Abstract
Arbuscular mycorrhiza (AM) is known to be a mutually beneficial
plant-fungal symbiosis; however, the effect of mycorrhization is
heavily dependent on multiple biotic and abiotic factors. Therefore,
for the proper employment of such plant-fungal symbiotic systems in
agriculture, a detailed understanding of the molecular basis of the
plant developmental response to mycorrhization is needed. The aim of
this work was to uncover the physiological and metabolic alterations in
pea (Pisum sativum L.) leaves associated with mycorrhization at key
plant developmental stages. Plants of pea cv. Finale were grown in
constant environmental conditions under phosphate deficiency. The
plants were analyzed at six distinct time points, which corresponded to
certain developmental stages of the pea: I: 7 days post inoculation
(DPI) when the second leaf is fully unfolded with one pair of leaflets
and a simple tendril; II: 21 DPI at first leaf with two pairs of
leaflets and a complex tendril; III: 32 DPI when the floral bud is
enclosed; IV: 42 DPI at the first open flower; V: 56 DPI when the pod
is filled with green seeds; and VI: 90–110 DPI at the dry harvest
stage. Inoculation with Rhizophagus irregularis had no effect on the
fresh or dry shoot weight, the leaf photochemical activity,
accumulation of chlorophyll a, b or carotenoids. However, at stage III
(corresponding to the most active phase of mycorrhiza development), the
number of internodes between cotyledons and the youngest completely
developed leaf was lower in the inoculated plants than in those without
inoculation. Moreover, inoculation extended the vegetation period of
the host plants, and resulted in increase of the average dry weight per
seed at stage VI. The leaf metabolome, as analyzed with GC-MS, included
about three hundred distinct metabolites and showed a strong
correlation with plant age, and, to a lesser extent, was influenced by
mycorrhization. Metabolic shifts influenced the levels of sugars, amino
acids and other intermediates of nitrogen and phosphorus metabolism.
The use of unsupervised dimension reduction methods showed that (i) at
stage II, the metabolite spectra of inoculated plants were similar to
those of the control, and (ii) at stages IV and V, the leaf metabolic
profiles of inoculated plants shifted towards the profiles of the
control plants at earlier developmental stages. At stage IV the
inoculated plants exhibited a higher level of metabolism of nitrogen,
organic acids, and lipophilic compounds in comparison to control
plants. Thus, mycorrhization led to the retardation of plant
development, which was also associated with higher seed biomass
accumulation in plants with an extended vegetation period. The
symbiotic crosstalk between host plant and AM fungi leads to
alterations in several biochemical pathways the details of which need
to be elucidated in further studies.
Keywords: Pisum sativum, Arbuscular mycorrhiza, Plant growth and
physiological state, Leaf, Metabolic profile
Introduction
The vast majority of land plants form arbuscular mycorrhizae (AM),
symbioses with obligatorily biotrophic Glomeromycota fungi.
Arbuscular-mycorrhizal fungi (AMF) feed on photosynthesis products and
utilize a considerable proportion of the assimilated carbon. They form
intraradical colonies with branched intracellular structures, the
arbuscules. The arbuscules are surrounded by a membrane which supports
bidirectional transport of nutrients: inorganic phosphate, ammonium,
and other mineral ions flow from the fungal cell to the plant cell, and
carbohydrates are transferred in the opposite direction ([50]Smith &
Read, 2008; [51]Kaschuk et al., 2009; [52]Gutjahr & Parniske, 2013;
[53]Tisserant et al., 2012; [54]Manck-Götzenberger & Requena, 2016).
Recently it has also been shown that the growth and development of AMF
depends on lipid transfer from the host plant ([55]Luginbuehl et al.,
2017). The AM is generally considered to be a mutually beneficial
symbiosis that facilitates plant nutrition and increases plant
tolerance to biotic and abiotic stresses ([56]Siddiqui, Akhtar & Futai,
2008; [57]Solaiman, Abbott & Varma, 2014). At the same time, it has
been increasingly recognized that interactions may follow a continuum
from mutualistic to parasitic. The effect of mycorrhization on a plant
depends on soil ecology, light conditions, plant and fungal species and
their mutual cross-adaptation ([58]Klironomos, 2003; [59]Xavier &
Germida, 2003; [60]Smith & Read, 2008; [61]Schweiger et al., 2014;
[62]Konvalinková & Jansa, 2016). Despite the negative growth response
to mycorrhization under certain conditions, plants with formed AM might
still have better fitness than the non-mycorrhizal control plants
because of their better nutrition ([63]Koide, 2010).
The pea (Pisum sativum L.) is an important legume crop, which forms
both AM and nitrogen-fixing root nodules with rhizobia ([64]Vance,
2008). Combined inoculation with both AMF and rhizobia can lead to a
3-fold and greater increase in plant biomass and seed mass in many pea
genotypes compared to mono-inoculation with rhizobia ([65]Jacobi et
al., 1999). A separate work showed combined inoculation to lead to an
increase in seed protein content in most tested lines ([66]Shtark et
al., 2006). However, P. sativum had a relatively low growth response to
mono-inoculation with AMF compared to Medicago varia, Secale cereale or
Hordeum vulgare, as was demonstrated in the geographical network
experiments of the All-Russia Research Institute for Agricultural
Microbiology (St. Petersburg) ([67]Yurkov et al., 2017). Furthermore,
many authors have described the complete absence of an increase of pea
growth parameters under conditions of mono-inoculation with AMF
([68]Rivera-Becerril et al., 2002; [69]Xavier & Germida, 2003;
[70]Borisov et al., 2004b; [71]Desalegn et al., 2016; [72]Zhukov et
al., 2017). Even pea genotypes having the highest growth response to
double inoculation may not show a positive response to mono-inoculation
with AMF ([73]Borisov et al., 2004b). All this data indicate the
necessity of a more detailed understanding of the molecular and genetic
bases of AMF interactions with the host plant. These aspects of the AM
formation per se are particularly well studied for Medicago truncatula
and Lotus japonicus ([74]Gutjahr & Parniske, 2013; [75]Gobbato, 2015;
[76]Pimprikar & Gutjahr, 2018). Information for Pisum sativum
([77]Borisov et al., 2004a; [78]Kuznetsova et al., 2010; [79]Zhukov et
al., 2016; [80]Shtark et al., 2016; [81]Leppyanen et al., 2017) is
scarce and its physiological and biochemical aspects need further
study.
Metabolomics is a powerful tool for investigating a plant’s
physiological/biochemical status varied under different environmental
conditions ([82]Iriti & Vitalini, 2018; [83]Peters et al., 2018).
Recent studies have revealed the species-specificity of leaf metabolic
responses to AM, implying that various metabolites can be affected.
Such mycorrhiza-mediated changes in the chemical composition of leaf
tissues can impart phytoprotection against different abiotic stresses
([84]Schweiger et al., 2014; [85]Schweiger & Mueller, 2015). In
P. sativum the studies were mainly focused on the seed metabolome, as
was expected for a pulse crop, and a lot of attention was also paid to
the effects of different stress conditions ([86]Charlton et al., 2008;
[87]Vigeolas et al., 2008; [88]Cechová et al., 2017; [89]Hradilová et
al., 2017; [90]Sistani et al., 2017; [91]Woźniak et al., 2017;
[92]Ellis et al., 2018; [93]Tasho, Shin & Cho, 2018). However,
[94]Desalegn et al. (2016) recently reported on leaf metabolome changes
driven by inoculation with AMF and rhizobia of healthy pea plants and
those infected with a pathogenic fungus Didymella pinodes.
The aim of the present study was to analyze the effect of inoculation
with AMF Rhizophagus irregularis on growth and the physiological and
biochemical state of pea plants. Detailed analysis of the leaf
metabolome using gas chromatography-mass spectrometry (GC-MS), along
with investigation of leaf photochemical activity and pigment content,
was performed to assess the changes caused in P. sativum leaves by
mycorrhization at the key stages of plant development. Despite the fact
that plants did not show a strong growth response to the inoculation,
and their photosynthetic activity was not affected by mycorrhization,
this study revealed significant metabolic alterations occurring in pea
leaves during the development of AM symbiosis. These alterations were
associated with a prolongation of the vegetation period and an increase
in the seed biomass of inoculated plants.
Materials & Methods
Plant and fungal material
The low, determinate pea (Pisum sativum L.) cv. Finale (Cebeco,
Rotterdam, The Netherlands) with dark green leaves, white flowers and
round green seeds ([95]Engvild, 1987) was used to study AM mediated
metabolic alterations in leaves. This cultivar was used because of its
stable yields and wide adaptation ([96]Engvild, 1987).
