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ORIGINAL RESEARCH
published: 05 November 2020
doi: 10.3389/fpls.2020.567388
Edited by:
Santiago Gutierrez,
Universidad De León, Spain
Reviewed by:
Francesca Mapelli,
University of Milan, Italy
Mara Novero,
University of Turin, Italy
*Correspondence:
Paolo Bonini
pb@ngalab.com
Specialty section:
This article was submitted to
Plant Pathogen Interactions,
a section of the journal
Frontiers in Plant Science
Received: 29 May 2020
Accepted: 15 October 2020
Published: 05 November 2020
Citation:
Bonini P, Rouphael Y,
Miras-Moreno B, Lee B, Cardarelli M,
Erice G, Cirino V, Lucini L and Colla G
(2020) A Microbial-Based
Biostimulant Enhances Sweet Pepper
Performance by Metabolic
Reprogramming of Phytohormone
Profile and Secondary Metabolism.
Front. Plant Sci. 11:567388.
doi: 10.3389/fpls.2020.567388
A Microbial-Based Biostimulant
Enhances Sweet Pepper
Performance by Metabolic
Reprogramming of Phytohormone
Profile and Secondary Metabolism
Paolo Bonini1*, Youssef Rouphael2, Begoña Miras-Moreno3, Byungha Lee1,
Mariateresa Cardarelli4, Gorka Erice5, Veronica Cirino5, Luigi Lucini3and
Giuseppe Colla6
1Next Generation Agronomics Laboratory (NGAlab), La Riera de Gaia, Tarragona, Spain, 2Department of Agricultural
Sciences, University of Naples Federico II, Portici, Italy, 3Department for Sustainable Food Process, Research Centre
for Nutrigenomics and Proteomics, Università Cattolica del Sacro Cuore, Piacenza, Italy, 4Consiglio per la ricerca
in agricoltura e l’analisi dell’economia agraria, Centro di ricerca Orticoltura e Florovivaismo, Pontecagnano Faiano, Italy,
5Atens, La Riera de Gaia, Tarragona, Spain, 6Department of Agriculture and Forest Sciences, Università degli Studi della
Tuscia, Viterbo, Italy
Microbial-based biostimulants can improve crop productivity by modulating cell
metabolic pathways including hormonal balance. However, little is known about the
microbial-mediated molecular changes causing yield increase. The present study
elucidates the metabolomic modulation occurring in pepper (Capsicum annuum
L.) leaves at the vegetative and reproductive phenological stages, in response to
microbial-based biostimulants. The arbuscular mycorrhizal fungi Rhizoglomus irregularis
and Funneliformis mosseae, as well as Trichoderma koningii, were used in this
work. The application of endophytic fungi significantly increased total fruit yield by
23.7% compared to that of untreated plants. Multivariate statistics indicated that the
biostimulant treatment substantially altered the shape of the metabolic profile of pepper.
Compared to the untreated control, the plants treated with microbial biostimulants
presented with modified gibberellin, auxin, and cytokinin patterns. The biostimulant
treatment also induced secondary metabolism and caused carotenoids, saponins,
and phenolic compounds to accumulate in the plants. Differential metabolomic
signatures indicated diverse and concerted biochemical responses in the plants
following the colonization of their roots by beneficial microorganisms. The above findings
demonstrated a clear link between microbial-mediated yield increase and a strong up-
regulation of hormonal and secondary metabolic pathways associated with growth
stimulation and crop defense to environmental stresses.
Keywords: Funneliformis mosseae,Rhizoglomus irregularis,Trichoderma koningii,Capsicum annuum L., plant
metabolomics, metabolic reprogramming
INTRODUCTION
Three major current global challenges are food security, environmental degradation, and climate
change. The first may be augmented, and the latter two diminished by improving nutrient
(nitrogen, phosphorus) use efficiency in agricultural crop production and stabilizing yield by
practicing sustainable agriculture (Searchinger et al., 2018). The application of plant biostimulants
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Bonini et al. Metabolic Reprogramming of Sweet Pepper Leaves
such as beneficial microbes [arbuscular mycorrhizal fungi
(AMF), Trichoderma spp., plant growth-promoting rhizobacteria
(PGPR)], and bioactive substances (humic and fulvic acids,
macroalgae and microalgae, protein hydrolysates and
silicon) used either separately or in combination may
help crops contend with the challenges mentioned above
(Rouphael and Colla, 2020).
Plant biostimulants were recently defined in the Regulations
of the European Parliament and Council (Regulation EU
2019/1009) as “. . .EU fertilising product(s) able to stimulate
plant nutrition processes independently of the product’s nutrient
content with the sole aim of improving one or more of the
following characteristics of the plant or the plant rhizosphere:
(1) nutrient use efficiency, (2) tolerance to abiotic stress, (3)
quality traits, or (4) availability of confined nutrients in the soil
or rhizosphere”. AMF comprise a very important category of
biostimulants (Rouphael et al., 2015;Bitterlich et al., 2018).
They are members of the Glomeromycotina subphylum and
establish mutualistic relationships with 74% of all terrestrial plant
species (Spatafora et al., 2016). AMF boost productivity and
enhance tolerance to abiotic stress (high temperature, drought,
and salinity) in crops (Rouphael et al., 2015). These findings are
due to the AMF-mediated enhancement of (1) growth and vigor
of the root apparatus in terms of biomass, length, density, and
branching; (2) macronutrient (N, P, and Fe) and micronutrient
(Mn and Zn) uptake and assimilation; (3) water relations and
photosynthetic activity; (4) secondary metabolism; (5) release
of low- and high-molecular-weight organic compounds such
as amino acids, phenolics, organic acids, and proteins into the
rhizosphere; (6) phytohormone signaling (Rouphael et al., 2015,
2020b;Yakhin et al., 2017;Rouphael and Colla, 2018). The
indirect and direct mechanisms of AMF influence shoot and root
function and augment crop agronomic performance. Other plant
beneficial endophytic fungi include Trichoderma spp. Several
of them are registered as microbial biological control agents
(López-Bucio et al., 2015;Rouphael et al., 2020a). However,
several studies reported that certain Trichoderma spp. including
T. atroviride,T. koningii,T. harzianum, and T. virens are
other plant biostimulants that boost crop performance (Colla
et al., 2015) and nutrient use efficiency and/or endue plants
with abiotic stress tolerance (Saia et al., 2020). The direct and
indirect mechanisms of the biostimulant action of Trichoderma
strains include (i) improvement of lateral root development,
(ii) induction of plant mitogen-activated protein 6, and (iii)
production and rhizosphere excretion of auxins and secondary
metabolites such as volatile and non-volatile substances that
stimulate various plant responses and enhance crop nutrient
uptake, resilience, and productivity (López-Bucio et al., 2015).
