Abstract
Iron (Fe) deficiency in plants is a major problem in agriculture.
Therefore, we investigated both the physiological features and
molecular mechanisms of plants’ response to low-Fe (LF) stress along
with the mitigation of LF with exogenous spermidine (Spd) in tomato
plants. The results showed that exogenous Spd foliar application
relieved the suppressing effect of LF stress on tomato plants by
regulating the photosynthetic efficiency, chlorophyll metabolism,
antioxidant levels, organic acid secretion, polyamine metabolism and
osmoregulatory systems. Analysis of transcriptomic sequencing results
revealed that the differentially expressed genes of iron-deficiency
stress were mainly enriched in the pathways of phytohormone signaling,
starch and sucrose metabolism and phenyl propane biosynthesis in both
leaves and roots. Moreover, Spd-induced promotion of growth under LF
stress was associated with upregulation in the expression of some
transcription factors that are related to growth hormone response in
leaves (GH3, SAUR, ARF) and ethylene-related signaling factors in roots
(ERF1, ERF2). We propose that traits associated with changes in
low-iron-tolerance genes can potentially be used to improve tomato
production. The study provides a theoretical basis for dealing with the
iron deficiency issue to develop efficient nutrient management
strategies in protected tomato cultivation.
Keywords: polyamine, tomato, iron-deficiency, oxidative stress,
transcriptomics
1. Introduction
Iron (Fe) is a trace mineral element necessary for the normal life
activities of almost all living organisms including plants. It is the
fourth most abundant element in the earth’s crust. Despite the high
total iron content in soils, the soluble iron (Fe^2+) fraction is
easily fixed to the insoluble form (Fe^3+) in an alkaline environment,
which seriously affects the normal uptake of iron by plants. Iron
deficiency impairs photosynthetic efficiency, plant growth and biomass
yield [[38]1,[39]2]. As a redox-active metal, Fe is engaged in
photosynthesis, mitochondrial respiration, nitrogen anabolism, hormone
(ethylene, gibberellic acid, jasmonic acid) synthesis and pathogen
defense [[40]3]. Iron also acts as the cofactor of many antioxidant
enzymes, and thus iron deficiency has a regulatory effect on the
antioxidant mechanisms, including the activities of superoxide
dismutase (SOD), peroxidase (POD) and catalase (CAT) in plants [[41]4],
which are in charge of protecting the biological system against the
harmful effects of reactive oxygen species (ROS) [[42]5]. ROS are
produced in all forms of aerobic life under stress or normal
conditions. The excessive production of ROS causes oxidative damage
that has a negative impact on the function of important macromolecules
[[43]6]. Thus, a better understanding of the mechanisms of plant
response to iron stress can be useful to improve crop stress resilience
and enhance crop yield and quality [[44]7].
When plants are exposed to a low-Fe environment, insufficient iron
uptake causes retarded growth, interveinal chlorosis and reduced plant
fitness. In severe iron deficiency, chloroplasts are dissociated or
vesiculated, thus affecting chlorophyll formation [[45]8]. To ensure
the normal growth of plants, phytohormone auxin accumulates in large
amounts in the roots, promoting the development of lateral roots and
positively regulating the transcriptional expression of the FIT1 and
AHA2 genes. Thus, growth factors are involved in the plant
Fe-deficiency response network through different pathways [[46]9].
Under low-iron stress, plants enhance Fe uptake by the root system
through two strategies: One strategy based on the reduction that occurs
in all dicotyledons and non-grass monocotyledons, called strategy I,
and another strategy relying on chelation, which is limited to
monocotyledons, called strategy II [[47]10]. Strategy-II plants produce
plant iron carriers capable of chelating Fe^3+, which are then absorbed
by specific epidermal root cell plasma membrane transporters [[48]11].
In tomatoes, on the one hand, as Strategy-I species, acidification of
the root mesenchyme by plasma membrane H^+-ATPase activity occurs to
dissolve Fe^3+, and reduction to Fe^2+ by Fe^3+-chelating reductase
(FCR) activity increases iron solubility. Afterward, translocation of
the resulting Fe^2+ to the root cell via a specific Fe transporter
(IRT1) takes place to accomplish iron acquisition in plants [[49]12].
On the other hand, nitric oxide (NO) accumulates in the roots and
promotes the expression of FER/FIT, as well as IRT and FRO genes, thus
participating in the response to iron-deficiency stress in plants
[[50]13].
Polyamines are highly bioactive, low-molecular-weight aliphatic amines
that occur as ubiquitous secondary metabolites in plants. Polyamines
can bind to phospholipids and other biomolecules with negatively
charged groups of nucleic acids and proteins through their ionic and
hydrogen bonds, which widely affect the biological activity of plants
[[51]14]. In previous research reports, polyamines have been shown to
perform an extremely important role in alleviating plant stress. Among
the three widely distributed major polyamines, spermidine (Spd) plays a
crucial role in abiotic stress tolerance. Due to its multivalent cation
property, its physiological function is stronger and more associated
with stress tolerance in plants [[52]15]. Spd is a common polyamine in
plants and is involved in adaptations to salinity [[53]16], drought
[[54]17], cold [[55]18] and heavy metals [[56]19]. Some studies have
shown that Spd modulates antioxidant enzyme activity and the expression
of related genes in tomato seedlings exposed to high temperatures
[[57]20]. Exogenous Spd has been found to play an important role in
remediating the effects of environmental stress on plants [[58]21].
However, to date, few studies have reported on the Spd-mediated
tolerance to iron stress, particularly in tomato plants.
In the present research, using ‘Micro-tom’ tomato as the object of
study, we explored the effect of exogenously sprayed Spd on the growth,
physiology and metabolism of tomato seedlings under low-Fe stress. The
physiological analysis, combined with transcriptomic analyses, shed new
light on the mechanism of Spd-mediated low-Fe tolerance in tomato
seedlings from both physiological and molecular perspectives, which
provides a theoretical basis for improving the uptake and utilization
of Fe in protected cultivation.
2. Materials and Methods
2.1. Plant Materials, Growth Conditions and Experimental Treatments
Tomato (Solanum lycopersicum L.) cv ‘Micro-Tom’ seeds were purchased
from the Ball Horticulture Company (West Chicago, IL, USA). Healthy
seeds were selected and sown on a petri dish with distilled water. The
germinated seedlings were transferred to 72-well trays and cultured
under artificial climate chamber conditions: temperature 28 °C/22 °C
(14 h day/10 h night), humidity 80% and light intensity 600 μmol
m^−2s^−1. When the plants had four fully expanded leaves, uniformly
grown tomato seedlings were planted in a hydroponic tank filled with
half-strength Japanese Yamazaki tomato formula nutrient solution
[[59]22]. After five days of seedling culture in hydroponics, the
following treatments were applied: (1) CK (control), Yamazaki formula
nutrient solution (Fe concentration was 100 μM); (2) LF, low-Fe
nutrient solution (Fe concentration was 10 μM); (3) Spd, Yamazaki
formula nutrient solution (100 μM Fe) + 0.25 mM Spd foliar spray; (4)
LF + Spd, low-Fe (10 μM) nutrient solution + 0.25 mM Spd foliar spray.
The Spd was purchased from the Beijing Solarbio Technology Company.
Both sides of the tomato leaves were sprayed with freshly prepared Spd
solution (approximately 10 mL per plant). Low-Fe stress was imposed 1d
after the Spd treatment. Foliar-spraying of Spd was repeated every two
days. The control tomato plants were foliar-sprayed with an equal
volume of distilled water. The nutrient solution was changed every
three days, the pH value was adjusted to 6.0 ± 0.2 and an intermittent
supply of oxygen was provided using an aeration pump. On the 10th day
of treatments, unless otherwise stated, samples were collected/used for
various analyses such as photosynthetic fluorescence indicators,
osmoregulatory substance content, organic acid and polyamine contents
and RNA sequencing. Biomass measurements were performed on day 15 of
treatment.