The fungal isolate Rhizophagus irregularis BEG144 used in the study was
previously characterized as forming highly effective AM symbioses with
many agricultural crops ([97]Muromtsev, Marshunova & Jacobi, 1989). The
isolate was initially provided by the International Bank for the
Glomeromycota (Dijon, France) and has been maintained in Plecthrantus
australis pot cultures. To produce the inoculum of the fungus for this
experiment, mycorrhizal Sorghum sp. plants were grown in pot cultures
(see ‘Fungal inoculum preparation’).
Plant growth conditions
All plants used in this work were grown in pots with a growth substrate
consisting of sterile soil and quartz sand mixture (1:2 v/v),
supplemented with 1 g L^−1 Ca[3]PO[4] as a source of phosphate. A loamy
sandy soddy-podzolic soil obtained from Gatchinsky district, Leningrad
Oblast, Russia, with the following characteristics was used: pH (KCl)
4.8; 3.6% organic matter; 35 mg kg^−1 available K[2]O (extraction with
0.2 N HCl); 33 mg kg^−1 available P[2]O[5] (extraction with 0.2 N HCl);
28,7 mg-equivalent kg^−1 hydrolytic acidity; 98 mg-equivalent kg^−1
base exchange materials. CaCO[3] (1.44 g kg^−1) was added to the soil
to correct pH. Pots with the growth substrate were autoclaved twice
with a two-day interval for 60 min at 134 °C and 0.22 MPa to remove
soil microbiota and kept for a month to eliminate volatile toxic
compounds. Plants were grown in a constant environment chamber (model
VB 1514; Vštsch, Hanau, Germany) at 16/8 h and 24/22C day/night regime,
75% relative humidity, and around 10,000 lux illumination. The pot
cultures were fertilized once a week with 0.5× modified Hoagland’s
solution without phosphate ([98]Shtark et al., 2016) (0.15 L L^−1 of
the growth substrate), and watered as needed.
Fungal inoculum preparation
Sorghum seeds were surface-disinfected as follows: 1 min in 96%
ethanol, a rinse with sterile water, 10 min in 0.15% KMnO[4] aqueous
solution and a thorough rinse with sterile water. Disinfected Sorghum
seeds were sown in the growth substrate (see ‘Plant growth
conditions’), supplemented with 1g L^−1 fresh and washed P. australis
mycorrhizal roots. After 120 days of cultivation under conditions
described in ‘Plant growth conditions’, Sorghum root systems were
extracted from the growth substrate, cut into one cm pieces, and dried
at room temperature and then mixed again with the growth substrate they
were extracted from at a 1:1 ratio (v/v).
Experimental design and collection of plant material
Pea seeds were surface-disinfected as follows: 1 min in 96% ethanol, a
rinse with sterile water, 8 min in a 5% NaClO aqueous solution, and a
thorough rinse with sterile water. Disinfected pea seeds were
germinated on sterile humid vermiculite in Petri dishes for 3 days at
27 °C in the dark. Two pea seedlings of equal size were planted into a
300-ml ceramic flower pot with the growth substrate described in ‘Plant
growth conditions’. Half of the pots were supplemented with 15 g L^−1
R. irregularis inoculum before planting; the other half was left as
control.
The plants were analyzed at six points in time, which corresponded to
specific developmental stages for the growth of this species. I: 7 days
post inoculation, DPI (Vegetative stage, second leaf fully unfolded
with one pair of leaflets, simple tendril); II: 21 DPI (Vegetative
stage, first leaf with two pairs of leaflets, complex tendril); III: 32
DPI (Reproductive stage, an enclosed floral bud); IV: 42 DPI
(Reproductive stage, first open flower); V: 56 DPI (Reproductive stage,
pod fill. Green seeds fill the pod cavity); and VI: 90–110 DPI
(Senescence stage, dry harvest stage. All pods dry and brown, seed
dry). The developmental stages were selected in accordance to [99]Knott
(1987) with the following difference: all the internodes were counted,
including those adjoining the two small scale leaves, resulting in two
additional nodes.
At stages I–V, ten or more plants per treatment were taken at random at
each stage. The plants were removed from the soil and their root
systems thoroughly washed. The total fresh weight, the fresh weight of
the aerial part of the plants and the number of internodes were
determined. After measuring the growth parameters, the youngest
fully-developed leaf from each analyzed plant was cut off. Leaves from
three to five plants were allocated to a single biological replicate,
weighed and snap-frozen in liquid nitrogen in two mL Eppendorf
Safe-Lock tube, and then stored at −80 °C. At least three biological
replicates for each time point were collected for pigment and
metabolome analysis. For arbuscular mycorrhiza analysis, fragments of
lateral roots were collected individually from each plant in two mL
Eppendorf tubes and were stored at –20 °C. At stage VI, the aerial
parts of all remaining plants were collected and their dry weight was
measured after drying at room temperature for three months.
Analysis of mycorrhization
Sheaffer Black Ink staining was performed according to [100]Vierheilig
et al. (1998) to visualize fungal structures in the root samples. Roots
were washed once with distilled water and covered in glycerol; root
fragments totaling a length of 30 cm for each plant (n = 10) were
mounted on glass slides. The AM development was examined using Axiovert
35 light microscope (Zeiss/Opton, Germany) and quantitatively assessed
according to [101]Trouvelot, Kough & Gianinazzi-Pearson (1986) by the
following parameters: M% = intensity of intraradical mycelium
development (reflects the proportion of the root length colonized by
the fungus), and a% = arbuscule abundance in mycorrhizal root fragments
(characterizes the functional state of the fungus). For statistical
analysis, the parameters were subjected to arcsine transformation to
normalize the data ([102]Little & Hills, 1978).
Leaf photochemical activity and pigment content analyses
Chlorophyll a fluorescence analysis was conducted at stages I–V on the
day before the plant material sampling (6, 20, 31, 41, and 55 DPI). The
photochemical activity of one of the leaflets in the first pair of the
youngest fully formed leaf ([103]Fig. S1) was measured. Plants (for
stage V n = 5, for the rest stages n = 3) in pots were placed in a
light-tight chamber and pre-adapted to darkness for 15 min before the
measurements. The kinetics of the chlorophyll a fluorescence induction
was acquired at room temperature by pulse amplitude modulation (PAM)
fluorometric analysis using a portable chlorophyll fluorometer PAM-2500
(Heinz Walz GmbH, Effeltrich, Germany). To secure the leaflets, a
2030-B clamp equipped with a quantum and temperature sensor was used.
The quantitative fluorescent parameters and related calculated factors
were derived using the PAMWin-3 Software and Instruction manual for
PAM-2500 (Heinz Walz, [104]https://www.walz.com). Using the original
data, the following values were obtained: F[v]∕F[m], the maximum PSII
photochemical efficiency in the darkness-adapted state ([105]Kitajima &
Butler, 1975); Y(II), the effective quantum yield of photochemical
energy conversion in PSII ([106]Genty, Briantais & Baker, 1989); qP,
the coefficient of photochemical quenching of chlorophyll fluorescence
([107]Schreiber, Schliwa & Bilger, 1986); qN, the coefficient of
non-photochemical quenching of chlorophyll fluorescence
([108]Schreiber, Schliwa & Bilger, 1986). The maximum electron
transport rate (ETRmax) at light saturation and minimum saturating
irradiance (Ik) were also calculated.
The leaflet area was calculated using Fovea Pro v. 4.0 for Adobe
Photoshop (Reindeer Graphics,
[109]http://reindeergraphics.com/products.html).
Leaf pigment analysis was conducted at stages I–V (for stage V n = 4,
for the remaining stages n = 3). The leaf samples (up to 0.02 g) were
ground three times for 2 min in two mL microtubes with 3 metal balls
three mm in diameter in liquid nitrogen by using a Tissue Lyser LT
(Qiagen, Hilden, Germany) bead mill at the 50 hits s^−1 frequency. The
pigments were quantitatively extracted with methanol as described by
[110]Smolikova et al. (2017). The absorption spectra of the extracts
were acquired at 470.0, 652.4, and 665.2 nm in quartz cuvettes with one
cm light path (Reachim, St. Petersburg, Russia) by using a UV/Vis
spectrophotometer Spekol 1300 (Analytik Jena AG, Jena, Germany). The
chlorophyll and carotenoid contents were calculated as recommended by
[111]Lichtenthaler (1987) and [112]Lichtenthaler & Buschmann (2001) and
normalized to fresh weights.
