A genome-wide expression profile analysis reveals active genes and pathways coping with phosphate starvation in soybean
Wang et al. BMC Genomics
A genome-wide expression profile analysis reveals active genes and pathways coping with phosphate starvation in soybean
Qing Wang 0 1
Jiao Wang 0 1
Yuming Yang 1
Wenkai Du 1
Dan Zhang 2
Deyue Yu 1
Hao Cheng 1
0 Equal contributors
1 National Center for Soybean Improvement, National Key Laboratory of Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University , Nanjing , China
2 Collaborative Innovation Center of Henan Grain Crops, College of Agronomy, Henan Agricultural University , Zhengzhou 450002 , China
Background: Phosphorus is one of the most important macronutrients that is required for plant growth and development. However, stress under low-P conditions has become a limiting factor that affects crop yields and qualities. Plants have developed strategies to cope with this, while few genes associated with low-P tolerance have been identified in soybean. Results: Genome-wide analyses were performed on the roots and leaves of a low-P-tolerant accession and a low-Psensitive accession which were identified by hydroponic experiments under different P treatments. Through comparative analyses on the differently expressed genes, we explored 42 common genes that were highly correlated to low-P stress. The functional classification of these genes revealed 24 Gene Ontology (GO) terms of biological process including response to oxidation reduction, hormone stimuli, and biotic and abiotic stimuli. Additionally, three common pathways were identified. Conclusions: These results could not only promote the work on the molecular regulation mechanism under low-P stress in soybean, but also facilitate the cultivation of high-phosphorus-acquisition and high-phosphorus-utilization soybean varieties.
Soybean; Root system; Low-P stress; Phosphorus deficiency; Expression profile
As an essential constituent of many important
compounds that are required for the development and
growth of plants, phosphorus (P) actively participates in
photosynthesis, respiration, carbohydrate metabolism,
energy transduction and other processes [
]. In many
agricultural systems, however, the concentration of
absorbable phosphorus in soil is insufficient to ensure
plant productivity [
As one of the most important grain and industrial
crops, soybean (Glycine max L. Merrill) is a vital source
of protein for human and animal food. However,
soybean growth and yield are limited by varieties of edaphic
factors, especially low phosphorus availability in soils [
Under low-P stress, soybean plants become dwarfed, leaf
area is reduced, and necrotic spots might appear on
lower leaves [
]. Also, flowering time is increased and
the number of pods could decrease during the fruiting
]. Due to these P-mediated phenotypic effects
on soybeans, phosphate fertilizers are used to overcome
undesirable potential yield outcomes. Large quantities of
phosphate fertilizer may cause environmental pollution
as it is easily immobilized in the soil to form chelates or
]. In order to alleviate the effects of
phosphorus deficiency on soybean growth, it is particularly
important for us to search for P-efficient soybean
materials and investigate high-phosphorus-efficient genes.
Recently, four major groups of genes induced by low-P
stress were reported [
]. One group was involved in
sensing low-P signals, including local phosphate sensing
that was proposed to occur in the primary root in
Arabidopsis thaliana [
], and long-distance phosphate
signaling which depends on the activity of transcription
factors, such as PHR1, PHL1 [
]. Members of another
group mediated phosphate distribution by involving an
internal regulation of phosphorus homeostasis, including
SPX family and PHO1 that contains SPX domain in
]. The remaining two groups of genes mainly
focus on the acquisition and transportation of
phosphate. For example, GmACP1 encodes an acid
phosphatase, the overexpression of which in roots could increase
the absorption of phosphate in the nutrient solution
]. The PHT1 family consisting of 15 PHT1 genes in
soybean encoded phosphate transporters was
demonstrated to be involved in not only directing phosphate
uptake in roots, but also transporting phosphorus as
]. Generally, the acquisition and
transportation of phosphate greatly depends on the root
system as root morphology changes under P-deficient
conditions to sustain plant growth. This includes an
increase in number and length of lateral roots in maize
(Zea mays L.) , longer and greater number of root
hairs in low-P tolerant accessions of barley (Hordeum
vulgare L.) and Arabidopsis [
], and relatively
longer primary roots in rice (Oryza sativa L.) when a
lowphosphorus-related transcription factor OsMYB2P-1 is
over expressed . Meanwhile, QTL (quantitative trait
loci) mapping associated with soybean phosphorus
efficiency have made processes. QTLs related to seed
