Graphical abstract
graphic file with name fx1.jpg
[37]Open in a new tab
Highlights
* •
Rehydration can alleviate ROS-damaged sunflowers accumulated by
drought stress
* •
DEGs in hormone, MAPK, and secondary metabolite pathways affect
drought resistance
* •
bHLH025, NAC53, and SINAT3 may play key roles in sunflower drought
resistance
* •
ROS scavenging, MAPK signaling, SMs, and AS aid in sunflower
drought resistance
__________________________________________________________________
Plant biology; Plant physiology; Transcriptomics
Introduction
Drought is one of the most important abiotic stresses and significantly
affects the growth and production of crops, especially in arid and
semiarid regions.[38]^1^,[39]^2 During the process of long-term
adaptation to drought environments, plants have gained drought
resistance mechanisms such as drought escape, dehydration avoidance,
drought tolerance, and drought recovery.[40]^3 Drought adaptability is
the overall performance of crops during drought and rehydration
processes, including drought resistance and rapid recovery after
rehydration.[41]^4 Due to its severe effects on crops, knowledge of
drought response and adaptation is a critical issue that urgently needs
to be resolved.
Revealing the physiological and molecular mechanisms of plant responses
to drought stress remains a major challenge. Plants rely on a variety
of physiological mechanisms to respond to drought stress, including
photosynthesis,[42]^5 antioxidant systems,[43]^6 plant hormones,[44]^7
and secondary metabolism,[45]^8 in attempts to alleviate adverse
effects on plant growth, development, and yield. Reactive oxygen
species (ROS) promote rapid systemic signaling in plants under drought
stress.[46]^9 Plants can induce ROS production, which leads to
oxidative stress under drought conditions.[47]^10 Plants actively
maintain physiological water balance mainly through osmotic
regulation[48]^11 and activation of stress response pathways, including
plant hormone signaling and antioxidant defense systems for scavenging
ROS.[49]^12 Elevated ROS levels not only affect the proteome, metabolic
flux, and transcription factors (TFs) but also regulate the level and
function of plant hormones.[50]^13 ROS signals coupled with the action
of the mitogen-activated protein kinase (MAPK) cascade form a flexible
feedback loop that amplifies the effect of hormonal signals. As a
multifunctional signaling molecule, MAPK interacts with ROS and
hormones to shape acclimation responses to abiotic stress.[51]^9
In general, multi-omics is considered a useful approach to elucidate
the underlying molecular mechanisms of plant responses to drought
stress,[52]^14 aiding in identifying candidate genes involved in
different abiotic stress tolerances.[53]^15 For example, combined
transcriptomics and metabolomics analysis showed that TaSnRK2.10 could
enhance the drought resistance of wheat by regulating stomatal aperture
and the expression of drought-responsive genes and increase
phosphoenolpyruvate supply and promote the degradation of TaERD15 to
enhance the drought tolerance of wheat.[54]^16 Several genes related to
drought tolerance have been identified using transcriptome technology,
such as NCED3, NCED5, ABI1, and PYL4, which are involved in abscisic
acid (ABA) synthesis and signaling and play essential roles in
alleviating drought stress in sunflower (Helianthus annuus L.).[55]^17
As a posttranscriptional mechanism, alternative splicing (AS) can
further regulate the response to drought in plants.[56]^18^,[57]^19 For
example, a truncated isoform of the zinc-induced facilitator-like 1
transporter can mediate the drought tolerance of Arabidopsis.[58]^20 AS
occurring in the key circadian rhythm gene CCA1 has been shown to
mediate the maize response to drought stress.[59]^21 In a study of
sunflower, it was found that different genotypes responded similarly to
drought stress, and AS events resulted in their expression
differences.[60]^22
Sunflower is an annual plant belonging to the Asteraceae (Compositae)
family, which is the fourth largest source of vegetable oil worldwide
and has important economic and ornamental value.[61]^23^,[62]^24
Sunflower has strong adaptability to various abiotic stresses and can
grow under drought, heavy metals, salinity, and other environmental
stresses.[63]^23^,[64]^25^,[65]^26^,[66]^27 It is widely cultivated in
Inner Mongolia, Xinjiang, Gansu, and other arid or semiarid regions in
China. However, the increasing shortage of water resources has
seriously affected the yield and quality of sunflower seeds. Therefore,
understanding the underlying molecular mechanism of drought resistance
in sunflower would be helpful for improving its drought resistance,
with important practical significance for its production.
To date, studies on the drought resistance of sunflower have mainly
focused on drought tolerance identification, physiological trait
analysis, molecular markers, quantitative trait locus mapping, and
expression profiling.[67]^28^,[68]^29 Seventeen genes involved in the
sunflower response to abiotic stimuli have been found, among which nine
genes might be associated with responses to water-related
stimuli.[69]^23 Accordingly, many sunflower TF families, such as bHLH
(basic-helix-loop-helix), WRKY, and bZIP (basic leucine zipper), have
experienced multiple gene expansions, which may also play important
roles in the strong adaptability of sunflower to various
stresses.[70]^30^,[71]^31^,[72]^32 Nevertheless, there are few reports
on the drought adaptation and rehydration mechanism of sunflower, and
most studies only focus on drought stress. In this study, the potential
mechanism of sunflower leaves was analyzed under drought stress and
rehydration conditions by integrating physiological and transcriptomic
analyses. Our results provide a theoretical basis for elucidating the
molecular mechanism underlying drought adaptation in sunflower.