Metabolome analysis
Leaves were sampled at stages II, IV and V. The samples (0.1–0.2 g)
were ground as described by [113]Puzanskiy et al. (2018) and subjected
to a single-stage extraction with two mL methanol: chloroform: water
(5:2:1) mixture. Tissue debris was removed by centrifugation at 12,000
g for 10 min at −5 °C. The supernatant was collected and evaporated in
a vacuum evaporator (Eppendorf, Germany). The dried material was
dissolved in pyridine with the internal tricosane standard (nC23). The
samples were then supplied with the silylating agent BSTFA: TMCS 99:1
(Sigma-Aldrich) and derivatizated at 90 °C for 20 min ([114]Puzanskiy
et al., 2015; [115]Puzanskiy et al., 2018).
GC-MS analysis was performed at Agilent 5860 chromatograph using
Agilent ChemStation E.02.02.1431 software (Agilent Technologies, Santa
Clara, CA, USA). Separation was performed on a J&W HP-5ms capillary
column 30 m long 0.25 mm in diameter, stationary phase film (95%
dimethylpolyoxane, 5% diphenyl), thickness 0.1 µm. The helium gas flow
rate was one mL min^−1. Inlet temperature was 250 °C at splitless mode.
The temperature conditions of the column thermostat were: an initial
temperature of 70 °C, increased by 5 °C per min up to 320 °C. The peaks
were recorded by an Agilent 5975S mass selective detector (Agilent
Technologies, Santa Clara, CA, USA). Electron impact ionization was
performed at 70 V and an ion source temperature of 230 °C.
The analysis of the GC-MS data was processed using the PARADISe program
(Department of Food Science Faculty of Science, University of
Copenhagen, Denmark, [116]http://www.models.life.ku.dk/paradise)
coupled with NIST MS Search (National Institute of Standards and
Technology (NIST), USA). In addition, the AMDIS (Automated Mass
Spectral Deconvolution and Identification System, NIST, USA) were used.
The following mass-spectrometer libraries were applied: NIST2010, the
library of the Resource Center of the Science Park “Center for
Molecular and Cell Technologies” (St. Petersburg University), the Golm
Metabolome Database (GMD) and MoNA (Massbank of North America).
Retention index (RI) was determined by calibration with standard
alkanes.
Leaf metabolome analysis was performed in three biological and two
technical replicates.
Statistical analysis
All data on plant growth, except that on the number of internodes, and
pigment accumulation at stages I–V was processed using two-way analysis
of variance (ANOVA) with normal distribution. The multiple comparison
procedure was used to isolate which group(s) differ from the others.
The data on the number of internodes between cotyledons and the
youngest completely formed leaf were processed with one-way ANOVA on
Ranks. Data on dry weight (stage VI), mycorrhiza development, and
chlorophyll fluorescence were processed with one-way ANOVA with normal
distribution. The SPSS 12.0 package (SPSS Inc Chicago, IL, USA) was
used for ANOVA. All data were expressed as mean ± standard error. The
differences were considered as significant at the confidence level of
p ≤ 0.05.
Metabolome data were processed in the environment of the R language
3.4.2 ([117]R Core Team, 2017). For quantitative interpretation, the
data were normalized against the internal standard (nC23), calculated
per mass. In addition, data were normalized against sample median. The
data were standardized and log[2]-transformed. Outlying values were
excluded on the basis of Dixon’s test. When a metabolite was not
detected but was present in the other replicated samples it was
considered a technical error and the missing values were imputed.
Missing data imputation was performed by KNN (k-nearest neighbors) with
“impute” R package ([118]Hastie et al., 2017). A heatmap was
constructed with ComplexHeatmap ([119]Gu, Eils & Schlesner, 2016). PCA
(Principal Component Analysis, PCA) was realized with pcaMethods
([120]Stacklies et al., 2007). LLE (Locally Linear Embedding) was
performed with RDRToolbox ([121]Bartenhagen, 2014). Random Forest
method (RF) was carried out in the randomForest toolkit ([122]Liaw &
Wiener, 2002), while (O)PLS-DA was performed in Ropls toolkit. Variable
Importance in Projection (VIP) was used as a statistic for the feature
selection ([123]Thevenot et al., 2015). Non-parametric multivariate
analysis of variance (PERMANOVA) ([124]Anderson, 2001) was used with
Vegan ([125]Oksanen et al., 2018). Euclidean distances were applied. A
hypergeometric test was used to perform an enrichment analysis
([126]Kachitvichyanukul & Schmeiser, 1985). Lists of metabolites tested
for overrepresentation was made by OPLS-DA (VIPs ≥ 1) and Random Forest
(Mean Decrease Accuracy). The KEGG database ([127]Kanehisa et al.,
2019) was used by the R package KEGGREST that provides a client
interface to the KEGG REST server ([128]Tenenbaum, 2018). The lists of
metabolic pathways which include identified metabolites were obtained
using KEGGREST. M. truncatula was used as a reference organism, because
it is closely related to P. sativum. For the compounds identified up to
class (hexose, disaccharide, among others), lists of metabolic pathways
for common compounds of these classes were used. Results were
visualized as the networks of metabolic pathways, where nodes
(pathways) share common edge if they include common metabolites. A
graph was built in the Cytoscape software environment ([129]Shannon et
al., 2003) using Prefuse Layout. Lengths of edges reflect the number of
metabolites shared between pathways. Metabolites with significant
(p < 0.05) and strong correlation coefficients (|r| > 0.8) of their
arbitrary content were mapped in the Cytoscape software environment
([130]Shannon et al., 2003), using “organic layout.”
Results
Arbuscular mycorrhiza development and its effect on host plant growth and
physiological state
In the plants inoculated with R. irregularis, the intensity of
intraradical mycelium development (M%; [131]Fig. 1A) increased up to
stage IV of plant development (see ‘Experimental design and collection
of plant material’ for description of the developmental stages) and
then plateaued. The most intensive growth of M% was observed in between
stages II and IV. Arbuscule abundance in mycorrhizal root fragments
(a%; [132]Fig. 1A) began to rapidly increase at stage I, reached the
maximum at stages II–III and then decreased, reflecting a reduction of
the functional activity of mycorrhiza. Mycorrhiza was not found in the
roots of non-inoculated plants, which indicated that the growth
substrate was well sterilized and there was no cross-contamination of
the plants during vegetation.
Figure 1. The effect of inoculation with R. irregularis on plant growth and
leaf photochemical activity of the pea cv. Finale at different stages of
plant development.
Figure 1
[133]Open in a new tab
The stages are: I: 7 days post inoculation (DPI) when the second leaf
is fully unfolded with one pair of leaflets and a simple tendril; II:
21 DPI at first leaf with two pairs of leaflets and a complex tendril;
III: 32 DPI when the floral bud is enclosed; IV: 42 DPI at the first
open flower; V: 56 DPI when the pod is filled with green seeds; and VI:
90-110 DPI at the dry harvest stage. Bars represent standard errors.
p ≤ 0.05. (A) The number of internodes between cotyledons and the
youngest completely formed leaf (pcs.) and parameters of arbuscular
mycorrhiza development in the inoculated plants: M% is the intensity of
intraradical mycelium development (no differences were observed between
the mean values for stages I and II, as well as between those for
stages IV and V), a% is the arbuscule abundance in mycorrhizal root
fragments (no differences found between the mean values for the stages
II and III, as well as between those for stages IV and V). (B) Average
dry weight (g) 1) of shoots 2) of seeds per plant 3) per seed (at stage
VI). (C) Photochemical activity of the youngest completely developed
leaf (one leaflet). Ik is the light saturation and minimum saturating
irradiance, ETRmax is the maximum electron transport rate. (A, C) The
values, which are not significantly different from each other, are
marked with the same letter. (B) * indicates a significant difference
between control and inoculated plants. contr signifies control plants,
AM signifies plants inoculated with R. irregularis.
