Skip to content


BMC Genomics

Open Access

RNA-seq analysis reveals alternative splicing under salt stress in cotton, Gossypium davidsonii

BMC Genomics201819:73

Received: 24 August 2017

Accepted: 14 January 2018

Published: 23 January 2018



Numerous studies have focused on the regulation of gene expression in response to salt stress at the transcriptional level; however, little is known about this process at the post-transcriptional level.


Using a diploid D genome wild salinity-tolerant cotton species, Gossypium davidsonii, we analyzed alternative splicing (AS) of genes related to salt stress by comparing high-throughput transcriptomes from salt-treated and well-watered roots and leaves. A total of 14,172 AS events were identified involving 6798 genes, of which intron retention (35.73%) was the most frequent, being detected in 3492 genes. Under salt stress, 1287 and 1228 differential alternative splicing (DAS) events were identified in roots and leaves, respectively. These DAS genes were associated with specific functional pathways, such as “responses to stress”, “metabolic process” and “RNA splicing”, implying that AS represents an important pathway of gene regulation in response to salt stress. Several salt response genes, such as pyrroline-5-carboxylate synthase (P5CS), K+ channel outward (KCO1), plasma membrane intrinsic protein (PIP) and WRKY33 which were involved in osmotic balance, ion homeostasis, water transportation and transcriptional regulation, respectively, were identified with differential alternative splicing under salt stress. Moreover, we revealed that 13 genes encoding Ser/Arg-rich (SR) proteins related to AS regulation were differentially alternatively spliced under salt stress.


This study first provide a comprehensive view of AS in G. davidsonii, and highlight novel insights into the potential roles of AS in plant responses to salt stress.


RNA-seqAlternative splicingSplicing factorSalt stress G. davidsonii


The removal of introns from immature mRNA by a process called “pre-mRNA splicing” occurs in the vast majority of eukaryotic protein-coding genes. In this process, particular exons of a gene may be included in or excluded from the final, processed messenger RNA (mRNA) from that gene. This process is known as alternative splicing (AS) [1]. AS is a ubiquitous mechanism in higher eukaryotes and contributes to both transcriptome and proteome diversity [2]. AS creates multiple mRNA transcripts from a single gene through the selection and utilization of alternative splice sites in the pre-mRNA via different splicing events, including exon skipping (ES), alternative donor site (AD), alternative acceptor site (AA), intron retention (IR) and other complicated forms of splicing [3]. The frequency of AS events varies significantly and some are gene- or species-specific. In animals, ES and IR are the most and least frequent, respectively. For example, approximately 35.2% of all AS events in humans are caused by ES, but only 0.01% by IR [4]. In contrast, IR is the most predominant form of AS in Arabidopsis [5], Zea mays [6], and Gossypium raimondii [7], whereas ES only accounts for a small proportion.

AS is accomplished by spliceosomes, which are high molecular weight complexes that are assembled at every intron [8, 9]. They contain five small nuclear ribonucleoprotein particles (snRNPs) and over 200 additional proteins. The identification of splice sites under particular cellular conditions is related to the interaction of additional proteins, globally designated as splicing factors (SFs), that guide spliceosomal components and thereby the spliceosome to the respective splice sites [10]. The main families of these SFs are the Ser/Arg-rich (SR) proteins and heterogeneous nuclear ribonucleoprotein particle (hnRNP) proteins. These proteins bind specific sequences in the pre-mRNA called intronic or exonic splicing enhancer or suppressor sequences [11]. Splice site selection reflects the relative occupation of these sequences and interactions between different proteins on a pre-mRNA molecule. In both animals and plants, many SFs/RNA binding proteins (RBPs) and some core spliceosomal components themselves undergo AS in response to signals, and control their own levels and those of other SFs via AS [12, 13]. In addition, the activity of SFs can be regulated by posttranslational modification in response to environmental cues [14].

AS is involved in many physiological processes, as well as responses to biotic and abiotic stresses in plants [12, 15, 16]. Since the significance of AS from SR protein splicing factor was reported [17], many studies had focused on how AS influences important developmental and signaling pathways. It has been demonstrated that ultraviolet (UV) irradiation can induce cell apoptosis by affecting the expression of apoptotic genes in an AS-dependent way [18]. Splicing variants of ABI3 were influenced by a plant splicing factor (SUA) in seed germination, implying that AS participates in both ABA signaling and response to abiotic stresses [19]. Transcriptomic analysis accelerates the identification of new splicing junctions [20]. Using RNA-seq data, genome-wide AS analysis has been conducted in several plant species, such as Oryza sativa [21], Zea mays [6], Glycine max [22] and Arabidopsis [5]. The potential roles of AS in the response to salt stress in Arabidopsis [23] and to heat stress in Physcomitrella patens have been further elucidated [24]. In addition, some splice variants of SR proteins, which are important splicing regulators, were identified in Arabidopsis under high- and low-temperature stress, and they might, in turn, alter the splicing of other pre-mRNAs [12, 25]. These findings demonstrated that AS is influenced by abiotic stress and, in turn, AS also plays a role in regulating gene expression.

Salinity is one of the most brutal environmental stresses that can hamper crop productivity worldwide. Although cotton is a relatively salt-tolerant species, its growth and development can still be greatly affected by adverse salt conditions [26]. Based on the analysis of the evolution and domestication of cotton using the sequenced G. hirsutum acc. TM-1 genome, it has been implied that the D-subgenome of tetraploid cotton species is a major donor for stress tolerance [7]. Although the mechanism of AS in G. raimondii has been investigated, little is known about AS in response to salt stress in cotton. In the previous study, using transcriptomes comparison from salt-treated and well-watered roots and leaves of G. davidsonii, a diploid D-genome wild salinity-tolerant cotton species, we detected that salt overly sensitive (SOS) and reactive oxygen species (ROS) signaling pathways were involved in salt stress tolerance, and photosynthesis pathways and metabolism played important roles in ion homeostasis and oxidation balance. We also found that alternative splicing is induced by salt stress in cotton [26]. However, deeper analysis between AS and salt stress remains to be investigated. Here, based on the high-throughput transcriptomes from salt-treated and well-watered roots and leaves in G. davidsonii, we systematically investigated the global dynamics of AS and elucidated the relationship between AS and salt stress in cotton. We found that the number of AS events under stress conditions was higher than that under normal conditions, indicating that AS plays important roles in the response to salt stress. We also detected SRs involved in AS regulation and investigated their differential expression and AS under salt stress. This study not only provides a comprehensive view of AS in cotton, but also highlights novel insights into the potential roles of AS in plant responses to salt stress.