2.2. RNA-Seq Analysis and Quantitative Real-Time PCR Analysis
Transcriptome sequencing was performed on samples from four
treatments—CK, Spd, LF and LF + Spd—collected on day 10 of treatments
by Hangzhou Lianchuan Biological Technology Co., Ltd. RNA-seq was
performed with three biological replicates for each treatment. All raw
sequencing data from the current study were deposited into the NCBI
database under the accession number “PRJNA834903”
([60]https://www.ncbi.nlm.nih.gov/sra/PRJNA834903), (accessed on 4 May
2022). Analysis of significant differences between samples was
performed using R packages edgeR or DESeq2. Genes with differential
fold FC > twofold or FC < 0.5-fold and a p-value < 0.05 were defined as
differentially expressed genes [[61]23]. GO (Gene Ontology) enrichment
and KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway enrichment
were analyzed using the clusterProfiler R package. GO functional
enrichment and KEGG pathway analysis were performed by Goatools
([62]https://github.com/tanghaibao/Goatools), (accessed on 6 June 2022)
and KOBAS ([63]http://kobas.cbi.pku.edu.cn/home.do), (accessed on 6
June 2022). The qRT-PCR test reaction system and primers used for
qRT-PCR are shown in [64]Supplementary Tables S1 and S2, respectively.
Samples were added to a 96-well plate and then reacted in an Applied
Biosystems Quant Studio three real-time fluorescence quantitative PCR
system (QuantStudio 3, ThermoFisher Scientific™, Waltham, MA, USA). The
qRT-PCR amplification procedure consisted of Stage 1: pre-denaturation,
one cycle 95 °C, 30 s; Stage 2: PCR reaction, 40 cycles of 95 °C for 10
s, 60 °C for 30 s, 72 °C for 40 s. Relative gene expression was
estimated using the 2^-∆∆Ct method [[65]24]. qRT-PCR experiments were
performed in biological triplicates.
2.3. Determination of Biomass and Root Morphology, Root Vigor and Root Fe^3+
Reductase Activity
After 15 days of treatment, five seedlings were randomly picked from
each treatment, and the selected plants were cut from the same part,
divided into above-ground and below-ground parts, any water on the
plant surface was dried with absorbent paper and the fresh weight was
measured. The samples were then placed in an electric thermostatic
drying oven (Heratherm™ General Protocol Ovens, 51028148, Thermo
Scientific™, Waltham, MA, USA) set to 105 °C for 30 min. After
adjusting the temperature to 80 °C, the material was dried to a
constant weight before measuring the dry weight. For root morphology
measurements, the whole root system of a plant was scanned with a root
system scanner (Epson Perfection V800 Photo, B11B223201, Epson America,
Inc., Los Alamitos, CA, USA). The analysis was done using a root
scanner (WinRhizo PRO, version 2017, Regent Instruments Inc., Quebec
City, QC, Canada), and parameters such as the total root length, total
surface area, total volume and average diameter were read [[66]25].
Root vigor was determined by the triphenyl tetrazolium chloride (TTC)
method [[67]26]. Fe^3+ reductase activity was determined according to
the method of Ekmekcioglu C [[68]27]. Three biological replicates for
each treatment were set in treatments.
2.4. Determination of Photosynthetic Pigment Content and Photosynthetic Index
On the 10th day, chlorophylls (Chl) such as Chla, Chlb and carotenoids
were measured in the third fully-expanded leaf [[69]28]. About 0.1 g of
leaf tissue was placed in a tube containing 96% ethanol in the dark for
about 24 h until the leaves turned completely white. The absorbance
values of chlorophyll extracts at 470 nm, 649 nm and 665 nm were
measured with a spectrophotometer (UV-2450, Shimadzu, Kyoto Prefecture,
Japan), and chlorophyll a, chlorophyll b and carotenoid contents were
calculated.
The photosynthetic indexes such as the net photosynthetic rate (Pn),
transpiration rate (Tr), intercellular CO[2] concentration (Ci) and
stomatal conductance (Gs) were measured using a portable photosynthetic
apparatus (LI-6800, Li-COR Inc., Lincoln, NE, USA) on a clear day at
around 10 a.m. The parameters were set to a flow rate of 500 μmol·s^−1,
leaf temperature of 28 °C and CO[2] concentration of 400 µmol·mol^−1; a
CO[2] cylinder was used to stabilize the CO[2] environment [[70]22].
Following 24 h of darkness, the seedling leaves were sampled to test
the maximum photochemical efficiency, i.e., Fv/Fm [[71]29]. In
addition, the actual photochemical efficiency of PSⅡ (ΦPSⅡ),
photosynthetic electron transfer rate (ETR), photochemical quenching
coefficient (qP) and non-photochemical quenching coefficient (NPQ) were
measured after 30 min of plant exposure to natural light conditions
[[72]30].
2.5. Determination of Antioxidant Properties and Osmoregulatory Substances
A fresh-leaf or root sample (0.3 g) was placed in a pre-cooled pestle
and mortar and ground to a fine frozen powder under liquid nitrogen,
followed by homogenization in 3 mL 50 mM phosphate buffer (pH 7.8) in
an ice bath. Then, homogenate centrifugation was done at 12,000× g for
15 min at 4 °C. The supernatant was used to determine the peroxidase
(POD) [[73]31], catalase (CAT) [[74]32] and superoxide dismutase (SOD)
[[75]33] activity. Activity analyses of POD, CAT and SOD were performed
as described previously [[76]34]. Three biological replicates for each
treatment were performed. The lipid peroxidation level was measured by
estimating the malondialdehyde (MDA) content in roots using
thiobarbituric acid (TBA) [[77]35]. Electrolyte leakage (%) was
estimated by measuring ion leakage from roots according to the method
of Shou [[78]36]. The roots (which weighed 0.1 g) were placed in
centrifuge tubes, then each tube was filled with 20 mL of distilled
water. The conductivity (A1) was first measured after shaking the tube
well, then the conductivity (A2) was again measured after shaking the
tube in the shaker for 2 h. Finally, the sample was boiled and cooled
to room temperature to measure the conductivity (A3). Relative
electrolyte leakage was measured as follows: Relative conductivity =
(A2−A1)/(A3−A1). The content of H[2]O[2] in leaves and roots was
determined by the method of Willekens [[79]37]. The content of O^2•− in
leaves and roots was analyzed by the method previously described by Li
et al. [[80]38]. Proline and soluble protein contents were determined
by the methods of Bates [[81]39] and Bradford [[82]40], respectively.
Meanwhile, the free amino acids and soluble sugar contents were
determined by the method of Zhang et al. [[83]41]. Each treatment was
repeated three times to ensure the reliability of the results. The
organic acid content was determined by high-performance liquid
chromatography [[84]42]. Parameter settings were as follows: a ZORBAX
Eclipse XDB-C18 column (4.6 × 250 mm, 5 mm) was used; the mobile phase
was set at 0.04 mol·mL^−1, pH 2.4, KH[2]PO[4]-H[3]PO[4] buffer
solution; the flow rate was 0.8 mL·min^−1; the column temperature was
30 °C, the detection wavelength was 210 nm and the injection volume was
10 µL.
2.6. Determination of Sucrose Content and Metabolism-Related Enzyme
Activities
The sucrose content was determined by the hydrochloric acid-resorcinol
method previously described by Zhang et al. [[85]43]. We accurately
weighed 0.1 g of leaves and roots and took 0.2 and 0.4 mL of
supernatants, respectively. After adding 200 µL NaOH, the solution was
boiled for 5 min at 100 °C, then cooled, before 2.8 mL 30% HCL and 0.8
mL 0.1% resorcinol were added, with the contents shaken well. Then,
they were placed in a water bath at 80 °C for 10 min for the reaction
to occur, and after cooling, the OD value was measured at 480 nm. Three
replicates of each treatment were performed. Standard curves with
different concentration gradients of sucrose were prepared with the
standard solution and used to calculate the actual sucrose content in
leaves and roots. To analyze the activities of sugar metabolism-related
enzymes, frozen samples of leaves were weighed to 0.1 g. Sucrose
synthase (SS), sucrose phosphate synthase (SPS), acid convertase (AI)
and neutral convertase (NI) activities were determined using the
corresponding enzyme activity assay kits (Beijing Solarbio Science &
Technology Co., Ltd., Beijing, China).
