Abstract
Soil salinity is a major constraint to crop growth and yield. The
primary and lateral roots of Arabidopsis thaliana are known to respond
differentially to a number of environmental stresses, including
salinity. Although the maize root system as a whole is known to be
sensitive to salinity, whether or not different structural root systems
show differential growth responses to salinity stress has not yet been
investigated. The maize primary root (PR) was more tolerant of salinity
stress than either the crown root (CR) or the seminal root (SR). To
understand the molecular mechanism of these differential growth
responses, RNA-Seq analysis was conducted on cDNA prepared from the PR,
CR and SR of plants either non-stressed or exposed to 100 mM NaCl for
24 h. A set of 444 genes were shown to be regulated by salinity stress,
and the transcription pattern of a number of genes associated with the
plant salinity stress response differed markedly between the various
types of root. The pattern of transcription of the salinity-regulated
genes was shown to be very diverse in the various root types. The
differential transcription of these genes such as transcription
factors, and the accumulation of compatible solutes such as soluble
sugars probably underlie the differential growth responses to salinity
stress of the three types of roots in maize.
Introduction
Plants are exposed to various environmental stresses during their life
cycle, and soil salinity is one of the leading constraints to plant
growth and productivity. Salinity stress involves a combination of both
ionic and osmotic stress, and these induce a range of conditions,
including membrane dysfunction, metabolic disorder and oxidative stress
[[33]1–[34]3]. Transgenic experiments have shown that the constitutive
expression of certain signaling pathway genes (in particular those
encoding certain protein kinases and transcription factors) can have a
positive effect on tolerance [[35]4–[36]8]. Salinity-regulated
transcription factors (TFs) control the expression of a wide range of
genes, and one of the most important pathways involved is the SOS (Salt
Overly Sensitive) signaling pathway, which is activated by cytoplasmic
Ca^2+ and has a major regulatory role over ion homeostasis
[[37]9–[38]11].
Salinity stress inhibits the growth of the Arabidopsis thaliana primary
root by suppressing cell division and elongation. It has been claimed
that it also induces agravitropic primary root (PR) growth by its
effect on the auxin efflux carrier PIN2 [[39]12, [40]13]. The effect of
salinity stress on the lateral root (LR) is less straight forward.
Osmotic stress, as induced by salinity, inhibits LR emergence, although
this can be rescued by the administration of exogenous auxin [[41]14,
[42]15]. Zhao et al. have shown that mild ionic stress stimulates both
the initiation and emergence of LR, and that LR emergence in the
loss-of-function sos1, 2 and 3 mutants is reduced in response to ionic,
but not to osmotic stress [[43]16]. Salinity has a major effect on root
growth and development, but most relevant studies have focused on the
root as a whole, even though the suspicion is that different roots from
the same plant may respond differentially to the same environmental
stress. Duan et al. have shown that LR growth is more strongly
suppressed by salinity than is that of the PR, and that this difference
is associated with ABA signaling [[44]17, [45]18]. The two types of
root also have a different gravitropic response, mediated by the effect
of PIN on the redistribution of auxin [[46]19, [47]20]. According to
Vidal et al. [[48]21], the microRNA miR393, which is inducibled by
nitrate, affects only LR growth. All these investigations suggest that
the differential growth dynamics between primary and lateral roots in
Arabidopsis are crucial for plants to adapt to the ever-changing
environmental conditions.
A. thaliana forms a taproot, comprising of a single embryonically
initiated PR and post-embryonically initiated LRs, but maize has a
typical fibrous root system comprising of more or less the same size of
embryonically and post-embryonically initiated branch roots [[49]22,
[50]23]. In maize, the embryonically initiated roots consist of the PR
and a variable number of seminal roots (SRs), which plays important
roles during the early stages of plant development. The
post-embryonically initiated roots are represented by a combination of
LR and shoot-borne roots initiated from stem nodes; those which emerge
above the surface are referred to as brace roots, and those which
emerge below the surface are known as crown roots (CR). The
post-embryonic root system is important for the physiology of the
mature plant.