The beneficial effects of combinations of AMF and
Trichoderma on vegetable crops were previously demonstrated
under both optimal and suboptimal conditions (Colla et al.,
2015;Saia et al., 2020). However, the physiological and molecular
mechanisms underlining biostimulant action have not been
fully elucidated. One strategy to clarify biostimulant efficacy is
to analyze metabolic profiling. In turn, this process serves as a
basis for subsequent transcriptomic analyses. The metabolomic
phytochemical characterization could identify numerous
physiological processes and metabolic pathways modulated
by biostimulants (Yakhin et al., 2017). The above approach
has been never used in an important vegetable crop such as
pepper (Capsicum spp.) where biostimulant applications (e.g.,
vegetal-derived substances, arbuscular mycorrhizal fungi, plant
growth-promoting microorganisms) have proven to be beneficial
in ameliorating the growth, yield and nutritional value of fruits
(Ertani et al., 2014;Pereira et al., 2016).
It has been hypothesized that AMF and Trichoderma can
induce and enhance fruit yield by modulating the hormonal
balance and secondary metabolic pathways.
In the present study, then, an untargeted metabolomics
approach was conducted on greenhouse pepper. The objectives
were to illuminate metabolomic reprogramming by microbial
biostimulants in leaf tissue at the vegetative and reproductive
phenological stages, elucidate biostimulant regulation of
key phytohormones, and correlate these molecular-level
biostimulant-promoted changes to observed fruit yield and
quality variations.
MATERIALS AND METHODS
Growth Conditions, Plant Material, Crop
Management, and Experimental Design
The trial was conducted in a greenhouse located at Paraje
Águilas Bajas, Santa María del Águila, Almería, Spain
(36◦4703900N 2◦4603200 W). The greenhouse was composed
of polycarbonate walls and a roof made of tri-laminated low-
density polyethylene (LDPE) film (200 µm thickness) with ∼60%
spectral transmittance in the photosynthetically active radiation
(PAR) region. The greenhouse was unheated and passively
ventilated with lateral side panels and flap roof windows. It had
an east-west orientation and a north-south crop row alignment.
The air temperature and relative humidity inside the greenhouse
were in the ranges of 12–32 ◦C and 50–70%, respectively.
Transplants of the sweet pepper (Capsicum annuum L.) hybrid
‘SV1204PB’ (Seminis, Montornés del Vallés, Barcelona, Spain)
at the 4–5 true-leaf stage were planted in “Enarenado” sandy
soil commonly used in greenhouse production in Almería.
This soil is formed by placing a 20 cm layer of sandy loam
soil, imported from a quarry, over the original stony, loam soil.
A 10 cm layer of coarse river sand is placed over the imported
sandy loam soil as a mulch (Thompson et al., 2007). The planting
date was 19 July 2017, and the planting density was 2.0 m−2.
The soil composition was 13.5% (w/w) clay, 72.8% (w/w) sand,
and 13.7% (w/w) silt. Soil pH was 7.52, with an organic matter
content of 0.71%, and total nitrogen, available phosphorus,
and exchangeable potassium of 690, 51.4, and 321 mg kg−1,
respectively. Aerial drip irrigation was used. The in-line emitters
were positioned at 0.30 m intervals, and the emitter flow rate
was 3.4 L h−1. Preplant fertilizer was broadcast at 90 kg ·ha−1
P, 120 kg ·ha−1K, and 15 kg ·ha−1Mg and incorporated into
the soil. Additional fertilizer in the form of K2SO4(80 kg ·ha−1
K) was applied through the drip irrigation system. Nitrogen
was applied via fertigation in the form of 27% NH4NO3soluble
fertilizer starting 10 days after transplanting until day 83. The
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Bonini et al. Metabolic Reprogramming of Sweet Pepper Leaves
total N supply was split into 10 weekly dressings. Powdery
mildew caused by Leveillula taurica was controlled by three foliar
applications of penconazole (Topas 10EC; Syngenta, Madrid,
Spain) at the label-recommended rate. Aphids and spider mites
were controlled by one foliar application each of imidacloprid
(Confidor 200 SL; Bayer Crop Science, Valencia, Spain) and
fenpyroximate (Miro; Bayer Crop Science, Valencia, Spain),
respectively. Weeds were controlled by hand hoeing. The control
and microbial-based biostimulant treatments were compared
in a randomized block design with four replicates for a total of
eight experimental plots. Each experimental plot was 30 m2and
contained 60 plants in four single rows. The microbial-based
biostimulants were applied through a drip irrigation system. The
first application was made at 15 days after transplanting (DAT)
(3 August 2017) at the rates of 1 ×106spores ha−1Rhizoglomus
irregularis BEG72 and 1 ×106spores ha−1Funneliformis
mosseae BEG234 in the form of 2.0 kg ha−1Team Horticola
(Agrotecnologías Naturales, S.L., Tarragona, Spain) plus 1 ×1012
CFU ha−1Trichoderma koningii TK7 in the form of 1.0 kg
ha−1Condor Shield (Agrotecnologías Naturales, S.L., Tarragona,
Spain). The second treatment was applied 43 DAT (31 August
2017) at the rate of 5 ×1011 CFU ha−1Trichoderma koningii
TK7 as 0.5 kg Condor Shield (Agrotecnologías Naturales,
S.L., Tarragona, Spain). Multiple applications of Trichoderma
inoculum are recommended especially in long-term crops
like greenhouse pepper under soils with low organic matter
to raise the population of this saprophytic beneficial fungus
in the soil rhizosphere. Because arbuscular mycorrhizal fungi
such as Rhizoglomus irregularis and Funneliformis mosseae are
symbiotic microorganisms, it is usually sufficient the application
of mycorrhizal inoculum just once at the beginning of cropping
cycle (Colla et al., 2008).
Yield Measurements and Arbuscular
Mycorrhizal Fungi (AMF) Root
Colonization
Fully mature pepper fruits were harvested from 139 DAT (5
December 2017) to 272 DAT (17 April 2018) on 26 plants per each
plot. Mean fruit weight and number and marketable yield were
determined for each experimental plot (replicate). Rotten fruit
and those weighing <100 g were considered unmarketable yield.