2.7. Determination of Polyamine Content
Polyamines extraction from tomato seedlings was performed using the
methods described by Flores and Galston [[86]44]. The content of
polyamines was determined by HPLC (high-performance liquid
chromatograph UltiMate3000, ThermoFisher Scientific™, Waltham, MA,
USA). The instrumentation and settings for endogenous polyamine
analyses were as follows: a ZORBAX Eclipse XDB-C18 column (4.6 × 250
mm, 5 mm) and mobile phase (methanol: acetonitrile: water =
58:2.5:39.5) were used with a detection wavelength of 230 nm, flow rate
of 1 mL·min^−1, column temperature of 30 °C and injection volume of 10
µL. The organic solvents used above were of chromatographic-grade
purity and the water was ultrapure. The mobile phase was configured for
use after ultrasonic sonication beforehand.
2.8. Statistical Analysis
All data were subjected to analysis of variance (ANOVA), analyzed with
SPSS 21.0 statistical software and plotted with Microsoft Excel 2016.
For multiple mean comparisons, differences between treatment means were
separated by Duncan’s multiple range test at p < 0.05.
3. Results
3.1. Overview of Sequencing Data-Quality Control
In this experiment, the leaves and roots of the Control (CK), Low Fe
(LF), Spermidine (Spd) and Low Fe + Spd (LFS) were sequenced, and each
treatment was replicated three times. The results showed that 99.98% of
the nucleotides in the transcriptome sequencing data reached Q20, and
97.25% of the nucleotides exceeded Q30 ([87]Table 1).
Table 1.
Statistics of transcriptome sequencing data. Sample, sample name; Raw
Read, the number of reads in total; Valid Read, the number of valid
reads after de-junctioning, de-low quality, etc.; Valid Ratio, the
proportion of valid reads; Mapped Reads, the number of reads that can
be compared to the genome; Unique Mapped Reads, can only uniquely match
to one position in the genome; Q20%, the percentage of bases with Q20%
quality value ≥ 20 (sequencing error rate less than 0.01); Q30%, the
percentage of bases with Q30% quality value ≥ 30 (sequencing error rate
less than 0.001).
Sample Raw Read Valid Read Valid Ratio (Reads) Mapped Reads Unique
Mapped Reads Q20% Q30%
CK_L1 51,425,238 47,618,720 92.60 45,445,100
(95.44%) 38,907,284
(81.71%) 99.99 97.68
CK_L2 41,647,086 39,867,334 95.73 38,103,955
(95.58%) 32,333,283
(81.10%) 99.99 97.78
CK_L3 36,237,924 34,971,030 96.50 33,505,416
(95.81%) 28,442,929
(81.33%) 99.99 97.59
CK_R1 41,884,204 40,904,806 97.66 35,303,817
(86.31%) 30,123,337
(73.64%) 99.99 97.38
CK_R2 47,810,190 46,772,676 97.83 42,676,467 (91.24%) 36,960,152
(79.02%) 99.98 98.37
CK_R3 51,562,904 50,495,438 97.93 44,954,365
(89.03%) 38,867,119
(76.97%) 99.98 98.30
LF_L1 45,353,480 42,425,970 93.55 40,385,907
(95.19%) 34,478,301
(81.27%) 99.99 97.79
LF_L2 45,112,774 43,318,042 96.02 41,436,986
(95.66%) 35,189,847
(81.24%) 99.99 97.62
LF_L3 47,262,530 45,751,138 96.80 43,773,386
(95.68%) 37,222,647
(81.36%) 99.99 97.73
LF_R1 43,262,284 42,427,286 98.07 39,214,339
(92.43%) 33,844,982
(79.77%) 99.99 98.39
LF_R2 52,854,702 51,760,038 97.93 47,780,225
(92.31%) 41,266,840
(79.73%) 99.99 98.51
LF_R3 52,525,326 51,551,094 98.15 45,715,483
(88.68%) 39,276,860
(76.19%) 99.99 98.45
LFS_L1 46,825,170 44,333,134 94.68 42,409,857
(95.66%) 36,246,131
(81.76%) 99.99 97.50
LFS_L2 35,688,744 34,009,554 95.29 32,356,247
(95.14%) 27,660,644
(81.33%) 99.99 97.25
LFS_L3 41,306,858 39,800,370 96.35 38,018,606
(95.52%) 32,451,594
(81.54%) 99.99 97.60
LFS_R1 53,734,354 52,663,752 98.01 47,503,814
(90.20%) 40,935,810
(77.73%) 99.99 98.41
LFS_R2 52,372,096 51,298,494 97.95 45,352,741
(88.41%) 39,262,642
(76.54%) 99.98 98.37
LFS_R3 54,358,180 53,210,976 97.89 48,848,829
(91.80%) 42,040,996
(79.01%) 99.99 98.43
Spd_L1 50,060,896 45,020,762 89.93 43,045,480
(95.61%) 36,727,980
(81.58%) 99.99 97.52
Spd_L2 35,827,860 34,695,530 96.84 33,367,320
(96.17%) 28,459,478
(82.03%) 99.99 97.74
Spd_L3 50,682,352 48,290,700 95.28 46,229,329
(95.73%) 39,485,340
(81.77%) 99.99 97.53
Spd_R1 52,910,480 51,911,130 98.11 46,563,164
(89.70%) 40,136,000
(77.32%) 99.98 98.42
Spd_R2 51,487,988 50,453,420 97.99 45,578,298
(90.34%) 39,515,003
(78.32%) 99.98 98.44
Spd_R3 53,683,760 52,593,000 97.97 46,823,515
(89.03%) 40,562,480
(77.13%) 99.98 98.48
[88]Open in a new tab
3.2. Analysis of Differentially Expressed Genes
To get a closer look at the differentially expressed genes, we mapped
volcanoes ([89]Figure 1). In the volcano maps, red represents
significantly upregulated differently expressed genes, blue represents
significantly downregulated differently expressed genes and gray
represents non-significant differently expressed genes.
Figure 1.
[90]Figure 1
[91]Open in a new tab
Volcano maps of expression differences. The horizontal coordinate
represents the different expression fold changes of the gene in
different samples, and the vertical coordinate represents the
statistical significance of the difference in the gene expression
change. LF_L vs. CK_L, Low Fe_Leaf sample vs. Control_Leaf sample; LF_R
vs. CK_R, Low Fe_Root sample vs. Control_Root sample; LFS_L vs. LF_L,
Low Fe + Spd_Leaf sample vs. Low Fe_Leaf sample; LFS_R vs. LF_R, Low Fe
+ Spd_Root sample vs. Low Fe_Root sample.
FPKM (fragments per kilobase of exon model per million mapped
fragments) was used to count the expression abundance of known genes in
different samples. In this experiment, we used the difference
multiplier FC ≥ 2 or FC ≤ 0.5 (i.e., the absolute value of log2FC ≥ 1)
as the threshold of change and a p-value <0.05 as the criterion for
screening differential genes. The number of differentially expressed
genes in each comparison group was counted, and a bar chart ([92]Figure
2) was used to visualize the number of significantly differentially
expressed genes in different comparison groups, as well as the specific
changes (up- and downregulation). Compared to the control, 227 genes
were upregulated and 201 genes were downregulated in the
low-iron-treated leaves (LF), whereas the number of differentially
expressed genes was higher in the root system, where 933 genes were
upregulated and 1199 genes were downregulated, which indicated that the
low-iron treatment had a more profound effect on transcription in the
root system than in the leaves. Again, compared to the LF treatment,
606 genes were upregulated in the LF + Spd-treated leaves, and 302
genes were downregulated, while 422 genes were upregulated and 619
genes were downregulated in the root sample.
Figure 2.
[93]Figure 2
[94]Open in a new tab
Number of significantly differentially expressed genes in different
treatments. LF_L vs. CK_L, Low Fe_Leaf sample vs. Control_Leaf sample;
Spd_L vs. CK_L, Spd_Leaf sample vs. Control_Leaf sample; LFS_L vs.
LF_L, Low Fe + Spd_Leaf sample vs. Low Fe_Leaf sample; LFS_L vs. Spd_L,
Low Fe + Spd_Leaf sample vs. Spd_Leaf sample; LF_R vs. CK_R, Low
Fe_Root sample vs. Control_Root sample; Spd_R vs. CK_R, Spd_Root sample
vs. Control_Root sample; LFS_R vs. LF_R, Low Fe + Spd_Root sample vs.