Till now, genetic studies have identified and characterized several
specific root mutants in maize. The mutant rtcs forms no shoot-borne
roots and the embryonic seminal roots and is a consequence of the
loss-of-function of RTCS, which encodes a LATERAL ORGAN BOUNDARIES
domain (LBD) protein [[51]24, [52]25]. The LR mutant rum1 is largely
unable to initiate either SR or LR from the PR, and the mutated gene
RUM1 encodes a truncated ZmIAA10 sequence, which interacts directly
with the auxin response factors ZmARF25 and ZmARF34 [[53]26].
Here, the growth response of the various types of the maize root system
to salinity stress was characterized, and the molecular basis of the
differential growth responses in response to salinity stress between
PR, SR and CR was investigated by physiological analysis and
transcriptome comparisons.
Material and Methods
Plant materials and salinity treatment
In the beginning, maize (Zea mays L cv. Chang 7–2) seeds were rolled
into soggy filter papers for several days before primary roots (PR)
elongated up to about 3 cm, and then the seedlings were grown for two
weeks in 1/2 Hoagland’s solution (0.51 g/L KNO[3], 0.82 g/L
Ca(NO[3])[2], 0.49 g/L MgSO[4]·7H[2]O, 0.136 g/L KH[2]PO[4], 0.6 ml/L
FeSO[4], 2.86 mg/L H[3]BO[3], 1.81 mg/L MnCl[2]·4H[2]O, 0.08 mg/L
CuSO[4]·5H[2]O, 0.22 mg/L ZnSO[4]·7H[2]O, 0.09 mg/L H[2]MoO[4]·4H[2]O)
(pH 6.0) in a growth chamber held at 28°C/25°C (day/night) and a
relative humidity of 60% under a 16 h photoperiod provided by 100 μmol
m^-2 s^-1 of photosynthetically active radiation. The culture solution
was renewed every two days. Early developmental stage of primary root
(PR) (3 days after germination), early developmental stage of seminal
root (SR) (7 days after germination), and early developmental stage of
crown root (CR) (15 days after germination) with lengths of
approximately 6 to 10 cm were selected for the experiments.
For salt treatment, 40 roots from 40 individual seedlings with lengths
of approximately 6 to 10 cm for each root type were transferred to
Hoagland’s solution containing either 0 or 100 mM NaCl for 24 h or 72
h, the root length was measured before and after the NaCl treatment.
Each root type was analyzed in triplicate.
For RNA sequencing, PR, SR and CR with lengths of approximately 6 to 10
cm were exposed for 24 h to the same Hoagland’s solution containing
either 0 or 100 mM NaCl. About 10–15 roots from 10–15 individual
seedlings with lengths of 1 cm from root tip for each root type were
chosen for this experiment. The harvested tissues were immediately
frozen in liquid nitrogen and stored at -80°C.
cDNA library preparation and sequencing
Library construction and sequencing were performed according to the
method described previously [[54]27]. Total RNA was extracted from the
root samples using the TRIzol reagent (Invitrogen, Carlsbad, CA, USA).
The cDNA first strand was generated using reverse transcriptase and
random primers. The resulting cDNA libraries were sequenced using an
Illumina HiSeq 2000 platform at the Beijing Genomics Institute
(Shenzhen, China). Sequencing data is available in the GEO Gene
Expression Omnibus (GEO) database under accession number [55]GSE53995.
Assessment of differential gene transcription
Raw reads were filtered to remove low quality reads, according to the
following procedure: 1) Remove reads with adaptor sequences. 2) Remove
reads in which the percentage of unknown bases (N) is greater than 10%.
