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. 2017 Apr 19;7:46552. doi: 10.1038/srep46552

Identification and characterization of miRNAs in two closely related C4 and C3 species of Cleome by high-throughput sequencing

Shuangcheng Gao 1,*, Wei Zhao 2,*, Xiang Li 2, Qingbo You 2, Xinjie Shen 2, Wei Guo 2, Shihua Wang 1, Guoan Shi 1, Zheng Liu 3,b, Yongqing Jiao 2,a
PMCID: PMC5396198  PMID: 28422166

Abstract

Cleome gynandra and Cleome hassleriana, which are C4 and C3 plants, respectively, are two species of Cleome. The close genetic relationship between C. gynandra and C. hassleriana provides advantages for discovering the differences in leaf development and physiological processes between C3 and C4 plants. MicroRNAs (miRNAs) are a class of important regulators of various biological processes. In this study, we investigate the differences in the characteristics of miRNAs between C. gynandra and C. hassleriana using high-throughput sequencing technology. In total, 94 and 102 known miRNAs were identified in C. gynandra and C. hassleriana, respectively, of which 3 were specific for C. gynandra and 10 were specific for C. hassleriana. Ninety-one common miRNAs were identified in both species. In addition, 4 novel miRNAs were detected, including three in C. gynandra and three in C. hassleriana. Of these miRNAs, 67 were significantly differentially expressed between these two species and were involved in extensive biological processes, such as glycol-metabolism and photosynthesis. Our study not only provided resources for C. gynandra and C. hassleriana research but also provided useful clues for the understanding of the roles of miRNAs in the alterations of biological processes in leaf tissues during the evolution of the C4 pathway.

C4 photosynthesis, which is a complex biological trait that enables plants to accumulate biomass at a much faster rate, has evolved independently multiple times within the angiosperms1,2. Considering that C4 crops have greater water and nitrogen use efficiency and photosynthesis efficiency than C3 crops, it has been suggested that integrating the characteristics of the C4 pathway into C3 crops could be used to increase yield3,4. However, other than the basic biochemistry, we still have a very limited understanding of C4 photosynthesis. Particularly, the genetic basis associated with the alterations in the cell biology and development of the C4 leaf is poorly understood5. Thus, the application of integrating the C4 pathway into C3 crops to increase yield remains restricted.

The Cleome species belongs to the family Cleomaceae, which was formerly Capparaceae6. Capparaceae was referred to as a sister to Brassicaceae and possessed a number of C4 species7. The genus Cleome contains a phylogenetic progression from C3 to C4 photosynthesis, which provides the potential to understanding the genetic changes between the C4 and C3 photosynthetic pathways during the evolutionary process7. Cleome gynandra (also known as Gynandropsis gynandra) and Cleome hassleriana (also known as Tarenaya hassleriana) are two typical species of Cleome. C. gynandra is a C4 photosynthesis species, while C. hassleriana is a C3 species. Phylogenetically, both C. gynandra and C. hassleriana are near relatives of Arabidopsis thaliana, which is a well-known C3 model plant5,7. C. gynandra, C. hassleriana and A. thaliana have high sequence similarities8. Approximately 70% of the RNA-seq reads of C. gynandra and C. hassleriana matched approximately 55.3% of the genes in the A. thaliana reference8. Therefore, the vast molecular data and well-annotated genome resources available for A. thaliana can be applied to C. gynandra and C. hassleriana. Moreover, these two species have small statures and short life cycles, are self-fertile and produce a large amount of seed6. These advantages make C. gynandra and C. hassleriana potential C4 and C3 models for identifying the differences in leaf development and physiological processes between C3 and C4 plants.

MicroRNAs are a class of 18–24 nucleotide (nt) small non-coding endogenous RNAs that are capable of regulating gene expression at the post-transcriptional level9,10,11,12. Research regarding A. thaliana, Oryza sativa and maize showed that plant miRNAs played important roles in regulating plant development13,14. For example, miR165 directs the cleavage of REV to regulate leaf morphogenesis15; miR319 regulates leaf senescence by controlling the TCP transcription factors10,16; and miR166 targets Leafbladeless1 (LBL1) to influence the specification of the adaxial/upper leaf surface17. The identification and characterization of miRNAs would provide helpful information for understanding the biological processes in plants.

Due to the merits of Cleome gynandra and Cleome hassleriana in studying the genetic basis of the differences in the cell biology and development between C4 and C3 plants, we used high-throughput sequencing technology to identify and characterize the miRNAs in the leaf tissues of these two species to gain some insights into the molecular changes that occur during the evolution of C3 plants to C4 plants. Thus, a number of known and novel miRNAs were detected in the two species, and the expression patterns of these miRNAs were profiled. By comparing our data with published data from Arabidopsis, the potential functions of these miRNAs were also investigated. Our study not only provided resources for studying C. gynandra and C. hassleriana but also provided useful clues for the understanding of the roles of the miRNAs in the differences in the biological processes in leaf tissues during the evolution of the C4 pathway.