During stages I–V, inoculation with R. irregularis had no influence on
either the shoot weight or the total fresh weight of cv. Finale pea
plants ([134]Table 1). No significant interaction between the factors
‘Stage’ and ‘Inoculation’ was observed ([135]Table 1). At stage III,
found to be the most active phase of mycorrhiza development, the number
of internodes between cotyledons and the youngest completely formed
leaf was lower in the inoculated plants ([136]Fig. 1A). At the same
time, correlations between the mycorrhization parameters and the number
of internodes in individual plants at this developmental stage
(p = 0.363 and 0.988, correspondingly, for M% and a%) were not
significant. This growth parameter increased up to stage IV and then
stabilized ([137]Fig. 1A) in accordance with the previous
characterizations of cv. Finale as exhibiting determinate stem growth
([138]Engvild, 1987).
Table 1. Probability (p) values that reflect influence of factors ‘Stage’ and
‘Inoculation’ on plant growth and pigment accumulation in the youngest
completely developed leaf of the pea cv. Finale.
p values were calculated using two-way ANOVA. For pairs with p ≤ 0.05,
the influence of the factor was considered significant (bold and
underline). Non-bold underlined p values indicate a marginally
non-significant trend of Inoculation factor influencing leaf
chlorophylls content (p ≤ 0.1).
Parameter Factor
Stage Inoculation Stage × inoculation
Shoot fresh weight
[MATH: <0.001<
/mrow>¯ :MATH]
0.106 0.721
Total fresh weight
[MATH: <0.001<
/mrow>¯ :MATH]
0.443 0.976
Chlorophyll a 0.182
[MATH: 0.080¯ :MATH]
0.909
Chlorophyll b 0.247
[MATH: 0.069¯ :MATH]
0.912
Carotenoids
[MATH: 0.026¯
:MATH]
0.108 0.793
[139]Open in a new tab
The vegetation of the control plants lasted, on average, 90 days, while
in the inoculated plants it continued for 2 to 3 weeks longer. Despite
the fact that the shoot dry weight was the same with and without AM,
inoculation led to an increase of the dry weight per seed ([140]Fig.
1B). Additionally, a marginally non-significant trend towards the
increase of the seed dry weight per plant (p = 0.054, n = 29) was
observed.
Further on, a measurement of the mycorrhization effect on the
photochemical activity of pea leaves, chlorophyll a fluorescence in a
leaflet of the youngest completely formed leaf was taken ([141]Fig.
S1). Both the maximum photochemical efficiency of PSII in the
darkness-adapted state, F[v]∕F[m], and the effective quantum yield of
photochemical energy conversion in PSII, Y(II), were generally constant
during vegetation (stages I–V). An exception to this was Y(II) at stage
I for inoculated plants, which was less than the corresponding value
for F[v]∕F[m] ([142]Fig. S2A). The coefficient of photochemical
quenching of chlorophyll fluorescence, qP, was also constant during
plant development, while the coefficient of non-photochemical quenching
of chlorophyll fluorescence, qN, changed with time relative to qP
([143]Fig. 2SB). The maximum electron transport rate at light
saturation, ETRmax, was constant in the control plants, whereas the
inoculated plants had a difference of ETRmax values corresponding to
stages III and V; namely, at stage III it was higher ([144]Fig. 2C).
The minimum saturating irradiance, Ik, changed during vegetation
([145]Fig. 2C). In the intermediate developmental stages, Ik was higher
than in the later stage V for both treatments. In the control, the
distinction was more pronounced: differences were also observed between
the middle stages and stage I. At the same time, there were no
differences between the control and inoculated plants in any of the six
characterized parameters of chlorophyll a photochemical activity at
each of the developmental stages ([146]Fig. 1C; [147]Fig. S2). Thus,
despite some differences between control and mycorrhizal plants in the
dynamics of changes of these parameters, it can be concluded that
mycorrhization, in general, did not affect the photochemical activity
of chlorophyll a in the leaves of the pea genotype Finale.
Figure 2. Heatmap of the arbitrary content of identified (at least up to
class) metabolites in the youngest completely developed leaf of the pea cv.
Finale at different developmental stages.
[148]Figure 2
[149]Open in a new tab
The heatmap is combined with a hierarchical clustering dendrogram
(distances represent the correlation of the metabolite concentrations,
Ward method). Data are normalized per sample median, log[2]-transformed
and standardized. The number after ‘_’ is the retention index (RI),
mag, monoacylglycerol, disac, trisac, di and trisacharides, or their
derivates. contr, control plants, AM, plants inoculated with R.
irregularis. Stage descriptions given in [150]Fig. 1.
Although a pair wise comparison of chlorophyll a and b accumulation did
not reveal significant differences between the control and inoculated
plants ([151]Fig. S3), there was a marginally non-significant trend of
‘Inoculation’ factor positively influencing both parameters ([152]Table
1). At the same time, chlorophyll a and b accumulation values were not
dependent on the stage of plant development, unlike the accumulation of
carotenoids ([153]Table 1; [154]Fig. S3). A multiple comparison
procedure revealed that this parameter differed only for the inoculated
plants between stages I and V ([155]Fig. S3). However, inoculation had
no effect on the carotenoid content ([156]Table 1; [157]Fig. S3).
Since it was shown that in the model plant M. truncatula mycorrhization
could lead to an increase in the intensity of photosynthesis by
increasing the leaf surface area ([158]Adolfsson et al., 2015), the
area of the leaflet used for chlorophyll fluorescence analysis was
measured. However, leaflet areas (measured at stages II and V) did not
differ significantly in control and inoculated plants and were,
respectively, 4.32 ± 0.93 and 5.24 ± 0.27 cm^2 (for stage II, p =
0.119) and 2.84 ± 0.05 and 2.52 ± 0.54 cm^2 (for stage V, p = 0.641).
Leaf metabolome
General characteristics of the metabolite profile
The metabolite profiles of pea leaves included about three hundred
metabolites, for half of which the metabolite class was identified
([159]Fig. 2 and [160]Tables S1, [161]S2). Twenty one amino acids (18
proteinogenic), more than ten carboxylic acids, mainly energy
metabolism intermediates, 13 fatty acids and their derivatives, as well
as nitrogenous bases, sugar alcohols, sterols, various secondary
metabolites and others were detected. Sugars (about 70), including
pentoses, hexoses and oligosaccharides, were the most widely
represented metabolite group in the obtained profiles.
Alterations in the metabolite-relative abundances were visualized as a
heat map combined with a hierarchical cluster dendrogram ([162]Fig. 2).
The metabolites formed three large clusters. Metabolites of the first
(I) and second (II) clusters ([163]Fig. 2) are characterized by a
higher content at the later stages of development and, vice versa,
metabolites of the third (III) smaller cluster received better
representation at earlier stages. The first and second clusters
contained a large number of sugars. The first, larger, cluster
primarily included monosaccharides. In addition, a number of key
organic acids was included in this cluster. The second cluster
included, for the most part, metabolites with a high content on the
last stage of development, and among them disaccharides were the most
abundant. In the third cluster ([164]Fig. 2), sugars were
underrepresented, but the group included the majority of fatty and
amino acids. Within these clusters, several smaller groups of
metabolites could be distinguished.
It was shown that pattern of correlations for metabolite content was
very similar in both the inoculated and control plants. A linear
relationship and strong correlation between Pearson coefficients in
control and inoculated plants was found (r = 0.75). On the other hand,
the number of strong correlations increased under mycorrhization. The
median number of strong correlations (|r| > 0.8) of each metabolite was
18.5 for control and 22 for inoculated plants and was statistically
(Wilcoxon test) different (p = 0.023). This reflected a slight, but
significant (p < 10^−15) increase in the level of mean absolute
correlation between inoculated (0.434) and control plants (0.418).
Analysis of alterations in leaf metabolome during plant development.
Differences in pea leaf metabolomes were analyzed in lower dimension
planes as obtained with two unsupervised dimension reduction methods
([165]Lee & Verleysen, 2010): the principal component analysis
(PCA)—most common in metabolic studies—and LLE (Locally Linear
Embedding). The latter gives advantages in the analysis of data with
nonlinear regularities. In [166]Fig. 3A, metabolite profiles are
visualized in the score space of the first two principal components
(PC) obtained from the PCA of the control and inoculated plants. It can
be seen that metabolomes were grouped along PC1 in accordance with the
plant age (ANOVA p = 2.2 × 10^−16, a Tukey test gives p values <10^−13
for all pairs). In addition, as can be seen in [167]Fig. 3A, an effect
of mycorrhization was much weaker, but clearly distinguished at both
stages IV (t-test for PC1 gives p = 0.0016) and V (p = 0.0003). A
similar result was obtained by LLE application ([168]Fig. 3B) (MANOVA
for first two dimensions p < 10^−13), but in this case the effect of
mycorrhiza was even more pronounced. For stage IV differences in the
first dimension (p = 0.003) and for stage V in the second dimension (p
= 0.0004) were observed. Also PERMANOVA confirmed effects of
development stage (p = 10^−15) and mycorrhization ( p = 0.04) on the
metabolome. It should be noted, that the leaf metabolic profiles of the
inoculated plants at stages IV and V were shifted towards those of the
control plants at earlier developmental stages ([169]Fig. 3). This
implies that mycorrhization slightly retarded plant development.
