Open Access

Genome-wide cataloging and analysis of alternatively spliced genes in cereal crops

  • Xiang Jia Min1, 2Email author,
  • Brian Powell3,
  • Jonathan Braessler3,
  • John Meinken2, 3, 5,
  • Feng Yu3 and
  • Gaurav Sablok4
BMC Genomics201516:721

https://doi.org/10.1186/s12864-015-1914-5

Received: 13 April 2015

Accepted: 9 September 2015

Published: 21 September 2015

Abstract

Background

Protein functional diversity at the post-transcriptional level is regulated through spliceosome mediated pre-mRNA alternative splicing (AS) events and that has been widely demonstrated to be a key player in regulating the functional diversity in plants. Identification and analysis of AS genes in cereal crop plants are critical for crop improvement and understanding regulatory mechanisms.

Results

We carried out the comparative analyses of the functional landscapes of the AS using the consensus assembly of expressed sequence tags and available mRNA sequences in four cereal plants. We identified a total of 8,734 in Oryza sativa subspecies (ssp) japonica, 2,657 in O. sativa ssp indica, 3,971 in Sorghum bicolor, and 10,687 in Zea mays AS genes. Among the identified AS events, intron retention remains to be the dominant type accounting for 23.5 % in S. bicolor, and up to 55.8 % in O. sativa ssp indica. We identified a total of 887 AS genes that were conserved among Z. mays, S. bicolor, and O. sativa ssp japonica; and 248 AS genes were found to be conserved among all four studied species or ssp. Furthermore, we identified 53 AS genes conserved with Brachypodium distachyon. Gene Ontology classification of AS genes revealed functional assignment of these genes in many biological processes with diverse molecular functions.

Conclusions

AS is common in cereal plants. The AS genes identified in four cereal crops in this work provide the foundation for further studying the roles of AS in regulation of cereal plant growth and development. The data can be accessed at Plant Alternative Splicing Database (http://proteomics.ysu.edu/altsplice/).

Keywords

Alternative splicing Cereal crops Expressed sequence tags mRNA

Background

Spliceosome mediated post-transcriptional modifications are the biggest challenges in understanding and predicting the degree of certainty and complexity of the proteome diversity [1, 2]. One of the most important mechanisms that contribute to the diversity in the protein isoforms is alternative splicing (AS), thus modulating the protein function as a consequence of the linking of the functional units (exons and introns) in a ubiquitous manner [3]. In addition, to the observed alternative splicing sub-types such as exon skipping (ES), alternative donor (AltD) or acceptor (AltA) site, and intron retention (IR), various complex types can be formed by combination of basic events [4, 5]. Apart from the four basic events, alternative transcripts may arise as a consequence of the alternative transcription initiation, alternative transcription termination, and alternative polyadenylation [2]. AS isoforms might encode distinct functional proteins, or might be nonfunctional, which harbor a premature termination codon. These nonfunctional isoforms generated through the process called “regulated unproductive splicing and translation” are degraded by a process known as nonsense-mediated decay [6].

Previous reports estimated around 90 % of human genes containing multiple exons are alternatively spliced [7, 8]. In line with the observed reports in humans, alternative splicing has been shown to be a major player in generation of the plant proteome diversity with 60 % of Arabidopsis thaliana multi-exon genes undergoing alternative splicing [9]. Genome-wide identification and physiological implications of AS have been reported in a number of model and non-model plant species including A. thaliana [1013], Oryza sativa [14], Nelumbo nucifera (sacred lotus) [15], Vitis vinifera [16], Brachypodium distachyon [5, 17]. AS transcripts are generally generated through three pathways: (1) IR in the mature mRNA; (2) alternative exon usage (AEU), resulting in ES; and (3) the use of cryptic splice sites that may elongate or shorten an exon that generates AltD or AltA site or both [14, 17]. Approximately 60–75 % of AS events occur within the protein coding regions of mRNAs, resulting changes in binding properties, intracellular localization, protein stability, enzymatic, and signaling activities [18]. In plants, IR has been shown to be the most dominant form with reports suggesting the proportions of intron containing genes undergoing AS in plants ranged from ~30 % to >60 % depending the depth of available transcriptome data [4, 5]. On contrast, recent reports suggest the down-regulation of the IR events and up-regulation of the alternative donor/acceptor site (AltDA) and ES under heat stress in model Physcomitrella patens [19]. With the advent of the Next Generation Sequencing (NGS) based approaches, fine scale physiological implications revealed alternative splicing as the prominent mechanism, which regulates the microRNA- mediated gene regulation by increasing the complexity of the alternative mRNA processing in Arabidopsis [20]. Complex networks of regulation of gene expression and variation in AS has played a major role in the adaptation of plants to their corresponding environment and additionally in coping with environmental stresses [13].

Rice (O. sativa ssp japonica and indica), maize (Zea mays), and sorghum (Sorghum bicolor) are important cereal crops as major sources of food in many countries. Previously several approaches have widely demonstrated the identification of the quantitative trait loci, genes and proteins linked to the functional grain content in these species [21]. However, a major portion of the gene functional diversity is controlled by a spliceosomal regulated AS. AS has been shown to be a critical regulator in grass clade, demonstrating several of the genes involved in flowering and abiotic stress depicting alternative splicing [4, 17, 22]. Identifying alternative splicing genes in these cereal plants is the first step toward understanding the functions and regulations of these genes in plant development and abiotic or biotic stress resistance. Previously, using the homology based mapping approach and expressed sequence tags (ESTs) representing the functional transcripts, we identified a total of 941 AS genes in B. distachyon, a model temperate grass [5, 17]. Previous and recent reports on the identification and prevalence of the alternative splicing events in O. sativa [4, 23], S. bicolor [24], and Z. mays [25] have shown the functional diversity changes through EST/RNA-seq approaches. Previous report by Ner-Gaon et al. suggested a 3.7-fold difference in AS rates between O. sativa and S. bicolor using EST pairs gapped alignment [26]. The lack of the identification of the comparative AS events in cereal plants and realizing the importance of these functional foods in climate changes, we attempted to carry out the large scale analysis using the so far currently ESTs and mRNA based information in cereal plants to identify species specific and conserved AS events across cereal plants. In this work, we compared the AS event landscape and the AS gene functional diversity in cereal plants, which includes O. sativa ssp japonica and indica, S. bicolor and Z. mays, with a much deeper coverage of the identified AS events and also comparatively analyzed these AS genes with AS genes identified from B. distachyon to reveal conserved patterns of the AS across the grass species. Identified AS events will allow for the experimental characterization of the AS genes involved in important physiological processes. Investigation of the genome-wide conserved AS events across different species will shed light on the understanding of the evolution of the functional diversity in cereal plant for crop improvement.