3) Remove low quality reads. If the percentage of the low quality base
(base with quality value ≤ 5) is greater than 50% in a read, we define
this read as low quality. The resulting set of reads was aligned with
the maize cv. B73 RefGen_V2 genomic DNA sequence using SOAP 2.21
software, and related to known genes by a BLAST (BLAST 2.2.23) analysis
([56]http://blast.ncbi.nlm.nih.gov/Blast.cgi). Transcript abundance was
calculated using the RPKM method (reads per kb per million reads)
[[57]28] and the formula is shown as follows:
[MATH:
RPKM=106CNL/10<
/mn>3 :MATH]
where C is number of reads that uniquely aligned to gene X, N is total
number of reads that uniquely aligned to all genes, and L is number of
bases of gene X.
The assignment of differential transcription relied on the probability
(p) value and the false discovery rate (FDR) [[58]29]. The former
corresponds to a differential gene transcription test, while the latter
is used to determine the threshold p-value. The thresholds applied were
FDR ≤ 0.001 and the absolute value of log[2] (treatment/control) ≥ 1
[[59]30].
All p-values were determined according to the following formula:
[MATH:
p(y|x)=(N2N
1<
none>)y(x+<
/mo>y)!x
!y!(1+N2N1)(x+y
+1) :MATH]
N[1] and N[2] denotes the total number of clean tags in two compared
libraries, respectively, while x and y represents the clean tags
mapping to gene X.
Functional classification of differentially transcribed genes (DTGs)
exploited the GO (Gene Ontology) ([60]http://www.geneontology.org/) and
KEGG (Kyoto Encyclopedia of Genes and Genomes)
([61]http://www.genome.jp/kegg/) databases. For GO analysis, annotating
the results that from BLAST (-p blastx-e 1e-5-m 7) sequences to Nr
database of NCBI to the terms of GO by use BLAST2GO (default
parameters), and the calculated p-value goes through Bonferroni
Correction, taking corrected p-value ≤ 0.05 as a threshold. GO terms
fulfilling this condition are defined as significantly enriched GO
terms in DTGs. For KEGG analysis, annotate to the KEGG database by
BLAST (-p blastx-e 1e-5-m 8).
Cluster analysis
Sets of genes showing a similar pattern of transcription were assumed
to imply that they were functionally correlated. Transcription patterns
were clustered using Cluster 3.0 [[62]31] with Euclidean distances and
the hierarchical cluster method of complete linkage clustering and Java
Treeview software [[63]32].
Validation of DTG status using real-time RT-PCR
RNA extraction and first strand cDNAs Synthesis were carried out as
described above. qRT-PCRs were performed using the Bio-Rad Real-time
PCR Detection System (Bio-Rad, USA) based on the FastStart Universal
SYBR Green Master mix (Roche, Basel, Switzerland), following the
manufacturer’s instructions. Each 20 μl PCR contained 10 μl 2 ×
real-time SYBR Green I PCR Mix, 0.4 μl of each primer (sequences given
in [64]S1 Dataset) and an appropriate quantity of cDNA. The temperature
cycling regime began with a pre-denaturation step (95°C / 30 s),
followed by 45 cycles of 95°C / 15 s, 55°C / 10 s and 72°C / 10 s. We
selected Zm18S rRNA, ZmActin and ZmUBQ housekeeping genes as a control
to evaluate expression levels of selected genes. Three biological
replicates were collected for each root type, and relative transcript
abundance was calculated using the delta-delta Ct method [[65]33].
Measurement of soluble sugar content
Soluble sugar was measured according to Kong et al. [[66]4]. In brief,
to measure the soluble sugar content, frozen root material (0.3 g) was
extracted with 10 mL H[2]O at 100°C for 10 min. The extracts were
filtered and analyzed for soluble sugar content using the
anthrone—sulphuric acid method. Briefly, 1 mL of the extract was mixed
with 1 mL of H[2]O, 0.5 mL of anthrone reagent (1 g anthrone and 50 mL
ethyl acetate) and 5 mL oil of vitriol, and then heated at 100°C for 1
min. After cooling, the mix was analysed by using UV spectrophotometry
at 620 nm.