Results

Overview of small RNA sequencing data

To identify the miRNAs in C. gynandra and C. hassleriana, two small RNA libraries were constructed for the fully expanded leaves (approximately 5 cm in length) of these two species and then sequenced by an Illumina HiSeq 2500 analyzer. In total, 6,698,595 and 7,177,444 raw reads were obtained (Table 1). The raw reads were further cleaned and trimmed by removing the low-quality reads and sequences that were either smaller than 18 nt or longer than 30 nt. Then, the clean reads were aligned with the Silva, GtRNAdb, Rfam and Repbase databases to filter the ribosomal RNA (rRNA), transfer RNA (tRNA), small nucleolar RNA (snoRNA) and the repeats, which in total, accounted for approximately 45.32% and 46.68% of the clean reads from the two libraries. Finally, a total of 1,761,554 and 1,687,208 clean unannotated reads were obtained for the two libraries (Table 1).

Table 1. Summary of the small RNA sequencing datasets.

RNA class Cleome gynandra Cleome hassleriana
Total % of Total Total % of Total
Raw reads 6,698,595 100 7,177,444 100
3′ adapter and length filter 1,859,667 27.76 2,120,400 29.54
rRNA 2,917,851 43.56 3,139,295 43.74
snoRNA 562 0.01 1,419 0.02
tRNA 117,536 1.75 209,775 2.92
Repeats 41,425 0.62 19,347 0.27
Unannotated reads 1,761,554 26.30 1,687,208 23.51
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We investigated the length distribution of the small RNA reads (Fig. 1a and b). The most abundant small RNA in C. gynandra and C. hassleriana was 21 nt in length, accounting for 55.78% and 60.58% of total sRNA reads, respectively. The second most abundant small RNA in C. gynandra was 20 nt, representing 10.91% of the total sRNA reads. In comparison, the second most abundant small RNA in C. hassleriana was 29 nt, representing 9.81% of total sRNA reads (Supplementary Table 1).

Figure 1. Length distribution and abundance of the small RNA sequences in Cleome gynandra and Cleome hassleriana.

Figure 1

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Percentage (line) and reads number (bar) of redundant (a) and mapped unique (b) sequences of 18–30 nt length for each sequenced library.

Identification of known miRNAs in C. gynandra and C. hassleriana

C. gynandra and C. hassleriana are members of Cleome, which is the most closely related genus to Arabidopsis thaliana5,7. Previous studies showed that C. gynandra, C. hassleriana and Arabidopsis thaliana had high sequence similarities8. We could use the well-annotated genome of Arabidopsis thaliana to identify the miRNAs and further investigate their expression in C. gynandra and C. hassleriana18.

The high-quality unannotated sRNA reads, which ranged from 18 to 30 nt, were aligned against the Arabidopsis thaliana genome. The mapped reads were further aligned with miRBase21.0 to identify the known miRNAs. In total, 94 and 102 known miRNAs were identified in C. gynandra and C. hassleriana, respectively, which were members of 32 plant conserved families (Table 2). Ninety-one known miRNAs were commonly identified in both C. gynandra and C. hassleriana (Table 2). Among these miRNA families, MIR156 contained the most abundant members, including nine members in C. hassleriana and eight members in C. gynandra. The second most abundant was the MIR166 family, which contained eight members in C. hassleriana and seven members in C. gynandra. Certain miRNA families were only detected in one species. For example, MIR397 and MIR828 were specific to C. hassleriana, while MIR161 and MIR8175 were specific to C. gynandra (Supplementary Fig. 1).

Table 2. Known miRNAs identified in Cleome gynandra and Cleome hassleriana.