Methods

Sequence datasets and sequence assembly

To identify the putative functional transcriptional changes across the Panicoideae lineage, we systematically queried and downloaded expressed sequence tags (ESTs) and mRNA sequences of O. sativa ssp japonica and indica, S. bicolor, and Z. mays from the dbEST and nucleotide repository of National Center for Biotechnology Information (NCBI; www.ncbi.nlm.nih.gov). Prior to aligning the ESTs/mRNAs to the corresponding genomic sequence, we applied stringent cleaning procedure using the strategy outlined below: 1) ESTs and mRNA sequences were subsequently cleaned using EMBOSS “trim” tool for trimming of the polyA or polyT ends; 2) Cleaned and trimmed ESTs and mRNA sequences were blasted using the BLASTN against UniVec and E. coli database for removal of vector and E. coli contaminants; 3) BLASTN searches against the plant repeat database which was built with TIGR gramineae repeat data and species specific repeat data including sorghum, maize, and rice available from ftp://ftp.plantbiology.msu.edu/pub/data/TIGR_Plant_Repeats/. Following stringent cleaning procedure, we assembled rice and sorghum cleaned EST and mRNA sequences using CAP3 with the following parameters: −p 95 –o 50 –g 3 –y 50 –t 1000 [27]. In case of the maize data, owing to the large number of available ESTs for this species, which is difficult to assemble, we followed an alternative way of assembling those ESTs. We first mapped ESTs and mRNA sequences to each individual chromosome of the maize genome using GMAP with default settings [28], and then chromosome specific-mapped ESTs and mRNAs were assembled individually using CAP3 with the parameters as mentioned above. The unmapped data and all assembled data from each individual assembly were combined and then re-assembled using CAP3 to generate a final consensus assembly for the further identification of the AS events. The raw data and assembled data for each organism were summarized in Table 1. For the prediction of the AS events, genome sequences, predicted protein coding DNA sequences (CDS), and related GFF data of O. sativa ssp japonica, Z. mays, and S. bicolor were downloaded from Phytozome database (http://www.phytozome.net/) [2932]. The genome sequences and CDS data of O. sativa ssp indica (strain 93–11) were downloaded from BGI database (http://rise2.genomics.org.cn/page/rice/index.jsp) [33].
Table 1

Summary of raw sequence data and assembled data in each organism

Species

ESTs

mRNAs

Total Sequences

Cleaned Sequences

Total PUTs

Average Length (bp)

O. sativa ssp japonica

987327

82451

1069868

1053842

163778

783

O. sativa ssp indica

207012

11953

219065

212768

102424

751

S. bicolor

209835

33248

243083

241690

60189

1002

Z. mays

2019524

91990

2111514

1822653

488243

466

PUTs putative unique transcripts

Putative unique transcripts to genome mapping, identification and functional annotation of AS isoforms

In the present study, taking into the account the genome duplication events in Z. mays and S. bicolor, accurate prediction of the alternative splicing events is a major concern over the decades. In our study, calling and predicting alternative splicing events is taken into account by mapping of EST and mRNA assemblies, i.e. putative unique transcripts (hereafter simply referred them as PUTs), to the corresponding genomic sequences were carried out using in-house developed algorithm, ASFinder (http://proteomics.ysu.edu/tools/ASFinder.html/) [34], which uses SIM4 program [35] to map PUTs to the corresponding genome and then subsequently identifies those PUTs that are mapped to the same genomic location but have variable exon-intron boundaries as AS isoforms. To avoid the call of the spurious alternative splicing events, we applied a threshold of minimum of 95 % identity of aligned PUT with a genomic sequence, a minimum of 80 bp aligned length, and >75 % of a PUT sequence aligned to the genome [17]. Application of the above identity percentage and the aligned length removes the chance of the false positive AS events calling as a result of genome duplication events. The output file (AS.gtf) of ASFinder was then subsequently submitted to AStalavista server (http://genome.crg.es/astalavista/) for AS event analysis [36]. The percentage of alternative splicing genes was estimated using the genome predicted gene models having alternative splicing PUT isoforms among total genes models having at least one PUT, the results were presented in Table 2.
Table 2

Percentage of alternative splicing genes

 

Total mapped PUTs (%)

PUT match to gene model

Total unique genes

AS genes

AS (%)

O. sativa ssp japonica

104447 (63.8)

71830

26191

7883

30.1

O. sativa ssp indica

47843 (46.7)

36467

17402

2414

13.9

S. bicolor

50224 (83.4)

38654

26540

3580

13.5

Z. mays

207332 (42.5)

119418

28698

9689

33.8

AS Alternative splicing

We further queried the coding potential and corresponding coding frame of each PUT using the ORFPredictor [37], and to assess the full–length transcript coverage using TargetIdentifier [38] as previously described. Functional classification was assigned to the PUTs by performing BLASTX searches with an E-value threshold of 1E-5 against UniProtKB/Swiss-Prot. Predicted protein sequences from ORFPredictor were further annotated using rpsBLAST against the PFAM database (http://pfam.xfam.org/). Gene Ontologies (GOs) were assigned on the basis of the functional homology obtained by the BLASTX searching algorithm against the UniProtKB/Swiss-Prot. The GO categories were further analyzed using GO SlimViewer using plant specific GO terms [39]. To assess the functional coverage of the assembled PUTs, we further compared PUTs against the predicted gene primary transcripts using BLASTN with a cut off E-value of 1E-10, ≥ 95 % identity and minimum aligned length of 80 bp.

Conserved alternatively spliced genes in cereal plants and visualization of AS

For the identification of the potentially conserved AS genes among O. sativa ssp japonica and indica, Z. mays and S. bicolor, reciprocal BLASTP (cutoff E-value 1E-10) were done using the longest (or longer) ORF of the AS PUT isoforms for classifying the conserved AS pairs between species or sub-species. Venn graphical visualization for conserved AS pairs were obtained using R programming language (http://www.r-project.org/). Visualization of the alternative splicing events with genome tracks is critically important from two points of views: (1) To have a graphic look at the corresponding genomic coordinate and associated genic functional changes; and (2) To extract the corresponding spliced region of interest for functional primer designing of putative AS events. Keeping in view the above points, AS events identified in this study along with the integrated genomic tracks are available from Plant Alternative Splicing Database (http://proteomics.ysu.edu/altsplice/) [15, 17]. The specific pages associated with the cereal plants offer several end-users functionalities such as querying using the PUT ID, gene ID, keywords in functional annotation, PFAM, or AS event types as “query fields”. Additionally, the identified AS events can be visualized and compared with predicted gene models using GBrowse for comparative assessment. Nevertheless, we also deployed BLASTN functionality to search for the PUTs and AS isoforms. The data analyzed along with the GO and PFAM annotations in the present research are publicly available at: http://proteomics.ysu.edu/publication/data/.