Results and Discussion
Differential growth of maize roots in response to salinity stress
Salinity stress is well known to inhibit root growth. The A. thaliana
PR is less sensitive to salinity than the LR [[67]18, [68]34]. Although
the maize root as a whole is also sensitive [[69]35], whether PR, SR
and CR have similar or differential growth response to salinity stress
is still unknown. When exposed to 100 mM NaCl for 24 h, the growth of
the maize PR was less inhibited (1% reduction) than either the CR (70%)
or the SR (60%) ([70]Fig. 1A). Similar results were observed in the
samples treated with 100 mM NaCl for 72 h ([71]Fig. 1B,C). The
assumption is that such a large difference must reflect distinct
patterns of transcription, and therefore the differential metabolites
in each root type.
Fig 1. Sensitivity of the three types of maize roots to the salt treatment.
[72]Fig 1
[73]Open in a new tab
(A) and (B) The relative elongation rate of the three types of maize
roots after 24 h and 72 h exposure in 100 mM NaCl. Different letters
represent significant difference at p< 0.05 (Duncan’s multiple range
test; data are represented as mean ± SEs; three biological repeats).
(C) The root morphology of PR, SR, CR with lengths of approximately 6
to 10 cm treated with 0 and 100 mM NaCl for72 h. Experiments were
repeated three times with similar results. Bar = 2.0 cm.
RNA-Seq analysis
Across the six RNA libraries (PR, PR-salt, CR, CR-salt, SR and
SR-salt), once the low quality reads had been removed, over 5 x 10^7
reads, representing 2.5 x 10^9 nt, were acquired ([74]Table 1). A high
proportion of the reads were readily mapped to the maize reference
genome sequence: 79.89% from PR, 78.87% from PR-salt, 77.80% from CR,
79.18% from CR-salt, 78.16% from SR, and 77.38% from SR-salt. A BLAST
analysis assigned around two thirds of these sequences (respectively
66.12%, 66.52%, 65.47%, 67.04%, 64.85% and 64.84%) to known genes
([75]Table 1).
Table 1. Summary of mapping result.
Sample ID PR PR-salt CR CR-salt SR SR-salt
Total Reads 8,278,219 (100.00%) 8,664,028 (100.00%) 8,641,655 (100.00%)
8,153,263 (100.00%) 8,265,737 (100.00%) 8,450,243 (100.00%)
Total BasePairs 405,632,731 (100.00%) 424,537,372 (100.00%) 423,441,095
(100.00%) 399,509,887 (100.00%) 405,021,113 (100.00%) 414,061,907
(100.00%)
Total Mapped Reads (mapping to reference genes) 5,473,192 (66.12%)
5,763,743 (66.52%) 5,658,068 (65.47%) 5,465,636 (67.04%) 5,360,742
(64.85%) 5,479,102 (64.84%)
Total Unmapped Reads (mapping to reference genes) 2,805,027 (33.88%)
2,900,285 (33.48%) 2,983,587 (34.53%) 2,687,627 (32.96%) 2,904,995
(35.15%) 2,971,141 (35.16%)
Total Mapped Reads (mapping to reference genome) 6,613,863 (79.89%)
6,833,217 (78.87%) 6,723,444 (77.80%) 6,455,367 (79.18%) 6,460,751
(78.16%) 6,539,064 (77.38%)
Total Unmapped Reads (mapping to reference genome) 1,664,356 (20.11%)
1,830,811 (21.13%) 1,918,211 (22.20%) 1,697,896 (20.82%) 1,804,986
(21.84%) 1,911,179 (22.62%)
[76]Open in a new tab
Identification of DTGs in the salinity stressed maize roots
A total of 444 genes proved to be regulated by salinity stress
([77]Fig. 2A, [78]S2 Dataset). Of these, 212 (13 up- and 199
down-regulated) were identified in the PR under NaCl treatment, whereas
183 genes were specifically regulated in PR after salinity stress
([79]Fig. 2C). Next, we compared the DTGs between CR with and without
NaCl treatment; 148 genes showed differential transcription in CR under
NaCl treatment, of which 54 showed up-regulated and 94 showed
down-regulated ([80]Fig. 2C). In addition, 94 genes were specifically
regulated in CR after salinity stress. A total of 33 up-regulated genes
and 126 down-regulated genes were found in SR after NaCl treatment
([81]Fig. 2C), whereas 101 genes were specifically regulated in SR
after salinity stress. After cluster analyses, as shown in [82]Fig. 2B,
PR showed distinct transcription profiles of DTGs compared with SR and
CR, which had similar transcription profiles of DTGs after salinity
stress. In conclusion, PR showed the largest total and specifically
regulated DTGs after salinity stress ([83]S2 Dataset). Furthermore, a
relatively larger portion of DTGs were down-regulated in PR (93.9%)
than in CR (63.5%) and SR (79.2%) after salinity stress ([84]Fig. 2C).