At the end of the trial, the roots of six pepper plants
per experimental plot were rinsed, and subsamples were used
to evaluate AMF root colonization. The root samples were
cleared with 10% (w/v) KOH, stained with 0.05% (w/v) trypan
blue in lactophenol, and microscopically (Stereo microscope
Leica EZ4V, 32x—Leica Microsystems Srl, Buccinasco, Italy)
examined for AMF colonization. The percentage of colonized
root segments was determined by the grid line intersect method
(Giovannetti and Mosse, 1980).
Quantitative Real-Time PCR (qPCR) for
Determining Concentration of Strain TK7
in Soil
At the end of the trial, rhizosphere soil was collected by
shaking the roots collected from 10 plants per plot. The
concentration of T. koningii TK7 in the rhizosphere was
determined using a qPCR approach with two strain-specific
primers, named RM3 (GGAGGCTTGAATGGGA) and RM4
(CAAAACGCTGCTAAGG), targeting to a coding sequence
annotated as hypothetical protein. The DNA template used in
qPCR experiments was extracted from the soil samples with a
DNeasyR
PowersoilR
kit (Cat. No. 12888-50; Qiagen, Hilden,
Germany) according to Qiacube (Qiagen, Hilden, Germany)
automation procedures. Amplification reactions were carried
out in a 20 µL final volume on a Rotor-Gene Q apparatus
(Qiagen, Hilden, Germany). Reactions contained: 4 µL of DNA
sample;10 µL of QuaniNovaTM SYBRR
Green Supermix (2x);
0.14 µL of 25 µM primers; 4.72 10-µL of water. The qPCR
cycling conditions were as follows: initial incubation at 95◦C for
2 min, 45 cycles of 95◦C for 5 s each, and 60◦C for 12 s. Two
technical replicates were performed per sample. After qPCR, the
number of colony forming unit (CFU) equivalent per gram of
soil was calculated by interpolation of calibration curves obtained
using serial dilutions (1:1,000, 1:10,000, and 1:100,000) of a DNA
preparation extracted from 109CFU mL−1culture aliquots of
the target strain.
Sample Collection and Untargeted
Metabolomics
Four leaves in the third position from the branch tip were
harvested for untargeted metabolomics at 43 DAT (31 August
2017) and at 131 DAT (27 November 2017). The leaves were flash-
frozen in liquid nitrogen and stored at −80 ◦C until subsequent
metabolomic analysis.
The four leaves from each replicate were pooled and
homogenized, then 1.0 g was extracted in 0.1% HCOOH in
80% methanol using an ultra-turrax, as previously described
(Paul et al., 2019). An untargeted metabolomics approach
was conducted in the UHPLC 1290 chromatographic system
coupled to a hybrid quadrupole-time-of-flight (Q-TOF) G6550
mass spectrometer (UHPLC/Q-TOF) (Agilent Technologies,
Santa Clara, CA, United States). A Waters Acquity UPLCR
BEH C18 column (100 ×2.1 mm i.d., 1.7 µm) (Waters
Corp., Milford, MA, United States) was used for reverse-phase
chromatographic separation. The binary gradient consisted of
water and acetonitrile and the Riken Plasma method was followed
(Tsugawa et al., 2019). The injection volume was 2 µL and the
mass spectrometer was run in positive polarity and SCAN mode
(range: 100–1,700 m/z; extended dynamic range setting). Quality
controls (QC) were prepared by pooling 10 µL samples. Five QCs
were acquired in data-dependent mode (auto MS/MS) at 1 Hz, 10
precursors/cycle, collision energies of 10 V, 30 V, and 50 V), and
in iterative mode with active exclusion to increase the number of
compounds targeted for tandem MS fragmentation.
Alignment, blank filtration, and identification were performed
in MSDIAL v. 4.0 (Riken, Tokyo, Japan) using the publicly
available library MoNA (Mass Bank of North America) and
an internal standard compound library as specified in the
Supplementary Table 1. Compounds lacking experimental
MS/MS spectra were annotated with MSFINDER (Riken,
Tokyo, Japan) following the procedure described in Blaženovi´
c
et al. (2019). The alternatives were filtered by retention time
prediction (Bonini et al., 2020). MSI (metabolomics standards
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Bonini et al. Metabolic Reprogramming of Sweet Pepper Leaves
initiative) levels for each identified compound are listed in
Supplementary Table 1.
Statistics and Data Analysis
Data were statistically analyzed with SPSS v. 21 (IBM Corp.,
Armonk, NY, United States). The microbial-based biostimulant
effects on mycorrhizal root colonization, Trichoderma
population, fruit yield and yield components were analyzed
by an unpaired Student’s t-test. A p-value of less than or equal to
0.05 was considered to indicate significant difference. Values are
presented as means plus/minus standard deviation.
Concerning metabolomics, the compound intensity table
exported from MSDIAL v. 4.0 (Riken, Tokyo, Japan) (Tsugawa
et al., 2015) was uploaded into MS-FLO (Riken, Tokyo,
Japan) (De Felice et al., 2017) to reduce false positives and
duplicates. The output was then imported into R v. 3.6.0 for
centring (normalization against the median), scaling, PCA, and
calculation of fold changes, ANOVA (Benjamini-Hochberg FDR
multiple testing correction, P<0.05). Venn diagrams were
plotted to identify metabolites common to 43 and 131 DAT
sampling points but not exclusive to a particular growth stage.
Compounds with P<0.05 were imported into ChemRICH
(Barupal and Fiehn, 2017) for enrichment analysis based on
their chemical similarity and MetaMapp (Barupal et al., 2012)
for chemical network analysis. Cytoscape (Saito et al., 2012)
displayed exported MetaMapp data and plotted the final images.
RESULTS
Soil Fungal Concentration and Crop Yield
By the end of the trial, the percentage of mycorrhizal root
colonization was significantly (P<0.01) higher under the
microbial inoculation treatment (33.6 ±11.7%) than it was
under the uninoculated control treatment (8.0 ±4.9%). The
total number of Trichoderma colonies estimated by qPCR in
the rhizosphere of inoculated pepper plants was significantly
(P<0.01) higher than that recorded for the untreated
control (2.2 ×105±0.6 ×105vs. 1.2 ×103±0.4 ×103
CFU g−1, respectively). It is worth mentioning that the weak
PCR amplification signal observed in control experiments with
metagenome from not inoculated soil did not interfere with the
quantitative PCR analysis.
