effects on plants through, e.g., manipulation of
hormone signaling and protection against pathogens. Plants communicate
with the microorganisms through metabolites exuded by the roots. The major
knowledge gaps for understanding the mechanisms of plant–microbe
interactions in the rhizosphere are shown in bold.
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dissection of the interconnection of plants and bacteria in
mineral nutrition, with emphasis on metabolic capacities of
plants and microbes. When reviewing this field, it must be
mentioned that there is a significant body of literature studying
how certain plants can receive nutritional benefits via symbiotic
associations with mycorrhiza and nodulating bacteria. To avoid
overlaps with some excellent recent reviews (Smith and Smith,
2011;Udvardi and Poole, 2013;Garcia et al., 2016;Kamel
et al., 2017), we do not focus on these well-characterized
symbiotic interactions. Instead, we focus on how plant nutrition
can be linked to the entire rhizospheric microbiome, an
emerging field that is currently undergoing rapid growth. We
focus the review on bacteria, although most of the concepts
are valid also for other soil organisms, particularly fungi.
We are making a case for a multidisciplinary approach that
combines plant and microbial genetics with biochemistry and
metabolic modeling. Together, these tools can enhance our
mechanistic understanding of the interactions between plants
and microbes, and how these processes can be optimized
to drive plant nutrition with lower applications of mineral
fertilizers.
EFFECTS OF PLANT NATURAL
VARIATION ON THE RHIZOSPHERE
MICROBIOME
To selectively breed plants for optimized nutritional interactions
with soil microbes, the genetic components of this trait
must first be discovered. Sequence analyses showed differences
between the composition of bacterial taxa in soil and plant
rhizosphere or the endophytic fraction, showing that plants
select for specific bacterial taxa and thus exert some control
over their microbiomes (Bulgarelli et al., 2012;Turner et al.,
2013;Zgadzaj et al., 2016). The next question is then to
define the key genetic determinants that underpin how different
plant genotypes interact with rhizospheric bacteria. Decades
of research have shown that the susceptibility to pathogenic
microorganisms is highly dependent on plant genome, between
different species as well as within accessions of the same
one (Zhang et al., 2013). Similarly, Arabidopsis accessions
showed large variation in supporting growth of rhizospheric
bacterium Pseudomonas fluorescens in a hydroponic system
(Haney et al., 2015). Indeed, sequence analyses have confirmed
different microbiome structures across plant taxa, with bigger
differences in more distant species and with also a larger
contribution of environment and soil to the variation (Turner
et al., 2013;Schlaeppi et al., 2014;Zgadzaj et al., 2016).
When comparing accessions or varieties of the same species,
genotypic effects on microbiome structure have been seen
amongst Arabidopsis, maize, and barley (Bulgarelli et al.,
2012, 2015;Peiffer et al., 2013). Regarding leaf microbiota,
clear differences were shown for leaf microbiomes across 196
Arabidopsis accessions (Horton et al., 2014). The variation
driven by plant-genome was particularly high for the most
abundant operational taxonomic units (OTU). The variation
was further explored by genome-wide association study (GWAS)
using the number of reads for individual OTUs as quantitative
phenotypes (Horton et al., 2014). There, GWAS revealed
that many of the significant SNPs linked to bacterial OTU
structure were categorized as defense response, which was the
most overrepresented gene ontology term among the candidate
genes. In addition, genes involved in cell wall synthesis and
kinase activity were enriched (Horton et al., 2014). Although
several candidate genes affecting the leaf microbiome were
identified, further confirmatory tests of mutants of these genes
have not been reported and therefore the functionality of the
genes in shaping the microbiome remains to be demonstrated.
The leaf microbiome GWAS can be of major importance
for understanding the processes in rhizosphere, because the
leaf and root microbiomes are overlapping (Bai et al., 2015)
and might be shaped by similar processes. In an alternative
approach to GWAS, Bodenhausen et al. (2014) monitored
changes to SynComs inoculated onto leaves of Arabidopsis
accessions and mutants of a priori selected genes (Bodenhausen
et al., 2014). Again, a clear genotype effect upon microbial
taxonomic composition has been observed in the 10 accessions
and in several mutants. In three mutants, the effects were
consistent and reproducible, two mutants were involved in cuticle
synthesis and one in ethylene signaling (ein2) (Bodenhausen
et al., 2014). Given that only some 40 mutants and highly
simplified SynComs were tested, the approach seems to be
promising for analysis of root microbiome as well, particularly
if mutants in nutrient uptake and assimilation would be
investigated.
GWAS of Bacteria-Mediated Plant Traits
The sequence analyses, however, explore only the taxonomical
composition of plant microbiome without taking into account
the whole bacterial genomes or addressing the functions
these microbes are performing. The best attempt so far to
assess how plant genotype affects functional interaction with
rhizobacteria is the analysis of variation in susceptibility
of Arabidopsis accessions to the plant growth-promoting
rhizobacterium Pseudomonas simiae WCS417r (Wintermans
et al., 2016). The authors cultivated 302 accessions with
and without the bacterium, which promotes changes in
root architecture and growth through volatile emission. The
accessions showed large difference in all three phenotypes
scored: fresh weight gain, proliferation of lateral roots, and
elongation of the primary root (Wintermans et al., 2016).
Statistical GWAS analysis resulted in several highly significant
associations, but despite some good correlation between the
fresh weight and root architecture data, none of the positive
SNPs were found with multiple phenotypes. The analysis
led to identification of several candidate genes, but without
further verification or confirmatory experiments (Wintermans
et al., 2016). The analysis proves that GWAS of Arabidopsis
accessions is a feasible approach to identify genetic loci that
control the phenotypic variation in plant–microbe interactions.
The challenge is to step beyond the relatively simple traits
analyzed so far and to design screens that would allow
to dissect the genetic architecture of the complex signaling
and metabolic networks leading to variation in composition
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FIGURE 2 | Plant natural variation in root exudates leading to distinctive microbial communities. We postulate that plant genotype has a strong effect on microbial
community composition, mediated via root exudate composition. Consider three plant genotypes with differing root exudate profiles. At time 0 (upper panel), the
three plants are transplanted from a sterile system onto a common soil where they are proximate to the same set of microbial strains. At time t(lower panel), the
three plant genotypes have recruited distinctive microbial strains to their roots, which confer differential growth-promoting effects manifesting in different plant sizes.
of root associated microbiota in different plant genotypes
(Figure 2).