These results suggested that the differential transcription trends of
the DTGs in PR, CR, and SR may contribute to the regulatory mechanism
of salinity sensitivity in different roots ([85]S3 Dataset).
Fig 2. Transcription patterns of stress-regulated genes in the maize PR, CR
and SR.
[86]Fig 2
[87]Open in a new tab
(A) The total number of genes regulated by salinity stress in the three
types of root. (B) Cluster analysis of salinity-regulated genes. Red
indicates that the gene has a higher expression level in the
salt-treated samples; green indicates that the gene has a lower
expression in the salt-treated samples, and gray indicates that the
gene has no expression in at least one sample. (C) The number of genes
responding to salinity stress in each root type.
A random sub-set of 16 of the 444 DTGs was subjected to real-time
quantitative PCR (qRT-PCR) to validate the RNA-Seq data. As shown in
[88]Fig. 2B, [89]Fig. 3 and [90]S1 Fig., the qRT-PCR output of about 70
percent of 16 genes confirmed the RNA-Seq based identification of DTGs.
For example, the expression of GRMZM2G339122 (EXP1) was down-regulated
in PR, but has no change in CR and SR after salt treatment, and
GRMZM2G010251 (NRT2:1) was found to be down-regulated in PR but
up-regulated in CR and SR. However, some genes did not correspond with
RNA-Seq data. For example, our qRT-PCR results showed that
GRMZM2G089506 (Eukaryotic aspartyl protease family protein) was
up-regulated in PR, but down-regulated in PR in RNA-Seq data.
Fig 3. qRT-PCR validation of differential transcription identified by
RNA-Seq.
[91]Fig 3
[92]Open in a new tab
Each column represents an average of three replicates, and bars
indicate SEs.
Functional classification of DTGs under salinity stress
The likely function of the set of DTGs was explored using the GO
classification system ([93]Fig. 4). With respect to biological process,
many of the genes fell into the categories cellular process,
establishment of localization and metabolic processes; with respect to
the cellular component, the main categories represented were response
to stimulus, cell part and membrane; and with respect to molecular
function, the key categories were binding, catalytic activity and
organelle. DTGs were more abundant in the PR than in the CR or SR after
salinity stress in all three categories. The outcome indicated that
salinity stress induced antioxidation and transcription factor
activity, which, in turn, generated the synthesis of solutes such as
proline, trehalose, mannitol and lycine, as maybe a common mechanism
underlying salinity tolerance in maize roots, especially in PR
[[94]36–[95]38].
Fig 4. Functional classification (GO) of salinity-regulated genes in the
maize PR, CR and SR.
[96]Fig 4
[97]Open in a new tab
When the DTGs were further subjected to KEGG analysis, the major
pathways regulated by salinity stress were revealed to oxidoreductase,
glycosyl hydrolysis, receptor-assotiated kinase and ABA signaling
([98]Fig. 5 and [99]S4 Dataset), a result consistent with what has been
reported elsewhere [[100]18, [101]39–[102]43].