phosphorus content [
], phosphorus efficiency relevant root
], and soybean tolerance to low phosphorus
stress based on flower and pod abscission rate [
been identified. Furthermore, screening of soybean
Pefficient materials identified P-efficient genotypes, such
as HP119, HP134, Huaxai 1, Huaxia 2 etc. [
As an abiotic stress, low-P conditions may induce a
series of complex responses. It was demonstrated that
phytohormone signaling pathways responded strongly to
phosphate deprivation [
]. The expression of a large
set of genes associated with plant hormone signaling
either induced or repressed root growth in Arabidopsis
and Zea mays when suffering low-P conditions [
In addition, changes of the secondary metabolites to
low-P stress were shown with an accumulation of
anthocyanin under long-term phosphorus deficiency [
Similar results were observed in Zea mays with genes
related to the phenylpropanoid pathway identified as
upregulated or down-regulated under P starvation [
A large set of genes and signaling pathways that were
associated with low-P stress had been uncovered using the
technique of gene expression profiles and transcriptome
analyses in Arabidopsis, rice, and Zea mays [
Recently, Zeng et al. identified
phosphate-deficiencyresponsive genes in soybean (Glycine max var. Williams
82) roots by high-throughput sequencing [
of representative materials and consideration of different
tissues will further reveal genes and pathways associated
with low-P stress. Here, we selected a low-P-tolerant
accession Chundou (CD) and a low-P-sensitive accession
Yunhefengwodou (YH) from 219 soybean accessions
through hydroponic experiments. Then microarray chips
were performed on the roots and leaves of these two
accessions. By analyzing the gene expression data, we
identified 42 candidate genes and three common pathways that
were induced by low-P stress. Our study provides
candidate loci for functional identification of
high-phosphorusefficient genes which may be of great significance for
cultivating P-efficient soybean accessions.
Identification of low-P-tolerant accession CD and low-Psensitive accession YH
With hydroponic experiments lasting for 10 days under
different P treated conditions of 219 soybean accessions
], we found that the soybean accession CD and YH
showed similar root morphology under + P treated
condition (Hoagland with 1.0 mmol/L P), while showing
obvious morphology differences under -P treated condition
(Hoagland with 0.01 mmol/L P). We then identified
accession CD as a low-P-tolerant accession, and accession
YH as a low-P-sensitive accession.
Then, we carried out a gradient experiment using
full, half and quarter Hoagland’s solution and found
that the + P and -P treated soybean plants showed
most obvious morphology difference when grown in
half Hoagland. Hence half Hoagland with 0.5 mmol/L
P and 0.005 mmol/L P were considered as normal
and low P treated conditions, respectively. The phenotypic
traits of CD and YH are shown in Additional file 1:
Figure S1. Primary root length was longer in CD than
that in YH. The number of root hairs and lateral
roots of CD were much more than those in YH.
Microarray experiments were performed on the roots
and leaves of CD and YH.
Significantly differentially expressed genes in different P treated conditions
Under low P condition, 257 and 11 up-regulated
differently expressed genes (DEGs) were found in the roots
and leaves of low-P-tolerant accession CD respectively;
while no up-regulated DEGs were found in the roots
and leaves of low-P-sensitive accession YH compared
with low-P treatment. For down-regulated DEGs, only
41 DEGs could be found in the roots of CD, 3 DEGs in
the roots, and 7 DEGs in the leaves of YH (Table 1).
Generally, the number of DEGs in CD was more than
that in YH, not only in the roots but also in the leaves
(Table 1), which suggested that CD could regulate a
series of genes to cope with the low-P stress. We found
only one common DEG, Glyma20g33710.2, in both the
roots of CD and YH, with the fold change 0.44 and 0.32
down-regulated in low-P-tolerant accession CD and
lowP-sensitive accession YH, respectively.
The number of DEGs in the roots was more than that
in the leaves. Interestingly, one common DEG,
Glyma01g04350.1 named as GmMMP2, which encodes a
matrix metalloproteinase, was found both in the roots
and leaves of CD. The fold change is 4.10 and 3.59,
respectively. Previous studies demonstrated that it was
associated with pathogenic infections in soybean [
A total of 317 non-redundant DEGs were found
between treated and control conditions in the roots and
leaves of both soybean accessions used in this
experiment. The hierarchical clustering analysis classified the
tolerant and sensitive soybean accessions into two
clusters; samples cluster and genes cluster. The compactness
of the clusters of the three replicates confirmed the high
reliability of our experimental design (Fig. 1).