PLANT ROOT EXUDATES—A SOURCE
OF MOLECULAR SIGNALS
Metabolic Signals to Recruit Favorable
Microbes
The growth of soil microbes is usually carbon-limited, so the
high amounts of sugars, amino acids, and organic acids that
plants deposit into the rhizosphere represent a valuable nutrition
source (Bais et al., 2006). However, deposition of this labile
carbon does not necessarily foster the recruitment of favorable
microbes, because pathogenic strains can also use these molecules
as growth substrates. Therefore, it can be postulated that plants
have evolved recognition mechanisms to discriminate beneficial
microorganisms from those that need to be repelled. In such
a case, the specific molecules present in root exudates that
contribute to shaping the microbial community structure are
potential targets for plant breeding strategies that seek to engineer
the rhizosphere microbiome. It has been shown that plant root
exudates contain components used in belowground chemical
communication strategies, such as flavonoids, strigolactones,
or terpenoids (Bais et al., 2006;Venturi and Fuqua, 2013;
Massalha et al., 2017). Studies on the microbiome of different
plant species and accessions revealed strong variations, leading
to the hypothesis that exudates are crucial in shaping plant–
microbe interactions (Hartmann et al., 2009). Furthermore, it has
been shown that plants specifically attract beneficial interaction
partners via root derived signals (Neal et al., 2012;Lareen et al.,
2016).
Up to now, most information about signal perception and
transduction in plant–microbe interactions comes from the field
of plant pathology, where plant receptor-like kinases (RLKs) play
a major role (Antolín-Llovera et al., 2012). In case of mutualistic
interactions, nodulation and mycorrhizal interactions serve as
model systems to identify recognition mechanisms between
plants and microbes (Delaux et al., 2015;Lagunas et al., 2015).
In parallel to recognizing the microbial interaction partner by the
plant, also microbes have to recognize their mutual interaction
partner (the plant root). It is widely accepted that root exudates
contribute to the establishment of the root microbiome (Massalha
et al., 2017). The term “root exudates” describes the molecules
that are selectively secreted by roots and distinguishes it from
the sloughing-off of root border cells (Walker et al., 2003). The
overall release of fixed carbon compounds (border cells and
exudates) into the surrounding soil is termed as rhizodeposition
(Jones et al., 2004;Dennis et al., 2010). Data about the amount
of rhizodeposition range between 5 and 30% of the total amount
of fixed carbon (Bekku et al., 1997;Hütsch et al., 2002;Dennis
et al., 2010), which generally means a large loss of reduced-C
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for biomass and represents a considerable impact on the carbon
budget of individual plants and also entire ecosystems (Badri
and Vivanco, 2009;Bardgett et al., 2014). In a 14C approach,
Hütsch et al. (2002) found remarkable differences in the amount
of C-release among six different plant species ranging from
11.6 (wheat) to 27.7 (oil radish) mg C/g root dry matter. Also,
the composition of these exudates varied between species, with
oil radish exudates being rich in organic acids whereas pea
exudates are rich in sugars. These data indicate that various
plant species differentially modulate the chemical composition
of their rhizospheres, which in turn might impact the associated
microbial community. The recruitment of beneficial microbes
might be crucial under environmental stress conditions such as
nutrient limitation, pathogen attack, pests, high salt, or heavy
metal stress.
Issues to Consider When Analyzing Root
Exudates
To fully understand the dynamic interactions between soil
microbes and plant roots, it is necessary to elucidate the
specific molecules within root exudates that can recruit favorable
microbial strains. This is a challenging problem in analytical
biochemistry, because various biological and methodological
issues must be addressed to undertake biologically insightful
analyses of plant root exudates (Rovira, 1969). Regarding
cultivation, artificial plant growth systems cannot mirror the
natural conditions in soil, but on the other hand, it is
difficult to unravel the relevant communication signals occurring
in soil, due to chemical interaction of metabolites with
the soil matrix, and background metabolites released from
decomposing organic matter or microbial exudation. Most
analyses therefore settle on hydroponic cultivation, sometimes
with an inert material to scaffold the roots. When sampling,
the experimenter must choose whether to collect exudates in
simple deionized water, or a more realistic medium containing
mineral salts. Furthermore, it is effectively impossible to design
an experimental approach that can differentiate exudates from
sloughed-off border cells. A comprehensive summary on exudate
collection and influences (e.g., pH, re-uptake by roots, incubation
period) is presented in Vranova et al. (2013). For data acquisition,
researchers are increasingly using unbiased mass spectrometry
(MS) approaches such as gas chromatography (GC)-MS and
liquid chromatography (LC)-MS. However, detection of all
metabolites in a sample is impossible due to physiochemical
biases imposed by the selected extraction method, sample
clean-up procedure, matrix effects and analytical technique
(Weston et al., 2015). Therefore, different methods have to be
combined for a comprehensive view on the metabolite profile.
The subsequent analysis of the derived MS data is a huge
challenge, beginning with data processing algorithms that enable
feature detection, peak alignment and different normalization
methods. These normalization and scaling algorithms have a
large impact on the outcome of an analysis (Worley and
Powers, 2013). To validate the identity of specific mass
spectral features, fragmentation data (MS2or MSn) are acquired
and compared against publicly available databases (Afendi
et al., 2013;Misra and van der Hooft, 2016), or authentic
standards (if available). Taken together, these challenges mean
that comprehensive analysis of root exudates is not trivial
(Figure 3).
Recent Approaches to Analyze Root
Exudate Composition
Various studies have described analyses of plant root exudates,
with Phillips et al. (2008) developing a method to collect exudates
from mature trees in the field, although microbial metabolism
probably makes a significant impact upon this non-sterile
system. That said, microbial nutrient uptake is an interesting
aspect of plant–microbe nutritional interactions, with a fast
degradation of flavonoid glucosides being observed by Carlsen
et al. (2012) when comparing the flavonoid content in two
soils and after different legume cultivations. To avoid microbial
impact upon root exudate profiles, researchers have established
diverse approaches of axenic hydroponic cultivation systems
(Badri et al., 2008;Oburger et al., 2013;Strehmel et al., 2014),
which are easier to control, even though they represent artificial
plant cultivation systems and plant responses might also include
stress reactions due to oxygen limitation and insufficient root
support. Furthermore, hydroponics is well suited for sampling of
exudates, as the total liquid can directly been taken for further
sample preparation procedures and root damage is minimized.
However, collection of exudates widely ranges in timescale and
the used collection medium (nutrient solution or water). Badri
et al. (2008) collected root exudates from Arabidopsis thaliana
in nutrient solution for 3 and 7 days for analysis by LC-MS
and revealed that most compounds are present only after the
longer incubation period. It could be hypothesized that this
observation is due to sloughed-off border cells. Nevertheless, they
also compared the exudate composition against root composition
and stated an 80% difference based on detected molecular masses
(Badri et al., 2008). Also Strehmel et al. (2014) applied a 7-
day collection period in nutrient solution to obtain sufficient
amounts of exudates from A. thaliana. In contrast, Carvalhais
et al. (2010) applied only a 6-h exudate collection period to
Zea mays plants to minimize the effect of sloughed-off border
cells, but used deionized water as collection medium. A similar
approach has been used for barley root exudates that were
collected for 4 h in deionized water (Tsednee et al., 2012).
For a short-term exudate collection period from Arabidopsis a
high amount of plants has been required to obtain sufficient
amounts of exudates for LC-MS analysis (Schmid et al., 2014).
