Abstract
Biological nitrogen fixation by free-living bacteria and rhizobial
symbiosis with legumes plays a key role in sustainable crop production.
Here, we study how different crop combinations influence the
interaction between peanut plants and their rhizosphere microbiota via
metabolite deposition and functional responses of free-living and
symbiotic nitrogen-fixing bacteria. Based on a long-term (8 year)
diversified cropping field experiment, we find that peanut co-cultured
with maize and oilseed rape lead to specific changes in peanut
rhizosphere metabolite profiles and bacterial functions and nodulation.
Flavonoids and coumarins accumulate due to the activation of
phenylpropanoid biosynthesis pathways in peanuts. These changes enhance
the growth and nitrogen fixation activity of free-living bacterial
isolates, and root nodulation by symbiotic Bradyrhizobium isolates.
Peanut plant root metabolites interact with Bradyrhizobium isolates
contributing to initiate nodulation. Our findings demonstrate that
tailored intercropping could be used to improve soil nitrogen
availability through changes in the rhizosphere microbiome and its
functions.
Subject terms: Agroecology, Microbial ecology, Metabolomics
__________________________________________________________________
Sustainability in agriculture can be improved harnessing biological
N[2]fixation in legumes. Here, the authors combine different crops with
peanut plants finding that maize and oilseed rape are the most
successful combinations which have potential to enhance rhizosphere
microbiota N[2] fixation.
Introduction
Chemical signaling between plants and soil microbiota plays a critical
role in microbial symbioses and rhizosphere microbiome
assembly^[47]1,[48]2. The secondary metabolites exuded by plant roots
are believed to attract and filter species-specific microbial
taxa^[49]3,[50]4, including microbiota that complement their host’s
functional repertoire with traits not encoded in the plant
genome^[51]5, such as biological nitrogen fixation and phosphorus
uptake^[52]6,[53]7. In turn, compounds released by rhizosphere microbes
trigger plant responses that further adjust microbiome specificity and
composition^[54]8,[55]9. This continuous chemical dialog is reflected
in the metabolic deposition of the host plant rhizosphere, also known
as rhizodeposition^[56]10–[57]12.
Although great mechanistic insights have been obtained on rhizosphere
chemical signaling and rhizomicrobiome assembly of individual plant
species^[58]1,[59]13,[60]14, much less is known about how these
processes are influenced by interspecific interactions between
coexisting plant species. Various studies have found that interspecific
neighbor-driven species recognition can induce a metabolic response in
the neighbor and change the chemical composition of its
rhizosphere^[61]15–[62]17. Such chemical alterations theoretically
drive subsequent changes in the structure and function of the
rhizomicrobiome. However, recent field studies on chemical feedbacks
between plant species have focused more on non-kin species
defense^[63]18–[64]20, and less on how these chemical cues may alter
the rhizomicrobiome and microbially mediated functions that affect the
plant fitness of the species involved^[65]21,[66]22.
The question of how interspecific effects on plant fitness are shaped
by rhizomicrobiome feedbacks is particularly relevant in the context of
diversified cropping systems, in which crop species diversity is
increased in space (e.g. intercropping) and time (crop rotation). Field
studies on such systems often demonstrate improved performance of key
food crops, especially in intercropping systems including
legumes^[67]23. One of the keys to the success in these systems is
improved nitrogen (N) availability through biological N[2] fixation,
both by free-living bacteria and rhizobial symbiosis with legumes. The
latter, in particular, requires finely tuned reciprocal signal
transduction systems as the host plant has to reprogram root growth and
invest in nodule structures before any gain from the symbiosis is
measurable^[68]24. These processes are likely to be influenced by the
other (non-legume) crops in the system (see e.g. Li et al.^[69]21). By
identifying specific rhizosphere metabolites that modulate legume
rhizomicrobiome assembly and biological N[2] fixation in these systems,
we are able to gain detailed mechanistic insights into
plant-rhizosphere feedback processes and understand why some crop
combinations work better than others.
To gain such mechanistic insights, we combined biochemistry, molecular
biology and crop ecology to investigate how the chemical signal
exchange between legumes and their rhizosphere microbiota is influenced
by different crop combinations, and how this in turn affects biological
N[2] fixation and legume crop performance. As a model for legumes we
focused on peanut, which is widely grown in the tropics and subtropics
and has edible seeds that develop underground. For this analysis we
gathered field data from an eight-year-old crop diversification
experiment (Fig. [70]S1), in which peanut was grown in monoculture
(PP), in rotation with oilseed rape (P-R), and in an intercropping
system with maize, rotated with oilseed rape (PM-R). Using multiomics
analysis (including nontargeted metabolomics and transcriptomics of
peanut roots), bacterial isolation and bacterial inoculation
experiments, we aimed to (i) identify metabolic cues in the peanut
rhizosphere that are influenced by the other crop species in the
system, and (ii) to investigate whether and how these metabolites
trigger rhizosphere microbiota and specific functions, biological N[2]
fixation in particular. Our results provide a mechanistic link between
plant diversity and belowground functioning and illustrate how the
chemical dialogs between plants and their rhizomicrobiome result in a
mutual plant-microbe alliance that improves fitness of both.
Results
Crop diversification enhances peanut production, root nodulation and
free-living N[2] fixation
Crop diversification had a significant positive effect on peanut
performance. In the system with highest crop diversity, i.e. where
peanut was intercropped with maize and rotated with oilseed rape,
average peanut height, biomass and fruit weight were increased by at
least 19%, 66% and 46%, respectively, compared with peanut monoculture
and peanut-rape rotation (p < 0.05, Turkey-HSD; Fig. [71]S2). It
finally resulted in 51% higher nitrogen uptake of peanut in crop
mixture than in the other two crop systems (p < 0.05, Turkey-HSD).