Prediction of gene isoforms

It is well known that multiple mRNA transcripts, also called gene isoforms, can be generated from a single gene via splicing events. Using RNA-seq data (Accessions: SRP061663), which were collected from both roots and leaves at 12, 24, 48, 96, and 144 h post salt stress (200 mM NaCl) in G. davidsonii, with data in normal conditions as controls [26], the splicing events involved in novel exons and novel intergenic transcripts were identified by mapping these data to the sequenced G. raimondii genome. The RNA-seq assays revealed 202,762 isoforms and 47,698 unigenes in total 40 libraries. Nevertheless, expression profiles revealed most isoforms were low abundance, with 70% lower than one fragments per kilobase per million reads (FPKM), about 20% between 1 and 10 FPKM, and less than 10% is over 10 FPKM (Fig. 1a). Expression level below 1 FPKM is thought to be beyond the limit of protein detection [2729]. It means more than 70 percentage isoforms predicted in our data will not be translated into function proteins. This explains why the number of detected proteins is much less than the number of predicted isoforms. Meanwhile, it also means the low abundance isoforms cannot represent the function of multi-isoforms genes. In order to investigate the proportion of each isoform in corresponding gene, we grouped 40 libraries into four groups, the roots of well-watered control plants (RC), the roots of salt-stressed plants (RS), the leaves of well-watered controls plants (LC) and the leaves of salt-stressed plants (LS), and performed the statistics of top five most abundant isoforms for multi-isoforms genes (Fig. 1b and Additional file 1: Figure S1). Top1 isoforms in each group occupied greater 60% gene abundance and top two occupied less than 30%. The remaining isoforms covered very small part in gene abundance. This result indicated that the genes with top one or top two abundance isoforms were major contributors for multi-isoforms. In order to reduce the false positive of computer predicting and effectively mine the functional isoforms, the expression threshold value of isoform was detected using the empirical method [30]. We identified 2.6 FPKM for each isoform with repeatedly detected in each biological replicate for further analysis (Additional file 2: Figure S2). Following the criteria, 58,909 isoforms with 29,368 high confidence unigenes were identified. Of the predicted genes, 46.13% (13,546) had two or more isoforms and 53.87% (15,822) had only one isoform. In addition, 81.51% of the isoforms (48,019) were involved in 21,527 unigenes with two or more exons and 18.49% (10,890) had only one exon (Additional file 3: Figure S3). These multi-isoforms genes or multi-exons isoforms have the potential to generate AS events. In addition, 40,016 isoforms were detected in RC group, 41,443 in RS group, 39,758 in LC group, and 40,572 in LS group. We found that the number of isoforms increased in both roots (3.57%) and leaves (2.05%) under salt stress (Table 1).
Fig. 1

Isoforms abundance distribution in G. davidsonii. a The isoforms abundance distribution across 20 samples with two biological replicates. The isoforms abundance is evaluated with FPKM. b Frequency of the top five most abundance isoforms in each gene across four groups. RC: the roots of well-watered control plants, RS: the roots of salt-stressed plants, LC: the leaves of well-watered controls plants, LS: the leaves of salt-stressed plants. The subsequent number represents the time point post treatment

Table 1

Increased isoforms under salt stress in roots and leaves, respectively


Isoform no.

Gene no.

Isoform per gene





















RC root well-watered control, RS root salt-stressed treatment, LC leaf well-watered control, LS leaf salt-stressed treatment

Identification of alternative splicing events

We used ASTALAVISTA to identify and classify the different types of alternative splicing. A total of 14,172 AS events were identified from 6798 genes in all 40 libraries, implying that nearly 31.58% of multi-exonic genes (6798/21,527) in roots or leaves underwent alternative splicing in G. davidsonii. We found that four basic AS types accounted for 85% of all AS events (Fig. 2a and Table 2). IR and ES events were the most and least frequent, and accounted for 35.73% (5063) and 7.71% (1093) of cases, respectively. AA events (29.25%) were more frequent than AD events (12.76%). These results are consistent with those of previous reports [5, 16, 31], which identified IR as the most frequent type of event in plant. Further, the distribution of four basic alternative splicing events in tissue- or treatment-specific groups were consistent with total events described above (Fig. 2b and Additional file 4: Table S1). As the isoforms increased under salt stress condition (Fig. 3a), all four basic events were increased in both roots and leaves post salt stress (Fig. 3b and Additional file 4: Table S1). It is consistent with the previous studies that AS was induced by abiotic stress [23, 32]. Only 2462 common events (17% of total events) in four groups were detected, which implied that most events were tissue or salt stress specific (Fig. 4a). In detail, 5069 AS events were present in both leaf and root tissues, while 5035 and 5893 events were root- or leaf-specific (Fig. 4b). Eight thousand two hundred seventy-one AS events were present in tissues under both stress and control conditions, while 3618 and 4108 events were specific to normal and salt stress conditions, respectively (Fig. 4c). Therefore, AS events in G. davidsonii differed more between tissues than under stress treatment (p < 0.001, Fisher’s exact tests).
Fig. 2

Distribution of alternative splicing events. a Four basic AS events in all 40 libraries. The first column illustrates the intron-exon structure of the AS events, followed by its description, the number of events and percentage. b The distribution of AS events in RC, RS, LC and LS group, respectively. The pie chart represents the frequency of each AS event

Table 2

Number of AS events in total libraries


Events no.

Events rate

Gene no.

Gene rateb

AS per gene





































aAA alternative acceptor site, AD alternative donor site, ES exon skipping, IR intron retention

bThe total gene rate is higher than 100% is due to one gene may undergo two or more AS events

Fig. 3

Number of isoform and AS events in four groups. a The number of isoforms in four groups. b The number of AS events in four groups

Fig. 4

Venn diagram of alternative splicing events in roots and leaves at normal and salt stress condition. a Venn diagram of the overlap of alternative splicing events in four groups. b The common and specific alternative splicing events between roots and leaves. c The common and specific alternative splicing events between well-watered and salt stress condition

Characteristics of different AS types

We found that the event length of the different AS types showed divergence (Fig. 5a). For IR, the retained intron length ranged from 21 to 2771 bp, with the largest number of events at 83 bp, which was similar to soybean [22] and shorter than rice [33]. Our analysis also showed that the most frequent AA length was 3 bp, and the most frequent AD length was 4 bp, which is in agreement with previous findings from other species [22, 34, 35]. The skipping length in ES has multi-peaks, ranging from 20 events at ~ 69 bp, 22 at ~ 72 bp, 22 at ~ 75 bp, and 19 at ~ 84 bp. We also found both length of intron in IR events and exon in ES events were significantly (p < 0.01, Student’s t test) shorter than the average length of intron and exon in total genome (Fig. 5b). In addition, according to the remainder when the length of events is divided by 3, we group each event into three categories: splicing events with length of multiples of three nucleotides named as AS0, the remainder 1 as AS1 and the remainder 2 as AS2. In general, splicing of AS1 or AS2 changes the reading frame, which either alters protein C termini or introduces premature termination codons (PTC) downstream from the splice junction, but splicing of AS0 does not. In total, AS0 type was dominant in four AS events and exon in total genome, except the intron (Fig. 5c). Especially, 41.8% in ES and 43.0% in AA were AS0 type, higher than AS1 and AS2 types, and the four peaks of ES length were all AS0 type. Even though each of three types in the intron accounted for 33.3% of total genome in average, all AS0 type of intron in IR events was 36.8% higher than the other two type which accounted for 31.5 and 31.7%, respectively. The result implies that more AS events in cotton undergo evolutionary pressure to preserve the reading frame.
Fig. 5

Length size of four basic alternative splicing events. a The length distribution of four basic alternative splicing events. To AA and AD events, the length more than 50 bp is few and not be shown in the chart. b length difference among exon in whole genome, intron in whole genome and four basic alternative splicing events. c Distribution in AS0, AS1, and AS2 types and their frequency, respectively

Changes in splicing patterns associated with stress response

To investigate the potential influence of salt-stress-induced AS on cellular processes, we analyzed functional categories related to genes with AS. Totally, we identified 1287 and 1228 differential alternative splicing events (DAS), involved in 808 genes in roots and 791 genes in leaves, under salt stress (Fig. 6a). The length distribution of these DAS events was generally consistent with the AS events described above (Additional file 5: Figure S4). And AS0 type was also dominant in four AS events, even occupied 84% of total DAS events in ES type (Additional file 6: Figure S5). To determine whether the DAS events were affected by gene transcription, we compared the DAS genes with the differential expressed genes identified in our previous study [26]. Only a small subset of DAS genes (18.02% in roots and 21.63% in leaves) overlapped with the differential expressed genes (Additional file 7: Figure S6). This result suggests that the transcriptional activity of genes induced by salt stress has no significant effect on DAS events, with the similar report in Physcomitrella patens [24].
Fig. 6