Results and discussion

EST assembly and annotation

Optimization of the assembly parameters and mapping functionally annotated PUTs is a key parameter to provide a robust identification and classification of the AS events. Table 1 represents the assembly information, including the final cleaned reads for the assembly, mRNA count for each species, assembled consensus sequence and average length of assembled consensus. In the present research, we assembled and generated consensus PUTs accounting for a total of 163,778 PUTs in O. sativa ssp japonica, 102,424 PUTs in O. sativa ssp indica, 60,189 PUTs in S. bicolor, and 488,243 PUTs in Z. mays. The average length (N50) of assembled PUTs was 783 bp in O. sativa ssp japonica, 751 bp in O. sativa ssp indica, 1,002 bp in S. bicolor, and 466 bp in Z. mays. To check for the coverage of the assembled functional transcriptome, we further checked for the functional assignments and all the assembled PUTs were structurally and functionally annotated including putative open reading frame (ORF) prediction, coding region full-length prediction, a putative function and PFAM prediction, which ensures the reliability of the assembly strategies in case of large complex ploidy genomes underwent whole genome duplication events. PUTs were mapped to their corresponding genomes and predicted gene models were also visualized using GBrowse.

Gapped alignments of PUTs to genome, detection and classification of alternative splicing events

Following the sequence assembly, resulting unique PUTs were mapped onto their corresponding genomic sequences using gapped alignments as implemented in SIM4 method that was integrated as part of ASFinder [34]. The numbers of mapped PUTs and matched gene models, as well as the number of the observed AS genes are presented in Table 2. We observed that a relatively larger proportion of PUTs in S. bicolor (83.4 %) and O. sativa ssp japonica (63.8 %) aligned to their genomes as compared to the other cereal plants. We identified a total of 8,734 in Oryza sativa subspecies (ssp) japonica, 2,657 in O. sativa ssp indica, 3,971 in Sorghum bicolor, and 10,687 in Zea mays AS genes (Table 3). The percentage of AS genes was estimated based on the proportion of predicted gene models having AS PUT isoforms over the total gene models having an EST (PUT) evidence (Table 2). The percentages of AS genes vary in different cereal plants, up to 30.1 % in O. sativa ssp japonica and 33.8 % in Z. mays, and relatively low in O. sativa ssp indica (13.9 %) and in S. bicolor (13.5 %). The difference in the mapping rate and AS rate might be due to the difference in the number of ESTs available for respective species. Previous reports on AS in B. distachyon clearly illustrates the fact that availability of the more ESTs/mRNAs reflects the prediction of the AS landscape [5, 17].
Table 3

Alternative splicing events in different cereal species

Species

IR(%)

AltD(%)

AltA (%)

ES (%)

Complex event (%)

Total events

Total AS genes

O. sativa ssp japonica

8288 (42.0)

1245 (6.3)

1950 (9.9)

762 (3.9)

7447 (37.8)

19692

8734

O. sativa ssp indica

2193 (55.8)

332 (8.5)

576 (14.7)

161 (4.1)

665 (16.9)

3927

2657

S. bicolor

4448 (23.5)

1072 (5.7)

1230 (6.5)

507 (2.6)

11681 (61.7)

18938

3971

Z. mays

11048 (40.4)

2080 (7.6)

3314 (11.4)

1568 (5.7)

5576 (20.4)

23386

10687

IR Intron Retention, AltD Alternative donor, AltA Alternative acceptor, ES exon skipping

Recent reports using the RNA-seq technology revealed that AS is common in plants—around 61 % of multi-exonic genes in A. thaliana are alternatively spliced under normal growth conditions [12], and ~40 % of intron containing genes that undergo AS in maize [25]. Classification of the AS events observed in the cereal plants are listed in Table 3 showing the prevalence of the IR as the major splicing type showing frequency as high as 55.8 % in O. sativa ssp indica and as low as 23.5 % in S. bicolor (Table 3). The high frequency of the IR in the mature mRNA is perfectly in line with the previously observed frequencies of IR (30–50 %) in AS landscape in A. thaliana and O. sativa [14]. It is worthwhile to mention that plant spliceosomal machinery supports the intron definition model, thus identifies the introns for pre-mRNA splicing as oppose to the abundant exon-spliceosome model observed in case of mammals. Previous arguments have clearly justified the cause and benefits of retaining the introns as potential cytoplasmic translatable transcripts [26] or as mediators of increasing the gene expression, a process widely described as intron-mediated enhancement (IME) of gene expression [40]. The abundance of IR as a major AS event is consistent with previous reports including Medicago tuncatula (39 %), Populus trichocarpa (34 %), A. thaliana (56 %), O. sativa (54 %), Chlamydomonas reinhardtii (50 %), Z. mays (58–62 %) and B. distachyon (55.5 %) [14, 17, 25, 41, 42]. In contrast, recently IR has been found remarkably repressed under elevated temperature in P. patens [19].

Alternative acceptor (AltA) and donor (AltD) represent the second most abundant and classified functional class of observed AS events with AltA showing a relatively higher frequency as compared to AltD (Table 3). Although ES events have been described as the rarest events in plants, which are in line with the observed results in this study, recently they have been proposed as the candidates of the transgene regulation using the conditional splicing [43]. We noted that 61.7 % events are complex events in sorghum, which have more than one basic event in compared paired PUTs. This is clearly related to the relative longer lengths of the PUTs in sorghum assembly. Recent reports suggest the differential up-regulation of the alternative donor/acceptor site (AltDA) and ES elucidating the importance of these events as indicators of early heat stress [19].

Our data in this work clearly showed that the number of AS genes and the percentage of genes with AS are different in different crops (Tables 2 and 3). However, this observation only reflects the current state in these plants based on the available data. Our previous analysis on AS in B. distachyon clearly demonstrated that more AS genes were identified with more available ESTs/mRNA data [5, 17]. This is also consistent with the finding of increasing frequency of occurrence of AS in Arabidopsis with time—a reflection of an accumulation of available transcriptome data, for example, only 1.2 % of the genes in Arabidopsis were reported undergo AS in 2003 and now it was estimated over 60 % of intron-containing genes undergo AS [13].

Features of exons and introns in protein coding genes: indicators of gene evolution

Understanding the patterns of gene evolution and identifying signatures of convergent and divergent evolution is of paramount importance, especially when we are addressing the genome complexity in terms of gene evolution. Exon-intron framework properties such as length distribution and GC content evolution have been previously used to demonstrate the gene evolution [44]. Additionally, longer introns as compared to short introns have been shown to play an important role in the gene expression [40, 45]. However, reports by Yang [46] demonstrate the negative correlation of the long introns with the levels of the expression in A. thaliana and O. sativa. Realizing the importance of the features of exon-intron in evolution and physiological responses, we extracted and plotted the length distribution of all internal exons and introns from each plant and the results are summarized (Table 4; Fig. 1; Fig. 2). Interestingly, we observed that the average internal exon lengths in O. sativa ssp indica and Z. mays are almost similar, and are relatively much shorter than the internal exon lengths in O. sativa ssp japonica and S. bicolor. On the other hand, Z. mays had the longer intron length (554 bp) and showed a wide variation in intron lengths as compared to the observed range of intron lengths (422–440 bp) in other three cereal plants in this study. We further analyzed deeply the exon size and intron size distribution frequencies demonstrating that Z. mays and O. sativa ssp indica had a relatively much higher proportion of internal exons of a smaller size (<120 bp) (Fig. 1). The observed frequency of internal exon lengths below 300 bp was 0.93 in Z. mays, 0.95 in O. sativa ssp indica, 0.89 in S. bicolor, and 0.90 in O. sativa ssp japonica. S. bicolor and O. sativa ssp japonica displayed more exons of relatively large size, whereas Z. mays displayed a higher number of long introns (Fig. 2). Prevalence of the introns richness and specifically long introns have been previously been shown to be widely associated with the increased expression of Adh1, Sh1, Bz1, Hsp82, actin, and GapA1 genes in Z. mays [4751] and salT, Act1, and tpi genes in rice [52, 53]. Additionally, a relative higher proportion of introns having a shorter length were observed in S. bicolor. We also observed ~2 % introns in maize and a small number of introns (<0.5 %) in other plants having a size >10 kb. However, taking into account the possible errors in PUT and genome assembly, these long introns were not included in the calculation of the average intron size. It is worthwhile to mention that the average internal exon size (180 bp) and intron size (440 bp) in O. sativa ssp japonica obtained in this work were close to the exon (193 bp) and intron (433 bp) size obtained previously in O. sativa, which presents the robustness of the implemented approach [14].
Table 4