Although peanut biomass was lower in the rotation system (P-R) than in
the monoculture system (p < 0.05, Turkey-HSD), this did not affect
fruit yield (p > 0.05, Turkey-HSD). In terms of root symbiotic N[2]
fixation, we investigated root nodulation. The differences between
treatments were striking (Fig. [72]1a, b): peanut roots in crop mixture
had a three-fold higher nodule density (p < 0.001, Turkey-HSD) and a
six-fold higher nodule-to-root mass ratio (p < 0.001, Turkey-HSD) than
peanuts grown in monoculture. When comparing the two rotation systems
with and without maize intercropping (PM-R versus P-R), peanut nodule
density was 50% higher and nodule-to-root mass ratio was twice as high
in crop mixture compared to peanut-rape rotation (p < 0.05,
Turkey-HSD). Linear regressions showed that peanut plant biomass
(Fig. [73]1d, e; p = 0.026, t-test) were significantly positively
correlated with nodule-to-root mass ratio, while rhizosphere ammonium
levels (Fig. [74]1g, h; p ≤ 0.005, t-test) were positively correlated
significantly with both nodule density and nodule-to-root mass ratio.
With respect to the rhizosphere free living N[2] fixation, microbial
immobilization of molecular ^15N was measured using inoculation of
^15N[2] isotope labeling. Obviously, soil δ^15N in crop mixture was the
highest, with 16% and 4% higher (p < 0.05, Turkey-HSD) than peanut
monoculture and peanut-rape rotation after 7 days of incubation,
respectively (Fig. [75]1c). Peanut rhizosphere ammonium and nitrate
nitrogen levels were positively correlated with soil ^15N fixation
(Fig. [76]1f and i; p ≤ 0.013).
Fig. 1. Effect of crop diversification on peanut root nodulation and
rhizosphere N availability.
[77]Fig. 1
[78]Open in a new tab
a, b Effect on peanut nodulation. c Effect on peanut rhizosphere ^15N
fixation. The data in a–c are shown as the mean ± SD. The error bars
with p values between groups were calculated using one-way ANOVA and
Tukey’s post-hoc tests (two-sided, n = 3 biologically independent
replicates from 9 samples per treatment). d, e Correlations between
root nodulation and peanut plant biomass. f Correlations between soil
^15N fixation and plant biomass. g, h Correlations between root
nodulation and rhizosphere nitrogen components including total (black
points with regression), ammonium (blue points with regression) and
nitrate (green points with regression) nitrogen. i Correlations between
soil ^15N fixation and rhizosphere nitrogen components including total,
ammonium and nitrate nitrogen. In d–i, solid lines represent the least
squares regression fits and shaded areas with dotted line border
represent the 95% confidence intervals. PP, P-R and PM-R represent
peanut monocropping, peanut-oilseed rape rotation, and peanut-maize
intercropping rotated with oilseed rape, respectively. Source data are
provided as a Source Data file.
Consistent with the observed treatment effects on peanut growth and
nitrogen fixation, crop diversification also led to improved nutrient
availability in the peanut rhizosphere (Supplementary Data [79]1). In
particular, nitrate (NO[3]^--N) and ammonium (NH[4]^+-N) levels were
52% and 125% higher in crop mixture than in peanut monoculture. This
effect was smaller for total N (only 9%), but still significant
(p < 0.05, Turkey-HSD). In the bulk soil, nutrients (including total N,
NO[3]^--N, NH[4]^+-N, total K and available K) did not differ between
cropping systems (p > 0.05, Turkey-HSD), except organic carbon (SOC),
total P and available P which were increased by at least 6%, 12% and
115%, respectively, in crop mixture or rotation compared with peanut
monoculture. In general, bulk soil nutrient levels were on average
17–59% lower than the corresponding rhizosphere values (p < 0.05),
except for total K (p < 0.001). The consistently lower soil nutrient
availabilities in bulk soil were associated with a significantly lower
soil pH in bulk soil, compared to rhizosphere soil (> 0.5 units
difference, see Supplementary Data [80]1, p < 0.001, Turkey-HSD). This
is typical for the highly weathered red soil at our experimental site,
where soil nutrients are easily lost due to the local rapid temperature
increases and heavy precipitation (see Methods)^[81]25,[82]26.
Crop diversification alters the composition of peanut rhizosphere metabolites
Using ultra-performance liquid chromatography-tandem mass spectrometry
(UHPLC–MS/MS) we found that of the total 2,891 mass features detected
by either positive (50.1%) or negative (49.9%) ionization, 447
metabolic features (15.5% of total) were identified and annotated with
>70% fragment score of the m/z Cloud best match using the database
([83]http://www.mzcloud.org)^[84]27,[85]28. These included benzenoids,
hydrocarbon derivatives, lipids and lipid-like molecules, nucleosides
and nucleotide analogs, organic acids and their derivates, organic
nitrogen compounds, organic oxygen compounds, organohalogen compounds,
organoheterocyclic compounds, and phenylpropanoids and polyketides
(Fig. [86]S3, Supplementary Data [87]S2)^[88]29. Principal component
analysis (PCA) showed that the annotated metabolites were strongly
clustered according to crop diversification, with PC1 and PC2
accounting for 29.7% and 13.5% of the variance, respectively
(R = 0.783, p[ANOSIM] = 0.001) (Fig. [89]2a). A similar grouping
emerged from the hierarchical clustering of the Heatmap of metabolic
features between treatments (Fig. [90]S3). Using on a twofold change
threshold, we found 49 metabolites that differed significantly between
crop mixture and peanut monoculture, and 202 metabolites that differed
significantly between three-crop mixture and -crop rotation (p < 0.05,
t-test, Supplementary Data [91]2). In particular, four metabolites
annotated with over 95% fragment score were specifically enriched in
crop mixture compared with monoculture and rotation (fold change>2,
p < 0.001, t-test, Fig. [92]2b; Supplementary Data [93]2). Based on
primary and secondary mass spectrum analyzes and the comparison of
standards’ mass spectrum, these four metabolites were putatively
identified as quercetin, hyperoside, scopoletin and syringaldehyde
(Fig. [94]S4, [95]S5; Supplementary Data [96]2). Thus, crop
diversification enriched the peanut rhizosphere with specific
flavonoids (i.e. quercetin and its 3-o-galactoside hyperoside),
coumarins (i.e. scopoletin) and their derivatives (i.e.
syringaldehyde).
Fig. 2. Effect of crop diversification on metabolic production in the peanut
rhizosphere.