Low Fe_Root sample; LFS_R vs. Spd_R, Low Fe + Spd vs. Spd_Root sample.
Then, we performed Venn diagram analysis for Control vs. Low Fe (CK vs.
LF) and Low Fe vs. Low Fe + Spd (LF vs. LFS), gene ontology (GO)
enrichment for Low Fe+ Spd_Leaf sample vs. Low Fe_Leaf sample (LFSL vs.
LFL) and Low Fe + Spd_Root sample vs. Low Fe_Root sample (LFSR vs. LFR)
and KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway enrichment
analysis for differently expressed genes as influenced by Spd
treatment.
The Venn diagram can visualize not only the number of differently
expressed genes in the different treatment groups but also the number
of genes that are differently expressed in each treatment group in
total. As shown in [95]Figure 3, a total of 104 differently expressed
genes were co-expressed among 428 differentially expressed genes in the
treatment group CK vs. LF, and there were 908 differentially expressed
genes in the treatment group LFS vs. LF in the leaves. In the case of
the root sample, a total of 677 differently expressed genes were
co-expressed among 2132 differently expressed genes in the treatment
group CK vs. LF, and there were 1041 differently expressed genes in the
treatment group LFS vs. LF.
Figure 3.
[96]Figure 3
[97]Open in a new tab
Venn diagram of significantly differentially expressed genes in
different treatment comparisons. LFSL. vs. LFL, Low Fe+ Spd_Leaf sample
vs. Low Fe_Leaf sample; CKL. vs. LFL, Control_Leaf sample vs. Low
Fe_Leaf sample; CKR.VS.LFR, Control_Root sample vs. Low Fe_Root sample;
LFSR. vs. LFR, Low Fe+ Spd_Root sample vs. Low Fe_Root sample.
The GO enrichment analysis of LFS vs. LF showed that enrichment in
biological processes (BP) was mostly in functions such as
transcriptional regulation with DNA as the template, protein
phosphorylation, defense responses, redox processes, signal
transduction processes, ethylene-activated signaling pathways, defense
responses against fungi, and protein ubiquitination ([98]Figure 4). In
cellular components (CC), differentially expressed genes were involved
in biological functions such as those of the nucleus, plasma membrane,
membrane components, cytoplasm, chloroplast and extracellular regions.
In molecular functions (MF), they were mainly enriched in
sequence-specific protein-binding, specific DNA sequence-binding
transcription factor activity, ATP-binding, DNA-binding, protein
serine/threonine kinase activity, and metal ion-binding.
Figure 4.
[99]Figure 4
[100]Open in a new tab
GO enrichment analysis of differently expressed genes. The abscissa
represents different GO terms, blue represents biological processes,
green represents cellular components, orange represents molecular
functions and ordinate represents the number of differentially
expressed genes. LFSL vs. LFL, Low Fe + Spd_Leaf sample vs. Low Fe_Leaf
sample; LFSR vs. LFR, Low Fe + Spd_Root sample vs. Low Fe_Root sample.
To further explore the most important biochemical/metabolic pathways
and signal transduction pathways involved in differentially expressed
genes due to Spd treatment in low-iron-supplied tomato plants, the
top-20 significantly enriched pathways were screened for KEGG
enrichment analysis by the number of genes enriched in this pathway,
and the enrichment results are presented in the form of bubble plots
([101]Figure 5). KEGG enrichment analysis was performed on 908
differentially expressed genes in leaves and 1041 differentially
expressed genes in roots, comparing the low-iron treatment and combined
treatment of Spd and low iron.
Figure 5.
[102]Figure 5
[103]Open in a new tab
Enrichment analysis of differentially expressed genes in the KEGG
pathway. The horizontal axis indicates the degree of enrichment (Rich
factor), and the vertical axis indicates the enriched KEGG pathway; the
size of the dots indicates the number of differentially expressed genes
enriched in a KEGG pathway; the color of the dots indicates different p
values; the Rich factor indicates the number of differentially
expressed genes belonging to a KEGG pathway/the total number of genes
belonging to this KEGG pathway. The larger the Rich factor, the higher
the enrichment of the KEGG pathway. LFSL vs. LFL, Low Fe + Spd_Leaf
sample vs. Low Fe_Leaf sample; LFSR vs. LFR, Low Fe + Spd_Root sample
vs. Low Fe_Root sample.
The results showed that a total of 707 differently expressed genes in
leaves were significantly enriched in 113 KEGG metabolic pathways,
concentrated in metabolic pathways such as plant-pathogen interaction
(79), phytohormone signaling (61), cytokinesis (30), amino and
nucleotide sugar metabolism (29), phenyl propane biosynthesis (23) and
starch and sucrose metabolism (23). A total of 867 differently
expressed genes were significantly enriched in 117 KEGG metabolic
pathways in the root system, concentrated in metabolic pathways such as
plant-pathogen interaction (58), phytohormone signaling (56), benzyl
propane biosynthesis (40), starch and sucrose metabolism (28), amino
and nucleotide sugar metabolism (23) and carbon metabolism (22). It was
found that the differentially expressed genes of iron-deficiency stress
were mainly enriched in the pathways of phytohormone signaling, starch
and sucrose metabolism and phenyl propane biosynthesis in both leaves
and roots.
The results of GO enrichment analysis and KEGG pathway enrichment
analysis showed that the differentially expressed genes in plant
hormone signaling processes and starch and sucrose metabolism were
significantly affected by low-Fe stress. Therefore, we performed a heat
map analysis of differentially expressed genes in phytohormone
signaling pathways and starch and sucrose metabolism. The results
showed that a total of nine differently expressed genes in the
phytohormone signal transduction pathway—Solyc00g174330.3(PR1),
Solyc05g009610.1(GID1), Solyc09g007010.1(PR1), Solyc06g062460.3(PIF3),
Solyc07g056000.2(TCH4), Solyc09g089930.2(ERF1), Solyc12g036470.2(PIF3),
Solyc01g107400.2(GH3) and Solyc03g093080.3(TCH4)—were common to leaves
in the comparison groups of LF vs. CK and LFS vs. LF ([104]Figure 6).
The expression of oleuropein sterol regulatory protein EBRU1 precursors
Solyc07g056000.2(TCH4) and Solyc03g093080.3(TCH4) was downregulated
under low-iron treatment, while all other genes were upregulated. In
contrast, all nine differently expressed genes were upregulated in
leaves after spraying with Spd under low-iron treatment. A total of 37
differently expressed genes were expressed in the root system, mostly
concentrated in the growth hormone and ethylene metabolic pathways. The
expression of Solyc03g082510.1(SAUR) and Solyc10g076790.2(AUX1) in the
growth hormone metabolic pathway and Solyc08g066660.1(ERF1) and
Solyc03g114310.3(CTR1) in the ethylene metabolic pathway were
downregulated under low-Fe stress, while their expression with Spd
treatment under low-iron stress was upregulated. It appears that
differentially expressed genes related to hormone metabolism showed
different trends in leaves and roots. For the upregulated genes, in
leaves, Spd foliar-spray treatment could further upregulate gene
expression, whereas, in roots, Spd foliar treatment downregulated genes
to the control level. Of the 18 differentially expressed genes in the
root system for starch and sucrose metabolic processes, seven
differently expressed genes were downregulated and 11 differently
expressed genes were upregulated, and the expression of genes related
to hormone signaling was consistent with the Spd treatment;
nonetheless, all of these were backregulated to the control level in
the root sample.
Figure 6.
[105]Figure 6
[106]Open in a new tab
Heat map of differentially expressed genes related to plant hormone
signal transduction and sucrose metabolism. Low Fe + Spd_Root sample
vs. Low Fe_Root sample.