A direct comparison of different plant cultivation and exudate
collection techniques revealed a huge impact on metabolite
patterns (Oburger et al., 2013). Especially, long incubations in
deionized water may lead to overestimated exudation rates due
to the high transmembrane gradient of solutes in low ionic
strength medium (Neumann and Römheld, 1999;Oburger et al.,
2013). To date, most published data on exudates concentrate
on specific metabolite classes such as primary metabolites
(Neumann and Römheld, 1999;Dakora and Phillips, 2002;
Rudrappa et al., 2008;Carvalhais et al., 2010;Tan et al., 2013;
Warren, 2015;Kawasaki et al., 2016), hormones (Foo et al.,
2013), flavonoids (Graham, 1991;Hughes et al., 1999;Weisskopf
et al., 2006;Cesco et al., 2010), or phytosiderophores (Oburger
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FIGURE 3 | Technical considerations for analyses of root exudates. Comprehensive analyses of root exudate composition are crucial to advance our knowledge of
plant–microbe interactions, but experimenters must consider the technical challenges at each step of the analytical workflow. First the growth system must be
carefully considered, particularly whether to use soil or hydroponics, which each have advantages and disadvantages. At sampling, those using hydroponic systems
must decide whether to collect the exudates in nutrient solution or deionized water, whereas those using soil systems must consider how to separate exudates from
the soil matrix. During sample preparation, there are a multitude of options for sample concentration and clean-up, which will influence sample composition. Clearly,
the mass spectrometry methodology used for data acquisition will play a crucial role in the workflow, because different setups allow the detection of different
molecules. Finally, there is a growing awareness that data processing and analysis strategies also play a key role in shaping the derived data.
et al., 2014). Non-targeted metabolite profiling approaches
of root exudates have been applied less frequently, although
Strehmel et al. (2014) provided a comprehensive overview
on secondary metabolites in Arabidopsis root exudates using
LC-MS. In follow-up experiments, the data collection was
complemented by GC-MS data and extended by a comparison
of 19 Arabidopsis accessions (Monchgesang et al., 2016), co-
cultivation with Piriformospora indica (Strehmel et al., 2016)
and data from phosphate limitation (Ziegler et al., 2016). As
MS technology continues to improve, it can be expected that
more studies will undertake untargeted analyses of root exudate
profiles.
How Root Exudates Differ Across Plant
Genotype and Nutrient Limitation
If root exudate profiles are to be a potential breeding target
for increasing plant–microbe nutritional cooperation (Kuijken
et al., 2015), then it must be first understood how exudate
composition varies across genotypes or in response to nutrient
deprivation. Recent studies have contributed to our knowledge
of this phenomenon, with the effects of phosphate limitation
investigated in Ziegler et al. (2016), and variation across
accessions shown in Monchgesang et al. (2016) and also
Micallef et al. (2009). Comparing exudate profiles across 19
natural Arabidopsis accessions showed a high natural variation
for glycosylated and sulfated metabolites, such as flavonoids,
glucosinolate degradation products, salicylic acid catabolites and
polyamine derivatives (Monchgesang et al., 2016). Regarding root
exudation changes induced by nutrient limitation, it seems that
phosphate deficiency results in a higher abundance of oligolignols
and a lower abundance of coumarins (Ziegler et al., 2016).
Future experiments could investigate a different panel of plant
genotypes, perhaps where previous experiments have defined a
phenotypic difference that is potentially linked to root exudation
profiles. One possibility involves comparing genotypes that have
shown contrasting affinity to recruit favorable microbes (Haney
et al., 2015), or genotypes that differ in their nutrient starvation
responses (Ikram et al., 2012). Another option is root exudate
profiling to analyze the phenotypic effects of mutants that were
identified by GWAS studies (Wintermans et al., 2016).
Specific Molecules in Root Exudates
Linked to Plant–Microbe Nutritional
Interactions
From our current knowledge of root exudates, is it possible to
pinpoint a set of molecules that are particularly promising for
recruiting favorable microbes to the rhizosphere? In legumes, it
is well described that the flavonoid pathway has a huge impact
on attracting rhizobia bacteria to roots and inducing NOD
gene expression (Eckardt, 2006;Maj et al., 2010;Abdel-Lateif
et al., 2012;Weston and Mathesius, 2013). Flavonoids are also
crucial for hyphal branching and thus promoting mycorrhizal
interaction (Abdel-Lateif et al., 2012;Hassan and Mathesius,
2012). Both of these interactions result in increased plant nutrient
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uptake, with mycorrhiza and root nodules boosting phosphorus
and nitrogen, respectively. Perhaps other plants and soil bacteria
have also tapped into this signaling pathway, and further analysis
of root exudates will give us clues as to whether flavonoids play a
role in this communication leading to increased nutrient uptake.
From analyzing plant mutants, candidate genes that have been
investigated so far are related to the transfer of metabolites into
the rhizosphere (Badri et al., 2008), hormonal signaling (Foo
et al., 2013;Carvalhais et al., 2015), or to biosynthesis, e.g., genes
from phenylpropanoid pathway (Wasson et al., 2006;Zhang et al.,
2009). These results give us some hints about candidate molecules
that are exuded by plant roots to recruit beneficial bacteria, such
as strigolactones and flavonols, and presumably further analyses
will expand this list. To fully exploit this knowledge, then it is
desirable to identify which microbial strains are recruited by these
molecules, and what benefits they confer to the plant.
USING SEQUENCE DATA TO PREDICT
MICROBIAL EFFECTS ON PLANT N, P,
AND S NUTRITION
Mechanisms of Microbial Nutrient
Provision to Plants
As described above, plants give large amount of carbon away
to the rhizosphere that nourishes soil microorganisms. So what
do plants get back? In natural soils the vast majority of N, P,
and S atoms are organically bound, while in the atmosphere
the vast majority of N is contained in the N2molecule. Due to
the different metabolic capacities of plants and microbes, these
nutrient sources are minimally bioavailable to plants, but can be
metabolized by various soil microbes. This means that nitrogen
fixing and nutrient mineralization processes carried out by soil
microbes are crucial for plant nutrition in natural ecosystems,
because these reactions metabolize recalcitrant forms of N, P, and
S to liberate these elements for plant nutrition (Rovira, 1965;
van der Heijden et al., 2008). It must be briefly stated that this
established paradigm has been somewhat questioned in recent
years, as several studies have demonstrated direct plant uptake
of various organic-N forms (Nasholm et al., 1998;Paungfoo-
Lonhienne et al., 2008). However, it is still generally accepted that
microbes are better competitors for these nutrients due to the
low diffusivity of organic-N molecules in soil, and the results of
isotope labeling studies generally support the concept that most
organic-N is first assimilated by microbial taxa, then subsequently
assimilated by plants upon microbial turnover (Richardson et al.,
2009;Kuzyakov and Xu, 2013).