[97]Fig. 2
[98]Open in a new tab
a Principal component analysis of the metabolites detected in peanut
rhizosphere soil. QC, quality control samples (composed of a small
aliquot of each sample). b Screening for specific enriched metabolites
(m/z Cloud database best match >95%). Four metabolites, identified as
quercetin (Qu), hyperoside (Hy), scopoletin (Sc), and syringaldehyde
(Sy), were selected based on the fold change (>2) of relative
concentration by two sided t-test. The data are shown as the mean ± SD
(n = 3 biologically independent replicates from 7 samples per
treatment). c Principal component analysis of root transcriptomic
variance of PP and PM-R. Colored shades in a and c represent 95%
confidence ellipses around each group to distinguish community
differences (n = 7 biologically independent samples per treatment). d
Volcano plot showing differentially regulated genes in peanut roots in
PM-R versus PP. Genes with fold change >2 and q < 0.05 are marked with
purple (down-regulated) and pink (up-regulated). e Top-10 of enriched
Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways of
differentially expressed genes (DEGs) in PM-R versus PP. PP, P-R, and
PM-R represent peanut monocropping, peanut-oilseed rape rotation, and
peanut-maize intercropping rotated with oilseed rape, respectively.
Source data are provided as a Source Data file.
Crop diversification enhances peanut root gene expression for the
biosynthesis of specific metabolites
To determine the origin of the metabolites that were enriched under
crop diversification, we performed root transcriptome sequencing to
compare gene expression in peanut roots between the least diverse (PP)
and most diverse system (PM-R). As expected, PCA of transcriptomic data
confirmed clearly separated clusters of monoculture and crop mixture
(Fig. [99]2c). In total, 2,911 differentially expressed genes (DEGs)
were regulated in the crop mixturegroup compared to the peanut
monoculture group, including 1322 downregulated genes and 1,589
upregulated genes (twofold change cut off, q < 0.05, Fig. [100]2d).
Using the Kyoto Encyclopedia of Genes and Genomes (KEGG) database, we
found that the pathways of ABC transporters (ko02010), phenylpropanoid
biosynthesis (ko00940), cytochrome P450 related metabolism (ko00982 and
ko00980) and degradation of other organics or metabolism were ranked
among the top 10 KEGG pathways that were enriched in crop mixture
compared to monoculture (p < 0.001, t-test, Fig. [101]2e). Given the
results of the metabolome analyzes (Fig. [102]2b; Fig. [103]S4), we
focused on the phenylpropanoid (ko00940) and flavonoid biosynthesis
(ko00941) pathways and found that 48 DEGs involved in the former and
eight DEGs involved in the latter were enriched in crop mixture
(p = 2.7 × 10^−10 and p = 0.016, respectively, t-test; Supplementary
Data [104]3 and [105]4). Based on the functional classification by Gene
Ontology (GO), most of these genes in crop mixture were specifically
enriched for peroxidase activity (indicating oxidative stress) and
flavonoid biosynthetic process (Fig. [106]S6a; Supplementary
Data [107]5). Using quantitative PCR (qPCR) of fifteen representative
genes, our results showed that the observed differences in gene
expression between monoculture and crop mixture were consistent with
the root transcriptome data (p < 0.05, t-test, Fig. [108]S6b), except
for gene 4GQZ4H (which participates in beta-glucosidase biosynthesis,
p > 0.05, t-test, Fig. [109]S6b). Collectively, these results provide
strong evidences that the presence of maize and oilseed rape in the
crop mixture system induces flavonoid and coumarin biosynthesis
pathways in peanut root, resulting in the accumulation of these
metabolites in the rhizosphere.
Crop diversification alters the peanut rhizosphere bacterial community
Given the effects of crop diversification on peanut root metabolic
biosynthesis and release (Fig. [110]2), we next investigated whether
these changes in rhizosphere chemistry have the ripple-on effect on the
root associated bacterial community. In total, 4649 amplicon sequence
variants (ASVs) were detected. Although increasing crop species appears
to negatively affected bacterial alpha-diversity, only Shannon index
showed significant difference (p < 0.05, Turkey-HSD) and no significant
differences were found based on the Chao1 index (p > 0.05,
Fig. [111]3a, Turkey-HSD). Also, crop diversification resulted in more
variability, as shown in the first axis of principal co-ordinates
analysis of multivariate homogeneity of group dispersions (Fig.[112]3b,
p = 0.01, Turkey-HSD). Relative to the monoculture, microbiome
composition heterogeneity in peanut rhizosphere decreased with crop
diversification (p = 0.011-0.361, Turkey-HSD), indicating that
different crop co-existence induces peanuts to make root-associated
microbial composition more homogenous. At the level of individual taxa,
crop diversification was found to increase the relative abundances of
some dominant phyla (average relative abundance >5%) such as
Chloroflexi and Gammaproteobacteria, while Alphaproteobacteria
decreased (Fig. [113]3c and Supplementary Data [114]6, p < 0.05,
Turkey-HSD). In particular, several ASVs belonging to
Gammaproteobacteria were enriched in the peanut rhizosphere in crop
mixture group (Fig. [115]3d; Fig. [116]S7; Supplementary Data [117]7).
Linear correlation analysis showed that the relative abundances of
three-quarters (49 of 68) of crop mixture group enriched ASVs were
positively correlated with at least one of specific flavonoids,
coumarins and derivatives (Fig. [118]3e).
Fig. 3. Effect of crop diversification on the peanut rhizosphere bacterial
community.
[119]Fig. 3
[120]Open in a new tab
a Shannon and Chao1 richness indices. The data are shown as the
mean ± SD. The error bars with p values between groups were calculated
using one-way ANOVA and Tukey’s post-hoc tests (two-sided, n = 3
biologically independent replicates from 9 samples per treatment). b
Principal coordinate analysis (PCoA) of bacterial beta dispersion among
different groups based on Bray–Curtis distance (left) and distance of
centroid beta-dispersal values for groups (right). Box plots indicate
median (black line), 25th, 75th percentile (box), and 5th and 95th
percentile (whiskers). p values were adjusted using multiple (95%
family-wise confidence level) comparisons using Tukey’s HSD (n = 3
biologically independent replicates from 9 samples per treatment). c
Phylum-level distribution of ASVs. d Ternary plot of bacterial ASVs
shared among the different peanut rhizosphere communities. Circle sizes
represent the relative abundances of the bacterial ASVs identified.