Figure 3. Age dependent metabolome shifts in pea leaves of cv. Finale at
different stages of plant development.
[170]Figure 3
[171]Open in a new tab
(A, B) Visualization of metabolic profiles in the low dimensional
spaces. (A) PCA score plot, % is the variance associated with a PC. (B)
Result of the dimension reduction by Local Linear Embedding (k = 18).
Ellipses—95% CI. (C, D) Supervised selection of features related to
aging: barplots of Mean Decrease Accuracy from Random Forest and VIPs
from PLS-DA combined with heatmap of means for every age for control
plants (C), and plants inoculated with R. irregularis (D). Stage
descriptions are given in [172]Fig. 1.
Further detailed analysis of age dependence of metabolite profiles
diversity was done with classification by supervised methods such as
PLS-DA and Random Forest. The PLS-DA ([173]Fig. S4 ) model made for the
control plants contained two PCs, explaining 41 and 21% of the
variance, respectively, at R^2Y = 0.99, Q^2Y = 0.96 ([174]Fig. S4A).
For both control and inoculated plants, plants that were 21 days old
were distinguished from the others by an association with PC1, while
those between 42 and 56 days old were distinguished by an association
with PC2 ([175]Fig. S4A). This indicates a larger metabolic shift
between 21 and 42 days compared with that of between 42 and 56 DPI.
Histograms of VIP and MDA ([176]Figs. 3C and [177]3D) values, which
reflect the relation of metabolite content to the age of the plant, are
shown in [178]Fig. S4B, [179]S4C. The age of plants strongly affected
the content of sugars, including a wide spectrum of disaccharides, such
as sucrose, as well as pentoses and hexoses, such as fructose and
glucose. According to the heatmap ([180]Figs. 2, [181]3C and [182]3D),
sugars were accumulated mainly at the age of 56 days. On the other
hand, a number of amino acids (e.g., proline, glutamate, glutamine,
histidine and asparagine) showed high values of VIP and MDA, but their
maximum abundance was observed at the beginning of plant development.
The effect of mycorrhization on the leaf metabolome.
A similar analysis was performed on the leaf metabolomes of the
inoculated plants ([183]Fig. S4 ). To evaluate the similarities and
differences in the metabolomic shifts during aging of the control and
inoculated plants, loadings of the first and second components of the
PLS-DA models were compared. Scatter plots in the spaces of the
loadings of the PCs of the PLS-DA models were created for analysis. In
the case of the PC1 ([184]Figs. S4C), which in both models were
associated with differences in the profiles of plant metabolites at the
21st day of development from the others, the dependence of their
loadings was close to linear and the values were very similar. The
correlation coefficient was r = 0.93 and highly significant
(p < 10^−15).
According to the loadings plot for the PC2 ([185]Fig. S4D), which was
related to differences between 42 and 56 DPI, a correlation was also
estimated. It was not so strong (r = 0.49) but significant
(p < 10^−15). Thus differences between inoculated and control plants
were much greater at the later stages. In the case of control plants, a
number of metabolites (in the higher left sector of [186]Fig. S4D)
decreased or did not show significant change at the last stage, but
they stayed at the same level or increased in the leaves of inoculated
plants. Several amino acids, intermediates of nitrogen metabolism, and
unsaturated fatty acids increased their content under mycorrhization.
In contrast, several sugars and sterols (in the right lower section of
[187]Fig. S4D) accumulated in the control plants and either decreased
or did not change their level, or accumulated at a lesser rate under
inoculation. This is consistent with the “shift” of the 56 day old
plants in the direction of 21 day old plants along PC2 at the PLS-DA
score plot ([188]Figs. S4A, [189]S4B). Thus, mycorrhiza modifies
specific segments of metabolic network at later stages of plant
development, making inoculated plants at stages II and V metabolically
closer to each other, at least partially. Moreover, mycorrhiza slightly
retards age-related metabolome alterations as can be seen above at the
PCA and LLE plots ([190]Figs. 3A [191]3B). As presented at [192]Figs.
S5C, [193]S5D, the samples of inoculated plants at later stages are
shifted toward samples of the preceding time points. In particular,
there is a quite recognizable shift from 42 DPI plants to 56 DPI plants
along PC1 on the PLS-DA score plot ([194]Fig. S5C).
In order to find out which pathways were the most affected by aging an
enrichment analysis was performed. A list of significant features was
identified with PLS-DA and Random Forest ([195]Fig. 3C). As was
expected according to the literature, plant age yielded the greatest
effects on sugar metabolism, including galactose metabolism (p =
0.037), starch and sucrose metabolism (p = 0.031), amino sugar and
nucleotide sugar metabolism (p = 0.035).
Moreover, the alterations of sterol metabolism (p = 0.06), the
components of lipids, were also associated with plant age. Thus, plants
at different stages of development demonstrate systemic differences
based on rearrangements in the metabolic pathways primarily associated
with carbohydrate metabolism, possibly including photosynthesis, carbon
transport and storage.
The effect of inoculation with R. irregularis on the leaf metabolite profile
at different developmental stages
Differences at stage II (21 DPI).
According to unsupervised dimension reduction by PCA ([196]Fig. 4A) and
LLE ([197]Fig. S6A) the samples clustered in relation to mycorrhization
status. With PCA, a clear difference was found in the space of PC2 and
PC3 (MANOVA p = 5.24 × 10^−7). The PERMANOVA for nonreduced data gave p
= 0.099. In order to determine the details of the differences in the
metabolic profiles of the control and inoculated plants, classification
by OPLS-DA was carried out. Sixteen percent of variation in metabolite
content was associated with the predictive component, with R^2Y = 0.99,
Q^2Y = 0.86. Thus, during this period, the role of mycorrhization in
the formation of the metabolite profile of leaf was significant, but
relatively minor. [198]Figure 4B shows the plot of the predictive
component loadings with VIPs >1. Negative loadings correspond to a
higher content in control plants and vice versa. As can be seen, a
greater number of metabolites demonstrates a higher content in
inoculated plants. Values of loadings ([199]Fig. 4B) show that the
inoculated plants contained more amino acids such as glutamate,
glutamine, glycine, homoserine, phenylalanine, proline, threonine,
valine. Also in the leaves of the inoculated plants, the content of
other nitrogen-containing compounds is higher, including such important
metabolites as urea and GABA. At the same time, the leaves of the
control plants contain more β-alanine, serine and N-acetylglutamate. On
the other hand, in the control plants, a higher content of TCA
intermediates such as citrate and aconitate was observed. Accumulation
of carbonic acids was associated with inhibition of synthesis of other
amino acids as well as with a more active catabolism and urea cycle.
Various sugars accumulated in both control and inoculated plants. Fatty
acids did not show a clear dependence on mycorrhiza in this period. It
should be noted that higher content of phosphate in the leaves was
observed in the inoculated plants, which may be the result of an
advantage in mineral nutrition.
Figure 4. Comparative analysis of metabolite profiles of mycorrhizal and
control pea plants of cv. Finale at different stages of plant development.
[200]Figure 4
[201]Open in a new tab
(A, B) 21 DPI, stage II; (C, D) 42 DPI, stage IV; (E, F) 56 DPI, stage
V. (A, C, E) PCA score plot, % is the variance associated with the PC.
Ellipses—95% CI, circles represent control, the triangles represent the
mycorrhizal plants. (B, D, F) Diagrams of loadings of the OPLS-DA
predictive component (VIP > 1). Negative values correspond to a higher
content in control plants and vice versa. Stage descriptions given in
[202]Fig. 1.
Enrichment analysis (list of metabolites with VIP >1) for 21 day old
plants showed that seven pathways were significantly affected by the
mycorrhization. Four of them are related to amino acid metabolism:
Alanine, aspartate and glutamate metabolism (p = 0.083), Aminoacyl-tRNA
biosynthesis (p = 0.036), Biosynthesis of amino acids (p = 0.014),
Cyanoamino acid metabolism (p = 0.074). Thus nitrogen metabolism and
various amino acids were primarily affected. Also 2-Oxocarboxylic acid
metabolism (p = 0.090), Pantothenate and CoA biosynthesis (p = 0.082),
and Porphyrin and chlorophyll metabolism (p = 0.082) were affected by
mycorrhiza.