Over several decades, the soil microbiology literature has
accumulated a list of microbial metabolic processes that are
linked to plant N, P, and S nutrition (summarized in Table 1).
Commercially, the symbiotic association between legumes and
bacteria is routinely exploited when field crops are inoculated
with nitrogen-fixing rhizobia strains (Bashan et al., 2014).
But, how can this phenomenon be refined and optimized for
widespread use in more sustainable agricultural systems—not
only for nitrogen fixation in legumes, but also for N, P, and S
nutrition in non-leguminous crops? Given that different bacterial
strains exhibit differing metabolic capabilities (Brader et al.,
2014;Timm et al., 2015), coupled with the huge amount of
genomic sequence data from soil microbes that has recently been
generated (Bai et al., 2015;Muller et al., 2016), one possibility
is to pinpoint the genes that encode metabolic pathways for
agriculturally beneficial N, P, and S metabolism, and to boost the
microbes that contain these specific genes in agricultural soils.
This section will briefly outline what are the key known bacterial
genes for boosting plant N, P, and S nutrition, and what strategies
exist for promoting the abundance of these genes in agricultural
soils.
Cultivation-Independent or
Cultivation-Dependent Approaches
The major difficulty in investigating soil microbial communities
is that only a small fraction of the inhabiting taxa can
be cultivated in the laboratory, where experimenters can
undertake detailed and controlled analyses (Pham and Kim,
2012). Therefore, the literature investigating root-associated
microbes can be roughly divided into either cultivation-
independent or cultivation-dependent studies. Generally, most
cultivation-independent approaches extract root-associated
microbes in situ, and then analyze the properties of this
community. In contrast, cultivation-dependent approaches
generally inoculate soil or root-associated microbes onto
laboratory growth medium, before analyzing distinct strains
that have been cultivated in the laboratory. For cultivation-
independent approaches, techniques typically used to analyze
soil microbiota often include: (1) 16S sequencing and PLFA
measurements to infer taxonomic breakdown; (2) metagenomic,
metatranscriptomic, or metaproteomic analyses to infer
functional capacity of microbial communities; and (3) enzyme
assays, respiratory measurements, or substrate utilization assays
to measure functional activity of microbial communities. In
cultivation-dependent approaches, the analytical possibilities
are multitude—once root-associated organisms have been
cultivated in a laboratory setting, they can be analyzed with
any available technique. In the literature, there are many
studies that use both cultivation-dependent and cultivation-
independent techniques to draw links between microbial
properties and plant nutrition (Kertesz and Mirleau, 2004;
Richardson et al., 2009). But, can these acquired data inform the
rational selection of microbes that will improve plant N, P, and S
nutrition?
Microbial Taxa and Metabolic Pathways
in Cultivation-Independent Literature
Linked to Plant–Microbe Nutrient
Transfers
Several studies have used cultivation-independent approaches
to investigate microbial community structure/function across
soils exposed to different fertilization regimes (Allison et al.,
2007;Williams and Hedlund, 2013;Bowles et al., 2014).
Typically, these studies compare soil microbiota from highly
fertilized soils against those from unfertilized or poorly fertilized
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TABLE 1 | Key microbial metabolic processes related to plant nutrition.
Element Biochemical Microbial Soil enzymology Culture-independent Culture-dependent
process genes literature literature literature
Nitrogen Nitrogen fixation nifD, nifH, nifK Reganold et al., 2010;
Xue et al., 2013
Bremer et al., 1990
Protein
depolymerization
apr, npr, sub Mader et al., 2002 Rasche et al., 2014 Kohler et al., 2007
Urea catabolism ureA, ureB, ureC Dick et al., 1988;
Bowles et al., 2014
Reganold et al., 2010;
Fierer et al., 2012,Xue
et al., 2013
Kohler et al., 2007
Phosphorous Phosphate ester
cleavage
phoA, phoD, phoX,
ACPase, glpQ, ushA, appA,
phyA, phyB
Mader et al., 2002;
Garcia-Ruiz et al., 2008
Fraser et al., 2015 Kohler et al., 2007
Phosphonate
breakdown
phnJ, phnX Bergkemper et al.,
2016
Sulfur Sulfate ester cleavage aslA, asfA Garcia-Ruiz et al., 2008 Schmalenberger et al.,
2008
Schmalenberger et al.,
2008
Sulfonate breakdown ssuD Kertesz and Mirleau,
2004
Soil microbial metabolism boosts plant nutrition by converting recalcitrant forms of N, P, and S to forms that are more bioavailable for plant uptake. This table collates a
set of well-known microbial metabolic processes that contribute to plant N, P, and S nutrition, highlighting the specific genes involved, and referencing specific studies
that have conclusively shown this link.
soils, in situations where the low-fertilization regimes have
encouraged mutualistic nutrient transfers between plants and
microbes. From analyses of microbial taxonomy, it has been
shown that the abundance of certain bacterial taxa is related
to amount of applied fertilizer, with the copiotrophic phylum
Actinobacteria being positively correlated with N fertilization,
whereas the oligotrophic phylum Acidobacteria is negatively
correlated (Ramirez et al., 2010, 2012). However, the results
of a meta-analysis suggest that it is difficult to generalize
a consistent response of microbial taxon abundance to N
fertilization, because local environment and management play
a dominant role in shaping microbial community structure
(Geisseler and Scow, 2014). The work of Hartmann et al. (2015)
examines bacterial 16S and fungal ITS2 sequences in a long-term
field experiment comparing organic with conventional farming
systems, showing correlations between taxon abundance and
fertilization regime, with the bacterial Firmicutes phylum and
several fungal taxa being more abundant on soils fertilized with
manure.
The last 15 years has seen an explosion in the number
of rhizospheric microbiome sequencing studies, offering new
taxonomical insights into the microbial communities associated
with plants (Bulgarelli et al., 2013). However, the utility
of taxonomic analyses for predicting microbial community
function can be questioned, because in the bacterial literature
it is becoming increasingly apparent that taxonomic groupings
derived from 16S homology are imperfect predictors of a
bacterial strain’s functionality (Beiko, 2015). Recent studies
have sequenced the whole genomes of several closely related
strains, and have discovered that although these strains are
categorized as closely related due to the presence of a homologous
core genome, in fact there can be considerable divergence in
the accessory genome, meaning that the encoded functional
capacities will also be significantly different. By sequencing
the bacterial 16S gene, researchers infer the phylogeny of a
strain’s core genome, but it can be argued that this gives
little information about metabolic traits, because many of the
key genes involved in N, P, and S metabolism are accessory
genes, which are not taxonomically conserved due to the high
prevalence of horizontal gene transfer between bacteria (Kumar
et al., 2015;Young, 2016). Therefore, metagenomics studies
that profile the abundance of all gene sequences (not just 16S)
should have more power to unravel links between microbial
genetics and plant nutrition, although it should be remembered
that only a small proportion of the soil DNA pool is actively
expressed. From metagenomic studies that compare the effects
of different fertilizer inputs, it seems apparent that certain
genes are more abundant in soils with lower fertilizer inputs,
such as urea metabolism (Fierer et al., 2012) and unclassified
metabolic genes (Leff et al., 2015). These genes are thus positioned
as potential targets for improving the microbial provision of
plant-bioavailable N, P, and S. However, the sheer complexity
of the soil microbiome makes it difficult to draw mechanistic
links between specific genes and ecosystem processes, which
is one of the reasons why many researchers are adopting
SynCom experiments that attempt to re-construct a simplified
rhizosphere microbiome in a controlled setting (Busby et al.,
2017).