Relative to the uninoculated control, inoculation with AMF
and Trichoderma koningii significantly increased fruit yield at
single harvests (139, 174, 272 DAT) and as a total (Table 1);
moreover, the biostimulant-mediated yield increase was more
pronounced during the first part of the reproductive cycle,
namely, early yield (139 and 174 DAT) (Table 1). The
comparatively higher production rates measured at 139 DAT
and 272 DAT for pepper plants inoculated with microbial-based
biostimulant was due to an increase in mean fruit weight. In
contrast, the relatively higher fruit yield determined for 174 DAT
was attributed to increases in both fruit number per plant and
mean fruit mass (Tables 1–3). The microbial-based biostimulant
significantly improved cumulative fruit yield by an average of
23.7% relative to uninoculated pepper plants (Table 1).
Yields and Modulation of Metabolomic
Profile
In the present study, we inoculated pepper plants with the
AMF species Rhizoglomus irregularis and Funneliformis mosseae
and Trichoderma koningii. Microbial treatments accelerated and
increased total crop yield by 24%, relative to uninoculated plants
(Table 1). Such increase in pepper yield was attributed to the gain
in fruit weight and/or number. Ultra-high-performance liquid
chromatography quadrupole-time-of-flight high-resolution mass
spectrometry (UHPLC-QTOF) and annotation in publicly
available databases and large metabolite groups were conducted
TABLE 1 | Effect of microbial-based biostimulant application on fruit yield of greenhouse-grown peppers at different days after transplanting (DAT).
Treatment Fruit yield (kg plant−1)
139 DAT 174 DAT 243 DAT 264 DAT 272 DAT Total
139 DAT 174 DAT 243 DAT 264 DAT 272 DAT Total
Control 0.57 ±0.04 0.85 ±0.07 0.59 ±0.09 0.73 ±0.06 0.63 ±0.13 3.37 ±0.07
Biostimulant 0.73 ±0.10 1.41 ±0.30 0.48 ±0.13 0.74 ±0.07 0.81 ±0.06 4.17 ±0.07
Significance * ** Ns Ns * **
Mean values plus/minus standard deviations (n = 4); Two-tailed unpaired Student’s t-test, ns = not significant, *P <0.05, and **P <0.01.
TABLE 2 | Effect of microbial-based biostimulant application on fruit number of greenhouse-grown peppers at different days after transplanting (DAT).
Treatment Fruit number (n. plant−1)
139 DAT 174 DAT 243 DAT 264 DAT 272 DAT Total
Control 2.35 ±0.37 3.65 ±0.29 2.90 ±0.53 2.20 ±0.10 2.09 ±0.45 13.20 ±0.50
Biostimulant 2.75 ±0.22 5.56 ±1.00 1.92 ±0.67 1.98 ±0.20 2.42 ±0.11 14.62 ±0.83
Significance ns ** Ns Ns ns *
Mean values plus/minus standard deviations (n = 4); Two-tailed unpaired Student’s t-test, ns = not significant, *P <0.05, and **P <0.01.
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TABLE 3 | Effect of microbial-based biostimulant application on fruit mean weight of greenhouse-grown peppers at different days after transplanting (DAT).
Treatment Fruit mean weight (g fruit−1)
139 DAT 174 DAT 243 DAT 264 DAT 272 DAT
Control 244.0 ±5.8 232.4 ±2.4 204.4 ±27.6 335.1 ±24.1 302.5 ±20.3
Biostimulant 264.7 ±7.9 254.4 ±12.8 250.4 ±27.5 368.3 ±3.5 334.3 ±15.4
Significance ** ** Ns * *
Mean values plus/minus standard deviations (n = 4); Two-tailed unpaired Student’s t-test, ns = not significant, *P <0.05, and **P <0.01.
FIGURE 1 | Principal Component Analysis (PCA) of identified metabolites in pepper plants following treatment with microbial biostimulants. Compounds were
profiled by untargeted metabolomics and samples harvested at two sampling dates: 43 (vegetative stage), and 131 days after transplanting (reproductive stage).
to obtain wide metabolome coverage. We applied UHPLC-
QTOF-based untargeted metabolomic profiling of crude extracts
to assess relative differences in the vegetative stage (43 DAT)
and reproductive stage (131 DAT) leaf metabolite profiles
between inoculated and uninoculated plants. A principal
component analysis (PCA) explained 79% of the overall
variance. The PCA score plot (Figure 1) showed two main
clusters accounting for the vegetative and reproductive stages,
respectively. Within each cluster, the metabolomic profiles
of leaves from inoculated and those from the uninoculated
(control) plants did not show overlapping, thus indicating
distinct phytochemical signatures. Notably, considering that PCA
provides unsupervised descriptions of relatedness/unrelatedness
across treatments, these patterns indicate a metabolomic shift in
plants following the biostimulant treatments. Thereafter, t-test
ANOVA (P<0.01) was carried out to identify differentially
accumulated metabolites at each plant growth stage. This analysis
disclosed >466 annotated metabolites (Sheets 2 and 3 of
Supplementary File 1) that had significantly changed between
the vegetative and reproductive stages. Of these, 327 were
common to 43 and 131 DAT sampling points (Figure 2). In
FIGURE 2 | Venn diagram of statistically different metabolites (P<0.05) in
pepper plants following treatment with microbial biostimulants, as a function
of the sampling date. Compounds were profiled by untargeted metabolomics
at two sampling dates: 43 (vegetative stage), and 131 days after transplanting
(reproductive stage).
contrast, 68 and 71 metabolites differentially accumulated during
the vegetative (43 DAT) and reproductive (131 DAT) stages,
respectively (Figure 2). The interactions between microbial
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Bonini et al. Metabolic Reprogramming of Sweet Pepper Leaves
inoculants and plants are complex. Nevertheless, metabolomics
effectively included the metabolic responses and mechanisms
involved in the plant-microbe interactions. Considering that
327 common metabolites (i.e., 70%) out of 395 and 398
metabolites (P<0.05) at 43 and 131 DAT, respectively, were
shared between vegetative and reproductive phenological stages
(Figure 2), the biostimulant-mediated metabolomic shifts we
recorded represented a common signature, irrespective from the
plant growth stage. On the other hand, certain stage-specific
responses could be identified as well.