miRNA family miRNA members Reference miRNA of Arabidopsis miRNA sequence TPM
Cleome gynandra Cleome hassleriana
MIR156 Cleome-miR156a-5p ath-miR156a-5p UGACAGAAGAGAGUGAGCAC 1791.13 361.33
Cleome-miR156b-5p ath-miR156b-5p UGACAGAAGAGAGUGAGCAC 1787.50 359.85
Cleome-miR156c-5p ath-miR156c-5p UGACAGAAGAGAGUGAGCAC 1791.13 361.33
Cleome-miR156d-5p ath-miR156d-5p UGACAGAAGAGAGUGAGCAC 2103.58 352.48
Cleome-miR156e ath-miR156e UGACAGAAGAGAGUGAGCAC 1776.60 352.48
Cleome-miR156f-5p ath-miR156f-5p UGACAGAAGAGAGUGAGCAC 1776.60 352.48
Cleome-miR156g ath-miR156g CGACAGAAGAGAGUGAGCAC 25.43 8.85
Cleome-miR156h ath-miR156h UGACAGAAGAAAGAGAGCAC 0 7.374
Cleome-miR156j ath-miR156j UGACAGAAGAGAGAGAGCAC 3.63 1.47
MIR157 Cleome-miR157a-3p ath-miR157a-3p GCUCUCUAGCCUUCUGUCAUC 0 1.4748
Cleome-miR157a-5p ath-miR157a-5p UUGACAGAAGAUAGAGAGCAC 1733.00 5276.87
Cleome-miR157b-3p ath-miR157b-3p GCUCUCUAGCCUUCUGUCAUC 0 1.4748
Cleome-miR157b-5p ath-miR157b-5p UUGACAGAAGAUAGAGAGCAC 1733.00 5276.87
Cleome-miR157c-5p ath-miR157c-5p UUGACAGAAGAUAGAGAGCAC 1827.46 5980.35
Cleome-miR157d ath-miR157d UGACAGAAGAUAGAGAGCAC 316.08 511.76
MIR159 Cleome-miR159a ath-miR159a UUUGGAUUGAAGGGAGCUCUA 12091.05 20914.26
Cleome-miR159b-3p ath-miR159b-3p UUUGGAUUGAAGGGAGCUCUU 2368.80 2325.77
Cleome-miR159c ath-miR159c UUUGGAUUGAAGGGAGCUCCU 6081.85 556.00
MIR160 Cleome-miR160a-3p ath-miR160a-3p GCGUAUGAGGAGCCAUGCAUA 948.25 42.77
Cleome-miR160a-5p ath-miR160a-5p UGCCUGGCUCCCUGUAUGCCA 1351.52 168.13
Cleome-miR160b ath-miR160b UGCCUGGCUCCCUGUAUGCCA 1271.59 162.23
Cleome-miR160c-5p ath-miR160c-5p UGCCUGGCUCCCUGUAUGCCA 1351.52 168.13
MIR161 Cleome-miR161.2 ath-miR161.2 UCAAUGCAUUGAAAGUGACUA 3.63 0
MIR162 Cleome-miR162a-3p ath-miR162a-3p UCGAUAAACCUCUGCAUCCAG 715.73 541.25
Cleome-miR162a-5p ath-miR162a-5p UGGAGGCAGCGGUUCAUCGAUC 276.12 79.64
Cleome-miR162b-3p ath-miR162b-3p UCGAUAAACCUCUGCAUCCAG 715.73 541.25
Cleome-miR162b-5p ath-miR162b-5p UGGAGGCAGCGGUUCAUCGAUC 257.95 79.64
MIR164 Cleome-miR164a ath-miR164a UGGAGAAGCAGGGCACGUGCA 4781.19 9599.53
Cleome-miR164b-5p ath-miR164b-5p UGGAGAAGCAGGGCACGUGCA 4879.29 9729.31
Cleome-miR164c-5p ath-miR164c-5p UGGAGAAGCAGGGCACGUGCG 105.36 126.83
MIR165 Cleome-miR165a-3p ath-miR165a-3p UCGGACCAGGCUUCAUCCCCC 279.75 2572.07
Cleome-miR165b ath-miR165b UCGGACCAGGCUUCAUCCCCC 279.75 2567.64
MIR166 Cleome-miR166a-3p ath-miR166a-3p UCGGACCAGGCUUCAUUCCCC 122665.26 119371.02
Cleome-miR166b-3p ath-miR166b-3p UCGGACCAGGCUUCAUUCCCC 122483.61 119376.92
Cleome-miR166c ath-miR166c UCGGACCAGGCUUCAUUCCCC 125281.11 136788.52
Cleome-miR166d ath-miR166d UCGGACCAGGCUUCAUUCCCC 125281.11 136794.41
Cleome-miR166e-3p ath-miR166e-3p UCGGACCAGGCUUCAUUCCCC 122549.00 119472.79
Cleome-miR166e-5p ath-miR166e-5p GGAAUGUUGUCUGGCACGAGG 0 1.47
Cleome-miR166f ath-miR166f UCGGACCAGGCUUCAUUCCCC 122545.37 119475.74
Cleome-miR166g ath-miR166g UCGGACCAGGCUUCAUUCCCC 125346.51 136890.28
MIR167 Cleome-miR167a-5p ath-miR167a-5p UGAAGCUGCCAGCAUGAUCUA 22623.48 5222.30
Cleome-miR167b ath-miR167b UGAAGCUGCCAGCAUGAUCUA 22568.98 5214.92
Cleome-miR167c-5p ath-miR167c-5p UAAGCUGCCAGCAUGAUCUUG 14.53 4.42
Cleome-miR167d ath-miR167d UGAAGCUGCCAGCAUGAUCUGG 4490.54 1278.66
MIR168 Cleome-miR168a-3p ath-miR168a-3p CCCGCCUUGCAUCAACUGAAU 406.91 622.37
Cleome-miR168a-5p ath-miR168a-5p UCGCUUGGUGCAGGUCGGGAA 2786.61 6180.92
Cleome-miR168b-5p ath-miR168b-5p UCGCUUGGUGCAGGUCGGGAA 2862.90 6310.71
MIR169 Cleome-miR169a-3p ath-miR169a-3p GGCAAGUUGUCCUUGGCUAC 0 8.85
Cleome-miR169a-5p ath-miR169a-5p CAGCCAAGGAUGACUUGCCGA 3.63 8.85
Cleome-miR169b-5p ath-miR169b-5p CAGCCAAGGAUGACUUGCCGG 174.39 8.85
Cleome-miR169c ath-miR169c CAGCCAAGGAUGACUUGCCGG 130.79 0
Cleome-miR169d ath-miR169d UGAGCCAAGGAUGACUUGCCG 0 1.47
Cleome-miR169e ath-miR169e UGAGCCAAGGAUGACUUGCCG 0 1.47
Cleome-miR169f-5p ath-miR169f-5p UGAGCCAAGGAUGACUUGCCG 47.23 1.47
Cleome-miR169g-5p ath-miR169g-5p UGAGCCAAGGAUGACUUGCCG 43.60 1.47
MIR170 Cleome-miR170-5p ath-miR170-5p UAUUGGCCUGGUUCACUCAGA 72.66 58.99