Results
Changes in ROS, antioxidant enzyme activity, and osmotic adjustment
substances
Sunflower seedlings under normal water supply (CK), mild drought stress
(MD), severe drought stress (SD), and rehydrated after severe drought
stress (WD) treatments were obtained as described in the [73]method
details ([74]Figure 1A). Based on the observation of phenotype,
sunflower seedlings displayed no obvious phenotypic change, which
further indicated that sunflower has strong drought resistance. To
further study the effects of drought and rehydration on sunflower
seedlings, we measured physiological indices, including ROS
accumulation, antioxidant enzyme activity, and osmotic adjustment
substances, of sunflower seedlings ([75]Figure 1). ROS accumulated
significantly in sunflower seedling leaves with the aggravation of
drought stress ([76]Figures 1B–1D). In addition, the contents of
[MATH: O2·¯ :MATH]
, H[2]O[2], and malondialdehyde (MDA) peaked in the SD treatment and
were significantly increased by 26.73%, 19.23%, and 26.59% compared to
CK, respectively. However, WD significantly reduced the ROS content
compared to SD (
[MATH: O2·¯ :MATH]
, H[2]O[2], and MDA contents were significantly reduced by 18.75%,
13.12%, and 15.09%, respectively), which returned to the CK or MD
level. With the aggravation of drought stress, the activities of
superoxide dismutase (SOD), catalase (CAT), and glutathione reductase
(GR) increased significantly, but the change in peroxidase (POD) was
not significant ([77]Figures 1E–1H). Rehydration treatment can
significantly increase the activities of these antioxidant enzymes in
sunflower seedling leaves. For instance, compared to SD, SOD and CAT
activities increased significantly by 10.09% and 9.50% in WD,
respectively.
Figure 1.
[78]Figure 1
[79]Open in a new tab
Experimental scheme and analysis of the physiological index
(A) A schematic plot representing the experimental design.
(B and C) Accumulation of reactive oxygen species (ROS), including
[MATH: O2·¯ :MATH]
and H[2]O[2].
(D) MDA content.
(E–H) Activities of antioxidant enzymes, including SOD, CAT, POD, and
GR.
(I–K) Contents of osmotic adjustment substances, including free
proline, soluble protein, and soluble sugar. CK, normal water supply;
MD, mild drought stress; SD, severe drought stress; WD, rehydrated
after severe drought stress. Different lowercase letters indicate
significant differences at the p value <0.05 level.
Osmotic adjustment substances significantly accumulated in sunflower
seedling leaves under drought stress ([80]Figures 1I–1K). Indeed, the
contents of free proline, soluble protein, and soluble sugar increased
significantly by 30.56%, 22.36%, and 43.99% in the MD treatment
compared to CK, respectively; with the aggravation of drought stress,
their contents continued to accumulate, increasing significantly by
49.53%, 32.44%, and 80.62%, respectively, in the SD treatment compared
to CK. Furthermore, rehydration treatment increased the content of
osmotic adjustment substances again. For instance, the free proline
content accumulated significantly by 16.21% under the WD treatment
compared to the SD treatment.
Expression profiles of sunflower leaves in response to drought stress
To further investigate the key genes of sunflower in response to
drought and rehydration, we performed transcriptomic analysis using
sunflower leaves from four treatments, as described in the [81]method
details (CK, MD, SD, and WD). As shown in [82]Table S1, 12 cDNA
libraries (three biological replicates per treatment) were constructed.
Approximately 515.15 million clean data points were obtained after
quality control and filtering. The Q30 value of each library was
approximately 94%. The mapping ratio ranged from 91.30% to 92.00%, of
which 92.35%–94.14% mapped uniquely, meeting the needs for subsequent
transcriptome analysis. Both the number of expressed genes and their
expression values were characterized in the samples. Across the 12
tested samples, ∼44.24% of genes were barely expressed, ∼18.16% of
genes were expressed at low levels (0 < FPKM ≤ 1), ∼33.77% of genes
were expressed at moderate levels (1 < FPKM ≤ 50), and ∼ 3.83% of genes
were expressed at high levels (FPKM >50) ([83]Figure S1).
SD affects gene expression more greatly than MD
First, 2,589 differentially expressed genes (DEGs) were identified
between any two treatments (MD/CK, SD/CK, WD/CK, SD/MD, WD/MD, and
WD/SD) based on DESeq with thresholds | log[2] (fold change) | > 1 and
p value <0.05. The number of upregulated DEGs was generally lower than
that of downregulated DEGs across all comparisons. For instance, there
were 203 DEGs (66 up- and 137 downregulated), 1,297 DEGs (377 up- and
920 downregulated), and 1,053 DEGs (244 up- and 809 downregulated)
identified in MD, SD, and WD compared to CK, respectively
([84]Figure 2A, MD/CK, SD/CK, and WD/CK). These genes may be regarded
as drought-related DEGs. Moreover, 469 DEGs (230 up- and 239
downregulated) were identified in WD compared to SD (WD/SD). Overall,
it seems that the expression profiles of MD and CK were more similar,
as were those of SD and WD, which was also confirmed by hierarchical
clustering of the expression profiles of all the DEGs ([85]Figure 2B).
These results indicate that SD treatment may affect gene expression
more than MD treatment.
Figure 2.
[86]Figure 2
[87]Open in a new tab
Comparative analysis of DEGs among three comparisons
(A) DEG numbers of six comparisons, including up- and downregulated
DEGs.
(B) Expression profiles for all 2,589 DEGs.
(C) Venn comparisons of up- and downregulated DEGs among three
comparisons (MD/CK, SD/CK, and WD/CK).
(D) Differentially expressed TFs identified among the three
comparisons.
(E) Venn comparison of TF family numbers among the three comparisons.
Then, we compared upregulated DEGs among three comparisons (MD/CK,
SD/CK, and WD/CK) and found that only 4 upregulated DEGs were shared in
all three comparisons ([88]Figure 2C). For instance, expression of the
sunflower PMEI7 gene was continuously upregulated among CK, MD, SD, and
WD. The expression of the CSLA9 gene was upregulated with the
aggravation of drought and downregulated after rehydration, but its
expression level was still higher than that in CK. For comparison of
downregulated DEGs among these three comparisons, there were 55
downregulated DEGs shared in all ([89]Figure 2C). Among them, protein
kinases (PKs), such as SRK2E, FLS2, and MAPKKK5, tended to show
downregulated expression with the aggravation of drought and
upregulated expression after rehydration. Similar trends were also
observed for other genes, such as TFs (ERF280, ERF252, ERF220 (also
named DREB1D), ERF040, WRKY53, WRKY27, and ZAT8), calcium-binding
proteins (CML38, CML19, and CAMBP25), stress-associated proteins (SAP5
and SAP12), RDUF1, and LOX31. Meanwhile, we further conducted a
comparison of WD/SD with the previous three comparisons
([90]Figure S2). It was found that 4 downregulated DEGs in WD/SD were
shared with those upregulated DEGs in SD/CK and WD/CK, and 9
upregulated DEGs were shared with their downregulated DEGs in SD/CK and
WD/CK. Among them, there were 1 TF (WRKY41), 9 genes, including YUC10,
U85C2, ALP1, PUB21, FLS2, GRXC9, SLD1, PUB23, and PMI10, and 3
unannotated genes.