DAS and functional categorization of the related genes in roots and leaves under salt stress. a The common and specific DAS genes between roots and leaves. b Functional categorization with biological process of DAS genes in roots and leaves under salt stress. The positive numbers indicate the increased isoforms and the negative numbers indicate the decreased isoforms under salt condition

Based on the Gene Ontology (GO) analysis, we found that the differential alternative splicing overrepresentation of different GO categories in both roots and leaves, and the number of isoforms involved in related terms increased under salt stress (Fig. 6b and Additional file 8: Table S2). Most terms were common enriched in roots and leaves, such as “response to salt stress”, “water transport” and “alcohol catabolic process”. In addition, photosynthesis related terms in leaves, such as “photosynthesis” and “chlorophyll metabolic process”, were exclusively overrepresented. Although most biological process were commonly overrepresented in roots and leaves, tissue-specific regulation of DAS was distinct, and less than 20% DAS genes were simultaneously detected in the two tissues (Fig. 6a).

We found 81 DAS events involved in 58 genes in “response to salt stress” terms (Additional file 9: Table S3). Most of them were regulated by IR and AA events. The expression of AS events was various under salt stress, with some increased and the other decreased (Fig. 7). We also performed the correlation analysis between AS frequency change values and fold change values of gene expression for salt response genes (Additional file 10: Table S4). The results showed that only 5% (4/81) DAS events were significantly correlated with their gene expression, implying that AS acts an independent regulatory pattern comparing to transcription regulation under salt stress. In the DAS genes, we found that some important genes involved in the response to salt stress, such as Pyrroline-5-carboxylate synthase (P5CS, Gorai.012G107700), K+ channel outward (KCO1, Gorai.013G153400) and plasma membrane intrinsic protein (PIP, Gorai.011G098100), generated novel AS isoforms. P5CS plays crucial roles in the proline biosynthesis pathway [36] and proline is an important compatible solute for osmosis homeostasis under salt stress [37]. KCO1 is involved in the K+ transport, which contributes to ion homeostasis [38]. PIP is a member of the aquaporin family, which is a crucial water channel protein involved in the salt stress response [39]. In addition, we also found some transcription factors that were related to the salt stress response, such as WRKY (Gorai.012G119600) [40], MYB (Gorai.009G288900) [41] and bHLH (Gorai.009G396900) [42]. Taken together, there exists the complex regulating mechanism in response on salt stress in cotton.
Fig. 7

Splicing pattern of DAS genes related to salt stress response. R: Change frequency under salt stress in roots. L: Change frequency under salt stress in leaves. The subsequent number represent the time point post salt stress. Change frequency indicates the percentage of AS events increase or decrease post salt stress: Change frequency = (FPKM (AS events isoforms)/FPKM (total isoforms))salt – (FPKM(AS events isoforms)/FPKM(total isoforms))CK. Red represents the increased and green represents the decreased AS events under salt stress

Splicing factors involved in AS under salt stress

RNA splicing genes, especially those encoding splicing regulators, are known to be actively alternatively spliced under stress conditions [43]. The serine/arginine-rich (SR) proteins are known to be involved in pre-mRNA splicing processes and regulate alternative splicing by changing the splice site selection in a concentration dependent-manner [44]. In this study, 29 SR orthologous genes (Additional file 11: Table S5) were identified in the G. raimondii annotation information ( Of them, 13 (12 in roots and seven in leaves) were differentially alternatively spliced under salt stress (Table 3). However, only two SR orthologous genes (Gorai.009G393200, Gorai.010G201600) were differential expression (up-regulated) under salt stress [26], indicating that SR genes were more inclined to be regulated by post-transcriptional level instead of transcriptional level. Following the Arabidopsis SR protein family divided into six subfamilies [25], we found that most differential splicing SR proteins in cotton belonged to RSZ subfamily, which was not reported in Arabidopsis, implying the different splicing regulation under salt stress between in cotton and Arabidopsis.
Table 3

The components of splicing machinery involved in regulation of salt stress responses in G. davidsonii

ID in G. raimondii

ID in A. thaliana

Gene symbol




































































RDAS differential alternative splicing types in roots, LDAS differential alternative splicing types in leaves

Validation of predicted AS and DAS events

In order to evaluate the reliability of computationally predicted AS events, nine AS events with the length ranging from 60 to 200 bp for detection by agarose gel analysis, were randomly selected and validated by RT-PCR using intron-flanking primers (Additional file 12: Figure S7 and Additional file 13: Table S6). As a result, the PCR products were more than one in agarose gel corresponding to the AS events. Meanwhile, we also found the amplification products difference in different tissues or treatments, implying that AS was complicated and sensitive in different conditions. Further, ten DAS genes, which included three salt stress response genes reported previously and seven other induced genes, were validated by evaluating the relative abundance of the splicing event by qRT-PCR (Fig. 8, Additional file 14: Table S7 and Additional file 15: Figure S8), and the ratio between normal condition and salt condition for each variant. In total, 80% (8/10) predicted events were able to be detected by qRT-PCR. The events that were unable to be validated may be the result of false computational predictions or low transcript expression. A plasma membrane intrinsic protein (GrPIP2.7, Gorai.011G098100) had three isoforms. GrPIP2.7_t1 was the dominant isoform that highly expressed in both roots and leaves, followed by GrPIP2.7_t2 and GrPIP2.7_t3. GrPIP2.7_t3 was generated by an AS event found at an alternative 3′ acceptor site (AA) which introduced a PTC at the MIP domain on the third exon. RNA-seq data and qRT-PCR revealed that the proportion of T3 increased under salt stress (Fig. 8a). K+ channel outward (GrKCO1, Gorai.013G153400) had two isoforms. GrKCO1_t1 was the dominant isoform and an alternative 5′ donor site (AD) located on the first exon within 5′ UTR region that would not change the integrity of CDS region. RNA-seq data and qPCR revealed that the proportion of GrKCO1_t 2 increased under salt condition (Fig. 8b). A WRKY transcription factor (GrWRKY33, Gorai.012G119600) had four isoforms. The gene had four introns and GrWRKY33_t1 was the dominant isoform. IR event was detected in GrWRKY33_t2, GrWRKY33_t3 and GrWRKY33_t4 on the second, third and fourth intron, respectively. Among these three IR events, only the last one was detected differential splicing and would be used to verify, however, the other two would not. IR in GrWRKY33_t3 and GrWRKY33_t4 introduced PTC caused the second function motif lost. Different from the former, IR in GrWRKY33_t2 introduced PTC at the 5′ CDS region but not affected the dominant function. RNA-seq data and qRT-PCR revealed that IR event on the fourth introns was decreased under salt stress (Fig. 8c), indicating that salt stress elevated the proportion of function isoforms of GrWRKY33.
Fig. 8

Validation on DAS events involved in salt stress by qRT-PCR. The first column showed the exon structure of each gene isoform. The red rectangle represents the PCR product at AS events loci, and the blue rectangle represents the PCR product at the constitutive exon loci. The second column showed the corresponding prediction of termination codon and functional domain. The third column showed the AS events ratio in RNA-seq data. The fourth column showed the AS events ratio in qRT-PCR analysis. a GrPIP2.7; b GrKCO1; c GrWRKY33 orthologs in G. davidsonii, respectively