Then, we analyzed the expression of genes related to hormone signaling
pathways and sucrose metabolism, as well as differentially expressed
genes of other metabolic pathways, as shown in [107]Figure 7. In leaf
blades, Solyc01g008620.3(GN1-2-3) expression was upregulated in the
starch and sucrose metabolic pathways, which potentially accelerated
glucose synthesis; Solyc02g071620.3(CHLP) and Solyc07g064720.3(CHLP)
expression were upregulated in porphyrin and chlorophyll metabolism,
which in turn, potentially functioned in the synthesis of chlorophyll a
and chlorophyll b, respectively. Solyc07g024000.3(NOL) expression was
downregulated, thus, perhaps, inhibiting the conversion of chlorophyll
b to hydroxy-chlorophyll a. In the photosynthetic pathway,
Solyc11g006910.2(PetF) iron oxytocin gene expression was upregulated
during photosynthetic electron transfer; in the peroxisome pathway,
i.e., the antioxidant enzyme system, Solyc12g094620.2(CAT) expression
was upregulated in the antioxidant enzyme system and so on. In the root
system, more genes are related to the expression of hormone metabolism,
and among them, the expression of Solyc10g076790.2(AUX1) and
Solyc03g082510.1(SAUR) was upregulated after Spd treatment under low
iron, both of which are jointly involved in plant cell growth. However,
Solyc09g089610.3(ETR), Solyc09g066360.1(ERF1) and
Solyc04g071770.3(ERF2) transcripts were downregulated, which
potentially alleviated the effect of ethylene on cell senescence.
Again, Solyc12g038580.2(TPS) expression was upregulated in the starch
and sucrose metabolic pathways, which affected sugar synthesis, and
Solyc12g009300.3(SUS) expression was downregulated, which might affect
sucrose synthase activity. In the peroxisome pathway, the epoxidation
process was promoted by upregulation of Solyc01g066457.1(EPHX2).
Upregulation of Solyc01g058210.2(HMGCL), Solyc10g007600.3(HAO) and
Solyc12g099930.2(AGXT) contributed to amino acid metabolism, and
upregulated expression of Solyc12g094620.2(CAT) in hydrogen peroxide
metabolism potentially increased redox levels. In addition to affecting
the expression of related metabolic genes in each pathway, Spd-spraying
under low iron upregulated the expression of Solyc02g069200.3(IRT1),
Solyc01g094890.3((FRO2) and Solyc01g094910.3(FRO), which potentially
improved the Fe uptake and transport capacity of the root system under
low-iron stress.
Figure 7.
[108]Figure 7
[109]Open in a new tab
Diagram of plant sucrose metabolism pathway.
Finally, expression trends of six selected differentially-expressed
genes related to iron transport or sucrose metabolism in the root were
validated by qRT-PCR. The trends for the gene expression in qRT-PCR
([110]Supplementary Figure S1) were approximately the same as the
transcriptome sequencing results, indicating that the results were
credible.
3.3. Exogenous Spd Improved the Growth and Photosynthetic Efficiency of
Tomato Plants under Low-Iron Stress
The growth of tomato seedlings was significantly inhibited by low-Fe
stress, along with significantly decreased dry and fresh weights by
28.57 and 27.91%, respectively. However, plant biomass was
significantly increased by Spd foliar treatment under low-iron stress
([111]Table 2). Likewise, root growth was significantly affected by
low-iron stress, but Spd foliar treatment under low-Fe conditions
increased the total root length, total root surface area and total root
volume by 78.63, 41.35 and 40.91%, respectively, compared to the
low-iron treatment ([112]Table 2). It is evident that exogenous
Spd-spraying has a mitigating effect on the growth of tomato seedlings
under low-iron stress.
Table 2.
Effects of exogenous Spd on tomato biomass under low-iron stress.
Treatments Shoot Fresh
Weight/g Root Fresh
Weight/g Shoot Dry
Weight/g Root Dry
Weight/g Total Fresh
Weight/g Total Dry
Weight/g
CK 5.43 ± 0.44 ab 1.45 ± 0.12 b 0.40 ± 0.01 b 0.09 ± 0.01 a 6.88 ± 0.43
b 0.49 ± 0.02 b
LF 3.80 ± 0.61 c 1.16 ± 0.13 c 0.29 ± 0.05 c 0.06 ± 0.01 b 4.96 ± 0.73
c 0.35 ± 0.03 c
Spd 6.19 ± 0.45 a 1.81 ± 0.12 a 0.48 ± 0.03 a 0.10 ± 0.01 a 7.99 ± 0.43
a 0.58 ± 0.03 a
LF + Spd 5.07 ± 0.24 b 1.42 ± 0.19 bc 0.36 ± 0.02 b 0.08 ± 0.01 a 6.49
± 0.35 b 0.45 ± 0.01 b
[113]Open in a new tab
CK, control; LF, Low Fe; Spd, spermidine; LF + Spd, Low Fe plus
spermidine. Data are shown as mean ± SD. Within each column, entries
followed by the same lowercase letters are not significantly different
according to Duncan’s test at p ≤ 0.05.
Moreover, root vigor and Fe^3+ reductase activity were significantly
increased by either low-iron stress or Spd-spraying ([114]Supplementary
Figure S2). When compared to the low-Fe treatment, root vigor and Fe^3+
reductase activity were further increased by 23.21 and 21.35%,
respectively, after spraying with Spd under low-Fe stress.
The photosynthetic pigment content of tomato leaves was repressed by
low-iron stress. However, the chlorophyll a, chlorophyll b and
chlorophyll a + b contents were significantly increased by 23.58, 12.50
and 21.58%, respectively, in Spd treatment under low-Fe stress compared
to low-iron stress only, though the carotenoid content was affected by
Spd treatment under low-iron stress ([115]Table 3).
Table 3.
Effects of exogenous Spd on chlorophyll content in tomato leaves under
low-iron stress.
Treatments Chl a
mg·g^−1 FW Chl b
mg·g^−1 FW Carotenoid
mg·g^−1 FW Chl a + b
mg·g^−1 FW
CK 1.47 ± 0.06 b 0.66 ± 0.02 b 0.24 ± 0.02 a 3.20 ± 0.08 b
LF 1.06 ± 0.03 d 0.56 ± 0.04 c 0.15 ± 0.01 b 2.41 ± 0.08 d
Spd 1.64 ± 0.02 a 0.70 ± 0.01 a 0.25 ± 0.02 a 3.52 ± 0.07 a
LF + Spd 1.31 ± 0.10 c 0.63 ± 0.02 b 0.18 ± 0.02 b 2.93 ± 0.16 c
[116]Open in a new tab
CK, control; LF, Low Fe; Spd, spermidine; LF + Spd, Low Fe plus
spermidine. Data are shown as mean ± SD. Within each column, entries
followed by the same lowercase letters are not significantly different
according to Duncan’s test at p ≤ 0.05.
In line with the photosynthetic pigment concentrations, the net
photosynthetic rate was inhibited by 49.07% in leaves under low-Fe
stress, and exogenous foliar-spraying of Spd alleviated the reduction
of gas-exchange parameters in tomato leaves caused by low-Fe stress,
and increased Pn, Tr, Gs and the intercellular CO[2] concentration (Ci)
([117]Table 4).
Table 4.
Effects of exogenous Spd on photosynthetic parameters in tomato leaves
under low-iron stress.
Treatments Pn/
(μmol·m^−1·s^−1) Gs/
(mmol·m^−1·s^−1) Ci/
(mmol·mol^−1) Tr/
(mmol·m^−1·s^−1)
CK 7.56 ± 0.10 b 180.55 ± 1.68 a 130.91 ± 1.58 c 3.55 ± 0.00 a
LF 3.85 ± 0.08 d 92.73 ± 1.55 c 136.26 ± 2.55 b 2.04 ± 0.02 c
Spd 8.92 ± 0.03 a 178.94 ± 1.28 a 122.71 ± 0.80 d 3.54 ± 0.02 a
LF + Spd 4.73 ± 0.12 c 139.87 ± 0.43 b 153.42 ± 1.47 a 2.90 ± 0.05 b
[118]Open in a new tab
CK, control; LF, Low Fe; Spd, spermidine; LF + Spd, Low Fe plus
spermidine. Data are shown as mean ± SD. Within each column, entries
followed by the same lowercase letters are not significantly different
according to Duncan’s test at p ≤ 0.05.
Under low-Fe stress, chlorophyll fluorescence parameters such as the
maximum photochemical efficiency of PSII (Fv/Fm), electron transfer
efficiency (ETR), actual photochemical quantum yield of PSII (ΦPSII)
and photochemical quenching coefficient (qP) of leaves significantly
decreased by 7.47, 37.21, 37.32 and 35.47%, respectively, and the
non-photochemical quenching coefficient (NPQ) increased by 85.94%.