To clarify and visualize the variations between metabolic
profiles at the vegetative and reproductive stages, we performed
a chemical enrichment analysis using ChemRICH (Figure 3
and Tables 4,5) and plotted the output by MetaMapp
Cytoscape (Figure 4;Barupal and Fiehn, 2017). Most of the
significantly upregulated and downregulated metabolites (fold-
change values ≤0.5 and ≥1.5, respectively; P≤0.01) had a
wide range of functions including growth stimulation, antifungal
activity, pathogen resistance, energy sources, and secondary
signaling cofactors.
Among other, secondary metabolites such as carotenoids
and other terpenes, saponins, and phenolic compounds, were
altered by the biostimulant treatment. Compared to the control,
at 131 DAT, foliar vitamin A and α-carotene were 1.5 ×and
8.5 ×higher, respectively, following treatment. Blumenols,
a class of apocarotenoids or cyclohexanone derivatives of
carotenoid cleavage, also accumulated in the biostimulant-
treated plants. In detail, blumenol B was 2 ×and 2.5 ×higher
at 43 and 131 DAT, respectively, after biostimulant application.
Regarding foliar saponins, their abundance was 1.5–10 ×higher
in plants treated with biostimulant than in the untreated
control. Furthermore, irrespective of growth stage, the phenolics
skullcapflavone I, pelargonidin-3-O-glucoside, kaempferol,
genistein, apiin, and myricatomentoside I accumulated to
levels 3–87 ×higher in the biostimulant-treated plants
than the control.
Phospholipids were also modulated by the treatment. In
more detail, the accumulation of phosphatidylethanolamines
[PE(P-16:0/20:5)], phosphatidic acid [PA(15:0/22:6), PA(O-
18:020:3)], phosphatidylinositol [PIM4(18:1/14:0)], and
phosphatidylserine [PS(P-16:013:0)] by 1.5–30 ×was recorded
in biostimulant-treated plants, compared to the control.
Furthermore, lysophospholipids [PA(P-16:0e18:2)] increased
by 6.5 ×in biostimulant-treated leaves at 131 DAT sampling
(reproductive stage).
Concerning hormones, the microbial-based treatment
induced also the accumulation of auxins (indole-3-acetamide
and indole-3-pyruvic acid increased by 1.7–7.5 ×relative to the
control), whereas a set of gibberellins precursors (GA81, GA36,
GA37, GA12, and GA20) increased by 1.3–16 ×compared to
control, at both 43 and 131 DAT. Still regarding phytohormones,
the biostimulant also triggered the accumulation of the cytokinin
trans-zeatin by 2.2–5.1 ×in pepper leaves, compared to control.
Purine metabolites also increased following the microbial
treatments. At 43 and 131 DAT, we observed sharp increases
in the guanosine (2.7 ×and 8.7 ×, respectively) and N6-
threonylcarbamoyladenosine (3- and 7.8-fold, respectively) levels
following microbial inoculation. Similar trends could be observed
for DAT, NAD, and FAD at both 43 and 131 DAT, with increases
by 1.5–4.4 ×in biostimulant-treated plants.
DISCUSSION
There is a growing interest in the use of beneficial microbial
inoculants such as AMF, Trichoderma spp., and PGPR in
horticulture as they have multiple beneficial effects on crops
(López-Bucio et al., 2015;Rouphael et al., 2015). Similarly, to
other studies (Conversa et al., 2013;Colla et al., 2015;Bakr
et al., 2018), microbial inoculation of pepper plants was effective
to raise significantly the mycorrhizal root colonization and the
Trichoderma population in the soil rhizosphere.
In the present study, we observed an increase of early and
total crop yield, compared to uninoculated plants. Colla et al.
(2015) reported that compared with uninoculated field-grown
zucchini plants, those supplied with live AMF G. intraradices
and T. atroviride inocula presented with greater early and
total yields. Similarly, in two field experiments, Ortas (2019)
reported that mycorrhizal inoculation increased yield of the
tomatoes, green peppers and eggplants and P and Zn uptake in
comparison with uninoculated plants. In the current experiment
the total yield increase resulting from inoculation of sweet pepper
plants with AMF and Trichoderma koningi was higher (24%)
than the value (18%) reported by Ombódi et al. (2019) using
an inoculum containing six different arbuscular mycorrhizal
species under unheated greenhouse conditions and the value
(12.7%) recorded by Almaca et al. (2013) using an inoculum
containing Glomus mosseae and G. etunicatum under field
conditions. The above differences in pepper yield response could
be attributed to the different mycorrhizal species used in the
trials and the addition of Trichoderma koningi in the current
experiment. Co-inoculation of Trichoderma spp. and AMF have
been found to promote growth and plant development of several
vegetable crops more than inoculation using only Trichoderma
spp. or AMF (Colla et al., 2015). Moreover, similarly to the
trial reported by Ombódi et al. (2019), we observed a better
yield response of pepper to mycorrhizal inoculation (+66% in
the second fruit harvest made on 9 January—174 DAT) when
the microclimate conditions for plant growth were suboptimal
(low light and temperature occurring during January). Finally,
in the current experiment the total yield increases induced
by inoculation with AMF and Trichoderma koningii were due
to both higher fruit number and mean fruit weight whereas
in the trial of Ombódi et al. (2019) the yield increases were
mostly due to higher number of fruits. The above findings
indicate a reduced activity of indigenous arbuscular mycorrhizal
fungi and Trichoderma spp. in enhancing crop productivity
in comparison with exogenous selected arbuscular mycorrhizal
fungi and Trichoderma species under field conditions. Similarly,
Ombódi et al. (2019) reported that inoculation of pepper plants
at transplanting with a commercial product containing six
different arbuscular mycorrhizal species was able to enhance
mycorrhizal root colonization, leaf chlorophyll content (SPAD
index) and fruit yield in comparison with naturally occurring
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Bonini et al. Metabolic Reprogramming of Sweet Pepper Leaves
FIGURE 3 | Chemical similarity enrichment analysis (ChemRICH) of statistically different annotated metabolites in microbial-based biostimulant treated leaves
compared to untreated control at 43 (vegetative stage) and 131 days after transplanting (reproductive stage). Color is according to proportion of increased or
decreased compounds (red = increased, blue = decreased, pink = mixed) within each cluster.
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Bonini et al. Metabolic Reprogramming of Sweet Pepper Leaves
TABLE 4 | Effect of microbial-based biostimulant application on compound chemical classes (CHEMRICH) of greenhouse-grown peppers at vegetative stage (43 DAT).