Plants require controlled systems to defend against various stresses. Previous studies have reported many genes that are responsive to stresses at the transcriptional level [45, 46], however, gene regulation at the posttranscriptional level was less investigated under stress, especially for salt stress. Since Berget et al. [47] discovered intervening sequences, an increasing amount of evidence has revealed that AS plays an important role in transcription regulation and contributes to the functional diversity of eukaryotic genomes. AS is involved in many physiological processes, including the responses to biotic and abiotic stresses [12, 15, 16]. Most of the AS events that have been found to be involved in the response to abiotic stress are linked to genes with regulatory roles, covering all levels of the regulation of gene expression [48]. Nevertheless, large-scale or genome-wide studies of AS dynamics under salt stress conditions are still relatively scarce. In this study, through comprehensive transcriptome analysis of high-throughput RNA-seq data, we revealed genome-wide AS events under salt stress in G. davidsonii. Our analysis suggests that 31.58% of the multi-exonic genes in the G. davidsonii genome are alternatively spliced under salt-stress conditions. Furthermore, four basic AS events was increased post salt stress, and we have identified DAS genes associated with several biological processes, such as “response to salt stress”, “water transport” and “metabolic process”. Moreover, we observed that genes encoding splicing factors are frequently alternatively spliced under salt stress.

An overview of AS in G. davidsonii under salt stress

In G. raimondii, 16,437 AS events involving 77,569 unique transcripts at 10,197 genes have been identified, indicating that ~ 32% of the multi-exonic genes had at least one AS event [7]. In this study, 14,172 AS events involving 6798 genes were identified in G. davidsonii suggested that 31.58% of the multi-exonic genes were alternatively spliced. The ratio of alternative splicing genes is lower than other plants [2224, 30] suggests difference between cotton and other plants. We also found that IR was less frequent and AA was more frequent in G. davidsonii compared with that in G. raimondii, implying interspecific differences in the types of AS events in the two diploid D-genome cotton species. Actually, the differences in alternative splicing have also been described between different ecotypes of Arabidopsis [49] and Vitis vinifera [31], giving support to the argument that changes in splicing may contribute to the evolutionary adaptation process. Furthermore, most AS events were tissue or stress specific, only 17% of total events were commonly detected in all four tissues/treatments conditions in G. davidsonii (Fig. 3a). Similar AS profiles are also observed in other plants, such as Arabidopsis [23], Vitis vinifera [31] and Zea mays [50]. The result implied that AS may contribute to the function diversity in the tissue or stress-specific genes.

AS was regulated in response to salt stress

We found that AS events were increased under salt stress no matter in roots or in leaves. This finding is consistent with the previous studies on AS under environmental stresses [23, 48]. These increased AS events may lead to a wider plasticity for plants to enable them to adapt to various stresses. To further understand the gene function affected by alternative splicing under salt stress, we performed gene ontology (GO) analysis of DAS and found that they were enriched in some biological processes, such as “response to salt stress”, “water transport” and “metabolic process”. This result further suggested that the DAS induced by salt stress could regulate the salt response. An interesting finding is that “response to calcium ion” is not only overrepresented in our study, but also frequently overrepresented in other plants [23] or other stress condition [32]. Calcium-signaling pathway plays a crucial role in stress response. Exposure to salinity activates the Salt Overly Sensitive (SOS) pathway, leading to Ca2+-dependent increased activity of SOS1, a plasma membrane Na+-H+ antiporter that enables adaptation through Na+ efflux [51, 52]. We suspect that Calcium regulation pathway is sensitive to stress condition at splicing level. Some important salt stress response genes or transcript factors, such as KCO, PIP and WRKY, were regulated by AS. The expression of these genes may have three fates, that decreased the abundance of function isoforms by upregulated the alternative splicing isoforms like GrPIP2.7, that increased the abundance of function isoforms by downregulated the alternative splicing isoforms like GrWRKY33, and that remained the abundance with AS events occur at 5′ UTR region that may not affect the translation of CDS region like GrKCO1. In addition, DAS genes induced by salt stress were enriched in pathways related to RNA splicing, indicating that AS events of the RNA splicing-related genes are induced by salt stress and regulated by themselves or other splicing factors [53, 54]. These AS events may introduce new domain and subsequently impact the function of gene [55, 56]. Consequently, AS is a complex and important regulatory mechanism in response to salt stress in cotton.

SR splicing factors were regulated by AS under salt stress

The splicing of introns from pre-mRNA is carried out by one of the largest molecular complexes of the cell, the spliceosome, which consists of five small nuclear ribonucleoproteins (snRNPs) and numerous additional proteins [10]. Members of the Serine/arginine (SR) protein family are well-known non-snRNP spliceosomal factors [57]. A few previous studies showed that over-expression of SRs or other splicing factors could increase plant tolerance to salt and other stresses [43, 5860]. Interestingly, the AS pattern of most SRs has been shown to change under stress conditions [12, 23]. It suggests that pre-mRNAs of SRs are themselves alternatively spliced and this splicing is under tight spatial, temporal, and environmental control. In this study, 29 orthologous SR genes were identified in G. davidsonii. Of them, 16 were differential splicing and only two were differential expression in roots or leaves. This result is consistent with the previous report on Arabidopsis [23], which show that splicing factors involved in salt stress function mainly via splicing pathways rather than regulated directly by salt stress. Thatcher et al. [30] also demonstrated that the expression level of known splicing factors was not the major driving force behind genotypic AS variation. Therefore, we speculated that pre-mRNAs of SRs were themselves alternatively spliced under salt stress and the diversity of SR splicing products consequently increased the number of novel AS isoforms identified (Figs. 3a and 6b), and SR genes belong to RSZ subfamily might play a crucial role in splicing regulation under salt stress condition in cotton.

AS plays a crucial role in salt stress response at the posttranscriptional level

The molecular mechanisms of the response to salt stress involve signal transduction pathways [61], transcription factors [62] and genes [63], which have been well documented at the transcriptional level. However, little is known about the regulation of salt stress-specific gene expression at the post-transcriptional level. Here, we found that AS events were abundant in the response to salt stress in G. davidsonii. However, one crucial question is whether the increase in AS events is an acclimation response or merely a consequence of splicing errors caused by stress damage. Several previous studies suggested that the stress-induced increase in AS could be ascribed to splicing errors and could weaken the function of the corresponding genes by decreasing the abundance of functional transcripts [23, 64]. However, some evidence showed that AS could promote stress tolerance by increasing proteomic diversity [65, 66]. Most aberrant splicing events (splicing errors) could be removed by mRNA surveillance mechanisms such as nonsense mediated mRNA decay (NMD) [67], thus, some splicing variants, neutral or beneficial to the organism, can be selectively fixed as functional AS events. A large-scale study using 39 million expressed sequence tags from 47 eukaryotic species revealed that the proportion of AS genes and the average number of AS isoforms per gene (AS level) have gradually increased over the past 1.4 billion years, indicating that AS complexity can be considered a strong predictor of organismal complexity [68]. In present study, we found the number of AS events and isoforms increased under salt stress condition (Figs. 3 and 6). In addition, DAS genes were overrepresented in terms of “response to salt stress” and “metabolic process”. We also found that the splicing pattern was diverse in tissues and developmental stage under salt stress (Fig. 7). Compared to the differential expression regulation in our previous study [26], salt stress response at splicing level is more complicated. The specific regulation pathway of response to salt stress is hard to verify only by transcriptome sequence, and further research at molecular level, based on the key genes regulated at splicing level such as P5CS and PIP, need to be conducted.