MIR171 Cleome-miR171a-3p ath-miR171a-3p UGAUUGAGCCGCGCCAAUAUC 3.63 1.47
Cleome-miR171a-5p ath-miR171a-5p UAUUGGCCUGGUUCACUCAGA 69.03 58.99
Cleome-miR171b-3p ath-miR171b-3p UUGAGCCGUGCCAAUAUCACG 112.63 39.82
Cleome-miR171c-3p ath-miR171c-3p UUGAGCCGUGCCAAUAUCACG 112.63 39.82
Cleome-miR171c-5p ath-miR171c-5p AGAUAUUGGUGCGGUUCAAUC 36.33 28.02
MIR172 Cleome-miR172a ath-miR172a AGAAUCUUGAUGAUGCUGCAU 846.52 886.36
Cleome-miR172b-3p ath-miR172b-3p AGAAUCUUGAUGAUGCUGCAU 846.52 886.36
Cleome-miR172c ath-miR172c AGAAUCUUGAUGAUGCUGCAG 14.53 10.32
Cleome-miR172d-3p ath-miR172d-3p AGAAUCUUGAUGAUGCUGCAG 14.53 8.85
Cleome-miR172e-3p ath-miR172e-3p GGAAUCUUGAUGAUGCUGCAU 3.63 5.90
MIR2111 Cleome-miR2111a-3p ath-miR2111a-3p GUCCUCGGGAUGCGGAUUACC 14.53 1.47
Cleome-miR2111a-5p ath-miR2111a-5p UAAUCUGCAUCCUGAGGUUUA 65.40 7.37
Cleome-miR2111b-5p ath-miR2111b-5p UAAUCUGCAUCCUGAGGUUUA 61.76 4.42
MIR319 Cleome-miR319a ath-miR319a UUGGACUGAAGGGAGCUCCCU 185.29 1.47
Cleome-miR319b ath-miR319b UUGGACUGAAGGGAGCUCCCU 207.09 1.47
Cleome-miR319c ath-miR319c UUGGACUGAAGGGAGCUCCUU 18.17 47.19
MIR390 Cleome-miR390a-3p ath-miR390a-3p CGCUAUCCAUCCUGAGUUUCA 21.80 10.32
Cleome-miR390a-5p ath-miR390a-5p AAGCUCAGGAGGGAUAGCGCC 1892.86 1259.49
Cleome-miR390b-5p ath-miR390b-5p AAGCUCAGGAGGGAUAGCGCC 1892.86 1259.49
MIR393 Cleome-miR393a-5p ath-miR393a-5p UCCAAAGGGAUCGCAUUGAUCC 1169.87 373.13
Cleome-miR393b-5p ath-miR393b-5p UCCAAAGGGAUCGCAUUGAUCC 1169.87 373.13
MIR394 Cleome-miR394a ath-miR394a UUGGCAUUCUGUCCACCUCC 461.41 168.13
Cleome-miR394b-5p ath-miR394b-5p UUGGCAUUCUGUCCACCUCC 461.41 168.13
MIR395 Cleome-miR395a ath-miR395a CUGAAGUGUUUGGGGGAACUC 21.80 4.42
Cleome-miR395b ath-miR395b CUGAAGUGUUUGGGGGGACUC 29.07 159.28
Cleome-miR395c ath-miR395c CUGAAGUGUUUGGGGGGACUC 29.07 159.28
Cleome-miR395d ath-miR395d CUGAAGUGUUUGGGGGAACUC 21.80 4.42
Cleome-miR395e ath-miR395e CUGAAGUGUUUGGGGGAACUC 21.80 4.42
Cleome-miR395f ath-miR395f CUGAAGUGUUUGGGGGGACUC 29.07 159.28
MIR396 Cleome-miR396a-3p ath-miR396a-3p GUUCAAUAAAGCUGUGGGAAG 1071.77 446.87
Cleome-miR396a-5p ath-miR396a-5p UUCCACAGCUUUCUUGAACUG 5649.51 2558.79
Cleome-miR396b-3p ath-miR396b-3p GCUCAAGAAAGCUGUGGGAAA 203.46 337.73
Cleome-miR396b-5p ath-miR396b-5p UUCCACAGCUUUCUUGAACUU 1594.94 1402.54
MIR397 Cleome-miR397a ath-miR397a UCAUUGAGUGCAGCGUUGAUG 0 10.32
MIR398 Cleome-miR398a-3p ath-miR398a-3p UGUGUUCUCAGGUCACCCCUU 101.73 42.77
Cleome-miR398b-3p ath-miR398b-3p UGUGUUCUCAGGUCACCCCUG 0 197.62
Cleome-miR398c-3p ath-miR398c-3p UGUGUUCUCAGGUCACCCCUG 0 197.62
MIR399 Cleome-miR399b ath-miR399b UGCCAAAGGAGAGUUGCCCUG 18.17 30.97
Cleome-miR399c-3p ath-miR399c-3p UGCCAAAGGAGAGUUGCCCUG 21.80 26.55
Cleome-miR399d ath-miR399d UGCCAAAGGAGAUUUGCCCCG 7.27 17.70
Cleome-miR399f ath-miR399f UGCCAAAGGAGAUUUGCCCGG 18.17 11.80
MIR403 Cleome-miR403-3p ath-miR403-3p UUAGAUUCACGCACAAACUCG 980.94 2153.22
MIR408 Cleome-miR408-3p ath-miR408-3p AUGCACUGCCUCUUCCCUGGC 18.17 1588.37
MIR8175 Cleome-miR8175 ath-miR8175 GAUCCCCGGCAACGGCGCCA 10.90 0
MIR824 Cleome-miR824-5p ath-miR824-5p UAGACCAUUUGUGAGAAGGGA 1860.16 920.28
MIR827 Cleome-miR827 ath-miR827 UUAGAUGACCAUCAACAAACU 559.50 244.82
MIR828 Cleome-miR828 ath-miR828 UCUUGCUUAAAUGAGUAUUCCA 0 1.47
MIR858 Cleome-miR858a ath-miR858a UUUCGUUGUCUGUUCGACCUU 185.29 535.36
Cleome-miR858b ath-miR858b UUCGUUGUCUGUUCGACCUUG 610.37 536.83
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Based on the number of sequencing reads, the miRNA families displayed a significantly varied abundance (Table 2). The miRNA families MIR159, MIR164, MIR166, MIR167 and MIR168 showed a higher expression level in both C. gynandra and C. hassleriana. MIR156, MIR157, MIR390, MIR393 and MIR396 also showed an average expression level of more than 1,000 TPM in both species. In contrast, certain miRNA families, such as MIR161, MIR170, MIR171, MIR395, MIR397 and MIR399, were observed to have a low expression level (Table 2).