Circles with red borders represent ASVs with the same marker sequence
as the subsequent bacterial isolates (Fig. [121]4a). e Heatmap of
specific enriched metabolites and PM-R enriched bacterial ASVs
according to Pearson’s correlations. Positive and negative correlations
are shown in blue and red, respectively. *p < 0.05, **p < 0.01,
***p < 0.001. PP, P-R, and PM-R represent peanut monocropping,
peanut-oilseed rape rotation, and peanut-maize intercropping rotated
with oilseed rape, respectively. Source data are provided as a Source
Data file.
Crop diversity-enhanced rhizosphere metabolites trigger bacterial nitrogen
fixation
As we expected, the rhizosphere bacterial community was selectively
influenced by the production and exudation of active specific
metabolites from peanuts in the system with maize and rape. However, it
is not clear why peanut alters its rhizosphere chemistry and
microbiota. It could be related to the higher plant nitrogen fixation
exhibited in the field of crop mixture group. To test this hypothesis,
we isolated and cultivated 109 bacterial species from the peanut
rhizosphere in crop mixture and tested the responses of selected
isolates, including free-living and symbiotic N[2] fixers, to specific
metabolites in microplate incubation assays (Figs. [122]4, [123]5).
These isolates represented five phyla: Firmicutes (54.1%),
Actinobacteria (19.3%), Gammaproteobacteria (12.8%),
Alphaproteobacteria (6.4%) and Betaproteobacteria (5.5%)
(Fig. [124]4a). From this set we selected and purified 27 isolates for
microplate incubation assays (Supplementary Data [125]8), which
included four strains (strains N4, OP14, N68 and N69) affiliated with
Pseudomonadales and Burkholderiales that had similar marker sequences
as the ASVs enriched in crop mixture group (ASV1, ASV335, ASV501,
ASV1260 marked in Fig. [126]3d) (Supplementary Data [127]9). Of these
four representative strains, three (N4, N68 and N69) were able to grow
on a nitrogen-free medium, indicating their capacity for free-living
N[2] fixation. In addition, we purified four isolates belonging to
Rhizobium and Bradyrhizobium (N43, N45, N47 and N59). Although these
strains were not phylogenetically consistent with specifically enriched
ASVs in crop mixture group, their potential for host invasion and
colonization and presence in the peanut rhizosphere could be related to
the increased host nodulation and rhizosphere N availability observed
in crop mixture group (Fig. [128]1). Finally, we selected 19 isolates
of other species, whose marker sequences were not similar to the
enriched ASVs, to serve as controls.
Fig. 4. Bacterial isolates from the PM-R peanut rhizosphere: effects of
typical metabolites on bacterial growth rates, free-living N[2] fixation.
[129]Fig. 4
[130]Open in a new tab
a Cladogram showing the phylogenetic relationships among 109
heterotrophic bacterial isolates from the PM-R rhizosphere and their
potential for N[2] fixation. The leaf labels indicate the
representative sequence IDs, with red labels indicating the four
selected isolates that were phylogenetically consistent with
PM-R-enriched ASVs at the order and genus levels, and blue labels
indicating the four selected rhizobia isolates. The rings, from the
inner to outside circles, represent (1) the phylum level taxonomy of
isolates; (2) the capacity for bacterial nitrogen fixation; and (3) the
strains selected for microplate incubation with typical rhizosphere
metabolites. b–e Effect of rhizosphere metabolites on the growth rate
(V) of selected bacterial strains, measured in microplate assays during
the bacterial logarithmic growth phase (n = 6 biologically independent
samples). V > 1 represents growth promotion with metabolite addition;
V < 1 represents growth inhibition. Bars with p values represent
significant differences as determined by two-sided t-test. f–h
Correlations between bacterial growth of free-living N[2] fixers and
their capability for N[2] fixation. Solid lines in f-h represent the
least squares regression fits and shaded areas with dotted line border
represent the 95% confidence intervals. Qu quercetin, Hy hyperoside, Sc
scopoletin, Sy syringaldehyde. Source data are provided as a Source
Data file.
Fig. 5. Bacterial isolates from the PM-R peanut rhizosphere: effects of
typical metabolites on Bradyrhizobium N47 colonization.
[131]Fig. 5
[132]Open in a new tab
a Diagram of symbiosis signaling pathway of Bradyrhizobium N47 in
peanut root, inducing nodule formation at lateral root bases. b, c
Expression of bradyrhizobial nodulation signaling genes, 48 h after
addition of metabolites. d–f Expression of host common symbiotic
signaling genes, measured in peanut roots 24 h after bacterial
inoculation and metabolite addition. g Peanut root nodule number 30
days after bacterial inoculation and metabolite addition. The data in
b–g are shown as the mean ± SD. The error bars with lowercase represent
significant differences between groups (p < 0.05) via one-way ANOVA and
Tukey’s post-hoc tests (two-sided, n = 6 biologically independent
samples). For exact statistical values, see Supplementary data [133]12.
C, control; Br Bradyrhizobium N47, Qu quercetin, Hy hyperoside, Sc
scopoletin, Sy syringaldehyde. Source data are provided as a Source
Data file.
Using these 27 strains, we conducted microplate incubation assays to
evaluate whether bacterial growth rates were influenced by the
metabolites that were specifically enriched in the rhizosphere of
peanut in mixture: quercetin, hyperoside, scopoletin and syringaldehyde
(Fig. [134]S8). On average, these individual metabolites were found to
affect the growth rate (V), either positively or negatively, of 48% of
the tested strains in 1/5 TSB medium containing 5 µg mL^-1 of one of
different metabolites (Fig. [135]4b–e, p < 0.05, t-test).
Interestingly, none of the metabolites had a negative effect on the
growth rates of the four representative isolates from Pseudomonadales
and Burkholderiales. Among these strains only neutral or positive
effects were detected (N4, p = 0.03–0.50; N68, p = 0.0003–0.04; N69,
p = 0.01–0.23; OP14, p = 0.02–0.66, t-test). In addition, for the
free-living N[2] fixers in this group (N4, N68 and N69), their
N[2]-fixing capacities under metabolite addition were consistently
positively correlated with their growth rates (Fig. [136]4f–h).