In this study, through comprehensive transcriptome analysis of high-throughput RNA-seq data, we revealed genome-wide AS events under salt stress in G. davidsonii. We found that that the number of AS events under stress conditions was significantly higher than that under normal conditions. The functions of these genes related to AS were enriched in several biological processes, such as “response to salt stress”, “water transport” and “metabolic process”. We also detected splicing factors (SFs) involved in AS regulation and found their alternative splicing under salt stress. In addition, several salt response genes, such as P5CS, KCO1, PIP and WRKY33, were identified with differential splicing. This study indicates that AS plays the important roles in plant response to salt stress.


Plant material and salt stress conditions

Diploid wild cotton species G. davidsonii was used for the study. Following our pervious study, the salt stress concentration of 200 mM NaCl was identified by comparing the salt tolerance of three cotton accessions, diploid wild cotton species G. davidsonii and two G. hirsutum cultivars, ZS9612 and Z9807, with sensitivities and insensitivities to salinity stress, respectively [26]. All necessary permits for collecting the G. davidsonii seeds were obtained from Nanjing Agricultural University, China.

The G. davidsonii seeds were surface-sterilized with 70% ethanol for 30–60s and 10% H2O2 for 60–120 min, followed by washing with sterile water. Sterilized seeds were germinated at 26 °C under long day conditions in a 16 h light/8 h dark cycle with a light intensity of 150 μmol m− 2 s− 1 on 1/2 MS solid medium. Three days after germination, the plants were transferred to 1/2 Hoagland nutrient solutions at pH 6.0.

The seedlings with two true leaves and one heart-shaped leaf were randomly selected and cultured in 1/2 Hoagland solutions supplemented with 200 mM NaCl for salinity stress treatment. Due to different salt damage mechanisms when plant exposure to salinity [69, 70], five time points, 12, 24, 48, 96, and 144 h after exposure, were set for the leaf and root sampling. All samples were frozen quickly in liquid N2 and stored at −70゜C for further use. Plant seedlings grown in normal 1/2 Hoagland nutrient solution was used as controls. All the cotton plants cultured in 1/2 Hoagland solutions were grown in chambers under long day conditions with a 16 h light/8 h dark cycle at 28/25゜C in Nanjing Agricultural University. The 1/2 Hoagland nutrient solution was replaced every day. These evaluations were not relevant to human subject or animal research. Therefore, they did not involve any endangered or protected species.

RNA extraction, cDNA library preparation, and RNA-seq

Total RNA was extracted from roots and leaves by the cetyltrimethylammonium bromide (CTAB)-sour phenol extraction method [71]. The RNA was digested with RNase-Free DNase (Qiagen) and checked for integrity by capillary gel electrophoresis. Library preparation for RNA-seq was performed using the TruSeq RNA Sample Preparation Kit (Illumina, Cat. NRS–122–2002) with 500 ng of total RNA. Accurate quantitation of cDNA libraries was performed using the QuantiFluor dsDNA System (Promega). The size range of the final cDNA libraries was determined by applying the DNA 1000 chip on the Bioanalyzer 2100 (Agilent; 280 bp). The cDNA libraries were amplified and sequenced using the cBot and HiSeq2000 systems from Illumina. Two biological replicates from each sample were used for all RNA-seq experiments.

Reads mapping and transcript assembly

After preprocessing the RNA-seq data with an NGS QC toolkit [72], the reads were mapped to the G. raimondii genome using a Tophat spliced aligner [73]. Based on default Tophat parameters, four mismatches were allowed for reads mapping. The sequence alignment/map files generated by Tophat were used as the input to the software Cufflinks [74], which assembles the alignments in the sequence alignment/map file into transfrags. Cufflinks does this assembly independently of the existing gene annotations and constructs a minimum set of transcripts that best describes the RNA-seq reads. The unit of measurement used by Cufflinks to estimate transcript abundance is FPKM. The Cufflinks statistical model probabilistically assigns reads to the assembled isoforms. Cuffcompare was used to merge the assemblies with the reference annotation (G.raimondii_221_v2.1.gene.gff) into a single GTF file that was used later to identify alternative splicing (AS) events. The class codes in the Cuffcompare output were used to identify novel isoforms, intergenic transcripts, and splice junctions.

Novel isoforms prediction and alternative splicing analysis

Using an empirical method [30], 2.6 FPKM was chosen as the expression cutoff for alternative splicing isoforms. In addition, the AS isoforms that were identified in both replications, were regarded as the stable isoforms. We used the ASTALAVISTA v2.2 software [75] with the parameters (-t asta -i) to quantify the type of AS events based on the assembled transcripts by the Cufflinks software. Four basic AS events (IR, AA, AD and ES) were performed and the remaining complex AS events were collectively grouped as other type.

Differential alternative splicing events analysis

Each AS event consist of constitutive and alternative splicing event (e.g. IR event consist of intron splicing and intron retain). First, FPKM of constitutive and alternative splicing event were calculated by sum of the corresponding isoforms (constitutive or alternative splicing event may have multiple isoforms). Then, fisher’s exact test was applied to above FPKM to analyze differential alternative splicing between well-watered and salt-stress treatments with p < 0.05 as significance. In order to assess the change of AS events caused by salt stress, each FPKM of event was then converted into a percentage of total (FPKM (AS events isoforms)/FPKM (total isoforms)) and subsequently calculated the difference.

Go enrichment

AgriGO software [76] was used for gene ontology analysis, and Singular Enrichment Analysis (SEA) was performed with the statistical method of Fisher’s exact tests. The input sample list was the G. raimondii gene ID (, which was converted from the original ID of the Cuffdiff default configuration, and the background was whole annotated genes in G. raimondii. The output of enrichment needed Benjamini and Hochberg-adjusted P-values (FDR) < 0.05.

RT-PCR and qRT-PCR validation

RT-PCR and qRT-PCR were done using a new set of RNAs extracted with the same tissues and treatment time points as that for RNA-seq analysis. The selected AS events were validated by RT-PCR using a set of primers (Table S6) that were designed based on each AS event. EF1-α was used as an internal standard. The amplification reactions were performed under the following conditions: 95 °C for 5 min, followed by 32 cycles of 95 °C for 30 s, 58 °C for 30 s, and 72 °C for 60 s. qRT-PCR validations was performed on three alternative splicing events in the salt stress response genes, using a Bio-Rad CFX96 Real-Time instrument and the light cycler fast start DNA Master SYBR Green I kit (Roche, Basel, Switzerland). Relative abundance of each splicing event by qRT-PCR using primers specific for each splicing variant or common primers for whole transcripts (Table S7). The GhHis3, which is a constitutive expression gene in cotton [77], its orthologous gene GrHis3 in G. raimondii was used to design the internal control primers (Table S7) and ΔΔCt method was used for data analysis [78].



Alternative acceptor site


Alternative donor site


Alternative splicing


Differential alternative splicing


Differential expression genes


Exon skipping


Fragments per kilobase per million reads


Gene Ontology


heterogeneous nuclear ribonucleoprotein particle


Intron retention


The leaves of well-watered controls plants


The leaves of salt-stressed plants


Nonsense mediated mRNA decay


Premature termination codons


RNA binding proteins


The roots of well-watered control plants


Reactive oxygen species


The roots of salt-stressed plants


Splicing factors


small nuclear ribonucleoprotein particles


Salt overly sensitive







The authors would like to thank Dr. Caiping Cai from College of Agriculture in Nanjing Agricultural University for helpful comments.