TFs play an important role in plants exposed to various abiotic
stresses. In total, 28 TF families and 330 members were identified from
all 1,781 DEGs among the three comparisons (MD/CK, SD/CK, and WD/CK).
As shown in [91]Figure 2D, the top 10 TF families exhibiting different
expression levels between any two different treatments were ERF, WRKY,
MYB, NAC, C2H2, bHLH, GRAS, C3H, G2-like, and HSF. The differentially
expressed TFs presented the largest number in SD/CK among the three
comparisons. There were seven shared TF families, namely, ERF, WRKY,
MYB, NAC, C2H2, GRAS, and G2-like families, among all three comparisons
([92]Figure 2E), which may further indicate that these TFs are involved
in sunflower resistance to drought stress.
Furthermore, we conducted an analysis of the sunflower ERF family and
found 288 members, much greater than those in Arabidopsis (122) and
rice (138) ([93]Figure 3A).[94]^33 Many of the members of the ERF gene
family were derived from segmental (143 genes) or tandem duplication
(59 genes) events ([95]Figure 3B; [96]Table S2). We compared the ERF
gene expression profiles of these duplicate pairs under different
treatments, and the results showed that the expression profiles of most
segmental duplicate gene pairs (101/122) were significantly divergent
(correlation test, p > 0.05), but a larger proportion of tandem
duplicate genes (15/63) were still significantly correlated after
duplication events (correlation test, p < 0.05, [97]Table S3). These
results may indicate neofunctionalization after ERF gene duplications.
Then, we compared differentially expressed ERF genes among the three
comparisons (15 in MD/CK, 55 in SD/CK, and 44 in WD/CK); there were 62
ERF members in total and 4 members (HaERF252, HaERF220, HaERF040, and
HaERF280) shared among them ([98]Figure 3C). We also examined the
expression profiles of these 62 ERF members and classified them into
five clusters based on expression ([99]Figure 3D). Members of cluster I
had high expression in CK and low expression in the other treatments.
Cluster V exhibited low expression in CK and high expression in the
other treatments, especially SD. The remaining clusters displayed high
expression in CK and MD but low expression in SD and WD.
Figure 3.
[100]Figure 3
[101]Open in a new tab
Characteristic analysis of ERF family members and RT-qPCR for RNA-seq
validation
(A) ERF numbers in Arabidopsis, rice, and sunflower.
(B) Schematic representation of collinear relationships of sunflower
ERF genes. Blue and gray lines indicate sunflower ERFs and all genes
resulting from segmental duplications. Red lines in chromosome blocks
present gene density on each chromosome.
(C) Comparison of differentially expressed ERFs among MD/CK, SD/CK, and
WD/CK.
(D) Expression profile of differentially expressed ERFs among the four
treatments.
(E) Quantitative analysis of gene expression levels for ten selected
DEGs using RT-qPCR. Different lowercase letters indicate significant
differences at the p value <0.05 level.
Finally, the reliability of the RNA-sequencing (RNA-seq) results was
verified by RT-qPCR analysis among the four treatments in sunflower
([102]Figure 3E). Similar expression trends were observed for the 10
selected genes between RNA-seq data and RT-qPCR, including LOX31,
E1314, RDUF1, ALP1, NIP1, INO1, SAP8, FLS2, PLA14, and GRXC9, showing a
significantly positive correlation (r = 0.66, p value = 1.20 × 10^−8)
for all the 10 genes among the different treatments.
Functional annotation and classification of DEGs
To explore functional differences of DEGs under drought stress and
rehydration, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and
Genomes (KEGG) enrichment analyses were performed by hypergeometric
tests to identify potential biological functions of these DEGs. First,
there were 158, 274, and 296 significantly enriched biological
processes (BP) in the three comparisons (MD/CK, SD/CK, and WD/CK),
among which 34 BPs were shared, including metabolic process
(S-glycoside, glycosinolate, glucosinolate, and isoprenoid), stimulus
response to reactions (response to water deprivation, wounding,
jasmonic acid (JA), and brassinosteroid (BR)), regulation of
transporter activity, and seedling development ([103]Figure 4B;
[104]Table S4). Moreover, there were 22 significantly enriched BPs
highly related to drought stress in both SD/CK and WD/CK, and 16 of
them were shared in these two comparisons ([105]Figure 4B).
Additionally, two BPs, namely, regulation of chlorophyll metabolic
process and auxin metabolic process, were significantly enriched only
in MD/CK, indicating their potential roles in sunflower responses to
moderate drought stress. There were 5 and 4 unique significantly
enriched BPs in SD/CK and WD/CK, respectively. The former includes
proline metabolic process, water homeostasis, cellular response to
water deprivation, response to cytokinin, and calcium ion homeostasis;
the latter includes regulation of hormone metabolic process, regulation
of lipid biosynthetic process, regulation of hormone biosynthetic
process, and BR-mediated signaling pathway. Finally, there were only
106 significantly enriched BPs in the WD/SD comparisons. In particular,
the significantly enriched BPs, water transport, response to hydrogen
peroxide, cytokinin metabolic process, response to ROS, regulation of
chlorophyll metabolic process, auxin metabolic process, calcium ion
homeostasis, and response to salicylic acid, were shared with the
previous three comparison groups, which may play important roles in the
rehydration process ([106]Table S4).
Figure 4.
[107]Figure 4
[108]Open in a new tab
GO term and KEGG pathway enrichment analyses of DEGs identified in
three comparisons
(A) Venn diagrams for significantly enriched BPs (biological processes)
and KEGG pathways for DEGs among different comparisons.
(B and C) show BP and KEGG pathway enrichment analyses of DEGs in
MD/CK, SD/CK, and WD/CK. The color scale in the middle represents the
-lg (p value).