To evaluate the potential functions of the DEGs
between low-P stress and normal conditions in both
soybean accessions, Gene Ontology (GO) categories were
applied for low-phosphorus-induced genes. The three
GO terms were categorized as biological processes,
molecular functions, and cellular components. We found
that DEGs were significantly associated with response to
wounding, response to jasmonic acid stimulus,
oxidoreductase activity, and cell wall, whether they were
upregulated genes or down-regulated genes. Furthermore,
we identified pathways that were associated with low-P
induced genes with the use of KEGG (Kyoto Encyclopedia
of Genes and Genomes). Overall, 45 pathways with P
values less than 0.05 were motivated under low-P stress
(Additional file 2: Table S1), and most pathways were
related to metabolic process. Notably, the most significant 3
pathways that were enriched included phenylpropanoid
biosynthesis, methane metabolism and phenylalanine
metabolism, all related to the metabolism of secondary
Significantly differentially expressed genes between different soybean accessions
Accessions CD and YH showed strong differences in
the ability to suffer low-P stress (Additional file 1:
Figure S1). There were much more DEGs in CD (309)
than in YH (10) (Table 1, Additional file 3: Table S2). To
further reveal the DEGs related with low-P condition
between CD and YH, we compared the DEGs between
soybean accessions CD and YH (Table 2).
DEGs induced by low-P conditions were classified as
three categories according to their expression patterns
in normal conditions. For 1777 up-regulated genes in
roots between CD and YH in low-P stress (Additional
file 4: Figure S2), the three categories were as follows: 1)
941 genes were up-regulated in CD/YH in normal
conditions, which is consistent with the expression pattern
in low-P stress; 2) 10 genes were down-regulated in
normal conditions, which showed the opposite expression
patterns between normal and low-P conditions; and 3)
826 genes showed no expression difference in normal
condition, with the expression difference less than two
fold between CD and YH. By the criterion that genes
with two fold expression change between experimental
group and control group were identified as DEGs, the
expression level in CD was more than two fold higher
than that in YH for the 1777 up-regulated genes
(Additional file 4: Figure S2). Among the 1777
upregulated genes, there were 826 genes with no
expression difference in normal conditions, the expression
difference of which between CD and YH was less
than two fold. To make the expression difference of
CD/YH between low-P stress and normal conditions
higher than two fold, we chose those genes with
larger than 4-fold expression difference between CD and
YH under low-P conditions as candidates. By this
criterion, 93 genes with larger than 4-fold expression difference
between CD and YH under low-P condition from the 826
genes were chosen as candidates. Hence, we got 40, 14,
and 38 DEGs from 975, 324, and 526 DEGs, respectively
(Table 2). Combined with the genes with opposite
expression patterns, they were considered as marked DEGs
between CD and YH. Therefore, 196 marked DEGs were
obtained. Among these genes, one gene was up-regulated
more than 4 fold between CD/YH under low-P conditions
in both roots and leaves. Thus, there were 195
nonredundant marked DEGs detected in total. Among these
marked DEGs, the majority (143 genes, 73.33 %) were
upregulated in CD/YH under low-P stress. Additionally,
there were 117 and 79 genes expressed differently between
CD and YH in roots and leaves, respectively. This result
suggested more genes were activated in roots than in
leaves under low-P stress during seedling stage.
We performed the enrichment of GO and KEGG
analysis for these 195 marked DEGs. GO analysis showed
that these genes were mainly involved in secondary
metabolic processes, for example flavonoid biosynthesis.
They were also involved in response to hormone stimuli
(jasmonic acid, auxin, ethylene, and salicylic acid), and
defense and abiotic stimuli (oxidative stress, salt stress
response, and hypersensitive response). In addition, they
were involved in electron carrier activity and peroxidase.
KEGG analysis revealed the pathways enriched were
mostly associated with the metabolism of secondary
metabolites, including phenylpropanoid biosynthesis,
methane metabolism, phenylalanine metabolism, flavonoid
biosynthesis and so on (Additional file 2: Table S1).
The 317 DEGs in different P-treated conditions were
considered as genes in response to low-P stress. The 195
marked DEGs in different soybean accessions were not
only induced by low-P stress but also by
materialsvariation dependent upon the response to low-P
conditions. Thus we compared 317 DEGs and 195 marked
genes, finding 85 overlapping genes that were considered
as active genes induced by low-P stress. Further study
revealed most of the common genes focused on roots,
which was consistent with a greater number of DEGs in
roots than leaves in different P conditions.
Interestingly, the 10 genes that showed opposite
expression patterns in roots all belonged to the overlapping
genes (Additional file 5: Table S3). Despite two genes
(ProbeSetID:11829195 and 12212657) that could not find
corresponding symbol and one gene (ProbeSetID:11858123)
that could not find functional annotation, seven genes
(GmNAC11 [Glyma16g04740.1], Glyma03g31940.1,
Glyma08g20220.1, Glyma13g27820.1, Glyma13g31580.1,
Glyma18g53170.1 and Glyma19g32700.1) with functional
annotations were shown to be associated with biotic and
Likewise, GO and KEGG enrichment analyses were
applied to evaluate the potential functions of the 85
overlapping DEGs. DEGs significantly enriched in GO
terms of biological processes such as oxidation reduction
and response to jasmonic acid stimulus. Molecular
functions such as oxidoreductase activity, and cellular
components such as the cell wall, were also enriched.