Exon and intron size in cereal plants

 

Exon

Intron

 

Sample size

Average size (bp)

SD

Sample Size

Average size (bp)

SD

O. sativa ssp japonica

127627

180

261

180575

440

695

O. sativa ssp indica

52330

133

113

79735

434

703

S. bicolor

106753

179

222

144860

422

747

Z. mays

137020

142

133

209139

554

1057

SD Standard deviation

Fig. 1

Distribution of internal exon size: The x-axis indicates the size of internal exons. Bin sizes are right inclusive (e.g., bin 100 comprises sequences of lengths 1–100 bp). The y-axis indicates the frequency of internal exons. The inset shows a detailed distribution of small internal exons

Fig. 2

Distribution of intron size: The x-axis indicates the size of introns. Bin sizes are right inclusive (e.g., bin 100 comprises sequences of lengths 1 –100 bp). The y-axis indicates the frequency of introns. The inset shows a detailed distribution of small introns

Functional classification of AS genes

AS and gene regulation can be observed at almost all levels of biological interactions [54]. The AS transcripts identified in the present study were functionally annotated for the Gene Ontologies (GOs) and for putative protein domains association by performing a BLASTX search of all PUTs against UniProt/Swiss-Prot database. The ORFs of PUTs were identified using ORFPredictor webserver [37]. The protein families of the AS genes, using the longest ORF of each AS gene, were predicted using rpsBLAST searching PFAM database. Among predicted ORFs of these AS genes, 6,900 in Z. mays, 4,939 in O. sativa ssp japonica, 1,362 in O. sativa ssp indica, and 2,890 in S. bicolor were classified with a putative protein family (Table 5, Additional file 1: Table S1). We further classified AS gene functional products into 2,030 unique protein families in Z. mays, 1,708 unique protein families in O. sativa ssp japonica, 757 unique protein families in O. sativa ssp indica, and 1,194 unique protein families in S. bicolor. Among the protein functions, encoded by these AS genes, widely includes protein kinase domain, RNA recognition motif, protein tyrosine kinase, ring finger domain, cytochrome P450, Myb-like DNA-binding domain, WRKY DNA-binding domain, Thioredoxin and protein phosphatase 2C (Table 5). A complete list of all the protein families encoded by AS genes is shown in Additional file 1: Table S1. Our analysis demonstrated that AS genes in cereal plants encode diverse protein families that play important roles in various biological processes. A classical example can be WRKY- DNA binding domains, which represents the largest and functionally diverse transcription factors in plants playing a major role in developmental and physiological processes. Previous studies have widely demonstrated the presence of the alternative ORF in the WRKY genes [55, 56]. Yang et al. [57] and Feng et al. [58] have clearly highlighted the role of the alternative splicing and WRKY in plant immunity. Previous functional studies have shown the presence of the splicing of the R-type intron and V-type intron in O. sativa WRKY genes and functionally correlated them to plant immunity [59]. MYB-domains play an important role in plant defense mechanism and are transcriptionally regulated by alterative splicing in A. thaliana and O. sativa and encode MYB- or MYB-related proteins [60]. Alternative splicing of MYB related genes MYR1 and MYR2 have clearly demonstrated the change in protein dimerization and folding as a consequence of alternative splicing thus affecting the transcriptional sensitivity in light mediated responses [61].
Table 5

Protein family classification of alternative genes in cereal plants

PFAM

Domain

Z. mays

O. sativa ssp japnoica

O. sativa ssp indica

S. bicolor

Putative Functions

pfam00069

Pkinase

205

228

55

74

Protein kinase domain

pfam00076

RRM_1

112

61

32

43

RNA recognition motif

pfam07714

Pkinase_Tyr

88

79

12

25

Protein tyrosine kinase

pfam13639

zf-RING_2

53

28

8

15

Ring finger domain

pfam00067

p450

45

56

4

37

Cytochrome P450

pfam00481

PP2C

45

25

11

11

Protein phosphatase 2C

pfam00249

Myb_DNA-binding

44

14

7

22

Myb-like DNA-binding domain

pfam00179

UQ_con

43

17

10

7

Ubiquitin-conjugating enzyme

pfam00010

HLH

41

5

1

7

Helix-loop-helix DNA-binding domain

pfam00071

Ras

38

20

11

7

Ras family

pfam00141

peroxidase

37

30

11

31

Peroxidase

pfam00153

Mito_carr

35

24

7

14

Mitochondrial carrier protein

pfam01559

Zein

35

0

0

0

Zein seed storage protein

pfam01490

Aa_trans

33

12

1

7

Transmembrane amino acid transporter protein

pfam02365

NAM

33

33

8

14

No apical meristem (NAM) protein

pfam00125

Histone

31

9

3

5

Core histone H2A/H2B/H3/H4

pfam01370

Epimerase

31

26

8

22

NAD dependent epimerase/dehydratase family

pfam00083

Sugar_tr

30

22

7

10

Sugar (and other) transporter

pfam00847

AP2

30

9

3

9

AP2 domain

pfam00106

adh_short

29

25

10

15

short chain dehydrogenase

pfam00657

Lipase_GDSL

29

5

1

16

GDSL-like Lipase/Acylhydrolase

pfam00085

Thioredoxin

28

14

6

11

Thioredoxin

pfam00226

DnaJ

28

18

9

9

DnaJ domain

pfam03151

TPT

27

9

2

6

Triose-phosphate Transporter family

pfam00004

AAA

26

18

6

14

ATPase family associated with various cellular

pfam00270

DEAD

24

21

5

8

DEAD/DEAH box helicase

pfam00504

Chloroa_b-bind

24

19

11

20

Chlorophyll A-B binding protein

pfam02309

AUX_IAA

24

13

5

5

AUX/IAA family

pfam00149

Metallophos

23

9

3

13

Calcineurin-like phosphoesterase

pfam00134

Cyclin_N

22

9

2

4

Cyclin

pfam00450

Peptidase_S10

21

18

6

18

Serine carboxypeptidase

pfam03106

WRKY

21

22

7

7

WRKY DNA -binding domain

pfam13041

PPR_2

21

30

1

12

PPR repeat family

Total

 