Based on KEGG enrichment analysis, there were 5, 11, and 12 pathways
that were significantly enriched in MD/CK, SD/CK, and WD/CK
([109]Figure 4A; [110]Table S5), some of which have been reported to be
drought-related pathways. For instance, metabolic pathways and
biosynthesis of secondary metabolites (SMs) were shared enriched
pathways among the three comparisons. In MD/CK, the other 3 enriched
pathways were diterpenoid biosynthesis, glycerophospholipid metabolism,
and RNA polymerase ([111]Figure 4C). Among them, RNA polymerase was
associated with MD/CK, whereas glycerophospholipid metabolism was
shared by MD/CK and WD/CK. In SD/CK and WD/CK, there were 6 other
common pathways that may be involved in the response to severe drought
and rehydration, including plant hormone signal transduction,
plant-pathogen interaction, MAPK signaling pathway, biosynthesis of
unsaturated fatty acids, alpha-linolenic acid (ALA) metabolism, and
phenylpropanoid biosynthesis. The phosphatidylinositol signaling system
and fatty acid metabolism in SD/CK and ether lipid metabolism, linoleic
acid metabolism, and carotenoid biosynthesis in WD/CK were also
enriched ([112]Figure 4C). Additionally, there were 8 significantly
enriched pathways WD/SD, among which 4 pathways were unique, namely, BR
biosynthesis, ascorbate and aldarate metabolism, glutathione
metabolism, and lysine biosynthesis ([113]Table S5).
Clustering analysis of functionally related DEGs
The 1,781 DEGs from the three comparison groups (MD/CK, SD/CK, and
WD/CK) were subdivided into 8 clusters through K-means clustering
analysis based on their expression patterns ([114]Figure 5A). Among
these clusters, cluster 3 contained 103 genes displaying a negative
response; cluster 4 contained 342 genes that displayed a positive
response to soil water deficit. In cluster 3, significantly enriched
BPs included localization (glucose import, glucose transmembrane
transport, hexose transmembrane transport, monosaccharide transmembrane
transport, carbohydrate transmembrane transport, and carbohydrate
transport) and response to stimulus (cytokinin-activated signaling
pathway, cellular response to cytokinin stimulus, and response to
cytokinin) ([115]Table S6). In cluster 4, DEGs significantly enriched
in BPs mainly included localization (organic acid transport, carboxylic
acid transport, organic anion transport, water transport, and fluid
transport) ([116]Table S6). Additionally, there were five and six
significantly enriched KEGG pathways identified for clusters 3 and 4,
respectively ([117]Figure 5B). For instance, metabolic pathways and
plant hormone signal transduction were shared enriched pathways in
clusters 3 and 4. Moreover, betalain biosynthesis, biosynthesis of
various SMs, and phagosomes were specifically enriched in cluster 3,
and biosynthesis of SMs, phenylpropanoid biosynthesis, stilbenoid,
diarylheptanoid and gingerol biosynthesis, and tryptophan metabolism
were specifically enriched in cluster 4.
Figure 5.
[118]Figure 5
[119]Open in a new tab
K-means analysis of DEGs identified in three comparisons
(A) Eight clusters reveal specific expression trends. The number in the
lower left corner indicates the number of genes in this cluster.
(B) KEGG enrichment analysis for genes from eight clusters.
(C and D) show the coexpression networks for clusters 3 and 4.
We also conducted expression correlation analysis for DEGs from
clusters 3 and 4 and regarded any two genes whose Pearson’s correlation
coefficient (r) value of expression values was greater than 0.9 and p
value was less than 0.05 as coexpressed genes. Two gene coexpression
networks were constructed for clusters 3 and 4 ([120]Figures 5C and
5D). In cluster 3, the top five connected genes were TUBB1, TUBB2,
NPF5.2, BGH3B, and GDL85, and the top connected TF was KAN4 (G2-like
family) ([121]Figure 5C). In cluster 4, more genes were located in the
center of the network, such as Phox/Bem1p, AAP7, FB119, OFP2, UGT85C2,
NPF5.1, and USPAL ([122]Figure 5D). ABF2 (bZIP family) and HAT22
(HD-ZIP family) were the two TF members with the most connections in
the network, suggesting their importance in response to drought stress.
Genes related to plant hormone signal transduction, MAPK, and secondary
metabolites
In total, 93 DEGs from plant hormone signal transduction were detected
based on KEGG pathway enrichment analysis ([123]Figure 6B), especially
involving the auxin, cytokinin, ABA, and BR signaling pathways. Eight
DEGs displayed a positive response to drought stress and rehydration,
including LECRKS4, ABF2, IAA1, SAUR50, PHL8, P2C24, SAPK2, and SAPK3.
Nine DEGs displayed a negative response, including LECRK1, AUX22,
IAA17, two GH3.1s, HHO3, KAN4, ARR17, and XTH22. Ninety-eight DEGs
involved in the MAPK signaling pathway were detected, and 7 genes
displayed a positive response to drought and rehydration, including
CATA, WRKY69, RBOHA, P2C24, P2C06 (Os01g0583100), SAPK2, and SAPK3
([124]Figure 6C). Twenty-eight DEGs involved in ROS-scavenging systems
were detected, the majority of which were continuously upregulated or
downregulated with drought stress aggravation. However, only five genes
(PER52, CSE, MED37C, CATA, and BT1) showed significant changes after
rehydration, which may indicate their potential function in response to
drought stress and rehydration ([125]Figure 6A). DEGs acting as
transporters or involved in SMs were also characterized, such as ABC
transporters and genes from the biosynthesis of unsaturated fatty acids
or ALA metabolism. Three ABC transporter genes (ABCB15, ABCG22, and
ABCG11) and one gene (KCR2) involved in the biosynthesis of unsaturated
fatty acids displayed a positive response under drought and rehydration
([126]Figures 6D and 6E). Three genes, CEQQRH, 4CLL5, and DOX1,
displayed a negative response in the ALA metabolism pathway
([127]Figure 6F). All the previously described genes may have certain
roles in the response to drought stress and rehydration.
Figure 6.
[128]Figure 6
[129]Open in a new tab
Expression profiles of DEGs
(A) ROS pathway.
(B) Plant hormone signal transduction.
(C) MAPK signaling pathway.
(D) ABC transporters.
(E) Biosynthesis of unsaturated fatty acids.
(F) Phenylpropanoid biosynthesis.