Fig 5. Pathway analysis (KEGG) of salinity-regulated genes in the maize PR,
CR and SR.
[103]Fig 5
[104]Open in a new tab
DTGs related to oxidoreductase activity
Salinity stress induces both osmotic and ionic stress. The former is
perceived rapidly by the plant, and soon inhibits root growth, water
uptake and cell expansion. A common cellular manifestation of this
stress is the production of reactive oxygen species (ROS), which damage
cell growth through their degradative effect on proteins, lipids and
DNA [[105]39, [106]44]. In order to mitiagate this damage, plants have
evolved a range of antioxidative enzymes and non-enzymic compounds
[[107]44]. Overexpression of a Mn-SOD (Superoxide Dismutase) in
transgenic Arabidopsis plants showed increased salt tolerance;
over-expressing OsAPXa or OsAPXb (Ascorbate Peroxidase) exhibited
increased salt tolerance in transgenic Arabidopsis plants due to higher
APX, low H[2]O[2] and MDA (Malondialdehyde) content. Thus the
constitutive expression of a superoxide dismutase or certain
peroxidases was able to enhance the level of salinity tolerance shown
by A. thaliana [[108]45]. Here, the transcription of the genes
GRMZM2G427815 (POX52) and GRMZM2G138450 (POX), identified as encoding
members of a peroxidase superfamily, contrasted between the PR and the
CR and SR in response to salinity stress ([109]Fig. 6A). In all, 49
genes related to oxidoreductase activity were up- or down-regulated in
response to salinity stress ([110]Fig. 2 and [111]Fig. 6A). A cluster
analysis recognized three types (I, II and III) among these genes. Type
I members, typified by GRMZM2G053720 (POX), GRMZM2G110192
(nine-cis-epoxycarotenoid dioxygenase 4, NCED4) and GRMZM2G117706
(POX), were down-regulated by salinity stress in all three root types.
The response of type II genes was a fall in transcript abundance in the
PR, but an increase in the CR and SR; an example is GRMZM2G052422, a
maize homolog of A. thaliana AtACO4 (which encodes an ACC oxidase)
([112]Fig. 6A). It has been previously shown that ethylene and its
precursor 1-aminocyclopropane-1-carboxylic acid (ACC) inhibit root cell
elongation in A. thaliana roots [[113]46]. It is possible that salt
stress stimulates a much stronger ethylene signaling effect on CR and
SR than PR growth inhibition. Finally, the type III genes displayed
down-regulation in the CR and SR, but slight up-regulation in the PR.
GRMZM2G111616 is one example of type III genes which belongs to formate
dehydrogenase family. These results suggested that genes involved in
redox reaction play an important role in maize root system in response
to salt stress, especially in PR.
Fig 6. Categories of salinity-regulated genes in the maize PR, CR and SR.
[114]Fig 6
[115]Open in a new tab
Genes encoding (A) oxidoreductases, (B) glycosyl hydrolases, (C)
phytohormone synthesis and (D) transcription factors. Red indicates
that the gene has a higher expression level in the salt-treated
samples; green indicates that the gene has a lower expression in the
salt-treated samples, and gray indicates that the gene has no
expression in at least one sample.
DTGs related to glycosyl hydrolysis
Glycosyl hydrolases have been classified into more than 45 families
[[116]47, [117]48], several of which have been implicated in the
abiotic stress response. An example is AtBG1, an A. thaliana
β-glucosidase, which is important in the drought stress response
[[118]49] and is negatively regulated by salinity in sweet almond
[[119]50]. CaChi2, a chitinase gene from pepper, is induced by ABA,
NaCl and drought [[120]51]. Here, 27 genes involved in glycosyl
hydrolysis were identified as being regulated by salinity ([121]Fig.