6900

4939

1362

2890

 

Note: a complete list is shown in Additional file 1: Table S1

GO analysis according to biological and molecular function revealed a wide visibility in all the major biological and molecular functions (Table 6; Table 7). Interestingly, even the data we collected are from pooled data in the public domain, i.e., not from a strictly controlled experiment, our GO analysis revealed that relative to the average of AS percentage, a higher percentage of genes involved in response to abiotic stimulus, photosynthesis, carbohydrate metabolic process, and cell death are involved in AS in cereal plants. In contrast, the genes involved in multicellular organismal development and reproduction had a lower percentage of AS (Table 6). GO molecular function analysis revealed that genes encoding proteins having DNA binding, sequence-specific DNA binding transcription factor activity, nuclease activity had a lower percentage of AS, and the gene coding proteins for protein binding and having kinase activity had a higher percentage of AS in the majority of plants (Table 7). Our observed results are consistent with literature reviewed recently by Reddy et al. [4] and Staiger and Brown [22] that AS is involved in most plant processes and plays regulated roles in plant development and stress responses.
Table 6

Classification of biological processes based on Gene Ontology (GO)

Table 7

Classification of molecular functions based on Gene Ontology (GO)

Conserved alternatively spliced genes

Classification of the conserved alternative splicing events provides a framework for understanding the evolution of the functional genes and their genic-regulation at the transcriptional level, which may initiate the cross-talks between the evolution of the genes under AS and between the transcriptional environment and the ecological adaptation. For the identification of the conserved AS pairs, longest ORFs of AS genes in each studied species were compared using the BLASTP (cutoff E-value 1E-10) to identify the best-reciprocal top hit as the conserved pairs. In total, we identified 1558 AS genes conserved between O. sativa ssp japonica and indica, 3,246 AS genes conserved between O. sativa ssp japonica and Z. mays, and 1,967 AS genes between S. bicolor and Z. mays (Additional file 2: Table S3). A total of 887 AS genes are conserved among Z. mays, S. bicolor, and O. sativa ssp japonica. More importantly, we identified 248 AS genes conserved among all four plants (Fig. 3). Furthermore, using the same approach, we identified a total of 53 AS genes conserved with B. distachyon belonging to BEP-clade of grass evolution. The co-orthologous conserved 53 AS genes are listed in Table  8 . The set of co-orthologs 248 AS genes conserved in the four plants, with 53 of them conserved to B. distachyon, are provided in Additional file 3: Table S2 (can be downloaded at http://proteomics.ysu.edu/altsplice/). Interestingly, one of the candidates among the conserved gene is Drought-induced protein (Di19). It has been previously suggested that the presence of the retained intron within the coding sequence may give rise to the non-sense mediated decay (NMD) [62]. Recent studies highlight the role of cycloheximide in introducing pre-mature termination codons (PTCs) and NMD in A. thaliana Di19, indicating the splicing mechanism in Di19 [63]. Identification of the Di19 mediated splicing will be of critical importance in increasing the drought resistance or increasing the captive yield of the cereal plants, which are acting as major suppliers of food in climate change. As current analysis were based on the pooled EST/mRNA sequences available in the public domain, more biologically functionally conserved AS genes will be identified when more transcriptome data are collected with improved technologies, various environmental conditions, developmental stages and tissues in these cereal crops. The present data is of immense potential for experimental validation and highlights the role of the AS and biological significance in plant, growth development and environmental regulation, which is a standing challenge in climate change.
Fig. 3

Conserved alternative splicing genes in rice (Oryza sativa) ssp japonica, rice ssp indica, sorghum (Sorghum bicolor), and maize (Zea mays) plants

Table 8

Conserved alternative splicing genes among five monocot plants

O. sativa ssp indica

O. sativa ssp japonica

Z. mays

S. bicolor

B. distachyon

CDD/Pfam

  

Osi19962

Osj954

Zm92934

Sb6267

Bd2565

pfam03171

2OG-FeII_Oxy

2OG-Fe(II) oxygenase superfamily

Osi18787

Osj44013

Zm40020

Sb12294

Bd28385

pfam00004

AAA

ATPase family associated with various cellular

Osi6875

Osj22392

Zm88316

45969421

Bd7352

pfam00248

Aldo_ket_red

Aldo/keto reductase family

Osi9356

Osj41340

Zm162

Sb17314

Bd6214

pfam00248

Aldo_ket_red

Aldo/keto reductase family

CX100091

Osj15328

Zm35072

Sb10885

Bd29210

pfam00439

Bromodomain

Bromodomain

Osi12568

Osj24409

Zm100060

Sb8817

Bd24009

pfam05042

Caleosin

Caleosin related protein

CT843009

Osj14649

Zm32705

Sb6709

Bd10918

pfam00571

CBS

CBS domain

Osi524

CT828785.1

Zm73067

Sb4586

Bd10523

pfam04733

Coatomer_E

Coatomer epsilon subunit

Osi21096

Osj16673

FL103380

2.42E + 08

Bd4031

pfam07876

Dabb

Stress responsive A/B Barrel Domain

Osi8549

Osj47391

Zm69871

Sb334

Bd7166

pfam05605

Di19

Drought induced 19 protein (Di19)

CT833644.1

CI258157

Zm20082

Sb10226

Bd7036

pfam05057

DUF676

Putative serine esterase (DUF676)

Osi21136

Osj16693

Zm46142

Sb13903

Bd7810

pfam05623

DUF789

Protein of unknown function (DUF789)

Osi19974

Osj14932

Zm70017

Sb10575

Bd3731595

pfam00676

E1_dh

Dehydrogenase E1 component

Osi1759

Osj22934

Zm35625

Sb15873

Bd7027

pfam01370

Epimerase

NAD dependent epimerase/dehydratase family

CT842225

Osj27697

Zm91971

Sb4930

Bd268

pfam00316

FBPase

Fructose-1-6-bisphosphatase

Osi20900

Osj16392

Zm58947

Sb3303

Bd7531597

pfam00210

Ferritin

Ferritin-like domain

Osi339

Osj20205

Zm20714

Sb12056

Bd6374

pfam00762

Ferrochelatase

Ferrochelatase

Osi11082

Osj491

Zm59942

Sb3313

Bd27405

pfam00125

Histone

Core histone H2A/H2B/H3/H4

Osi13655

Osj36042

Zm81325

Sb15256

Bd28446

pfam00403

HMA

Heavy-metal-associated domain

Osi11360

Osj36865

Zm38497

Sb20674

Bd9583

pfam00447

HSF_DNA-bind

HSF-type DNA-binding

Osi17520

Osj35947

Zm27347

Sb9471

Bd7833

pfam01156

IU_nuc_hydro

Inosine-uridine preferring nucleoside

Osi13902

Osj25885

Zm23750

Sb12436

Bd28318

pfam00013

KH_1

KH domain

Osi11280

Osj28328

Zm35841

Sb9907

Bd13744

cd00116

LRR_RI

Leucine-rich repeats (LRRs)