In contrast to the generally positive responses of free-living N[2]
fixers to the tested metabolites, the growth rates of the four
rhizobial isolates (i.e. potential symbiotic N[2] fixers) were
generally insensitive or even showed a negative response to flavonoids
and coumarins (N43, p = 0.0001–0.64; N45, p = 0.04–1.00; N47,0.30–0.84;
and N59, p = 0.01–0.95, t-test), except for N43 which responded
positively to scopoletin (p = 0.012, t-test) and syringaldehyde
(p < 0.001, t-test). Since flavonoids are common signals for the
establishment of legume-rhizobia symbioses^[137]6, we wondered about
the poor growth response of rhizobia to these metabolites. Considering
the large increase in peanut nodulation in crop mixture (Fig.[138]1),
we hypothesized that increased rhizodeposition of flavonoids and their
derivates in three-crop mixture enhanced the rhizobia’s ability to
colonize host roots, rather than their growth rates. To test this, we
first inoculated peanut seedlings with the individual N[2]-fixing
rhizobial isolates (N43, N45, N47 and N59), and found that only
Bradyrhizobium N47, through lateral root base invasion and intra- and
inter-cellular colonization^[139]30, successfully induced nodulation of
peanut host plants within 30 days (Fig. [140]5a, Fig. [141]S9a–d).
Therefore, we focused on this strain to assess whether the enriched
root metabolites increased nodulation signaling during the
establishment of the symbiosis. Indeed, the addition of flavonoids and
coumarin to pure Bradyrhizobium cultures increased the expression of
Bradyrhizobial nodD1 and nodC genes by 21-126% (NodD1) and 216-430%
(NodC), compared to controls (Fig. [142]5b, c). Similar stimulating
effects on the nod genes (NodD1 and NodC) have also been observed in
the model microorganism Sinorhizobium meliloti (strain1021) for such
metabolites (Fig. [143]S10). Simultaneously, the addition of these
metabolites to peanut seedlings inoculated with Bradyrhizobium N47
enhanced peanut root gene expression of AhSYMRK, AhCCaMK and AhNIN by
43-169% at the transcriptional level compared to Bradyrhizobium
inoculation without added metabolites (Fig. [144]5d–f). These
upregulated plant genes play crucial roles in nodule organogenesis by
encoding leucine-rich repeat receptor-like kinase (SYMRK), calcium
spikes by a calcium calmodulin-dependent protein kinase (CCaMK) and an
RWP-RK transcription factor (NIN)^[145]31,[146]32. Together, the
metabolite-triggered increases in bacterial and root gene expression
eventually resulted in a 21-90% increase in nodule number, measured 30
days after inoculation and metabolite addition (Fig. [147]5g). Thus, it
appeared that the flavonoids and coumarins acted as a chemical
communication signal between peanut and Bradyrhizobium. The fourth
metabolite, syringaldehyde, did not have such effect (Fig. [148]5b–g,
p > 0.05, Turkey-HSD). Overall, these results suggest that peanut
rhizosphere metabolites produced under the influence of coexisting
crops enhance the peanut- Bradyrhizobium symbiosis, but not growth
rate.
Discussion
In both natural and agricultural ecosystems, belowground facilitation
between legume and non-legume plants has been found to regenerate soil
fertility, especially N availability^[149]33–[150]35. Many studies
simply attribute this positive plant-soil feedback to the innate
function of legumes to fix atmospheric N[2] and focus on how this
service improves the productivity of non-legume plants, such as maize
and wheat in crop systems (e.g., Li et al and Zhao et
al)^[151]23,[152]36. Here, we shift the focus to the legumes themselves
to examine how their capacity to contribute N to the system is
influenced by coexisting crops. Data from our field study showed that
peanut biomass, root nodulation (including nodule density and
nodule-to-root mass ratio) and soil ^15N[2] fixation were significantly
increased in the most diverse system (including both rotation with
oilseed rape and intercropping with maize), compared to the peanut
monoculture and peanut-oilseed rape rotation without maize
intercropping (Fig.[153]1: PM-R versus PP and P-R). Moreover, the
increased nodulation and free-living N[2] fixation were positively
correlated with rhizosphere nitrogen accumulation. These findings
suggested an interspecific positive feedback, from maize in particular,
on peanut N[2] fixation.
To understand the mechanisms underlying the increased nodulation,
peanut N uptake and N availability, we zoomed in on the peanut roots
and rhizosphere. Chemical analysis of peanut rhizosphere soil showed
that peanut grown in the most diverse cropping system accumulated
specific metabolites in its rhizosphere: flavonoids (quercetin and
hyperoside), coumarins (scopoletin) and their derivatives
(syringaldehyde). These active secondary metabolites, which many plant
species are able to produce, are synthesized via the phenylpropanoid
pathway^[154]37,[155]38 and regulated by cytochrome P450 enzymes and
ATP-binding cassettes (ABC)-transporters for flavonoids and coumarins
biosynthesis and transport^[156]38,[157]39. Coincidentally, these
pathways (KEGG pathway top-10) were significantly enriched in peanuts
grown in the most diversified system. By examining the increased
relative expression of functional genes that participate in the
specific metabolic biosynthesis and transport processes in peanut roots
(Fig. [158]S6b), it appeared that the metabolites were mainly produced
by the peanut plants their own. Although we cannot completely exclude
the possibility that some of the measured metabolites were originated
from gradient diffusion from neighboring (maize) or previously grown
(rape) plants, soil pore space structure and soil microbial substrate
consumption have been reported to greatly diminish the diffusion
efficiency of these soluble compounds from neighboring or historical
plant species at long distance (such as >10 cm) in
soil^[159]9,[160]40,[161]41.
A relevant question is why the peanut rhizosphere accumulated these
metabolites specifically when peanut was intercropped with maize.