6B), with the majority contrasting between the PR and the CR/SR with
respect to their transcript accumulation. Both GRMZM2G051943_T01 and
GRMZM2G133781_T01 (both encoding chitinases) were up-regulated by
salinity in the CR and SR, but not in the PR. The gene
GRMZM2G065585_T01 (glycosyl hydrolysis family 17) was down-regulated in
the PR, but in neither the CR nor the SR, while At4g16260, an A.
thaliana homolog of GRMZM2G065585_T01 (Glycosyl hydrolase superfamily
protein), is up-regulated by salinity stress [[122]52].
GRMZM2G055802_T01 (BBTI13) and [123]AC208221.3_FGT002 (BBTI2), homologs
of, respectively, the rice genes OsBBTI13 and OsBBTI2, were
differentially transcribed between the PR and the CR in salinity
stressed plants ([124]Fig. 6B). OsBBTI13 is a component of the abotic
stress response of rice [[125]53]. Our results indicated that genes
involved in glycosyl hydrolasis played an important role in different
maize roots in response to salt stress.
DTGs related to phytohormone synthesis
Plant growth and development relies on the interplay of a suite of
phytohormones. The synthesis of abscisic acid (ABA) is an early
response to many abiotic stresses [[126]18, [127]41]. ABA metabolism is
under the control of the cytochrome P450 type enzyme CYP707A; in the
absence of this enzyme, the level of tolerance to drought stress is
enhanced [[128]54], which suggested that CYP707A plays a negative role
in abiotic stress through determining the endogenous level of ABA.
Here, the KEGG analysis showed that, in the transcriptome of
salinity-stressed plants, genes encoding cytochrome P450 type enzymes
were under-represented among the PR DTGs, but over-represented among
the CR and SR ones ([129]Fig. 5 and [130]S3 Dataset). ABA signaling in
the endodermis is known to represent an important mechanism underlying
the differential response of the A. thaliana PR and LR to salinity
[[131]18]. The transcription profiles of genes which are related to
synthesis of phytohormones and signaling, such as GRMZM2G096171 (type-A
response regulator, involved in cytokinin signaling), GRMZM2G031724
(GA2OX1, involved in gibberellin signaling) and GRMZM2G031065
(gibberellin receptor GID1L2, involved in gibberellin signaling),
contrasted between the PR and the CR/SR ([132]Fig. 6C), while those of
other genes, such as GRMZM2G140721 (CRK25, involved in brassinosteroid
signaling) responded differentially to salinity stress in each of the
three root types ([133]Fig. 6C).
Differentially transcribed TFs
It has been established that transcription factors (TFs) including NAC,
WRKY, MYB, AP2, bHLH, and C2H2 like zinc finger, HSF, and bZIP play
central roles in plant abiotic stress responses by regulating
downstream genes via specific binding to cis-acting elements in the
promoters of target genes. Overexpression of Arabidopsis SNAC-A genes
such as RD26 and ATAF1, and rice SNAC-A genes such as OsNAC6 and OsNAC5
can improve drought and salinity tolerance [[134]55, [135]56];
OsWRKY30, which is induced by PEG, NaCl and ABA treatment, confers the
osmotic stress tolerance in transgenic Arabidopsis [[136]57];
Constitutively overexpressing any one of DREB1A/B/C genes which belongs
to AP2/ERF family display significantly improved tolerance to freezing,
drought and high salinity in transgenic Arabidopsis [[137]58, [138]59].
Our transcriptome analysis revealed that 25 TFs were induced or
repressed in response to salt stress, and these genes were identified
from 13 different families including C2H2 like zinc finger, NAC,
AP2/ERF, LBD, C3H zinc finger, HSF, MYB, WRKY, bHLH, TALE, DBB, as well
as Trihelix and RAV ([139]Fig. 6D) and our results showed that most of
detected TFs were down-regulated after salt stress in all three
different maize roots. Previous studies showed that overexpression of
TaNAC2, OsNAC6 and OsNAC5 enhanced tolerances to drought, salt, and
freezing stresses [[140]6, [141]55, [142]56]. GRMZM2G127379 (NAC
family) was up-regulated by salinity in the CR and SR, but
down-regulated in the PR, as well as GRMZM2G001205 (C2H2 like zinc
finger). These trends may be due to the longer treatment period (24 h).