Identification of novel miRNAs in C. gynandra and C. hassleriana

To investigate the novel miRNAs in C. gynandra and C. hassleriana, the unannotated sRNA sequences were used to predict novel miRNA by exploring the hairpin structures based on comparisons with the genome sequence of Arabidopsis thaliana, the Dicer cleavage site and the minimum free energy using miREvo and mirdeep2 software. Finally, only four novel miRNA candidates were identified and temporarily named in the format of Cleome-novel-miR-number (Table 3). Cleome_novel_miR1 and Cleome_novel_miR2 were detected in both C. gynandra and C. hassleriana. Cleome_novel_miR3 was specific to C. gynandra, and Cleome_novel_miR4 was specific to C. hassleriana. These novel miRNAs showed a relatively low expression level in both species (Table 3).

Table 3. Novel miRNA candidates identified in Cleome gynandra and Cleome hassleriana.

miRNA name miRNA sequence TPM
Cleome gynandra Cleome hassleriana
Cleome_novel_miR1 uuuccuucuucuuguugc 69.03 14.75
Cleome_novel_miR2 uucguccccacagacggcgcca 374.21 97.34
Cleome_novel_miR3 cgauccccggcaacggugcca 7.27 0
Cleome_novel_miR4 acaggugguggaacaaauaugagu 0 16.22
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Differentially expressed miRNAs between C. gynandra and C. hassleriana

In total, sixty-seven differentially expressed miRNAs were identified between C. gynandra and C. hassleriana, 21 of which were down-regulated and 46 were up-regulated in C. gynandra compared with those in C. hassleriana (Fig. 2a and b).

Figure 2. Expression patterns of miRNAs between Cleome gynandra and Cleome hassleriana.

Figure 2

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(a) Hierarchical cluster analysis of differentially expressed miRNAs between Cleome gynandra and Cleome hassleriana. The abundance used in the heatmap refers to the high (in red) and low (in green) expression levels of the miRNAs (TPM value of each miRNA). (b) Expression patterns of the differentially expressed miRNAs between Cleome gynandra and Cleome hassleriana. The color scales indicate the species-specific expressed miRNAs (grey bar) and non-species-specific expressed miRNAs (blue bar).

Among these miRNAs, some showed drastically changed expression patterns. For example, the expression levels of two members of the miR165 family, Cleome-miR165a-3p and Cleome-miR165b, were both reduced nearly ten-fold in C. gynandra compared with those in C. hassleriana (Supplementary Table 2); Cleome-miR159c had a greater than ten-fold higher expression level in C. gynandra than that in C. hassleriana (Supplementary Table 2). Certain miRNAs were expressed in a species-specific manner. For example, Cleome_novel_miR3, Cleome-miR8175 and Cleome-miR169c were only detected in C. gynandra, while Cleome-miR169a-3p, Cleome-miR397a, Cleome_novel_miR4, Cleome-miR398b-3p and Cleome-miR398c-3p were only expressed in C. hassleriana (Fig. 2b and Supplementary Table 2). Some miRNAs that belong to the same miRNA family exhibited different expression patterns. For example, three members of the MIR395 family, Cleome-miR395a, Cleome-miR395d, and Cleome-miR395e, were up-regulated in C. gynandra, while three other members of this family, Cleome-miR395b, Cleome-miR395c and Cleome-miR395f, were down-regulated (Supplementary Table 2).

We identified 194 putative target genes for the differentially expressed miRNAs that were identified in our study (Supplementary Table 3). Külahoglu et al. (2014) had investigated the dynamic changes in the gene expression levels during leaf development between C. gynandra and C. hassleriana by high-throughput sequencing19. We analyzed these publicly available RNA-seq datasets to explore the expression levels of our putative target genes. In total, 51 of the 194 genes were found to be differentially expressed between C. gynandra and C. hassleriana, 38 of which had opposite miRNAs expression patterns between these two species (Supplementary Table 4). These 38 target genes were found to be the targets of 8 conserved and 1 putative novel miRNA families, including Cleome-miR156a-5p, Cleome-miR160a-5p, Cleome-miR164a, Cleome-miR171b-3p, Cleome-miR319c, Cleome-miR394a, Cleome-miR395a, Cleome-miR396a-5p and Cleome_novel_miR1 (Supplementary Table 4).