Shaped through a long evolutionary process, rhizosphere deposition is
among a plant’s most sophisticated strategies to adapt to changing
environments^[162]14. Flavonoids and coumarins, the secondary
metabolite deposits that we found to be increased in the rhizosphere of
peanuts co-grown with maize, are known to be produced by plants in
response to environmental changes, including light intensity,
ultraviolet radiation, temperature variation and drought^[163]42. Shade
from the maize canopy and associated competition for light may have
triggered the peanut transcriptional response and induced flavonoid
production^[164]43,[165]44. In addition, maize root exudates may have
enhanced peanut flavonoid biosynthesis, as suggested by Li et al in a
faba bean-maize intercropping system^[166]21. Unlike the neighboring
maize effect, peanuts that were rotated with oilseed rape did not
result in biomass promotion, despite increasing nitrogen fixation in
soil. This suggests that, in addition to resource availability, other
factors in the legacy effect stemming from historical species may play
a role in determining peanut development^[167]18,[168]19,[169]22.
Apart from their origin, we also shed light on the function of these
metabolites, i.e. their role in the chemical dialog between peanut and
its rhizosphere microbial community. In general, we found that the
higher specific metabolic deposition in the peanut rhizosphere
triggered by crop diversity had a greater impact on bacterial beta
diversity than alpha diversity. Taxonomic analysis showed that ASVs
affiliated with the orders Pseudomonadales and Burkholderiales were
specifically enriched in the peanut rhizosphere of the most diverse
system, PM-R. Three of the isolated strains (N4, N68, and N69), with
marker sequences similar to the enriched Pseudomonadales and
Burkholderiales ASVs in PM-R, were found to be capable of free-living
N[2] fixation. Remarkably, their growth and correlated level of
N[2]-fixation were promoted by the specific metabolites that had
accumulated in the peanut rhizosphere of the most diverse system. Thus,
the positive growth response of these strains to plant diversity-driven
specific metabolites may have synergistically promoted free-living N[2]
fixation in the peanut rhizosphere, as reflected by the increased
rhizosphere N availability observed in the field. In the case of these
N[2]-fixers, specific flavonoids and coumarin act more like as carbon
resources, supporting microbial survival and nitrogen fixation. Given
the potentially antimicrobial effect of flavonoids^[170]4,[171]38, the
positive response of the observed strains to flavonoids and coumarins
suggested their adaptative plasticity under the filtering effect of
rhizosphere chemical selection^[172]45.
The observed chemical interactions between peanut and rhizosphere
bacteria involved not only free-living but also symbiotic N[2]-fixers.
Focusing on Bradyrhizobium (N47, which was isolated from the peanut
rhizosphere and tested for symbiotic potential), we found that the
metabolites (quercetin, hyperoside and scopoletin) helped to initiate
the plant-microbe symbiosis and thus aided the survival of both by
nodulation. This is in line with findings that flavonoids can act as a
chemical language between rhizobia and legumes to initiate root
nodulation^[173]6,[174]46. In this dialog, the microbial Nod genes
regulate the production of Nod factors
(lipochitooligosaccharide)^[175]6,[176]31, which in turn activate plant
downstream signalling genes involving SYMRK, CCaMK and NIN. These
factors are needed to trigger the nodule developmental
program^[177]31,[178]32 which in peanut initiates at the lateral root
base (Fig. [179]S9c)^[180]30,[181]31. Thus, the specific deposits that
accumulated in the peanut rhizosphere of the most diverse cropping
system helped to activate a common symbiosis signaling pathway. This
molecular-level finding may explain the increased nodule density with
crop diversification observed in the field (Fig. [182]1).
It is worth noting that scopoletin did induce the expression of
bradyrhizobial genes involved in root nodule-forming symbioses but did
not increase the number of root nodules compared to the control
treatment with bradyrhizobial inoculation alone (Fig. [183]5). To the
best of our knowledge, evidence for the role of coumarins in root
nodulation or N[2]-fixation is scarce^[184]47. When we used the model
plant Medicago truncatula (A17) to establish symbiosis with its
rhizobia Sinorhizobium meliloti (strain1021), the addition of trace
scopoletin (SML, 5 µg mL^-1) resulted in the upregulation of genes
involved in root nodulation (including MtCCaMK, MtNIN, MtERN1,
MtVAPYRIN, MtENOD11, MtRIP1 and MtFLOT4) and plant defense (including
MtPR4, MtPR10 and MtGST) (p < 0.05). This ultimately led to an increase
in both root nodules and biomass in Madicago compared with control (C)
and single bacterial inoculation (SM). Comparatively, the addition of
high scopoletin (SMH, 50 µg mL^-1) weakens the effect on plant
nodulation and defense compared with low scopoletin addition
(Fig. [185]S11). Some coumarins (such as scopoletin and esculetin) are
recognized for their efficiencies as antibiofilm and antimicrobial
compounds, contributing to plant immunity^[186]38. This rhizosphere
selective effect through specific metabolites could promote microbially
elicited plant-microbe interactions, providing benefits for nutrient
acquisition in the rhizosphere and plant health^[187]47.
Interestingly, whether flavonoids and coumarins induced by maize and
oilseed rape coexistence or gaseous ethylene which was previously found
to be stimulated by non-legume species recognition^[188]17, these plant
secondary metabolites have been reported to not only involved in the
plant defense of biotic and abiotic stresses but also act as the
chemical cue for “belowground cry-for-help”: specific components of
root exudates to recruit root-associated and soil-associated
microbiomes to enhance plant fitness^[189]14,[190]48. Here, we provide
a mechanistic understanding of why specific crop combinations may show
higher levels of leguminous N[2] fixation than expected. By combining a
field investigation of multi-omics (rhizosphere metabolome analysis and
peanut root amplicon sequencing) with a molecular-level analysis of
laboratory plant-microbe systems, we showed that crop diversification,
specifically intercropping with maize, induces a chemical dialog in
which peanut plants select specific free-living and symbiotic
N[2]-fixing bacteria through rhizosphere metabolite deposition
(Fig. [191]6). The substrate adaptation of free-living N[2]-fixers and
changes in symbiont life strategies in response to such chemical cues
ultimately determines rhizosphere functions in the holobiont comprised
of the host plant and its microbiota^[192]49. Such plant-microbe
functional alliances, with their mutual fitness benefits, present a new
perspective for understanding the relationship between aboveground
plant diversity and belowground ecosystem
functioning^[193]33,[194]45,[195]50. Finally, our findings also provide
relevant mechanistic insights for the design of intelligent crop
combinations and targeted manipulation of the rhizosphere microbiome
and functionality in sustainable agroecosystems.