This program was financially supported in part by the National Transgenic Program (2016ZX08005-004), Key R & D program in Jiangsu Province (BE2015360), Six talent peaks project in Jiangsu province (2015-NY-002), the Fundamental Research Funds for the Central Universities (KYYJ201701) and JCIC-MCP project (No.10).

Availability of data and materials

RNA-seq data in this study have been deposited at the National Center of Biotechnology Information (NCBI, under the accessions SRP061663.

Authors’ contributions

Experiments were designed by WZG. Experiments were performed by GZZ, WXL and FZ. GZZ and WZG drafted the manuscript, WZG revised the manuscript. All authors read and approved the final manuscript.

Ethics approval and consent to participate

Diploid wild cotton species G. davidsonii was used in the study. All necessary permits for collecting the G. davidsonii seeds and seedlings were obtained from Nanjing Agricultural University, China.

Consent for publication

Not applicable.

Competing interests

The authors declared that they had no competing interests.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (, which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

State Key Laboratory of Crop Genetics & Germplasm Enhancement, Hybrid Cotton R & D Engineering Research Center, Ministry of Education, Nanjing Agricultural University, Nanjing, China


  1. Black DL. Mechanisms of alternative pre-messenger RNA splicing. Annu Rev Biochem. 2003;72:291–336.View ArticlePubMedGoogle Scholar
  2. Chen M, Manley JL. Mechanisms of alternative splicing regulation: insights from molecular and genomics approaches. Nat Rev Mol Cell Biol. 2009;10(11):741–54.View ArticlePubMedPubMed CentralGoogle Scholar
  3. Keren H, Lev-Maor G, Ast G. Alternative splicing and evolution: diversification, exon definition and function. Nat Rev Genet. 2010;11(5):345–55.View ArticlePubMedGoogle Scholar
  4. Wang ET, Sandberg R, Luo SJ, Khrebtukova I, Zhang L, Mayr C, Kingsmore SF, Schroth GP, Burge CB. Alternative isoform regulation in human tissue transcriptomes. Nature. 2008;456(7221):470–6.View ArticlePubMedPubMed CentralGoogle Scholar
  5. Marquez Y, Brown JW, Simpson C, Barta A, Kalyna M. Transcriptome survey reveals increased complexity of the alternative splicing landscape in Arabidopsis. Genome Res. 2012;22(6):1184–95.View ArticlePubMedPubMed CentralGoogle Scholar
  6. Lu XD, Chen DJ, Shu DF, Zhang Z, Wang WX, Klukas C, Chen LL, Fan YL, Chen M, Zhang CY. The differential transcription network between embryo and endosperm in the early developing maize seed. Plant Physiol. 2013;162(1):440–55.View ArticlePubMedPubMed CentralGoogle Scholar
  7. Li Q, Xiao G, Zhu YX. Single-nucleotide resolution mapping of the Gossypium raimondii transcriptome reveals a new mechanism for alternative splicing of introns. Mol Plant. 2014;7(5):829–40.View ArticlePubMedGoogle Scholar
  8. Wahl MC, Will CL, Luhrmann R. The spliceosome: design principles of a dynamic RNP machine. Cell. 2009;136(4):701–18.View ArticlePubMedGoogle Scholar
  9. Will CL, Luhrmann R. Spliceosome structure and function. CSH Perspect Biol. 2011;3(7): a003707.Google Scholar
  10. Bessonov S, Anokhina M, Will CL, Urlaub H, Luhrmann R. Isolation of an active step I spliceosome and composition of its RNP core. Nature. 2008;452(7189):846–50.View ArticlePubMedGoogle Scholar
  11. Erkelenz S, Mueller WF, Evans MS, Busch A, Schoneweis K, Hertel KJ, Schaal H. Position-dependent splicing activation and repression by SR and hnRNP proteins rely on common mechanisms. RNA. 2013;19(1):96–102.View ArticlePubMedPubMed CentralGoogle Scholar
  12. Reddy AS. Alternative splicing of pre-messenger RNAs in plants in the genomic era. Annu Rev Plant Biol. 2007;58:267–94.View ArticlePubMedGoogle Scholar
  13. Reddy AS, Shad Ali G. Plant serine/arginine-rich proteins: roles in precursor messenger RNA splicing, plant development, and stress responses. Wires RNA. 2011;2(6):875–89.View ArticlePubMedGoogle Scholar
  14. Luco RF, Allo M, Schor IE, Kornblihtt AR, Misteli T. Epigenetics in alternative pre-mRNA splicing. Cell. 2011;144(1):16–26.View ArticlePubMedPubMed CentralGoogle Scholar
  15. ASN R, Marquez Y, Kalyna M, Barta A. Complexity of the alternative splicing landscape in plants. Plant Cell. 2013;25(10):3657–83.View ArticleGoogle Scholar
  16. Filichkin SA, Priest HD, Givan SA, Shen R, Bryant DW, Fox SE, Wong WK, Mockler TC. Genome-wide mapping of alternative splicing in Arabidopsis Thaliana. Genome Res. 2010;20(1):45–58.View ArticlePubMedPubMed CentralGoogle Scholar
  17. Ge H, Manley JL. A protein factor, ASF, controls cell-specific alternative splicing of SV40 early pre-mRNA in vitro. Cell. 1990;62(1):25–34.View ArticlePubMedGoogle Scholar
  18. Munoz MJ, Santangelo MSP, Paronetto MP, de la Mata M, Pelisch F, Boireau S, Glover-Cutter K, Ben-Dov C, Blaustein M, Lozano JJ, et al. DNA damage regulates alternative splicing through inhibition of RNA polymerase II elongation. Cell. 2009;137(4):708–20.View ArticlePubMedGoogle Scholar
  19. Sugliani M, Brambilla V, Clerkx EJ, Koornneef M, Soppe WJ. The conserved splicing factor SUA controls alternative splicing of the developmental regulator ABI3 in Arabidopsis. Plant Cell. 2010;22(6):1936–46.View ArticlePubMedPubMed CentralGoogle Scholar
  20. Hillier LW, Reinke V, Green P, Hirst M, Marra MA, Waterston RH. Massively parallel sequencing of the polyadenylated transcriptome of C. elegans. Genome Res. 2009;19(4):657–66.View ArticlePubMedPubMed CentralGoogle Scholar
  21. Lu T, Lu G, Fan D, Zhu C, Li W, Zhao Q, Feng Q, Zhao Y, Guo Y, Li W, et al. Function annotation of the rice transcriptome at single-nucleotide resolution by RNA-seq. Genome Res. 2010;20(9):1238–49.View ArticlePubMedPubMed CentralGoogle Scholar
  22. Shen Y, Zhou Z, Wang Z, Li W, Fang C, Wu M, Ma Y, Liu T, Kong LA, Peng DL, et al. Global dissection of alternative splicing in paleopolyploid soybean. Plant Cell. 2014;26(3):996–1008.View ArticlePubMedPubMed CentralGoogle Scholar
  23. Ding F, Cui P, Wang Z, Zhang S, Ali S, Xiong L. Genome-wide analysis of alternative splicing of pre-mRNA under salt stress in Arabidopsis. BMC Genomics. 2014;15:431.View ArticlePubMedPubMed CentralGoogle Scholar