Meanwhile, sixteen pathways were enriched based on
0.05 significance level. Similar to the results of the 195
marked genes, the pathways of phenylpropanoid
biosynthesis, methane metabolism, phenylalanine
metabolism, and flavonoid biosynthesis were also detected
(Additional file 2: Table S1).
Significantly differentially expressed genes between roots and leaves
In our study, under low-P treatment, more DEGs were
detected in roots than leaves, suggesting there were
more genes in response to low-P stress during seedling
stage in roots. DEGs between roots and leaves were
found (Table 2). DEGs induced by low-P conditions were
classified into three categories according to their
expression patterns in normal conditions. For 6370
a Significantly up-regulated expression pattern under low-P stress;
b Significantly up-regulated expression pattern under normal P condition;
c The expression patterns were not significantly changed under normal P condition;
d Significantly down-regulated expression pattern under normal P condition;
e The number in parentheses indicates the number of genes identified by our criteria (Additional file 4: Figure S2)
regulated genes between roots and leaves in CD under
low-P stress, three categories of DEGs were 5137
upregulated genes, one down-regulated gene, and 1232 genes
showing no different expression in roots/leaves in normal
conditions. Among the 1232 genes, there were 97 genes
that showed larger than 4-fold expression difference
between roots and leaves under low-P conditions.
Additionally, we found 63, 19, and 73 genes showing larger than
4fold expression differences under low-P conditions from
the 1207, 893, and 1075 genes, respectively (Table 2). With
one opposite-expressed gene, there were 253 marked
DEGs between roots and leaves.
We performed the enrichment of GO and KEGG
analysis for these 253 marked DEGs. GO analysis showed
these genes enriched in a group of biological processes,
for example, oxidation reduction, response to oxidative
stress, and response to abiotic stimulus. KEGG analysis
revealed most of these genes enriched in methane
metabolism, phenylpropanoid biosynthesis, and
phenylalanine metabolism (Additional file 2: Table S1).
The 253 marked DEGs between roots and leaves were
related with low-P stress. There were 317 DEGs in roots
and leaves considered to be caused by low-P stress.
Thus, we compared 317 DEGs and 253 marked genes
and found 68 overlapping genes. Interestingly, 64
overlapping genes were up-regulated in CD between roots
and leaves, consistent with the results that more genes
were induced in CD than YH under low-P stress. GO
enrichment analysis showed the 68 overlapping genes
enriched in biological processes such as response to
cadmium ion, defense, jasmonic acid stimulus (P < 0.05). In
the molecular function term and cellular component,
these genes were significantly enriched in transcription
regulator activity and in the nucleus (P < 0.05) (Additional
file 2: Table S1). Furthermore, we identified pathways
including methane metabolism and flavonoid biosynthesis
(P < 0.05).
Common DEGs among treatments, materials, and tissues
The 195 marked DEGs between CD and YH under
lowP stress were identified as DEGs between materials
which were associated with low-P stress, while the 253
marked DEGs between roots and leaves in low-P
conditions were regarded as DEGs between tissues that were
related to low-P stress. We compared the 195, 253
marked DEGs with the 317 DEGs between different P
treatments and 42 common DEGs were found to be in
all three classes. That is to say, the 42 DEGs occurred
not only between treatments, but also between materials
and tissues under low-P stress, which suggested they
were active genes under low-P conditions. Further
KEGG analyses enriched in three general pathways:
methane metabolism, phenylalanine metabolism, and
phenylpropanoid biosynthesis. Notably, the 5 genes enriched
in the three pathways were all the same. Among them,
four genes including Glyma02g40000, Glyma11g29890,
Glyma18g06250 and Glyma20g30910 acted as
peroxidase to generate p-Hydroxyphenyl lignin (H lignin),
Guaiacyl lignin (G lignin) and Syringyl lignin (S lignin)
from corresponding p-Coumaryl alcohol, Coniferyl
alcohol and Sinapyl alcohol (Fig. 2). One gene named
Glyma02g14450 functioned in the production of
pCoumaroyl-CoA, which involved in the biosynthesis of
Quantitative Real-time PCR (RT-qPCR) verification
To verify the expression results obtained by gene chips,
a total of 13 differently expressed genes either between
soybean accessions CD and YH in low-P stress or
between different P treatments in roots of CD and YH
were selected for RT-qPCR. The results of RT-qPCR
analysis were consistent with the data obtained from
gene chips (Fig. 3), indicating the reliability of the results
from gene chips.