However, all these indicators, except for qP, increased significantly
after spraying with Spd, suggesting that exogenous foliar-spraying with
Spd under low-iron stress had a strong ameliorative effect on leaf
chlorophyll fluorescence characteristics ([119]Table 5).
Table 5.
Effects of exogenous Spd on fluorescence parameters in tomato leaves
under low-iron stress.
Treatments Fv/Fm ETR ΦPSII qP NPQ
CK 0.763 ± 0.007 b 120.562 ± 0.95 b 0.276 ± 0.002 b 0.468 ± 0.026 b
1.380 ± 0.038 c
LF 0.706 ± 0.009 d 75.706 ± 3.14 d 0.173 ± 0.007 d 0.302 ± 0.012 d
2.566 ± 0.056 a
Spd 0.776 ± 0.008 a 146.958 ± 0.77 a 0.337 ± 0.002 a 0.518 ± 0.004 a
1.314 ± 0.012 c
LF + Spd 0.741 ± 0.002 c 106.407 ± 1.21 c 0.243 ± 0.003 c 0.416 ± 0.006
c 1.851 ± 0.022 b
[120]Open in a new tab
CK, control; LF, Low Fe; Spd, spermidine; LF + Spd, Low Fe plus
spermidine. Data are shown as mean ± SD. Within each column, entries
followed by the same lowercase letters are not significantly different
according to Duncan’s test at p ≤ 0.05.
3.4. Effect of Exogenous Spd on ROS Accumulation, Antioxidant System and
Osmoregulatory Substances in Tomatoes under Low-Iron Stress
Low-iron stress increased the accumulation of intracellular
[MATH: O2−
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and H[2]O[2], leading to increased membrane permeability, and
disruption of plant cell membranes as evidenced by a significant
increase in the relative electrolyte leakage and MDA content in the
root sample. However, exogenous spraying of Spd decreased the
[MATH: O2−
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and H[2]O[2] contents, which, in turn, reduced the levels of MDA and
relative conductivity, thereby effectively alleviating the deleterious
effects of low iron on the cell membrane ([121]Figure 8,
[122]Supplementary Figure S3).
Figure 8.
[123]Figure 8
[124]Open in a new tab
Effect of exogenous Spd on superoxide anion (
[MATH: O2−
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) content and hydrogen peroxide (H[2]O[2]) content under low-iron
stress in tomato plants (A,B). Means denoted by the different lower
case letters are significantly different according to Duncan’s multiple
range test (p ≤ 0.05); the mean represents the average of three
replicates and the vertical bar indicates ± standard deviation (SD).
Next, we observed the ultrastructure of tomato leaves to reveal the
effect of low-iron-induced oxidative stress on the plant cell
structure. [125]Figure 9 shows that under low-iron stress, the cell
exhibited the phenomenon of plasma-wall separation, the cell membrane
was damaged, the chloroplast and starch granules in the leaf were
deformed, the chloroplast was irregularly spherical and the starch
granule swelled obviously. However, with Spd foliar treatment under
low-Fe stress, chloroplast deformity was recovered to some extent with
elliptical bands, and the shape of starch grains was restored.
Figure 9.
[126]Figure 9
[127]Open in a new tab
Ultra-structure of leaf cells revealed by transmission electron
microscopy. CW, cell wall; Va, vacuole; Chl, chloroplast; Mi,
mitochondria; S, starch grain.
To study whether the alleviation of low-iron stress by exogenous Spd
was related to the change in antioxidant enzyme activity in tomatoes,
we analyzed the activities of SOD, POD and CAT in leaves and roots. The
results, shown in [128]Figure 10, revealed that the activities of SOD,
POD and CAT decreased in leaves and roots under low-iron stress, which
potentially indicated a weakened ROS scavenging ability. However,
foliar-spraying with Spd increased the activities of SOD, POD and CAT
to varying degrees, thereby effectively alleviating the ROS-induced
damage to the cell membrane.
Figure 10.
[129]Figure 10
[130]Open in a new tab
Effect of exogenous Spd on antioxidant enzyme activity under low-iron
stress in tomatoes. The first row represents the leaves and the second
row represents the roots. Means denoted by the different lower case
letters are significantly different according to Duncan’s multiple
range test (p ≤ 0.05); the mean represents the average of three
replicates and the vertical bar indicates ± standard deviation (SD).
We also analyzed the levels of osmoregulatory substances such as
proline, sugars and proteins, which are vital for osmotic regulation
under stressful conditions in plants. The proline contents in both
leaves and roots significantly increased by 40.91 and 32.05%,
respectively, and the free amino acid content significantly decreased
by 31.20 and 14.79%, respectively, under low-Fe stress when compared
with the control. Interestingly, the proline and free amino acid
contents in leaves and roots increased with Spd foliar treatment under
low-Fe stress compared to low-Fe stress only. The soluble protein
content decreased in leaves and roots under low-Fe stress; however, it
increased by 13.45% in leaves and 31.16% in roots after Spd foliar
treatment under low-Fe stress. The soluble sugar content in leaves
significantly decreased by 38.62% under low-Fe stress, while there was
no significant change in this in roots. However, compared to low-Fe
stress alone, treatment with Spd and low-Fe stress increased the
soluble sugar content significantly in both leaves and roots
([131]Figure 11).
Figure 11.
[132]Figure 11
[133]Open in a new tab
Effect of exogenous Spd on proline content, free amino acid content,
soluble protein content and soluble sugar content under low-iron stress
in tomato plants (A–D). Means denoted by the different lower case
letters are significantly different according to Duncan’s multiple
range test (p ≤ 0.05); the mean represents the average of three
replicates and the vertical bar indicates ± standard deviation (SD).
3.5. Effect of Exogenous Spd on the Organic Acid Content in Roots and the
Polyamine Content in Leaves under Low-Iron Stress in Tomato Plants
Oxalic, malic, acetic and citric acids in the root system responded
differently to low-Fe stress ([134]Table 6). The oxalic acid level was
not significantly altered by low-Fe stress compared to the control;
however, the citric and malic acid contents increased by 78.12 and
69.58%, respectively, and the acetic acid content decreased by 49.76%
in tomato roots under low-Fe stress. Spd treatment under low-Fe stress
significantly increased the contents of citric (49.15%), malic
(172.76%) and acetic acids (310.88%) compared to low-Fe stress alone,
suggesting that exogenous Spd treatment-induced increased secretion of
organic acids from the root potentially enhanced the Fe transport
capacity.
Table 6.
Effects of exogenous Spd on organic acid content in tomato roots under
low-iron stress.
Treatments Oxalic Acid/(mg·g^−1) Malic Acid/(µg·g^−1) Citric
Acid/(µg·g^−1) Acetic Acid/(µg·g^−1)
CK 2.68 ± 0.13 a 357.77 ± 28.45 c 90.95 ± 4.85 c 114.52 ± 15.87 c
LF 2.73 ± 0.13 a 606.70 ± 97.39 bc 162.00 ± 23.73 b 57.54 ± 12.40 d
Spd 2.24 ± 0.09 b 684.47 ± 167.26 b 133.55 ± 9.60 bc 210.01 ± 11.88 b
LF + Spd 2.75 ± 0.35 a 1654.85 ± 218.26 a 241.63 ± 40.10 a 236.42 ±
15.10 a
[135]Open in a new tab
CK, control; LF, Low Fe; Spd, spermidine; LF + Spd, Low Fe plus
spermidine. Data are shown as mean ± SD. Entries within each column
followed by the same lowercase letters are not significantly different
according to Duncan’s test at p ≤ 0.05.
Meanwhile, under low-Fe stress, soluble and bound Put, Spd and Spm
concentrations increased in the leaves, while free Put decreased and
free Spd and Spm did not significantly change ([136]Table 7). It is
likely that free polyamines were converted to bound polyamines, which
increased the bound polyamines under stress conditions. However, all
three forms of polyamines, except for bound Spd, increased to different
degrees after Spd foliar-spraying under low-Fe stress. This showed that
exogenous Spd treatment could improve the biosynthesis and
interconversion of endogenous polyamines to increase the plants’
ability to withstand stress.