Cluster name Cluster size p-values FDR Key compound Increased Decreased
Carotenoids 10 2.2E-20 9.2E-20 Fucoxanthinol, Vit A, Alpha Carotene 7 3
Diterpenes 17 2.2E-20 9.2E-20 NCGC00385284-01_C32H54O13 17 0
Flavonoids 9 2.2E-20 9.2E-20 Skullcapflavone I 20-(200 -E-cinnamoylglucoside) 7 2
Isoflavones 6 2.2E-20 9.2E-20 Genistein 6 0
Lignans 4 2.2E-20 9.2E-20 Myricatomentoside I 3 1
Phosphatidic Acids 9 2.2E-20 9.2E-20 PA(18:0/18:2) 8 1
Phosphatidylethanolamines 12 2.2E-20 9.2E-20 PE(P-16:0/20:5) 4 8
Phosphatidylinositols 5 2.2E-20 9.2E-20 PIM4(18:1/14:0) 5 0
Phosphatidylserines 12 2.2E-20 9.2E-20 PS(P-16:0/13:0) 7 5
Saponins 15 2.2E-20 9.2E-20 Borassoside A 8 7
Triterpenes 14 2.2E-20 9.2E-20 Cussoracoside F 10 4
Glucosides 5 1.1E-16 3.9E-16 Luteolin-40-O-glucoside 5 0
Amino Acids 5 7.8E-16 2.2E-15 Arginine 2 3
Phenols 6 1.7E-15 4.5E-15 Gibbilimbol B 5 1
Glycosides 4 7.7E-14 1.7E-13 Melissoidesin D 2 2
Macrolides 10 2.6E-14 5.9E-14 Capsianoside 8 2
Piperidines 3 9.1E-13 1.9E-12 Andrachcinidine 3 0
Iridoids 3 1.5E-12 3E-12 NCGC00168877-02_C15H20O8 3 0
Catechols 3 5.4E-12 9.9E-12 (S)-[8]-Gingerol 3 0
Flavonols 3 1.6E-11 2.7E-11 Kaempferol 3 0
Auxins 12 6E-11 9.8E-11 Indole-3-acetamide 12 0
Glycerides 4 3.7E-09 5.4E-09 DG(19:1(9Z)/22:4(7Z,10Z,13Z,16Z)/0:0)[iso2] 3 1
Limonins 4 5.9E-09 8.4E-09 11beta-Acetoxydihydrocedrelone 2 2
Dipeptides 6 1.2E-08 1.7E-08 Ala-Phe 4 2
Sesquiterpenes 4 4.5E-08 6.1E-08 Leucascandrolide A 4 0
Coumarins 3 5.8E-08 7.6E-08 Coumarin 2 1
Monoterpenes 4 9.6E-08 1.2E-07 NCGC00384740-01_C21H34O9 4 0
Amino Acids, Aromatic 5 0.0000002 2.5E-07 Tryptophan 4 1
Glycolipids 3 3.1E-07 3.6E-07 Lyciumoside IV 2 1
Anthocyanins 3 3.7E-07 4.3E-07 Cyanidine-3-O-sambubioside 3 0
DiHODE 3 6.8E-07 7.6E-07 8(R)-Hydroperoxylinoleic acid 3 0
Gibberellins 4 0.0000019 0.0000021 Gibberellin A20 4 0
Oligopeptides 3 0.0000047 0.000005 Indole-3-acetyl-L-isoleucine 3 0
Saturated FA 3 0.000024 0.000025 Capric acid 2 1
Phosphatidylglycerols 4 0.00037 0.00037 PG(18:2/13:0) 3 1
mycorrhizal fungi in untreated control. The above findings may
be explained by the depression of native mycorrhizal fungi
in horticultural production systems caused by the frequent
soil tillage and the overuse of chemical inputs. Under these
conditions, AMF inoculation may compensate for the loss of
indigenous microbial communities to support plant growth
(Yu et al., 2020). The results of the current experiment
proved that exogenously-applied beneficial fungi such as AMF
and Trichoderma koningi act as phytostimulation agents and
improve plant nutrient uptake. The phytostimulation efficacy
of beneficial fungi is explained by complex signal exchange
and crosstalk between the host plants and the microorganisms
affecting phytohormone balance and plant metabolism (Sbrana
et al., 2017). Metabolomics helps elucidate the metabolic
pathways and processes involved in plant-microbe interactions.
Growth stage has a hierarchically strong effect on the leaf
metabolome. Nevertheless, microbial biostimulants significantly
alter the metabolome such that it is readily distinguishable
from the control. The microbial treatments elicited several
processes related to plant secondary metabolism. Microbial-
based biostimulants promote the accumulation of different
classes of secondary metabolites and phospholipids.
Plant responses to microbial-based biostimulants involved
the modulation of phytohormone network. Treatments with
beneficial fungi alter auxins, cytokinins, and gibberellins.
Modification of the hormone profile may be associated to
the yield increases we observed. Several studies demonstrated
that microbial biostimulants promote yield by changing the
phytohormone balance, increasing nutrient availability and
uptake, and enhancing abiotic stress tolerance (Rouphael et al.,
2015;Saia et al., 2020). Certain putative mechanisms for
the biostimulant activity of microbial-based inoculant (AMF
+Trichoderma) in pepper have been proposed. Microbial-
based inoculants promote root biomass, length, density, and
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Bonini et al. Metabolic Reprogramming of Sweet Pepper Leaves
TABLE 5 | Effect of microbial-based biostimulant application on compound chemical classes (CHEMRICH) of greenhouse-grown peppers at reproductive
stage (131 DAT).