Low phosphorus induced systemic changes in soybean
Phosphorus is important to soybean growth and
development. To detect low-P-stress related genes, multiple
comparative analyses were conducted. First, we
compared the expression level changes between different P
treatments in the given materials and tissues, which
represented intra-material and intra-tissue DEGs in
coping with low-P conditions. Simultaneously, we analyzed
inter-materials and inter-tissues gene expression patterns
in the case of different concentrations of phosphorus.
Finally, we obtained overlapping DEGs between
intermaterials and intra-materials, between inter-tissues and
intra-tissues, and common genes among different P
treatments, materials and tissues. Through these
comparative analyses, we acquired stable genes responding
to low-P stress, and obtained common genes and
pathways related to low-P stress in soybean.
Zeng et al. revealed 17 biological processes in GO
enrichment analysis of phosphate-deficiency-responsive
genes in soybean roots (Glycine max var. Williams 82)
]. Among these processes, photosynthesis, iron ion
transport, fatty acid metabolic process, and stress
responses were also detected in our study. Through the
analyses of the GO enrichment results, we found
oxidative response, as well as peroxidases were repeatedly
detected. As earlier studies demonstrated, the expression
levels of peroxidases were closely related to the
resistance to biotic and abiotic stresses, such as salt stress,
wounding, diseases, hypersensitive reactions, and so on
]. Recent studies confirmed that under P-limited
conditions, alterations of oxidative stress- related genes
including peroxidase genes were observed in Zea mays,
rice and Arabidopsis [
23, 24, 27
Simultaneously, the results of GO enrichment analyses
also showed many genes induced by low-P stress were
associated with plant hormone signal transduction, such
as ethylene and jasmonic acid. Previous studies
supported that many genes induced by low-P stress were
associated with plant hormone signal transduction [
]. An increase of ethylene content in root systems of
common bean indicated that ethylene regulated changes
in root morphology (i.e. root length) under low-P stress
. Meanwhile, the effect of ethylene on the elongation
of primary roots in Arabidopsis and the emergence of
root hairs under low-P stress was shown with the use of
a method based on image processing [
]. Gao et al.
studied the effect of ethylene on physiological changes
of soybean plants grown in P-deficient solution. Their
results suggested that ethylene increased root vigor and
acid phosphatase activity in the root system, as well as
root:shoot ratio under low-P stress [
]. In addition,
jasmonic acid promoted plants’ freezing tolerance with the
application of exogenous jasmonic acid and blocking-up
biosynthesis of jasmonic acid [
The three common pathways, methane metabolism,
phenylalanine metabolism, and phenylpropanoid
biosynthesis, were enriched from comparative analyses, which
suggested that these pathways responded actively when
plants suffered low-P stress. These results were further
supported by the facts that transcript profiling analyses
of Zea mays and Arabidopsis identified that many genes
involved in biosynthesis of phenylpropanoids either
upregulated or down-regulated when exposed to low-P stress
]. Phenylpropanoid was a member of phenolic
compound, which pertained to plant secondary metabolites.
For example, Hall et al. found that the phenolic content
was stimulated by an increase in nutrient stress in
Helianthus annuns . Similar phenomenon occurred in
Tageteserecta when suffering from water stress with a
significant increase of phenolic content [
]. Koeppe et al.
observed that more phenol compounds were leached from
living intact roots, dried roots, and tops of
phosphatedeficient plants than from phosphate-sufficient ones [
Although the effect of methane metabolism on the
response to low-P stress had not been clearly stated, a recent
study suggested plants released more methane at high
], and methane could play an important
role in regulating plant growth [
]. Methane metabolism
in association with low-P stress may provide a new
direction in studying the mechanism of low-P tolerance in
Our results suggested that the responses of soybean to
low-P stress were complex cross-talks; it not only
depended on other nutrition (iron) and plant hormones
levels (ethylene and jasmonic acid), but also on the
metabolism of secondary metabolites. This information can
be used to focus on these systemic connections induced
by low-P stress to devise strategies aimed at improving
soybean yield in P-deficiency soils.