Table 7.
Effects of exogenous Spd on the polyamine content in tomato leaves
under low-iron stress.
Treatments Free Polyamine (nmol·g^−1) Soluble Conjugated Polyamine
(nmol·g^−1) Bound Polyamine (nmol·g^−1)
Put Spd Spm Put Spd Spm Put Spd Spm
CK 662.64 ± 16.76 b 429.20 ± 57.33 b 142.69 ± 1.68 b 111.21 ± 39.69 c
30.44 ± 3.91 d 60.37 ± 2.54 c 828.56 ± 91.03 c 238.62 ± 27.29 c 451.48
± 36.43 c
LF 545.76 ± 43.10 c 568.52 ± 43.73 b 160.10 ± 3.17 ab 229.51 ± 46.45 b
85.60 ± 4.36 b 96.29 ± 2.73 b 6909.74 ± 433.32 a 2064.22 ± 130.05 a
794.57 ± 43.54 b
Spd 848.99 ± 64.31 a 574.61 ± 30.55 b 160.37 ± 13.66 ab 129.79 ± 18.81
c 55.02 ± 4.26 c 63.07 ± 2.60 c 1060.74 ± 47.86 c 308.22 ± 14.31 c
496.38 ± 18.08 c
LF + Spd 702.82 ± 55.56 b 878.03 ± 140.96 a 170.73 ± 12.87 a 408.15 ±
78.32 a 103.22 ± 4.38 a 119.36 ± 9.19 a 4963.23 ± 340.45 b 1479.83 ±
102.24 b 1103.26 ± 55.84 a
[137]Open in a new tab
CK, control; LF, Low Fe; Spd, spermidine; LF + Spd, Low Fe plus
spermidine. Data are shown as mean ± SD. Entries within each column
followed by the same lowercase letters are not significantly different
according to Duncan’s test at p ≤ 0.05.
3.6. Effect of Exogenous Spd on Sugar Metabolism in Tomato Leaves under
Low-Iron Stress
In addition to being a source of energy for plant metabolism, sucrose
has also been identified as a signaling molecule involved in the
regulation of Fe deficiency. The sucrose content in leaves increased by
41.52, 28.24 and 48.57% with time after low-Fe treatment, and was
higher than the control. Spd-spraying under low-Fe stress significantly
reduced the sucrose content in the leaves compared to the low-Fe
treatment ([138]Figure 12).
Figure 12.
[139]Figure 12
[140]Open in a new tab
Effects of exogenous Spd on the sucrose content in tomato leaves under
low-iron stress. Means denoted by the different lowercase letters on
the same color bars are significantly different according to Duncan’s
multiple range test (p ≤ 0.05); the mean represents the average of
three replicates and the vertical bar indicates ± standard deviation
(SD).
The results of the measurement of enzymes’ activities related to sugar
metabolism showed that under low-iron stress, the activities of both SS
and SPS enzymes decreased, while the activities of two conversion
enzymes, NI and AI, increased on day 10 after low-Fe treatment,
although the sucrose content increased rather than decreased. This
shows that the catabolic direction of SS and SPS enzyme activities was
greater than the synthetic direction under low-Fe stress, and with the
decrease in enzyme activities, the transport of photosynthetic products
was blocked, causing the accumulation of sucrose in leaves, while the
degradation and utilization of sucrose were weakened, which, in turn,
stimulated the activities of two converting enzymes, NI and AI, and
maintained the stability of sucrose anabolism. Exogenous Spd treatment
significantly increased the SS activity and decreased the sucrose
content, indicating that Spd promotes the degradation of sucrose,
accelerates the consumption of sucrose transported from the leaves,
promotes the transfer of photosynthetic products from the source to the
reservoir and prevents the inhibitory effect of sucrose accumulation on
photosynthetic efficiency ([141]Supplementary Table S4).
4. Discussion
Iron is a vital element for the metabolism, growth and development of
plants. Nevertheless, the lack of adaptive mechanisms to combat iron
deficiency severely impairs plant biomass accumulation. Biomass is a
direct manifestation of plant growth variation and can be an important
basis for assessing the degree of plant injury due to stress [[142]45].
Roots not only provide structural support to the above-ground parts of
the plant but also provide nutrients and water. Therefore, the survival
of a plant depends on its proper growth, development and root function
[[143]46]. Under low-iron stress, a decrease in above-ground and
below-ground biomass ([144]Table 2), and a suppressed total root
length, total root surface area and total root volume of seedlings were
observed ([145]Supplementary Table S3). Morphological inhibition is one
of the adverse effects caused by low-iron stress, and our results were
consistent with earlier accounts of iron-deficiency effects on crop
plants [[146]47]. This is because adverse stress conditions inhibit
both the division and growth of root cells, causing a significant
decline in root biomass [[147]48]. However, foliar-spraying with Spd
increased not only above-ground and below-ground biomass but also the
total root length, total root surface area, total root volume, root
vigor and Fe^3+ reductase activity, which potentially improved nutrient
acquisition and alleviated low-iron stress in tomato seedlings.
Since photosynthesis is the most essential plant process, its
efficiency has a significant influence on growth, yield and stress
resistance in plants [[148]49]. In this study, the photosynthetic
pigment content of tomato leaves was significantly inhibited under
low-iron stress and leaf photosynthetic activity was drastically
reduced ([149]Table 3 and [150]Table 4), which is consistent with the
findings of Yao et al. [[151]50]. This is because iron-deficiency
stress hinders chlorophyll synthesis in tomato seedlings, leading to a
reduction in chloroplast lamellae and disruption of the chloroplast
structure. However, the chlorophyll contents in tomato leaves increased
significantly after Spd foliar-spraying. Such effects support the
hypothesis that the ability to capture and convert light energy was
restored, and the exogenously sprayed Spd could safeguard chloroplasts
and protect the photosynthetic mechanism from the adverse effects of
environmental stress [[152]51]. Moreover, chlorophyll fluorescence
parameters such as Fv/Fm, PSII, ETR, etc., decreased significantly and
NPQ increased under low-iron stress ([153]Table 5), which was in
agreement with the previous findings [[154]52]. This is because damage
to the photosystem II reaction centered on low-iron stress-inhibited PS
II photochemical activity, reduced PS II primary light energy
conversion efficiency and hindered the photosynthetic electron transfer
process. Consistent with the previous reports in Sweet Corn [[155]53],
exogenous Spd increased the chlorophyll content and stabilized the
photosynthetic system in tomato seedlings, thus alleviating the damage
to the photosystem and enhancing or restoring photosynthetic
efficiency. It can be inferred that exogenous Spd-spraying is crucial
to improve the photosynthetic efficiency of tomato seedlings, leading
to increased biomass and dry matter accumulation.
Polyamines protect plants from environmental stress by regulating the
accumulation of sugar, proline and other osmotic substances [[156]54].
Proline is an important osmotic adjustment substance in plants that
functions in maintaining the membrane structure and is used as a
physiological and biochemical indicator for the plant stress response
[[157]55]. Du [[158]56] showed that the proline content in plants under
stress increased, and was further increased by Spd treatment, which is
in agreement with our results showing that proline content in leaves
and roots of tomato seedlings under low-iron stress increased
significantly compared to the control, and were further significantly
increased after foliar-spraying of Spd under low-iron stress compared
to LF treatment. The proteins synthesized and stored during plant
growth are degraded to free amino acids for biosynthesis to maintain
normal plant life activities [[159]57]. When plants are subjected to
stress, particularly osmotic stress, the soluble sugar content
increases, which can improve the osmoregulatory capacity of leaves and
provide carbon and nitrogen sources for plant organic matter synthesis
[[160]58]. The soluble sugar content in leaves and roots of tomato
seedlings decreased under low-iron stress; however, exogenous Spd
treatment increased the soluble sugar content in tomato seedlings under
low-iron stress, suggesting that Spd improves the ability of plants to
synthesize sugars [[161]59]. To improve the plant tolerance to iron
deficiency, roots can reduce the inter-root pH by secreting organic
acids and increasing Fe^3+ solubility [[162]60]. Exogenous spraying
with Spd significantly increased the content of citric and malic acids
in the root system, which indicates that Spd potentially increases the
secretion of organic acids in the root system, thus enhancing the iron
transport in plants [[163]61].