  24. Chang CY, Lin WD, Tu SL. Genome-wide analysis of heat-sensitive alternative splicing in Physcomitrella patens. Plant Physiol. 2014;165(2):826–40.View ArticlePubMedPubMed CentralGoogle Scholar
  25. Barta A, Kalyna M, Reddy AS. Implementing a rational and consistent nomenclature for serine/arginine-rich protein splicing factors (SR proteins) in plants. Plant Cell. 2010;22(9):2926–9.View ArticlePubMedPubMed CentralGoogle Scholar
  26. Zhang F, Zhu G, Du L, Shang X, Cheng C, Yang B, Hu Y, Cai C, Guo W. Genetic regulation of salt stress tolerance revealed by RNA-Seq in cotton diploid wild species, Gossypium davidsonii. Sci Rep. 2016;6:20582.View ArticlePubMedPubMed CentralGoogle Scholar
  27. Fagerberg L, Oksvold P, Skogs M, Algenas C, Lundberg E, Ponten F, Sivertsson A, Odeberg J, Klevebring D, Kampf C, et al. Contribution of antibody-based protein profiling to the human Chromosome-centric Proteome Project (C-HPP). J Proteome Res. 2013;12(6):2439–48.View ArticlePubMedGoogle Scholar
  28. Hebenstreit D, Fang M, Gu M, Charoensawan V, van Oudenaarden A, Teichmann SA. RNA sequencing reveals two major classes of gene expression levels in metazoan cells. Mol Syst Biol. 2011;7:497.View ArticlePubMedPubMed CentralGoogle Scholar
  29. Vogel C, Marcotte EM. Insights into the regulation of protein abundance from proteomic and transcriptomic analyses. Nat Rev Genet. 2012;13(4):227–32.PubMedPubMed CentralGoogle Scholar
  30. Thatcher SR, Zhou W, Leonard A, Wang BB, Beatty M, Zastrow-Hayes G, Zhao X, Baumgarten A, Li B. Genome-wide analysis of alternative splicing in Zea mays: landscape and genetic regulation. Plant Cell. 2014;26(9):3472–87.View ArticlePubMedPubMed CentralGoogle Scholar
  31. Vitulo N, Forcato C, Carpinelli EC, Telatin A, Campagna D, D’Angelo M, Zimbello R, Corso M, Vannozzi A, Bonghi C, et al. A deep survey of alternative splicing in grape reveals changes in the splicing machinery related to tissue, stress condition and genotype. BMC Plant Biol. 2014;14:99.View ArticlePubMedPubMed CentralGoogle Scholar
  32. Li WF, Lin WD, Ray P, Lan P, Schmidt W. Genome-wide detection of condition-sensitive alternative splicing in Arabidopsis roots. Plant Physiol. 2013;162(3):1750–63.View ArticlePubMedPubMed CentralGoogle Scholar
  33. Zhang GJ, Guo GW, Hu XD, Zhang Y, Li QY, Li RQ, Zhuang RH, Lu ZK, He ZQ, Fang XD, et al. Deep RNA sequencing at single base-pair resolution reveals high complexity of the rice transcriptome. Genome Res. 2010;20(5):646–54.View ArticlePubMedPubMed CentralGoogle Scholar
  34. Campbell MA, Haas BJ, Hamilton JP, Mount SM, Buell CR. Comprehensive analysis of alternative splicing in rice and comparative analyses with Arabidopsis. BMC Genomics. 2006;7:327.View ArticlePubMedPubMed CentralGoogle Scholar
  35. Akerman M, Mandel-Gutfreund Y. Alternative splicing regulation at tandem 3′ splice sites. Nucleic Acids Res. 2006;34(1):23–31.View ArticlePubMedPubMed CentralGoogle Scholar
  36. Himuro Y, Ishiyama K, Mori F, Gondo T, Takahashi F, Shinozaki K, Kobayashi M, Akashi R. Arabidopsis galactinol synthase AtGolS2 improves drought tolerance in the monocot model Brachypodium distachyon. J Plant Physiol. 2014;171(13):1127–31.View ArticlePubMedGoogle Scholar
  37. Verbruggen N, Hermans C. Proline accumulation in plants: a review. Amino Acids. 2008;35(4):753–9.View ArticlePubMedGoogle Scholar
  38. Hamamoto S, Marui J, Matsuoka K, Higashi K, Igarashi K, Nakagawa T, Kuroda T, Mori Y, Murata Y, Nakanishi Y, et al. Characterization of a tobacco TPK-type K+ channel as a novel tonoplast K+ channel using yeast tonoplasts. J Biol Chem. 2008;283(4):1911–20.View ArticlePubMedGoogle Scholar
  39. Zhao YY, Yan F, Hu LP, Zhou XT, Zou ZR, Cui LR. Effects of exogenous 5-aminolevulinic acid on photosynthesis, stomatal conductance, transpiration rate, and PIP gene expression of tomato seedlings subject to salinity stress. Genet Mol Res. 2015;14(2):6401–12.View ArticlePubMedGoogle Scholar
  40. Wei Y, Shi H, Xia Z, Tie W, Ding Z, Yan Y, Wang W, Hu W, Li K. Genome-wide identification and expression analysis of the WRKY gene family in cassava. Front Plant Sci. 2016;7:25.PubMedPubMed CentralGoogle Scholar
  41. Cui MH, Yoo KS, Hyoung S, Nguyen HTK, Kim YY, Kim HJ, Ok SH, Yoo SD, Shin JS. An Arabidopsis R2R3-MYB transcription factor, AtMYB20, negatively regulates type 2C serine/threonine protein phosphatases to enhance salt tolerance. FEBS Lett. 2013;587(12):1773–8.View ArticlePubMedGoogle Scholar
  42. Babitha KC, Vemanna RS, Nataraja KN, Udayakumar M. Overexpression of EcbHLH57 transcription factor from Eleusine coracana L. in tobacco confers tolerance to salt, oxidative and drought stress. PLoS One. 2015;10(9):e0137098.View ArticlePubMedPubMed CentralGoogle Scholar
  43. Palusa SG, Ali GS, Reddy AS. Alternative splicing of pre-mRNAs of Arabidopsis serine/arginine-rich proteins: regulation by hormones and stresses. Plant J. 2007;49(6):1091–107.View ArticlePubMedGoogle Scholar
  44. Graveley BR, Hertel KJ, Maniatis T. Correspondence - SR proteins are ‘locators’ of the RNA splicing machinery. Curr Biol. 1999;9(1):R6–7.View ArticlePubMedGoogle Scholar
  45. Kakumanu A, Ambavaram MM, Klumas C, Krishnan A, Batlang U, Myers E, Grene R, Pereira A. Effects of drought on gene expression in maize reproductive and leaf meristem tissue revealed by RNA-Seq. Plant Physiol. 2012;160(2):846–67.View ArticlePubMedPubMed CentralGoogle Scholar
  46. Singh R, Pandey N, Kumar A, Shirke PA. Physiological performance and differential expression profiling of genes associated with drought tolerance in root tissue of four contrasting varieties of two Gossypium species. Protoplasma. 2016;253(1):163–74.View ArticlePubMedGoogle Scholar
  47. Berget SM, Moore C, Sharp PA. Spliced segments at the 5′ terminus of adenovirus 2 late mRNA. Proc Natl Acad Sci USA. 1977;74(8):3171–5.View ArticlePubMedPubMed CentralGoogle Scholar
  48. Mastrangelo AM, Marone D, Laido G, De Leonardis AM, De Vita P. Alternative splicing: enhancing ability to cope with stress via transcriptome plasticity. Plant Sci. 2012;185-186:40–9.View ArticlePubMedGoogle Scholar