Massive genes involved in response to low-P stress in low-P-tolerant accession
We selected a low-P-tolerant accession, CD, and a
lowP-sensitive accession, YH. The numbers of different
expression genes between different P treatments in CD
were 30-fold more than those in YH. To explore
whether the DEGs were accession-specific between CD
and YH, we compared the DEGs between these two
accessions. Our results showed that among the 10 DEGs
in YH, one gene (Glyma20g33710) was also found in
CD. A homologous gene was identified in Lotus
japonicus (Accession No. AB378629) with the tool of BLAST
in the National Center for Biotechnology Information
(NCBI) database (http://blast.ncbi.nlm.nih.gov/Blast.cgi),
which exhibited 54 % sequence identify to Glyma20g33710.
This gene was predicted as a nodulation-associated bZIP
transcription factor gene [
]. For a DEG Glyma09g18770
in YH, a homologous gene Glyma17g10510 was detected
in the DEGs from CD. Glyma09g18770 encoded a
putative E3 ligase protein with metal ion binding and
DNA binding domains, homologous with BRUTUS
and OsHRZ in Arabidopsis thaliana and Oryza sativa
which regulates the response to iron deficiency [
The rest annotated DEGs in YH, three genes
(Glyma03g28630, Glyma19g31361 and Glyma03g28611)
encoded the transcription factor ORG2-like proteins. The
homologous gene in Arabidopsis thaliana was bHLH038,
which played an important role in the iron-deficiency
responses and uptake of this nutrient [
was a plastid-lipid-associated fibrillin protein, transferring
phosphorus-containing groups. Glyma20g33710 encoded
a member of basic leucine zipper transcription gene
family, homologs with TGA4 in Arabidopsis thaliana. TGA4
acts as a regulator in response to defense signals [
Although no homologous genes of the rest of the DEGs were
detected in CD; gene response to iron-deficiency, uptake,
and defense were detected in CD. These genes might be
located in different pathways or different locations of the
In conclusion, most DEGs were specific between CD
and YH (Additional file 3: Table S2), however, the
involved pathways found in YH were included in CD. In
addition, other low-P stress related pathways were
detected in CD, for example, oxidation-reduction and
metabolic processes. The much more genes induced by
low-P stress existed in CD than in YH suggested massive
genes involved in the response to low-P condition in
tolerant accession CD. In addition to involving in biotic
and abiotic stress responses, some of these genes were
related to plant growth and development. On one hand,
these massive genes could response to low-P condition
through mediating the secretion of phytohormone and
secondary metabolites. On the other hand, they could
improve uptake of P through controlling soybean root
traits, such as main root elongation and increased root
hair number. All of these responses could make CD
more adaptive to low-P stress and more resistant than
YH. Likewise, more stress-responsive genes were
identified in tolerant soybean line than those in sensitive
soybean line in the response to common cutworm feeding
]. Moreover, more DEGs were detected in CD than
those in YH under low-P stress (Table 1) could be due
to different genetic backgrounds between these two
materials, which was further supported by the result that
there were 64,534 SNPs detected between CD and YH
by a genome-wide NJAU 355 K SoySNP array
(unpublished data). The selection of P-efficient soybean
accessions could be helpful in exploring low-P related genes
and creating new low-P-tolerant soybean accessions.
Roots played a crucial role in P metabolism during seedling stage
Compared with 310 DEGs detected in roots under low-P
stress, only 18 DEGs were detected in leaves (Table 1).
Plants under low-P conditions depended on the root
system during seedling stage to absorb phosphorus
nutrition. Previous studies showed an increase in lateral
roots, root hair density, root:shoot ratio when plants
were exposed to low-P stress [
]. Among them,
uptake through root hairs contributed up to 90 % of the
phosphorus acquired by plants . Some phosphate
transporters specifically located in roots had been
identified to be involved in Pi uptake at the root-soil interface
in P-deficient soils. These proteins transport available
phosphorus that could be absorbed by plants, mainly
H2PO4−, HPO42−and PO43− [
]. The apparent changes of
root systems under low-P stress in our hydroponic
experiments also confirmed that roots played an
important role in acquiring P during seedling stage. As such,
we found some genes associated with phosphorus
absorption in roots. The gene Glyma04g19450 showed
homology with AtSPX2-4, members of a sub-family of
Arabidopsis genes with the SPX domain. Proteins
harboring SPX domain are believed to be involved in
phosphorus acquisition and phosphorus signaling network.
Duan et al. found several Pi starvation-responsive genes
were regulated by AtSPX2-4, positively or negatively.