Characterization of AS events and comparative analysis of DEGs and DSGs
AS also plays important roles in responding to environmental stress.
Hence, we conducted AS analysis to identify candidate genes that may be
involved in drought resistance. In total, 685, 780, and 662
differentially alternative splicing (DAS) events were identified in
MD/CK, SD/CK, and WD/CK, which corresponded to 502, 578, and 498
differentially spliced genes (DSGs), respectively ([130]Figure 7A).
Then, all these DAS events were further subdivided into 5 patterns,
including differentially alternative splicing (DAS) events of
alternative 3’ splice site (A3SS), alternative 5’ splice site (A5SS),
retained intron (RI), mutually exclusive exons (MXE) and skipped exon
(SE). Among these patterns, A3SS and SE accounted for the largest
proportion ([131]Figure 7B). To further identify the potential
functions for these DSGs, GO and KEGG enrichment analyses were
performed, and 19 BPs, such as histone modification and phospholipid
biosynthetic and metabolic process, were significantly enriched among
all three comparisons ([132]Table S7). In MD/CK and SD/CK, the
phosphatidylinositol biosynthetic process, membrane lipid biosynthetic
process, regulation of cell shape, and regulation of ABA biosynthetic
process were significantly enriched. The GO terms regulation of
response to osmotic stress, positive regulation of response to water
deprivation, and lipid transport were enriched in SD/CK and WD/CK.
Water homeostasis was specifically enriched in MD/CK, and regulation of
protein kinase activity, alternative mRNA splicing via the spliceosome,
and response to calcium ions were specifically enriched in SD/CK. Two
significantly enriched pathways, the metabolic pathway and biosynthesis
of SMs, were shared among the three comparisons ([133]Table S8).
However, only one enriched KEGG pathway was shared by MD/CK and SD/CK
(lysine degradation). Glycosylphosphatidylinositol-anchor biosynthesis
and arginine and proline metabolism were specifically enriched in the
MD/CK group. Eight KEGG pathways were specifically enriched in the
SD/CK comparison. For instance, cysteine and methionine metabolism,
pentose phosphate pathway, sulfur metabolism, and terpenoid backbone
biosynthesis. In WD/CK, 3 KEGG pathways were enriched, including
2-oxocarboxylic acid metabolism, starch and sucrose metabolism, and
other glycan degradation ([134]Table S8).
Figure 7.
[135]Figure 7
[136]Open in a new tab
Effect of drought stress on alternative splicing events in sunflower
seedlings
(A) Numbers of differentially alternative splicing (DAS) events and
corresponding genes (DSGs).
(B) Distribution of different types of AS events.
(C) Venn diagrams showing overlapping genes between DEGs and DSGs.
(D) TFs and PK genes among DSG-DEG overlapping genes are shown.
To elucidate the relationship between AS and transcriptional
regulation, we conducted comparison analysis using the genes that
experienced DAS and DEGs in response to drought stress. In total, we
found 1, 13, and 6 overlapping genes in the three comparisons
([137]Figure 7C). Among them, two TFs (bHLH025 and NAC53) and one PK
(SINAT3) were identified, which may play central roles in the response
to severe drought stress ([138]Figure 7D). Interestingly, bHLH025 and
SINAT3 were also shared between DSGs and DEGs in WD/CK.
Discussion
Drought stress can lead to an insufficient water supply for plants and
affect their normal growth.[139]^2 For instance, it can increase the
production of
[MATH: O2·¯ :MATH]
, H[2]O[2], ·OH, and other ROS, and the accumulation of ROS can
seriously damage plants by increasing lipid peroxidation, protein
degradation, and even cell death.[140]^34^,[141]^35 MDA is a product of
cell membrane lipid peroxidation and is an important indicator of the
damage to plasma membrane system.[142]^34^,[143]^35To prevent such
oxidative damage, plants have evolved a complex antioxidant defense
system, such as SOD, POD, CAT, and other enzymes.[144]^36^,[145]^37 In
this study, drought stress increased the accumulation of ROS, such as
[MATH: O2·¯ :MATH]
and H[2]O[2], in sunflower seedlings ([146]Figure 1). Our results
showed that rehydration treatment significantly reduced the
[MATH: O2·¯ :MATH]
, H[2]O[2], and MDA contents and increased the activities of SOD, POD,
and CAT in sunflower seedlings under drought stress, indicating that
rehydration effectively mitigates drought-induced ROS damage by
increasing the activity of antioxidant enzymes.
Osmotic adjustment has been considered an important physiological
mechanism involved in acclimation to drought stress.[147]^38 The
physiological parameters related to osmotic adjustment mainly include
free proline, soluble protein, soluble sugar, and osmotic
potential,[148]^3^,[149]^39 the contents of which increase to protect
cell structure and function by maintaining cell filling or other
physiological mechanisms under drought stress.[150]^40 Similar to the
study of Wu et al. (2022) in sunflower[151]^41 and Khan et al.
(2019)[152]^6 in rapeseed, the contents of free proline, soluble
protein, and soluble sugar were significantly positively related to
drought resistance in our study ([153]Figure 1). After rehydration, the
contents of osmotic adjustment substances continued to increase,
especially free proline. These results indicate that free proline
correlated significantly with drought resistance and recovery in
sunflower. Based on the previously described physiological results,
sunflower seedlings can regulate their antioxidant enzyme activity, ROS
content, and osmotic adjustment when responding to drought stress, and
rehydration can effectively alleviate this stress damage.
Under drought stress, plants show a variety of regulatory mechanisms
coupled with extensive gene expression changes,[154]^42^,[155]^43 and
the number of DEGs reflects the response intensity of the crops. In our
study, more DEGs were detected under severe stress than under mild
stress and rehydration ([156]Figure 2), indicating that the number of
DEGs increased with decreasing water content in potting soil. Plant
adaptation to drought stress is closely related to TFs, especially in
highly resistant plants, and TFs interact with the promoter-specific
elements of resistance genes. Some TFs respond to drought stress by
regulating downstream genes.[157]^44 Previous studies have shown that
many TF families play important roles in enhancing drought resistance
and improving water use efficiency.[158]^45^,[159]^46 According to our
results, the ERF, WRKY, MYB, NAC, C2H2, GRAS, and G2-like TF families
are important in sunflower under drought stress and rehydration. These
TFs function as activators or repressors to regulate target genes and
form a transcriptional regulatory network in response to abiotic stress
response and tolerance.[160]^47 The AP2/ERF family is one of the
largest plant-specific TF families,[161]^48 and numerous studies have
shown that genes belonging to the AP2/ERF family can improve drought
tolerance in Arabidopsis[162]^49 and tobacco.[163]^50 Many ERF family
members are differentially expressed when exposed to drought stress,
and many duplication events in the ERF family may contribute to
resistance to drought stress in sunflower seedlings.