Fig. 6. Effect of crop diversification on metabolite deposition and
biological N[2] fixation in the leguminous rhizosphere.
[196]Fig. 6
[197]Open in a new tab
The precrops (e.g. legacy effects from oilseed rape) and neighbors
(e.g. interspecific interactions of maize) induce changes in the
metabolites deposited into the legume rhizosphere. The deposited
flavonoids and coumarins selectively influence the functional
activities of free-living and symbiotic N[2] fixers which enhance
rhizosphere N[2] fixation in the field.
Methods
Field experiment site and design
For this study, samples were collected from a long-term field
experiment, established in 2011 at the Yingtan Red Soil Ecological
Experimental Station of the Chinese Academy of Sciences in Jiangxi
Province, China (28°12’N, 116°55’E). The climate is subtropical (see
Chen et al.^[198]17 for more detailed description). The soil is an
acidic, loamy clay derived from Quaternary Red Clay and classified as a
Ferralic Cambisol^[199]51. Before the establishment of this experiment,
the site was a mass pine (Pinus massonina Lamb.) natural secondary
forest.
The field experiment compared three crop systems based on common
practices in local intensive agricultural systems of tropics and
subtropics^[200]36,[201]52: (I) a single-crop system (PP, monoculture
of peanut, Arachis hypogaea), (II) a two-crop system (P-R, rotation of
peanut and oilseed rape Brassica campestris), and (III) a three-crop
system (PM-R, intercropping peanut and maize Zea mays L., rotated with
oilseed rape) (Fig. [202]S1). Each treatment was carried out in three
randomized blocks, with three replicate plots of 20 m × 5 m × 1.5 m
(width × length × depth), each plot further divided into three
subplots. The plots were separated by 10 cm (thickness) concrete baffle
plates, and the subplots by ridges.
Field cropping practices
The design of the three-crop system (PM-R, intercropping peanut and
maize, rotated with oilseed rape) during the spring-summer season
(April-August) included a 1.0 m peanut strip (2 rows of peanut, with a
0.5 m interrow distance) and a 1.0 m maize strip (2 rows of maize, with
a 0.5 m interrow distance). The interplant distance within the same row
was 0.2 m for peanuts and 0.25 m for maize. In the single-crop system
(PP, monoculture of peanut) and two-crop system (P-R, rotation of
peanut and oilseed rape) treatments, the interrow and interplant
distances for peanut were 0.5 m and 0.2 m, respectively, which made the
peanut density identical to that in a comparable area of the PM-R
treatment. During the autumn-winter season (September-March), the
interrow and interplant distances for oilseed rape were 0.5 m and
0.2 m, respectively, in the P-R and PM-R treatments. Peanut and maize
were sown on the 1st–15th of April, and oilseed rape was sown on the
1st–15th of September. The topsoil (0–25 cm) was ploughed before
cultivation every year. All plots were irrigated and weeded during the
growing period.
Field soil characteristics and annual fertilizations
The basic soil characteristics of the field experiment in 2012 were as
follows: organic matter 4.58 g kg^-1, total N 0.45 g kg^-1, total P
0.35 g kg^-1, total K 11.84 g kg^-1, available P 1.68 mg kg^-1,
available K 54.17 mg kg^-1, NH[4]^+-N 5.24 mg kg^-1, NO[3]^--N
2.59 mg kg^-1, and pH 4.84. All treatment groups received urea
(containing 46% N), calcium superphosphate (containing 12.5% P[2]O[5])
and potassium chloride (containing 60.0% K[2]O) chemical fertilizers at
rates of 150 kg ha^-1 y^-1, 75 kg ha^-1 y^-1 and 60 kg ha^-1 y^-1,
respectively, 10-15 days before seeds were sown in the spring. In the
oilseed rape planting season (P-R and PM-R treatments), 1/2 doses of
chemical fertilizers were applied before seed sowing.
Collection of soil and plant samples
Soil and peanut plant samples were collected on 10 June 2019 at the
peanut flowering stage. To sample rhizosphere soil, we randomly
selected six peanut plants from each subplot and gently brushed off the
soil adhering to the root systems, resulting in one composite
rhizosphere sample per subplot (n = 9 per treatment). The removed
plants were used for determining plant height, biomass and nodulation.
To sample the bulk soil, we collected six soil cores in each subplot
(5–20 cm depth), 25 cm away from crop roots, using an “S” sampling
pattern, and then pooled the cores into one composite bulk soil sample
per subplot (n = 9 per treatment). All samples were immediately sieved
at 4 mm to remove plant debris and stones. From each soil sample, 3 g
was stored at ‒80 °C for microbial molecular analysis, while another
20 g was stored at 4 °C for soil chemical analyzes. The soil chemical
properties were characterized by standard methods below. In addition,
3 g of each rhizosphere soil sample was frozen in liquid nitrogen and
transported on dry ice for soil metabolic analysis. Of the latter
group, two samples per treatment were set aside for pre-analysis but
were subsequently lost due to a broken freezer; hence for the
metabolite analysis n = 7 instead of n = 9. Finally, 5 g of each
rhizosphere sample from the PM-R treatment was stored at 4 °C for
functional bacterial isolation.
Based on the results of the rhizosphere metabolic analysis, we
collected fresh peanut root samples from the PP and PM-R treatments on
15 June 2020 for root transcriptome analysis and gene expression. For
each treatment, seven randomly selected peanut plants (2–3 plants per
replicate plot) were dug out using a shovel. Roots were immediately
separated from the plants and washed twice with sterile water. Next,
tissues 3–9 cm from the root bases were cut into small pieces (2 cm
length), frozen in liquid nitrogen, and transported to the laboratory
for RNA extraction, transcriptome sequencing and quantitative reverse
transcription polymerase chain reaction (qRT-PCR).