Further studies demonstrated the expression levels of
AtSPX2 and AtSPX3 increased under phosphorus
]. Interestingly, we also found three DEGs in
roots; namely Glyma13g24810.1, Glyma07g31630.1 and
Glyma13g31290.1 respectively, showed homology with
PHO2, the ubiquitin-conjugating enzyme that contained
SPX domain, which is crucial for phosphorus acquisition
and translocation in plants. Results of Huang et al.
suggested PHO2 modulated Pi absorption, specifically
transport at the root surface by regulating the abundance of
The limited number of DEGs in leaves probably resulted
from genes in the leaves primarily involved in P
distribution and utilization in later stages. Three DEGs from leaves
(Glyma03g28630, Glyma19g31361 and Glyma03g28611)
were homologous to bHLH038 in Arabidopsis thaliana,
functioning in the iron-deficiency responses and uptake
]. Thus, we concluded roots played an important role in
coping with low-P stress during seedling stage. An
indepth study on DEGs in roots will advance the functional
study of highly P-efficient genes.
P starvation leads to systemic changes in the gene
expression of soybean. Here, the gene expression patterns
of soybean under low-P stress were surveyed on the
roots and leaves of a low-P-tolerant accession and a
lowP-sensitive accession by microarray chips. Through
comparative analyses on the differently expressed genes, we
identified 42 candidate genes and three common
pathways, including methane metabolism, phenylalanine
metabolism and phenylpropanoid biosynthesis, which were
highly correlated to low-P stress. These results not only
promote our understandings of the molecular bases of
the responses to P deficiency, but also facilitate research
in improving Pi usage in soybean and designing highly
phosphate-efficient soybeans. This process could optimize
fertilizer use and promote development of sustainable
Through hydroponic experiments, we selected two
soybean accessions, Chundou (CD) and Yunhefengwodou
(YH), from 219 soybean materials [
]. The 219 soybean
seedlings were sown in plastic pots which contained the
nutrient soil and vermiculite with a ratio of 1:3. After
five days of germination, we picked three shoots of each
accession with similar growth vigor which were then
transplanted to half Hoagland under different P treated
conditions in hydroponic boxes with a 16 h/8 h (day/
night) photoperiod and a temperature of 26-28 °C/22 °C
(day/night) temperature cycle. Hoagland nutrient
solution was composed of macroelements (1.0 mM KH2PO4,
5.0 mM KNO3, 5.0 mM Ca(NO3)2, 2.0 mM MgSO4),
microelements (2.86 mg/L H3BO3, 1.81 mg/L MnCl2 ·
4H2O, 0.22 mg/L ZnSO4 · 7H2O, 0.08 mg/L CuSO4 ·
5H2O, 0.0269 mg/L Na2MoO4 · 2H2O) and ferric salts
(5.56 mg/L FeSO4 · 7H2O, 7.64 mg/L EDTA · Na).
Hoagland with 1.0 mmol/L P was considered as + P treated
condition, while Hoagland with 0.01 mmol/L P was
considered as -P treated condition. To satisfy the demands
of plant growth, we substituted equal concentrations of
KCl for KH2PO4. Ventilation was performed three times
per day for 30 min. Nutrient solution was exchanged
every three days.
Then Roots and leaves of soybean accession CD and
YH were harvested after 10 days of hydroponics (normal
and low P treated conditions), each with three biological
replicates. All samples were stored at −80 °C for
additional experiments, including RNA isolation and
Total RNA isolation and quantitative RT-PCR
Total RNA was extracted from roots and leaves separately
of soybean accessions CD and YH, each with three
biological replicates. A total of 24 samples were isolated to
exact RNA according to the manufacturer’s instructions
with the use of Plant RNA Extract Kit (TianGen, Beijing,
China). A total of 50–100 mg leaf and 50–100 mg root
were used to isolate RNA for each sample, respectively.
Then about 1 μg RNA was used to configure 20 μL system
to synthesize cDNA with the application of HiScript® II Q
RT SuperMix for qPCR (+gDNA wiper) (Vazyme,
Nanjing, China). The constitutive expression gene Gmtublin
(GenBank accession number: AY907703) was used as a
reference gene for RT-qPCR [
], and each sample was
measured with three replicates. The RT-qPCR was
conducted on an ABI 7500 real-time PCR system (Applied
Biosystems, Forster City, CA, USA) with the use of SYBR
Green Realtime Master Mix (Toyobo). The ABI 7500
system Sequence Detection System (SDS) software v.1.4 was
applied to analyze the data. The primers used were listed
in Additional file 6: Table S4.
Microarray experimental design and data analysis
To obtain the differently expressed genes under different
P treated conditions in different soybean accessions, we
used 24 Affymetrix Soybean Gene 1.1 ST Array Strip.