CT844279

CB642464

Zm3338

Sb7337

Bd28467

pfam01717

Meth_synt_2

Cobalamin-independent synthase

Osi1437

Osj37916

Zm4695

Sb5119

Bd7994

pfam00635

Motile_Sperm

MSP (Major sperm protein) domain

Osi231

Osj25397

Zm37411

Sb10332

Bd28960

pfam14360

PAP2_C

PAP2 superfamily C-terminal

Osi8815

Osj32580

Zm61468

Sb11226

Bd6619

pfam01195

Pept_tRNA_hydro

Peptidyl-tRNA hydrolase

Osi16666

Osj19199

Zm104454

Sb12015

Bd16056

pfam00450

Peptidase_S10

Serine carboxypeptidase

Osi12736

Osj14309

Zm22618

Sb7927

Bd8683

pfam00141

peroxidase

Peroxidase

Osi833

Osj39350

Zm92939

Sb19533

Bd5931597

pfam00069

Pkinase

Protein kinase domain

Osi3301

Osj17780

Zm29726

Sb14730

Bd29285

pfam00069

Pkinase

Protein kinase domain

Osi6061

Osj15126

Zm59883

Sb673

Bd15932

PLN02756

PLN02756

S-methyl-5-thioribose kinase

Osi6187

Osj42201

Zm39790

Sb2138

Bd8363

pfam00348

polyprenyl_synt

Polyprenyl synthetase

Osi13092

Osj21144

Zm33939

Sb5787

Bd23758

pfam14299

PP2

Phloem protein 2

Osi11891

NM_001070568.2

Zm87952

Sb2001

Bd2595

pfam00854

PTR2

POT family

Osi20788

Osj19691

Zm39384

30944654

Bd10083

pfam07992

Pyr_redox_2

Pyridine nucleotide-disulphide

CT837906.1

Osj7689

Zm101865

Sb5520

Bd25885

pfam00719

Pyrophosphatase

Inorganic pyrophosphatase

Osi21504

Osj17274

Zm6058

Sb11340

Bd21664

pfam00072

Response_reg

Response regulator receiver domain

Osi15366

Osj24220

Zm5068

Sb10671

Bd23705

pfam02453

Reticulon

Reticulon

Osi9029

Osj47510

Zm24118

Sb11323

Bd8231593

pfam03214

RGP

Reversibly glycosylated polypeptide

Osi5643

Osj25267

Zm80771

Sb227

Bd11010

pfam01246

Ribosomal_L24e

Ribosomal protein L24e

Osi8310

Osj36859

Zm101179

Sb11303

Bd6311

pfam00076

RRM_1

RNA recognition motif

Osi1456

Osj43479

Zm371

Sb12579

Bd15819

pfam00076

RRM_1

RNA recognition motif

Osi773

Osj43052

Zm24001

Sb2305

Bd20070

pfam00464

SHMT

Serine hydroxymethyltransferase

Osi9812

Osj14203

Zm39491

2.42E + 08

Bd28258

pfam01406

tRNA-synt_1e

tRNA synthetases class I (C) catalytic

Osi9653

Osj44577

Zm33457

Sb5144

Bd6360

pfam00443

UCH

Ubiquitin carboxyl-terminal hydrolase

Osi2251

Osj35805

Zm98577

2.42E + 08

Bd20683

pfam12076

Wax2_C

WAX2 C-terminal domain

Osi15508

Osj14495

Zm95

Sb10474

Bd4536

pfam05495

zf-CHY

CHY zinc finger

Osi21052

Osj4519

Zm39479

Sb9831

Bd24331

No Pfam predicted

 

Osi8778

Osj195

Zm100142

Sb1070

Bd2265

No Pfam predicted

 

Osi20728

Osj16408

Zm34294

57806619

Bd19455

No Pfam predicted

 

Osi17233

Osj20996

Zm100171

Sb1504

Bd16477

No Pfam predicted

 

CT830510.1

Osj3010

Zm96019

Sb2210

Bd12504

No Pfam predicted

 

Conclusions

In the present work, we investigated the functional landscape of the four most important cereal plants O. sativa ssp indica and japonica, S. bicolor and Z. mays using the updated EST and mRNA sequences available in NCBI thus bridging the knowledge gap and updating the conserved AS catalog with functional elucidation. The availability of the conserved AS genes among the four cereal plants will facilitate to understand the regulation of the alternative physiological processes in global climate change biology and their subsequent impact on the genic-environmental interactions.

Availability of supporting data

The data described in the work can be searched or downloaded at the Plant Alternative Splicing Database (http://proteomics.ysu.edu/altsplice/). Other detailed analysis data can be downloaded at http://proteomics.ysu.edu/publication/data/CerealAS/.

Abbreviations

AltA: 

Alternative acceptor site

AltD: 

Alternative donor site

AS: 

Alternative splicing

CDS: 

Coding DNA sequence

ES: 

Exon skipping

IR: 

Intron retention

PUT: 

Putative unique transcript

ssp: 

Subspecies

Declarations

Acknowledgements

The work was funded by the Ohio Plant Biotechnology Consortium (Grant 2013-003) through Ohio State University, Ohio Agricultural Research and Development Center to XJM. XJM was also supported by the College of Science, Technology, Engineering, and Mathematics Dean’s reassigned time for research. JM was supported with a graduate research assistantship by the Center for Applied Chemical Biology, Youngstown State University.

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), 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 (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
Department of Biological Sciences, Youngstown State University
(2)
Center for Applied Chemical Biology, Youngstown State University
(3)
Department of Computer Science and Information Systems, Youngstown State University
(4)
Plant Functional Biology and Climate Change Cluster (C3), University of Technology Sydney
(5)
Present address: Center for Health Informatics, University of Cincinnati