Plant performance under multiple abiotic stresses is linked to the
accumulation of Put, Spd and Spm [[164]62]. In this study, the content
of all three forms of polyamines increased to different degrees after
Spd-spraying, which is consistent with the results of Shan et al.
[[165]61]. It is highly likely that exogenous Spd treatment potentially
improves the biosynthesis of endogenous polyamines and significantly
enhances the ability of plants to withstand adversity. Moreover, the
study also found that Spd treatment significantly increased SS enzyme
activity, reduced sucrose content, promoted sucrose degradation,
accelerated sucrose consumption, facilitated the transfer of
photosynthetic products from source to sink and prevented the
inhibitory effect of sucrose accumulation on photosynthetic efficiency
in tomato seedlings [[166]63], which is consistent with the results of
our study.
Under stress conditions, reactive oxygen species (ROS) are profusely
generated in plants, causing oxidative stress and damage to important
molecules in plants [[167]55,[168]64]. The cell membrane is a barrier
that maintains the relative stability of plant cells. Under stress
conditions, the degree of membrane lipid peroxidation intensifies due
to excessive accumulation of ROS, which changes the membrane
permeability and affects the normal physiological and biochemical
reactions [[169]65]. In this study, low-iron stress reduced SOD, POD
and CAT activities in tomato plants and weakened their ability to
scavenge ROS, resulting in excessive intracellular
[MATH: O2−
:MATH]
and H[2]O[2] accumulation, increased membrane permeability and
disruption of plant cell membranes ([170]Figure 8). This relies on the
fact that iron acts as a component of enzymes such as SOD, POD and CAT,
and the three enzymes’ activities were significantly inhibited when
plants were subjected to a low-iron environment. After exogenous
spraying of Spd treatment, the SOD, POD and CAT activities increased to
different degrees and
[MATH: O2−
:MATH]
, H[2]O[2], MDA and the relative conductivity decreased, indicating
that Spd effectively alleviated the extent of cell membrane disruption.
In iron-chelating reductase FRO7 mutant plants, the iron content in
chloroplasts and the activity of iron reductase are significantly lower
than in wild-type plants, and the electron transport chain in the
photosystem is interrupted, causing impaired photosynthesis [[171]66].
Moreover, FRO7 mutant plants show a severe yellowing phenotype, along
with the occurrence of seedling lethality, indicating that the FRO7
gene is important for maintaining iron homeostasis in chloroplasts and
for the proper performance of photosynthesis in the plant [[172]66]. In
the present study, Spd treatment under low-iron stress upregulated the
expression of the FRO gene and related Fe transporter genes IRT1 and
IRT2 in the root, which is consistent with the results of a previous
study in Pyrus betulaefolia [[173]67].
Previous studies established that IAA plays an important role as a
signaling molecule in the response to iron deficiency in plants, and
that the local iron supply affects the plant lateral root growth and
development by inducing the growth hormone AUX-1 transporter [[174]68].
The strategy-I plants induce ethylene synthesis in response to
iron-deficiency stress, and ethylene positively regulates the
iron-deficiency response [[175]69]. The ethylene response factor
ERF4/ERF72 is involved in iron-deficiency response in apple rootstocks,
and interference with these two genes results in upregulated expression
of iron-uptake genes in Ziziphus jujube roots, promoting iron uptake by
the roots [[176]70]. Accordingly, we also found that transcript levels
of ERF1 and ERF2 genes were upregulated in the root system under
low-iron stress, and exogenous spraying of Spd treatment further
upregulated the expression of ERF1 genes in the leaves, while it
downregulated them in the root. Differential expression of these genes
related to growth hormones and ethylene, together with the expression
of downstream FRO and IRT1 genes, potentially contributed to improved
iron acquisition and transport under low-iron stress.
Meanwhile, sucrose accumulation in leaves increased under low-iron
stress, which indicated that the translocation capacity of sucrose to
the root system was possibly reduced; nonetheless, sucrose could act as
a long-range signal to regulate the response of plants to Fe deficiency
[[177]71]. It is worth noting at this point that the expression of
genes such as COX15 in chlorophyll metabolism was downregulated under
low-iron stress, indicating that the transport of sucrose to the lower
part of the ground was inhibited [[178]72]. The upregulated expression
of genes such as CHLP, PetF and CAT, which are involved in chlorophyll
synthesis and antioxidant enzyme activities, as well as significantly
upregulated SUS and TPS gene expression and significantly increased
sucrose synthase activity after Spd-spraying, indicated that Spd
treatment also affected sugar metabolism to confer tolerance to
low-iron stress in tomato plants.
5. Conclusions
Iron (Fe) deficiency severely limits agricultural crop yield due to its
low availability, particularly in soils with a high pH. The success of
iron fertilization largely depends on soil pH management, which is very
challenging in field conditions. In this study, we showed that foliar
application of exogenous plant growth regulator Spd could improve plant
tolerance to low-iron stress. Briefly, the transcriptomic analysis
revealed that exogenous Spd could regulate the plant response to
low-iron stress by modulating the expression of genes involved in the
processes of hormone metabolism, sucrose metabolism, antioxidant
defense system, photosynthesis, chlorophyll metabolism and Fe uptake
and transport. Besides this, biochemical and physiological analyses
revealed that low-iron stress-induced suppression, in photosynthesis
and growth of tomato seedlings, were significantly alleviated by
exogenous Spd treatment, which was closely associated with differential
modulation of photosynthetic pigment contents, gas exchange,
chlorophyll fluorescence capacity, proline content, sucrose content,
root vigor, citric and malic acid contents, ROS metabolism and
polyamine synthesis and interconversion. Overall, this study reveals
the critical mechanism of exogenous Spd-induced enhanced tolerance to
low-iron stress in tomatoes and provides a novel characterization of
the key traits associated with the adaptation of tomatoes to a low-iron
environment. Traits associated with changes in low-iron-tolerance genes
can potentially be used to improve yields of greenhouse tomatoes in
low-iron environments. Nonetheless, large-scale experimentation is
required to unveil and extend this knowledge, to develop better
agricultural practices.
Supplementary Materials
The following supporting information can be downloaded at:
[179]https://www.mdpi.com/article/10.3390/antiox11071260/s1, Figure S1:
The relative expression of selected differentially expressed genes
verified by qRT-PCR; Figure S2: Effect of exogenous Spd on (A) root
vigor and (B) Fe^3+ reductase activity of tomato under low-iron stress;
Figure S3: Effect of exogenous Spd on lipid peroxidation (A) and ion
leakage (B) in tomato roots under low-iron stress; Table S1: qRT-PCR
test reaction system; Table S2: Primers used for qRT-PCR; Table S3:
Effects of exogenous Spd on tomato root morphological indexes under
low-iron stress; Table S4: Effects of Spd on enzyme activities related
to sucrose metabolism in tomato leaves under low-iron stress.
[180]Click here for additional data file.^ (566.8KB, zip)
Author Contributions
Y.S., conceptualization, methodology, formal analysis, investigation
and writing—original draft. Y.Z. (Yihong Zhao), formal analysis,
investigation and writing—original draft. Q.Y., formal analysis and
investigation. F.L., methodology and funding acquisition. X.L., formal
analysis and investigation. X.J., formal analysis and investigation.
Y.Z. (Yi Zhang), conceptualization, supervision, resources,
writing—original draft, funding acquisition and project administration.
G.J.A., conceptualization, writing—review and editing, funding
acquisition and project administration. All authors have read and
agreed to the published version of the manuscript.
Institutional Review Board Statement
Not applicable.
Informed Consent Statement
Not applicable.
Data Availability Statement
Data are contained within the article and [181]Supplementary Materials.
Conflicts of Interest
The authors declare no conflict of interest.
Funding Statement
This work was supported by the National Key R&D Program of China
(2019YFD1000300), Shanxi Province Key R&D Plan (201903D211011), the
Basic Research Program in Shanxi (20210302123366) and the National
Natural Science Foundation of China (3195041055, 31501750,
31550110201).
Footnotes
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References