In the soil biology literature, enzyme assays have established
a set of enzymes linked to high-functioning soil microbiota,
such as protease, urease, various phosphatases, and sulfatase
(Garcia-Ruiz et al., 2008;Bowles et al., 2014). Therefore, the
bacterial strains that possess the genes encoding these proteins
are candidates for boosting nutrient transfers to plants. However,
one challenge involves managing the stoichiometric availability
of different nutrients to promote the activity of these enzymes. In
the priming literature, it is generally accepted that soil microbiota
are usually limited by the amount of labile C. Supply of labile
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carbon (e.g., root exudates) can relieve this limitation, such that
N, P, or S then becomes the limiting nutrient, and microbes
then express enzymes that can depolymerize recalcitrant forms
of these nutrients. So, even if the soil microbiota contains
strains with genes encoding the aforementioned enzymes
linked to soil health, soil conditions must be optimized for
these microbial proteins to be expressed and active (Paterson,
2003). Another method to measure metabolic capacity of
soils involves community level physiological profiling assays,
which measures substrate degradation affinity across different
fertilization regimes, although usually these assays are designed
with an emphasis on degradation of C-sources rather than
sources of N, P, and S. This technique has been applied
to soil receiving different fertilization practices, and it has
been shown that the capacity to degrade a diverse range of
substrates is correlated to other aspects of soil health, such
as organic carbon content and disease suppression (Perez-
Piqueres et al., 2006;Nair and Ngouajio, 2012;Dumontet
et al., 2017). Perhaps future studies could modify this approach
to develop assays that measure the capacity of soil microbes
to degrade various sources of N, P, and S. However, the
mechanistic insight derived from these assays is sometimes
questioned, because they measure the capacity of soil microbiota
to grow on specific nutrients under laboratory conditions.
This setup may therefore select for a small number of fast-
growing taxa and not correlate with the in situ activity of these
substrate degradation pathways (Ros et al., 2008;Rutgers et al.,
2016).
Microbial Genes in
Cultivation-Dependent Literature Linked
to Soil Fertility
It can be posited that soils with high rates of microbial N, P,
and S cycling should harbor microbes with specialized genes
encoding these traits, and therefore that microbial strains isolated
from these soils should possess useful metabolic attributes
for boosting plant nutrition. Interestingly, Rhizobia strains
isolated from N-fertilized soils exhibited a lower capacity to
promote plant growth compared to strains isolated from adjacent
unfertilized plots (Weese et al., 2015), indicating that the
management history of the isolation site impacts the degree of
mutualism in the resulting isolate. In the P literature, a similar
phenomenon has been observed, with phosphate mineralization
being more common in isolates from soils where bioavailable-
P was less abundant (Hu et al., 2009;Mander et al., 2012).
For sulfur, one example is from soils from the Rothamsted
Broadbalk experiment, where different S-fertilization practices
had led to fields that exhibit high versus low sulfatase activity.
Bacterial strains were isolated from these contrasting soils, and
functional assays such as enzyme measurements and growth
on minimal media revealed that the strains isolated from low-
SO42−soils contained several mechanisms for depolymerizing
organic-S (Schmalenberger et al., 2008). Together, these results
imply that research programs seeking “elite” microbial strains
that can maximally boost plant nutrition could begin with
inocula from sites that favor plant–soil feedbacks, such as
unfertilized soils or organic farms (Xia et al., 2015;Melo et al.,
2016).
Microbial Strains That Promote Plant
Growth by Enhancing N, P, and S
Nutrition
To truly be useful in an agricultural setting, it must be proven
that candidate growth-promoting strains can be re-inoculated
onto plants, successfully colonize the rhizospheric niche, and
then mediate nutrient mobilization that benefits plant growth.
This can be tested through plant–microbe interaction assays,
where candidate strains are tested for their ability to promote
plant growth and nutrient acquisition (Ahemad and Kibret,
2014). Once again, this research field is most mature for the
case of nitrogen-fixing Rhizobia, where decades of research have
endeavored to define the optimal inoculation practice, searching
for the right combination of plant genotypes and rhizobia strains
to suit specific climates and soils (Lindstrom et al., 2010).
Regarding the taxonomy of nitrogen-fixing symbioses, it should
be mentioned that nitrogenase genes are present in diverse
bacterial taxa (Gyaneshwar et al., 2011), and that non-leguminous
plants have been documented to host N2-fixing bacterial strains
(Santi et al., 2013), perhaps implying that other plant–microbe
combinations (not just legumes and Rhizobia) could be similarly
optimized to promote nitrogen fixation (Mus et al., 2016).
There are also reports of plant growth promotion via microbial
mobilization of other nitrogen sources, shown by higher yield in
plants inoculated with bacterial strains (Shaharoona et al., 2008;
Adesemoye et al., 2009), although for one of these experimental
setups, it seems that the source of this N was directly from
the ammonium sulfate fertilizer rather than from organically
bound soil N (Adesemoye et al., 2010). It has been shown that
unsterilized grass seeds can better access protein-N compared
to sterilized seeds, but the specific strains that provide this
service were not elucidated (White et al., 2015). The ability of
the fungi Glomus intraradices to transfer organic nitrogen to
plants has also been shown (Thirkell et al., 2016), suggesting
that future experiments could focus on documenting other fungal
strains with this capacity and characterizing the relevant genes
and mechanisms. For phosphorous, the literature contains a
large number of reports of both fungal and bacterial strains
with the capacity to solubilize inorganic P, and also many
reports of strains that can mineralize organic P (Plassard et al.,
2011;Ahemad and Kibret, 2014). Many of these P-mobilizing
strains are also characterized as growth promoting microbes, but
microbial promotion of plant growth can operate through a wide
variety of mechanisms, and sometimes it is not conclusive that
P-mobilization is responsible for the plant growth promotion
elicited by these strains (Richardson and Simpson, 2011). For
sulfur, studies of a plant growth-promoting Pseudomonas strain
have used genetic knockout of the sulfonate monooxygenase
enzyme to show that organic-S mineralization accounts for
some fraction of the growth-promoting phenotype (Kertesz and
Mirleau, 2004).