However, GRMZM2G011236 (ERF family) showed an opposite expression
pattern between PR and CR, SR ([143]Fig. 6D). Taken together, these
results reveal that the differential expression trends of these TFs,
especially GRMZM2G127379 (NAC family), in PR, SR, and CR may contribute
to the regulatory mechanism of salinity sensitivity in different roots.
PR accumulate more soluble sugar after salt stress
It has been determined previously that the accumulation of soluble
sugar in plants is correlated with increased tolerance to salt stress
[[144]4]. To test if the higher tolerance of PRs to salt stress is
attributed to the higher level of soluble sugars, we measured the
soluble sugar contents in PR, SR and CR. As shown in [145]S2 Fig., PR
accumulated a higher level of soluble sugar under NaCl treatment even
under normal conditions, which is consistent with the higher tolerance
of PR to salt stress.
Conclusion
The overall finding from these experiments was that the three maize
root types showed a distinct response to salinity stress, and that the
PR was more tolerant of the stress than was either the CR or the SR.
The RNA-Seq analysis identified over 400 genes as being differentially
transcribed in response to salinity stress, and the functional analysis
of these DTGs suggested that the most important genes involved in the
stress response were, apart from various upstream TFs, those dealing
with ROS, with glycosyl hydrolysis, with hormone signal perception and
transduction, and with the synthesis of compatible solutes such as
soluble sugars. The study revealed that the PR, SR, and CR each had its
own distinct transcriptional profile, which might underlie the
differential growth of each root type in the face of environmental
challenge.
Supporting Information
S1 Dataset. PCR primers used in this study.
(XLSX)
[146]Click here for additional data file.^ (11.6KB, xlsx)
S2 Dataset. Differentially Transcribed Genes (DTGs) (log2 Ratio ≥ 1,
FDR ≤ 0.001).
(XLSX)
[147]Click here for additional data file.^ (100.7KB, xlsx)
S3 Dataset. Genes regulated specifically in each root type after NaCl
treatment (log[2] Ratio ≥ 1, FDR ≤ 0.001).
(XLSX)
[148]Click here for additional data file.^ (28.5KB, xlsx)
S4 Dataset. KEGG pathway enrichment analysis of DTGs.
A: KEGG pathway enrichment analysis of DTGs in PR salt/PR. B: KEGG
pathway enrichment analysis of DTGs in CR salt/CR. C: KEGG pathway
enrichment analysis of DTGs in SR salt/SR.
(XLSX)
[149]Click here for additional data file.^ (22.9KB, xlsx)
S1 Fig. Validation of the differential transcription of 16 of the genes
identified by RNA Seq, based on qRT-PCR, which was made by SigmaPlot
11.0 program.
**: The transcript abundance in the nontreated samples and the salt
treatment samples differs significantly at p< 0.01.
(TIF)
[150]Click here for additional data file.^ (330.8KB, tif)
S2 Fig. Soluble sugar contents in PR, SR and CR under salt stress.
Soluble sugar content in each root type treated with 100 mM NaCl for 24
h. Each column represents an average of three replicates and bars
indicate SDs. ** and * indicate significant differences in comparison
with PR at P < 0.01 and P < 0.05, respectively.
(TIF)
[151]Click here for additional data file.^ (537.7KB, tif)
Data Availability
Sequencing data is available in the GEO Gene Expression Omnibus (GEO)
database under accession number GSE53995.
Funding Statement
Z.D. was supported by Grants from the National Natural Science
Foundation of China (No. 31222005 and No. 31270327) and the
“1000-talents Plan” from China for young researchers (11200095551303).
X.K. was supported by the China Postdoctoral Science Foundation (No.
2014T70631 and No. 2013M540543). The funders had no role in study
design, data collection and analysis, decision to publish, or
preparation of the manuscript.
References