  49. Gan X, Stegle O, Behr J, Steffen JG, Drewe P, Hildebrand KL, Lyngsoe R, Schultheiss SJ, Osborne EJ, Sreedharan VT, et al. Multiple reference genomes and transcriptomes for Arabidopsis thaliana. Nature. 2011;477(7365):419–23.View ArticlePubMedPubMed CentralGoogle Scholar
  50. Thatcher SR, Danilevskaya ON, Meng X, Beatty M, Zastrow-Hayes G, Harris C, Van Allen B, Habben J, Li B. Genome-wide analysis of alternative splicing during development and drought stress in maize. Plant Physiol. 2016;170(1):586–99.View ArticlePubMedGoogle Scholar
  51. Chung JS, Zhu JK, Bressan RA, Hasegawa PM, Shi H. Reactive oxygen species mediate Na+-induced SOS1 mRNA stability in Arabidopsis. Plant J. 2008;53(3):554–65.View ArticlePubMedGoogle Scholar
  52. Shi H, Ishitani M, Kim C, Zhu JK. The Arabidopsis thaliana salt tolerance gene SOS1 encodes a putative Na+/H+ antiporter. Proc Natl Acad Sci USA. 2000;97(12):6896–901.View ArticlePubMedPubMed CentralGoogle Scholar
  53. Saltzman AL, Pan Q, Blencowe BJ. Regulation of alternative splicing by the core spliceosomal machinery. Genes Dev. 2011;25(4):373–84.View ArticlePubMedPubMed CentralGoogle Scholar
  54. Thomas J, Palusa SG, Prasad KV, Ali GS, Surabhi GK, Ben-Hur A, Abdel-Ghany SE, Reddy AS. Identification of an intronic splicing regulatory element involved in auto-regulation of alternative splicing of SCL33 pre-mRNA. Plant J. 2012;72(6):935–46.View ArticlePubMedGoogle Scholar
  55. Marquez Y, Hopfler M, Ayatollahi Z, Barta A, Kalyna M. Unmasking alternative splicing inside protein-coding exons defines exitrons and their role in proteome plasticity. Genome Res. 2015;25(7):995–1007.View ArticlePubMedPubMed CentralGoogle Scholar
  56. Yu H, Tian C, Yu Y, Jiao Y. Transcriptome survey of the contribution of alternative splicing to proteome diversity in Arabidopsis thaliana. Mol Plant. 2016;9(5):749–52.View ArticlePubMedGoogle Scholar
  57. Carvalho RF, Feijao CV, Duque P. On the physiological significance of alternative splicing events in higher plants. Protoplasma. 2013;250(3):639–50.View ArticlePubMedGoogle Scholar
  58. Duque P. A role for SR proteins in plant stress responses. Plant Signal Behav. 2011;6(1):49–54.View ArticlePubMedPubMed CentralGoogle Scholar
  59. Isshiki M, Tsumoto A, Shimamoto K. The serine/arginine-rich protein family in rice plays important roles in constitutive and alternative splicing of pre-mRNA. Plant Cell. 2006;18(1):146–58.View ArticlePubMedPubMed CentralGoogle Scholar
  60. Feng J, Li J, Gao Z, Lu Y, Yu J, Zheng Q, Yan S, Zhang W, He H, Ma L, et al. SKIP confers osmotic tolerance during salt stress by controlling alternative gene splicing in Arabidopsis. Mol Plant. 2015;8(7):1038–52.View ArticlePubMedGoogle Scholar
  61. Zhu JK. Cell signaling under salt, water and cold stresses. Curr Opin Plant Biol. 2001;4(5):401–6.View ArticlePubMedGoogle Scholar
  62. Schmidt R, Mieulet D, Hubberten HM, Obata T, Hoefgen R, Fernie AR, Fisahn J, San Segundo B, Guiderdoni E, Schippers JH, et al. Salt-responsive ERF1 regulates reactive oxygen species-dependent signaling during the initial response to salt stress in rice. Plant Cell. 2013;25(6):2115–31.View ArticlePubMedPubMed CentralGoogle Scholar
  63. Chen S, Jiang J, Li H, Liu G. The salt-responsive transcriptome of Populus simonii x Populus nigra via DGE. Gene. 2012;504(2):203–12.View ArticlePubMedGoogle Scholar
  64. Cui P, Zhang S, Ding F, Ali S, Xiong L. Dynamic regulation of genome-wide pre-mRNA splicing and stress tolerance by the Sm-like protein LSm5 in Arabidopsis. Genome Biol. 2014;15(1):R1.View ArticlePubMedPubMed CentralGoogle Scholar
  65. Braunschweig U, Gueroussov S, Plocik AM, Graveley BR, Blencowe BJ. Dynamic integration of splicing within gene regulatory pathways. Cell. 2013;152(6):1252–69.View ArticlePubMedPubMed CentralGoogle Scholar
  66. Syed NH, Kalyna M, Marquez Y, Barta A, Brown JWS. Alternative splicing in plants - coming of age. Trends Plant Sci. 2012;17(10):616–23.View ArticlePubMedPubMed CentralGoogle Scholar
  67. Chang YF, Imam JS, Wilkinson MF. The nonsense-mediated decay RNA surveillance pathway. Annu Rev Biochem. 2007;76:51–74.View ArticlePubMedGoogle Scholar
  68. Chen L, Bush SJ, Tovar-Corona JM, Castillo-Morales A, Urrutia AO. Correcting for differential transcript coverage reveals a strong relationship between alternative splicing and organism complexity. Mol Biol Evol. 2014;31(6):1402–13.View ArticlePubMedPubMed CentralGoogle Scholar
  69. Flowers TJ. Improving crop salt tolerance. J Exp Bot. 2004;55(396):307–19.View ArticlePubMedGoogle Scholar
  70. Cramer GR, Nowak RS. Supplemental manganese improves the relative growth, net assimilation and photosynthetic rates of salt-stressed barley. Physiol Plantarum. 1992;84(4):600–5.View ArticleGoogle Scholar
  71. Zhang T, Jiang J. Extraction of total RNA in cotton tissues with CTAB-acidic phenolic method. Cotton Sci. 2003;15:166–7.Google Scholar
  72. Patel RK, Jain M. NGS QC toolkit: a toolkit for quality control of next generation sequencing data. PLoS One. 2012;7(2):e30619.View ArticlePubMedPubMed CentralGoogle Scholar
  73. Trapnell C, Pachter L, Salzberg SL. TopHat: discovering splice junctions with RNA-Seq. Bioinformatics. 2009;25(9):1105–11.View ArticlePubMedPubMed CentralGoogle Scholar
  74. Trapnell C, Roberts A, Goff L, Pertea G, Kim D, Kelley DR, Pimentel H, Salzberg SL, Rinn JL, Pachter L. Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks. Nat Protoc. 2012;7(3):562–78.View ArticlePubMedPubMed CentralGoogle Scholar
  75. Sammeth M. Complete alternative splicing events are bubbles in splicing graphs. J Comput Biol. 2009;16(8):1117–40.View ArticlePubMedGoogle Scholar
  76. Du Z, Zhou X, Ling Y, Zhang Z, Su Z. agriGO: a GO analysis toolkit for the agricultural community. Nucleic Acids Res. 2010;38:W64–70.View ArticlePubMedPubMed CentralGoogle Scholar
  77. Xu YH, Wang JW, Wang S, Wang JY, Chen XY. Characterization of GaWRKY1, a cotton transcription factor that regulates the sesquiterpene synthase gene (+)-delta-cadinene synthase-A. Plant Physiol. 2004;135:507–15.View ArticlePubMedPubMed CentralGoogle Scholar
  78. Livak KJ, Schmittgen TD. Analysis of relative gene expression data using real-time quantitative PCR and the 2(T)(-Delta Delta C) method. Methods. 2001;25(4):402–8.View ArticlePubMedGoogle Scholar


© The Author(s). 2018