The research field is beginning to build large collections of
genomically sequenced bacterial isolates that can be re-assembled
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into SynComs (Bai et al., 2015). Xia et al. (2015) isolated
endophytic bacterial strains from plants grown under organic
management, and showed that over half of these strains can
boost tomato growth in a greenhouse experiment. This high
proportion of growth-promoting isolates shows that the capacity
to promote plant growth is widespread amongst plant-associated
bacteria, but to gain a mechanistic understanding into how these
growth-promoting effects are manifested, it will be necessary to
conduct detailed investigations into the genetics, biochemistry
and physiology of these growth-promoting strains. Furthermore,
microbial community experiments should also consider how
the interaction between different strains affects plant growth
promotion. Such knowledge will enable the rational selection of
growth-promoting strains and communities, driven by defined
genetic and biochemical mechanisms.
A GROWING COMMUNITY:
CHALLENGES AND PERSPECTIVES IN
MODELING PLANT–MICROBE
INTERACTIONS
With the vast quantity of data being generated by high-
throughput experimental techniques, new opportunities are
arising to integrate theoretical and computational approaches
with experiments. Potential synergies include designing
hypothesis-driven experiments based on the results of modeled
scenarios, or using modeling as a tool to mechanistically interpret
the results of high-throughput experiments (O’Brien et al., 2015).
In the bacterial field, there is currently a major initiative
to integrate genomic sequencing data with computational
modeling, in order to predict the function of individual
bacterial strains and whole bacterial communities (Blaser et al.,
2016). Already, microbial engineering is a mature technology that
exploits the power of computational modeling to optimize strains
and communities for industrial processes like bioremediation
and fermentation (Perez-Garcia et al., 2016). Regarding the study
of the rhizosphere microbiome, over the past 15 years this field
has accumulated a huge volume of sequencing data, but these
data are generally descriptive, and to date they have not been
used to significantly further our mechanistic understanding
of nutrient exchange processes in the rhizosphere. We see
mathematical modeling as the most promising approach to
bridge this gap, and in the following section we want to elucidate
through examples the impact mathematical modeling can have
for future improvement of rhizosphere interactions to boost
plant growth.
One of the principal aims of mathematical models is the
reduction of complexity in order to capture the fundamental
principles behind the phenomena of interest. There is nowadays
a positive trend in biology to develop predictive models of the
system under study, with some disciplines at a more advanced
stage than others in the integration of theory and experiments.
The study of microbial communities is a clear illustrative example
of a field where computational approaches are essential (Widder
et al., 2016). While bioinformatics is instrumental to analyze
high throughput data from meta-omics experiments, theoretical
models are developed to gain mechanistic understanding
of complex biological systems. Mathematical descriptions of
population dynamics were pioneered in the 19th century by
Verhulst’s law of logistic growth (Verhulst, 1838) and are now a
fundamental part of ecology (Murray, 2002). In the same way,
over the last five decades models of metabolism have started
converging into sound mathematical methods to investigate
cellular functions (Heinrich and Schuster, 1996). It is also
relevant to point out that another characteristic of mathematical
models is that, as long as the described mechanism holds true,
they can be easily generalized to different organisms. Therefore,
as an example, a model originally built to describe bacteria might
be also applied to other microbes.
Theoretical biology is a rapidly expanding field and it is nearly
impossible to condense and classify it in few words, but we can
roughly point out three main classes of methods: kinetic models,
stochastic models, and network-based models. It is important
to point out that depending on the system and phenomena
under study, certain modeling techniques will be more suited
than others. Kinetic models are dynamic and deterministic,
typically constructed as systems of differential equations (Holmes
et al., 1994), solved nowadays with computational integration
algorithms. These models can offer precise predictions but
require either a priori knowledge or inference (e.g., through
a fit to data) of the equation parameters. Typically, these
models are well suited to describe small-scale metabolic pathways
where most enzyme kinetic constants are measured and few
parameters are reasonably constrained and then fit. Stochastic
models encompass any representation that implements some
random components with a Monte Carlo procedure and they
are needed to capture effects like noise or individual variability.
A widely used method is the Gillespie algorithm (Gillespie,
1977) which simulates in randomized steps the evolution
in time of, e.g., a chain of biochemical reactions, each of
which has an associated probability to happen. Under the
general term network-based models (Milo et al., 2002;Feist
et al., 2009) we include different static methods that have in
common the treatment of metabolic pathways as networks,
where metabolites are connected to reactions either as substrates
or products. Reactions can be represented either as edges
connecting nodes (the metabolites) or, in bipartite networks,
as a disjoint set of nodes (the enzymes) that connect to
the metabolites nodes via edges carrying, e.g., stoichiometric
information. Reversibility of a reaction is determined by
physical principles and is taken into account in directed
networks.
Mostly, systems of plant–microbes have been considered in
light of host–pathogen interactions. The classic zig-zag model
(Jones and Dangl, 2006) is an illustrative scheme proposed to
explain the function of the plant immune system in response to
pathogens. It distinguishes two branches of the plant immune
system, one reacting to microbe-associated molecular patterns
with pattern-triggered immunity (PTI), the other responding
to effectors trying to suppress PTI with effector-triggered
immunity. This picture is, however, a purely expository model
and is not suited to capture the interaction dynamics we
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FIGURE 4 | Considerations for combining modeling and experimental approaches. A major goal in biology is to integrate computational predictions with
experimental data to generate predictive models of biological systems. While the temporal scale is an intrinsic property of the phenomena under study, the choice of
the physical scale of the model/experiment is chosen by the scientist: one can investigate soil microbial communities at an ecosystem macroscale or at the DNA
sequencing level. In general, the physical scale will have a strong impact on the choice of experimental technique, while the temporal scale will mostly influence the
experimental design (this is represented by thicker/thinner arrows). On the contrary, theoretical methods will be highly constrained by the temporal scale to be
modeled, while they are rather flexible regarding the physical scale to be operated at. In a truly interdisciplinary approach, the experimental and theoretical methods
have to be planned together to ensure that the reciprocal results are compatible and can be integrated. This will allow further improvements in both experimental
design and model development.
are interested in (Pritchard and Birch, 2014). Indeed, if the
objective is to build a predictive model of plant–microbe
interactions, and in particular beneficial ones, the fundamental
ingredients include: (i) the actual molecular factors driving the
interactions, like the ones discussed in the previous sections
of this review; (ii) environmental conditions inducing different
reactions, e.g., to stresses; (iii) temporal and spatial scales of
the phenomena under study. While it would be nice to have a
single model drawn from a universal theory of plant–microbe
interactions, we are mostly at the stage where mathematical
models are first needed to answer a limited number of
focused and well-defined biological questions. With the current
advancement in “omics” experimental techniques and with the
development of computational methods to understand metabolic
pathways, quantitative models of plant–microbes ecosystems at
the molecular level become an appealing possibility.