As shown in [164]Figure 4, the DEGs were significantly enriched in the
signaling pathway of hormones (such as ABA, JA, and salicylic acid),
components of the redox system and photosynthetic metabolism under
different degrees of drought stress and rehydration, consistent with
previous studies in sunflower.[165]^51 Furthermore, the proline
metabolic process was only significantly enriched under severe drought
stress, and the response by ROS was significantly enriched under severe
drought stress and rehydration compared to CK ([166]Figure 4), which
further explains the results of our physiological analysis.
Pathway enrichment analyses of both DEGs and DSGs showed that metabolic
pathways and biosynthesis of SMs may be involved in the drought stress
response ([167]Figure 4; [168]Table S8). In our study, the biosynthesis
of unsaturated fatty acids, plant hormone signal transduction, and ALA
metabolism pathways were significantly enriched under severe drought
stress and rehydration compared to CK ([169]Figure 4). Accordingly,
polyunsaturated fatty acids are associated with plant adaptation to
abiotic stress.[170]^52 ALA is also a precursor of JA, which can
alleviate damage due to drought stress through ROS scavenging, stomatal
closure, and other multiple strategies.[171]^53 In addition, numerous
DEGs were identified as being involved in plant hormone signal
transduction pathways, especially ABA signaling pathways
([172]Figure 6). ABA plays a key role in the response to drought
stress[173]^54^,[174]^55 and can effectively improve water use
efficiency and drought resistance.[175]^56 Among the core components of
ABA signaling, overexpression of PYL2, PYL8, PP2C, SnRK2, and ABF
enhances drought resistance in plants.[176]^57 In our study, P2C24,
SAPK2, and SAPK3 and an ABF gene were upregulated with increasing
drought severity and then downregulated after rehydration, which
indicates that they play positive regulatory roles in the ABA signaling
pathway and drought resistance. Similar results have been reported in
sorghum and ginseng.[177]^58^,[178]^59 Notably, the ABA pathway plays
an important role in the MAPK signaling pathway and ROS-scavenging
systems.[179]^60 MAPK is a signaling molecule that interacts with ROS
and hormones to form adaptive responses.[180]^9 As depicted in
[181]Figure 6, 7 genes (CATA, WRKY69, RBOHA, P2C24, P2C06
(Os01g0583100), SAPK2, and SAPK3) in the MAPK signaling pathway and 5
genes (PER52, CSE, MED37C, CATA, and BT1) in ROS-scavenging systems
showed a positive response to drought stress, which may indicate their
potential function in response to drought stress and rehydration.
Hence, as important members of the plant stress signal network, plant
hormones, MAPK, and ROS crosstalk with each other to alleviate drought
stress in sunflower.
It has been reported that AS plays an important role in coping with
various environmental stresses by affecting transcript abundance,
increasing transcriptome diversity, and enhancing the functional
diversity of proteins.[182]^61^,[183]^62^,[184]^63 Accordingly, AS and
transcription regulation functions occur relatively independently under
abiotic stress.[185]^64^,[186]^65 The AS of genes in tea leaves was
triggered by drought stress and enhanced the transcriptome adaptation
in response to stress.[187]^64 In our study, more AS events occurred
under severe drought than under mild drought or rehydration
([188]Figure 7A), indicating that the number of AS events increases
along with drought severity and decreases after rehydration. A3SS and
SE were the most abundant AS patterns of the total AS events under the
different water conditions ([189]Figure 7B), consistent with a previous
study in soybean.[190]^61 Hence, these changes may play pivotal roles
in the response to drought stress. To date, the biological functions of
AS in plants have been defined in many genes, especially those involved
in the regulation of stress responses, such as TFs.[191]^64 In
comparisons between DSGs and DEGs, we found only a few shared TFs, such
as two TFs (bHLH025 and NAC53) and one PK (SINAT3), indicating that
they may function in the response to drought stress through both
transcriptional and posttranscriptional regulation.
In conclusion, we propose a model for the underlying mechanism of
sunflower seedlings responding to varying levels of drought stress
([192]Figure 8). First, drought stress can cause the accumulation of
ROS (such as
[MATH: O2·¯ :MATH]
and H[2]O[2]). As a secondary signal, ROS can interact with hormone
signals and the MAPK cascade to activate drought-related TFs (such as
bHLH025 and NAC53). Next, the TFs further activate the expression of
downstream-related genes, such as ROS scavenging, SMs, and other genes,
and extensively promote the drought resistance of sunflower. Moreover,
AS may also play important roles in drought resistance. Overall, our
results not only deepen insight into the underlying mechanism of
drought stress and rehydration in sunflower seedlings but also provide
a theoretical basis for genetic breeding and water-efficient irrigation
of this crop.
Figure 8.
[193]Figure 8
[194]Open in a new tab
Possible response mechanisms of sunflower seedlings under drought
stress
Limitations of the study
Through physiological measurement and transcriptomics for sunflower, we
have identified some potential genes positively or negatively
responding to drought stress and rehydration and highlighted the
underlying regulatory mechanisms. However, the biological function of
these genes remained to be verified through genetic modification.