These detected 66,473 genes from Glyma1.01 database,
each with 19 probes, and 8250 genes from GeneBank
database, each with 16 probes. After RNA
concentrations quantified by ultraviolet Spectrophotometer
(NanoDrop Technologies, ND-1000) and RNA quality
assessed by formaldehyde agarose gel electrophoresis,
the total mRNA was hybridized on Affymetrix Soybean
Gene 1.1 ST array strips. Microarray experiments were
performed by CapitalBio Technology. All microarrays
were scanned with an Affymetrix scanner named
GeneChip® Scanner 3000, and images acquired were saved as
.JPG pictures. AGCC software (Affymetrix®GeneChip®
Command Console® Software) was applied to convert
the image signals to digital signals, which recorded the
signal intensity of every probe. After subtracting
background and consolidating probe signals, normalization
between arrays was carried out using RMA algorithm to
remove variances between samples caused by abiologic
]. We compared gene expression quantities
between different P-treated conditions and different
soybean accessions. Differently expressed genes
simultaneously satisfied all of the following criteria: (a)
|log2Ratio| ≥ 1, ratio represented fold change of expression
between experimental group control group, (b) the P
value after FDR correction was less than 0.05, (c) three
biological replicates existed. The analyses of differently
expressed genes were performed using SAM (significance
analysis of microarray). For DEGs between different P
treatments, A1, A2 and A3 were defined as three
biological replicates under normal condition (0.5 mmol/L P),
and A7, A8 and A9 were defined as three biological
replicates under low-P condition (0.005 mmol/L P) (Fig. 1).
Gene Ontology (GO) and KEGG pathway enrichment analysis
Gene Ontology database was a structured typical
biological model; it consisted of three terms which were
cellular component (CC), biological process (BP) and
molecular function (MF). All differently expressed genes
were mapped to GO terms in the GO databases (http://
www.geneontology.org/), then calculated GO terms that
were significantly enriched in differently expressed genes
compared with genome background with the application
of hypergeometric distribution. Results showed
biological functions that were significantly associated with
genes (P < 0.05).
The main metabolic pathways and signal transduction
pathways that differently expressed genes may be
identified by pathway enrichment analysis. KEGG (Kyoto
Encyclopedia of Genes and Genomes) database is an
important public database associated with pathways which
integrateds genomics, biochemistry and system functional
omics. Using KEGG PATHWAY as a unit, we found
pathways that were significantly associated with differently
expressed genes compared with genome background.
Expression profile data were analyzed using a Molecule
Annotation System (http://mas.capitalbiotech.com/).
Availability of supporting data
The data sets supporting the results of this article are
included within the article and its additional files. The data
from the 24 chips are publicly available in the National
Center for Biotechnology Information (NCBI) Gene
Expression Omnibus (GEO) under accession number
Additional file 1: Figure S1. Roots morphological of soybean accession
CD and YH under low-P condition (0.005 mmol/L P) (A) Root
morphological before low-P treatments (B) Root morphological after
10 days of low-P treatments. (DOC 190 kb)
Additional file 2: Table S1. KEGG and Go enrichment analysis of
differently expressed genes. (a) 317 DEGs between different P treatments
(b) 195 DEGs between different materials (c) 253 DEGs between different
tissues (d) 85 overlapping genes between the 195 marked DEGs between
CD and YH and 317 DEGs in roots and leaves (e) 68 overlapping genes
between the 253 marked DEGs between roots and leaves and 317 DEGs
in roots and leaves (f) 42 common DEGs. (XLS 86 kb)
Additional file 3: Table S2. The list of DEGs between different P
treatments in the roots and leaves of CD and YH. (XLS 55 kb)
Additional file 4: Figure S2. Identification of differently up-regulated
genes in roots between different soybean accessions. (DOC 26 kb)
Additional file 5: Table S3. 10 genes showing opposite expression
patterns in roots between different soybean accessions. (DOC 42 kb)
Additional file 6: Table S4. The list of primers used in this article.
(DOC 32 kb)
BP: biological process; CC: cellular component; CD: Chundou;
DEGs: Differently expressed genes; GO: Gene Ontology; KEGG: Kyoto
Encyclopedia of Genes and Genomes; MF: molecular function; P: phosphorus;
QTL: quantitative trait loci; RT-qPCR: Quantitative Real-time PCR;
SDS: Sequence Detection System; YH: Yunhefengwodou.
The authors declare that they have no competing interests.
DY, HC and DZ conceived and designed the experiments. QW, YY and WD
carried out most of the experiments in this paper. QW and JW performed
bioinformatics analysis. QW, JW and HC wrote the paper. All authors read
and approved the final manuscript.
This work was supported in part by the National Natural Science Foundation
of China (31301342, 31370034 and 31301336), Key Transgenic Breeding
Program of China (2014ZX08004-003), and Jiangsu Collaborative Innovation
Center for Modern Crop Production (JCIC-MCP).
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