In the following section, we will outline some examples that
illustrate how different techniques can better capture different
phenomena. It is essential, when developing a theoretical model,
to understand at which temporal and spatial scale the biological
system operates. The temporal scale is an intrinsic property
of each biophysical process and therefore sets specific limits
on the mathematical method to use. On the other hand, the
spatial scale to investigate is chosen by the modeler based on
the biological question to address, since we can chose to describe
the same microbial community as a single metabolic unit or as a
population of individual organisms (Figure 4). These choices are
critical since they will determine at the same time the degree of
complexity of the model (an Earth-wide ecosystem model can be
simpler than a model of a Escherichia coli metabolism) and the
requirements for the integration of experimental data (Succurro
et al., 2017).
Dynamic Models of the Rhizosphere
The rhizosphere comprises the shared environment between
the plant roots and microbes. Understanding the dynamics of
nutrient competition in the rhizosphere is an important aspect
to identify the driving mechanisms of community assembly. The
work of Darrah (1991a,b) presents one of the first differential
equation models of microbial population dynamics in the
rhizosphere, parameterizing how root growth and exudation
of soluble C fuels microbial growth at specific sites. These
models accounted for root elongation and release of soluble
organic compounds which diffuse through the surrounding area
populated by bacteria. The simulations showed how different
exudation patterns can control the distribution of bacterial
biomass. Being based on simple equations representing the spatial
gradient of soluble substrates, this model is easily expanded to
include the presence in the rhizosphere of other solutes like
minerals essential to plant growth (Darrah et al., 2006). Recently,
a similar approach was used to model the colonization of the
root tip by bacteria, introducing bacterial motility and under the
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assumption that carbon is the growth-limiting nutrient (Dupuy
and Silk, 2016).
Borrowing concepts from microeconomics, Schott et al.
(2016) modeled the nutrient trade system between plants
and arbuscular mycorrhizal fungi as a minimal network,
and investigated the partnership relation in terms of costs
and revenues. First, they demonstrated that a simple model
where the nutrient exchange via the periarbuscular space is
only regulated by proton pumps (H+-ATPase) and nutrient
transporters (H+/sugars and H+/phosphate cotransporters)
could reproduce the situation where C and P exchange is
stable at the thermodynamic equilibrium. This result shows
that this small set of transporters can explain C and P fluxes
between the plant and the fungi, although the presence of further
transporters cannot be excluded from this result. Interestingly,
while the observable highly cooperating behavior might suggest
altruistic behavior in a “reward-based” system, the symbiosis is
the result of a selfish optimization of personal benefit. A change
in the costs to revenues ratio, for example, a well-fertilized
plant that places a lower value on fungal-derived P would
therefore lead to a different nutrient exchange rate (Schott et al.,
2016).
Soil nutrient fluxes also play a role in more global scenarios.
Earth system models are extended climate models that include
the effects from biogeochemical cycles, and different competition
theories can be embedded in them. Recently, Zhu et al. (2016)
demonstrated that a kinetic model considering plant, microbes
and nutrients as a coupled network, and based on the principle
that nutrient uptake rate is controlled by specialized transporter
enzymes, can reproduce data of N competition in a grassland
ecosystem where other competition theories fail (Zhu et al.,
2016).
Genome-Scale Metabolic Network
Models
Differential equation models are perfectly suited to simulate
dynamic processes like enzymatic or diffusion reactions, but
rely on fixed parameters that characterize specific states. In
principle it would be possible to obtain exact kinetic models
of metabolic networks at the “genome scale” (genome-scale
models, GEMs) if we were to know the function and the
kinetic parameters of each enzyme in the picture. Such
models would be, however, computationally expensive to solve
and difficult to manage in terms of number of equations,
variables and parameters. Instead, a convenient approach is the
reconstruction of GEMs from automated genome annotation
and their representation as networks (Thiele and Palsson,
2010). In an automated process of mining the information
from databases (Henry et al., 2010), the enzymes encoded in
the genome are assigned metabolic functions. The resulting
GEM, however, still requires subsequent careful manual curation
as a consequence of possible inaccurate annotations besides
our incomplete knowledge of genes associated to metabolic
activities.
Once a metabolic network has been reconstructed, it can be
translated into a stoichiometric matrix. This mathematical
formalism, connecting reactions to metabolites via the
stoichiometric coefficients, presents itself to a number of
methods developed to study the function of biochemical
pathways (Fell and Small, 1986;Varma et al., 1993;Papin et al.,
2004). Constraint-based models investigate the distribution
of reaction fluxes along the directed metabolic network (first
constraint, imposing irreversibility or reversibility under
physiological conditions from thermodynamics principles)
under the assumption that the system is at a steady state
(second constraint, meaning that there is internal balance
of production and consumption of metabolites) and that
enzymes operate at limited capacities (third constraint,
setting upper or lower bounds to the rate of an enzymatic
reaction based on experimental observations). This set of
constraints leads to a narrower space of possible solutions
for the metabolic fluxes, but a further step is needed in
order to identify a unique solution. A widely used method
is flux balance analysis (FBA) (Orth et al., 2010), where an
optimization problem is defined by introducing an objective
function (usually a linear combination of metabolic rates)
either to be maximized (like in the case of biomass as objective
function) or minimized (like in the case of total sum of cellular
fluxes).
GEMs are in general compartmentalized according to the
cellular structure. The simplest division, typically for prokaryotic
organisms, is between an external and an internal (cytosolic)
compartment, while plant GEMs will have compartments for
different tissues as well. Exchange of metabolites between
compartments happens through transport reactions, which are
not always correctly annotated and often added to the network
during the gap-filling process of the reconstruction. An exchange
flux with the external compartments is equivalent to an uptake or
secretion rate.
The GEM of the nitrogen fixing bacterium Rhizobium etli, a
known symbiont of legumes, was studied with constraint-based
modeling to understand its physiological capabilities during
the third developmental stage of the symbiosis in the plant
nodule, when it differentiate into a bacteroid and provides
N to the plant (Resendis-Antonio et al., 2007). The authors
explored how N fixation can be influenced by succinate (the
carbon source provided by the plant to the bacteroid) and
oxygen (nodules protect the nitrogenase enzyme from oxidative
damage by providing a microaerobic environment). For a
given oxygen uptake rate, an increase in succinate uptake rate
leads to higher symbiotic N fixation, until a threshold value
in succinate uptake rate is reached. After that an inhibitory
effect, caused by insufficient oxygen for succinate reduction,
drastically lowers N fixation rate. These kinds of studies, quick
to perform in silico, can be easily transferred to other pathways
of interest to generate hypothesis relating environment to
metabolism.
Available GEMs can be browsed at the BioModels database
(Le Novere et al., 2006), where most of the reconstructed models
are deposited and usually classified according to their level
of curation. We have to point out that efforts are still on-
going to converge on model standards, and the trustworthiness
of simulation results depends on the quality of the GEM
(Ravikrishnan and Raman, 2015;King et al., 2016).
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FIGURE 5 | Metabolic interconnections between plants and soil microbes. A major research goal is to generate a mechanistic understanding of the metabolic
interactions between plants and rhizosphere microbes. This scheme illustrates a set of research themes that will deepen our knowledge of this phenomenon.