References

  1. Graveley BR. Alternative splicing: increasing diversity in the proteomic world. Trends Genet. 2001;17:100–7.View ArticlePubMedGoogle Scholar
  2. Roberts GC, Smith CW. Alternative splicing: combinatorial output from the genome. Curr Opin Chem Biol. 2002;6:375–83.View ArticlePubMedGoogle Scholar
  3. Hiller M, Huse K, Platzer M, Backofen R. Creation and disruption of protein features by alternative splicing - a novel mechanism to modulate function. Genome Biol. 2005;6:R58.PubMed CentralView ArticlePubMedGoogle Scholar
  4. Reddy AS, Marquez Y, Kalyna M, Barta A. Complexity of the alternative splicing landscape in plants. Plant Cell. 2013;25:3657–83.PubMed CentralView ArticlePubMedGoogle Scholar
  5. Sablok G, Gupta PK, Baek JM, Vazquez F, Min XJ. Genome-wide survey of alternative splicing in the grass Brachypodium distachyon: an emerging model biosystem for plant functional genomics. Biotechnol Lett. 2011;33:629–36.View ArticlePubMedGoogle Scholar
  6. Lewis BP, Green RE, Brenner SE. Evidence for the widespread coupling of alternative splicing and nonsense-mediated mRNA decay in humans. Proc Natl Acad Sci U S A. 2003;100:189–92.PubMed CentralView ArticlePubMedGoogle Scholar
  7. Wang ET, Sandberg R, Luo S, Khrebtukova I, Zhang L, Mayr C, et al. Alternative isoform regulation in human tissue transcriptomes. Nature. 2008;456:470–6.PubMed CentralView ArticlePubMedGoogle Scholar
  8. Chen L, Tovar-Corona J M, Urrutia AO. Alternative splicing: a potential source of functional innovation in the eukaryotic genome. Int J Evol Biol. 2012, doi:10.1155/2012/596274Google Scholar
  9. Carvalho RF, Feijão CV, Duque P. On the physiological significance of alternative splicing events in higher plants. Protoplasma. 2013;250:639–50.View ArticlePubMedGoogle Scholar
  10. Filichkin SA, Priest HD, Givan SA, Shen R, Bryant DW, Fox SE, et al. Genome-wide mapping of alternative splicing in Arabidopsis thaliana. Genome Res. 2010;20:45–58.PubMed CentralView ArticlePubMedGoogle Scholar
  11. Zhang PG, Huang SZ, Pin AL, Adams KL. Extensive divergence in alternative splicing patterns after gene and genome duplication during the evolutionary history of Arabidopsis. Mol Biol Evol. 2010;27:1686–97.View ArticlePubMedGoogle Scholar
  12. 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;22:1184–95.PubMed CentralView ArticlePubMedGoogle Scholar
  13. Syed NH, Kalyna M, Marquez Y, Barta A, Brown JW. Alternative splicing in plants - coming of age. Trends Plant Sci. 2012;17:616–23.PubMed CentralView ArticlePubMedGoogle Scholar
  14. Wang B, Brendel V. Genome wide comparative analysis of alternative splicing in plants. Proc Natl Acad Sci U S A. 2006;103:7175–80.PubMed CentralView ArticlePubMedGoogle Scholar
  15. VanBuren R, Walters B, Ming R, Min XJ. Analysis of expressed sequence tags and alternative splicing genes in sacred lotus (Nelumbo nucifera Gaertn.). Plant Omics J. 2013;6:311–7.Google Scholar
  16. Vitulo N, Forcato C, Carpinelli EC, Telatin A, Campagna D, D'Angelo M, 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.PubMed CentralView ArticlePubMedGoogle Scholar
  17. Walters B, Lum G, Sablok G, Min XJ. Genome-wide landscape of alternative splicing events in Brachypodium distachyon. DNA Res. 2013;20:163–71.PubMed CentralView ArticlePubMedGoogle Scholar
  18. Stamm S, Ben-Ari S, Rafalska I, Tang Y, Zhang Z, Toiber D, et al. Function of alternative splicing. Gene. 2005;344:1–20.View ArticlePubMedGoogle Scholar
  19. Chang CY, Lin WD, Tu SL. Genome-wide analysis of heat-sensitive alternative splicing in Physcomitrella patens. Plant Physiol. 2014;165:826–40.PubMed CentralView ArticlePubMedGoogle Scholar
  20. Yang X, Zhang H, Li L. Alternative mRNA processing increases the complexity of microRNA-based gene regulation in Arabidopsis. Plant J. 2012;70:421–31.View ArticlePubMedGoogle Scholar
  21. Mao H, Sun S, Yao J, Wang C, Yu S, Xu C, et al. Linking differential domain functions of the GS3 protein to natural variation of grain size in rice. Proc Natl Acad Sci U S A. 2010;107:19579–84.PubMed CentralView ArticlePubMedGoogle Scholar
  22. Staiger D, Brown JW. Alternative splicing at the intersection of biological timing, development, and stress responses. Plant Cell. 2013;25:3640–56.PubMed CentralView ArticlePubMedGoogle Scholar
  23. 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.PubMed CentralView ArticlePubMedGoogle Scholar
  24. Panahi B, Abbaszadeh B, Taghizadeghan M, Ebrahimie E. Genome-wide survey of alternative splicing in Sorghum bicolor. Physiol Mol Biol Plants. 2014;20:323–9.PubMed CentralView ArticlePubMedGoogle Scholar
  25. Thatcher SR, Zhou W, Leonard A, Wang BB, Beatty M, Zastrow-Hayes G, et al. Genome-wide analysis of alternative splicing in Zea mays: landscape and genetic regulation. Plant Cell. 2014;26:3472–87.PubMed CentralView ArticlePubMedGoogle Scholar
  26. Ner-Gaon H, Leviatan N, Rubin E, Fluhr R. Comparative cross-species alternative splicing in plants. Plant Physiol. 2007;144:1632–41.PubMed CentralView ArticlePubMedGoogle Scholar
  27. Huang X, Madan A. CAP3: A DNA sequence assembly program. Genome Res. 1999;9:868–77.PubMed CentralView ArticlePubMedGoogle Scholar
  28. Wu TD, Watanabe CK. GMAP: a genomic mapping and alignment program for mRNA and EST sequences. Bioinformatics. 2005;21:1859–75.View ArticlePubMedGoogle Scholar
  29. Goodstein DM, Shu S, Howson R, Neupane R, Hayes RD, Fazo J, et al. Phytozome: a comparative platform for green plant genomics. Nucleic Acids Res. 2012;40:D1178–1186.PubMed CentralView ArticlePubMedGoogle Scholar
  30. Ouyang S, Zhu W, Hamilton J, Lin H, Campbell M, Childs K, et al. The TIGR rice genome annotation resource: improvements and new features. Nucleic Acids Res. 2007;35:D883–887.PubMed CentralView ArticlePubMedGoogle Scholar
  31. Schnable PS, Ware D, Fulton RS, Stein JC, Wei F, Pasternak S, et al. The B73 maize genome: complexity, diversity, and dynamics. Science. 2009;326:1112–25.View ArticlePubMedGoogle Scholar
  32. Paterson AH, Bowers JE, Bruggmann R, Dubchak I, Grimwood J, Gundlach H, et al. The Sorghum bicolor genome and the diversification of grasses. Nature. 2009;457:551–6.View ArticlePubMedGoogle Scholar
  33. Yu J, Hu S, Wang J, Wong GK, Li S, Liu B, et al. A draft sequence of the rice genome (Oryza sativa L. ssp indica). Science. 2002;296:79–92.View ArticlePubMedGoogle Scholar