Function analysis of the identified miRNAs

To investigate the roles of the miRNAs in C. gynandra and C. hassleriana, the putative target homolog genes of these miRNAs in Arabidopsis thaliana were predicted and analyzed using miRNA family assignment. Two hundred twenty-eight putative target genes were predicted for 68 miRNAs in C. gynandra. In addition, two hundred twenty-three target genes were predicted for 72 miRNAs in C. hassleriana (Table 4). Of the known miRNAs, the MIR156 family could target the most abundant genes, which has 15 putative target genes. The second most abundant family was the MIR161 family, which could match 13 putative target genes. Of the novel miRNAs, only Cleome_novel_miR1 and Cleome_novel_miR3 were predicted to have target genes. Notably, the novel miRNA, Cleome_novel_miR1, could match 129 putative target genes (Supplementary Table 3). No target genes were found for the remaining 29 miRNAs in C. gynandra and 33 miRNAs in C. hassleriana.

Table 4. Summary of the putative target genes of the miRNAs in Cleome gynandra and Cleome hassleriana.

Species Number of all miRNAs Number of miRNAs with Target genes Number of Target genes
Cleome gynandra 97 68 228
Cleome hassleriana 105 72 223
Total 109 76 238
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GO categories and KEGG pathway analyses were performed for the differentially expressed miRNAs between C. gynandra and C. hassleriana based on the annotations of their putative target homolog genes in Arabidopsis thaliana. For the biological processes, the enriched GO terms included cellular process, single-organism process, metabolic process, biological regulation, response to stimulus, etc. For the molecular function, these target genes were mainly enriched in binding, catalytic activity, and nucleic acid binding transcription factor activity. For the cell component category, the target genes were mainly involved in cell, cell part and organelle part (Fig. 3a). The KEGG pathway analysis showed that these target genes were enriched in twenty pathways, including pathways for plant hormone signal transduction, ribosome, starch and sucrose metabolism, nitrogen metabolism, porphyrin and chlorophyll metabolism, and sulfur metabolism (Fig. 3b).

Figure 3. GO categories and KEGG pathway analysis for the putative target genes of the differentially expressed miRNAs in Cleome gynandra and Cleome hassleriana.

Figure 3

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(a) GO categories for the putative target genes of the differentially expressed miRNAs involved in biological processes, cellular components and molecular functions. (b) KEGG pathway analysis for the putative target genes of the differentially expressed miRNAs involved in processes.

Quantitative real-time PCR of miRNAs

To confirm the expression patterns of the differentially expressed miRNAs that were obtained from the sequencing analysis, we confirmed the expression levels of fourteen miRNAs using quantitative real-time PCR (qRT-PCR) and compared the differences in their expression levels between C. gynandra and C. hassleriana (Fig. 4). The results showed that 13 of the14 miRNAs had expression patterns that were similar to those of the sequencing data, which indicated that our sequencing data were reliable.

Figure 4. Quantitative analysis of the fourteen miRNAs levels by poly-A tail extension qRT-PCR in the leaf of Cleome gynandra and Cleome hassleriana.

Figure 4

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U6 was used as the internal control. The line represents the fold change of the expression level by sRNA-seq.

Discussion

In this study, we used two closely related C4 and C3 species, C. gynandra and C. hassleriana, to explore the genetic difference between C4 and C3 plants using high-throughput sequencing technology. Two small RNA libraries were constructed and sequenced. The obtained small RNA sequences were between 18–29 nt in length. Generally, the 21 nt small RNAs represent typical, mature miRNAs in plants. The most abundant small RNAs in C. gynandra and C. hassleriana were 21 nt in length, accounting for 55.78% and 60.58% of the total small RNAs, respectively. This result was consistent with that in the maize leaf20. The second most abundant sRNA in C. gynandra was 20 nt in length, while the second most abundant sRNA in C. hassleriana was 29 nt in length (Table S2). In some plants, such as soybeans, peanuts, sweet oranges and rice, the lengths of the most and second most abundant small RNAs were 24 nt and 21 nt, respectively21,22,23,24,25. The different length distributions of the small RNAs indicate the different characteristics of the small RNAs among various plant species.

MiRNAs are important post-transcriptional regulators of gene expression and play important roles in biological processes, including plant development10,15,17,26,27,28. Since the first plant miRNAs were reported in Arabidopsis thaliana in 2002, various miRNAs have been identified29. For example, Zhao et al. (2010) reported 75 conserved miRNAs and 14 novel miRNAs in peanuts21; Kang et al. (2012) detected 125 and 127 known miRNAs in maize seeds and leaves, respectively20; and Wang et al. (2015) discovered 95 known miRNAs and 23 novel miRNA candidates in cotton seeds30. Here, in our study, 94 known and 3 novel miRNAs were identified in C. gynandra, and 102 known and 3 novel miRNAs were identified in C. hassleriana. Ninety-one known and 2 novel miRNAs were commonly detected in both species. Our study is the first to report the identification of miRNAs in C. gynandra and C. hassleriana, which are valuable additions to the plant miRNA kingdom.

Some miRNAs in our study showed species-specific expression patterns. For example, Cleome-miR161.2 was present only in C. gynandra, and Cleome-miR397a was present only in C. hassleriana (Table 2). MiR161 has two forms, miR161.1 and miR161.2, which target PPR-P and regulate cytoplasmic male sterility31,32,33. OsmiR397 targets the laccase-like gene OsLAC and affects grain size and panicle branching34. The roles of these species-specific miRNAs in the development and function of leaf tissues in C. gynandra and C. hassleriana need to be investigated in the future.