Regarding genetic variation, a major goal is to define the genetic elements responsible for variation in plant root exudate composition, and also the genes that
encode for desired bacterial metabolic pathways. Metabolic profiling is a key tool in this field, because it can provide direct measurements of root exudate
composition and be used to identify specific metabolites linked to bacterial recruitment. Gene expression studies of plant–microbe interactions should reveal the key
genes that are induced or suppressed during favorable nutritional interactions. Finally, mathematical modeling is a crucial tool to integrate the derived data, with the
goal of building predictive models that enable us to rationally design agricultural systems for beneficial nutritional interactions between plants and microbes.
From Single Species to Soil Communities
Even without considering the associated plant, mathematical
modeling of microbial consortia is a very diverse research field
where multiple methods can (and should) be applied. For a
wide overview we point the interested reader to specific reviews
(Song et al., 2015;Zomorrodi and Segre, 2016), while here we
will focus on metabolic models, pointing out advantages and
limitations. A clear asset of methods like FBA is the possibility of
manipulating GEMs with ease, obtaining direct information on
active reactions. There are straightforward observable quantities
that can be experimentally measured to inform or to challenge
the model prediction, like nutrient uptake, oxygen levels, or
biomass growth. However, as mentioned before, the simulation
results are determined by the choice of an optimality principle,
which is still an extremely subjective and approximate choice
(Schuetz et al., 2007). If the concept of optimality is debatable
for a single organism undergoing metabolic regulation by
circadian rhythms and environmental factors, is it easy to
imagine that defining optimality in a microbial community,
where many other dynamics enter the picture, is far from
trivial.
Considering the temporal scale of constraint-based models,
the steady state condition implies that we obtain a static
representation of the mass-balanced metabolic system. If we
want to recover some dynamic information, we can consider
that metabolism adjusts quickly to small perturbations and
that it will be at a “quasi-steady-state” compared to slower
external processes (Harcombe et al., 2014). Moving to the spatial
scale, there is no unique way to build a community metabolic
network model (Perez-Garcia et al., 2016). Generally speaking,
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Jacoby et al. Soil Microorganisms in Plant Nutrition
we can consider the results on a single organism metabolic
model as a proxy of a colony of a single species, and different
approaches have been proposed to describe communities of
various species. For example, one possibility is to lump the
metabolic pathways of each species together, obtaining a single
“super-organism” where the biochemical reactions are acting
together to optimize a common objective function. Another
approach is to build a compartmentalized model, where each
species is a separate compartment and transport fluxes allow
the exchange of metabolites between them, and the modeler
can choose to implement a common objective function or
differentiate it among the organisms. While both choices are
valid, one strategy can be more appropriate than the other in
the specific context drawn by the biological question under
investigation.
The recent work of Pfau (2013) is, to our knowledge,
the first example of a metabolic network model of a plant–
microbe system (Pfau, 2013). The authors first reconstructed
the GEM of Medicago truncatula, an annual legume used
as model plant for studies on legume–rhizobia symbiosis,
obtaining a highly curated multi-tissue model describing root
and shoot. The metabolic network model of the nitrogen
fixing symbiont Sinorhizobium meliloti was then connected to
the root tissue model through exchange reactions extracted
from literature. Simulations on the GEM of plant and
symbiont under different N availability quantified the benefit,
in terms of growth, for the symbiotic partners. The insight
on exchange fluxes showed that S. meliloti exported only
alanine as N source to the plant. A simulation repeated for
an alanine dehydrogenase knock-out version of the bacterium
showed that N was provided to the plant as ammonia,
with an increased need for oxygen. These first results,
backed-up by experimental data, already show the potential
of theoretical models to provide testable hypotheses and
mechanistic understanding of symbiotic interactions involving
nutrient exchange.
CONCLUSION
For plants to access recalcitrant soil-borne nutrients, they are
dependent upon the metabolic activities of soil microbiota.
Considering the environmental damage associated with current
fertilization practices, a current research priority is to optimize
plant–microbe nutritional interactions for more sustainable
agricultural systems. However, the specific mechanisms
governing the assembly of the plant microbiome and its
modulation according to plant nutritional status are extremely
complex and difficult to predict. Despite the experimental
challenges described here, we argue that many important jigsaw
pieces have been identified that will enable us to understand
the mechanisms governing dynamic plant–microbe interactions
(Figure 5). We know that although soil is the major determinant
of the microbial community associated with plant roots, plants
have a significant effect on taxonomic assembly. Therefore,
comparative genetic approaches such as GWAS promise to
identify plant genes and processes important for controlling
how plants shape the rhizospheric microbiome. Particularly
interesting are genes in plant metabolic pathways that affect the
composition of root exudates and thus the actual signals in the
rhizosphere. Therefore, continued progress in our capability to
collect and analyze exudates will be important for assessing the
molecules plants use for communication with soil microbes,
and also the pathways the microbes use to decrypt these
signals.
Recent genomic studies are beginning to reveal the specific
microbial strains that contain metabolic pathways favorable
for plant nutrition (Muller et al., 2016). The big question,
however, remains: to what extent can plants attract specific
microbes for specific environmental/nutritional conditions?
For a number of microbes the mechanisms of their plant
growth promoting effects have been deciphered, however, it
seems that almost every individual organism uses different
processes. How do these various mechanisms integrate in
real soil with a multitude of taxa? Rhizosphere microbiome
research has already started to move from description of
the communities to assessing their dynamics due to changes
in environmental conditions (Busby et al., 2017). However,
still very little is known about which specific microbial
strains are the key contributors to plant nutrition, or about
how nutrient availability affects the composition of the
rhizospheric microbiome. How can we merge the progress
in the individual areas of research to obtain an integrated
view? Already we can design experiments with synthetic
microbial communities to identify processes important for the
establishment of effective communities using different plant
genotypes (Castrillo et al., 2017). However, there is still an
enormous knowledge gap regarding a sound theory of plant–
microbe interactions. The huge volume of data obtained from
the characterization of the microbiome is clearly calling for
a modeling approach, e.g., to construct nutritional networks
integrating metabolic pathways of plants and microbiota. Such
models would allow researchers to assemble various SynComs
with defined metabolic capacities, and facilitate dissection of the
mechanisms that shape microbial community composition and
function. This knowledge could mechanistically guide the highly
promising approaches to use microbes for more sustainable plant
nutrition.
AUTHOR CONTRIBUTIONS
RJ, MP, AS, AK, and SK together designed the scope of the
manuscript, wrote individual chapters, and prepared the figures.
FUNDING
The research in SK’s lab is funded by the Deutsche
Forschungsgemeinschaft (EXC 1028). RJ is funded by the
Horizon 2020 Marie Curie Sklodowska Action project No.
705808 – PINBAC.
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Conflict of Interest Statement: The authors declare that the research was
conducted in the absence of any commercial or financial relationships that could
be construed as a potential conflict of interest.
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Frontiers in Plant Science | www.frontiersin.org 19 September 2017 | Volume 8 | Article 1617