Differences at stage IV (42 DPI). As in the case of 21 day old plants,
unsupervised dimension reduction ([203]Figs. 4C, [204]S6B) showed that
the samples grouped according to the mycorrhization status. But in this
case PCA, in contrast to 21 DPI, stood out in the space of PC1 and PC2
(MANOVA p = 0.0005). PERMANOVA for nonreduced data gave p = 0.004.
Thus, the effect of mycorrhiza was more pronounced than at stage II.
OPLS-DA showed that 25% of the variance of the metabolites content was
associated with the predictive component, and R^2Y = 0.94, Q^2Y = 0.87.
Thus, for this period, the role of mycorrhization in leaf metabolome
formation increased as compared to that of 21 day old plants.
Considering the loadings of the predictive component ([205]Fig. 4D), it
can be seen that positive values corresponding to a higher content in
inoculated plants were characteristic for a larger number of
metabolites. The differences in the metabolic profiles of inoculated
and control plants had a number of common features at 21 and 42 DPI.
After six weeks of mycorrhization, leaves showed higher levels of a
number of amino acids and intermediates of nitrogen and phosphorus
metabolism. Organic acids such as aconitate, lactate, malonate, among
others, also increased. The content of some sterols also became higher
at 42 DPI in the inoculated plants. During this period the list of
metabolites, whose content was higher in the leaves of the control
plants, mainly contained sugars. Exceptions were the amino acids serine
(as on day 21), and isoleucine. Differences were found in the content
of secondary metabolites, for example, 4-hydroxycinnamic acid.
The effects of aging and mycorrhization were compared at 42 DPI. A
scatter plot was created in the spaces of the loadings of the
predictive components from the two OPLS-DA models: one for comparing
the 21 and 42 DPI plants, and the other for the control and inoculated
plants at 42 DPI ([206]Fig. S5A). Values of loadings of predictive
components from these two models are strongly correlated. The
correlation coefficient was r = − 0.61 and highly significant
(p < 10^−15). Thus, in general, mycorrhization has an opposite effect
on metabolite content to that of aging. In addition, the ‘rejuvenating’
effect of mycorrhization clearly appeared on the score plot (produced
by PLS-DA) of the 42 DPI plants of both the control and the treatment
groups and the 21 DPI control plants ([207]Fig. S5C). As can be seen,
the inoculated plants at 42 DPI are closer to the 21 DPI control plants
than the 42 DPI control plants.
Enrichment analysis ([208]Figs. 5, [209]S7) showed that the range of
pathways, which were affected by mycorrhization at the 42nd day,
expanded compared to the 21st day and included ([210]Fig. 5A, [211]Fig.
S4A): Aminoacyl-tRNA biosynthesis (p = 0.20), Arginine and proline
metabolism (p = 0.091), Biosynthesis of amino acids (p = 0.060),
Cyanoamino acid metabolism (p = 0.009), Cysteine and methionine
metabolism (p = 0.027), Glycine, serine and threonine metabolism (p =
0.043), monobactam biosynthesis (p = 0.027), Porphyrin and chlorophyll
metabolism (p = 0.066), Steroid biosynthesis (p = 0.091), Vancomycin
resistance (p = 0.066). [212]Figs. 5A and [213]S7A show that
mycorrhization primarily affects segments of metabolic network related
to amino acid and protein biosynthesis, and lipophilic compound
metabolism.
Figure 5. The effect of mycorrhiza development revealed by enrichment
analysis (hypergeomertric test) on the metabolic pathways in the pea leaves
of cv. Finale.
[214]Figure 5
[215]Open in a new tab
Fragment of pathway network based on the KEGG database using Medicago
truncatula as a reference species. Nodes (pathways) share common edge
if they share metabolites. The graph was built in the Cytoscape using
Prefuse Layout, where lengths of edges reflect the number of
metabolites shared between pathways. The larger red nodes correspond to
p < 0.05, smaller pink nodes to p < 0.1, grey nodes to pathways sharing
metabolites with significantly affected ones. (A) 42 DPI (stage IV);
(B) 56 DPI (stage V). Full versions of the graphs are shown in the
[216]Fig. S6.
Differences at stage V (56 DPI).
Just as with the 21 DPI and 42 DPI plants, dimension reduction showed
([217]Figs. 4E, [218]S6C) that samples grouped according to
mycorrhization status. In this case a clear difference was observed in
the space of PC1 and PC3 (MANOVA p = 1.2 × 10^−7). The PERMANOVA for
nonreduced data gave p = 0.008. The OPLS-DA showed that 23% of the
variance of the content of metabolites was associated with the
predictive component, with R^2Y = 0.99, Q^2Y = 0.86. Thus, during this
period, mycorrhization exerted the same influence on the formation leaf
profile metabolites as that at 42 DPI. Analysis of the predictive
component loadings ([219]Fig. 4F) showed that negative values
corresponded to a higher metabolite content in control plants where a
larger number of metabolites was observed. Lipophilic compounds,
sterols, terpenes, and fatty acids were among the metabolites with
higher content in the leaves of control plants. Several intermediates
of TCA demonstrated the same trend. At the same time there were no
fatty acids, sterol or acylglycerol among metabolites which
demonstrated higher content in the inoculated plants. Interestingly,
major monosaccharides and few amino acids also demonstrated positive
factor loadings. Comparative analysis of the aging and mycorrhiza
effects (in the same way as for 42 DPI) revealed ([220]Figs. S5B,
[221]S5D) that these two factors have the opposite effect (r = − 0.37,
p < 10^−10).
Enrichment analysis ([222]Figs. 5B, [223]S4B) showed that the range of
pathways affected by mycorrhization during this period was very
different from that at 42 and 21 DPI. At the same time, the
mycorrhiza-affected pathways were similar to those affected by aging.
Changes in the metabolism of sugars, including glycolysis and
lipophilic compounds were more pronounced. The following pathways were
affected: AGE-RAGE signaling pathway in diabetic complications (p =
0.048), Amino sugar and nucleotide sugar metabolism (p = 0.069),
Galactose metabolism (p = 0.067), Glycolysis/Gluconeogenesis (p =
0.067), Pentose phosphate pathway (p = 0.079), Starch and sucrose
metabolism (p = 0.079), Steroid biosynthesis (p = 0.031).
Discussion
The main goal of the present study was to reveal whether and how
mycorrhization affects growth and metabolism of the aerial part of pea
plants at the key plant developmental stages. For this purpose gas
chromatography-mass spectrometry (GC-MS) was used ([224]Van Look,
Simchen & Heberle, 1995; [225]Lisec et al., 2006; [226]Puzanskiy et
al., 2018). The youngest completely developed leaf was studied, with
the assumption that it had the most active metabolism. The study also
included analyses of its photochemical activity and pigment
accumulation, which showed that AM development did not affect the
physiological state of the plants. The major achievement of this study
is not only that remarkable alterations of the leaf metabolite profile
with plant age were demonstrated, but it was also found that
mycorrhization can influence age-related changes in the leaf,
prolonging the active phase of its metabolism. This is also concurs
with the observed retardation of plant growth and delayed senescence in
mycorrhizal plants.
The effect of mycorrhization on plant growth and physiological state
Under the conditions of this experiment, no positive effect of
mycorrhiza on the growth parameters of pea during the vegetative and
reproductive stages (I-V) was revealed. The data obtained here are
consistent with numerous observations made by other authors who noted a
low growth response of P. sativum to AMF mono-inoculation
([227]Rivera-Becerril et al., 2002; [228]Xavier & Germida, 2003;
[229]Borisov et al., 2004b; [230]Desalegn et al., 2016; [231]Zhukov et
al., 2017). It was also previously shown that the effect of mycorrhiza
was often not manifested under artificial lighting conditions
([232]Konvalinková & Jansa, 2016), so it is likely that the
illumination in the growth chambers used in this study was not
sufficient for the growth response to occur. Additionally, in the
present study, plants were grown under conditions of severe phosphate
deficiency; as was shown in previous works under such conditions the
plant strongly stimulates root colonization by AMF ([233]Smith & Read,
2008; [234]Bonneau et al., 2013). Taking into consideration the
specific conditions, one can presume that excessive colonization by the
fungus was not beneficial for the plant at certain developmental
stages, since the carbon cost of maintaining the microsymbiont may
outweigh the positive effects of mycorrhization.