In this study, most of the miRNAs, approximately 61.5%, were differentially expressed between C. gynandra and C. hassleriana. Of these miRNAs, some were reported to participate in the cell biology and development process of the leaf. For example, the MIR165 family directly cleaves the adaxial identity genes REV (AT5G60690) and PHB (AT2G34710) and was involved in the process of leaf morphogenesis15,35. In our study, the expression levels of two members of the MIR165 family, Cleome-miR165a-3p and Cleome-miR165b, were both reduced nearly ten-fold in C. gynandra (Supplementary Table 2). This expression pattern might be associated with the specified morphogenesis of the C4 leaf in C. gynandra. miR164 mediates the cleavage of NAC1 (AT1G56010) and NAC2 (AT5G39610), which were involved in the processes of lateral root emergence and aging-induced cell death and leaf senescence, respectively36,37. The over-expression of ath-miR164a repressed the EIN3-induced early senescence phenotypes in Arabidopsis thaliana38. Whether miR164 still functions as an aging regulator or has a new function in the C4 pathway needs to be further investigated. We also found that some miRNAs that belong to the same miRNA family have different expression patterns between C. gynandra and C. hassleriana. For example, the Cleome-miR395a/d/e was significantly up-regulated, while Cleome-miR395 b/c/f was significantly down-regulated in C. gynandra compared with those in C. hassleriana (Supplementary Table 2). The target genes of miR395 include GUN5 (AT5G13630), APS1 (AT3G22890) and AST68 (AT5G10180), which were reported to be involved in chlorophyll synthesis, the abscisic acid (ABA) pathway, and the sulfate metabolism pathway38,39,40. The diverse expression patterns of miRNAs suggested an orchestrated temporal and spatial regulation of the gene expression levels in various biological processes between these two species at the post-transcriptional level. One hundred ninety-four putative target genes were predicted for the identified miRNAs in our study (Supplementary Table 3). Among them, 38 were confirmed to have expression patterns that were opposite to those of the miRNAs in the publicly available data19, which indicated that these 38 genes might be regulated by miRNAs (Supplementary Table 4). We investigated the expression patterns of the putative target genes mentioned above and found that the target of miR164, NAC1 (AT1G56010), and the two targets of miR395, APS1 (AT3G22890) and AST68 (AT5G10180), were among the 38 genes that had expression patterns that were opposite to those of the miRNAs in the published data (Supplementary Table 4). However, the two targets of miR165, REV (AT5G60690) and PHB (AT2G34710), one target of miR164, NAC2 (AT5G39610), and one target of miR395, GUN5 (AT5G13630), had no such expression patterns. The miRNA-mediated gene regulation is complicated, which means that it might not cause the down-regulation of the target genes, e.g., translational inhibition41. In addition, different leaf samples and growth conditions were applied in the previous and our experiments. These differences might be the reasons why large numbers of target genes were not found to be differentially expressed or have opposite miRNAs expression patterns. To obtain more reliable results, small RNAome, transcriptome and degradome sequencings must be simultaneously conducted using the same leaf samples in the future.

The functions of differentially expressed miRNAs were investigated according to the annotation information for Arabidopsis (Table 4). However, there were still 29 miRNAs in C. gynandra that were not predicted to have target genes. These miRNAs might target species-specific genes, which could be validated in the future when the genome sequence and gene annotation of C. gynandra are available.

In this study, we explored the differences in the characteristics of miRNAs between C. gynandra and C. hassleriana using high-throughput sequencing technology. In addition to the identified known and novel miRNAs, the differentially expressed miRNAs and their functions were investigated in these two species. Because two different species were used, some of the observed differences in the miRNAs may be due to the differences in the genetic backgrounds rather than the differences between C3 and C4 photosynthesis. Future studies about the functions of these differentially expressed miRNAs will be helpful to clarify the question above and confirm the roles of these miRNAs in the process of photosynthesis. Our study not only provides a foundation for the further elucidation of miRNA function in C. gynandra and C. hassleriana but is also helpful for scientists to obtain some insights into the alteration in molecular pathways during the evolution of the C3 pathways to C4 pathways.

Methods

Plant material and RNA isolation

C. gynandra (also known as Gynandropsis gynandra) and C. hassleriana (also known as Tarenaya hassleriana) were grown in a standard potting mix in a 25 °C greenhouse under a 16:8 (light: dark) photoperiod. The fully expanded leaves (approximately 5 cm in length) were collected, immediately frozen in liquid nitrogen and stored at −80 °C. The total RNA was isolated from the leaves using TRIzol reagent (Life Technology, CA, USA) according to the manufacturer’s protocol. The total RNA concentration and purity were assayed with a NanoDrop ND-1000 spectrophotometer (NanoDrop Technologies, Wilmington, DE, USA). The RNA integrity was assessed on an Agilent 2100 Bioanalyzer Lab-on-Chip system (Agilent Technologies, Palo Alto, CA, USA). Small RNA fractions of 10–40 nucleotides were isolated from the total RNA pool with a Novex 15% TBE-urea gel (Invitrogen, USA).