  34. Min XJ. ASFinder: a tool for genome-wide identification of alternatively spliced transcripts from EST-derived sequences. Int J Bioinformatics Res Appl. 2013;9:221–6.View ArticleGoogle Scholar
  35. Florea L, Hartzell G, Zhang Z, Rubin GM, Miller W. A computer program for aligning a cDNA sequence with a genomic DNA sequence. Genome Res. 1998;8:967–74.PubMed CentralPubMedGoogle Scholar
  36. Foissac S, Sammeth M. ASTALAVISTA: dynamic and flexible analysis of alternative splicing events in custom gene datasets. Nucleic Acids Res. 2007;35:W297–299.PubMed CentralView ArticlePubMedGoogle Scholar
  37. Min XJ, Butler G, Storms R, Tsang A. OrfPredictor: predicting protein-coding regions in EST-derived sequences. Nucleic Acids Res. 2005;33:W677–680.PubMed CentralView ArticlePubMedGoogle Scholar
  38. Min XJ, Butler G, Storms R, Tsang A. TargetIdentifier: a web server for identifying full-length cDNAs from EST sequences. Nucleic Acids Res. 2005;33:W669–72.PubMed CentralView ArticlePubMedGoogle Scholar
  39. McCarthy FM, Wang N, Magee GB, Williams WP, Luthe DS, Burgess SC. AgBase: a functional genomics resource for agriculture. BMC Genomics. 2006;7:229.PubMed CentralView ArticlePubMedGoogle Scholar
  40. Mascarenhas D, Mettler IJ, Pierce DA, Lowe HW. Intron-mediated enhancement of heterologous gene expression in maize. Plant Mol Biol. 1990;15:913–20.View ArticlePubMedGoogle Scholar
  41. Baek JM, Han P, Iandolino A, Cook DR. Characterization and comparison of intron structure and alternative splicing between Medicago truncatula, Populus trichocarpa. Arabidopsis Rice Plant Mol Biol. 2008;67:499–510.View ArticlePubMedGoogle Scholar
  42. Labadorf A, Link A, Rogers MF, Thomas J, Reddy ASN, Ben-Hur A. Genome-wide analysis of alternative splicing in Chlamydomonas reinhardtii. BMC Genomics. 2010;111:14.Google Scholar
  43. Hickey SF, Sridhar M, Westermann AJ, Qin Q, Vijayendra P, Liou G, et al. Transgene regulation in plants by alternative splicing of a suicide exon. Nucleic Acids Res. 2012;40:4701–10.PubMed CentralView ArticlePubMedGoogle Scholar
  44. Zhu L, Zhang Y, Zhang W, Yang S, Chen JQ, Tian D. Patterns of exon-intron architecture variation of genes in eukaryotic genomes. BMC Genomics. 2009;10:47.PubMed CentralView ArticlePubMedGoogle Scholar
  45. Niu D-K, Yang Y-F. Why eukaryotic cells use introns to enhance gene expression: Splicing reduces transcription-associated mutagenesis by inhibiting topoisomerase I cutting activity. Biol Direct. 2011;6:24.PubMed CentralView ArticlePubMedGoogle Scholar
  46. Yang H. In plants, expression breadth and expression level distinctly and non-linearly correlate with gene structure. Biol Direct. 2009;4:45.PubMed CentralView ArticlePubMedGoogle Scholar
  47. Rose AB, Beliakoff JA. Intron-mediated enhancement of gene expression independent of unique intron sequences and splicing. Plant Physiol. 2000;122:535–42.PubMed CentralView ArticlePubMedGoogle Scholar
  48. Maas C, Laufs J, Grant S, Korfhage C, Werr W. The combination of a novel stimulatory element in the first exon of the maize Shrunken-1 gene with the following intron 1 enhances reporter gene expression up to 1000-fold. Plant Mol Biol. 1991;16:199–207.View ArticlePubMedGoogle Scholar
  49. Sinibaldi RM, Mettler IJ. Intron splicing and intron-mediated enhanced expression in monocots. In: Cohn WE, Moldave K, editors. Progress in Nucleic Acid Research and Molecular Biology, vol. 42. New York: Academic Press; 1992. p. 229–57.Google Scholar
  50. Donath M, Mendel R, Cerff R, Martin W. Intron-dependent transient expression of the maize GapA1 gene. Plant Mol Biol. 1995;28:667–76.View ArticlePubMedGoogle Scholar
  51. Rethmeier N, Seurinck J, Van Montagu M, Cornelissen M. Intron-mediated enhancement of transgene expression in maize is a nuclear, gene-dependent process. Plant J. 1997;12:895–9.View ArticlePubMedGoogle Scholar
  52. McElroy D, Zhang W, Cao J, Wu R. Isolation of an efficient actin promoter for use in rice transformation. Plant Cell. 1990;2:163–71.PubMed CentralView ArticlePubMedGoogle Scholar
  53. Xu Y, Yu H, Hall TC. Rice triosephosphate isomerase gene 5′ sequence directs β-glucuronidase activity in transgenic tobacco but requires an intron for expression in rice. Plant Physiol. 1994;106:459–67.PubMed CentralView ArticlePubMedGoogle Scholar
  54. Kelemen O, Convertini P, Zhang Z. Function of alternative splicing. Gene. 2013;514:1–30.View ArticlePubMedGoogle Scholar
  55. Wu KL. The WRKY family of transcription factors in rice and Arabidopsis and their origins. DNA Res. 2005;12:9–26.View ArticlePubMedGoogle Scholar
  56. Xie Z. Annotations and functional analyses of the rice WRKY gene superfamily reveal positive and negative regulators of abscisic acid signaling in aleurone cells. Plant Physiol. 2005;137:176–89.PubMed CentralView ArticlePubMedGoogle Scholar
  57. Yang S, Tang F, Zhu H. Alternative splicing in plant immunity. Int J Mol Sci. 2014;15:10424–45.PubMed CentralView ArticlePubMedGoogle Scholar
  58. Feng B, Yang S, Du H, Hou X, Zhang J, Liu H, et al. Molecular characterization and functional analysis of plant WRKY genes. African J Biotechnol. 2012;11:13606–13.Google Scholar
  59. Peng Y. OsWRKY62 is a negative regulator of basal and Xa21- mediated defense against Xanthomonas oryzae pv. Oryzae in rice Mol Plant. 2008;1:446–58.View ArticlePubMedGoogle Scholar
  60. Li J, Li X, Guo L, Lu F, Feng X, He K, et al. A subgroup of MYB transcription factor genes undergoes highly conserved alternative splicing in Arabidopsis and rice. J Exp Bot. 2006;57:1263–73.View ArticlePubMedGoogle Scholar
  61. Zhao C, Beers E. Alternative splicing of Myb-related genes MYR1 and MYR2 may modulate activities through changes in dimerization, localization, or protein folding. Plant Signal Behav. 2013;11:e27325.View ArticleGoogle Scholar
  62. Morello L, Breviario D. Plant spliceosomal introns: not only cut and paste. Curr Genomics. 2008;9:227–38.PubMed CentralView ArticlePubMedGoogle Scholar
  63. Kalyna M, Simpson CG, Syed NH, Lewandowska D, Marquez Y, Kusenda B, et al. Alternative splicing and nonsense-mediated decay modulate expression of important regulatory genes in Arabidopsis. Nucleic Acids Res. 2012;40:2454–69.PubMed CentralView ArticlePubMedGoogle Scholar

Copyright

© Min et al. 2015

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