In the present study, the dynamics of AM development were similar to
those for other annual plants. This growth curve rises until plant
flowering, and then the fungi stop growing and require carbon only for
maintenance. However, the relative costs of AM symbiosis are larger in
early plant development when AM fungal colonization is indispensable
for plants because the root system is small and hyphae are more
efficient in reaching P, which is poorly-mobile in soil (for review,
see [235]Kaschuk et al., 2009). This might have led to the observed
slight holdback in the formation of stem nodes by the middle of the
life cycle in mycorrhizal plants.
Despite the lack of a significant increase in the shoot and seed dry
weight per plant under the influence of AMF, the inoculated plants had
larger seeds at the dry harvest stage (VI). The inoculated pea plants
could probably accumulate additional seed biomass due to an extension
of their vegetation period. These observations showed that the
inoculated pea plants did have a longer vegetation period, compared
with the control plants. Previously, the effect of extending the
vegetation period was noted for pea inoculated with both AMF and
rhizobia, but no measurements that would confirm retardation in the
life cycle of pea plants had been carried out ([236]Naumkina, 2007). In
the present study, the pea plants inoculated with AMF prolonged the
active phase of the vegetation period, as evidenced by the results of
the metabolome analysis of the youngest fully developed leaf (see
‘Influence of the stage of plant development and mycorrhization on the
metabolite profile of pea leaves’).
One possibility is that control plants accelerated the completion of
their life cycle, since their mineral nutrition probably was worse than
that of mycorrhizal plants. It is known that poor nutrition can cause
acceleration of flowering and the completion of the life cycle
([237]Takeno, 2016), as well as premature flowering cessation, as shown
for pea ([238]Jeuffroy & Sebillote, 1997). Changes in hormonal balance,
potentially occurring in inoculated plants, may also contribute to the
observed differences. For example, gibberellic acid accelerates
flowering ([239]Fleet & Sun, 2005), while abscisic acid detains this
process ([240]McCourt & Creelman, 2008).
A similar delay in the aging of inoculated plants has been previously
described for Capsicum annuum, but there the effect only manifested
under salinity stress ([241]Beltrano et al., 2013). In a contrary
example, for Medicago sativa it was shown that mycorrhization can
shorten the vegetation period and at the same time increase the growth
parameters of the plant as well as increase photosynthetic acclimation.
However, such results were demonstrated only against the background of
increased CO[2] content ([242]Goicoechea et al., 2014). Interestingly,
mono-inoculation of pea with rhizobia extended the vegetation period,
while simultaneous use of rhizobia and foliar micronutrient fertilizer
did not. At the same time, increase of seed yield compared to control
was equal in both treatments ([243]Zając, Klimek-Kopyra & Oleksy,
2013). Thus, the change in the vegetation period of the mycorrhizal
plants may be associated with both environmental conditions and the
individual characteristics of a particular plant genotype.
It is assumed that the enhanced outflow of carbon from the aerial part
of a plant into its root system should stimulate the photosynthesis
process ([244]Kaschuk et al., 2009). Nevertheless, in the present
study, no changes were found to be associated with AMF inoculation,
either in the photochemical activity or in the surface area of the
leaves. Only a marginally non-significant trend was observed for
chlorophyll a, b accumulation. At the same time, changes in some
parameters of photochemical activity at certain stages of development,
both in the control and inoculated plants, were revealed indicating
normal development and functioning of PSII. Given the fact that
enhancement of photosynthesis due to mycorrhization as described in the
literature usually occurs under conditions of abiotic stress ([245]Wu &
Xia, 2006; [246]Beltrano et al., 2013; [247]Rozpądek et al., 2014;
[248]Hashem et al., 2015; [249]Liu et al., 2015; [250]Shinde & Thakur,
2015; [251]Yang et al., 2015; [252]Yooyongwech et al., 2016;
[253]Mathur, Sharma & Jajoo, 2018), it can be assumed that
mycorrhization by itself may not have direct impact on the function of
the photosynthetic apparatus. Rather, it might indirectly affect
photosynthesis owing to the overall increase in plant fitness and the
increase of its aerial part. The question of how the plant manages to
compensate for the carbon outflow from the aerial part to mycorrhizal
roots remains.
Influence of the stage of plant development and mycorrhization on the
metabolite profile of pea leaves
The GS-MS profiling of pea leaf revealed more than three hundred
substances. Simple unsupervised (PCA, LLE) methods showed significant
differences in pea leaf metabolome between different stages of plant
growth. These differences were much more pronounced than alterations
triggered by mycorrhization. Similar phenomenon of lesser importance of
AM formation compared to such factors as age, species specificity,
fertilization, season of experiment etc. has been described by other
researchers ([254]Fester et al., 2011; [255]Schweiger et al., 2014;
[256]Hill et al., 2018).
Further analysis (PLS-DA) indicated more drastic leaf metabolome
alterations between stages II and IV in comparison with those between
stages IV and V. At stage II plants grew intensively, while at stages
IV and V (first open flower and pod fill, green seeds stages) they
stopped growing and formed reproductive organs. Thus, plant development
was possibly accompanied by drastic changes in patterns of
donor–acceptor relations between organs and this coincided with
alterations in biochemical pathways.
Enrichment analysis and PLS-DA revealed intensive changes in
carbohydrate metabolism. Elevation in sugar content was associated
mostly with pea plant aging. Alterations in sucrose, fructose, and
glucose might suggest changes in the activity of photoassimilate
outflow. The pea plants at the flowering period and, especially, at the
pod fill stage were characterized by a high content of disaccharides
and hexoses in the leaves, which may be the result of an active
synthesis of transport forms of sugars for their subsequent transition
to the reproductive organs ([257]Troughton & Currie, 1977).
The sugars with content growing during the flowering and pod fill
stages were not limited to sucrose. Unfortunately, these sugars were
not identified definitively and thus for the moment their role remains
unclear. They might be intermediates of starch or other
polysaccharides, containing sugar fragments or transport forms of
carbon found in plants ([258]Lalonde, Wipf & Frommer, 2004). Moreover,
different transport glycosides of active compounds such as hormones
might be among them ([259]Park et al., 2017).
Metabolite profiles of young leaves (stage II) were characterized by
higher levels of amino acids. This indicates a higher level of nitrogen
metabolism associated with growth in this period. This is consistent
with the previously published data concerning raised content of free
amino acids in young leaves of P. sativum ([260]Storey & Beevers,
1978). Leaves at this age were shown to accumulate higher contents of
C14-18 fatty acids, especially unsaturated ones, and their amount
decreased with aging. All this could be the result of age-dependent
changes in the structure of membranes and lipid metabolism associated
with cessation of growth. It is known that the intensity of synthesis
of different types of lipids can vary during plant development
([261]Troncoso-Ponce et al., 2013). In pea plants in particular, a
change in the activity of the synthesis of both different types of
glycerolipids and sterols was reported ([262]Hellgren & Sandelius,
2001). The process slowed down with age. This indicates that the
metabolism of leaves of different ages from one plant can vary
significantly ([263]Dietz & Heilos, 1990; [264]Desbrosses et al., 2005;
[265]Puzanskiy et al., 2018).
Turning back to the role of mycorrhiza effects on the metabolome and
possible alterations during development, it should be noted that the
observed profile changes were uneven. According to the results of
OPLS-DA, the mycorrhiza effect on metabolism was less pronounced at the
earlier stage II than at stages IV and V. This phenomenon may be
associated with AM colonization (M%) (at stage II it was very low
whereas at stages IV and V it reached its maximum level). The
alterations between stages IV and V were also significantly different
between inoculated and control plants. In the process of plant
development, the number of metabolites that were more abundant in
inoculated plants increased in comparison to control plants. It is
known that in some cases mycorrhiza contributes to the accumulation of
proteins, carbohydrates, primary and secondary metabolites, probably
due to a better supply of phosphate and nitrogen ([266]Fester et al.,
2011; [267]Pedone-Bonfim et al., 2013; [268]Goicoechea et al., 2014;
[269]Hodge & Storer, 2015); it may also promote photosynthetic plant
acclimation at the late stages of vegetation as was shown in Medicago
sativa and other plants ([270]Goicoechea et al., 2014; [271]De Souza et
al., 2016). The observed metabolite shift probably indicates an
adaptation of the plant to new nutritional conditions and further
redistribution of metabolites in the plant-fungi symbiosis.