Cluster name Cluster size p-values FDR Key compound Increased Decreased
Carotenoids 12 2.2E-20 1.3E-19 Fucoxanthinol, Vit A, Alpha Carotene 9 3
Diterpenes 15 2.2E-20 1.3E-19 Traversianal 15 0
Flavonoids 9 2.2E-20 1.3E-19 Skullcapflavone I 20-(200-E-cinnamoylglucoside) 7 2
Lignans 5 2.2E-20 1.3E-19 Myricatomentoside I 3 2
Macrolides 9 2.2E-20 1.3E-19 Capsianoside 8 1
Phosphatidic Acids 9 2.2E-20 1.3E-19 PA(O-18:020:3(8Z11Z14Z)) 9 0
Phosphatidylserines 12 2.2E-20 1.3E-19 PS(P-16:013:0) 11 1
Triterpenes 15 2.2E-20 1.3E-19 Tricalysioside T 13 2
Phosphatidylglycerols 8 3.3E-16 1.7E-15 PG(P-18:017:2(9Z12Z)) 8 0
Phosphatidylethanolamines 8 4.9E-15 2.1E-14 PE(P-16:020:5(5Z8Z11Z14Z17Z)) 5 3
Saponins 9 1.2E-14 4.9E-14 Namonin E 8 1
Isoflavones 6 1.8E-14 6.8E-14 Genistein 6 0
Amino Acids 5 1.6E-12 5.4E-12 L-Valine 3 2
Phosphatidylinositols 5 3.1E-12 1E-11 PIM4(18:1(9Z)14:0) 5 0
Glucosides 6 4.3E-12 1.3E-11 Daedaleaside D 6 0
Glycolipids 3 5.3E-12 1.5E-11 Capsoside A 3 0
Phenols 6 7E-12 1.9E-11 Gibbilimbol B 6 0
Glycosides 5 3.2E-11 7.6E-11 Cyclopassifloside VII 5 0
Auxins 12 5.1E-11 1.2E-10 INDOLE-3-PYRUVIC ACID 12 0
Monoterpenes 5 1.2E-10 2.5E-10 beta-Thujaplicin 5 0
Amino Acids, Aromatic 4 2.3E-10 4.6E-10 34-Dihydroxy-L-phenylalanine 4 0
Purine Nucleosides 4 6.9E-10 1.2E-09 Adenosine 4 0
Sesquiterpenes 4 1.4E-09 2.4E-09 (+)-vulgraon B 4 0
Oligopeptides 5 2.7E-08 4.3E-08 Indole-3-acetyl-L-isoleucine 4 1
Limonins 4 2.9E-08 4.4E-08 Toonaciliatin D 3 1
Xanthophylls 3 5.6E-08 8.3E-08 Spirilloxanthin 2 1
Chlorophyllides 3 8.2E-08 1.2E-07 chlorophyllide a 3 0
Anthocyanins 3 8.4E-08 1.2E-07 Delphinidin-3-O-sambubioside 3 0
Saturated FA 3 1.8E-07 2.5E-07 Petroformyne 1 2 1
Iridoids 4 4.1E-07 5.1E-07 Eleganoside B 4 0
Flavonols 3 5E-07 6.1E-07 Kaempferol 3 0
Coumarins 3 1.3E-06 1.5E-06 Coumarin 2 1
Diglycerides 3 1.7E-06 0.000002 DG(15:1(9Z)22:6(4Z7Z10Z13Z16Z19Z)0:0)iso2 3 0
Saturated_Fatty Acids 3 2.5E-06 2.8E-06 Capric acid 2 1
Catechols 3 4.3E-06 4.8E-06 330440-Tetrahydroxy-550-diisopropyl-220-dimethylbiphenyl 1 2
Lysophospholipids 3 4.9E-06 5.2E-06 PC(O-17:00:0) 3 0
Cinnamates 6 8.2E-06 8.5E-06 Sinapine 6 0
Disaccharides 3 0.000011 0.000011 Melibiose 1 2
Gibberellins 3 0.000036 0.000036 Gibberellin A20 3 0
branching, in turn increasing macronutrient and micronutrient
uptake and boosting crop productivity. They also regulate key
phytohormones such as gibberellins, cytokinins, and auxins
(López-Bucio et al., 2015;Rouphael et al., 2015).
Gibberellins are diterpenoid phytohormones that regulate
plant development, flowering, and senescence (Shu et al., 2018).
In response to microbial-based inoculant treatment, gibberellins
precursors increased. Although the precursor gibberellin A20
was recently linked to increased yields in maize (Tucker
et al., 2019), the concurrent increase in auxins we observed
(i.e., hormones upregulating the genes encoding 2-oxidases)
suggests the promotion of gibberellins catabolism (Frigerio
et al., 2006). Indeed, the coordination between gibberellins
biosynthesis (mediated by 20- and 3-oxidases) and their 2-
oxidases inactivation affects pollination and fruit set in tomato
(Serrani et al., 2007). On the other hand, auxins and gibberellins
overlap in terms of root growth and fruit set regulation
(Bermejo et al., 2018). The microbial-based biostimulant
also increased the accumulation of trans-zeatin; cytokinins
interact with auxins to fine-tune root and shoot development.
Trans-zeatin modulates meristem activity and mediates plant
responses to variable extrinsic factors such as abiotic stress
(Werner and Schmülling, 2009).
The modulation of plant signaling compounds in response to
the microbial-based biostimulant treatment also involved
membrane lipids. Phospholipids are plasma membrane
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Bonini et al. Metabolic Reprogramming of Sweet Pepper Leaves
FIGURE 4 | MetaMapp Metabolomic network maps of pepper leaves at 43 (vegetative stage) and 131 days after transplanting (reproductive stage). Microbial-based
biostimulant treated plants were compared to control ones. The red squares are compounds with an increase in fold change, while the blue ones represent
compounds with a decrease in fold changes. Chemical similarity and KEGG reaction were utilized to draw the clusters and nodes.
components that play important roles in cell signaling,
membrane trafficking, and apoptosis (Xue et al., 2009).
The microbial-based biostimulant treatment changed the
phospholipids profile. It altered 20 foliar metabolites at 43
DAT (vegetative stage) and 31 foliar metabolites at 131 DAT
(reproductive stage). Lysophospholipids release calcium from
the endoplasmic reticulum, promote cell division and inhibit
apoptosis (Ye, 2008;Hou et al., 2016).
The microbial treatment also modulated secondary
metabolism, i.e., a set of processes often altered in response
to plant interactions with the environment, including agronomic
practices and plant-microbe interactions (Yang et al., 2018). In
our experiments, plant responses to microbial biostimulants
entail the coordinated modulation of several unrelated pathways.
The carotenoids vitamin A and α-carotene increased following
the microbial treatment. Carotenoids absorb light energy,
participate in photosynthesis, protect plants against oxidative
damage, and are precursors of visual pigment chromophores
and volatile apocarotenoids that attract pollinators (Heath et al.,
2013;Sun et al., 2018). Moreover, they are involved in plant
responses to abiotic stresses and plant-microbe interactions
(Felemban et al., 2019). Among carotenoids, blumenols also
accumulated in the biostimulant-treated plants. Noteworthy,
they are reported to accumulate in roots and shoots of
mycorrhized plants and have been proposed as markers of
arbuscular mycorrhizal fungi colonization (Wang et al., 2018).