STAR★Methods
Key resources table
REAGENT or RESOURCE SOURCE IDENTIFIER
Biological samples
__________________________________________________________________
Seeds of sunflower cultivar MH8361 ZHONG KE MAO HUA company, Hebei,
China N/A
__________________________________________________________________
Chemicals, peptides, and recombinant proteins
__________________________________________________________________
Ethylenedinitrilotetraacetic acid (EDTA) Sigma-Aldrich Cat#E6758
Hydroxylamine Hydrochloride Sigma-Aldrich Cat#[195]HX0770
2-Thiobarbituric acid Sigma-Aldrich Cat#T5500
Sulfosalicylic acid Sigma-Aldrich Cat#390275
Ninhydrin Sigma-Aldrich Cat#NX0403
Coomassie Brilliant Blue G-250 Sigma-Aldrich Cat#1.15444
Guaiacol Sigma-Aldrich Cat#G5502
Nitro-blue tetrazolium Sigma-Aldrich Cat#N6876
Trichloroacetic acid Sigma-Aldrich Cat#T6399
4-aminobenzenesulfonic acid Sigma-Aldrich Cat#09180
1-naphthylamine Sigma-Aldrich Cat#N9005
Potassium iodide (KI) Sigma-Aldrich Cat#P2963
Polyvinylpyrrolidone Sigma-Aldrich Cat#81440
Ethanol Sigma-Aldrich Cat#E9508
Anthrone Sigma-Aldrich Cat#52445
Acetic acid Sigma-Aldrich Cat#A6283
Toluene Sigma-Aldrich Cat#TX0750
2-Mercaptoethanol Thermo Fisher Cat#21985023
Acetone Thermo Fisher Cat#T_702A060015
Anhydrous ethanol Thermo Fisher Cat#E/0550DF/15
__________________________________________________________________
Critical commercial assays
__________________________________________________________________
Total Miniprep Kit Axygen Cat#RH175821
RNAprep Pure Plant Plus Kit Tiangen Cat#DP452
PrimeScript™ 1st Strand cDNA Synthesis Kit Takara Cat#6210B
__________________________________________________________________
Deposited data
__________________________________________________________________
Raw data generated in this study This study NCBI Sequence Read
Archive:PRJNA869183. (the SRA accession NO. for each sample is listed
in [196]Table S1).
__________________________________________________________________
Oligonucleotides
__________________________________________________________________
Primers used are shown in [197]Table S9 This study N/A
__________________________________________________________________
Software and algorithms
__________________________________________________________________
Cutadapt N/A [198]http://cutadapt.readthedocs.io/en/stable/
HISAT2 Kim et al., 2019[199]^66
[200]http://ccb.jhu.edu/software/hisat2/index.shtml
DESeq Wang et al., 2010[201]^67
[202]https://www.bioconductor.org/packages//2.10/bioc/html/DESeq.html
ASprofile Florea et al., 2013[203]^68 N/A
rMATS Shen et al., 2014[204]^69 N/A
Metware N/A [205]https://cloud.metware.cn
Cytoscape v.3.8.2 Otasek et al., 2019[206]^70 N/A
Primer3 software Untergasser et al., 2012[207]^71
[208]http://primer3.ut.ee
SPSS software IBM V19.0.0
[209]Open in a new tab
Resource availability
Lead contact
Further information and requests for resources should be directed to
and will be fulfilled by the lead contact, Ake Liu
([210]akeliu@126.com).
Materials availability
This study did not generate new unique reagents.
Experimental model and study participant details
The sunflower cultivar MH8361 was used as the material for drought
stress experiments. The potting soil (Pindstrup Mosebrug, Ltd.,
Denmark) used contained more than 98% sphagnum moss and less than 2%
impurities. The soil was weighed and quantified before seeds were
planted, and 2 seeds were sown in each pot. Then, they were cultured in
a growth chamber (25°C, 16 h light/20°C, 8 h dark cycle). Water was
replenished regularly every day to ensure normal seedling growth.
Method details
Drought stress treatments
The seedlings were randomly divided into four groups with three
replicates each after the sixth true leaves fully expanded. A natural
progressive drought was imposed by withholding watering based on daily
measurements of pot weight. The drought stress treatments included a
normal water supply (CK, 75 ± 5% water content), mild drought stress
(MD, 40 ± 5% water content) and severe drought stress (SD, 20 ± 5%
water content) treatments applied for five days, and another treatment
(WD, water content of 75 ± 5%) in which the plants were rehydrated with
a normal water supply for 2 days after 3 days of severe drought stress
([211]Figure 1A). During this process, the pots were weighed regularly
(every day) to maintain the water content in the four different drought
treatments. After the experimental treatments, two leaves of the last
fully expanded sunflower leaves were collected, immediately frozen in
liquid nitrogen, and stored at -80°C until further physiological and
RNA sequencing.
Detection of physiological indicators
The production rate of
[MATH: O2·¯ :MATH]
was detected by the hydroxylamine method.[212]^72 0.1 g of leaf tissue
was macerated in a mortar with 65 mM phosphate buffer (pH 7.8) and
ground fully. After centrifuging at 12000 ×g for 15 min, supernatant
was mixed with 65 mM phosphate buffer (pH 7.8) and 10 mM hydroxylamine
chloride, and incubated at 25°C for 20 min. Then, the reaction mixture
was added with 17 mM 4-aminobenzenesulfonic acid and 7 Mm
alpha-naphthylamine, and mixed well. After incubating at 30°C for
30 min, absorbance was measured at 530 nm using a spectrophotometer.
The content of H[2]O[2] was obtained based on the acetone extraction
method.[213]^28 0.5 g of leaf tissue was macerated in a mortar with
0.1% (w/v) trichloroacetic acid solution. After centrifuging at
12000 × g for 12 min, supernatant was mixed with 0.1 M potassium
phosphate buffer (pH 7.0) and KI solution; absorbance was measured at
390 nm using a spectrophotometer. The malondialdehyde (MDA) content was
measured by the thiobarbituric acid chromogenic method.[214]^73
Briefly, 0.2 g of penultimate leaves, a small amount of quartz sand,
and 0.1% trichloroacetic acid were mixed together and ground in an ice
bath. Then, 0.5% thiobarbituric acid was added to the mixture, followed
by a boiling-water bath for 15 min, and then centrifuged at 1000 × g
for 15 min after cooling to room temperature. The absorbance values of
the supernatant at 532 nm and 600 nm were measured using 0.5%
thiobarbituric acid as the control.