The leaves of AM plants contained higher levels of amino acids and
unsaturated fatty acids, a fact which coincides with more active
metabolism. Sterol levels were accordingly lower than in non-inoculated
plants. Thus, mycorrhization partially slows down the aging process and
makes the leaf profiles of old mycorrhizal plants a little “younger”.
This effect was associated with prolongation of the vegetation period
and the increase in seed biomass of the inoculated plants. The present
data are consistent with recent discoveries in the field of proteomic
research. It was found that at the stage of seed maturation a highly
symbiotically effective genotype K-8274 (cv. Vendevil) under combined
inoculation with AMF and rhizobia had the proteomic signatures of
ongoing seed filling as compared to non-inoculated plants, which shows
that inoculation prolongs the active phase of seed filling in some
genotypes ([272]Mamontova et al., 2019).
Conclusions
To the best of our knowledge, this is the first study in which the
effect of mycorrhization on the pea leaf metabolome has been examined
at different plant developmental stages. Although there was no
significant effect of mycorrhization on the aerial biomass, or on the
accumulation of chlorophyll a, b and carotenoids in the leaves or their
photochemical activity, it did influence the age-related changes in the
plant leaf metabolome. This effect was more pronounced at later stages
of plant development. The results of the analysis allow us to conclude
that mycorrhization prolongs the period of ‘youth’ in plant leaves, and
possibly leads to better accumulation of metabolites such as amino
acids and unsaturated fatty acids. Thus, it can be assumed that
mycorrhiza partially halts senescence due to a boost of some segments
of metabolic network at later stages of plant development. This effect
promises to be beneficial for agriculture, especially for northern
regions where prolongation of the pea life cycle does not lead to
preliminary drying of the seeds and plants in general, and for the
green pea cultivars which are harvested before seed maturation.
Supplemental Information
Figure S1. Measurement of the photochemical activity of pea leaf using
a portable chlorophyll fluorometer.
The arrow points to a leaflet in the first pair of the youngest fully
formed leaf. The analyzed leaflet is secured in place using the clamp
equipped with a quantum and temperature sensor and connected to the
fluorometer.
[273]Click here for additional data file.^ (955.4KB, png)
DOI: 10.7717/peerj.7495/supp-1
Figure S2. Photochemical activity of the youngest completely developed
leaf (one leaflet) of the pea cv. Finale at the different stages of
plant development.
The stages are: I: 7 days post inoculation (DPI) when the second leaf
is fully unfolded with one pair of leaflets and a simple tendril; II:
21 DPI at first leaf with two pairs of leaflets and a complex tendril;
III: 32 DPI when the floral bud is enclosed; IV: 42 DPI at the first
open flower; V: 56 DPI when the pod is filled with green seeds; and VI:
90-110 DPI at the dry harvest stage. The values, which are not
significantly different from each other (p ≤ 0.05) are marked with the
same letter. Bars represent standard errors. (A) F[v]∕F[m], the maximum
PSII photochemical efficiency in the darkness-adapted state, Y(II),
effective quantum yield of photochemical energy conversion in PSII; (B)
qP, coefficient of photochemical quenching of chlorophyll fluorescence,
qN, coefficient of non-photochemical quenching of chlorophyll
fluorescence. contr, control plants, AM, plants inoculated with
Rhizophagus irregularis.
[274]Click here for additional data file.^ (32.6KB, png)
DOI: 10.7717/peerj.7495/supp-2
Figure S3. Accumulation of pigments in the youngest completely
developed leaf of the pea cv. Finale at the different stages of plant
development (see descriptions in Fig. S2).
The values for each parameter, which are not significantly different
from each other (p ≤ 0.05) are marked with the same letter. Bars
represent standard errors. contr, control plants, AM, plants inoculated
with R. irregularis.
[275]Click here for additional data file.^ (637.7KB, png)
DOI: 10.7717/peerj.7495/supp-3
Figure S4. Comparative analysis of metabolome shifts during plant
development in the pea cv. Finale control and mycorrhizal plants.
PLS-DA score plots with model parameters for control plants (A) and
plants inoculated with R. irregularis (B). Scatter plots in the spaces
of the PLS-DA loadings of: (C) PC1, (D) PC2.
[276]Click here for additional data file.^ (2.6MB, png)
DOI: 10.7717/peerj.7495/supp-4
Figure S5. Systemic aging delay in the pea cv. Finale plants inoculated
with R. irregularis.
(A, B) Scatter plot in the spaces of the loadings of the predictive
components from two OPLS-DA models: (A) First, for comparing 21 and 42
DPI plants; second, for control and inoculated (AM) 42 DPI plants, (B)
First, for comparing 42 and 56 DPI plants; second, for control and AM
56 DPI plants. PLS-DA score plots with model parameters for the (C) 42
DPI control and AM and 21 DPI control, (D) 56 DPI control and AM and 42
DPI control.
[277]Click here for additional data file.^ (2.7MB, png)
DOI: 10.7717/peerj.7495/supp-5
Figure S6. Comparative analysis of metabolite profiles of mycorrhizal
and control pea cv. Finale plants at different stages of plant
development by using LLE (Locally Linear Embedding).
(A) 21 DAI (stage II), k = 10; (B) 42 DAI (stage IV), k = 6; (C) 56 DAI
(stage V), k = 10. Black –control, red –plants inoculated with R.
irregularis, ellipses –90% CI, DIM –dimension.
[278]Click here for additional data file.^ (220.5KB, png)
DOI: 10.7717/peerj.7495/supp-6
Figure S7. The effect of mycorrhiza development revealed by enrichment
analysis (hypergeomertric test) on the metabolic pathways in the pea
cv. Finale leaves. Full versions of the graphs shown in [279]Fig. 5.
Pathway network based on KEGG database using Medicago truncatula as a
reference species. Nodes (pathways) share common edge if they share
metabolites. Graph was built in the Cytoscape environment using Prefuse
Layout, where lengths of edges reflect the number of metabolites shared
between pathways. The bigger red nodes correspond to p < 0.05, smaller
pink nodes to p < 0.1, grey nodes to pathways sharing metabolites with
significantly affected ones. (A) 42 DPI (stage IV); (B) 56 DPI (stage
V).
[280]Click here for additional data file.^ (9.3MB, png)
DOI: 10.7717/peerj.7495/supp-7
Table S1. Metabolite content, a. u. determined as peak areas (sum for
isomers) normalized by area of internal standard (tricosane, 20 µg)
peak area and normalized per weight.
Abbreviations for compound names: mag, monoacylglycerol; disac, trisac,
unidentified di- or trisacharide or derivate; sim, similar to; ni,
unidentified compounds; RI, retention index; _number is RI for
compounds identified just up to class. AM, plants inoculated with R.
irregularis, DPI, days post inoculation. Bold type marks values imputed
by KNN, see materials and methods
[281]Click here for additional data file.^ (144.6KB, xlsx)
DOI: 10.7717/peerj.7495/supp-8
Table S2. Metabolite content normalized by sample (row) median.
Abbreviations for compound names: mag, monoacylglycerol; disac, trisac,
unidentified di- or trisacharide or derivate; sim, similar to; ni,
unidentified compounds; RI, retention index; _number is RI for
compounds identified just up to class. AM, plants inoculated with R.
irregularis, DPI, days post inoculation. Bold type marks values imputed
by KNN, see materials and methods.
[282]Click here for additional data file.^ (123.4KB, xlsx)
DOI: 10.7717/peerj.7495/supp-9
Supplemental Information 1. Raw GC-MS data presented in chromatograms
for metabolite content in the leaves of control and mycorrhizal pea
plants (21 days post inoculation, stage II).
[283]Click here for additional data file.^ (19.3MB, zip)
DOI: 10.7717/peerj.7495/supp-10
Supplemental Information 2. Raw GC-MS data presented in chromatograms
for metabolite content in the leaves of control and mycorrhizal pea
plants (42 days post inoculation, stage IV).
[284]Click here for additional data file.^ (17.2MB, zip)
DOI: 10.7717/peerj.7495/supp-11
Supplemental Information 3. Raw GC-MS data presented in chromatograms
for metabolite content in the leaves of control and mycorrhizal pea
plants (56 days post inoculation, stage V).
[285]Click here for additional data file.^ (22MB, zip)
DOI: 10.7717/peerj.7495/supp-12
Acknowledgments