However, their functions in processes other than allelopathy
are still unknown. Their levels are strongly correlated with
the degree of mycorrhization (Fester et al., 2002). Concerning
saponins, they are constitutively produced in plants and comprise
part of plant defense, having both antifungal and antifeedant
activity. Though they are generally associated with pathogenesis,
it was reported that saponins may participate in mutualistic
relationships among plants, rhizobacteria, and mycorrhizae
(Szakiel et al., 2011). Despite not focusing on root metabolome
(where such mutualistic associations take place), our results
indicate that saponins may also be involved in aboveground
response to microbial inoculation with the biostimulants.
Compared to control, plants subjected to the microbial
treatments presented higher levels of phenolic compounds.
Phenolic metabolites are essential for lignin and pigment
biosynthesis and participate in plant responses to pathogens
and external stimuli (Bhattacharya et al., 2010). Mycorrhizae
elicit phenolic biosynthesis in other plant species (Baslam
and Goicoechea, 2012;Jugran et al., 2015). They also trigger
plant defense against abiotic and biotic stresses and improve
nutrient availability and use efficiency (Sharma and Sharma,
2017). Phenolics are associated with plant defense mechanisms.
Flavones may protect plants from both biotic and abiotic
stress (Martinez et al., 2016). Lignans have high antioxidant
activity (Hu et al., 2007;Durazzo et al., 2013). Compared
with the uninoculated, the gibbilimbol B level was 1.5 ×and
4.2 ×higher at 43 and 131 DAT sampling dates, respectively,
in the inoculated plants. Gibbilimbol B was reported to have
fungicidal activity against Fusarium oxysporum f. sp. dianthi.
Coumarin upregulation is related to iron nutrition (Curie and
Mari, 2017), allelochemistry (Niro et al., 2016), and abiotic
stress tolerance (Saleh and Madany, 2015) in plants. Plant
coumarins may influence the shape of the root microbiome
(Voges et al., 2019).
Relative to the control, the levels of several purines were
altered in the plants treated with the microbial biostimulant here.
Several studies have focused on the effects of increased levels of
adenosine and purines. These compounds are recycled by the so-
called “salvage pathway” (Ashihara et al., 2018). Nicotinamide
adenine dinucleotide (NAD) and flavin adenine dinucleotide
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Bonini et al. Metabolic Reprogramming of Sweet Pepper Leaves
(FAD) are reducingequivalent exchange cofactors that participate
in several redox reactions.
Overall, our metabolomics study revealed that microbial
biostimulant treatment had two major effects on pepper. First,
the biostimulant modulated the phytohormone profile and
phospholipid signaling in the plants. Next, it altered various
secondary metabolic processes involving saponins, blumenols,
carotenoids, and phenolic compounds. Phytohormones and
biochemical messengers are associated with various metabolic
processes (Ashihara et al., 2018) and might account for the
observed biostimulant-mediated increases in crop productivity.
Although it is difficult to ascribe the increased yield to few/some
specific compounds, we can postulate that the altered balance
of hormone profile may have played a pivotal role in fruit
setting and development. Indeed, it is well recognized that
yields are tightly connected to hormones profile, with an
important role actually played by auxins (that increased in
our experiments) (An et al., 2020). On the other hand, the
involvement of phytohormones in the connection between
beneficial microbes and plant productivity has already been
postulated (Bhattacharyya and Jha, 2012). Comparatively, much
less is known to date regarding the signaling related to membrane
lipids, and future research is advisable on this topic.
The secondary metabolites modulated by biostimulant
treatment have numerous positive influences on plant
productivity, such as the enhancement of nutrient uptake and
assimilation and biotic and abiotic stress tolerance. The elicitation
of secondary metabolism by plant beneficial microbes deserves
further investigation in terms of abiotic stress tolerance and
induced systemic response (ISR) induction. Noteworthy, looking
at food nutritional traits, carotenoids and phenolics improve
quality and promote health in many fruits, including pepper.
Thus, the microbial biostimulant treatments applied here could
have nutritional implications as well.
CONCLUSION
Recent scientific investigations have focused on improving
sustainable farming practices that stabilize yield under optimal
and suboptimal conditions and comply with changing legislation
regarding the application of low-input agrochemicals. Microbial-
based biostimulants (for example, AMF and/or Trichoderma)
may sustainably enhance crop productivity. Our greenhouse
experiment on pepper confirmed that inoculation with a
combination of AMF and Trichoderma koningii TK7 increased
marketable fruit yield by 23.7% relative to that of the untreated
control. Metabolomics analysis revealed that the biostimulant
treatment reprogrammed the leaf metabolome at the vegetative
and reproductive stages. Likely, several biochemical processes
underly the observed increase in fruit yield. Here, we showed
that the biostimulant modulated the phytohormone profile
and elicited secondary metabolism. Specifically, the microbial-
based biostimulant upregulated compounds such as carotenoids,
saponins, and phenolics that participate in plant nutrition,
defense, and stress response. The results of the present study
confirm that biostimulant amendments improved the plant
health status since the vegetative stage, favoring stable increases
in fruit yield. This leads the way toward future investigations
into their effects on plants under challenging conditions such
as abiotic and biotic stress, environmental perturbations, and
physicochemical imbalances.
DATA AVAILABILITY STATEMENT
The original contributions presented in the study are included
in the article/Supplementary Material, further inquiries can be
directed to the corresponding author.
AUTHOR CONTRIBUTIONS
PB, GC, YR, VC, MC, and LL designed the experiment. GC,
MC, YR, and GE measured and made the interpretation of
agronomical data. PB and BL acquired the metabolomics and
qPCR data. PB, BM-M, and LL analyzed the metabolomics
data. All authors discussed the results and contributed to the
final manuscript.
ACKNOWLEDGMENTS
We thank Tobias Kind from UC Davis, Davis, CA, United States
for his assistance with manuscript revision. We also thank prof.
Maurizio Ruzzi for the qPCR data revision.
SUPPLEMENTARY MATERIAL
The Supplementary Material for this article can be found
online at: https://www.frontiersin.org/articles/10.3389/fpls.2020.
567388/full#supplementary-material
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Conflict of Interest: VC and GE were employed by the company Atens SL. BL and
PB were employed by the laboratory NGAlab.
The remaining 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.
Copyright © 2020 Bonini, Rouphael, Miras-Moreno, Lee, Cardarelli, Erice, Cirino,
Lucini and Colla. This is an open-access article distributed under the terms of
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