Approximately 0.1 g of penultimate leaves, a small amount of quartz
sand, and 50 mM phosphate buffer (containing 0.1 mM
ethylenediaminetetraacetic acid and 1% polyvinylpyrrolidone) were mixed
together and ground in an ice bath. Then, the mixture was centrifuged
at 15000 × g for 15 min at 4°C. The supernatant was used to determine
the amounts of SOD, CAT, POD, GR and soluble proteins. The activities
of SOD and CAT were detected by the nitroblue tetrazolium
photoreduction method and UV spectrophotometry.[215]^74 The activities
of POD were assessed by the guaiacol colorimetric method.[216]^75 The
activity of GR was detected based on the method of Foyer and Halliwell
(1976).[217]^76 Soluble proteins were determined using Coomassie
Brilliant Blue G-250 colorimetry.
To determine soluble sugar content, 0.1 g of penultimate leaves was
accurately weighed in a centrifuge tube with 80% ethanol solution.
After water bath for 30 min at 80°C and centrifuging the extract at
3500 × g for 10 min, the supernatant was transferred into a new
centrifuge tube. Then 80% ethanol solution was added to the
precipitation, repeat extraction as above. The supernatant was for the
determination soluble sugar content by anthrone-sulfuric acid
method.[218]^77 The detection of free proline was performed by the acid
ninhydrin method.[219]^77 Briefly, 0.2 g of fresh leaves were placed in
a mortar and ground with a small amount of quartz and 3% sulfosalicylic
acid on ice. Placed in a boiling water bath for10 min. After cooling,
the grinding liquid was centrifuged at 2000 × g for 10 min.
Supernatant, glacial acetic acid and acid ninhydrin were mixed and then
placed in a boiling water bath for 30 min. After cooling to room
temperature, toluene was added to the mixture in the dark for
extraction. The absorbance of the toluene phase (red) was measured at
520 nm using a spectrophotometer.
RNA isolation and transcriptome sequencing
Total RNA was extracted from the collected samples of all groups using
a total Miniprep Kit (Axygen Bioscience, USA) according to the
manufacturer’s instructions. RNA integrity and purification were
determined using an Agilent 2100 BioAnalyzer (Agilent Technologies,
USA). High-quality RNA with an RNA integrity number of > 8 and of
sufficient quantity was used to construct a sequencing library. After
completion of library construction, PCR amplification was performed to
enrich the library fragments. Library selection was conducted according
to fragment size (300 ∼400 bp). The size and total concentration of the
library were quality inspected using an Agilent 2100 BioAnalyzer and
fluorescence quantification, respectively. After construction of the
library, next-generation sequencing technology based on the Illumina
HiSeq™ 2000 Sequencing platform was used, and paired-end (PE)
sequencing was performed on these libraries.
Transcriptome evaluation and gene expression analysis
Raw RNA-Seq data were collected and deposited in the NCBI (National
Center for Biotechnology Information) database under Bioproject
PRJNA869183 (the SRA accession NO. for each sample is listed in
[220]Table S1). The Cutadapt program
([221]http://cutadapt.readthedocs.io/en/stable/) was used to filter the
data, including removing adapters, unknown nucleotides, and low-quality
(Q-value ≤ 20) bases. The obtained clean reads were further mapped to
the sunflower reference genome[222]^78 (downloaded from the NCBI genome
database) using HISAT2
([223]http://ccb.jhu.edu/software/hisat2/index.shtml).[224]^66 HTSeq
statistics were applied to compare the read count value of each gene as
the original expression amount of the gene. FPKM (fragments per
kilobases per million fragments) was employed to normalize gene
expression levels, making them comparable among genes or samples.
Differentially expressed genes (DEGs) were analyzed using DESeq
([225]https://www.bioconductor.org/packages//2.10/bioc/html/DESeq.html)
[226]^67 with the criteria |log[2]-fold-change| > 1.0 and P
value < 0.05.
Alternative splicing (AS) analysis
After read mapping and transcript assembly, AS events were identified
and classified using ASprofile.[227]^68 SE, RI, MXE, A5SS, and A3SS
events were analyzed by rMATS[228]^69 and screened by FDR < 0.05. Genes
involved in any DAS event were considered differentially spliced genes
(DSGs).
Functional enrichment analysis of DEGs and DSGs
Functional enrichment analysis was conducted for the identified DEGs
and DSGs to predict their potential functions and biological pathways.
Gene Ontology (GO) and KEGG (Kyoto Encyclopedia of Genes and Genomes,
[229]https://www.genome.jp/kegg) enrichment analyses of all DEGs and
DSGs were carried out using an online tool from Metware
([230]https://cloud.metware.cn). The significance for each GO term and
KEGG pathway was calculated against the whole-genome background by the
hypergeometric test (p value < 0.05). The p value calculation was
performed with FDR correction.
TF family analysis and gene coexpression network analysis
TFs and their families were predicted by comparison with the Plant
Transcription Factor Database (PlantTFDB). Gene coexpression network
analysis was performed by calculating r of expression values
(log[2](FPKM+1)) between any two genes under different treatments to
reveal the relationship between target genes (File S1). An r value
greater than 0.9 and a p value less than 0.05 were selected as the
screening thresholds. The networks were visualized using Cytoscape
v.3.8.2.[231]^70
Quantitative RT‒qPCR validation
To verify the reliability of the transcriptome sequencing results, we
randomly selected 10 DEGs for quantitative real-time PCR (RT‒qPCR)
analysis. Total RNA of 12 samples of sunflower leaves was extracted
using the RNAprep Pure Plant Plus Kit following the manufacturer’s
instructions (Tiangen Biotech, China). After confirming the integrity
of the RNA, reverse transcription was carried out using the
PrimeScript™ 1st Strand cDNA Synthesis Kit (Takara Bio), and RT‒qPCR
was performed using AceQ® qPCR SYBR Green Master Mix (Vazyme Biotech
Co., Ltd.). Gene-specific primers were designed using Primer3 software
([232]http://primer3.ut.ee);[233]^71 the primer sequences are shown in
[234]Table S9. All reactions were carried out in three replicates, and
gene expression levels were calculated by 2^−▵▵Ct.
Quantification and statistical analysis
Bioinformatic analysis was described in the [235]method details
section. SPSS (V19.0.0) software was used for statistical analysis.
Expression differences were analyzed by one-way ANOVA and LSD’s test,
and a p value < 0.05 was considered statistically significant.
Acknowledgments