Library construction and high-throughput sequencing of small RNAs

The small RNA (sRNA) sequencing libraries were generated using the NEBNex Multiplex Small RNA Library Prep Set for Illumina (NEB, USA) following the manufacturer’s instructions. The libraries were quantified on the Agilent Bioanalyzer 2100 (Agilent Technologies, Waldbroon, Germany) system using DNA High Sensitivity Chips and were pooled so that each index-tagged sample was present in equimolar amounts. The clustering of the pooled index-coded samples was performed on a cBot Cluster Generation System using TruSeq SR Cluster Kit v3-cBot-HS (Illumia, USA) according to the manufacturer’s instructions. After the cluster generation, the libraries were sequenced on an Illumina HiSeq 2500 platform, and 50 bp single-end reads were generated. The dataset has been deposited in the National Center for Biotechnology Information (NCBI; accession number SRR5298259, SRR5298260).

Data processing

The raw data (raw reads) were first processed through custom scripts. In this step, clean data (clean reads) were obtained by removing the reads that contained ploy-N, 3′adapter and insert tags, and low-quality reads from the raw data. The remaining unique reads were mapped to the Arabidopsis thaliana genome sequence by Bowtie242. Then, the mapped sRNA reads were aligned with the Silva, GtRNAdb, Rfam and Repbase databases by Bowtie2 to remove the reads that originated from repeat sequences, rRNA, tRNA, snRNA, and snoRNA. The remaining clean unannotated reads could be used to detect the known and novel miRNAs.

The mapped clean unannotated sRNA reads were further used to identify the known miRNA with alignment against miRBase21.0 (http://www.mirbase.org/), mirdeep243 and srna-tools-cli software. Matched sequences with no more than two mismatches were considered known miRNAs. The available software miREvo44 and mirdeep2 were integrated to predict novel miRNA through exploring the hairpin structure, the Dicer cleavage site and the minimum free energy of the small RNA unannotated reads.

Expression and function of miRNAs

The MiRNA expression levels were estimated for each sample through the following steps: First, the sRNA sequences were mapped back onto the precursor sequence, the counts of the reads for each miRNA were obtained from the mapping results, and the TPM (transcripts per million) values of each miRNA were calculated45. The differential expression analysis of the miRNAs between the C. gynandra and C. hassleriana samples was performed using the DEseq46 R package. The P-value was adjusted using the q-value47. FDR ≤ 0.05 and |log2(foldchange)| ≥ 1 were set as the threshold for significant differential expression by default.

The prediction of the putative target genes of the miRNAs was performed by psRobot_tar in psRobot48 for plants based on a comparison with the annotations of Arabidopsis genome. A Gene Ontology (GO) enrichment analysis was used on the putative target genes of the miRNAs with the GOseq R packages based on the Wallenius non-central hyper-geometric distribution49. The KOBAS50 software was used to test the statistical enrichment of the putative target genes of the miRNAs in the KEGG pathways (http://www.genome.jp/kegg/)51.

Differential gene expression profiling

The dataset of the publicly available RNA-seq data for the leaves of C. gynandra and C. hassleriana was downloaded from NCBI (accession number: SRP036637 and SRP036837)19. The differential expression analysis of genes between C. gynandra and C. hassleriana was performed using the DEGseq R package52. The P-value was adjusted using the q-value47. FDR ≤ 0.01 and |log2(foldchange)| ≥ 1 were set as the threshold for significant differential expression by default.

Quantitative real-time PCR

To confirm the predicted results, qRT-PCR for miRNAs detection was performed to examine their expression. Fourteen miRNAs were randomly selected for the qRT-PCR assays in the samples of C. gynandra and C. hassleriana by Platinum SYBR Green-based qPCR (Invitrogen, USA) with the High-Specificity miRNA QuantiMir RT Kit (RA610A-1, System Biosciences) on the ViiA™ 7 Dx platform (ABI, USA). Amplified primers for all miRNAs were designed according to Varkonyi-Gasic et al.53. The miRNA-specific forward primers of the 14 selected miRNAs and the internal control U6 are listed in the supplementary table (Supplementary Table 5). The qRT-PCR procedure was as follows: 95 °C for 10 min, 40 cycles at 95 °C for 15 s and 60 °C for 30 s, and a final step at 95 °C for 15 s, 60 °C 1 min and 95 °C for 15 s. After the qRT-PCR amplification, the melting curve and amplification curve were examined to evaluate the specific amplification. The relative expression levels of the miRNAs were analyzed by the 2−ΔΔct method, and U6 was used as the internal control. All qRT-PCR reactions were assayed in triplicates.

Additional Information

How to cite this article: Gao, S. et al. Identification and characterization of miRNAs in two closely related C4 and C3 species of Cleome by high-throughput sequencing. Sci. Rep. 7, 46552; doi: 10.1038/srep46552 (2017).

Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Material

Supplementary Information
srep46552-s1.pdf (1.3MB, pdf)

Acknowledgments

This work was supported by the National Natural Science Foundation of China [grant number 31371654, 31522042, 31501655 and 31501334], the Natural Science Foundation of Hebei Province (C2015201176), the Scientific Research Foundation for the Returned Overseas Chinese Scholars by State Education Ministry of China (2011–1139), the Chinese Academy of Agricultural Sciences, and the Wuhan Chenguang Plan (2015070404010193).

Footnotes

The authors declare no competing financial interests.

Author Contributions S.G., W.Z. and Z.L. carried out the data analysis. W.Z. and Y.J wrote and revised the manuscript. X.L., Q.Y., X.S., W.G., S.W. and G.S. helped to analyze the data. Y.J. and Z.L conceived and coordinated the study. All authors read and approved the final manuscript.

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Supplementary Materials

Supplementary Information
srep46552-s1.pdf (1.3MB, pdf)

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