Soil chemical properties measurement
The soil chemical properties were measured as follows: the pH was
determined with a glass electrode and a water-to-soil ratio of 2.5:1
(v:w). The SOC content was determined by the Walkley-Black wet
digestion method^[203]53. The TN and nitrate and ammonium nitrogen
(NO[3]^--N and NH[4]^+-N, respectively) concentrations were measured by
the Kjeldahl method^[204]54. Total phosphorus (TP) was digested with
HF-HClO[4], AP was extracted with sodium carbonate and sodium
bicarbonate and then determined with the molybdenum blue
method^[205]55, and AK was determined by flame photometry after
extraction with ammonium acetate^[206]56.
Determination of plant physiological indexes
Fresh peanut plants were immediately transferred to the laboratory to
measure plant height and biomass after removing soil attached to the
roots. For the quality and quantity of nodule density, we measured the
number of nodules (n), the biomass of nodules and root per plant, and
the cumulative length of each plant root (diameter > 1 mm).
[MATH: Noduledensity(n<
/mstyle>10cm<
/mrow>−1)<
mo>=ThenumberofnodulesperplantCumulativerootlengthofeachplant×10 :MATH]
1
[MATH: Noduletorootmassratio=
ThenodulebiomassperplantTherootbiomassperplant :MATH]
2
Soil incubation for ^15N detection
Peanut rhizosphere soil (10 g) from PP, P-R and PM-R field treatments
was spread flat in small glass serum vials (50 mL). Each vial was
sprayed with 1 mL distilled water and then underwent a pre-incubation
period (25 °C for one day) to activate soil microbiota. At the start of
the incubations, an anaerobic indicator strip was placed in the vial to
monitor O[2] depletion before closing the vial with butylrubber
stoppers and aluminum crimp seals. Then, 20 mL of the gas phase in each
vial was evacuated and replaced with an artificial gas mix containing
78%^15N[2] (99% atom) and 22% O[2] using a syringe. Vials were placed
in the dark at 28 °C for 7 days. On the fourth day of cultivation,
vials were opened for natural gas exchange and then sealed again. The
same volume of gas (20 mL) was replaced with an artificial gas mix
again. After incubation, soil was collected and sieved at 0.15 mm for
soil δ^15N analysis. Each soil treatment was conducted with nine
replicates. Soil of PP treatment that was incubated with natural air
instead of the artificial gas mix was processed identically and
detected as the control.
Soil ^15N detection
Soil δ^15N analysis was carried out using an Isolink NC elemental
analyzer (EA; Thermo Scientific, MA, USA) coupled under continuous flow
ConFlo IV (universal continuous flow interface) to the DELTA V
Advantage Isotope Ratio Mass Spectrometers (IRMS) (IRMS; Thermo
Scientific, MA, USA). Soil samples (ca. 30 mg) were weighed into tin
capsules for sample combustion and subsequent reduction over heated
copper (Cu) wires within the EA^[207]57. The resulting N[2] was
transferred into the IRMS to determine δ^15N values, where isotope
ratios were calculated as δ^15N.
The delta (δ) notation is conventionally used to express the difference
in isotope ratios of the sample (sa), relative to a reference standard
(st)-- R[sa] and R[st], respectively.
[MATH: δ15N(‰)
=Rsa−RstRst×
1000 :MATH]
3
where the isotope ratio, R is the amount of heavy isotope over the
amount of lighter isotope.
Bacterial high-throughput sequencing
Bacterial community composition was assessed using 16 S rRNA
high-throughput sequencing. DNA was extracted from 1.0 g of soil using
the FastDNA SPIN Kit according to the manufacturer’s instructions. The
quality and quantity were measured using a NanoDrop spectrophotometer
(NanoDrop Technologies, Wilmington, DE, USA). The V3-V4 regions of the
bacterial 16 S rRNA gene were targeted with the primer pair 338 F
(5′-ACTCCTACGGGAGGCAGCAG-3′) and 806 R (5′-GGACTACHVGGGT
WTCTAAT-3′)^[208]58. The PCR systems and conditions were consistent
with Duan et al. ^[209]58. Amplicon sequencing libraries were
constructed using the MiSeq Reagent Kit v3 according to the
manufacturer’s instructions. High-throughput paired-end sequencing was
performed on the Illumina HiSeq 2500 platform (Illumina, San Diego, CA,
USA) by OE Biotech Co., Ltd. (Shanghai, China).
The adapt and primer sequences were removed using Cutadapt
(v4.4)^[210]59. Then DADA2 (Divisive Amplicon Denoising Algorithm 2)
(version 1.26) was used to merge, filter, trim, and denoise the raw
data, and finally generate amplicon sequence variants (ASVs)^[211]60.
Taxonomic assignment for the ASVs was performed using RDP Classifier
against the SILVA rRNA database (version 138)^[212]61. After removing
mitochondria and chloroplast, samples were rarefied to the same
sequencing depth (16000) for further analysis.
Rhizosphere soil nontargeted metabolic analysis
To determine the effect of crop diversification on metabolic deposition
in the peanut rhizosphere, the metabolic composition of peanut
rhizosphere soil samples was determined by nontargeted metabolic
profiling, using ultra-performance liquid chromatography-tandem mass
spectrometry (UHPLC–MS/MS). Each sample was ultrasonically extracted in
an acetonitrile diluent before the analysis. A nontargeted approach
with a Q-Exactive quadrupole-Orbitrap mass spectrometer was used to
identify metabolites^[213]62. Mass spectra were acquired in positive
and negative ionization modes through full MS and higher energy
collisional dissociation (HCD) data-dependent MS/MS analysis (full
MS-ddMS2). The mass ranged from 50-2000 m/z. Data were acquired using
Xcalibur 2.1 software (Thermo Scientific, Rockford, USA). The datasets
from the Q-Exactive analysis were processed with a metabolomics
processing workflow using Compound Discoverer 3.0 software (Thermo
Scientific, San Jose, CA, USA) to match the primary and secondary mass
spectra and retention time (RT) from the database. The advanced Mass
Spectral Database (mzCloud, [214]https://www.mzcloud.org/), [215]HMDB
and ChemSpider ([216]http://www.chemspider.com/) were chosen as
references for the nontargeted metabolomics workflow. All calibration